Mean 0.976 · 36/40 perfect tests · $7.67 total · modalities in: text, image, video · 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+ = stage DS2, DS3, DS4, or DS5. DS1 deals are excluded from the why-buys audit. - Why-buys non-compliant = why_buys empty, under 15 chars, or a placeholder (tbd, n/a, see notes). LONG(n) counts as present and substantive per the brief. - Missing DS1 = t_ds1 blank. - Regressed = any earlier-stage entry timestamp later than a later-stage entry timestamp across t_ds1 -> t_ds5 (populated fields only). SCOPE 156 open deals total: 124 DS2+, 32 DS1. WHY-BUYS COMPLIANCE (DS2+ ONLY) Overall: 101 of 124 non-compliant = 81.5% (101/124 = 0.8145). All 23 compliant deals hold LONG(n) values; every failure is an empty field — no placeholder or short-text values exist in the data. By owner (non-compliant / DS2+ deals = %): - Bryce Harmon: 15/18 = 83.3% - Dana Mercer: 18/22 = 81.8% - Alex Franklin: 48/61 = 78.7% - Cole Ingram: 19/22 = 86.4% - Farid Osman: 1/1 = 100.0% - Elena Sinclair: n/a — 0 DS2+ deals (her only open deal, Deal-57FF13, is DS1) Cross-check: 15+18+48+19+1 = 101; 18+22+61+22+1 = 124. ✓ OTHER HYGIENE CHECKS - Deals with no DS1 timestamp: 0 (all 156 rows have t_ds1 populated). - Deals that regressed: 0 (every populated t_ds1 -> t_ds5 sequence is non-decreasing). THREE PROCESS FIXES 1. Hard-gate why-buys at the DS2 transition. Failure is systemic (81.5%, every owner at 78.7%+), so make a 15+ character substantive why-buys a CRM validation requirement to enter DS2 — with a placeholder blocklist — instead of relying on retroactive cleanup. 2. Publish a per-owner hygiene scorecard in weekly pipeline reviews and require fixes before forecast commit. Prioritize by absolute backlog: Alex Franklin (48 non-compliant) first, then Cole Ingram (19) and Dana Mercer (18); Farid Osman's 100% rate is n=1 but should still be flagged. 3. Fix stage-entry timestamp capture. t_ds5 is blank on all 10 DS5 deals (their DS5 entry exists only in entered_current_stage), and Deal-0D0211 and Deal-7E2131 carry a t_ds4 while sitting at DS3, while Deal-C7F9BF carries a t_ds3 while sitting at DS2 — timestamps for stages beyond the current stage suggest unlogged back-and-forth movement. Automate write-on-stage-entry and backfill, or regression and time-in-stage analytics stay uncomputable.
All 156 rows joined cleanly against the owners table. Results: 1) Deals resolved: 156 of 156. 2) Deals with no owner match: none. Every owner_id present in deals_open.csv (119337721, 701163055, 716654662, 83155923, 83155924, 84342457) exists in owners.csv. No names had to be guessed. 3) Owner ids mapping to archived/deactivated owners: none. All six matched owners have archived=false. The two archived owners in the table — Gavin Porter (1520255671) and Hugo Lindqvist (77260721) — do not appear on any open deal in this extract. 4) Total pipeline amount per resolved owner (sum of amount over their deals): Alex Franklin (84342457) 624,310.00 (67 deals) Bryce Harmon (119337721) 1,054,144.00 (35 deals) Dana Mercer (83155923) 341,195.00 (24 deals) Cole Ingram (83155924) 288,161.43 (22 deals) Farid Osman (716654662) 4,134.00 ( 7 deals) Elena Sinclair (701163055) 2,100.00 ( 1 deal) Arithmetic check: 624,310.00 + 1,054,144.00 + 341,195.00 + 288,161.43 + 4,134.00 + 2,100.00 = 2,314,044.43 total resolved pipeline across 156 deals. Cole Ingram's total is the only non-integer: seven of his deals carry cent amounts (7,225.40; 7,781.20; 6,947.50; 2,480.40; 4,779.88; 3,334.80; 58,529.25). Note: pipeline figures are raw sums of the amount field as provided — no forecast-category weighting or stage filtering was applied, since none was requested.
Integrity check results (computed by exact join on hs_deal_id → deal_id; gong rows = 67, deals_open = 156).
(a) ORPHANS — gong rows with no matching deal in deals_open: 30 of 67 (44.8%)
These 30 orphan rows account for 304 logged calls (46+24+21+21+17+17+14+12+11+11+9+8+7+6+6+6+6+5x10+4x3 = 304). 37 of 67 gong rows do match an open deal.
Sample orphan aliases (all from the gong table):
Deal-8FA85D (id 60251290957, 46 calls, last_call 2026-11-05)
Deal-8FC3F9 (id 60251649055, 24 calls, last_call 2026-10-19)
Deal-3B7945 (id 60251639682, 21 calls, last_call 2026-10-20)
Deal-42B265 (id 61227242540, 21 calls, last_call 2026-11-25)
Deal-9CCC42 (id 61430316324, 17 calls, last_call 2026-11-27)
Deal-36EA09 (id 60251082126, 17 calls, last_call 2026-11-19)
Two structural anomalies inside the orphan set, both taken straight from the data:
- The highest-volume orphans are future-dated: 8 orphan rows have last_call after 2026-09-05 (up to 2026-11-27), which is impossible for a "calls in the last 90 days" extract as of today. Every matched row's last_call falls on/before 2026-09-16.
- The entire orphan block with 46/24/21/21/17/17/14/12/11/11/9/8/7/6/5... calls has calls_with_ds2_date=0 AND calls_with_sqo_date=0, and its gong_owner_id values (e.g. 373679877710534937, 6440417017135074779, 7971335727414442543, 4654480998631630424, 7441100256678694562, 247470326654037859) never appear on any matched row. This looks like a different Gong source/segment (or a different pipeline whose deals aren't in the default-pipeline deals_open extract), not random key breakage. Per the note, gong_owner_id can't be cross-walked to HubSpot owner_id to verify.
(b) DUPLICATE CONVERSATION KEYS: 0 rows.
Every one of the 67 gong rows has calls_90d == distinct_conversation_keys; no row has calls_90d exceeding distinct_conversation_keys (and none below it). Conversation keys are unique per row across the file.
(c) DS3+ open deals with at least one logged call: 25 of 85 = 29.4%
Arithmetic: open deals at stage DS3/DS4/DS5 = 61 + 14 + 10 = 85. Of these, 25 have their deal_id present in the gong table (12 of 61 DS3, 6 of 14 DS4, 7 of 10 DS5). 25/85 = 0.2941 → 29.4%. 60 DS3+ deals have no logged call in the 90d window, including COMMIT-stage Deal-403845 (DS5), Deal-2465CE (DS5), Deal-FD9F4E (DS5) and BEST_CASE Deal-7BBDFA (DS3, amount 37440), Deal-530B50 (DS3, 31200), Deal-B25F40 (DS3, 40000).
Coverage rises sharply with stage: DS3 12/61 = 19.7%, DS4 6/14 = 42.9%, DS5 7/10 = 70.0%.
Caveat: if the orphan rows belong to deals outside the default pipeline (deals_open is default-pipeline only), the 29.4% figure is understated for DS3+; the data provided does not include other pipelines, so that cannot be confirmed here.
```sql
-- Per customer company, first calendar month as a customer:
-- unique givers, recognition count, successful redemption count.
--
-- Catalog-driven decisions:
-- * The catalog contains NO raw giving/recognition event table, so historical giving counts
-- can only come from pre-aggregated M1_* columns. Consequently the deleted-giver exclusion
-- (which per business rules must NOT be applied to historical giving counts) is not applied
-- anywhere -- there is no giver-level table in this catalog to apply it to.
-- * PRODUCTION.DEPRECATED_RECOGNITION.REDEMPTION_RECORDS_V2 is the documented raw source for
-- redemption counts (STATE = 'succeeded' only), but the catalog documents no columns for it
-- (no company key, no event timestamp), so joining it for a per-company first-month count
-- would require inventing schema. M1_REDEMPTIONS, the documented month-1 redemption count,
-- is used instead.
-- * Stale/unpopulated/retired tables avoided: HUBSPOT_HUB_1973303.V2_LIVE.OBJECTS_DEALS
-- (unpopulated), PRODUCTION.HUBSPOT.DEALS (stale since 2023-03),
-- PRODUCTION.HUBSPOT.GONG_HUBSPOT_MAP_FAST (retired).
-- * Coverage caveat: this table covers self-serve companies only; the catalog offers no
-- giving/redemption source for sales-led customers, so they cannot be reported here.
SELECT
COMPANY_ID, -- catalog states one row per self-serve company but does not name the key column; COMPANY_ID is the assumed identifier
DATE_TRUNC('MONTH', FIRST_SUB_PAYMENT_DATE)
AS FIRST_CUSTOMER_CALENDAR_MONTH, -- first subscription payment = became a customer; truncated to its calendar month
M1_USERS AS UNIQUE_GIVERS, -- PROXY ONLY: catalog documents no unique-givers metric; M1_USERS (month-1 users) is the only month-1 people count available
M1_GIVES AS RECOGNITION_COUNT, -- month-1 gives = recognitions sent
M1_REDEMPTIONS AS SUCCESSFUL_REDEMPTION_COUNT -- documented month-1 redemption count (see note above on the raw events table)
-- FROM PRODUCTION.PLG.COMPANY_COHORT_SUMMARY: the only cataloged table holding per-company
-- giving and redemption metrics; one row per self-serve company with M1_* = first-month values,
-- so first-calendar-month metrics require no event scan and no other table.
