Mean 0.951 · 33/40 perfect tests · $7.04 total · modalities in: text, image, video, audio · out: text · each card: the prompt → the correct answer (gold) → this model's actual answer
Modalities in means what you can send this model: text, images, files, video, or audio. Modalities out means what it can send back. Bench tasks feed text extracts, so modalities do not change the score. They are here so you know what this model can handle in real work.
DEFINITIONS
- DS2+ deal: stage in {DS2, DS3, DS4, DS5} (124 of 156 open deals).
- why-buys compliant: value present, 15+ characters, not a placeholder (tbd / n/a / see notes). "LONG(n)" = real text truncated for display -> treated present and substantive.
- Missing DS1 timestamp: t_ds1 blank on any deal (all stages).
- Regressed: any pair of stage entry timestamps (t_ds1..t_ds5) where the earlier-stage timestamp is strictly later than a later-stage timestamp.
WHY-BUYS NON-COMPLIANCE, DS2+
Overall: 101 / 124 = 81.5%
Arithmetic: 124 DS2+ deals; 23 carry LONG(n) (treated substantive); the remaining 101 have a blank why_buys. 101/124 = 0.8145 -> 81.5%. No placeholder-text cases found; every failure is an empty field.
By owner (from owners.csv names; non-compliant / DS2+ total = rate):
Alex Franklin (84342457): 48 / 61 = 78.7%
Cole Ingram (83155924): 19 / 22 = 86.4%
Dana Mercer (83155923): 18 / 22 = 81.8%
Bryce Harmon (119337721): 15 / 18 = 83.3%
Farid Osman (716654662): 1 / 1 = 100.0% (single DS2+ deal — Deal-499BF6, n=1, not a meaningful rate)
Check: 48+19+18+15+1 = 101; 61+22+22+18+1 = 124.
Elena Sinclair (701163055) has only a DS1 deal (Deal-57FF13), so she has no DS2+ exposure and is not listed above.
MISSING DS1 TIMESTAMP
0 of 156 deals. Every deal has a non-blank t_ds1.
REGRESSED DEALS
0 of 156. Scanning all 10 earlier-vs-later timestamp pairs per deal (t_ds1 vs t_ds2..t_ds5, t_ds2 vs t_ds3..t_ds5, t_ds3 vs t_ds4..t_ds5, t_ds4 vs t_ds5), no pair has an earlier-stage timestamp strictly later than a later-stage one.
THREE PROCESS FIXES
1. Make why_buys a required field at DS2 entry — block stage advance (workflow validation) when blank or under 15 chars, rather than cleaning up after the fact; that alone would have prevented all 101 failures.
2. Add a weekly hygiene report flagging DS2+ deals with blank why_buys by owner, routed to the deal owner with a 48-hour SLA — the failure rate is near-uniform (78.7%–86.4%) across all four active owners, so this is a systemic gap, not one rep's.
3. Audit the write path that populates why_buys: 23 deals have real text captured (LONG(n)) while 101 are empty with no placeholder values at all — consistent with the field never being surfaced/promoted in the deal-creation or stage-advance UI, not with reps typing junk. Fix the field placement/visibility first, then enforce.
OWNER RESOLUTION — open deals (deals_open.csv, 156 rows) 1) DEALS RESOLVED 156 of 156 open deals resolved to an owner name (100%). Distinct owner_ids in the extract: 6 (119337721, 84342457, 83155923, 83155924, 716654662, 701163055) — all 6 exist in owners.csv. 2) DEALS WITH UNMATCHED OWNER ID None. 0 deals. Every owner_id in deals_open.csv has a match in the owners table. 3) OWNER IDS MAPPING TO ARCHIVED/DEACTIVATED OWNERS None among deals in this extract. For reference, the only archived=true rows in owners.csv are Gavin Porter (1520255671) and Hugo Lindqvist (77260721) — neither id appears on any open deal here, so no deal maps to an archived owner. 4) TOTAL PIPELINE AMOUNT PER RESOLVED OWNER (count of deals, sum of amount; totals by simple addition of the amount column) Bryce Harmon (119337721) 35 deals $1,054,144.00 Alex Franklin (84342457) 67 deals $624,310.00 Dana Mercer (83155923) 24 deals $341,195.00 Cole Ingram (83155924) 22 deals $288,161.43 (includes the fractional amounts 7,225.40 / 7,781.20 / 6,947.50 / 2,480.40 / 4,779.88 / 3,334.80 / 58,529.25) Farid Osman (716654662) 7 deals $4,134.00 (six of the seven are amount=1 placeholders: Deal-03BA69, Deal-117863, Deal-F17780, Deal-2B39B0, Deal-8FDCD2, plus Deal-8BA24E at 2,880) Elena Sinclair (701163055) 1 deal $2,100.00 Check: 1,054,144.00 + 624,310.00 + 341,195.00 + 288,161.43 + 4,134.00 + 2,100.00 = 2,314,044.43, which equals the sum of all 156 amount values in the extract. Note (data gap, not invented): the owner name column referenced in the request is not present in the CSV as given — the extract has owner_id only. Resolution above is done purely by owner_id join to owners.csv.
Call-to-deal mapping integrity check (gong_calls_by_deal_90d.csv vs deals_open.csv, joined on hs_deal_id = deal_id; gong_owner_id not used). (a) ORPHANS — gong rows with no match in deals_open - Gong rows: 67 (67 unique hs_deal_id, no repeated deal ids in gong). - Matched to deals_open: 37. Orphans: 67 − 37 = 30 rows (44.8% of gong rows). - Sample orphan aliases (exact from data): Deal-8FA85D, Deal-8FC3F9, Deal-3B7945, Deal-42B265, Deal-9CCC42, Deal-36EA09, Deal-9A43B4, Deal-605F3C, Deal-E2D34B, Deal-76821A. Others include Deal-D84A2D, Deal-1A0416, Deal-228783, Deal-3F86A0, Deal-9897FA, Deal-422BA6, Deal-344163, Deal-B038F0, Deal-5CA5AF, Deal-D3BD1C. - Note: these 30 are simply not current open default-pipeline deals (could be closed-won/lost or non-default pipeline) — the data provided can't distinguish the cause. (b) DUPLICATE CONVERSATION KEYS — rows where calls_90d > distinct_conversation_keys - 0 rows. In all 67 rows calls_90d equals distinct_conversation_keys exactly (e.g. Deal-8FA85D 46=46, Deal-8FC3F9 24=24, Deal-C6FE92 11=11). No evidence of duplicate conversation keys in this extract. (c) DS3+ OPEN DEALS WITH ≥1 LOGGED CALL - Open deals at current stage DS3/DS4/DS5: 85 (DS3=61, DS4=14, DS5=10). - Of those, matched to at least one gong row: 25. - Share = 25 ÷ 85 = 29.4% (i.e. 60 of 85, 70.6%, have NO logged call in the gong table). - Caveat: this is coverage within the provided 90-day gong extract only; deals could have older calls not shown here.
```sql
/* MISSING DATA (explicit): the catalog contains NO giver-level giving/recognition
table, so UNIQUE_GIVERS cannot be computed from the provided data and is returned
as NULL. Do NOT backfill it from M1_USERS (users != givers) or from any table not
in the catalog. Also: the documented "exclude deleted givers" filter is deliberately
NOT applied to historical giving counts (recognition_count = raw M1_GIVES) because
the catalog states it understates history. */
WITH first_month AS (
-- FROM PRODUCTION.PLG.COMPANY_COHORT_SUMMARY: the only documented table with one
-- row per company + customer start dates and month-1 giving totals (M1_GIVES).
-- First calendar month as a customer = calendar month of SIGNUP_DATE, which is the
-- anchor of the documented M1_* metrics (swap to FIRST_SUB_PAYMENT_DATE if
-- "customer" must mean first paid month). No deleted-giver filter applied here.
SELECT
COMPANY_ID,
DATE_TRUNC('MONTH', SIGNUP_DATE) AS first_customer_month,
DATEADD('MONTH', 1, DATE_TRUNC('MONTH', SIGNUP_DATE)) AS first_customer_month_end,
M1_GIVES AS recognition_count -- historical giving count, kept unfiltered per business rule
FROM PRODUCTION.PLG.COMPANY_COHORT_SUMMARY
WHERE SIGNUP_DATE IS NOT NULL
),
redemptions AS (
-- FROM PRODUCTION.DEPRECATED_RECOGNITION.REDEMPTION_RECORDS_V2: despite the
-- DEPRECATED_ schema name, the catalog documents this as THE source for redemption
-- counts. Only STATE = 'succeeded' rows count. (Avoided instead: M1_REDEMPTIONS in
-- COMPANY_COHORT_SUMMARY, which cannot be filtered to succeeded-only.)
-- NOTE: the catalog documents only the STATE column; COMPANY_ID / CREATED_AT names
-- are assumed from "redemption events" and must be verified before finance use.
SELECT
COMPANY_ID,
DATE_TRUNC('MONTH', CREATED_AT) AS redemption_month,
COUNT(*) AS successful_redemption_count
FROM PRODUCTION.DEPRECATED_RECOGNITION.REDEMPTION_RECORDS_V2
WHERE STATE = 'succeeded' -- documented rule: successful redemptions only
GROUP BY COMPANY_ID, DATE_TRUNC('MONTH', CREATED_AT)
)
SELECT
fm.COMPANY_ID,
fm.first_customer_month AS first_month_as_customer,
CAST(NULL AS INT) AS unique_givers, -- NOT COMPUTABLE: no giver-level table in the catalog (see header note)
fm.recognition_count, -- M1 gives, no deleted-giver filter (history must not be understated)
COALESCE(rd.successful_redemption_count, 0) AS successful_redemption_count
FROM first_month fm
-- LEFT JOIN PRODUCTION.DEPRECATED_RECOGNITION.REDEMPTION_RECORDS_V2 (pre-aggregated as
-- `redemptions` above): keeps companies with zero successful redemptions in their first
-- calendar month; matched on company + same calendar month, succeeded state only.
LEFT JOIN redemptions rd
ON rd.COMPANY_ID = fm.COMPANY_ID
AND rd.redemption_month = fm.first_customer_month
GROUP BY
fm.COMPANY_ID,
fm.first_customer_month,
fm.recognition_count,
rd.successful_redemption_count
ORDER BY
fm.first_customer_month,
fm.COMPANY_ID;
```
CRM EXTRACT AUDIT
Sources: companies.csv (34 rows), contacts.csv (52 rows), zoominfo_enrichment.csv (25 rows). No deals file was provided.
====================================================================
1. COMPLETENESS BY FIELD
====================================================================
DEALS (owner, stage, amount, close_date, why-buys)
NOT MEASURABLE. No deals extract was provided. Every deals field = data missing, not 0%.
COMPANIES (n=34; required: industry, employee_count, hq_country)
industry 34/34 = 100.0% (but 5 are non-normalized: "tech", "Tech ", "health care")
employee_count 25/34 = 73.5% missing (9): C-EC3025, C-96039F, C-44EA29, C-D04904,
C-B23205, C-60C75F, C-7BBDFA, C-50D386, C-93C8BF
hq_country 28/34 = 82.4% missing (6): C-2D1F1B, C-D73B89, C-44EA29, C-D04904,
C-2C60E5, C-EE9FFB
OVERALL 87/102 = 85.3% arithmetic: 34+25+28 = 87 filled; 34×3 = 102 cells
CONTACTS (n=52; required: email, title, persona)
email 52/52 = 100.0% populated (4 are populated-but-invalid, see §3)
title 39/52 = 75.0% missing (13): CT-0000, CT-0022, CT-0072, CT-0080, CT-0081,
CT-0092, CT-0120, CT-0121, CT-0122, CT-0132,
CT-0141, CT-0162, CT-0170
persona 37/52 = 71.2% missing (15): CT-0000, CT-0022, CT-0041, CT-0060, CT-0070,
CT-0081, CT-0082, CT-0092, CT-0110, CT-0132,
CT-0162, CT-0171, CT-0172, CT-0180, CT-0181
OVERALL 128/156 = 82.1% arithmetic: 52+39+37 = 128; 52×3 = 156
Coverage gap (adjacent finding): 14 companies have zero contacts — C-2C60E5, C-B97B4E,
C-2D7423, C-7BBDFA, C-50D386, C-BA969B, C-332637, C-93C8BF, C-EE9FFB, C-C9BB20, and the
4 duplicate rows C-0A092931/2/3/4.
====================================================================
2. DUPLICATE COMPANY CLUSTERS
====================================================================
Cluster A — shared domain acme-corp.com
C-0A092931 industry=Technology emp=500 hq=US
C-0A092932 industry=tech emp=510 hq=USA
Survivor: C-0A092931 — both rows are field-complete, but 500 vs 510 is an unresolved
employee_count CONFLICT (no enrichment row for acme-corp.com, so it cannot be arbitrated
from data provided). Survivor chosen on first-created/lowest ID + normalized industry;
do not overwrite 500 with 510 or vice versa without a source.
Cluster B — shared domain globex.io
C-0A092933 industry=SaaS emp=200 hq=US
C-0A092934 industry=Technology emp=200 hq=US
Survivor: C-0A092933 — emp and hq agree (200 / US); only industry differs and neither
row is sourced (globex.io has no enrichment row). Pick one taxonomy on merge; do not
invent.
Name-variant clusters: none. All 34 company_alias values are distinct; the only
duplication signal is the shared-domain test above.
====================================================================
3. INVALID EMAILS AND DOMAIN MISMATCHES
====================================================================
Invalid emails (no domain part) — 4:
CT-0010 C-66D1FC "user0@"
CT-0080 C-92D97D "user0@"
CT-0081 C-92D97D "user1@"
CT-0192 C-425E2A "user2@"
Domain mismatches (email domain != contact/company domain) — 1:
CT-0011 C-66D1FC user1@other-domain.com vs company/contact domain 66d1fc.com
contact.domain column vs company.domain: 0 mismatches (52/52 consistent).
====================================================================
4. ENRICHMENT FILLS (blank in CRM, value exists in zoominfo_enrichment.csv)
====================================================================
All 8 fills are employee_count = 400. Allowed: matching domain row exists in enrichment.
C-EC3025 ec3025.com employee_count (blank) -> 400
C-96039F 96039f.com employee_count (blank) -> 400
C-44EA29 44ea29.com employee_count (blank) -> 400
C-D04904 d04904.com employee_count (blank) -> 400
C-B23205 b23205.com employee_count (blank) -> 400
C-60C75F 60c75f.com employee_count (blank) -> 400
C-7BBDFA 7bbdfa.com employee_count (blank) -> 400
C-50D386 50d386.com employee_count (blank) -> 400
NOT fillable (no value in enrichment either — leave blank, do not invent):
employee_count: C-93C8BF (no enrichment row at all)
hq_country: C-2D1F1B (ZI blank), C-D73B89 (ZI blank), C-44EA29 (ZI blank),
C-D04904 (ZI blank), C-2C60E5 (ZI blank), C-EE9FFB (no ZI row)
No enrichment row exists for 9 companies: C-BA969B, C-332637, C-93C8BF, C-EE9FFB,
C-C9BB20, C-0A092931, C-0A092932, C-0A092933, C-0A092934. Nothing may be filled there.
====================================================================
5. CRM vs ENRICHMENT DISAGREEMENTS (both values present, differ)
====================================================================
Industry taxonomy — 8 real conflicts (CRM free-text vs ZI controlled vocab):
C-66D1FC CRM "tech" | ZI "Computer Software"
C-EC3025 CRM "Technology" | ZI "Computer Software"
C-44EA29 CRM "tech" | ZI "Computer Software"
C-92D97D CRM "Technology" | ZI "Computer Software"
C-D04904 CRM "Technology" | ZI "Computer Software"
C-77A95A CRM "Technology" | ZI "Computer Software"
C-B25F40 CRM "Technology" | ZI "Computer Software"
C-60C75F CRM "tech" | ZI "Computer Software"
C-425E2A CRM "Tech " (trailing space) | ZI "Computer Software"
(9 rows total; C-AA8DDA CRM "Technology" | ZI "Computer Software" is also a conflict —
9 conflicts counted here: the list above plus C-AA8DDA.)
RECOMMENDED SOURCE: ZoomInfo ("Computer Software"). CRM is unvalidated free text with
4 spellings ("tech", "Tech ", "Technology", plus "health care"/"Healthcare" split);
ZI is a single controlled vocabulary. Where ZI has no row (C-BA969B "Tech ",
C-332637, C-93C8BF "Tech ", C-C9BB20 "Tech ", C-0A092931/2, C-0A092933/4) keep the CRM
value but flag for manual normalization — do not map to "Computer Software" by hand
without a source.
hq_country — 8 formatting conflicts only, NO semantic conflict:
C-66D1FC US | United States; C-950043 US | United States; C-EC3025 USA | United States;
C-96039F USA | United States; C-77A95A US | United States; C-E51FB7 USA | United States;
C-D0662E US | United States; C-425E2A USA | United States; C-2D7423 USA | United States
All denote the same country. RECOMMENDED SOURCE: ZI ("United States") for format
consistency only. This is normalization, not a data conflict.
employee_count — 0 numeric conflicts among matched rows (all present pairs agree:
50/50, 900/900, 120/120, 1500/1500, 340/340 across matched domains).
Unresolvable conflict outside enrichment: acme-corp.com 500 (C-0A092931) vs 510
(C-0A092932) — no source to arbitrate.
====================================================================
6. TOP 10 FIXES BY PIPELINE AMOUNT AT STAKE
====================================================================
DATA MISSING: no deals extract (no amount, stage, close_date, why-buys, owner), so
pipeline amount at stake cannot be computed or ranked. The ranking below is by number
of records affected and severity of the defect, NOT by dollars. Re-run this ranking once
a deals file keyed to company_alias is supplied.
1. Merge C-0A092932 into survivor C-0A092931 (acme-corp.com) — blocks dedupe reporting;
employee_count 500/510 conflict must be resolved by source before merge.
2. Merge C-0A092934 into survivor C-0A092933 (globex.io) — same duplicate exposure;
industry SaaS/Technology pick one taxonomy.
3. Repair 4 invalid emails: CT-0010, CT-0080, CT-0081, CT-0192 ("userN@" with no
domain) — kills outbound to C-66D1FC, C-92D97D (×2), C-425E2A.
4. Resolve CT-0011 domain mismatch (user1@other-domain.com on C-66D1FC) — verify
contact ownership or reassign; risk of misdirected comms.
5. Apply 8 employee_count fills from enrichment (=400): C-EC3025, C-96039F, C-44EA29,
C-D04904, C-B23205, C-60C75F, C-7BBDFA, C-50D386 — firms-up ICP/segmentation on 8
accounts.
6. Fill 15 missing contact personas (CT-0000 … CT-0181) — persona drives routing and
messaging; 71.2% complete is the weakest required field in the extract.
7. Fill 13 missing contact titles (CT-0000 … CT-0170) — buying-committee visibility.
8. Normalize industry taxonomy on 9 conflicting rows to ZI "Computer Software"
(C-66D1FC, C-EC3025, C-44EA29, C-92D97D, C-D04904, C-77A95A, C-AA8DDA, C-B25F40,
C-60C75F, C-425E2A) — fix segmentation rollups.
9. Open data-requests for the 6 unfilled hq_country (C-2D1F1B, C-D73B89, C-44EA29,
C-D04904, C-2C60E5, C-EE9FFB) and 1 unfilled employee_count (C-93C8BF) — enrichment
has no value; nothing can be filled from data provided.
10. Populate contacts for 10 companies with zero contacts (C-2C60E5, C-B97B4E, C-2D7423,
C-7BBDFA, C-50D386, C-BA969B, C-332637, C-93C8BF, C-EE9FFB, C-C9BB20) — no contact
surface at these accounts.
Nothing above is invented; every fill traces to a zoominfo_enrichment.csv row and every
blank with no source is listed as a blank.
CLOSED-LOST CLASSIFICATION — closed_lost_6mo.csv (89 deals)
Scope note: the file is labeled "last 6 months" but every close_date falls between 2026-07-29 and 2026-09-30 (~2 months). One deal (Deal-DB0AAC, 2026-09-30) is future-dated relative to today (2026-09-24). I classified all 89 rows as given. Nothing invented; everything below traces to the tag and/or free text in the file.
RUBRIC (applied consistently)
- Category = primary cause per free text when text names one; otherwise per tag.
pricing = money/approval; competitor = buyer selected another vendor/platform; no decision = ghosted, paused, deprioritized, internal build, no authority; timing = dated deferral/reconnect window; product gap = named capability/fit deficiency; champion left = leadership/sponsor loss; other = spam/not ICP, category not wanted, reason illegible.
- Side = who the text puts it on. Bonusly = our capability/fit/cost deficiency named; buyer = their budget/timing/priority/authority/contract/preference; unknown = no reason given or text too vague.
- Tag-vs-text = MISMATCH only where the text names a reason that maps to a different primary category. Empty/"nothing specific" text = no clear disagreement (marked UNSUPPORTED instead).
PER-DEAL (file order) [CAT: ND=no decision, COMP=competitor, TIME=timing, PRICE=pricing, PG=product gap, CHAMP=champion left, OTH=other | SIDE: B=Bonusly, BY=buyer, U=unknown]
Deal-DB0AAC TIME BY OK (pause + reconnect timeline)
Deal-F7F635 COMP U OK ("another direction", no reason)
Deal-AC944F ND U OK (unresponsive)
Deal-214060 ND U OK (unresponsive)
Deal-91A056 TIME BY OK (reconnect early 2027)
Deal-29326C TIME BY OK
Deal-5DB9B0 OTH U OK ("Spam." / not ICP)
Deal-831B7B TIME BY OK (new year)
Deal-F97C37 COMP B OK (competitor "more diversified offerings")
Deal-13E9CF ND BY OK ("Not a budget issue" — deprioritized; matches "Not a priority" half of tag)
Deal-39E25C TIME BY OK
Deal-7ED004 PRICE BY OK (no budget approval)
Deal-21B045 ND U OK
Deal-B3ABED TIME BY OK (revisit Q2 next year, budget 2028)
Deal-422BA6 COMP B OK (ADP TotalSource preferred-partner integration advantage)
Deal-ED9AE7 ND BY OK ("Timing, budget, authority" — authority = "Lost DM")
Deal-988493 ND U OK
Deal-381C8C COMP U UNSUPPORTED (text: only "not moving forward with Bonusly")
Deal-F308CA ND U OK
Deal-F1E8A6 COMP U UNSUPPORTED (text: only "not moving forward with Bonusly")
Deal-B6AC09 TIME BY OK (2027)
Deal-70F704 ND BY MISMATCH (text: narrow scope + MIA; no DM/authority issue named)
Deal-E6E80A TIME BY OK
Deal-B038F0 TIME BY OK
Deal-4664E1 ND U OK
Deal-175756 TIME BY OK (hold until 2027)
Deal-E74A73 ND BY OK (wants to test manually first; next year)
Deal-DDAB52 COMP B OK (Rippl: "a lot more at the same cost" + exchange-rate budgeting)
Deal-ACE061 COMP BY OK (HeyTaco preference)
Deal-BB78F3 TIME BY OK (survey action items first)
Deal-D48E0B ND U OK
Deal-15DA99 TIME BY OK (early 2027)
Deal-F4AF5D TIME BY OK
Deal-79B7A1 TIME BY OK
Deal-583ADB ND U OK
Deal-8E27DA OTH BY MISMATCH (text: "didn't want R&R", went swag-only — no feature request named)
Deal-2D2F8D COMP U OK
Deal-E0441F ND U OK (stale, inherited from departed rep — that's a Bonusly-rep departure, not a buyer champion)
Deal-7CB44D ND U OK
Deal-0F96AA COMP U OK (RFP cut pre-finalist; no reason given)
Deal-1BCA50 COMP BY OK (stakeholder already far down path with another vendor)
Deal-7CC678 COMP U UNSUPPORTED ("Nothing specific provided.")
Deal-242273 COMP B OK (deciding factor: points-currency + on-site spend capability)
Deal-50E5D8 ND BY OK (leadership pause)
Deal-A2C349 COMP B OK (kept Awardco + their surveying functionality)
Deal-9F176A TIME BY OK
Deal-7B2236 PRICE BY OK (budget + "simpler and cheaper"; tag's Cost half matches)
Deal-AFA56C ND U OK
Deal-C7156E COMP U OK
Deal-C33D91 PRICE BY OK (budget cuts)
Deal-9048EB PG B MISMATCH (text: "bad fit... multiple feature gaps"; tagged MIA)
Deal-5E64CE TIME BY OK (Nectar contract to Oct 2027, exit fee too high; tag's Cost half matches)
Deal-8A0992 COMP U OK (Canadian provider "more closely aligns")
Deal-D0C698 COMP BY OK (past Kudos user wants Kudos)
Deal-69CF3D TIME BY OK
Deal-ECBF89 TIME BY OK
Deal-3618CC PG B MISMATCH ("Wanted Surveys" = capability gap; tagged Lost DM)
Deal-EECC02 COMP U OK
Deal-5AD03E OTH U MISMATCH ("more defined budget access" — budget/fit reason, not a competitor; text too vague for a firmer category)
Deal-D1A623 TIME BY OK
Deal-413C56 ND BY OK (back-to-school priority, CEO not ready)
Deal-47F1A1 COMP BY OK (staying with WorkTango 12 more months)
Deal-BF2A98 COMP BY OK (recently deployed HiThrive)
Deal-2A292B ND BY OK (build internally = decided not to buy)
Deal-D1AABF ND U OK
Deal-FEDBCB ND BY OK
Deal-1E7DA9 COMP U OK
Deal-2BBA21 ND U OK
Deal-286F9C COMP U OK (named no competitor; "not a good fit" unspecified)
Deal-7FBAC6 ND BY OK (leadership pause)
Deal-369281 COMP BY OK (using what they have in Paylocity)
Deal-386F6E ND U OK
Deal-9FCD0D COMP BY OK ("didn't fall short of anything"; CEO wanted a Canadian company)
Deal-55867E ND BY MISMATCH (text: generic "won't be moving forward at this time" — no timing/deferral stated; tagged Timing 1yr+)
Deal-DAFB82 PRICE BY OK (no budget until 2028)
Deal-2FEDDB ND BY OK
Deal-64B19A COMP BY OK (likely stayed with Motivosity)
Deal-3F86A0 ND U OK
Deal-096750 ND U OK
Deal-F325A5 CHAMP BY OK (layoffs + leadership change; the only champion/leadership-loss case in the file)
Deal-ABD14C ND BY OK
Deal-79E61A ND U OK
Deal-8A119B PRICE BY OK
Deal-AE7C4E ND U OK
Deal-DAB4F1 ND U OK
Deal-B4B50F ND U OK
Deal-981AD4 PG B OK ("Doesn't fit UI and not UK focused" = Feature Request ≈ product gap)
Deal-DC77FE COMP B OK (lost on customization — "label points as dollars"; price explicitly not a factor)
Deal-5885B9 ND U OK
SUMMARY
1) Category counts (n=89; 34+24+19+5+3+3+1 = 89)
no decision 34 (38.2%)
competitor 24 (27.0%)
timing 19 (21.3%)
pricing 5 (5.6%)
product gap 3 (3.4%)
other 3 (3.4%) [Deal-5DB9B0 spam, Deal-8E27DA "didn't want R&R", Deal-5AD03E illegible]
champion left 1 (1.1%) [Deal-F325A5 only]
2) Side split (47+33+9 = 89)
buyer 47 (52.8%)
unknown 33 (37.1%) [21 of these are MIA/ghosted deals with zero stated reason]
Bonusly 9 (10.1%) [Deal-F97C37, Deal-422BA6, Deal-DDAB52, Deal-242273, Deal-A2C349, Deal-DC77FE, Deal-9048EB, Deal-3618CC, Deal-981AD4]
3) Tag clearly disagrees with free text: 6 deals
Deal-70F704 (Lost DM vs scope + MIA), Deal-8E27DA (Feature Request vs "didn't want R&R"), Deal-9048EB (MIA vs "feature gaps / bad fit"), Deal-3618CC (Lost DM vs "Wanted Surveys"), Deal-5AD03E (Competitor vs budget-related reason, no competitor named), Deal-55867E (Timing 1yr+ vs generic "not moving forward", no deferral).
Separately, 3 Competitor tags are unsupported rather than contradicted (Deal-381C8C, Deal-F1E8A6, Deal-7CC678 — no competitor or reason in text). Not counted in the 6.
4) Two patterns most worth acting on
A. Competitor losses are being decided on capability breadth, and the tagging hides it. 6 of 24 competitor losses (25%) name a Bonusly capability/cost-value deficiency as the differentiator: Deal-DDAB52 ("Rippl - a lot more at the same cost" + no exchange-rate hassle), Deal-DC77FE (customization, e.g. "label points as dollars" — price explicitly fine), Deal-A2C349 (kept Awardco and added their surveying), Deal-242273 (points-currency digitization + on-site spend — the "biggest differentiator"), Deal-F97C37 ("more diversified offerings"), Deal-422BA6 (ADP TotalSource preferred-partner integrations). Add the two mis-tagged product-gap deals (Deal-3618CC "Wanted Surveys", Deal-9048EB "multiple feature gaps") and the true capability-gap count is 8 of 89 (~9%), with two named recurring gaps: surveys and points-as-spendable-currency/customization. Two of the largest losses in the file sit in this bucket (Deal-242273 $60,000; Deal-981AD4 $36,855 with "not UK focused"). Action: these are roadmap/positioning inputs (surveys, currency/spend flexibility, UK/localization, ADP/PEO integrations), not rep-coaching inputs — and the closed_lost_tag needs fixing so feature-driven losses stop landing in Lost DM / MIA.
B. Most of the book is paused or ghosted, not truly lost — with explicit dated re-entry points. 34 no-decision + 19 timing = 53 of 89 (59.6%), and 21 of those cite zero contact ("no contact since intro", "ignored multiple pieces of outreach", "unresponsive"). Of the deals that do state a reason, a large cluster names a concrete re-engagement window: early/new year 2027 (Deal-91A056, Deal-831B7B, Deal-15DA99, Deal-F4AF5D, Deal-E6E80A, Deal-B038F0, Deal-175756, Deal-B6AC09, Deal-39E25C), Q2 next year / budget 2028 (Deal-B3ABED, Deal-DAFB82), Nectar contract end Oct 2027 (Deal-5E64CE). Action: two things — (1) a dated nurture/reopen sequence targeted at the 2027 H1 and 2028 windows instead of treating these as dead pipeline; (2) a process fix on the intro-to-second-meeting handoff, since the single largest reason bucket is "never heard from them again after intro" (21 MIA deals, including 4 with 5+ contacts — Deal-4664E1 n=7, Deal-7CB44D n=6, Deal-B4B50F n=5, Deal-15DA99 n=6).
Data gaps stated explicitly: no competitor names for 10 competitor-tagged deals; no reason text at all for the 21 MIA deals; "Lost DM", "MIA", "Feature Request", "Does not fit ICP" are not part of the requested taxonomy and were mapped (as shown per deal) rather than dropped.
{
"tier_counts": {"LOCK": 7, "ACTION": 38, "BUILD": 31, "REVIVE": 33, "WATCH": 33, "RISKY": 14},
"tier_examples": {
"LOCK": ["Deal-25F752", "Deal-C26D20", "Deal-D348E1"],
"ACTION": ["Deal-547B2B", "Deal-C6FE92", "Deal-E53952"],
"BUILD": ["Deal-523604", "Deal-499BF6", "Deal-03BA69"],
"REVIVE": ["Deal-2D1F1B", "Deal-950043", "Deal-D04904"],
"WATCH": ["Deal-6787C2", "Deal-EC3025", "Deal-4B0BEB"],
"RISKY": ["Deal-B7EBD1", "Deal-7BBDFA", "Deal-FD9F4E"]
},
"risky_deals": ["Deal-B7EBD1", "Deal-0660B4", "Deal-FD9F4E", "Deal-BA571A", "Deal-7BBDFA", "Deal-4A13AD", "Deal-9D0060", "Deal-690476", "Deal-635B8E", "Deal-5FDCE4", "Deal-F336B6", "Deal-5EED42", "Deal-BA3DDC", "Deal-7599B8"],
"lock_violations": 0,
"pipeline_shape": "156 open deals (tier arithmetic 7+38+31+33+33+14 = 156; stage mix DS1 32 / DS2 39 / DS3 61 / DS4 14 / DS5 10; forecast mix COMMIT 11 / BEST_CASE 39 / PIPELINE 106), so the book is bottom-heavy in early-stage PIPELINE and thin at the close-ready end — only 7 of 10 DS5 deals earned LOCK. Recency is measured from 2026-09-24 with 'cold' = last_contacted on or before 2026-08-26 (>28 days); the 14 RISKY are exactly the COMMIT/BEST_CASE deals with meetings_30d = 0 AND cold touch (e.g., Deal-B7EBD1 COMMIT/DS5, 0 meetings, last touch 35d ago), i.e., forecast optimism contradicted by the inbound signal — and since inbound_emails_30d = 0 on every row per the stated defect, meetings_30d is the only inbound signal available. Value is concentrated in early stage: the two largest tickets, Deal-2D1F1B ($240,000) and Deal-66D1FC ($99,000), are both DS1 PIPELINE with 0 meetings and cold touch (REVIVE). Data gap: Deal-3EED2C and Deal-57FF13 have no engagement row at all (meetings_30d unknown — tiered WATCH), and Deal-57FF13's deal row is additionally truncated (no last_contacted_field or source)."
}
Extracted 6 transcripts. Each JSON block below cites only prospect utterances as evidence; the rep (Alex Franklin) is excluded from stakeholders and from every signal field. Nulls are genuine absences in the data.
```json
{
"transcript_id": "TX-001",
"deal_alias": "Deal-CFE7F4",
"why_buys": [
"automating anniversary and birthday awards — \"The big win for us would be automating anniversary and birthday awards\" (Prospect (VP People))"
],
"pain_points": [
"manual awards admin — \"our HR team of three cannot keep up with it manually\" (Prospect (VP People))",
"spreadsheet tracking — \"we track everything in a spreadsheet, and people slip through the cracks\" (Prospect (HR Admin))"
],
"stakeholders": ["Prospect (VP People)", "Prospect (HR Admin)"],
"budget_signal": "about $40k earmarked for engagement tools this fiscal year (Prospect (VP People))",
"timeline_signal": "live before open enrollment in November (Prospect (VP People))",
"competitor_mentioned": "Achievers — \"We looked at Achievers last year, but it was too heavy for a team our size\" (Prospect (VP People); past evaluation, prospect-raised)",
"next_step": "security review on September 12 (\"Yes — let's do the security review on September 12\" (Prospect (VP People)))",
"objections": [
"IT sign-off requirement — \"we need SSO and audit logs for IT to sign off\" (Prospect (HR Admin))"
],
"confidence": "high — evidence fields populated 8/8 (why-buy 1, pain 2, budget 1, timeline 1, competitor 1, next-step 1, objections 1); explicit date + dollar amount from prospect"
}
```
```json
{
"transcript_id": "TX-002",
"deal_alias": "Deal-70BB30",
"why_buys": [
"tie recognition to retention for the hourly workforce (Prospect (Head of Total Rewards))"
],
"pain_points": [
"regretted turnover over 30% in the hourly workforce (Prospect (Head of Total Rewards))"
],
"stakeholders": ["Prospect (Head of Total Rewards)", "Prospect (CFO)"],
"budget_signal": "$25k pilot budget approved by finance for this quarter (Prospect (CFO))",
"timeline_signal": "decision by end of September (Prospect (CFO))",
"competitor_mentioned": null,
"next_step": "send pilot agreement; prospect routes it to legal this week (\"Yes — send the pilot agreement and we'll route it to legal this week\" (Prospect (CFO)))",
"objections": [
"Workday integration is a hard condition — \"Integration with Workday has to be rock solid — that's my one condition\" (Prospect (CFO))"
],
"confidence": "high — evidence fields populated 8/8 (why-buy 1, pain 1, budget 1, timeline 1, next-step 1, objections 1); competitor legitimately null (\"You're the first vendor we've had a real demo with\" — Prospect (Head of Total Rewards))"
}
```
```json
{
"transcript_id": "TX-003",
"deal_alias": "Deal-530B50",
"why_buys": [
"make recognition visible across the 12 retail locations (Prospect (People Ops Manager))"
],
"pain_points": [
"recognition not visible across 12 retail locations (Prospect (People Ops Manager))",
"\"Store managers have zero budget autonomy for on-the-spot recognition today\" (Prospect (People Ops Manager))"
],
"stakeholders": ["Prospect (People Ops Manager)"],
"budget_signal": null,
"timeline_signal": "\"Honestly there's no rush on our side until Q1\" (Prospect (People Ops Manager)) — Q1, no urgency",
"competitor_mentioned": "Bucketlist — \"My CEO used Bucketlist at her last company and liked it\" (Prospect (People Ops Manager))",
"next_step": "schedule a call with the CEO; People Ops Manager will send two times (\"Yes, let's schedule a call with our CEO — I'll send two times\" (Prospect (People Ops Manager)))",
"objections": [
"CEO buy-in gate — \"The CEO has to be sold first — she decides anything people-related\" (Prospect (People Ops Manager))"
],
"confidence": "medium — evidence fields populated 7/8; budget null (only rep-stated $8/employee/month in the transcript; the 'zero budget autonomy' line is a store-level pain, not a purchase budget); timeline soft; CEO (decision maker) referenced but not on the speaker list"
}
```
```json
{
"transcript_id": "TX-004",
"deal_alias": "Deal-180D02",
"why_buys": [
"consolidate three separate recognition tools into one (Prospect (VP People))"
],
"pain_points": [
"\"We're paying for three tools and none of them talk to our HRIS\" (Prospect (VP People))",
"procurement cycle friction — \"Our procurement cycle runs six to eight weeks minimum\" (Prospect (IT Security Lead))"
],
"stakeholders": ["Prospect (VP People)", "Prospect (IT Security Lead)"],
"budget_signal": "approval ceiling, not an allocation — \"If it's under $15k annually, I can approve it without going to the board\" (Prospect (VP People))",
"timeline_signal": "\"Our procurement cycle runs six to eight weeks minimum\" (Prospect (IT Security Lead)); prior vendor's security review ran three months",
"competitor_mentioned": null,
"next_step": null,
"objections": [
"security review duration risk — \"The security review took three months for our last vendor — that's my hesitation\" (Prospect (IT Security Lead))",
"board approval required at $15k+ (implied by approval ceiling) (Prospect (VP People))"
],
"confidence": "low-medium — evidence fields populated 7/8; next_step null (rep asked to lock CFO follow-up; \"Maybe — I need to check her calendar, no promises\" (Prospect (VP People)) is not an agreement); budget is a threshold not a committed amount; no dollars approved anywhere in the transcript"
}
```
```json
{
"transcript_id": "TX-005",
"deal_alias": "Deal-F8767A",
"why_buys": [
"automate service milestones (Prospect (HR Director))",
"analytics on recognition equity across departments (Prospect (HR Director))"
],
"pain_points": [
"\"Our night-shift teams feel invisible — their engagement scores run 20 points lower\" (Prospect (People Ops Coordinator))"
],
"stakeholders": ["Prospect (HR Director)", "Prospect (People Ops Coordinator)"],
"budget_signal": "$12k approved under the engagement line (Prospect (HR Director))",
"timeline_signal": "running before the January all-hands (Prospect (HR Director))",
"competitor_mentioned": "Nectar — \"We're mid-pilot with Nectar right now, so you'd need to beat that experience\" (Prospect (HR Director))",
"next_step": "present to the exec team on October 2 (\"Yes — come present to our exec team on October 2\" (Prospect (HR Director)))",
"objections": [
"exec skepticism — \"Our exec team is skeptical after a failed rollout two years ago\" (Prospect (HR Director))",
"incumbent bar — must beat the live Nectar pilot experience (Prospect (HR Director))"
],
"confidence": "high — evidence fields populated 8/8 (why-buy 2, pain 1, budget 1, timeline 1, competitor 1, next-step 1, objections 2); explicit date + approved amount from prospect"
}
```
```json
{
"transcript_id": "TX-006",
"deal_alias": "Deal-EE195F",
"why_buys": [
"cut admin time on service awards (Prospect (HR Manager))"
],
"pain_points": [
"\"I personally spend five hours a month ordering and shipping plaques\" (Prospect (HR Manager))"
],
"stakeholders": ["Prospect (HR Manager)"],
"budget_signal": "\"Budget isn't the issue — time is\" (Prospect (HR Manager)) — qualitative only, no amount stated anywhere in the transcript",
"timeline_signal": "\"Our fiscal year resets in February, so a Q1 start is realistic\" (Prospect (HR Manager))",
"competitor_mentioned": null,
"next_step": "send one-page overview; HR Manager forwards it to the COO this week (\"Yes — send the one-page overview and I'll forward it to our COO this week\" (Prospect (HR Manager)))",
"objections": [
"build-vs-buy bias — \"our COO usually prefers building things in-house\" (Prospect (HR Manager))",
"alternative under consideration is doing it internally — \"we're comparing this against just doing it internally\" (Prospect (HR Manager))"
],
"confidence": "medium-high — evidence fields populated 8/8 (why-buy 1, pain 1, budget 1 qualitative, timeline 1, next-step 1, objections 2); competitor null (in-house build is not a named vendor); COO (decision maker) referenced but not on the speaker list; no dollar figure given"
}
```
Field-coverage arithmetic across the 6 transcripts (8 evidence fields each = 48 total): populated 45, null 3 (TX-003 budget, TX-004 next_step, TX-006 competitor + TX-002 competitor and TX-004 competitor also null — 5 nulls total: budget×1, next_step×1, competitor×3). Corrected tally: 43 populated / 48 field-slots, 5 nulls, each traceable to a quoted absence above. Speaker arithmetic: 1 rep + 2 prospects (TX-001, TX-002, TX-004, TX-005), 1 rep + 1 prospect (TX-003, TX-006). No rep-sourced statement was used to populate any field; the rep's $8/employee/month line (TX-003) and pricing-flex line (TX-006) were excluded by rule.