FROM PRODUCTION.PLG.COMPANY_COHORT_SUMMARY
WHERE FIRST_SUB_PAYMENT_DATE IS NOT NULL -- restrict to companies that actually became customers (made a first subscription payment); M1 anchor is not documented and is assumed to align with this first month
;
```
CRM AUDIT — COMPLETENESS & FIXES
(3 files received: companies.csv [34 rows], contacts.csv [52 rows], zoominfo_enrichment.csv [25 rows]. NO deals file was provided — see §1.)
════════════════════════════════════════
1. COMPLETENESS PER FIELD
════════════════════════════════════════
DEALS — NOT AUDITABLE. No deals.csv was included in the extract. Completeness for owner, stage, amount, close_date, and why_buys cannot be computed. Fix 1 below is to supply the file; until then pipeline amount at stake is unquantifiable for every fix.
COMPANIES (n = 34)
industry 34/34 = 100.0% (but non-normalized values: 'tech', 'Tech ' [trailing space], 'health care' — see §6)
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
(also non-normalized: 'US', 'USA', 'United States', 'UK', 'Canada' all in use)
CONTACTS (n = 52)
email 52/52 = 100.0% populated, but only 48/52 = 92.3% are valid-format (4 malformed, §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
COVERAGE GAP (not a field, but a completeness issue): 14 of 34 companies (41%) have ZERO contacts: C-2C60E5, C-B97B4E, C-2D7423, C-7BBDFA, C-50D386, C-BA969B, C-332637, C-93C8BF, C-EE9FFB, C-C9BB20, C-0A092931, C-0A092932, C-0A092933, C-0A092934. No orphan contacts (every contact's company_alias resolves).
════════════════════════════════════════
2. DUPLICATE COMPANY CLUSTERS (shared domain)
════════════════════════════════════════
Cluster A — domain acme-corp.com
C-0A092931 industry='Technology', employees=500, hq='US'
C-0A092932 industry='tech', employees=510, hq='USA'
SURVIVOR: C-0A092931 (fully populated, canonical-style industry label, lower ordinal).
Unresolved conflict: employee_count 500 vs 510 — ZoomInfo has NO row for acme-corp.com, so the data cannot arbitrate. Do not guess; verify manually, then drop C-0A092932.
No contacts or deals reference either alias, so the merge loses nothing.
Cluster B — domain globex.io
C-0A092933 industry='SaaS', employees=200, hq='US'
C-0A092934 industry='Technology', employees=200, hq='US'
SURVIVOR: C-0A092934 (matches the 'Technology' taxonomy used by the rest of the file).
Unresolved conflict: industry 'SaaS' vs 'Technology' — no ZoomInfo row for globex.io; confirm with the business, then drop C-0A092933. Employees/country agree.
No name-variant clusters beyond these two (all other domains unique).
════════════════════════════════════════
3. INVALID EMAILS & DOMAIN MISMATCHES
════════════════════════════════════════
Malformed emails (4) — all are truncated 'user@' with no domain:
CT-0010 (C-66D1FC) 'user0@'
CT-0080 (C-92D97D) 'user0@'
CT-0081 (C-92D97D) 'user1@'
CT-0192 (C-425E2A) 'user2@'
Fix: the local part + company domain strongly suggest the intended address (e.g. user0@66d1fc.com), but that is a reconstruction, not data — verify with the source system before writing. Do not auto-fill.
Domain mismatch (1):
CT-0011 (C-66D1FC) email 'user1@other-domain.com' vs company domain '66d1fc.com'
Fix: flag for rep review — either the contact is mis-associated to C-66D1FC, or the email belongs to a different employer. Data cannot resolve which.
════════════════════════════════════════
4. ENRICHMENT FILLS (ZoomInfo) — only where a matching domain row exists
════════════════════════════════════════
FILLABLE (9 fills across 7 companies — ZI row exists and ZI value is non-blank):
C-EC3025 (ec3025.com) employee_count = 400
C-96039F (96039f.com) employee_count = 400
C-44EA29 (44ea29.com) employee_count = 400
C-D04904 (d04904.com) employee_count = 400
C-B23205 (b23205.com) employee_count = 400
C-60C75F (60c75f.com) employee_count = 400
C-7BBDFA (7bbdfa.com) employee_count = 400
C-50D386 (50d386.com) employee_count = 400
(arithmetic: 8 company fills listed, 9 fields — C-EC3025 employee_count only; its industry is already populated)
Correction — recount: the 9 employee_count-blank companies; 8 have a ZI value, C-93C8BF does not. So 8 fills, not 9.
NOT FILLABLE — ZI row exists but ZI value also blank (do not invent):
hq_country: C-2D1F1B, C-D73B89, C-44EA29, C-D04904, C-2C60E5
NOT FILLABLE — no ZI row at all:
C-93C8BF (employee_count), C-EE9FFB (hq_country)
Post-fill projected completeness: employee_count 33/34 = 97.1%; hq_country unchanged at 82.4%.
════════════════════════════════════════
5. CRM vs ZOOMINFO DISAGREEMENTS & RECOMMENDED SOURCE
════════════════════════════════════════
VALUE CONFLICTS — industry, 8 companies. CRM says Technology/tech/Tech; ZI says 'Computer Software':
C-66D1FC, C-EC3025, C-44EA29, C-92D97D, C-D04904, C-77A95A, C-AA8DDA, C-B25F40, C-60C75F, C-425E2A
(recount from output: 10 companies, not 8 — C-66D1FC, C-EC3025, C-44EA29, C-92D97D, C-D04904, C-77A95A, C-AA8DDA, C-B25F40, C-60C75F, C-425E2A)
These are the same segment under two labels, not a factual dispute. RECOMMENDATION: keep CRM as system of record, adopt ONE canonical taxonomy ('Technology'), and map ZI 'Computer Software' -> 'Technology' on every future import. Also normalize within CRM: 'tech' -> 'Technology', 'Tech ' -> 'Technology' (trim whitespace), 'health care' -> 'Healthcare' (C-7BBDFA, C-50D386), 'SaaS' resolved via Cluster B.
FORMATTING-ONLY — hq_country, 10 companies. CRM 'US'/'USA' vs ZI 'United States':
C-66D1FC, C-950043, C-EC3025, C-96039F, C-77A95A, C-B23205, C-E51FB7, C-D0662E, C-425E2A, C-2D7423
Same country, different strings. RECOMMENDATION: neither source is wrong — standardize on one code list (ISO-2 'US' is the cheapest to enforce at entry) and apply it to both CRM and the ZI import mapping. Within the dup Cluster A, C-0A092931 'US' vs C-0A092932 'USA' is the same non-issue; the real conflict there is 500 vs 510 employees (see §2, unresolved — no ZI row).
Employee_count: no CRM-vs-ZI numeric conflicts outside the dup cluster (wherever both sides have a number, they agree: 900, 1500, 120, 340, 50 all match).
════════════════════════════════════════
6. TOP 10 FIXES BY PIPELINE AMOUNT AT STAKE
════════════════════════════════════════
No deals file exists, so no amount can be attached to any fix. Pipeline at stake = UNKNOWN ($0 quantifiable) for every item; ranked instead by revenue risk severity. If a deal is later found tied to any alias below, that fix jumps the queue.
1. Supply the deals extract. 0 of 5 required deal fields (owner, stage, amount, close_date, why_buys) are auditable. Amount at stake: the entire pipeline — unquantifiable until provided.
2. Merge dup Cluster A (acme-corp.com): survivor C-0A092931, resolve 500 vs 510 manually. Amount: unknown — no deals data. Risk: any future deal double-counted across two records.
3. Merge dup Cluster B (globex.io): survivor C-0A092934, confirm industry. Amount: unknown. Same double-count risk.
4. Repair 4 malformed emails (CT-0010, CT-0080, CT-0081, CT-0192) via source system. Amount: unknown — three of the four are on companies (C-66D1FC, C-92D97D, C-425E2A) whose other contacts are champions/EBs; broken emails kill sequenced outreach.
5. Resolve CT-0011 domain mismatch (user1@other-domain.com at C-66D1FC). Amount: unknown. Risk: champion contact pointed at the wrong company.
6. Apply the 8 ZoomInfo employee_count fills (C-EC3025, C-96039F, C-44EA29, C-D04904, C-B23205, C-60C75F, C-7BBDFA, C-50D386). Amount: unknown. Unblocks territory/segment scoring.
7. Fill the 15 missing personas and 13 missing titles (keys listed in §1), prioritizing companies with open pipeline once deals exist. Amount: unknown. Persona-based routing is blind on 29% of contacts.
8. Create contact coverage for the 14 zero-contact companies (§1) — 41% of the account base is unreachable. Amount: unknown; this is the single largest structural gap after the missing deals file.
9. Normalize taxonomies: industry -> {Technology, Manufacturing, Retail, Healthcare, Finance, ...}; country -> ISO-2. Covers 'tech', 'Tech ', 'health care', 'US'/'USA'/'United States'. Amount: none directly, but every report/filter built on these fields is currently wrong.
10. Re-pull ZoomInfo (or alternate source) for the 7 unfillable fields: hq_country on C-2D1F1B, C-D73B89, C-44EA29, C-D04904, C-2C60E5, C-EE9FFB; employee_count on C-93C8BF. Amount: unknown. ZI returned blanks/no-row; left empty, these stay empty — flagging rather than inventing.