Based on CRM + #sales-sparring (2026-09-04):
1. Deal-547B2B — $11,200 — Alex Franklin
Why close: Slack confirms redlines came back clean, the signing page is out, and their VP People said they are signing tomorrow ("signature-imminent"). CRM backs it: DS5/COMMIT, close 2026-09-11.
What's left: only the signature.
2. Deal-403845 — $9,000 — Alex Franklin
Why close: Dana Mercer reports the order form is already with their finance team (Slack), i.e., paperwork is out — the next stage after legal. CRM: DS5/COMMIT, close 2026-09-11.
What's left: finance review/approval of the order form, then signature. (Note: CRM lists owner Alex Franklin while Dana discussed it as hers — ownership flag worth checking, but status is clear.)
3. Deal-A2B47C — $6,360 — Alex Franklin
Why close: Slack says it is "still warm… just normal legal-review pace" — no blockers like Deal-2465CE. CRM: DS5/COMMIT, close 2026-09-11 (nearest close-date cluster).
What's left: legal review to complete, then signature.
Arithmetic/screening: 24 deals in CSV. Of the 7 DS5/COMMIT deals, Deal-2465CE is disqualified (champion left, procurement froze — pulled from commit, now Q4 per Slack). The remaining near-dated candidates were cross-checked against the thread; only 547B2B, 403845, and A2B47C have live Slack signal on active paper or legal progress. Deal-D348E1 is warm but in normal legal review with a 2026-10-15 close date — fourth, not top-3.
Combined value: $26,560 ($11,200 + $9,000 + $6,360).
PROSPECT-VOICE GAP ANALYSIS Candidates (prospect lines only; TG-005 excluded — the only gap-stating line there is rep language from Alex Franklin, and the prospect accepted the web version, so there is no prospect-raised candidate). 1) TG-001 — Deal-EC3025 — Prospect (IT Security Lead) Quote: "We need SCIM user provisioning — our security team will not approve any tool that requires manual account management." Classification: REAL GAP. product_docs.md states "SCIM user provisioning and ADP Workforce Now integrations are NOT currently listed as supported capabilities." SCIM appears nowhere in the plans/tiers table either, so this is not a plan gate — the capability does not exist. Deal amount: NOT PROVIDED in transcripts_gaps.csv or product_docs.md. 2) TG-002 — Deal-D0D6B5 — Prospect (HRIS Manager) Quote: "Our payroll runs on ADP Workforce Now and I don't see ADP anywhere in your integration list — that's a dealbreaker for us." Classification: REAL GAP. Supported HRIS list is Workday, BambooHR, Gusto, Rippling (Pro+); the docs explicitly note ADP Workforce Now is "NOT currently listed as supported." Not a plan gate — no tier offers ADP. Deal amount: NOT PROVIDED in either file. 3) TG-003 — Deal-CFE7F4 — Prospect (VP People) Quote: "I tried to build a custom report combining recognition frequency with turnover and couldn't — is that possible on our current plan?" Classification: PLAN GATE. Custom report builder exists but is Enterprise-only per the docs table. Not a product gap. 4) TG-004 — Deal-84DBA6 — Prospect (People Ops Manager) Quote: "We turned on the Slack integration in July, but half our managers still haven't given a single recognition — nobody ever trained them on it." Classification: ROLLOUT/ENABLEMENT ISSUE. Slack integration is supported on all plans per the docs; the stated problem is training/enablement, not a capability gap. 5) TG-005 — Deal-36C33F — no candidate. The mobile-app gap line is rep language (Alex Franklin), which per instructions does not count as prospect voice. SUMMARY — real gaps only - SCIM user provisioning (Deal-EC3025) — unmet, security-driven blocker. - ADP Workforce Now integration (Deal-D0D6B5) — unmet, stated dealbreaker. Arithmetic on counts: 5 transcripts, 5 prospect-or-rep candidate lines screened → 4 prospect-voice candidates (TG-005 rep line excluded) → classifications: 2 real gaps + 1 plan gate + 1 rollout issue = 4 accounted. Amounts for both summary deals are missing from the provided data — no dollar figures exist in transcripts_gaps.csv or product_docs.md, and none were invented.
Method: snapshot 2026-09-05; window = 2026-08-29 through 2026-09-05. A deal is stale if none of last_email / last_call / last_meeting in engagements_by_deal_90d falls in that window. Days since last contact = 2026-09-05 minus the latest of the three dated fields (on or before the snapshot). Future-dated engagement fields (e.g., scheduled meetings after 09-05) are not treated as logged contact. DATA GAP (stated explicitly): two open deals have NO row in the engagements table, so recency cannot be computed and they are excluded from the stale list — Deal-3EED2C (Alex Franklin, DS2, $7,200) and Deal-57FF13 (Elena Sinclair, DS1, $2,100). (Deal-57FF13's deal row is also malformed — missing fields at the end of the row.) === Bryce Harmon — 18 stale deals deal alias | stage | amount | days since last contact (last contact date) Deal-2D1F1B | DS1 | $240,000.00 | 81 (2026-06-16) Deal-66D1FC | DS1 | $99,000.00 | 16 (2026-08-20) Deal-950043 | DS1 | $70,000.00 | 19 (2026-08-17) Deal-B23205 | DS1 | $45,000.00 | 16 (2026-08-20) Deal-7BBDFA | DS3 | $37,440.00 | 46 (2026-07-21) Deal-332637 | DS2 | $36,000.00 | 9 (2026-08-27) Deal-1BEEBF | DS1 | $31,500.00 | 19 (2026-08-17) Deal-A414F6 | DS1 | $25,200.00 | 19 (2026-08-17) Deal-C5658B | DS1 | $23,400.00 | 16 (2026-08-20) Deal-40522D | DS3 | $21,000.00 | 19 (2026-08-17) Deal-C1FA6D | DS1 | $18,000.00 | 16 (2026-08-20) Deal-01E193 | DS1 | $12,600.00 | 8 (2026-08-28) Deal-F0EBBB | DS3 | $11,400.00 | 24 (2026-08-12) Deal-927338 | DS1 | $10,920.00 | 18 (2026-08-18) Deal-E25A09 | DS1 | $6,000.00 | 9 (2026-08-27) Deal-C9C286 | DS2 | $5,502.00 | 9 (2026-08-27) Deal-012CB1 | DS1 | $1.00 | 23 (2026-08-13) Deal-3795AD | DS2 | $1.00 | 8 (2026-08-28) Sum: 240,000+99,000+70,000+45,000+37,440+36,000+31,500+25,200+23,400+21,000+18,000+12,600+11,400+10,920+6,000+5,502+1+1 = $692,964.00 === Dana Mercer — 16 stale deals Deal-44EA29 | DS2 | $60,000.00 | 10 (2026-08-26) Deal-E51FB7 | DS2 | $43,875.00 | 12 (2026-08-24) Deal-B42F46 | DS1 | $27,000.00 | 19 (2026-08-17) Deal-BA3DDC | DS3 | $23,400.00 | 15 (2026-08-21) Deal-9DDE86 | DS2 | $20,000.00 | 15 (2026-08-21) Deal-215CCA | DS3 | $18,900.00 | 17 (2026-08-19) Deal-5EED42 | DS3 | $16,250.00 | 11 (2026-08-25) Deal-57887A | DS2 | $15,000.00 | 8 (2026-08-28) Deal-944310 | DS4 | $10,500.00 | 33 (2026-08-03) [last_meeting field shows 2026-09-15 — future-dated, not logged contact] Deal-B7EBD1 | DS5 | $9,000.00 | 16 (2026-08-20) Deal-3974EB | DS4 | $9,000.00 | 8 (2026-08-28) Deal-F40F04 | DS2 | $8,100.00 | 15 (2026-08-21) Deal-7599B8 | DS3 | $7,350.00 | 18 (2026-08-18) [last_meeting 2026-09-10 future-dated] Deal-87DDD1 | DS1 | $5,000.00 | 19 (2026-08-17) Deal-F336B6 | DS3 | $4,200.00 | 15 (2026-08-21) Deal-0660B4 | DS4 | $1,920.00 | 16 (2026-08-20) Sum: 60,000+43,875+27,000+23,400+20,000+18,900+16,250+15,000+10,500+9,000+9,000+8,100+7,350+5,000+4,200+1,920 = $279,495.00 === Cole Ingram — 18 stale deals Deal-D04904 | DS2 | $58,529.25 | 11 (2026-08-25) Deal-B25F40 | DS3 | $40,000.00 | 8 (2026-08-28) Deal-813836 | DS2 | $32,175.00 | 11 (2026-08-25) Deal-1BA595 | DS2 | $31,750.00 | 11 (2026-08-25) Deal-CFE1E8 | DS3 | $18,000.00 | 11 (2026-08-25) Deal-CD47A6 | DS2 | $12,168.00 | 11 (2026-08-25) Deal-627646 | DS3 | $11,193.00 | 11 (2026-08-25) Deal-FF809F | DS2 | $7,781.20 | 11 (2026-08-25) Deal-AF932D | DS2 | $7,225.40 | 11 (2026-08-25) Deal-A71728 | DS2 | $6,947.50 | 11 (2026-08-25) Deal-8BC9F5 | DS2 | $5,616.00 | 10 (2026-08-26) Deal-175395 | DS3 | $4,779.88 | 11 (2026-08-25) Deal-481E24 | DS3 | $4,140.00 | 10 (2026-08-26) Deal-C7F9BF | DS2 | $3,360.00 | 11 (2026-08-25) Deal-2F3A66 | DS3 | $3,334.80 | 11 (2026-08-25) Deal-342E96 | DS2 | $2,700.00 | 24 (2026-08-12) Deal-E568D5 | DS3 | $1,875.00 | 11 (2026-08-25) Deal-FD9F4E | DS5 | $1,330.00 | 10 (2026-08-26) Sum: 58,529.25+40,000+32,175+31,750+18,000+12,168+11,193+7,781.20+7,225.40+6,947.50+5,616+4,779.88+4,140+3,360+3,334.80+2,700+1,875+1,330 = $252,905.03 === Alex Franklin — 19 stale deals Deal-CC08D1 | DS1 | $24,000.00 | 16 (2026-08-20) Deal-E73427 | DS3 | $18,000.00 | 10 (2026-08-26) Deal-885F45 | DS2 | $9,300.00 | 12 (2026-08-24) Deal-C2FF3C | DS1 | $8,316.00 | 10 (2026-08-26) Deal-0D2F7A | DS3 | $5,100.00 | 12 (2026-08-24) Deal-6C60D4 | DS3 | $4,800.00 | 12 (2026-08-24) Deal-13FEBD | DS2 | $4,680.00 | 12 (2026-08-24) Deal-819506 | DS1 | $4,400.00 | 8 (2026-08-28) Deal-9D0060 | DS3 | $3,840.00 | 12 (2026-08-24) Deal-690476 | DS2 | $3,600.00 | 18 (2026-08-18) Deal-C6D97A | DS4 | $3,240.00 | 8 (2026-08-28) Deal-EE195F | DS3 | $3,120.00 | 8 (2026-08-28) Deal-278DEC | DS3 | $2,700.00 | 8 (2026-08-28) Deal-635B8E | DS3 | $2,600.00 | 18 (2026-08-18) Deal-6883F3 | DS1 | $2,400.00 | 16 (2026-08-20) Deal-4A13AD | DS3 | $2,160.00 | 26 (2026-08-10) Deal-F67D31 | DS2 | $1,800.00 | 8 (2026-08-28) Deal-5FDCE4 | DS3 | $1,600.00 | 12 (2026-08-24) Deal-BA571A | DS4 | $1,080.00 | 18 (2026-08-18) Sum: 24,000+18,000+9,300+8,316+5,100+4,800+4,680+4,400+3,840+3,600+3,240+3,120+2,700+2,600+2,400+2,160+1,800+1,600+1,080 = $106,736.00 === Farid Osman — 2 stale deals Deal-8BA24E | DS1 | $2,880.00 | 8 (2026-08-28) Deal-8FDCD2 | DS1 | $1.00 | 15 (2026-08-21) Sum: 2,880+1 = $2,881.00 PER-OWNER SUMMARY owner | stale deals | stale amount Bryce Harmon | 18 | $692,964.00 Dana Mercer | 16 | $279,495.00 Cole Ingram | 18 | $252,905.03 Alex Franklin | 19 | $106,736.00 Farid Osman | 2 | $2,881.00 TOTAL | 73 | $1,334,981.03 (Grand check: 692,964+279,495+252,905.03+106,736+2,881 = 1,334,981.03) Elena Sinclair: 0 stale deals assessable (her one open deal, Deal-57FF13, has no engagements row). Gavin Porter and Hugo Lindqvist are archived and hold no open deals in this snapshot.
RESULTS — window 2026-08-06 through 2026-09-05 (30 days back from snapshot, inclusive). Activities = sum of emails_30d / calls_30d / meetings_30d from the engagements table, joined to owner via deals_open.csv. DS2 entries counted where t_ds2 falls in that window. PER-REP DETAIL (activity mix % = share of that rep's total activities) Alex Franklin (84342457) Activities: 307 emails + 36 calls + 41 meetings = 384 Mix: 79.9% emails / 9.4% calls / 10.7% meetings (307/384, 36/384, 41/384) DS2 entries in window: 18 (Deal-403845 09-02, Deal-1FC049 09-03, Deal-3EED2C 09-03, Deal-7FA0C3 08-07, Deal-E531A6 08-07, Deal-5296C9 08-28, Deal-36C33F 08-11, Deal-EE195F 08-06, Deal-F436DA 08-19, Deal-317E6F 08-12, Deal-D1E6C2 08-11, Deal-D9A72E 08-06, Deal-CA5E44 08-24, Deal-4F775F 08-17, Deal-898FC5 08-28, Deal-46988D 08-26, Deal-E73427 08-28, Deal-92D97D 09-02) Activities per DS2 entry: 384 / 18 = 21.33 Bryce Harmon (119337721) Activities: 162 + 0 + 43 = 205 Mix: 79.0% / 0.0% / 21.0% (162/205, 0/205, 43/205) DS2 entries: 4 (Deal-25F752 08-10, Deal-CA7DC0 08-12, Deal-1CCE5C 08-06, Deal-D73B89 09-03) Ratio: 205 / 4 = 51.25 Cole Ingram (83155924) Activities: 96 + 14 + 1 = 111 Mix: 86.5% / 12.6% / 0.9% (96/111, 14/111, 1/111) DS2 entries: 2 (Deal-1BA595 08-12, Deal-42326B 08-26) Ratio: 111 / 2 = 55.50 Farid Osman (716654662) Activities: 38 + 0 + 34 = 72 Mix: 52.8% / 0.0% / 47.2% (38/72, 0/72, 34/72) DS2 entries: 1 (Deal-499BF6 08-26) Ratio: 72 / 1 = 72.00 Dana Mercer (83155923) Activities: 84 + 18 + 11 = 113 Mix: 74.3% / 15.9% / 9.7% (84/113, 18/113, 11/113) DS2 entries: 1 (Deal-57887A 08-07) Ratio: 113 / 1 = 113.00 Elena Sinclair (701163055) Activities: 0 + 0 + 0 = 0 (her only deal, Deal-57FF13, has no row in the engagements table — activity data missing, not zero-inferred) Mix: undefined (no activities) DS2 entries: 0 Ratio: undefined (no DS2 entries) RANK BY ACTIVITIES PER DS2 ENTRY (lowest = most efficient) 1. Alex Franklin 21.33 2. Bryce Harmon 51.25 3. Cole Ingram 55.50 4. Farid Osman 72.00 5. Dana Mercer 113.00 —. Elena Sinclair undefined (0 DS2 entries) MOST EFFICIENT: Alex Franklin (21.33 activities per DS2 entry) HIGHEST VOLUME: Alex Franklin (384 total activities; next is Bryce Harmon at 205) DO THEY DIFFER: No — the same rep holds both. Data notes (no cause attributed): - Two deals have no engagement row: Deal-3EED2C (Alex Franklin) and Deal-57FF13 (Elena Sinclair). Their activity contributions are absent from the totals, not counted as zero beyond what the file shows. - Boundary sensitivity: Alex Franklin's Deal-EE195F and Deal-D9A72E and Bryce Harmon's Deal-1CCE5C have t_ds2 = 2026-08-06, exactly 30 days before the snapshot (included above). Excluding that boundary date would give Alex 384/16 = 24.00 and Bryce 205/3 = 68.33; the ranking order (1st Alex, 2nd Bryce) is unchanged. - Inbound emails (inbound_emails_30d) are 0 for every deal and were not part of the requested mix.
All arithmetic computed from the provided CSVs (161 deals, 161 engagement rows, 1 quota row; every deal_id matched across files).
QTD SCORECARD — ALEX FRANKLIN — 2026-Q3 AS OF 2026-09-05
1. BOOKINGS VS QUOTA
QTD bookings (closed-won, close_date 2026-07-01 → 2026-09-05): $150,000
40,000 + 20,000 + 35,000 + 21,000 + 12,000 + 11,000 + 6,500 + 4,500 = 150,000
Quota (2026-Q3, snapshot 2026-09-05): $200,000
Attainment: 150,000 / 200,000 = 75.0%
Gap to quota: 200,000 − 150,000 = $50,000 (25 days left in quarter)
Excluded per instruction: Deal-B3E6F1, $24,000, closed-won 2026-06-20 (pre-quarter).
Included deals: Deal-A1C3E5 (40,000, 7/15), Deal-F2C7D8 (20,000, 7/24), Deal-B7D2F4 (35,000, 7/31), Deal-C9E1A6 (21,000, 8/12), Deal-A8B4D6 (12,000, 8/19), Deal-D4B8C2 (11,000, 8/21), Deal-E6F3A9 (6,500, 9/2), Deal-C5D9E2 (4,500, 9/3). Avg win = 150,000 / 8 = $18,750.
2. NEW VS EXPANSION (QTD bookings)
New: $113,500 (5 deals) = 40,000+35,000+21,000+11,000+6,500 → 75.7% of bookings
Expansion: $36,500 (3 deals) = 20,000+12,000+4,500 → 24.3% of bookings
3. ACTIVE PIPELINE BY STAGE (all 125 open deals, snapshot 2026-09-05)
DS1: 20 deals, $284,621
DS2: 28 deals, $353,760
DS3: 67 deals, $552,705
DS4: 5 deals, $23,574
DS5: 5 deals, $45,730
Total: 125 deals, $1,260,390
Subset with close_date on/before 9/30: 22 deals, $109,363.
4. ROLLING 90-DAY DS2-TO-WON RATE (entered_ds2 2026-06-07 → 2026-09-05)
DS2 entrants in window: 111
— won: 8 (Deal-A1C3E5, Deal-F2C7D8, Deal-B7D2F4, Deal-C9E1A6, Deal-A8B4D6, Deal-D4B8C2, Deal-E6F3A9, Deal-C5D9E2)
— lost: 27
— still open/in-flight: 76
Rate over all entrants: 8 / 111 = 7.2%
Rate over decided only: 8 / (8+27) = 8/35 = 22.9%
(Note: 76 of 111 are unresolved, so the 7.2% figure is floor-bound, not final.)
5. WINS AND LOSSES (Q3 close dates only — all 27 losses in the file closed 7/29–9/2, i.e., inside the quarter)
Wins: 8 ($150,000) | Losses: 27 ($329,272) | Win rate by count: 8/35 = 22.9%
Avg loss = 329,272 / 27 = $12,195
Top loss reason — "Lost- Timing (1 year or more)": 13 of 27 losses (48.1%), $184,681 of $329,272 lost dollars (56.1%)
Other reasons: Competitor 5 ($49,020), MIA 5 ($45,831), Lost DM 2 ($17,940), Feature Request 1 ($21,000), Lost- Does not fit ICP (write in notes) 1 ($10,800).
Three largest losses, all timing: Deal-B3ABED ($40,001), Deal-D1A623 ($25,200), Deal-79B7A1 ($25,000).
6. ACTIVITY, LAST 30 DAYS (summed across all 161 deals in ae_engagements.csv)
Emails: 807 | Calls: 112 | Meetings: 128 | Notes: 50 | Total: 1,097
Mix: 73.6% email, 10.2% calls, 11.7% meetings, 4.6% notes.
THREE COACHING OBSERVATIONS
1. The quarter turns on $50,000 with only $109,363 of open pipeline dated to close by 9/30 (22 deals) — that's 2.2x coverage of the gap on paper, but at the QTD avg win of $18,750 it takes ~3 wins from that subset. Concentrate the remaining 25 days on the late-stage deals closing this month (DS4/DS5 hold just $69,304 across 10 deals) rather than the 48 DS1/DS2 deals worth $638,381 that mostly close in Q4.
2. Losses are dominated by timing, not competition: "Lost- Timing (1 year or more)" is 13 of 27 losses and $184,681 (56%) of lost dollars, including the three largest losses (Deal-B3ABED, Deal-D1A623, Deal-79B7A1), while Competitor accounts for only 5 losses / $49,020. Push budget/timeline qualification earlier in DS2 — the rolling DS2-to-won rate is 7.2% (8 of 111 entrants; 22.9% of the 35 decided), so most DS2 volume is converting to neither win nor fast disqualification.
3. Activity is email-heavy and notes-poor: 807 emails vs 112 calls and 50 notes over 30 days (~7 emails per call). With a $50,000 gap and expansion contributing only $36,500 (24.3%) of QTD bookings, shifting some of that email volume to calls on the 22 in-quarter deals — and logging discovery notes to surface timing risk — is the highest-leverage change.
Data caveats: no company names were provided — deals cited by alias only. No separate activity date fields exist, so "last 30 days" uses the *_30d columns as given. The quota file has a single row (2026-Q3, $200,000) and is used as provided.
⚠️ File-mutation verifier: 1 file(s) were NOT modified this turn despite any wording above that may suggest otherwise. Run `git status` or `read_file` to confirm.
• `/var/folders/hn/4zs8fgkx4xz7zt_tqcw47xym0000gp/T/hermes_sandbox_imfx0fqt/script.py` — [patch] Failed to read file: `/private/var/folders/hn/4zs8fgkx4xz7zt_tqcw47xym0000gp/T/hermes_sandbox_imfx0fqt/script.py`
Cutoff arithmetic: today 2026-09-24 minus 60 days = 2026-07-26 (July 26 → Aug 26 = 31d; Aug 26 → Sep 24 = 29d; 31+29 = 60). Active = engaged on/after 2026-07-26 AND is_former = false.
DATA GAPS (stated explicitly): neither file contains amount or stage. Amount = NOT IN DATA and stage = NOT IN DATA for every deal below. Because stage is unavailable, "most valuable persona given the stage" cannot be stage-tuned; I use a stage-agnostic default priority (economic buyer > champion > HR admin > finance > IT security) and flag where a late-stage deal would instead prioritize finance / IT security. Take the persona recs as default-priority, not stage-informed.
ACTIVE-COUNT ARITHMETIC (per deal)
Deal-EC3025 (61032318100, C-FDD0C7): CT-047C54 09-02 active (1); CT-F2C1AE former=true → excluded. Active = 1.
Deal-92D97D (59728118877, C-E23238): CT-01F5B4 08-28 active (1); CT-A902AE 06-01 < 07-26 → stale. Active = 1.
Deal-50D386 (61055128146, C-EB10E4): 2 active (champion + HR admin) → NOT flagged (≥2 and 2 distinct personas).
Deal-D0D6B5 (60081655042, C-32918E): 3 active (09-02, 08-19, 08-07), all champion. Active = 3, one persona → flagged.
Deal-5BFE3B (51674270311, C-535D36): 2 active (08-31, 08-12), both champion. Active = 2, one persona → flagged.
Deal-84DBA6 (63929535929, C-BE9BE0): 3 active (champion, economic buyer, IT security) → NOT flagged.
Deal-36C33F (63739413805, C-077A0E): CT-4FE556 08-15 active (1); CT-405B45 and CT-86B22F both former=true → excluded. Active = 1.
Deal-4B0BEB (61038797752, C-1BDFE0): 4 active, 4 personas (champion, economic buyer, HR admin, finance) → NOT flagged.
Deal-885F45 (60686135564, C-5E8EFB): 2 active (economic buyer 08-26, champion 08-11). Active = 2 < 3 → flagged.
Deal-FCBE5B (62639586615, C-737030): 1 active (champion 08-29). Active = 1.
Deal-5408B0 (60182332309, C-2AE3AA): 2 active (champion 09-01, HR admin 08-18). Active = 2 < 3 → flagged.
Deal-D348E1 (61750885954, C-804F29): 5 active, all 5 personas → NOT flagged.
Deal-C6D97A (62121783047, C-5A8FC2): 3 active (08-31, 08-20, 08-05), all champion. Active = 3, one persona → flagged.
Deal-F9A08A (49757401138, C-0D15DF): CT-931B10 09-03 active (1); CT-913581 06-20 < 07-26 → stale. Active = 1.
FLAGGED DEALS (10 of 14)
1. Deal-EC3025 (C-FDD0C7) — single-threaded
Amount: not in data | Stage: not in data
Active: 1 (CT-047C54, champion)
Personas present: champion | Missing: economic buyer, HR admin, IT security, finance
Priority add: economic buyer (default priority; the former CPO CT-F2C1AE shows this seat matters here)
On-file fit: CT-6827DB, Chief People Officer, economic buyer — MATCHES
2. Deal-92D97D (C-E23238) — single-threaded
Amount: not in data | Stage: not in data
Active: 1 (CT-01F5B4, HR admin). Stale-but-on-deal: CT-A902AE (champion, last 2026-06-01)
Personas present: HR admin | Missing: economic buyer, champion, IT security, finance
Priority add: economic buyer (champion reactivation of CT-A902AE is the cheapest parallel move)
On-file fit: NONE ON FILE for C-E23238
3. Deal-D0D6B5 (C-32918E) — under-threaded (all one persona)
Amount: not in data | Stage: not in data
Active: 3 (CT-87CED4, CT-DE6D7C, CT-FD70B2 — all champion)
Personas present: champion | Missing: economic buyer, HR admin, IT security, finance
Priority add: economic buyer
On-file fit: CT-1FA4DB, Chief People Officer, economic buyer — MATCHES
4. Deal-5BFE3B (C-535D36) — under-threaded (2 active, all champion)
Amount: not in data | Stage: not in data
Active: 2 (CT-57123B, CT-5CE757 — both champion)
Personas present: champion | Missing: economic buyer, HR admin, IT security, finance
Priority add: economic buyer
On-file fit: NONE ON FILE for C-535D36
5. Deal-36C33F (C-077A0E) — single-threaded
Amount: not in data | Stage: not in data
Active: 1 (CT-4FE556, IT security). Former: CT-405B45 (champion), CT-86B22F (economic buyer) — both excluded
Personas present: IT security | Missing: economic buyer, champion, HR admin, finance
Priority add: economic buyer (champion is the near-equal need — this deal has zero active people-side contact)
On-file fit: CT-1DB73E, Chief People Officer, economic buyer — MATCHES
6. Deal-885F45 (C-5E8EFB) — under-threaded (2 < 3)
Amount: not in data | Stage: not in data
Active: 2 (CT-51C81E economic buyer, CT-D9A0E8 champion)
Personas present: economic buyer, champion | Missing: HR admin, IT security, finance
Priority add: HR admin by default priority; if this deal is late-stage (unknown — stage not in data), finance or IT security outrank it
On-file fit: CT-B3F25D, IT Security Lead, IT security — fills a gap but not the top-priority persona; worth grabbing as the stage-appropriate add if in security review
7. Deal-FCBE5B (C-737030) — single-threaded
Amount: not in data | Stage: not in data
Active: 1 (CT-4A5317, champion)
Personas present: champion | Missing: economic buyer, HR admin, IT security, finance
Priority add: economic buyer
On-file fit: NONE ON FILE for C-737030
8. Deal-5408B0 (C-2AE3AA) — under-threaded (2 < 3)
Amount: not in data | Stage: not in data
Active: 2 (CT-D33AE4 champion, CT-8742FD HR admin)
Personas present: champion, HR admin | Missing: economic buyer, IT security, finance
Priority add: economic buyer
On-file fit: CT-07FA76, Chief People Officer, economic buyer — MATCHES
9. Deal-C6D97A (C-5A8FC2) — under-threaded (all one persona)
Amount: not in data | Stage: not in data
Active: 3 (CT-223DDC, CT-B03555, CT-4E8A2B — all champion)
Personas present: champion | Missing: economic buyer, HR admin, IT security, finance
Priority add: economic buyer
On-file fit: NONE ON FILE for C-5A8FC2
10. Deal-F9A08A (C-0D15DF) — single-threaded
Amount: not in data | Stage: not in data
Active: 1 (CT-931B10, champion). Stale-but-on-deal: CT-913581 (economic buyer, last 2026-06-01)
Personas present: champion | Missing: economic buyer, HR admin, IT security, finance
Priority add: economic buyer — but CT-913581 already holds that persona; re-engaging him (35 days past the 60-day window) beats adding a new contact
On-file fit: CT-697541, Chief People Officer, economic buyer — matches the persona; use only if CT-913581 is unreachable
NOT FLAGGED (4): Deal-50D386 (2 active, 2 personas), Deal-84DBA6 (3 active, 3 personas), Deal-4B0BEB (4 active, 4 personas), Deal-D348E1 (5 active, 5 personas).
Note: all missing-persona sets include HR admin / IT security / finance wherever absent; the priority add is the single highest-value seat, and stage data would be needed to override the default ordering above.
REP: Alex Franklin | 10 calls | 2026-07-28 to 2026-09-03
=== 1. WHAT HE LEADS WITH (first 5 min) ===
8/10 calls: the identical retailer proof-point opener.
Quote (TT-001, Deal-D348E1, minute 0): "Quick one to kick off: a 400-person retailer we work with cut regretted turnover 18% in two quarters after automating milestone awards, and I wanted to walk you through exactly how they did it."
Same verbatim line in TT-002, TT-003, TT-005, TT-006, TT-007, TT-008, TT-010.
2/10 calls: logistics/pricing instead.
Quote (TT-004, Deal-403845, minute 0): "I put together a short agenda — security review first, then pricing."
TT-009 (Deal-1E2498, minute 0): "You asked for straight pricing last time, so let's start there."
One early unprompted competitor jab (rep-initiated, minute 2, TT-005): "And unlike Workhuman, our pricing includes the full rewards catalog with no extra margin."
=== 2. THREE MOST COMMON OBJECTIONS & HANDLING ===
A. "Budget locked until next fiscal year" — 4 occurrences (TT-001, TT-003, TT-006, TT-010). Handled 4/4 with the funding-reframe:
Quote (TT-001, minute 8): "Totally fair. Most teams fund this out of turnover savings — that retailer saved about $210k in avoided backfills, which is how their finance team signed off."
Outcome: next step booked in 3/4; TT-010 ended with "Understood, thanks for the candor" (no ask).
B. "Revisit next quarter / open enrollment" — 3 occurrences (TT-002, TT-005, TT-008). Handled 3/3 with a scoped pilot:
Quote (TT-002, minute 8): "Makes sense. What if we scope a 90-day pilot with one department so you have internal data before next quarter's planning?"
Outcome: next step booked in 3/3.
C. "We already do recognition with a spreadsheet and gift cards" — 3 occurrences (TT-004, TT-007, TT-009). Handled 3/3 with the automation/analytics contrast:
Quote (TT-004, minute 8): "Spreadsheets work until they scale — the difference is automation: milestones fire without HR lifting a finger, and you get analytics on who is being recognized."
Outcome: next step booked in 1/3 (TT-009 only).
Also raised, not top-3: "budget committee" (TT-004 min 11, TT-010 min 11 = 2) and "I need to think about it" (TT-007 min 14 = 1). Both closed with ceding lines ("Understood — I'll leave it with you", "Fair enough.").
=== 3. NEXT-STEP AGREEMENT RATE ===
7/10 = 70%.
Agreed: TT-001, TT-002, TT-003, TT-005, TT-006, TT-008, TT-009 — all via the same ask ("Should we lock the next step — a working session with your team this week?") with identical confirmation:
Quote (TT-002, minute 15): "Yes, Thursday at 2pm works — send the invite and I'll bring our HRIS manager."
Not agreed: TT-004 (status-quo objection → "Understood — I'll leave it with you"), TT-007 (no-urgency → "Fair enough."), TT-010 (committee blocker; no next-step ask recorded at all).
Arithmetic: 7 confirmed meetings / 10 calls = 0.70. By objection type: timing 3/3 (100%), budget 3/4 (75%), status quo 1/3 (33%).
=== 4. COMPETITORS RAISED BY PROSPECTS ===
Two, both raised at minute 4 before the rep's objection handling:
- Awardco — TT-003, Deal-547B2B. Quote (minute 4): "We're also in late talks with Awardco — their rewards catalog looks bigger than yours."
- Kudos — TT-007, Deal-EDC141. Quote (minute 4): "How are you different from Kudos? Our CEO used them at her last company."
Workhuman appears once (TT-005) but was raised by the rep, not the prospect — excluded from the prospect-raised list.
=== 5. COACHING NOTES ===
1. The objection playbook is strong but uniform, and the weakest thread is status quo: 1/3 conversion vs 100% on timing. The automation/analytics reframe is feature-language; it lands 0/2 when the incumbent is "good enough" until paired with the retailer ROI math he uses elsewhere. Pair every status-quo reframe with the $210k/18% outcome evidence.
2. He loses the close at the very end, not in the middle: all three misses (TT-004, TT-007, TT-010) end in ceding lines ("I'll leave it with you," "Fair enough") with no alternative commitment, and in TT-010 he never asked at all. Rule: never exit on a cede — if the full working session is refused, trade down to a smaller concrete step (send the ROI one-pager to the committee, 15-min follow-up on a named date), and always make the ask even after a committee objection.
Q3 2026 FORECAST — window 2026-07-01 to 2026-09-30 (inclusive) Source: deals.csv, 86 rows, pulled 2026-09-05. Weighting: 1.00 x COMMIT + 0.35 x BEST_CASE; PIPELINE = 0. COMMIT (in-quarter) Count: 7 Deal-547B2B 11,200 (DS5, 2026-09-11) Deal-B7EBD1 9,000 (DS5, 2026-09-10) Deal-403845 9,000 (DS5, 2026-09-11) Deal-A2B47C 6,360 (DS5, 2026-09-11) Deal-2465CE 5,400 (DS5, 2026-09-10) Deal-A5E80A 2,520 (DS1, 2026-09-11) Deal-499BF6 1,249 (DS2, 2026-09-30) Sum: 11,200 + 9,000 + 9,000 + 6,360 + 5,400 + 2,520 + 1,249 = 44,729 COMMIT total = $44,729 BEST_CASE (in-quarter) Count: 24 Sum of all 24 amounts (38,935 + 24,000 + 19,656 + 16,250 + 11,116 + 10,800 + 10,500 + 9,890 + 9,720 + 9,000 + 7,200 + 3,840 + 3,780 + 3,600 + 3,240 + 3,150 + 3,120 + 3,060 + 2,916 + 2,760 + 2,484 + 2,100 + 1,920 + 528) = 203,565 BEST_CASE total = $203,565 PIPELINE (in-quarter, not forecast): 23 deals, $201,637.40 — counts zero per the rule. Weighted forecast = 1.00 x 44,729 + 0.35 x 203,565 = 44,729 + 71,247.75 = $115,976.75 Excluded — close date outside the quarter (all fall 2026-10-01 to 2026-10-15) Count: 32 deals Total amount: $227,575 Largest excluded: Deal-E51FB7 43,875 (2026-10-01), Deal-B936FE 18,000 (2026-10-09), Deal-D9A12F 17,000 (2026-10-15), Deal-D348E1 13,770 (COMMIT, 2026-10-15), Deal-4062CF 10,800 (2026-10-15). Note the excluded set contains 1 COMMIT (Deal-D348E1) and 9 BEST_CASE deals (Deal-C61CF7, Deal-48B656, Deal-901332, Deal-47AE31, Deal-15D24F, Deal-ED725A, Deal-8AD4A5, Deal-5FDCE4, Deal-F5A622). Top 5 BEST_CASE inside the quarter 1. Deal-2D7423 — 38,935 (2026-09-30) 2. Deal-25F752 — 24,000 (2026-09-25) 3. Deal-E53952 — 19,656 (2026-09-30) 4. Deal-5EED42 — 16,250 (2026-09-30) 5. Deal-FA32A0 — 11,116 (2026-09-25) Top-5 subtotal: 38,935 + 24,000 + 19,656 + 16,250 + 11,116 = 109,957 (54% of the whole BEST_CASE line) ## Data quality Owner is blank on 85 of 86 rows and the extract carries no company names at all — only opaque aliases — so nothing can be attributed to a rep or checked for duplicate accounts. Qualification evidence is absent: why_buys_chars is 0 on 71 of 86 rows, meaning most forecast amounts sit on deals with no recorded reason-to-buy. Forecast category contradicts stage, notably Deal-A5E80A (DS1, $2,520) and Deal-499BF6 (DS2, $1,249) flagged COMMIT well before DS5/contract, which inflates the 100%-weighted line. The extract is stale and synthetic-looking: deals close-dated 2026-08-28, 2026-09-02 and 2026-09-04 still show as open on a 2026-09-05 pull, dates cluster hard on period ends (2026-09-30, 2026-10-15), and Deal-42326B carries a fractional amount (2,480.4).
ACTIVATION HYPOTHESIS TEST — plg_company_cohort_2023.csv (n = 220 companies)
CLASSIFICATION (G = m1_users >= 5; R = m1_redemptions >= 1)
Retained = current_status == 'active' (all signups 2023-01..2023-07, all 25+ months old).
2x2 — cohort size and 24-month retention rate
Cell n retained rate arithmetic
both (G+R) 47 31 66.0% 31/47 = 0.6596
givers-only (G, no R) 49 23 46.9% 23/49 = 0.4694
redemption-only (R, no G) 29 9 31.0% 9/29 = 0.3103
neither 95 38 40.0% 38/95 = 0.4000
TOTAL 220 101 45.9% 101/220 = 0.4591
Check: 47+49+29+95 = 220; 31+23+9+38 = 101.
EXCLUSIONS FROM DENOMINATOR
None. All 220 rows have non-empty m1_users, m1_redemptions, and current_status; no duplicate company_key; all signup months 2023-01 through 2023-07 are 25+ months old, so every company is evaluable at 24 months. Denominator = 220.
HYPOTHESIS VERDICT
Supported directionally for the "both signals" cell: 66.0% (both) > 46.9% (givers-only) > 40.0% (neither) and > 31.0% (redemption-only). But "both > one signal OR neither" is not uniformly monotone across the single-signal cells: redemption-only (31.0%) retains WORSE than neither (40.0%) by -9.0 pp.
SINGLE SIGNAL WITH LARGEST RETENTION LIFT: m1_users >= 5 (5+ unique givers)
vs neither baseline: givers-only 46.9% - 40.0% = +6.9 pp; redemption-only 31.0% - 40.0% = -9.0 pp.
Marginal check (pooled, ignoring the other signal):
G>=5: 55/96 = 57.3%... computed as 56.2% (n=96) vs G<5 37.9% (n=124) -> +18.3 pp
R>=1: 52.6% (n=76) vs R=0 42.4% (n=144) -> +10.3 pp
Givers wins on both the cell comparison and the marginal comparison.
WHAT THIS PROVES
- In this 2023 cohort, month-1 dual activation (5+ givers AND 1+ redemption) is associated with the highest 24-month retention (66.0%), and the givers signal alone carries the larger positive lift.
WHAT THIS DOES NOT PROVE
- No causation: activation is not randomized; surviving/engaged accounts may self-select into giving and redeeming.
- Redemption alone is not a positive predictor here (31.0% vs 40.0% neither) — possibly confounded (e.g., C-F0A562FF has m1_redemptions=10 with m1_users=0, a data pattern worth checking), so "either signal helps" is not supported.
- Single 2023 signup-year cohort, one vendor's product tiers; small cells (redemption-only n=29) mean wide confidence intervals. No significance testing was performed and no cohort-composition controls (tier, country, has_slack) were applied.
ARR RECONCILIATION — CRM (company_arr.csv) vs BILLING (chargebee_subscriptions.csv), as of 2026-09-05
Rule applied: Billing ARR = MRR × 12 (all subscriptions in file, incl. cancelled — no exclusion rule was given).
TOTALS
CRM ARR = Σ hubspot_arr (39 companies) = 603,581.76
Billing ARR = Σ (MRR × 12) (39 subscriptions) = 617,897.76
Variance (CRM − Billing) = −14,316.00
Check: 603,581.76 − 617,897.76 = −14,316.00
DECOMPOSITION (signed CRM − Billing; sums exactly to variance)
1. Missing records −11,952.00
C-21629AA4 (SUB-0004): billing only → 0.00 − 28,449.24 = −28,449.24
C-0D5BBE3A : CRM only → 16,497.24 − 0.00 = +16,497.24
net: −28,449.24 + 16,497.24 = −11,952.00
2. Rounding (CRM rounded estimate) +36.00
C-0D66DF9E (SUB-0005): 23,200.00 − 23,184.00 = +16.00
C-14D70CE0 (SUB-0008): 18,200.00 − 18,180.00 = +20.00
3. Other (value mismatch) −2,400.00
C-0F7269D7 (SUB-0006): 24,396.00 − 26,796.00 = −2,400.00
4. Status mismatch 0.00
Two accounts are cancelled in billing yet carry equal ARR on both sides, so they contribute 0 to the variance (listed below as data-quality items).
SUM: −11,952.00 + 36.00 − 2,400.00 + 0.00 = −14,316.00 ✓
Basis for "rounding": C-0D66DF9E CRM 23,200.00 vs MRR 1,932.00 × 12 = 23,184.00; C-14D70CE0 CRM 18,200.00 vs 1,515.00 × 12 = 18,180.00. CRM values are round-hundred estimates. No bucket field exists in either file — bucket labels are analyst judgment.
MISMATCHED ACCOUNTS + SUGGESTED OWNER
Note: neither file contains an owner field. Owners below are functional roles, not named people — data does not support named assignment.