Bottom line: contacts are 100% email-populated but 92.3% email-valid, 75.0% title, 71.2% persona; companies are 100% industry, 73.5% employee_count (97.1% after ZI fills), 82.4% country; deals are 0% auditable. Send the deals file and I can convert this severity ranking into a dollar-ranked one.
Classification rules applied (only tag + free-text used):
- "MIA" tag + silence text -> no decision. "Doing nothing/Not a priority/Cost" + pause/deprioritize -> no decision, unless text names an incumbent contract (then competitor) or explicit budget failure (then pricing). "Feature Request" -> product gap unless text says they bought something else (then competitor). "Lost DM" is ambiguous and was classified from text only. Timing required a stated delay/reconnect window; "at this time" with no re-engagement path -> no decision. Side = Bonusly when text cites something we control (feature gaps, platform fit); buyer when the cause sits on their side; unknown when text is silent or generic ("went another direction", "MIA").
PER-DEAL CLASSIFICATION (alias | tag -> primary category | side)
1. Deal-DB0AAC | Lost- Timing -> timing | buyer
2. Deal-F7F635 | Competitor -> competitor | unknown
3. Deal-AC944F | MIA -> no decision | unknown
4. Deal-214060 | MIA -> no decision | unknown
5. Deal-91A056 | Lost- Timing -> timing | buyer
6. Deal-29326C | Lost- Timing -> timing | unknown
7. Deal-5DB9B0 | Does not fit ICP -> other | unknown (text: "Spam.")
8. Deal-831B7B | Lost- Timing -> timing | buyer
9. Deal-F97C37 | Competitor -> competitor | buyer
10. Deal-13E9CF | Doing nothing -> no decision | buyer ("Not a budget issue - ... deprioritized")
11. Deal-39E25C | Lost- Timing -> timing | buyer
12. Deal-7ED004 | Lost- Budget/Price -> pricing | buyer
13. Deal-21B045 | MIA -> no decision | unknown
14. Deal-B3ABED | Lost- Timing -> timing | buyer
15. Deal-422BA6 | Competitor -> competitor | buyer
16. Deal-ED9AE7 | Lost DM -> no decision | buyer ("Timing, budget, authroity" — multi-factor stall, no single driver)
17. Deal-988493 | MIA -> no decision | unknown
18. Deal-381C8C | Competitor -> competitor | unknown
19. Deal-F308CA | MIA -> no decision | unknown
20. Deal-F1E8A6 | Competitor -> competitor | unknown
21. Deal-B6AC09 | Lost- Timing -> timing | buyer
22. Deal-70F704 | Lost DM -> no decision | unknown
23. Deal-E6E80A | Lost- Timing -> timing | buyer
24. Deal-B038F0 | Lost- Timing -> timing | buyer
25. Deal-4664E1 | MIA -> no decision | unknown
26. Deal-175756 | Lost- Timing -> timing | buyer
27. Deal-E74A73 | Doing nothing -> no decision | buyer (manual test first)
28. Deal-DDAB52 | Competitor -> competitor | buyer (Rippl)
29. Deal-ACE061 | Competitor -> competitor | buyer (likely HeyTaco)
30. Deal-BB78F3 | Lost- Timing -> timing | buyer
31. Deal-D48E0B | MIA -> no decision | unknown
32. Deal-15DA99 | Lost- Timing -> timing | buyer
33. Deal-F4AF5D | Lost- Timing -> timing | buyer
34. Deal-79B7A1 | Lost- Timing -> timing | unknown
35. Deal-583ADB | MIA -> no decision | unknown
36. Deal-8E27DA | Feature Request -> competitor | buyer (bought a swag provider instead)
37. Deal-2D2F8D | Competitor -> competitor | unknown
38. Deal-E0441F | MIA -> no decision | unknown
39. Deal-7CB44D | MIA -> no decision | unknown
40. Deal-0F96AA | Competitor -> competitor | unknown
41. Deal-1BCA50 | Competitor -> competitor | buyer
42. Deal-7CC678 | Competitor -> competitor | unknown
43. Deal-FAC17C | Lost DM -> no decision | buyer (internal approval blocked)
44. Deal-242273 | Competitor -> product gap | Bonusly (couldn't solution onsite points spending)
45. Deal-50E5D8 | Doing nothing -> no decision | buyer
46. Deal-A2C349 | Competitor -> competitor | buyer (stayed with Awardco)
47. Deal-9F176A | Lost- Timing -> timing | buyer
48. Deal-7B2236 | Doing nothing -> pricing | buyer ("simpler and cheaper")
49. Deal-AFA56C | MIA -> no decision | unknown
50. Deal-C7156E | Competitor -> competitor | unknown
51. Deal-C33D91 | Lost- Budget/Price -> pricing | buyer
52. Deal-9048EB | MIA -> product gap | Bonusly (text: "bad fit ... multiple feature gaps")
53. Deal-5E64CE | Doing nothing -> competitor | buyer (locked into Nectar through Oct 2027)
54. Deal-8A0992 | Competitor -> competitor | buyer (Canadian provider)
55. Deal-D0C698 | Competitor -> competitor | buyer (past Kudos user)
56. Deal-69CF3D | Lost- Timing -> timing | buyer
57. Deal-ECBF89 | Lost- Timing -> timing | buyer
58. Deal-3618CC | Lost DM -> product gap | Bonusly ("Wanted Surveys")
59. Deal-EECC02 | Competitor -> competitor | unknown
60. Deal-5AD03E | Competitor -> product gap | Bonusly ("Wanted more defined budget access")
61. Deal-D1A623 | Lost- Timing -> timing | unknown
62. Deal-413C56 | Doing nothing -> timing | buyer (CEO not ready, school-year priority)
63. Deal-47F1A1 | Competitor -> competitor | buyer (WorkTango)
64. Deal-BF2A98 | Competitor -> competitor | buyer (HiThrive)
65. Deal-2A292B | Doing nothing -> competitor | buyer (build internally = build-vs-buy competitor)
66. Deal-D1AABF | MIA -> no decision | unknown
67. Deal-FEDBCB | Doing nothing -> no decision | unknown
68. Deal-1E7DA9 | Competitor -> competitor | unknown
69. Deal-2BBA21 | MIA -> no decision | unknown
70. Deal-286F9C | Competitor -> competitor | buyer
71. Deal-7FBAC6 | Doing nothing -> no decision | buyer (paused again)
72. Deal-369281 | Competitor -> competitor | buyer (Paylocity native)
73. Deal-386F6E | MIA -> no decision | unknown
74. Deal-9FCD0D | Competitor -> competitor | buyer (Canadian company, CEO preference)
75. Deal-55867E | Lost- Timing -> no decision | buyer ("not moving forward at this time", no reconnect date given)
76. Deal-DAFB82 | Lost- Budget/Price -> pricing | buyer (no budget until 2028)
77. Deal-2FEDDB | Doing nothing -> timing | buyer (unsure when she can move it)
78. Deal-64B19A | Competitor -> competitor | buyer (likely stayed with Motivosity)
79. Deal-3F86A0 | MIA -> no decision | unknown
80. Deal-096750 | MIA -> no decision | unknown
81. Deal-F325A5 | Lost DM -> champion left | buyer (layoffs + change in leadership)
82. Deal-ABD14C | Doing nothing -> no decision | buyer
83. Deal-79E61A | MIA -> no decision | unknown
84. Deal-8A119B | Lost- Budget/Price -> pricing | buyer
85. Deal-AE7C4E | MIA -> no decision | unknown
86. Deal-DAB4F1 | MIA -> no decision | unknown
87. Deal-B4B50F | MIA -> no decision | unknown
88. Deal-981AD4 | Feature Request -> product gap | Bonusly (UI fit, not UK-focused)
89. Deal-DC77FE | Competitor -> competitor | buyer (customization — label points as dollars; price explicitly not a factor)
90. Deal-5885B9 | MIA -> no decision | unknown
SUMMARY
Total deals: 90
Category counts (count / % / $):
- no decision: 31 (34.4%) $281,844.20
- competitor: 27 (30.0%) $352,594.96
- timing: 20 (22.2%) $257,611.00
- pricing: 5 (5.6%) $172,450.00
- product gap: 5 (5.6%) $178,245.00
- other: 1 (1.1%) $10,800.00
- champion left: 1 (1.1%) $14,400.00
Arithmetic: 31+27+20+5+5+1+1 = 90. Total $ = 281,844.20+352,594.96+257,611.00+172,450.00+178,245.00+10,800.00+14,400.00 = $1,267,945.16
Side split (count / % / $):
- buyer: 49 (54.4%) $593,874.16
- unknown: 36 (40.0%) $495,826.00
- Bonusly: 5 (5.6%) $178,245.00
Arithmetic: 49+36+5 = 90. $593,874.16 + $495,826.00 + $178,245.00 = $1,267,945.16 (ties to total)
Tag-vs-text clear disagreements: 10 deals
- Deal-8E27DA (Feature Request tag; text = chose a swag provider)
- Deal-242273 (Competitor tag; text = we couldn't meet the need)
- Deal-7B2236 (Doing nothing tag; text = wanted simpler/cheaper)
- Deal-9048EB (MIA tag; text = bad fit + multiple feature gaps)
- Deal-5E64CE (Doing nothing tag; text = locked into Nectar contract)
- Deal-5AD03E (Competitor tag; text = wanted defined budget access — a capability ask)
- Deal-413C56 (Doing nothing tag; text = timing/CEO not ready)
- Deal-2A292B (Doing nothing tag; text = building internally)
- Deal-55867E (Timing tag; text = not moving forward, no reconnect date)
- Deal-2FEDDB (Doing nothing tag; text = timing uncertainty)
Note: "Lost DM" was treated as unmapped, so those reclassifications aren't counted in the 10.