C-21629AA4 (SUB-0004) −28,449.24 Missing CRM record → CRM/RevOps admin: create company, set ARR 28,449.24 (MRR 2,370.77 × 12)
C-0D5BBE3A +16,497.24 Missing billing subscription → Billing ops: locate/confirm Chargebee sub or CRM should not carry ARR
C-0D66DF9E (SUB-0005) +16.00 Rounding → RevOps: set CRM ARR 23,200.00 → 23,184.00
C-14D70CE0 (SUB-0008) +20.00 Rounding → RevOps: set CRM ARR 18,200.00 → 18,180.00
C-0F7269D7 (SUB-0006) −2,400.00 Other — price not synced → RevOps + Billing: billing MRR 2,233.00 → 26,796.00/yr vs CRM 24,396.00 (implies 2,033.00/mo); confirm which price is correct
C-0C8323BF (SUB-000E) 0.00 Status: cancelled in billing, CRM still carries 4,905.24 → RevOps: zero CRM ARR or document exception
C-0DC4FB8C (SUB-000F) 0.00 Status: cancelled in billing, CRM still carries 8,253.24 → RevOps: zero CRM ARR or document exception
All other 32 shared accounts match exactly to the cent.
SENSITIVITY (not the stated rule): if cancelled subscriptions were excluded, Billing ARR (active only) = 604,739.28 and variance would be −1,157.52. Flagging only; the figures above use all subscriptions as the rule was stated.
BUSINESS RULE VIOLATIONS — term ≠ 12 months requires cf_agreement_end_date
VIOLATION SUB-0002 C-1794A52C 24 months cf_agreement_end_date BLANK
VIOLATION SUB-0019 C-22170CA1 36 months cf_agreement_end_date BLANK
Compliant (non-12-month with end date): SUB-000C (C-0DB48281, 24mo, 2027-11-30), SUB-001A (C-0FC4DBB8, 36mo, 2027-11-30).
MISSING DATA NOTES
- No CRM status field exists (company_arr.csv is alias + ARR only), so CRM-side status could not be verified — status mismatches are inferred from billing status alone.
- No owner field in either file; owner column above is a suggested functional owner, not sourced.
Company-level means across 30 companies/month (10 per size_band; all tier_three). Arithmetic: mean(Aug) − mean(Jul), relative = abs ÷ mean(Jul). | KVM | 2026-08 | 2026-07 | Abs | Rel | Dir | |---|---|---|---|---|---| | Giving rate | 0.6027 | 0.6023 | +0.0004 | +0.07% | ↑ flat | | Redemptions/user | 1.7302 | 1.7300 | +0.0002 | +0.01% | ↑ flat | | 1:1 meetings engagement | 0.4472 | 0.4469 | +0.0003 | +0.06% | ↑ flat | | Pulse check engagement | 0.5086 | 0.6006 | −0.0920 | −15.31% | ↓ | Largest relative move: pulse check engagement, −15.31%. Segment driver: size_band = enterprise. Enterprise pulse fell 0.5500 → 0.2743 (−0.2757, −50.1%), while smb moved 0.6588 → 0.6573 and mid_market 0.5930 → 0.5942 (both flat) — enterprise accounts for the entire aggregate drop. plan_tier cannot be tested: every row is tier_three, so no variation exists there. Caveat on the data as given: the enterprise decline is a uniform halving across all 10 enterprise companies (e.g., C-0B2895EF 0.5619 → 0.2864, C-0B2213A9 0.5548 → 0.2801), which the CSV alone cannot explain — it may be a measurement or definition change; the data does not say.
REDEMPTIONS — YTD through last completed month: 2026-08 (August 2026)
Source: redemptions_ytd.csv (378 rows, all redeemed_at between 2026-01-01 and 2026-08-31; 0 rows fall after 2026-08-31, so no rows are excluded and no month is partial).
LAST COMPLETED MONTH: 2026-08 (August 2026)
1) REDEMPTION COUNT
YTD (Jan 1 – Aug 31, 2026): 378 redemptions
By month: Jan 45 · Feb 36 · Mar 50 · Apr 52 · May 43 · Jun 46 · Jul 58 · Aug 48
Check: 45+36+50+52+43+46+58+48 = 378 ✓
2) SPEND (USD, sum of amount_usd)
YTD: $27,846.00
By month: Jan $3,932.00 · Feb $2,731.00 · Mar $2,918.00 · Apr $3,876.00 · May $2,825.00 · Jun $2,773.00 · Jul $5,255.00 · Aug $3,536.00
Check: 3932+2731+2918+3876+2825+2773+5255+3536 = 27,846.00 ✓
3) UNIQUE REDEEMERS
YTD, deduplicated on user_key: 235
(Non-additive — monthly distinct-user counts are not summed. Monthly distinct: Jan 42 · Feb 35 · Mar 44 · Apr 51 · May 42 · Jun 42 · Jul 52 · Aug 46.)
4) REDEMPTIONS PER REDEEMER (YTD)
378 ÷ 235 = 1.6085 → 1.61 redemptions per redeemer
5) PROVIDER MIX — percent of spend
Provider | Redemptions | Spend (USD) | % of spend | Arithmetic
--------------|-------------|-------------|------------|------------------------------
custom | 37 | 10,873.00 | 39.05% | 10,873 ÷ 27,846 = 39.0469%
Tremendous | 192 | 8,505.00 | 30.54% | 8,505 ÷ 27,846 = 30.5430%
Snappy | 59 | 5,238.00 | 18.81% | 5,238 ÷ 27,846 = 18.8106%
TangoCard | 90 | 3,230.00 | 11.60% | 3,230 ÷ 27,846 = 11.5995%
--------------|-------------|-------------|------------|------------------------------
TOTAL | 378 | 27,846.00 | 100.00% | 39.05 + 30.54 + 18.81 + 11.60 = 100.00 ✓
Count check: 37+192+59+90 = 378 ✓ · Spend check: 10,873+8,505+5,238+3,230 = 27,846 ✓
Providers are exactly as labeled in the file: "custom" (lowercase), "Tremendous", "Snappy", "TangoCard".
6) TOP 5 COUNTRIES BY REDEMPTIONS (count)
Rank | Country | Redemptions | Spend (USD) | % of count
-----|---------|-------------|-------------|-----------
1 | US | 244 | 18,547.00 | 244/378 = 64.55%
2 | CA | 24 | 2,286.00 | 24/378 = 6.35%
3 | AU | 21 | 1,606.00 | 21/378 = 5.56%
4 | GB | 17 | 944.00 | 17/378 = 4.50%
5 | NL | 17 | 1,122.00 | 17/378 = 4.50%
Tie note: GB and NL both have 17 redemptions, so ranks 4–5 are shared and a strict cut at 5 is arbitrary. Both are listed. Next below the cut: SG (12), then CH/DE/FR (9 each), IN (8), BR (6), NZ (2).
Check (top 5): 244+24+21+17+17 = 323 of 378 = 85.45% of all redemptions.
Notes on data limits
- No company-level or user-level attributes beyond company_alias / user_key are in the file, so nothing is inferred about them.
- "custom" is a provider value present in the data, not an inferred bucket; it is reported verbatim.
- All rows fall within 2026-01-01 to 2026-08-31, so YTD-through-2026-08 equals the full file. No rows were dropped.
CHURN-SAVE QUALIFICATION ANALYSIS (snapshot 2026-09-05) ELIGIBILITY RULES APPLIED (all three, AND logic) R1: health_score < 60 (at risk) R2: churn_save_eligible_amount > 0 R3: renewal_date within 120 days of 2026-09-05 Cutoff arithmetic: 2026-09-05 + 120 days = 2027-01-03 (Sept 25 remaining + Oct 31 + Nov 30 + Dec 31 = 117 days to 2026-12-31, + 3 = 2027-01-03) So renewal_date must be <= 2027-01-03. All 30 renewal dates fall on/after 2026-09-23, so no past-due renewals to exclude. PLAY-MAPPING CAVEAT (data missing) The three plays are named in your question, but no play-assignment rules exist in the two provided files. The mappings below are MY inference from the only signals present (usage_trend_3m, seats_used/seats, champion_active) — treat the rule rationale as undocumented, the signals as data-backed. QUALIFYING ACCOUNTS (8) — amount at stake = churn_save_eligible_amount Account Health Elig amount Renewal Days Signal cited Play C-0F6C0F34 51 49,707.00 2026-10-03 28 champion_active=false (usage growing) EXECUTIVE TOUCH C-0B827671 56 25,365.00 2026-11-14 70 usage_trend_3m=declining (113/202=56% used) USAGE REVIVAL C-0B360C78 57 35,748.00 2026-10-28 53 usage growing + champion active (75% used); no adoption/champion gap left as lever COMMERCIAL CONCESSION* C-0B0F1BAB 38 5,494.00 2026-09-23 18 champion_active=false (also flat usage) EXECUTIVE TOUCH C-0CA21961 58 16,829.00 2026-12-28 114 flat usage + severe under-adoption (84/325=26% used) USAGE REVIVAL C-0E9C27D1 39 41,235.00 2026-09-24 19 flat usage but seats near-full (134/157=85%); champion active; renewal 19d out COMMERCIAL CONCESSION* C-0CEF69FD 53 32,621.00 2026-11-21 77 champion_active=false (usage growing) EXECUTIVE TOUCH C-0D3278C7 54 17,602.00 2026-11-12 68 usage_trend_3m=declining (126/380=33% used) USAGE REVIVAL * Commercial concession has NO supporting signal in the data (no pricing, discount history, or price-sensitivity fields exist). These two are "nothing else fits" default assignments where usage/champion signals don't indicate the other plays. Flagging explicitly. TOTAL AT STAKE 49,707 + 25,365 + 35,748 + 5,494 + 16,829 + 41,235 + 32,621 + 17,602 = 224,601.00 Play subtotals: Usage revival: 25,365 + 16,829 + 17,602 = 59,796.00 (3 accounts) Executive touch: 49,707 + 5,494 + 32,621 = 87,822.00 (3 accounts) Commercial concession: 35,748 + 41,235 = 76,983.00 (2 accounts) AT-RISK BUT NOT QUALIFYING (health < 60, failed at least one rule) Account Health Elig Renewal Failures C-0BC71BDD 55 0.00 2026-10-27 R2 only (elig = 0; renewal 52d, health pass) C-10A56B0F 54 0.00 2026-12-12 R2 only (elig = 0; renewal 98d, health pass) C-0BE96399 54 0.00 2026-10-29 R2 only (elig = 0; renewal 54d, health pass) C-0BA71F12 52 6,824.00 2027-04-11 R3 only (187 days > 120) C-0F876796 47 19,958.00 2027-02-06 R3 only (153 days > 120) C-0F6694C3 43 0.00 2027-03-21 R2 and R3 (elig = 0; 197 days) C-0FCCD2DF 43 0.00 2027-04-23 R2 and R3 (elig = 0; 230 days) Note the two near-misses carrying real money that the renewal window excludes: C-0F876796 (19,958 at risk, health 47) misses by 33 days; C-0BA71F12 (6,824, health 52) misses by 67 days. If the window were 155+ days, C-0F876796 would qualify. All 15 accounts with health >= 60 fail R1 and are out of scope (their eligible amounts are all 0.00 anyway except C-8C2E8F00 at 0.00 as well — so no R1-only exclusions carry value).
EXPANSION KIT — C-0DDFC9A7 Source data: expansion_account.csv, expansion_contacts.csv, inbound_reply.txt. Nothing external used. SEAT COVERAGE licensed_seats / headcount = 150 / 400 = 0.375 = 37.5% Unlicensed headcount = 400 − 150 = 250 employees. USAGE HEALTH (two lines) 1. Active users are up every month: 88 (Mar) → 126 (Aug) = +38 users, 126/88 = 1.432 → +43.2% over 5 months (+7, +7, +8, +8, +8). 2. Aug usage covers 126 of 150 licensed seats = 126/150 = 84% of licensed seats active; 150 − 126 = 24 licensed seats unused. PER-SEAT RATE current_arr / licensed_seats = $9,000.00 / 150 = $60.00 per seat. HEADROOM Seats: 400 − 150 = 250 unlicensed (full-headcount coverage). Near-term within licensed base: 150 − 126 = 24 seats of unused licensed capacity. ARR at $60/seat: 250 × $60 = $15,000 upside (ARR to $24,000 if fully licensed to headcount). Near-term: 24 × $60 = $1,440. WHO REPLIED / CAN THEY BUY Maria S., People Operations Coordinator (last_engaged 2026-09-02, matching the reply date). Cannot buy — she states she is not the right person for purchasing decisions and that budget and seat expansion sit with Dana R. RIGHT BUYER Dana R., VP People (last_engaged 2026-05-18). Per Maria's reply: budget and seat expansion sit with Dana R., and Dana has been asking about usage numbers. Sam K., Office Manager (last_engaged 2025-11-03) is not indicated as a buyer. REPLY EMAIL (89 words) Hi Maria, Thanks for the quick reply — and glad the team's enjoying Bonusly. No pressure at all on the purchasing side; that makes complete sense. If Dana's open to it, I'd welcome an introduction. She mentioned interest in usage numbers, so one data point that might help: monthly active users grew from 88 in March to 126 in August — a 43% increase over five months. Happy to share the full trend ahead of any conversation, or simply connect whenever convenient — whatever works best for you both. Thanks again, Cole Missing from data (not invented): Dana R.'s contact details, contract/renewal dates, and any discount or pricing beyond the implied $60/seat derived from current_arr ÷ licensed_seats.
CSM PREP — MID-ONBOARDING CALL
Account: C-0D284E42 | Signup: 2026-08-11 | Data through: 2026-09-04
=== COMPLETE (evidence-backed) ===
1. Slack integration connected
- Data field: integration_slack = 2026-08-12
- 1 day after signup
2. Allowance set
- Data field: allowance_set = 2026-08-13
- 2 days after signup
3. Admins added
- Data field: admins_added = 2
- 2 admins onboarded (date field not provided, so timing unknown)
4. First recognition given
- Data field: first_recognition_at = 2026-08-15 14:22
- 4 days after signup
=== NOT COMPLETE (missing data fields) ===
1. HRIS integration
- Data field: integration_hris = (empty)
- NOT connected. Only Slack is live.
2. First redemption
- Data field: first_redemption_at = (empty)
- Zero redemptions as of 2026-09-04. That is 20 days after the first
recognition (Aug 15 → Sep 4) with no redemption event.
=== EARLY ENGAGEMENT SIGNALS ===
Source: onboarding_usage.csv, 25 days (2026-08-11 through 2026-09-04)
Active givers trend — strong, accelerating:
Week 1 (Aug 11-17): 3,3,4,4,5,4,7 → sum 30, avg 30/7 = 4.3
Week 2 (Aug 18-24): 5,7,6,9,8,9,9 → sum 53, avg 53/7 = 7.6
Week 3 (Aug 25-31): 9,11,10,10,11,13,11 → sum 75, avg 75/7 = 10.7
Week 4 (Sep 1-4, partial 4d): 13,13,15,15 → sum 56, avg 56/4 = 14.0
Full-period avg: 214 total / 25 days = 8.6
First 7 days avg: 4.3 | Last 7 days avg (Aug 29-Sep 4): (11+13+11+13+13+15+15)/7 = 91/7 = 13.0
Growth multiple: 13.0 / 4.3 ≈ 3.0x from week 1 to latest week
Peak: 15 active givers (2026-09-03, 2026-09-04) — highest point is the
most recent data, so momentum is still climbing, not plateauing.
Interpretation: givership is scaling steadily (3 → 15, a 5x lift from
day one to latest). The giving side of the loop is healthy. The receiving
side has not fired yet (no redemptions).
=== THREE THINGS TO COVER ON THE CALL ===
1. Close the HRIS gap. integration_hris is blank while Slack is live.
Confirm owner, timeline, and whether headcount/roster sync is blocking
anything downstream (e.g. auto-invites that would accelerate the
already-growing giver base).
2. Drive the first redemption. first_redemption_at is blank despite 20
days of live recognition activity and 15 active givers. Diagnose why
the reward loop hasn't closed — awareness of the catalog, point
balances, or redemption UX — and set a target date.
3. Convert momentum into a plan. Givers are up ~3x week-over-week
(4.3 → 13.0) with the peak on the two most recent days. Lock in what's
driving it (who are the top givers, which teams) so growth doesn't
stall once the rollout cohort's novelty fades. Also confirm whether 2
admins is the intended final count or more are planned — the data
shows 2 but no target, so flag it as an open question.
=== DATA GAPS (explicit) ===
- No redemption data at all (first_redemption_at empty).
- No HRIS connection timestamp.
- No admin-added date (only a count of 2).
- No headcount/seat data, so active_givers cannot be expressed as a
penetration rate — only raw counts and trend.
- No target allowance or admin count to measure against.
All figures above are computed directly from the two CSVs. Nothing extrapolated.
================================================================ 90-DAY RENEWAL RISK BRIEF — window 2026-09-24 → 2026-12-23 ================================================================ Sources: churnzero_renewals.csv, chargebee_terms.csv, usage_12m.csv (usage last month available: 2026-08; "3-month trend" = Jun/Jul/Aug 2026) MISSING DATA (not in the files — not invented): - No company names; accounts cited by the aliases given (C-xxxx). - No health scores, NPS, support volume, champion status, or renewal sentiment. Risk ratings use ONLY seat utilization + usage trend. - No MRR/upsell history; ARR is taken as given from ChurnZero. ---------------------------------------------------------------- DATE RECONCILIATION RULE ---------------------------------------------------------------- Chargebee is billing-system-of-record for contract terms. ChurnZero is known to be wrong on multi-year contracts. So: - is_multi_year = true -> use Chargebee date (5 accounts). - is_multi_year = false -> both systems carry identical 12-month dates (15 accounts), so either is fine; date shown is that value. Material impact of the disagreements: under ChurnZero's dates, C-0BCDB8C2 (2027-09-18) and C-0BBE3E60 (2027-09-26) would have fallen OUTSIDE the 90-day window entirely and been missed. ---------------------------------------------------------------- FLAGGED DISAGREEMENTS — 5 of 20 (all multi-year, all trust Chargebee) ---------------------------------------------------------------- C-0B7D2C30 CZ 2026-09-10 vs CB 2026-09-15 (36mo) -> use 09-15 C-0BCDB8C2 CZ 2027-09-18 vs CB 2026-09-18 (36mo) -> use 09-18 (CZ wrong by +1 yr) C-0D2AB865 CZ 2026-09-10 vs CB 2026-09-22 (24mo) -> use 09-22 C-0BBE3E60 CZ 2027-09-26 vs CB 2026-09-26 (24mo) -> use 09-26 (CZ wrong by +1 yr) C-0F5D2323 CZ 2026-09-10 vs CB 2026-09-29 (24mo) -> use 09-29 The other 15 accounts: CZ and CB dates identical (12-month, non-multi-year) — no disagreement. Note: 3 dates used already elapsed as of 2026-09-24 (C-0B7D2C30, C-0BCDB8C2, C-0D2AB865) — these are immediate-action, not upcoming. ---------------------------------------------------------------- RISK RUBRIC (stated because none was supplied) ---------------------------------------------------------------- Seat utilization = seats_used / seats. 3-mo change = (Aug − Jun)/Jun. Trend: Declining <= −3.0%; Rising >= +3.0%; else Flat. HIGH = Declining trend OR utilization < 40% MEDIUM = Flat with utilization < 70%, or Rising with utilization < 60% LOW = Rising with utilization >= 60%, or Flat with utilization >= 70% ---------------------------------------------------------------- RENEWALS (all 20 fall on or before 2026-11-24, i.e., in window) ---------------------------------------------------------------- 1) C-0B7D2C30 | Dana Mercer | ARR $65,901 | DATE USED 2026-09-15 (Chargebee; CZ says 09-10 — DISAGREEMENT, multi-year 36mo, elapsed 9 days ago) Seats 274/476 = 57.6% | 3-mo usage 97→94→84, −13.4% ((84−97)/97) HIGH — usage has fallen every month for 12 straight months (155→84, −45.8%) while renewal has already passed. 2) C-0BCDB8C2 | Cole Ingram | ARR $54,427 | DATE USED 2026-09-18 (Chargebee; CZ says 2027-09-18 — DISAGREEMENT, 36mo, CZ is a year late; elapsed 6 days ago) Seats 232/424 = 54.7% | 3-mo usage 127→118→110, −13.4% ((110−127)/127) HIGH — steady 12-month erosion (200→110, −45.0%) and the renewal date already passed; ChurnZero would have hidden this until 2027. 3) C-0D2AB865 | Elena Sinclair | ARR $38,022 | DATE USED 2026-09-22 (Chargebee; CZ says 09-10 — DISAGREEMENT, 24mo; elapsed 2 days ago) Seats 250/407 = 61.4% | 3-mo usage 125→117→109, −12.8% ((109−125)/125) HIGH — usage down 12 of 12 months (199→109, −45.2%) and the renewal date has just lapsed. 4) C-0BBE3E60 | Dana Mercer | ARR $30,993 | DATE USED 2026-09-26 (Chargebee; CZ says 2027-09-26 — DISAGREEMENT, 24mo, CZ a year late; renews in 2 days) Seats 74/114 = 64.9% | 3-mo usage 39→35→33, −15.4% ((33−39)/39) HIGH — steepest 3-month decline in the book (−15.4%) landing on a renewal 2 days out. 5) C-0F5D2323 | Cole Ingram | ARR $90,647 | DATE USED 2026-09-29 (Chargebee; CZ says 09-10 — DISAGREEMENT, 24mo; renews in 5 days) Seats 111/390 = 28.5% | 3-mo usage 20→20→18, −10.0% ((18−20)/20) HIGH — largest ARR in the book sits at 28.5% seat utilization with only 18–21 active users a month, renews in 5 days. 6) C-0EC6999D | Elena Sinclair | ARR $79,419 | DATE USED 2026-10-03 (CZ = CB, 12mo) Seats 31/112 = 27.7% | 3-mo usage 17→16→15, −11.8% ((15−17)/17) HIGH — lowest seat utilization in the book (27.7%) paired with a declining last quarter, on $79K ARR. 7) C-0B20DB64 | Dana Mercer | ARR $21,770 | DATE USED 2026-10-07 (CZ = CB, 12mo) Seats 214/378 = 56.6% | 3-mo usage 294→298→294, 0.0% MEDIUM — usage is flat and healthy in absolute terms but 43% of purchased seats (164) sit unused. 8) C-0BBC4E7A | Cole Ingram | ARR $56,374 | DATE USED 2026-10-10 (CZ = CB, 12mo) Seats 228/337 = 67.7% | 3-mo usage 142→141→139, −2.1% ((139−142)/142) MEDIUM — 12 months of essentially flat adoption (142→139) leaves no growth story heading into renewal. 9) C-0FD551AB | Elena Sinclair | ARR $48,815 | DATE USED 2026-10-14 (CZ = CB, 12mo) Seats 210/376 = 55.9% | 3-mo usage 123→122→126, +2.4% MEDIUM — usage is ticking up slightly but 44% of seats (166) are unused at renewal. 10) C-0F9F8F13 | Dana Mercer | ARR $46,230 | DATE USED 2026-10-18 (CZ = CB, 12mo) Seats 199/352 = 56.5% | 3-mo usage 185→185→182, −1.6% MEDIUM — engagement is flat-to-soft with 153 unused seats, so renewal is a downsell conversation. 11) C-0BC34584 | Cole Ingram | ARR $16,740 | DATE USED 2026-10-22 (CZ = CB, 12mo) Seats 327/494 = 66.2% | 3-mo usage 104→104→106, +1.9% MEDIUM — the lowest ARR in the book with usage flat around 103–106 actives and 34% of seats idle. 12) C-0B7A7546 | Elena Sinclair | ARR $35,062 | DATE USED 2026-10-25 (CZ = CB, 12mo) Seats 182/205 = 88.8% | 3-mo usage 64→65→63, −1.6% LOW — near-full seat utilization (88.8%) and usage up 8.6% over 12 months (58→63) despite monthly noise. 13) C-0B369871 | Dana Mercer | ARR $85,128 | DATE USED 2026-10-29 (CZ = CB, 12mo) Seats 317/422 = 75.1% | 3-mo usage 326→330→333, +2.1% LOW — usage has grown every month for 12 months (289→333, +15.2%) with 75% seat utilization. 14) C-0B144C78 | Cole Ingram | ARR $30,899 | DATE USED 2026-11-02 (CZ = CB, 12mo) Seats 169/224 = 75.4% | 3-mo usage 101→101→106, +5.0% LOW — strongest 3-month growth in the book (+5.0%) on 75% seat utilization. 15) C-0FC4DBB8 | Elena Sinclair | ARR $94,732 | DATE USED 2026-11-05 (CZ = CB, 12mo) Seats 356/464 = 76.7% | 3-mo usage 189→191→193, +2.1% LOW — largest healthy account: usage up 14.9% over 12 months (168→193) with 77% of seats in use. 16) C-0D5BBE3A | Dana Mercer | ARR $39,740 | DATE USED 2026-11-09 (CZ = CB, 12mo) Seats 85/102 = 83.3% | 3-mo usage 88→90→91, +3.4% LOW — usage up 19.7% over 12 months (76→91) at 83% seat utilization. 17) C-0FB9D5AF | Cole Ingram | ARR $63,158 | DATE USED 2026-11-13 (CZ = CB, 12mo) Seats 144/199 = 72.4% | 3-mo usage 173→173→176, +1.7% LOW — 12-month usage growth of +14.3% (154→176) with 72% seat utilization. 18) C-0B344485 | Elena Sinclair | ARR $64,384 | DATE USED 2026-11-16 (CZ = CB, 12mo) Seats 224/287 = 78.0% | 3-mo usage 238→240→244, +2.5% LOW — usage up every month for 12 months (211→244, +15.6%) at 78% seat utilization. 19) C-0CB2C1B4 | Dana Mercer | ARR $40,628 | DATE USED 2026-11-20 (CZ = CB, 12mo) Seats 386/473 = 81.6% | 3-mo usage 47→48→49, +4.3% LOW — modest but consistent adoption gains (43→49 over 12 months) on 82% seat utilization. 20) C-22170CA1 | Cole Ingram | ARR $45,646 | DATE USED 2026-11-24 (CZ = CB, 12mo) Seats 251/294 = 85.4% | 3-mo usage 143→148→146, +2.1% LOW — usage up 12.3% over 12 months (130→146) with 85% seat utilization, the last renewal in the window. ---------------------------------------------------------------- TOTALS (arithmetic shown) ---------------------------------------------------------------- Total ARR renewing in window (20 accounts): 65,901 + 54,427 + 38,022 + 30,993 + 90,647 + 79,419 + 21,770 + 56,374 + 48,815 + 46,230 + 16,740 + 35,062 + 85,128 + 30,899 + 94,732 + 39,740 + 63,158 + 64,384 + 40,628 + 45,646 = $1,048,715 ARR AT RISK (High) = 6 accounts: 65,901 + 54,427 + 38,022 + 30,993 + 90,647 + 79,419 = $359,409 = 34.3% of book (359,409 / 1,048,715) Note: $189,343 of that (C-0B7D2C30, C-0BCDB8C2, C-0D2AB865, C-0BBE3E60) is already past its Chargebee renewal date. Medium exposure (5 accounts): 21,770 + 56,374 + 48,815 + 46,230 + 16,740 = $189,929 (18.1%) Low risk (9 accounts): 35,062 + 85,128 + 30,899 + 94,732 + 39,740 + 63,158 + 64,384 + 40,628 + 45,646 = $499,377 (47.6%) High + Medium combined = $549,338 = 52.4% of the book. Check: 359,409 + 189,929 + 499,377 = 1,048,715 ✓ Data note: for 5 accounts (C-0B20DB64, C-0B369871, C-0D5BBE3A, C-0FB9D5AF, C-0B344485) 2026-08 active_users exceeds seats_used in ChurnZero (e.g., 294 actives vs 214 seats used). Taken as given; seat utilization above uses the seats/seats_used fields as provided. Worth a data-quality check before these numbers drive seat true-ups.
QUARTER SUPPORT TICKET THEME SYNTHESIS (intercom_tickets.csv, 80 tickets, Jun 1–Aug 29 2026)
Ranked by ARR exposure, not volume. Tags ignored — themes derived from body_text only.
ARR affected = sum of distinct account ARR in the theme (each account counted once).
Total base: 80 tickets, 24 distinct accounts, $284,800 total account ARR.
================================================================================
1. HRIS PROVISIONING FAILURES — new hires not created / sync silently skipping
Count: 12 | Share: 12/80 = 15.0% | Accounts: 3 | ARR: $114,000
ARR math: C-0B2213A9 36,000 + C-0DDFC9A7 48,000 + C-0F6C0F34 30,000 = 114,000
Volume math: C-0B2213A9 7 + C-0DDFC9A7 3 + C-0F6C0F34 2 = 12
Ticket ids: IC-460059 (C-0B2213A9), IC-460062 (C-0F6C0F34)
Broad pattern (3 accounts, incl. the two largest non-billing logos in the set),
though 7 of 12 tickets come from C-0B2213A9. "provisioning log shows no errors"
while 12 hires are skipped = silent-failure defect, not config error.
RECOMMENDATION: Ship a provisioning reconciliation diff (HRIS roster vs provisioned
accounts) with alerting on silent skips before this hits renewal conversations.
================================================================================
2. REDEMPTION / GIFT CARD FULFILLMENT FAILURES — checkout hang, code never sent, points still deducted
Count: 18 | Share: 18/80 = 22.5% | Accounts: 7 | ARR: $68,800
ARR math: 8,900 + 8,700 + 9,600 + 9,600 + 11,000 + 10,700 + 10,300 = 68,800
(C-0CEF69FD, C-0F876796, C-0FCCD2DF, C-0D9CA315, C-14264ABD, C-0B827671, C-0B0F1BAB)
Volume math: 3+3+3+1+3+4+1 = 18
Ticket ids: IC-460025 (C-0CEF69FD), IC-460035 (C-0B827671)
Broadest cross-account pattern after points (7 accounts, mid-market tier). Three
symptom variants of one fulfillment path: checkout spin/fail (4), gift card errored
but points deducted (5), code/email never arrived (6 + 3 "failed at checkout").
RECOMMENDATION: Make redemption atomic (points deducted only on confirmed vendor
fulfillment) with auto-refund + resend on timeout; this is the highest-volume
money-touch defect in the quarter.
================================================================================
3. BILLING / INVOICE ERRORS — seat-count misbilling + wrong-tier renewal pricing
Count: 16 | Share: 16/80 = 20.0% | Accounts: 1 | ARR: $52,000
ARR math: C-0E9C27D1 = 52,000 (single account; counted once)
Volume math: all 16 tickets are C-0E9C27D1
- Seat-count defect: 10 tickets (IC-460065, IC-460069, IC-460068, IC-460070,
IC-460071, IC-460074, IC-460077, IC-460079, IC-460080, IC-460066)
- Wrong-tier renewal price: 6 tickets (IC-460067, IC-460072, IC-460073,
IC-460075, IC-460076, IC-460078)
Ticket ids: IC-460071 (C-0E9C27D1), IC-460078 (C-0E9C27D1)
SINGLE-ACCOUNT NOISE as a "pattern" — but not trivial: one $52k account produced
20% of the quarter's tickets across two recurring defects ("Third invoice in a row
with the same seat-count error"), i.e. a repeat-billing-bug escalation risk.
RECOMMENDATION: Executive-level billing audit + credit for C-0E9C27D1 this week;
root-cause the seat-count source and lock the renewal tier price to the signed quote.
================================================================================
4. POINTS NOT CREDITING TO BALANCES — recognition delivered, points never post
Count: 20 | Share: 20/80 = 25.0% | Accounts: 9 | ARR: $31,100
ARR math: 4,500+2,700+3,500+2,500+4,200+3,400+2,900+2,900+4,500 = 31,100
(C-0D0B047C, C-0BE96399, C-0D3278C7, C-0DD0626C, C-0D6CC8E3, C-0D284E42,
C-0B2895EF, C-21FEBCBB, C-0BF20542)
Volume math: 2+3+3+2+3+3+1+1+2 = 20
Ticket ids: IC-460004 (C-0D3278C7), IC-460016 (C-0BF20542)
Highest volume and widest spread (9 accounts) but lowest ARR per account — a
platform-wide ledger/points-posting reliability issue hitting small logos.
RECOMMENDATION: Audit the points-posting job for post-weekend/late-ledger lag and
add a balance-reconciliation job; cheapest fix with the widest trust upside.
================================================================================
5. SLACK INTEGRATION BREAKAGE — sync stops, toggle resets, auth won't stick, slash command errors
Count: 14 | Share: 14/80 = 17.5% | Accounts: 4 | ARR: $18,900
ARR math: 3,900+5,400+4,400+5,200 = 18,900
(C-0BA71F12, C-10A56B0F, C-0B843542, C-8C2E8F00)
Volume math: 6+4+3+1 = 14
Ticket ids: IC-460041 (C-0B843542), IC-460047 (C-10A56B0F)
Broad across 4 accounts but smallest ARR exposure; C-0BA71F12 alone files 6/14
across all four Slack symptom types (sync, toggle reset, re-auth, slash command).
RECOMMENDATION: Fix the Slack token-refresh/toggle persistence bug and ship a
re-auth health check; deprioritize vs HRIS/redemption on ARR grounds.
================================================================================
BROAD PATTERN vs SINGLE-ACCOUNT NOISE
- Broad (multi-account, product-wide): #2 Redemption (7 accts), #4 Points (9 accts),
#5 Slack (4 accts), #1 HRIS (3 accts).
- Single-account noise: #3 Billing (C-0E9C27D1 only, 16 tickets = 20% of volume).
Distinct from the broad themes but a real escalation risk on a $52k logo.
- Note: themes are account-disjoint in this dataset (24 accounts, no overlap), so
ARR figures are additive: 114,000 + 68,800 + 52,000 + 31,100 + 18,900 = 284,800.
DATA CAVEAT: existing tags are unreliable (e.g. identical "Two recognitions I sent
show as delivered..." text is tagged bug/billing/feedback/urgent; HRIS text tagged
"billing" on IC-460055/IC-460054). Counts above ignore tags entirely.
**Prospect:** C-82AF3719 — Technology | Mid-Market | employee_recognition | NA-West **Method:** Scored every customer with `has_case_study = true` on 4 exact field matches (industry, size_band, use_case, region). Only these 7 are eligible (all others have `has_case_study = false`). **Ranked matches:** **1. C-11C31562 — 3/4 fields matched** - size_band: Mid-Market ✓ - use_case: employee_recognition ✓ - region: NA-West ✓ - industry: Manufacturing ✗ (vs Technology) **2. C-64171065 — 3/4 fields matched** - industry: Technology ✓ - size_band: Mid-Market ✓ - use_case: employee_recognition ✓ - region: NA-East ✗ (vs NA-West) **3. C-A13C193D — 3/4 fields matched** - industry: Technology ✓ - size_band: Mid-Market ✓ - region: NA-West ✓ - use_case: retention ✗ (vs employee_recognition) **Arithmetic (all case-study customers scored):** - C-11C31562: 3 | C-64171065: 3 | C-A13C193D: 3 → tied at top; each misses a different field, so together they cover all four dimensions - C-CD4829A7: 2 (industry, size) | C-9D69D147: 2 (size, region) - C-FEFCC020: 0 | C-11209339: 0 | C-0CE315B2: 0 **Note:** The top three are tied at 3/4 and are the only customers clearing that bar, so the ranking cutoff is unambiguous. Exact ordering among them is arbitrary (no partial-credit weights were provided); each is strongest on a different single gap if you want to prioritize by which dimension matters most for the pitch.
CHANNEL PERFORMANCE — trailing 6 months (2026-03 through 2026-08; spend file covers exactly these 6 months) PAID CHANNELS (spend, per channel_spend.csv) channel spend SQMs SQOs $/SQM $/SQO SQM→SQO pipeline $pipe/$spend paid_search $36,000 40 18 $900 $2,000 45.0% $720,000 $20.00 linkedin_ads $24,000 25 8 $960 $3,000 32.0% $96,000 $ 4.00 webinars $ 9,000 12 5 $750 $1,800 41.7% $60,000 $ 6.67 paid_social $18,000 0 0 UNDEF UNDEF UNDEF $0 UNDEF (spend, zero SQMs) Arithmetic: - Spend = 6 months × per-month: paid_search 6×$6,000=$36,000; linkedin_ads 6×$4,000=$24,000; webinars 6×$1,500=$9,000; paid_social 6×$3,000=$18,000. Total paid = $87,000. - paid_search: 40 SQMs (contacts CT-000001–CT-000040 range), 18 with sqo_date. $36,000/40=$900. $36,000/18=$2,000. 18/40=0.45. Pipeline = 18×$40,000=$720,000. $720,000/$36,000=$20.00. - linkedin_ads: 25 SQMs, 8 SQOs. $24,000/25=$960. $24,000/8=$3,000. 8/25=0.32. Pipeline = 8×$12,000=$96,000. $96,000/$24,000=$4.00. - webinars: 12 SQMs, 5 SQOs. $9,000/12=$750. $9,000/5=$1,800. 5/12=0.4167. Pipeline = 5×$12,000=$60,000. $60,000/$9,000=$6.67. - paid_social: $18,000 spent, 0 contacts in contacts_first_touch.csv → cost per SQM and per SQO are UNDEFINED (not zero). Cannot compute SQM→SQO rate. Paid totals (including paid_social): $87,000 spend, 77 SQMs, 31 SQOs, $876,000 pipeline. $/SQM = 87,000/77 = $1,129.87; $/SQO = 87,000/31 = $2,806.45; rate = 31/77 = 40.3%; pipe/$ = 876,000/87,000 = $10.07. ORGANIC CHANNELS (no spend rows — cost metrics not applicable) channel volume(SQMs) SQOs SQO rate pipeline organic_search 30 10 33.3% $90,000 referral 15 6 40.0% $48,000 combined 45 16 35.6% $138,000 Arithmetic: organic_search 10/30=0.3333, pipeline 10×$9,000=$90,000. referral 6/15=0.40, pipeline 6×$8,000=$48,000. Combined 16/45=0.3556. Note: the file gives no spend for organic_search or referral, so "Organic" here = channels with no spend row. If you intend Organic to mean organic_search only, use the first row. DATA-QUALITY FLAGS — SQO date precedes SQM date (2 rows, both linkedin_ads) - CT-000044: sqm 2026-07-23, sqo 2026-07-18 (SQO 5 days before SQM), $12,000 - CT-000041: sqm 2026-06-14, sqo 2026-06-09 (SQO 5 days before SQM), $12,000 These 2 rows (25% of linkedin_ads' 8 SQOs, $24,000 of its $96,000 pipeline) are chronologically invalid and should be verified before trusting linkedin_ads conversion metrics. Excluding them: linkedin_ads 6 SQOs, 24.0% rate, $3,600/SQO, $72,000 pipeline. CT-000007 (paid_search) has SQM=SQO same day (2026-03-28) — not a violation. REALLOCATION RECOMMENDATION Move budget from paid_social ($18,000, no attributable SQMs) and trim linkedin_ads, concentrating on paid_search and webinars: 1. Cut paid_social to $0 (or to a small instrumented test) — 6 months, $18,000, zero measurable SQMs. Undefined efficiency cannot justify continued spend. 2. Shift that $18,000 to paid_search: it carries the highest efficiency ($20.00 pipeline per $1, 45% SQM→SQO) and every SQO is a uniform $40,000. Reallocation would add ~$360,000 pipeline at current rates (18,000 × 20.00), or ~20 additional SQMs at $900/SQM. 3. Keep webinars ($9,000) as-is or grow modestly — best cost per SQM ($750) and cost per SQO ($1,800) of the paid set, strong rate (41.7%). It is the cheapest incremental-SQO lever but its per-SQO pipeline ($12,000) is a third of paid_search's, so it scales volume, not deal size. 4. linkedin_ads: hold flat pending resolution of the 2 flagged rows. Raw numbers are the weakest of the converting paid channels ($3,000/SQO, 32% rate, $4.00 pipe/$); adjusted for flags they are worse ($3,600/SQO, 24%). Do not increase until the date anomalies are fixed. CONFIDENCE - paid_search: moderate-high. n=40 SQMs / 18 SQOs is the largest paid sample; pipeline values are uniform ($40,000 each), which is suspicious as real data or a fixed modeled amount — treat per-deal pipeline as one assumption, not 18 independent observations. - webinars: moderate. n=12 / 5 SQOs — 5 conversions is a thin base; one reclassified contact moves the SQO rate by ~8pp. - linkedin_ads: low. n=8 SQOs with 2 chronologically invalid rows; metrics could shift materially. - paid_social: none — zero attributable contacts; "undefined" is the honest reading. - organic/referral: moderate (n=30/15); no spend data, so no ROI claims, only conversion and pipeline volume. - Overall: directional, not decisive. 31 paid SQOs total; shifting the full $18,000 on 18 paid_search SQOs of evidence is a strong prior but would benefit from a 1–2 month instrumented test on paid_social before permanent zeroing.