Two patterns most worth acting on:
1. We are losing to specific capabilities, not to competitors wholesale. At least 6 competitor/product-gap losses name an exact feature the winner had and Bonusly lacked: surveys (Deal-3618CC, Deal-A2C349), defined budget access (Deal-5AD03E), points-as-dollars customization (Deal-DC77FE), onsite/internal points spending (Deal-242273), diversified offerings beyond R&R (Deal-F97C37), swag (Deal-8E27DA). Add Deal-9048EB ($41,790, "multiple feature gaps") and the 5 product-gap deals alone total $178,245. These are roadmap-addressable — surveys and flexible points/budget configuration recur. Fix the features and a meaningful slice of the 27 competitor losses gets winnable. All Bonusly-side losses are only 5.6% of deals but are the most directly controllable.
2. The pipeline leaks at the top and we aren't learning why. No decision (31 deals, 34.4%) is the largest category, and 21 of those 31 are pure ghosting (MIA/unresponsive/no response). Several died immediately after intro/demo with 1-2 contacts engaged (Deal-F308CA $30,321, Deal-4664E1, Deal-2BBA21, Deal-096750) and one was inherited stale from a departed rep (Deal-E0441F). On top of that, 36 of 90 deals (40%) have unknown side — generic notes like "went another direction" / "Nothing specific provided" (Deal-0F96AA $76,800, Deal-EECC02 $66,690) mean we're flying blind on our biggest losses. Action: multi-thread deals past 1-2 contacts, tighten post-demo follow-up SLAs, and enforce structured competitor/exit fields at closed-lost so $495,826 of unknown-side pipeline isn't unexplainable.
```json
{
"tier_counts": {"LOCK": 3, "ACTION": 20, "BUILD": 39, "WATCH": 80, "REVIVE": 7, "RISKY": 7},
"tier_examples": {
"LOCK": ["Deal-D348E1", "Deal-C26D20", "Deal-403845"],
"ACTION": ["Deal-25F752", "Deal-547B2B", "Deal-944310"],
"BUILD": ["Deal-D73B89", "Deal-523604", "Deal-2B39B0"],
"WATCH": ["Deal-C6FE92", "Deal-93C8BF", "Deal-036E80"],
"REVIVE": ["Deal-2D1F1B", "Deal-F0EBBB", "Deal-1BEEBF"],
"RISKY": ["Deal-B7EBD1", "Deal-0660B4", "Deal-BA571A"]
},
"risky_deals": ["Deal-B7EBD1", "Deal-0660B4", "Deal-BA571A", "Deal-7BBDFA", "Deal-4A13AD", "Deal-690476", "Deal-635B8E"],
"lock_violations": 0,
"pipeline_shape": "156 open deals, and the shape is bottom-heavy with a thin, healthy top: only 3 LOCKs (all DS5 COMMIT with meetings in the last 30d, fresh engagement, 3+ contacts), backed by 20 ACTION deals clustered in DS4/DS5/COMMIT-BEST_CASE that are actively touched but meeting-starved — the single gap keeping them out of LOCK. 7 RISKY deals carry optimistic forecasts (COMMIT/BEST_CASE) contradicted by zero meetings_30d and stale or near-zero 30-day activity, including DS5 COMMIT Deal-B7EBD1 (nothing since 2026-08-20) and BEST_CASE Deal-7BBDFA (silent since 2026-07-21). The middle is the bulk: 80 WATCH deals, mostly DS2/DS3 running on email-only engagement, and 39 BUILD deals (DS1/DS2) showing real meeting momentum. 7 REVIVE deals are effectively dormant, and hygiene flags include several $1-amount placeholders (Deal-012CB1, Deal-483B2D, Deal-3795AD, the 716654662-owned DS1 batch) and at least one close_date already in the past (Deal-333EBB, 2026-08-28)."
}
```
```json
[
{
"transcript_id": "TX-001",
"deal_alias": "Deal-CFE7F4",
"why_buys": [
"Automate anniversary and birthday awards — 'The big win for us would be automating anniversary and birthday awards' (VP People)"
],
"pain_points": [
"HR team of three cannot keep up with awards manually",
"Everything tracked in a spreadsheet; 'people slip through the cracks' (HR Admin)"
],
"stakeholders": ["VP People", "HR Admin"],
"budget_signal": "~$40k earmarked for engagement tools this fiscal year (VP People)",
"timeline_signal": "Ideally live before open enrollment in November",
"competitor_mentioned": "Achievers — looked at last year; 'too heavy for a team our size'",
"next_step": "Security review with IT lead on September 12 (explicitly agreed)",
"objections": [
"Requires SSO and audit logs for IT to sign off (HR Admin)"
],
"confidence": "high — budget, timeline, competitor, and a dated next step are all prospect-stated"
},
{
"transcript_id": "TX-002",
"deal_alias": "Deal-70BB30",
"why_buys": [
"Tie recognition to retention for the hourly workforce (Head of Total Rewards)"
],
"pain_points": [
"Regretted turnover over 30% in the hourly workforce"
],
"stakeholders": ["Head of Total Rewards", "CFO"],
"budget_signal": "$25k pilot budget approved by finance for this quarter (CFO)",
"timeline_signal": "Decision by end of September (CFO)",
"competitor_mentioned": null,
"next_step": "Send pilot agreement; prospect will route it to legal this week (explicitly agreed)",
"objections": [
"Workday integration 'has to be rock solid' — CFO's one condition"
],
"confidence": "high — approved budget, decision date, and agreed next step with CFO in the room"
},
{
"transcript_id": "TX-003",
"deal_alias": "Deal-530B50",
"why_buys": [
"Make recognition visible across 12 retail locations (People Ops Manager)"
],
"pain_points": [
"Store managers have zero budget autonomy for on-the-spot recognition today"
],
"stakeholders": ["People Ops Manager"],
"budget_signal": null,
"timeline_signal": "'No rush on our side until Q1' (People Ops Manager)",
"competitor_mentioned": "Bucketlist — CEO used it at her last company and liked it",
"next_step": "Schedule a call with prospect's CEO; People Ops Manager to send two times (explicitly agreed)",
"objections": [
"CEO has to be sold first — 'she decides anything people-related'",
"CEO already likes Bucketlist (competitor affinity)"
],
"confidence": "medium-low — agreed CEO intro call, but no prospect budget signal, no urgency until Q1, and a gatekeeping CEO who likes the competitor"
},
{
"transcript_id": "TX-004",
"deal_alias": "Deal-180D02",
"why_buys": [
"Consolidate three separate recognition tools into one (VP People)"
],
"pain_points": [
"Paying for three tools and none of them talk to the HRIS"
],
"stakeholders": ["VP People", "IT Security Lead"],
"budget_signal": "Under $15k annually, VP People can approve without going to the board (approval threshold, not committed spend)",
"timeline_signal": "Procurement cycle runs six to eight weeks minimum (IT Security Lead)",
"competitor_mentioned": null,
"next_step": null,
"objections": [
"Security review took three months for their last vendor — IT Security Lead's stated hesitation",
"Anything at or above $15k annually requires board approval"
],
"confidence": "medium — clear pain and a stated approval threshold, but long procurement, security hesitation, and no agreed next step ('no promises')"
},
{
"transcript_id": "TX-005",
"deal_alias": "Deal-F8767A",
"why_buys": [
"Automate service milestones (HR Director)",
"Analytics on recognition equity across departments (HR Director)"
],
"pain_points": [
"Night-shift teams feel invisible; their engagement scores run 20 points lower (People Ops Coordinator)"
],
"stakeholders": ["HR Director", "People Ops Coordinator"],
"budget_signal": "$12k approved under the engagement line (HR Director)",
"timeline_signal": "Needs to be running before the January all-hands",
"competitor_mentioned": "Nectar — prospect is mid-pilot with Nectar; 'you'd need to beat that experience'",
"next_step": "Present to prospect's exec team on October 2 (explicitly agreed)",
"objections": [
"Exec team is skeptical after a failed rollout two years ago",
"Must beat the active Nectar pilot experience"
],
"confidence": "high — approved budget, hard deadline, dated exec presentation; active incumbent pilot is the main risk"
},
{
"transcript_id": "TX-006",
"deal_alias": "Deal-EE195F",
"why_buys": [
"Cut the admin time on service awards (HR Manager)"
],
"pain_points": [
"HR Manager personally spends five hours a month ordering and shipping plaques"
],
"stakeholders": ["HR Manager"],
"budget_signal": "No figure stated; prospect: 'Budget isn't the issue — time is.'",
"timeline_signal": "Fiscal year resets in February; a Q1 start is realistic",
"competitor_mentioned": null,
"next_step": "Send the one-page overview; HR Manager will forward it to the COO this week (explicitly agreed)",
"objections": [
"COO usually prefers building things in-house (prospect's 'fair warning')"
],
"confidence": "medium — champion with agreed next step, but no budget figure, Q1 timeline, and COO build-vs-buy risk"
}
]
```
Explicit gaps and exclusions per your rules:
- TX-003 budget_signal is null: the only pricing in the transcript ($8/employee/month) came from the rep, not the prospect, so it was excluded.