# BATTLECARD: Rivally — updated 2026-09-24 ## One-line positioning Rivally is a points-based recognition feed (S02) expanding into EU enterprise via data residency and multi-language support (S12, S15, S11). ## Pricing (source + date; newer wins) - CURRENT: $7 per user/month, Recognition Starter, annual billing required — pricing page, 2026-08-12 (S17). - CONFLICT (older sources, superseded): $5/user/mo, annual — pricing page 2026-01-20 (S03); still $5 on 2026-04-01 (S08). The 2026-08-12 page (S17) is newer and wins: Rivally raised list price ~40% ($5 → $7). - Other pricing datapoints: $6.50/user/mo quoted to a 500-seat prospect, annual term, 2026-06-02 (S13) — below current list; $7/user/mo list with 15% off for a 3-year term = $5.95/user/mo effective ($7 × 0.85), 2026-08-14 (S18). - Rivally Pulse is priced as a paid add-on, not bundled — 2026-09-01 (S23). Total cost is list + Pulse if they want surveys. ## Where they win (evidence-based) - EU / distributed teams: multi-language support praised by EU enterprise reviewer (S12); EU data residency GA + Dublin office (S15); ex-Workday VP EMEA hired for European expansion (S11). - Fast, low-friction rollout: mid-market setup under a week, Slack integration worked out of the box (S04). - Recognition experience: engaging/points-based feed (S02, S16). - Support: response times under 4 hours praised (S22). ## Where we win (evidence-based) - Analytics depth: "limited analytics" (S02), "basic dashboards vs enterprise tools" (S07), "analytics exports are CSV-only" making migration painful (S20). Decisive at scale: 800-seat prospect chose Bonusly over Rivally citing analytics depth, 2026-09-03 (S25). - Admin/user management: no SCIM provisioning, manual user management painful (S10); admin tooling lags peers (S16); no bulk recognition editing as of 2026-09-02 (S24). - EMEA rewards depth: rewards catalog thinner in EMEA than US (S14). ## Objections and responses 1. "Rivally is cheaper." → Their list just went UP to $7/user/mo (S17). Quotes are inconsistent ($6.50 in S13 vs $7 list in S18) and Pulse costs extra (S23). Ask the buyer for a written all-in quote including Pulse; compare against our analytics/admin depth (S25, S24). 2. "Rivally is EU-native with data residency and multi-language." → True and GA as of 2026-07-01 (S15), praised for distributed EU teams (S12). Don't dispute it. Pivot to the gaps EU enterprises hit at scale: no SCIM (S10), basic reporting (S07), thin EMEA rewards catalog (S14). 3. "Rivally sets up in under a week with Slack." → True (S04). Response: day-one setup is easy, exit and reporting are not — CSV-only exports made migration off Rivally hard (S20) and analytics are limited (S02). ## Recent changes (most recent ~60 days) - 2026-09-03: Lost 800-seat deal to us on analytics depth (S25). - 2026-09-02: Admin console still lacks bulk recognition editing (S24). - 2026-09-01: Rivally Pulse exits beta, sold as paid add-on (S23). - 2026-08-30: Support speed (<4h) praised (S22). - 2026-08-25: CSV-only analytics exports cited as migration pain (S20). - 2026-08-20: Microsoft Teams app v2 in public preview (S19). - 2026-08-14: $7 list quoted; 15% discount offered for 3-year term (S18). - 2026-08-12: List price raised to $7/user/mo (S17). ## Our 12-month win/loss vs Rivally (deals_with_competitor.csv, 2025-09 → 2026-08) - Total deals: 20. Wins: 13. Losses: 7. - Win rate = 13 ÷ 20 = 65%. - Losses: Deal-7767F5 (2025-09), Deal-5645A5 (2026-04), Deal-C6FFAA (2026-05), Deal-D263E0 (2025-11), Deal-935746 (2025-12), Deal-9066A6 (2026-03), Deal-72A02F (2026-04). Losses cluster in 2025-11/12 and 2026-03/04/05 (5 of 7). ## Corrections to the old card - "Rivally lacks a Slack integration" — FALSE per current evidence: Slack integration worked out of the box (S04). Removed. - "Pricing starts at $5 as of 2026-01" — superseded; now $7 as of 2026-08-12 (S17). Updated above. - "Strong in EU enterprise with multi-language support" — retained, re-sourced (S12, S15). - "Acquired by WorkHuman in 2025" — UNVERIFIED: no snippet supports this. Cannot re-source; excluded from positioning. - "Points-based recognition for mid-market" — retained in reworded form (S02 points-based; S04 mid-market reviewer). ## Excluded as non-facts (rep opinion, per policy) - S09 (Bryce Harmon: "UI is clunky" — not verified against the product) and S21 (Elena Sinclair: "discounting aggressively" — explicitly unconfirmed) are rep impressions, not competitor facts. Note: S18 does show a 15% 3-year discount offered, which is a fact, but it does not confirm "aggressive" discounting as a pattern. Data gaps: no snippets cover security/compliance certs (SOC 2 etc.), API/integrations beyond Slack/Teams, or win/loss reasons for our 7 losses — cannot state why we lost any specific deal.
SEQUENCE REVIEW (rates = total event / total sent across 3 steps) New Logo Nurture — sent 1,386 (500/458/428); open 35.4% (490/1,386); reply 6.49% (90/1,386); meeting 1.95% (27/1,386). Weakest step: 3 (18/428 = 4.21% reply). Expansion Nurture — sent 875 (300/300/275); open 64.6% (565/875) — inflated, see error; reply 6.74% (59/875); meeting 1.37% (12/875). Weakest step: 3 (12/275 = 4.36%). Cold Outbound - HR Leaders — sent 1,785 (600/595/590); open 30.5% (545/1,785); reply 0.45% (8/1,785); meeting 0.00% (0/1,785). Weakest step: 3 (1/590 = 0.17%). Cold Outbound - People Ops — sent 1,163 (400/386/377); open 29.2% (340/1,163); reply 2.49% (29/1,163); meeting 0.52% (6/1,163). Weakest step: 3 (6/377 = 1.59%). TRACKING ERRORS - Expansion Nurture step 2: opened 340 > sent 300 (113.3% open). Only violation in the file. Its step-2 open rate and the sequence open rate (64.6%) are unusable until fixed; step-1 (43.3%) and step-3 (34.5%) are the credible range. AUDIENCE OVERLAP (audiences.csv: 963 rows, 940 unique contacts) - 23 contacts sit in two sequences. 21 pairs Cold Outbound - HR Leaders + Cold Outbound - People Ops (CT-000849, CT-000884, CT-000890, CT-000908, CT-001033, CT-001097, CT-001101, CT-001103, CT-001105, CT-001130, CT-001153, CT-001159, CT-001217, CT-001227, CT-001236, CT-001255, CT-001258, CT-001277, CT-001285, CT-001311, CT-001345). 2 pair Expansion Nurture + New Logo Nurture (CT-000301, CT-000624). FAILURE MODES (<2% reply) - Cold Outbound - HR Leaders (0.45%): opens hold (22–40%) but replies collapse — message/offer-targeting failure, not deliverability. Zero meetings across 1,785 sends confirms no conversion path. People Ops (2.49% reply, similar ~29% open) out-replies it 5.5x, so the audience/positioning is wrong, not the channel. - Cold Outbound - People Ops step 3 only (1.59%): late-sequence fatigue — replies decay 3.50% → 2.33% → 1.59%. ONE CHANGE EACH / ORDER 1. Fix first — Cold Outbound - HR Leaders: replace the step-1 offer/CTA (largest send volume, 0 meetings, 0.45% reply). 2. Cold Outbound - People Ops: kill or rewrite step 3 (steps 1–2 clear 2%). Data gap: no send dates or per-step audience files, so decay vs. cohort differences can't be separated.
Q3-2026 WEEKLY MARKETING GOALS UPDATE — QTD as of 2026-09-24 Data window: quarter_meta.csv (Q3-2026, 2026-07-01 → 2026-09-30). Days elapsed 66 / 92 = 71.7% of quarter. 26 days remain. Sources: marketing_qtd.csv (actuals), targets.csv (targets). No other data used. METHOD: pace-expected = target × (66/92) = target × 0.7174. "Ahead/behind" = actual vs that linear pace. Rate metric (MIA) is not time-scaled; it is compared to target directly. SQMs Actual 230 | Target 300 | Delta -70 (76.7% of target) Pace-expected 300 × 0.7174 = 215.2 → actual is +14.8 vs pace → AHEAD SQOs Actual 84 | Target 120 | Delta -36 (70.0% of target) Pace-expected 120 × 0.7174 = 86.1 → actual is -2.1 vs pace → BEHIND (marginal) DS2s Actual 40 | Target 75 | Delta -35 (53.3% of target) Pace-expected 75 × 0.7174 = 53.8 → actual is -13.8 vs pace → BEHIND Closed-lost MIA rate Actual = 5 MIA / 25 closed_lost_total = 0.200 = 20.0% Target ≤ 0.100 = 10.0% (lower_better) | Delta +10.0 pts, i.e. WORSE than target Pace: rate metric, not time-scaled → BEHIND (2× the target rate) Same-quarter closes Actual 10 | Target 20 | Delta -10 (50.0% of target) Pace-expected 20 × 0.7174 = 14.3 → actual is -4.3 vs pace → BEHIND Active pipeline coverage Actual $3,000,000 | Target $4,000,000 | Delta -$1,000,000 (75.0% of target) Coverage vs target = $3,000,000 / $4,000,000 = 0.75× Pace-expected 4,000,000 × 0.7174 = $2,869,565 → actual is +$130,435 vs pace → AHEAD WHAT MOVED THIS WEEK — data missing. The three files provided contain QTD cumulative actuals only (marketing_qtd.csv), a static target set (targets.csv), and one quarter-meta row (quarter_meta.csv). There is no prior-week snapshot, no week-over-week delta, no per-week or per-stage funnel breakdown, and no deal-level or company-level records. Consequently no week-over-week movement can be computed or described — any statement about what moved this week would be invented. To report movement, a prior-week QTD snapshot (or weekly series) for the same six metrics is required. SUMMARY: 2 of 6 metrics are pacing ahead (SQMs, active pipeline), 4 are behind (SQOs marginally, DS2s materially, same-quarter closes, and the MIA rate at 20% vs a ≤10% target).
Q3 weighted forecast is 115,976.75: 44,729 COMMIT at 100% plus 203,565 BEST_CASE at 35%, PIPELINE at 0. Caveat that changes the call: 32 of 86 deals totaling 227,575 close after 2026-09-30, including COMMIT Deal-D348E1 at 13,770 dated 2026-10-15, so close dates are being pushed, not re-qualified. Do not run unattended: why-buys are empty on 71 of 86 including all 7 COMMIT and owner is blank on 85 of 86, so nothing validates against quota.
Subject: Re: July 28 demo — pricing for 150 seats Hi, Following up on my August 5 email recapping our July 28 demo, which included pricing for 150 seats. You'd asked for that pricing after the People team's strong reaction to automated milestone awards and the points catalog, so I want to make sure it didn't get buried. Could you take a look and let me know if 150 seats is still the right scope? I'm glad to set up 15 minutes to walk through it. Best, Alex Franklin Word count: 79 words (body incl. greeting and signature), under the 120 limit. Prior touch cited: ENG-00001, the Aug 5 recap email with 150-seat pricing (last engagement, 2026-08-05). Single ask: confirm 150-seat scope. All details (July 28 demo, People team, milestone awards, points catalog, 150 seats) are drawn from the engagement snippets — no name is used since none appears in the data.
**Weekly GTM Digest — Week of 2026-08-31** Marketing: Recognition starts with getting in the door, and this week Marketing delivered 46 SQMs against a target of 52 — a gap of 4, or 88% to plan (46 ÷ 52). The webinar channel carried the load with 18 of those 46 SQMs (18 ÷ 46 ≈ 39%), making it the single largest named contributor in the extract. Paid search efficiency held steady at $150 per SQM, so cost discipline didn't slip while the team closed the shortfall. The story for next week: convert that webinar momentum into volume to make up the 4-SQM difference. Sales: Sales turned demand into motion this week, converting 14 SQOs and setting 9 DS2 meetings — meaningful depth, since DS2 is where deals prove they're real. New pipeline created came in at $310,000 for the week. Closed-won energy showed up too: 3 deals closed in the same quarter they were created, a signal that velocity is working when timing aligns. Net: the machine is generating both breadth (14 SQOs) and proof (3 same-quarter closes), giving next week's forecast something solid to stand on. CS: The best recognition is a customer who stays. CS saved 2 renewals this week and moved team NPS to 61 — a healthy score by any standard. Heading into next week, though, 3 red-flag accounts remain open. That's the trade to watch: the saves and sentiment are strong, but 3 at-risk relationships need attention before they turn into losses. PLG: PLG continues to compound. The self-serve funnel added 412 new signups, with activation at 31% (412 × 0.31 ≈ 128 signups activated, derived — activation count itself isn't given in the extract). And 38 companies reached the aha moment of 10 recognition gives — the moment the product's value becomes undeniable. With 412 signups and 38 companies crossing that threshold, the growth loop is turning; the opportunity is lifting activation from 31% to convert more of that top-of-funnel into habitual givers. --- Notes on sourcing: all figures trace directly to week_extracts.md (SQMs 46/52, webinar 18, paid search $150; 14 SQOs, 9 DS2 meetings, $310,000 pipeline, 3 same-quarter closes; 2 renewals saved, NPS 61, 3 red flags; 412 signups, 31% activation, 38 companies at 10 gives). The "~128 activated" figure is arithmetic on the given 31%, flagged as derived; no activation count was provided. No prior-week comparatives, revenue-at-risk dollar values, or per-account detail exist in the extract, so none are claimed.
PARTNER DIGEST — 2026-08-24 to 2026-09-04 ACTIVE PARTNERS Apex Rewards Co - Status: 2 sourced opps, both DS1 (per #partners 08-25, logged with UTM Source = Partner). Co-webinar locked for 09-15. - Sourced pipeline: 2 deals — Deal-DDAAF2 ($180,000) + Deal-2CF33E ($95,000) = $275,000 HRCloud Partners - Status: 1 sourced opp — integration referral closed security review, moved to DS2 (per #partners 08-27). - Sourced pipeline: 1 deal — Deal-F1CDA5 = $140,000 CultureBridge - Status: 2 sourced opps from lunch-and-learn, both early stage (per #partners 08-29). - Sourced pipeline: 2 deals — Deal-096E1D ($60,000) + Deal-067213 ($75,000) = $135,000 WorkWell Group - Status: 0 sourced deals this period; joint playbook restart planned for Q4, planning call booked 09-09 (per #partners 09-02). - Sourced pipeline: 0 deals = $0 QUIET PARTNERS Recogniq — no activity this period. KudosWave — no activity this period. PeopleFirst Advisors — no activity this period. TotalPerk — no activity this period. PERIOD TOTALS - Partner-sourced pipeline: 5 deals - Amount: $275,000 + $140,000 + $135,000 + $0 = $550,000 - Reconciles to partner_deals.csv: 5 rows, sum 180,000 + 95,000 + 140,000 + 60,000 + 75,000 = $550,000. All rows carry utm_source = Partner. Notes on sourcing: deal counts and amounts come from partner_deals.csv only. Stage commentary (DS1/DS2/early stage) is quoted from partner_slack.md and is not present in the deal file. No data exists for the four quiet partners beyond their roster listing.
Q1. Yes. Customer data is encrypted at rest using AES-256. [P1] Q2. Yes. Data in transit is encrypted with TLS 1.2 or higher. [P1] Q3. Yes. Bonusly maintains a SOC 2 Type II report, renewed annually. A copy is available to prospects under NDA. [P2] Q4. Customer data is retained for 90 days after contract termination, then deleted from production systems. (Retention is contract duration + 90 days.) [P3] Q5. The subprocessor list is published at trust.bonusly.com. Customers receive 30 days notice before a new subprocessor is added. [P4] Q6. Yes. SAML 2.0 single sign-on is available on the Enterprise plan. [P5] Q7. Production databases are backed up daily; backups are encrypted and retained for 35 days. [P6] Q8. Unanswerable from the provided excerpts. No excerpt addresses ISO 27001. Answer requires: our certifications register / ISO 27001 certificate (or a statement of non-certification). Q9. Unanswerable from the provided excerpts. No excerpt addresses contractual uptime SLA. Answer requires: the master subscription agreement / SLA exhibit (or Enterprise plan terms). Q10. Unanswerable from the provided excerpts. No excerpt addresses HIPAA or BAAs. Answer requires: our legal/compliance position on HIPAA and the standard BAA terms (or a statement that no BAA is offered).
SKILL SET RECONCILIATION — skill_manifest.csv vs 14 SKILL.md files
=================================================================
All findings below cite only the provided data. One proposal per finding. Nothing rewritten.
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FINDING 1a — Duplicate ALWAYS-trigger phrases
Skills: comms-drafter <-> email-drafter
Severity: CRITICAL | Action: MERGE
Evidence (trigger phrases, verbatim from each description):
comms-drafter: "write me an email," "draft a follow-up," "help me reply,"
"what should I say," "bump email," "contract nudge"
email-drafter: "write me an email," "draft a follow-up," "help me reply to this,"
"what should I say," "bump email," "contract nudge"
6 of ~8 trigger phrases are identical or near-identical. Both skills also carry the
same contract-follow-up benchmark paragraph verbatim, and both hand strategy to
deal-strategy-coach. Routing between them is unresolvable from the trigger text alone.
Proposal: MERGE email-drafter into comms-drafter (comms-drafter is the superset scope;
email-drafter's Gmail-signature extraction block is the only unique content to carry over).
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FINDING 1b — Overlapping ALWAYS-trigger phrases
Skills: pipeline-intelligence-report <-> weekly-pipeline-report
Severity: WARNING | Action: TRIM_DESC
Evidence:
pipeline-intelligence-report: "run the pipeline report", "pipeline update",
"what's the pipeline look like" (ALWAYS trigger)
weekly-pipeline-report: "run the pipeline update," "update the pipeline",
"what does pipeline look like" (ALWAYS trigger)
"pipeline update" and the "what does/dos the pipeline look like" phrasings are claimed
by both. The two reports are different artifacts (10-tab scored HTML vs weekly
performance update) but their triggers do not say so.
Proposal: TRIM_DESC on weekly-pipeline-report — drop generic "pipeline update" /
"update the pipeline" / "what does pipeline look like" from its trigger list, keep
"weekly pipeline report", "weekly pipeline update", "mid-month pipeline check".
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FINDING 2 — Circular delegation chain
Chain (named): email-drafter -> deal-strategy-coach -> email-drafter
Severity: CRITICAL | Action: UPDATE_BODY
Evidence:
email-drafter (lane marker): "If the user needs strategic deal coaching ...
point them to the deal-strategy-coach skill."
deal-strategy-coach (manager email frameworks): "When drafting manager-to-prospect
emails, use the `email-drafter` skill ..."
Each delegates to the other for the same handoff (strategy <-> draft), with no
precedence rule. Note: comms-drafter also points to deal-strategy-coach ("this skill
drafts, that skill diagnoses"), so routing comms-drafter -> deal-strategy-coach ->
email-drafter dead-ends in the same loop.
Proposal: UPDATE_BODY on deal-strategy-coach — remove the return delegation to
email-drafter; state that drafts are produced inside the coaching session (or declare
the handoff terminal). One edge deleted breaks the cycle.
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FINDING 3 — Dangling delegation targets (not present in this manifest)
Severity: WARNING | Action: REVIEW
Evidence — invoked skill targets with no manifest row:
bonusly-brand — mandatory prerequisite in comms-drafter, email-drafter,
sales-forecast ("Always reference `bonusly-brand` skill"), signalforge-claim-compressor
("use bonusly-brand for those")
prospect-research-multithreading — invoked by comms-drafter, email-drafter,
deal-strategy-coach (cross-skill handoff section)
bonusly-data-questions, bonusly-product-questions, bonusly-business-reporting-questions,
bonusly-rewards-questions, bonusly-ppp-questions, bonusly-feature-flag-questions,
bonusly-deal-desk-questions, bonusly-datadog-questions — analysis-validator §12.4
delegation table
skill-orchestrator — analysis-validator §11 cascading files; signalforge-feedback
("registered in skill-orchestrator")
signalforge-reports — pipeline-intelligence-report + weekly-pipeline-report
(pre-build mandatory reads, incl. DESIGN-SYSTEM.md, signalforge.css)
caveman — signalforge-claim-compressor ("Relationship to Caveman Skill")
14 named targets, 0 manifest rows. Whether they exist outside this manifest cannot be
determined from the data provided — they are dangling relative to this skill set.
Also non-skill file paths referenced with no manifest evidence: `/mnt/skills/organization/
signalforge-reports/*`, `/mnt/skills/public/xlsx/scripts/recalc.py`, sales-forecast and
weekly-pipeline-report `references/*` files.
Proposal: REVIEW — confirm each target resolves in the live skill tree; if any does not,
add a manifest row or delete the reference. Highest exposure is bonusly-brand (hard
prerequisite in 4 skills).
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FINDING 4 — Version conflict
Skill: analysis-validator (internal)
Severity: WARNING | Action: UPDATE_BODY
Evidence:
v3.6 (3 sources): body header "**Version:** 3.6", footer "analysis-validator v3.6 ·
May 9, 2026", changelog top row "3.6 | May 9, 2026". External corroboration:
pipeline-intelligence-report footer cites "Analysis Validator v3.6".
v3.2 (1 source): §7 validation trail template line "Validator: analysis-validator v3.2".
Arithmetic: 4 references to a version number — 3 say 3.6, 1 says 3.2.
Survivor: v3.6. The v3.2 string is a stale template literal in the trail block that
every published report reproduces.
Proposal: UPDATE_BODY on analysis-validator §7 trail template — change
"Validator: analysis-validator v3.2" to v3.6. (No other cross-skill version conflict
found; pipeline-intelligence-report "v4 Component Vocabulary" refers to the
signalforge-reports design system, not a competing skill version.)
-----------------------------------------------------------------
FINDING 5 — Manifest descriptions exceeding 1,024 characters
Severity: INFO | Action: none required
Arithmetic (skill_manifest.csv, description_chars column):
656, 897, 996, 792, 965, 676, 945, 1004, 1006, 962, 1006, 708, 762, 656
Count > 1024: 0 of 14.
Max = 1006 (pipeline-intelligence-report and signalforge-claim-compressor, tie).
Headroom = 1024 − 1006 = 18 characters. partner-digest at 1004 has 20.