- TX-002, TX-004, TX-006 competitor_mentioned are null: no competitor was named by the prospect. TX-002's "you're the first vendor we've had a real demo with" and TX-006's in-house alternative are not competitor mentions; TX-004's three incumbent tools were never named.
- TX-004 next_step is null: the CFO follow-up was rep-proposed and the VP People said "Maybe — I need to check her calendar, no promises," which is not explicit agreement.
- Stakeholders are limited to prospect speakers as instructed. People mentioned but not on the speaker list (IT lead in TX-001, CEO in TX-003, CFO in TX-004, COO in TX-006) appear only in next_step/objections where the prospect referenced them.
- No arithmetic was required — all dollar figures and dates are direct prospect quotes, restated as given.
As of 2026-09-05 (Slack export dated 2026-09-04): 1. Deal-547B2B — $11,200 — Alex Franklin, DS5/COMMIT, CRM close 2026-09-11 (6 days out). Why close: redlines came back clean, signing page is out, and their VP People said they are signing "tomorrow" = 2026-09-05. Signature-imminent. Left: the signature itself. 2. Deal-403845 — $9,000 — DS5/COMMIT, CRM close 2026-09-11 (6 days). Why close: order form is with their finance team and "moving fine." Left: finance team action on the order form; no signature date given. (Note: Slack update came from Dana Mercer; CRM owner is Alex Franklin.) 3. Deal-A2B47C — $6,360 — Alex Franklin, DS5/COMMIT, CRM close 2026-09-11 (6 days). Why close: Slack confirms it is "still warm," normal legal-review pace. Left: legal review to complete, then signature. Not ranked: Deal-2465CE ($5,400) — CRM still shows DS5/COMMIT for 2026-09-10 (5 days), but Slack supersedes: champion left, procurement froze new vendors, Dana Mercer is pulling it from commit; realistically Q4. Deal-D348E1 is warm but CRM-dated 2026-10-15 (40 days). Deal-B7EBD1 (DS5/COMMIT, 2026-09-10) has no Slack status, so I ranked the Slack-confirmed movers ahead of it.
CANDIDATE REVIEW
TG-001 | Deal-EC3025 | amount: not provided in data
Prospect (IT Security Lead): "We need SCIM user provisioning — our security team will not approve any tool that requires manual account management."
Classification: REAL GAP. Docs state SCIM user provisioning is "NOT currently listed as supported capabilities." No tier offers it.
TG-002 | Deal-D0D6B5 | amount: not provided in data
Prospect (HRIS Manager): "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. Docs state ADP Workforce Now is "NOT currently listed as supported capabilities." Supported HRIS list is Workday, BambooHR, Gusto, Rippling only.
TG-003 | Deal-CFE7F4 | amount: not provided in data
Prospect (VP People): "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 in the product (Enterprise tier per the docs table). The capability is not missing; the prospect's plan doesn't include it. Excluded from summary.
TG-004 | Deal-84DBA6 | amount: not provided in data
Prospect (People Ops Manager): "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; the problem is adoption/training, not product capability. Excluded from summary.
TG-005 | Deal-36C33F | excluded
The only gap statement (no native mobile app) comes from Alex Franklin (rep), not the prospect. Rep language does not count as prospect voice. The prospect's own line ("The web version should be fine for our office staff for now") raises no gap.
SUMMARY — TRUE PRODUCT GAPS (prospect-voiced)
1. SCIM user provisioning — Deal-EC3025 — amount: not provided in data
"We need SCIM user provisioning — our security team will not approve any tool that requires manual account management." (IT Security Lead)
2. ADP Workforce Now integration — Deal-D0D6B5 — amount: not provided in data
"Our payroll runs on ADP Workforce Now and I don't see ADP anywhere in your integration list — that's a dealbreaker for us." (HRIS Manager)
Missing data, stated explicitly:
- Deal amounts: no amount field exists in transcripts_gaps.csv or product_docs.md, so amounts cannot be named.
- Deal status: no field indicates which deals are "active," so all five listed deals were evaluated as candidates.
Snapshot 2026-09-05. "Last 7 days" window = 2026-08-29 through 2026-09-05 inclusive. Recency = max(last_email, last_call, last_meeting) from engagements table, ignoring last_meeting values dated AFTER the snapshot (scheduled meetings are not logged activity). Days since last contact = 2026-09-05 minus that date. Two deals (Deal-3EED2C, Deal-57FF13) have no engagement row at all, so days-since-contact cannot be computed — flagged as "no engagement data." **Bryce Harmon** | Deal | Stage | Amount | Days since last contact | |---|---|---|---| | Deal-2D1F1B | DS1 | $240,000 | 81 (last: 2026-06-16) | | Deal-66D1FC | DS1 | $99,000 | 16 (last: 2026-08-20) | | Deal-950043 | DS1 | $70,000 | 19 (last: 2026-08-17) | | Deal-B23205 | DS1 | $45,000 | 16 (last: 2026-08-20) | | Deal-7BBDFA | DS3 | $37,440 | 46 (last: 2026-07-21) | | Deal-332637 | DS2 | $36,000 | 9 (last: 2026-08-27) | | Deal-1BEEBF | DS1 | $31,500 | 19 (last: 2026-08-17) | | Deal-A414F6 | DS1 | $25,200 | 19 (last: 2026-08-17) | | Deal-C5658B | DS1 | $23,400 | 16 (last: 2026-08-20) | | Deal-40522D | DS3 | $21,000 | 19 (last: 2026-08-17) | | Deal-C1FA6D | DS1 | $18,000 | 16 (last: 2026-08-20) | | Deal-01E193 | DS1 | $12,600 | 8 (last: 2026-08-28) | | Deal-F0EBBB | DS3 | $11,400 | 24 (last: 2026-08-12) | | Deal-927338 | DS1 | $10,920 | 18 (last: 2026-08-18) | | Deal-E25A09 | DS1 | $6,000 | 9 (last: 2026-08-27) | | Deal-C9C286 | DS2 | $5,502 | 9 (last: 2026-08-27) | | Deal-012CB1 | DS1 | $1 | 23 (last: 2026-08-13) | | Deal-3795AD | DS2 | $1 | 8 (last: 2026-08-28) | Bryce Harmon: 18 stale deals, $692,964 total. **Dana Mercer** | Deal | Stage | Amount | Days since last contact | |---|---|---|---| | Deal-44EA29 | DS2 | $60,000 | 10 (last: 2026-08-26) | | Deal-E51FB7 | DS2 | $43,875 | 12 (last: 2026-08-24) | | Deal-B42F46 | DS1 | $27,000 | 19 (last: 2026-08-17) | | Deal-BA3DDC | DS3 | $23,400 | 15 (last: 2026-08-21) | | Deal-9DDE86 | DS2 | $20,000 | 15 (last: 2026-08-21) | | Deal-215CCA | DS3 | $18,900 | 17 (last: 2026-08-19) | | Deal-5EED42 | DS3 | $16,250 | 11 (last: 2026-08-25) | | Deal-57887A | DS2 | $15,000 | 8 (last: 2026-08-28) | | Deal-944310 | DS4 | $10,500 | 33 (last: 2026-08-03) | | Deal-B7EBD1 | DS5 | $9,000 | 16 (last: 2026-08-20) | | Deal-3974EB | DS4 | $9,000 | 8 (last: 2026-08-28) | | Deal-F40F04 | DS2 | $8,100 | 15 (last: 2026-08-21) | | Deal-7599B8 | DS3 | $7,350 | 18 (last: 2026-08-18) | | Deal-87DDD1 | DS1 | $5,000 | 19 (last: 2026-08-17) | | Deal-F336B6 | DS3 | $4,200 | 15 (last: 2026-08-21) | | Deal-0660B4 | DS4 | $1,920 | 16 (last: 2026-08-20) | Dana Mercer: 16 stale deals, $279,495 total. **Cole Ingram** | Deal | Stage | Amount | Days since last contact | |---|---|---|---| | Deal-D04904 | DS2 | $58,529.25 | 11 (last: 2026-08-25) | | Deal-B25F40 | DS3 | $40,000 | 8 (last: 2026-08-28) | | Deal-813836 | DS2 | $32,175 | 11 (last: 2026-08-25) | | Deal-1BA595 | DS2 | $31,750 | 11 (last: 2026-08-25) | | Deal-CFE1E8 | DS3 | $18,000 | 11 (last: 2026-08-25) | | Deal-CD47A6 | DS2 | $12,168 | 11 (last: 2026-08-25) | | Deal-627646 | DS3 | $11,193 | 11 (last: 2026-08-25) | | Deal-FF809F | DS2 | $7,781.20 | 11 (last: 2026-08-25) | | Deal-AF932D | DS2 | $7,225.40 | 11 (last: 2026-08-25) | | Deal-A71728 | DS2 | $6,947.50 | 11 (last: 2026-08-25) | | Deal-8BC9F5 | DS2 | $5,616 | 10 (last: 2026-08-26) | | Deal-175395 | DS3 | $4,779.88 | 11 (last: 2026-08-25) | | Deal-481E24 | DS3 | $4,140 | 10 (last: 2026-08-26) | | Deal-C7F9BF | DS2 | $3,360 | 11 (last: 2026-08-25) | | Deal-2F3A66 | DS3 | $3,334.80 | 11 (last: 2026-08-25) | | Deal-342E96 | DS2 | $2,700 | 24 (last: 2026-08-12) | | Deal-E568D5 | DS3 | $1,875 | 11 (last: 2026-08-25) | | Deal-FD9F4E | DS5 | $1,330 | 10 (last: 2026-08-26) | Cole Ingram: 18 stale deals, $252,905.03 total. **Alex Franklin** | Deal | Stage | Amount | Days since last contact | |---|---|---|---| | Deal-CC08D1 | DS1 | $24,000 | 16 (last: 2026-08-20) | | Deal-E73427 | DS3 | $18,000 | 10 (last: 2026-08-26) | | Deal-885F45 | DS2 | $9,300 | 12 (last: 2026-08-24) | | Deal-C2FF3C | DS1 | $8,316 | 10 (last: 2026-08-26) | | Deal-3EED2C | DS2 | $7,200 | no engagement data | | Deal-0D2F7A | DS3 | $5,100 | 12 (last: 2026-08-24) | | Deal-6C60D4 | DS3 | $4,800 | 12 (last: 2026-08-24) | | Deal-13FEBD | DS2 | $4,680 | 12 (last: 2026-08-24) | | Deal-819506 | DS1 | $4,400 | 8 (last: 2026-08-28) | | Deal-9D0060 | DS3 | $3,840 | 12 (last: 2026-08-24) | | Deal-690476 | DS2 | $3,600 | 18 (last: 2026-08-18) | | Deal-C6D97A | DS4 | $3,240 | 8 (last: 2026-08-28) | | Deal-EE195F | DS3 | $3,120 | 8 (last: 2026-08-28) | | Deal-278DEC | DS3 | $2,700 | 8 (last: 2026-08-28) | | Deal-635B8E | DS3 | $2,600 | 18 (last: 2026-08-18) | | Deal-6883F3 | DS1 | $2,400 | 16 (last: 2026-08-20) | | Deal-4A13AD | DS3 | $2,160 | 26 (last: 2026-08-10) | | Deal-F67D31 | DS2 | $1,800 | 8 (last: 2026-08-28) | | Deal-5FDCE4 | DS3 | $1,600 | 12 (last: 2026-08-24) | | Deal-BA571A | DS4 | $1,080 | 18 (last: 2026-08-18) | Alex Franklin: 20 stale deals, $113,936 total. **Farid Osman** | Deal | Stage | Amount | Days since last contact | |---|---|---|---| | Deal-8BA24E | DS1 | $2,880 | 8 (last: 2026-08-28) | | Deal-8FDCD2 | DS1 | $1 | 15 (last: 2026-08-21) | Farid Osman: 2 stale deals, $2,881 total. **Elena Sinclair** | Deal | Stage | Amount | Days since last contact | |---|---|---|---| | Deal-57FF13 | DS1 | $2,100 | no engagement data | Elena Sinclair: 1 stale deal, $2,100 total. Total: 75 stale open deals, $1,344,281.03 across 6 owners.
SNAPSHOT: 2026-09-05 | 30-day window: 2026-08-06 through 2026-09-05 (inclusive) | Activities = emails_30d + calls_30d + meetings_30d from engagements table, aggregated per owner across their deals.
PER-REP TOTALS (last 30 days)
==============================
Alex Franklin (84342457)
Emails 307, Calls 36, Meetings 41 -> Total 384
Mix: Emails 307/384 = 79.9% | Calls 36/384 = 9.4% | Meetings 41/384 = 10.7%
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 = 21.3
Bryce Harmon (119337721)
Emails 162, Calls 0, Meetings 43 -> Total 205
Mix: Emails 162/205 = 79.0% | Calls 0/205 = 0.0% | Meetings 43/205 = 21.0%
DS2 entries in window: 4
Deal-25F752 (08-10), Deal-CA7DC0 (08-12), Deal-1CCE5C (08-06), Deal-D73B89 (09-03)
Activities per DS2 entry: 205/4 = 51.3
Cole Ingram (83155924)
Emails 96, Calls 14, Meetings 1 -> Total 111
Mix: Emails 96/111 = 86.5% | Calls 14/111 = 12.6% | Meetings 1/111 = 0.9%
DS2 entries in window: 2
Deal-42326B (08-26), Deal-1BA595 (08-12)
Activities per DS2 entry: 111/2 = 55.5
Farid Osman (716654662)
Emails 38, Calls 0, Meetings 34 -> Total 72
Mix: Emails 38/72 = 52.8% | Calls 0/72 = 0.0% | Meetings 34/72 = 47.2%
DS2 entries in window: 1
Deal-499BF6 (08-26)
Activities per DS2 entry: 72/1 = 72.0
Dana Mercer (83155923)
Emails 84, Calls 18, Meetings 11 -> Total 113
Mix: Emails 84/113 = 74.3% | Calls 18/113 = 15.9% | Meetings 11/113 = 9.7%
DS2 entries in window: 1
Deal-57887A (08-07)
Activities per DS2 entry: 113/1 = 113.0
Elena Sinclair (701163055)
One deal (Deal-57FF13, DS1, no t_ds2). No row in the engagements table -> 0 emails, 0 calls, 0 meetings.
DS2 entries: 0. Ratio undefined (0/0) — excluded from ranking.
Gavin Porter (1520255671) and Hugo Lindqvist (77260721): archived, no deals in the file — no data to compute.
EFFICIENCY RANKING (lowest activities per DS2 entry = most efficient)
=====================================================================
1. Alex Franklin 21.3 (384 / 18)
2. Bryce Harmon 51.3 (205 / 4)
3. Cole Ingram 55.5 (111 / 2)
4. Farid Osman 72.0 (72 / 1)
5. Dana Mercer 113.0 (113 / 1)
ANSWER
======
Most efficient rep: Alex Franklin (21.3 activities per DS2 entry).
Highest-volume rep: Alex Franklin (384 total activities).
They do NOT differ — the same rep is both most efficient and highest volume.
DATA NOTES
==========
- Boundary convention: "within 30 days" treated as t_ds2 >= 2026-08-06. Deals at exactly 2026-08-06 are included (Deal-1CCE5C, Deal-EE195F, Deal-D9A72E); Deal-55164C at 2026-08-05 and Deal-13FEBD at 2026-08-04 are excluded. If the boundary were exclusive, ratios become Bryce 205/3 = 68.3 and Alex 384/16 = 24.0; the ranking is unchanged.
- Deal-3EED2C (Alex Franklin) has no row in the engagements table; counted as 0 activities.
- Bryce Harmon and Farid Osman logged zero calls in the window; Farid's mix is meeting-heavy (47.2% meetings vs ~10% or less for email-led reps).
- No cause attributed to any difference in volume, mix, or efficiency.
QTD SCORECARD — Alex Franklin — as of 2026-09-05 (2026-Q3: 07-01 → 09-30)
1. BOOKINGS VS QUOTA
Quota: $200,000
QTD bookings: $150,000 across 8 closed-won deals
Deal-A1C3E5 $40,000 (07-15) + Deal-F2C7D8 $20,000 (07-24) + Deal-B7D2F4 $35,000 (07-31)
+ Deal-C9E1A6 $21,000 (08-12) + Deal-A8B4D6 $12,000 (08-19) + Deal-D4B8C2 $11,000 (08-21)
+ Deal-E6F3A9 $6,500 (09-02) + Deal-C5D9E2 $4,500 (09-03) = $150,000
Attainment: 150,000 / 200,000 = 75.0%
Excluded per instructions: Deal-B3E6F1 $24,000, closed-won 2026-06-20 — before Q3 start, not counted.
2. NEW VS EXPANSION SPLIT (of $150,000)
New: $113,500 (5 deals: Deal-A1C3E5, Deal-B7D2F4, Deal-C9E1A6, Deal-D4B8C2, Deal-E6F3A9) — 75.7% (113,500/150,000)
Expansion: $36,500 (3 deals: Deal-F2C7D8, Deal-A8B4D6, Deal-C5D9E2) — 24.3% (36,500/150,000)
3. ACTIVE PIPELINE BY STAGE (open deals, all close dates)
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 open deals, $1,260,390
Note: only 22 of these ($109,363) carry a close_date on or before 09-30; $1,151,027 is dated in Q4 or later.
4. ROLLING 90-DAY DS2-TO-WON RATE (window 2026-06-07 → 2026-09-05)
Cohort: deals with entered_ds2 in window that have a closed outcome.
Won: 8 (the 8 QTD wins) | Lost: 27 | Decided: 35
Rate: 8 / 35 = 22.9%
(If measured on all deals decided in the window regardless of DS2 entry date: 9 won / 36 decided = 25.0%.)
5. WIN/LOSS COUNTS AND TOP LOSS REASON
Wins QTD: 8 | Losses QTD: 27 (all 27 closed-lost fall in Q3; total lost value $329,272 vs $150,000 won)
Loss reasons: Lost- Timing (1 year or more) 13 deals / $184,681 — TOP REASON (48% of losses: 13/27)
Competitor 5 / $49,020 · MIA 5 / $45,831 · Lost DM 2 / $17,940
Feature Request 1 / $21,000 · Lost- Does not fit ICP 1 / $10,800
6. ACTIVITY VOLUME, LAST 30 DAYS (161 deal rows in ae_engagements.csv)
Emails: 807 | Meetings: 128 | Calls: 112 | Notes: 50 | Total: 1,097
Mix: 73.5% email, 11.7% meetings, 10.2% calls, 4.6% notes.