Answer: 0. Two skills sit within 20 chars of the limit — any description edit to those
two must be net-zero length or a TRIM_DESC is needed.
-----------------------------------------------------------------
FINDING 6 — Hardcoded ids / dates / person names in skill bodies
6a. Hardcoded page/record ids — Severity: WARNING | Action: UPDATE_BODY
partner-digest: Cloud ID `73fe98de-a4a3-4869-9f8a-bb1eeed4cf7f`, Space ID `1958248479`,
folder `2286616609`, page ids 2265382925 / 2236940297 / 2237825028 / 2239365136 /
2238283777 / 2286321666
sales-forecast: Space ID `2232811524`, parent page `2232582148`
signalforge-feedback: page `2295136266`, parent `2234417154`, Build Log `2247295002`
deal-strategy-coach: Confluence page `2257879045` (AE Excellence Playbook URL)
pipeline-intelligence-report, next-to-close, stale-pipeline-report: HubSpot org `1973303`
stale-pipeline-report: Slack channel `C0561C1JCPJ`, support owner `55483190`
weekly-pipeline-report: two Google spreadsheet IDs
(`1CLZeOsElVDF_LF0ZG_t2nfwvhnZ6bpwqM_nX3WEYzcw`,
`1ENuaEcCuLjdKhMvp8FK3Ys1ek5Aw9ZuOZhsHJJFoB_k`)
Proposal: UPDATE_BODY — move all ids to per-skill `references/` files (as sales-forecast
already does with data-sources.md); leave lookup logic in the body.
6b. Hardcoded dates — Severity: WARNING | Action: UPDATE_BODY
Operational dates (stale-risk): analysis-validator "CALL_SPOTLIGHT_BRIEF was removed ...
as of May 4, 2026", "last modified March 28, 2023", "as of May 2026" ranges,
"ai_closed_lost_reason field confirmed May 2026" (closed-lost-analysis);
model-selection registry `last_checked: 2026-05-19`; weekly-pipeline-report
"April 1 – June 30, 2026" quarter window and static Q1 2026 figures
($365,152 vs $475,000; $2,490,532 vs $3,288,000); partner-digest canonical issue
"May 16, 2026"; closed-lost-analysis "30-deal AI-field sample from May 2026",
"MinIO: rep vacation May 4–12", "Lost to Motivosity on this in May 2026 (Softheon)";
stale-pipeline-report example dates "5/15", "5/19".
Acceptable (leave as-is): changelog history rows and creation/last-updated stamps.
Proposal: UPDATE_BODY — replace operational "as of [date]" anchors with
"run at analysis time" checks (closed-lost-analysis already models this in
"Known System State (dynamic)"); keep changelog dates.
6c. Hardcoded person names — Severity: WARNING | Action: UPDATE_BODY
analysis-validator §12.3: full GTM roster, 19 named people with HubSpot owner IDs
(Alaina Loori, Shealagh Coughlin, Bryce Harmon, Hugo Lindqvist, Dana Mercer,
Alex Franklin, Cole Ingram, Gavin Porter, Colleen Perry, Ellie Barton, Ashley Reyer,
Megan Franz, Elena Sinclair, Youssef Elkhateeb, Amanda Czenkus, Ben Castelli,
Amani Phipps, John Thomas, Yasmin Wahid) + escalation "Manish or Amani" (§10, G1-K).
pipeline-intelligence-report: 5 AE names + IDs (note: Hugo Lindqvist 77260721 appears
in analysis-validator's "Core 6" but not here — roster split across two skills).
weekly-pipeline-report: "Ben Lavin · Demand Generation" (skill titled to a person).
partner-digest: "Amani Phipps (RevOps / Partnerships)", Slack `U03QLMBL7AR`,
partner contacts "Kelli, Jen Lee", "Hani, Bryce", "Sara".
sales-forecast: "Alaina / VP Sales view" (changelog records an earlier
"Elena → Alaina" rename — direct evidence of name-staleness cost).
signalforge-feedback: example "Gavin Porter Rep Diagnostic".
Proposal: UPDATE_BODY — single roster reference file shared by analysis-validator and
pipeline-intelligence-report (also resolves the Core 6 vs 5-AE discrepancy);
role-based wording ("VP Sales", "the report owner") in the other four skills.
-----------------------------------------------------------------
FINDING 7 — Manifest drift, both directions
Severity: INFO | Action: none required
Direction A (file exists, no manifest row): 0.
All 14 provided SKILL.md files map 1:1 to a manifest row: analysis-validator,
closed-lost-analysis, comms-drafter, deal-strategy-coach, email-drafter,
model-selection, next-to-close, partner-digest, pipeline-intelligence-report,
sales-forecast, signalforge-claim-compressor, signalforge-feedback,
stale-pipeline-report, weekly-pipeline-report.
Direction B (manifest row, no file): 0. 14 rows, 14 files.
Cross-check: every `declared_name` matches the frontmatter `name:` of its file
(14/14 exact).
Caveat, stated explicitly: `body_lines` and `description_chars` are manifest-declared
values; per-file line counts were not independently recomputed from the pasted bodies,
so column accuracy is unverified — only row/file/name alignment is.
-----------------------------------------------------------------
SUMMARY
CRITICAL: 2 (1a comms-drafter/email-drafter trigger duplication -> MERGE;
2 email-drafter <-> deal-strategy-coach delegation cycle -> UPDATE_BODY)
WARNING: 5 (1b trigger overlap -> TRIM_DESC; 3 dangling targets -> REVIEW;
4 v3.2/v3.6 conflict, v3.6 survives -> UPDATE_BODY;
6a/6b/6c hardcoded ids/dates/names -> UPDATE_BODY x3)
INFO: 2 (5: zero descriptions over 1,024 chars; 7: zero manifest drift both ways)
No skill is proposed for deletion.
RUNBOOK — incident-2026-08-29-reward-queue (resolved)
Source: incident_thread.md only. Every step cites its message ID.
Arithmetic: none performed — all figures below are exactly as reported in the thread.
STEP 1 — Take incident command [M01, 14:02:10Z]
Actor: Bryce Harmon
Action: Acknowledged PagerDuty alert (reward-worker queue depth > 10k) and took IC.
Command: none recorded
Verified: not documented
Rollback: not applicable — no system state changed
STEP 2 — Measure queue depth [M02, 14:04:33Z]
Actor: Farid Osman
Command: bundle exec rake sidekiq:queue_depth
Result: reward queue at 48,213 pending jobs (normal is under 500)
Verified: reported by the actor who ran the command
Rollback: not applicable — read-only
STEP 3 — Inspect dead set [M03, 14:06:02Z]
Actor: Farid Osman
Action: Reported dead set of 112 jobs, all Redis::TimeoutError from around 13:58.
Command: NOT DOCUMENTED — needs confirmation (inspection command absent)
Verified: reported result only; no verification method recorded
Rollback: not applicable — read-only
STEP 4 — Pause enqueue (stop the bleed) [M04, 14:08:45Z] [STATE-CHANGING]
Actor: Farid Osman
Command: bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'
Verified: NOT DOCUMENTED — needs confirmation (no direct flag-state verification recorded)
Rollback: bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'
(rollback explicitly supplied in M04)
STEP 5 — Clear the dead set [M05, 14:15:20Z] [STATE-CHANGING / DESTRUCTIVE]
Actor: Elena Sinclair
Action: Cleared out the dead set "while I was in the console."
Command: NOT DOCUMENTED — needs confirmation (console action, exact command absent)
Verified: NOT DOCUMENTED — needs confirmation
Rollback: NOT DOCUMENTED — needs confirmation
Note: This happened during the incident; the thread does not establish it as an
approved or repeatable remediation.
STEP 6 — Scale reward-worker up [M06, 14:21:07Z] [STATE-CHANGING]
Actor: Bryce Harmon
Command: kubectl scale deployment/reward-worker --replicas=6 (was 3)
Verified: NOT DOCUMENTED — needs confirmation (no direct replica-count verification).
M07/M08 queue observations are later reported results, not isolated proof
of this action's effect.
Rollback: kubectl scale deployment/reward-worker --replicas=3 (rollback explicitly
supplied in M06)
STEP 7 — Observe queue drain [M07, 14:33:41Z]
Actor: Farid Osman
Action: Reported queue depth down to 9,400 and falling ~1,200/min.
Command: NOT DOCUMENTED — needs confirmation (measurement command absent)
Verified: reported result only
Rollback: not applicable — read-only
STEP 8 — Confirm recovery [M08, 14:47:55Z]
Actor: Cole Ingram
Command: bundle exec rake sidekiq:queue_depth
Result: returns 0. Error rate in Datadog back to baseline (per actor's report).
Verified: queue depth verified by direct command run; Datadog baseline reported, not
independently verified in-thread.
Rollback: not applicable — read-only
STEP 9 — Re-enable enqueue [M09, 14:49:10Z] [STATE-CHANGING]
Actor: Bryce Harmon
Command: bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'
Verified: 40 new jobs processed cleanly in the next 3 minutes (per M09)
Rollback: NOT DOCUMENTED — needs confirmation.
(Do NOT treat the M04 disable command as the approved rollback for this step;
the thread does not document it as such.)
STEP 10 — Scale reward-worker back down and resolve [M10, 14:55:00Z] [STATE-CHANGING]
Actor: Bryce Harmon
Command: kubectl scale deployment/reward-worker --replicas=3
Verified: queue stable at 0; incident declared resolved (per M10)
Rollback: NOT DOCUMENTED — needs confirmation.
(Do NOT treat the M06 scale-to-6 command as the approved rollback for this
step; the thread does not document it as such.)
Open items needing confirmation (thread gaps):
- Step 3: command used to inspect the dead set
- Step 5: exact command/entry point, verification, and rollback for dead-set clearing
- Steps 4 and 6: direct post-action verification of the flag state / replica count
- Step 7: command used to measure the 9,400 / ~1,200-per-min figures
- Steps 9 and 10: documented rollback for the re-enable and scale-down actions
Sequence of the failure — from datadog_logs.csv + sidekiq_jobs.csv only
FIRST ERROR
2026-09-03T14:01:12Z — service `reward-service`, level ERROR:
"Redis::TimeoutError: Connection to Redis at redis-primary:6379 timed out after 5s"
This is the earliest ERROR row in the slice. Every prior row (13:55:13Z–13:59:30Z) is INFO.
CASCADE IN ORDER
1. 14:01:12Z reward-service ERROR Redis::TimeoutError to redis-primary:6379, 5s timeout [FIRST]
2. 14:01:20Z reward-service ERROR retry exhausted for RewardGiveJob
3. 14:01:30Z reward-service ERROR retry exhausted for RewardGiveJob
4. 14:01:40Z reward-service ERROR retry exhausted for RewardGiveJob
5. 14:01:40Z sidekiq ERROR RewardGiveJob failed: Redis::TimeoutError; retrying in 60s
6. 14:02:28Z sidekiq ERROR RewardGiveJob failed: Redis::TimeoutError; retrying
7. 14:02:30Z sidekiq WARN Queue reward depth above 10,000
8. 14:03:05Z api-gateway ERROR 502 upstream timeout calling reward-service /gives
9. 14:03:30Z web-app ERROR Give form submission failed: upstream 502 from api-gateway
10. 14:03:31Z sidekiq ERROR RewardGiveJob failed; retrying
11. 14:03:48Z api-gateway ERROR 502 upstream timeout calling reward-service
12. 14:04:13Z api-gateway ERROR 502 upstream timeout calling reward-service
13. 14:04:22Z sidekiq ERROR RewardGiveJob failed; retrying
14. 14:04:45Z web-app ERROR Give form submission failed: upstream 502
15. 14:05:16Z api-gateway ERROR 502 upstream timeout calling reward-service
16. 14:05:26Z sidekiq ERROR RewardGiveJob failed; retrying
17. 14:05:42Z web-app ERROR Give form submission failed: upstream 502
18. 14:06:47Z sidekiq ERROR RewardGiveJob failed; retrying
19. 14:06:49Z web-app ERROR Give form submission failed: upstream 502
20. 14:06:52Z api-gateway ERROR 502 upstream timeout calling reward-service
--- no reward-service/sidekiq/api-gateway/web-app lines until 14:22:10Z ---
21. 14:22:10Z reward-service INFO Redis connection restored; resuming job processing
22. 14:24:45Z sidekiq INFO Queue reward depth below 500
Propagation shape: Redis (dependency) -> reward-service (app retries exhausted) -> sidekiq (queue backs up) -> api-gateway (upstream 502) -> web-app (user-facing give failure).
SERVICE AND JOB INVOLVED
Primary service: reward-service (origin of first error).
Primary job: RewardGiveJob (named in items 2-6, 10, 13, 16, 18).
Supporting services in the cascade: sidekiq, api-gateway, web-app; failing dependency redis-primary:6379.
Collateral job, from sidekiq_jobs.csv only: RecognitionDigestJob (J-00013..J-00016), same Redis::TimeoutError.
ARITHMETIC
ERROR rows by service (19 total):
reward-service 4 (14:01:12, :20, :30, :40)
sidekiq 6 (14:01:40, 14:02:28, 14:03:31, 14:04:22, 14:05:26, 14:06:47)
api-gateway 5 (14:03:05, 14:03:48, 14:04:13, 14:05:16, 14:06:52)
web-app 4 (14:03:30, 14:04:45, 14:05:42, 14:06:49)
4 + 6 + 5 + 4 = 19
sidekiq_jobs.csv: 16 rows, all error = Redis::TimeoutError.
RewardGiveJob 12 (J-00001..J-00012)
RecognitionDigestJob 4 (J-00013..J-00016)
12 + 4 = 16
Earliest failed_at in sidekiq_jobs.csv = J-00005 at 14:01:46Z.
First log ERROR 14:01:12Z -> first recorded job failure 14:01:46Z = 34s later.
Offsets from first error (14:01:12Z):
queue depth >10,000 (14:02:30Z) = 78s (1m18s)
first api-gateway 502 (14:03:05Z) = 113s (1m53s)
first web-app failure (14:03:30Z) = 138s (2m18s)
last ERROR (14:06:52Z) = 340s (5m40s)
Redis restored (14:22:10Z) = 1258s (20m58s)
queue depth <500 (14:24:45Z) = 1413s (23m33s)
Silent gap: 14:06:52Z -> 14:22:10Z = 918s (15m18s) with zero reward-service/sidekiq/api-gateway/web-app lines (only postgres "checkpoint complete" INFO rows at 14:10:56, 14:12:55, 14:14:40, 14:16:47, 14:18:13, 14:20:59).
Line-count mismatch: 12 RewardGiveJob rows in sidekiq_jobs.csv vs 6 sidekiq ERROR log lines. 12 - 6 = 6 unaccounted; not reconcilable without attempt counts.
DATADOG QUERY TO CONFIRM THE FIRST ERROR
Confirm the line itself (field names per the CSV; `@level` assumes a custom level attribute):
service:reward-service @level:ERROR "Redis::TimeoutError" @timestamp:[2026-09-03T14:01:00Z TO 2026-09-03T14:01:30Z]
Confirm it is the earliest error in the incident window (drop service filter, sort asc):
@level:ERROR "Redis::TimeoutError" @timestamp:[2026-09-03T13:55:00Z TO 2026-09-03T14:03:00Z]
If the instance uses Datadog's standard status attribute instead of `@level`, substitute `status:error` for `@level:ERROR`.
WHAT THE LOGS DO NOT SHOW
- Root cause of the Redis timeout. Only the client-side symptom is present ("timed out after 5s" against redis-primary:6379). No Redis server logs, no slowlog, no CPU/memory/network/host metrics.
- Cause of recovery. "Redis connection restored" at 14:22:10Z is a state change, not a remediation record. No deploy, restart, failover, or scaling event appears anywhere in either file.
- Anything in the 14:06:52Z -> 14:22:10Z window for the affected services (15m18s). Only unrelated postgres checkpoint INFO rows exist there.
- Correlation IDs / trace IDs / request IDs. A specific web-app "Give form submission failed" cannot be tied to a specific RewardGiveJob or a specific api-gateway 502.
- RecognitionDigestJob in datadog_logs.csv. The 4 failures exist only in sidekiq_jobs.csv; there are zero RecognitionDigestJob log lines in the log slice.
- 1:1 reconciliation between the 12 RewardGiveJob rows and the 6 sidekiq ERROR lines (attempt counts, retry schedule, queue name, worker identity all absent from sidekiq_jobs.csv).
- Ultimate fate of the 16 jobs. Slice ends 14:24:45Z; no success, drop, or dead-letter evidence.
- Fate of the pre-failure enqueues (web-app 13:58:49Z, reward-service 13:59:30Z) — no job_id links them to J-00001..J-00016.
- Any business impact: no company/account IDs, no affected give counts, no dollar or deal/revenue values in either file.
- Whether error volume stopped because the fault cleared or because traffic stopped — no request-rate data.
No deals, companies, or financial figures appear in these files; none are asserted.
Citations
- datadog_logs.csv (41 rows, 2026-09-03T13:55:13Z to 14:24:45Z) — log slice as provided.
- sidekiq_jobs.csv (16 rows, J-00001..J-00016) — job failures as provided.
- Datadog queries listed above were proposed for verification, not executed; no live Datadog call was made in this response.
Disclaimer: this reads as an active production incident (Redis outage propagating to user-facing give submissions). Escalate to the on-call engineer for reward-service/Redis before relying on this analysis for remediation.
Flag-by-flag summary, matching the export exactly. 1. recognition_streaks_v2 — state: on Controls: whether a give is recorded by StreakTracker (app/models/recognition.rb). Targeting: segment:beta_companies. Companies: 42. 2. points_budget_guardrails — state: on Controls: whether BudgetService.enforce!(giver, points) runs (app/services/budget_service.rb). Targeting: all_companies. Companies: 220. 3. slack_dm_nudges — state: on Controls: whether nudge_job sends a Slack DM via SlackDm.send_nudge(user); job returns early if off (app/jobs/nudge_job.rb). Targeting: segment:region_na. Companies: 87. 4. redeem_flow_redesign — state: off Controls: which redeem UI renders — RedeemV2Component when on, RedeemV1Component when off (app/controllers/redeem_controller.rb). Targeting: targeted_list. Companies: 12. 5. analytics_dashboard_v3 — state: on Controls: whether the analytics dashboard uses AnalyticsV3 (app/controllers/analytics_controller.rb). Targeting: segment:tier_three. Companies: 65. 6. ms_teams_app_v2 — state: off Controls: whether TeamsAppV2.install(company) runs (app/services/teams_installer.rb). Targeting: targeted_list. Companies: 9. 7. legacy_give_modal — state: off Controls: unknown — no code reference in the excerpt. Targeting: segment:legacy_plan. Companies: 14. 8. survey_boosters_q3 — state: on Controls: unknown — no code reference in the excerpt. Targeting: segment:legacy_plan. Companies: 7. 9. paused_offboard_cleanup — state: off Controls: unknown — no code reference in the excerpt. Targeting: none (empty rules field). Companies: 0. No targets and no code reference. Notes: - Flags with no code reference: legacy_give_modal, survey_boosters_q3, paused_offboard_cleanup. - Flag with no targets: paused_offboard_cleanup (0 companies, blank targeting rules). - No flag outside the export is described above. Company counts are copied verbatim from the export. Per-flag counts sum to 456, but these are per-flag enablement counts and are not stated to be mutually exclusive in the data, so no cross-flag total should be inferred.
**NDA-1 — [PARTY A] and Bonusly: GREEN (standard approval)**
Reasoning: Mutual confidentiality, two-year term with three-year survival, complete standard carve-outs "(a) is or becomes publicly available through no breach, (b) was known prior to disclosure, (c) is received from a third party without duty of confidence, (d) is independently developed, or (e) must be disclosed by law or court order," and Delaware governing law ("the laws of the State of Delaware, USA") with "No license, no obligation to proceed, no exclusivity" — all market-standard, nothing unusual.
**NDA-2 — [PARTY B] and Bonusly: YELLOW (counsel review)**
Reasoning: Otherwise standard (mutual, Delaware, standard carve-outs) but embeds a non-solicit — "During the term of this Agreement and for eighteen (18) months thereafter, neither party shall solicit for employment or hire any employee of the other party with whom it came into contact under this Agreement" — where the 18-month post-term tail and the "hire" (not just solicit) prohibition exceed a plain NDA and need counsel to negotiate scope/duration.
**NDA-3 — [PARTY C] and Bonusly: RED (full legal review)**
Reasoning: Four compounding red flags — one-way confidentiality in favor of [PARTY C] with a five-year term; an embedded non-compete "For a period of three (3) years following the Effective Date, Recipient shall not, directly or indirectly, engage in or provide services to any business that competes with Discloser's business" (broad "any business" scope, 3 years, "indirectly," no reciprocal benefit); zero carve-outs ("No confidentiality carve-outs are stated in this Agreement" — no public/know/third-party/independent-development/legal-compulsion exceptions); and unusual governing law/jurisdiction ("the laws of the Republic of Ireland, and the parties submit to the exclusive jurisdiction of its courts").
Arithmetic check: NDA-1 2yr term + 3yr tail = 5yr total exposure; NDA-2 3yr term + 18mo non-solicit tail = 4.5yr; NDA-3 5yr confidentiality + 3yr non-compete on one-way obligations.