THREE COACHING OBSERVATIONS
1. Attainment risk is real, and September won't save it. 75.0% attained with $50,000 to go, but late-stage cover is thin: DS4+DS5 totals just $69,304 (10 deals) across ALL close dates, and only $34,204 of it (6 deals) closes by 09-30. Pipeline coverage on the remaining gap is ~1.4x using total late-stage ($69,304/$50,000) and just 0.68x using in-quarter late-stage — well below the ~3x rule of thumb. The four September-closing DS5/DS4 deals with zero 30-day calls or meetings — Deal-547B2B ($11,200, 0 meetings), Deal-A2B47C ($6,360, 0 meetings), Deal-F9A08A ($2,484, 14 emails but 0 calls/meetings) — need direct engagement now, not email sequences.
2. Losses are a qualification and multi-threading problem, not an activity problem. QTD loss count runs 27 against 8 wins (a 23% QTD win rate on closed deals), and the two biggest controllable buckets — MIA (5 deals, $45,831) and Lost DM (2 deals, $17,940) — point to single-threaded deals going dark. The activity data supports this: 73.5% of all touches are emails. Compare the 8 QTD wins, which averaged 11.1 emails + 3.9 calls + 2.9 meetings in 30 days, against late-stage open deals riding on email alone. The dominant reason, Lost- Timing (13 deals, $184,681 — including the $40,001 Deal-B3ABED), suggests deals entering DS2 without validated urgency; tighter exit criteria at DS1→DS2 would protect cycle time.
3. The pipeline is large but stale and back-weighted. $1.26M open looks healthy, but 91% of it ($1,151,027) is dated Q4+, and 29 open DS2+ deals worth $282,960 entered DS2 more than 90 days ago — several from Q1 (Deal-F9A08A entered DS2 2025-11-24 and is still sitting in DS4; Deal-60C2C2 $19,000 in DS3 since 2026-02-23). Given a 22.9% rolling DS2-to-won rate, these aged deals are statistically more likely to be future "Timing" losses than future bookings. Recommend a formal pipeline scrub: re-qualify or close out the 29 aged deals, and rebuild Q4 pipeline with deals created in-quarter, where his win rate is demonstrably stronger.
Reference date: 2026-09-05 (today). 60-day active window = last_engaged_date on/after 2026-07-07.
Data gaps stated up front: deal amount and deal stage do not exist in either file, so those fields are "not provided" for every deal below. Because stage is unavailable, the persona-priority rule applied is: if the deal has no active economic buyer, that is the most valuable add (no deal closes without budget authority); otherwise IT security (most common late-stage blocker in HR-tech sales). If you can supply stages, I'll re-rank.
10 of 13 open deals flagged. 3 not flagged (Deal-84DBA6, Deal-4B0BEB, Deal-D348E1: each has 3+ active contacts across 3+ personas).
FLAGGED DEALS
1. Deal-EC3025 (C-FDD0C7) — single-threaded
Amount: not provided. Stage: not provided.
Active contacts: 1 (CT-047C54, champion, 2026-09-02)
Personas present: champion
Personas missing: economic buyer, HR admin, IT security, finance
Priority add: economic buyer
On-file unengaged fit: CT-6827DB, Chief People Officer, economic buyer — yes, on file.
Note: CT-F2C1AE (economic buyer) is marked former and does not count.
2. Deal-92D97D (C-E23238) — single-threaded
Amount: not provided. Stage: not provided.
Active contacts: 1 (CT-01F5B4, HR admin, 2026-08-28)
Personas present: HR admin
Personas missing: economic buyer, champion, IT security, finance
Priority add: economic buyer
On-file unengaged fit: none on file.
Note: CT-A902AE (champion) last engaged 2026-06-01, 96 days out — outside the 60-day window.
3. Deal-50D386 (C-EB10E4) — under-threaded (2 active)
Amount: not provided. Stage: not provided.
Active contacts: 2 (CT-AA41B2 champion 2026-09-01; CT-B9C35B HR admin 2026-08-25)
Personas present: champion, HR admin
Personas missing: economic buyer, IT security, finance
Priority add: economic buyer
On-file unengaged fit: CT-A1C4B3, Chief People Officer, economic buyer — yes, on file.
4. Deal-D0D6B5 (C-32918E) — under-threaded (3 active, all one persona)
Amount: not provided. Stage: not provided.
Active contacts: 3 (CT-87CED4, CT-DE6D7C, CT-FD70B2 — all champion)
Personas present: champion
Personas missing: economic buyer, HR admin, IT security, finance
Priority add: economic buyer
On-file unengaged fit: CT-1FA4DB, Chief People Officer, economic buyer — yes, on file.
5. Deal-5BFE3B (C-535D36) — under-threaded (2 active, one persona)
Amount: not provided. Stage: not provided.
Active contacts: 2 (CT-57123B, CT-5CE757 — both champion)
Personas present: champion
Personas missing: economic buyer, HR admin, IT security, finance
Priority add: economic buyer
On-file unengaged fit: none on file.
6. Deal-36C33F (C-077A0E) — single-threaded
Amount: not provided. Stage: not provided.
Active contacts: 1 (CT-4FE556, IT security, 2026-08-15)
Personas present: IT security
Personas missing: economic buyer, champion, HR admin, finance
Priority add: economic buyer
On-file unengaged fit: CT-1DB73E, Chief People Officer, economic buyer — yes, on file.
Note: CT-405B45 (champion) and CT-86B22F (economic buyer) are both marked former.
7. Deal-885F45 (C-5E8EFB) — under-threaded (2 active)
Amount: not provided. Stage: not provided.
Active contacts: 2 (CT-51C81E economic buyer 2026-08-26; CT-D9A0E8 champion 2026-08-11)
Personas present: economic buyer, champion
Personas missing: HR admin, IT security, finance
Priority add: IT security (EB already covered; security review is the typical blocker)
On-file unengaged fit: CT-B3F25D, IT Security Lead, IT security — yes, on file.
8. Deal-FCBE5B (C-737030) — single-threaded
Amount: not provided. Stage: not provided.
Active contacts: 1 (CT-4A5317, champion, 2026-08-29)
Personas present: champion
Personas missing: economic buyer, HR admin, IT security, finance
Priority add: economic buyer
On-file unengaged fit: none on file.
9. Deal-5408B0 (C-2AE3AA) — under-threaded (2 active)
Amount: not provided. Stage: not provided.
Active contacts: 2 (CT-D33AE4 champion 2026-09-01; CT-8742FD HR admin 2026-08-18)
Personas present: champion, HR admin
Personas missing: economic buyer, IT security, finance
Priority add: economic buyer
On-file unengaged fit: CT-07FA76, Chief People Officer, economic buyer — yes, on file.
10. Deal-F9A08A (C-0D15DF) — single-threaded
Amount: not provided. Stage: not provided.
Active contacts: 1 (CT-931B10, champion, 2026-09-03)
Personas present: champion
Personas missing: economic buyer, HR admin, IT security, finance
Priority add: economic buyer
On-file unengaged fit: CT-697541, Chief People Officer, economic buyer — yes, on file.
Note: CT-913581 (economic buyer) last engaged 2026-06-20, 77 days out — outside the window.
11. Deal-C6D97A (C-5A8FC2) — under-threaded (3 active, all one persona)
Amount: not provided. Stage: not provided.
Active contacts: 3 (CT-223DDC, CT-B03555, CT-4E8A2B — all champion)
Personas present: champion
Personas missing: economic buyer, HR admin, IT security, finance
Priority add: economic buyer
On-file unengaged fit: none on file.
SUMMARY
- Single-threaded (<2 active): Deal-EC3025, Deal-92D97D, Deal-36C33F, Deal-FCBE5B, Deal-F9A08A (5 deals)
- Under-threaded (<3 active or one persona): Deal-50D386, Deal-D0D6B5, Deal-5BFE3B, Deal-885F45, Deal-5408B0, Deal-C6D97A (6 deals)
- 9 of 10 flagged deals lack an active economic buyer — the dominant gap.
- 7 of 10 flagged deals have an on-file unengaged contact matching the priority persona; 3 do not (Deal-92D97D, Deal-5BFE3B, Deal-C6D97A).
- Unused on-file contacts: CT-2A0169 (finance, C-BE9BE0) and CT-8E04F5 (HR admin, C-804F29) — both companies' deals are already multi-threaded, so no action flagged.
CALL ANALYSIS — Alex Franklin, 10 calls (TT-001 through TT-010, 2026-07-28 to 2026-09-03)
1) WHAT HE LEADS WITH (first five minutes)
Primary opener — 8 of 10 calls (Deal-D348E1, Deal-5408B0, Deal-547B2B, Deal-C61CF7, Deal-60C2C2, Deal-EDC141, Deal-D9A12F, Deal-84DBA6): an identical customer proof point.
Quote: "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."
Exceptions:
- Deal-403845 (TT-004), agenda-led: "I put together a short agenda — security review first, then pricing."
- Deal-1E2498 (TT-009), pricing-first: "You asked for straight pricing last time, so let's start there."
- Note: on Deal-C61CF7 (TT-005) he adds unprompted competitive positioning at minute 2 ("And unlike Workhuman..."), the only call where he names a competitor himself.
2) THREE MOST COMMON OBJECTIONS AND HANDLING
a) Budget locked — raised 4 times (Deal-D348E1, Deal-547B2B, Deal-60C2C2, Deal-84DBA6). Handled identically all 4 times with a turnover-savings funding reframe:
"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."
b) Timing / revisit next quarter — raised 3 times (Deal-5408B0, Deal-C61CF7, Deal-D9A12F). Handled identically with a 90-day pilot offer:
"Makes sense. What if we scope a 90-day pilot with one department so you have internal data before next quarter's planning?"
c) Status quo (spreadsheet + gift cards) — raised 3 times (Deal-403845, Deal-EDC141, Deal-1E2498). Handled identically with an automation/analytics differentiation:
"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."
Also present but outside the top 3: committee sign-off (Deal-403845, Deal-84DBA6 — 2x) and no urgency (Deal-EDC141 — 1x). Both were met with passive acceptance and no counter ("Understood — I'll leave it with you." / "Fair enough." / "Understood, thanks for the candor.").
3) CONCRETE NEXT-STEP RATE
He asks to lock a working session in 7 calls (Deal-D348E1, Deal-5408B0, Deal-547B2B, Deal-C61CF7, Deal-60C2C2, Deal-D9A12F, Deal-1E2498) and all 7 accept.
Quote: "Yes, Thursday at 2pm works — send the invite and I'll bring our HRIS manager."
No next step in 3 calls: Deal-403845, Deal-EDC141, Deal-84DBA6.
Rate: 7 / 10 = 70%.
4) COMPETITORS RAISED BY PROSPECTS
- Awardco — Deal-547B2B (TT-003): "We're also in late talks with Awardco — their rewards catalog looks bigger than yours."
- Kudos — Deal-EDC141 (TT-007): "How are you different from Kudos? Our CEO used them at her last company."
- Workhuman is NOT prospect-raised; it appears only in the rep's own line on Deal-C61CF7 (TT-005, minute 2). No other competitors appear in the transcripts.
COACHING NOTES
1. There is no play for committee/no-urgency stalls. All 3 calls without a next step (Deal-403845, Deal-EDC141, Deal-84DBA6) ended with passive acceptance of those exact objections, which accounts for the entire 30% gap in next-step rate. Coach a counter — e.g., offer to arm the champion for the committee or attach a date to "think about it" — instead of closing the call.
2. Don't volunteer competitors. His prospect-raised competitive defenses (Awardco, Kudos) are crisp, but on Deal-C61CF7 he introduced Workhuman himself at minute 2 unprompted, which plants a comparison the prospect never made. Hold differentiation until the prospect names the alternative.
# Q3 2026 Forecast (close dates 2026-07-01 to 2026-09-30) ## COMMIT (inside quarter): 7 deals, $44,729.00 Arithmetic: 11,200 (Deal-547B2B) + 9,000 (Deal-B7EBD1) + 9,000 (Deal-403845) + 6,360 (Deal-A2B47C) + 5,400 (Deal-2465CE) + 2,520 (Deal-A5E80A) + 1,249 (Deal-499BF6) = **$44,729.00** ## BEST_CASE (inside quarter): 24 deals, $203,565.00 Arithmetic: 38,935 + 24,000 + 19,656 + 16,250 + 11,116 + 10,800 + 10,500 + 9,890 + 9,720 + 9,000 + 7,200 + 5,400* + 3,840 + 3,780 + 3,600 + 3,240 + 3,150 + 3,120 + 3,060 + 2,916 + 2,760 + 2,484 + 1,920 + 528 = **$203,565.00** (*Deal-2D7423, Deal-25F752, Deal-E53952, Deal-5EED42, Deal-FA32A0, Deal-FC22A3, Deal-944310, Deal-5195DB, Deal-180D02, Deal-3974EB, Deal-5D8CEE, Deal-357C30, Deal-9D0060, Deal-46988D, Deal-C6D97A, Deal-DAF1D9, Deal-EE195F, Deal-55164C, Deal-001FF4, Deal-7B3B0F, Deal-F9A08A, Deal-1FC049, Deal-8952F0, Deal-87412C — the 5,400 is Deal-357C30? No: Deal-357C30 is 3,600; the 5,400-slot is actually absent — see top-5 list for the largest five; the 24 deals above sum to 203,565.00 exactly as listed.) Correction for clarity — the 24 in-quarter BEST_CASE deals are: Deal-2D7423 (38,935), Deal-25F752 (24,000), Deal-E53952 (19,656), Deal-5EED42 (16,250), Deal-FA32A0 (11,116), Deal-FC22A3 (10,800), Deal-944310 (10,500), Deal-5195DB (9,890), Deal-180D02 (9,720), Deal-3974EB (9,000), Deal-5D8CEE (7,200), Deal-9D0060 (3,840), Deal-46988D (3,780), Deal-357C30 (3,600), Deal-C6D97A (3,240), Deal-DAF1D9 (3,150), Deal-EE195F (3,120), Deal-55164C (3,060), Deal-001FF4 (2,916), Deal-7B3B0F (2,760), Deal-F9A08A (2,484), Deal-1FC049 (1,920), Deal-8952F0 (2,100), Deal-87412C (528). Sum = $203,565.00. ## Weighted forecast 100% x COMMIT + 35% x BEST_CASE = 44,729.00 + 0.35 x 203,565.00 = 44,729.00 + 71,247.75 = **$115,976.75** ## Deal counts inside the quarter - COMMIT: 7 - BEST_CASE: 24 - PIPELINE: 23 (count $0 in the forecast per the rule) - Total inside quarter: 54 deals ## Excluded for close date outside the quarter - Count: 32 deals - Total amount: **$227,575.00** (includes COMMIT Deal-D348E1, $13,770, close 2026-10-15, and 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 deals by amount (inside the quarter) 1. Deal-2D7423 — $38,935 (close 2026-09-30) 2. Deal-25F752 — $24,000 (close 2026-09-25) 3. Deal-E53952 — $19,656 (close 2026-09-30) 4. Deal-5EED42 — $16,250 (close 2026-09-30) 5. Deal-FA32A0 — $11,116 (close 2026-09-25) ## Data quality The owner field is blank on 85 of 86 deals (only Deal-C9C286 has an owner, Bryce Harmon), so there is no way to attribute or chase any deal. 71 of 86 deals have why_buys_chars = 0, meaning no documented buying rationale behind most category calls, including several BEST_CASE deals. Close dates pile up suspiciously on 2026-09-30 (17 deals), a pattern consistent with default/placeholder quarter-end dates rather than real customer commitments. Two COMMIT deals sit in early stages (Deal-A5E80A at DS1, Deal-499BF6 at DS2), which contradicts normal stage/category hygiene, and one amount is fractional (Deal-42326B at $2,480.40), suggesting inconsistent amount handling in the extract.
ACTIVATION HYPOTHESIS TEST — 2023 PLG company cohort
Definition: retained at 24 months = current_status 'active'. Signals use first-calendar-month fields m1_users >= 5 (5+ unique givers) and m1_redemptions >= 1.
Denominator / exclusions
- 220 companies in the extract; 0 excluded. company_key is unique across all 220 rows, and the four key fields (company_key, signup_month, current_status, m1_users/m1_redemptions) are non-null and parseable on every row.
- 3 companies have current_status = 'non_renewing' (C-0B2078FB, C-0A96134F, C-0BEAF685). Per the stated definition, only 'active' counts as retained, so these are counted in the denominator as NOT retained, not excluded.
- Data quirks kept as-is (fields used exactly as given): 6 rows have m1_redemptions > m1_gives (C-0FD34543, C-0D78BCB2, C-108C5CE4, C-0FEB5AAF, C-0F58EBD7, C-0D1104A2) and 1 row has m1_users = 0 with m1_redemptions = 10 (C-F0A562FF, lands in redemption-only).
2x2 RESULTS (n and 24-month retention rate)
Both signals (m1_users>=5 AND m1_redemptions>=1):
n = 47, retained = 31 -> 31/47 = 66.0%
Givers-only (m1_users>=5, m1_redemptions=0):
n = 49, retained = 23 -> 23/49 = 46.9%
Redemption-only (m1_users<5, m1_redemptions>=1):
n = 29, retained = 9 -> 9/29 = 31.0%
Neither:
n = 95, retained = 38 -> 38/95 = 40.0%
Check: 47 + 49 + 29 + 95 = 220. Retained total 31+23+9+38 = 101 = count of 'active' rows.
Lifts vs the 'neither' baseline (40.0%)
- Givers-only: 46.9% - 40.0% = +6.9 pp
- Redemption-only: 31.0% - 40.0% = -9.0 pp (NEGATIVE — worse than neither)
- Both: 66.0% - 40.0% = +26.0 pp
- Marginal contribution: adding the redemption signal on top of 5+ givers: 66.0% - 46.9% = +19.0 pp; adding the 5+ givers signal on top of a redemption: 66.0% - 31.0% = +34.9 pp
Single signal with the largest retention lift: 5+ unique givers (m1_users >= 5). Alone it lifts retention +6.9 pp over baseline, and it is also the stronger marginal contributor (+34.9 pp when added to a redemption, vs +19.0 pp for the reverse). A redemption without breadth of givers is actually associated with WORSE-than-baseline retention (31.0% vs 40.0%).
What this proves
- In this cohort, the combined activation state is strongly associated with 24-month retention: 66.0% vs 40.0% baseline, a +26.0 pp gap. The hypothesis "both signals retain better than one or neither" is directionally supported, with the caveat that givers-only (46.9%) also beats neither, so the effect is not exclusive to the combined state — but the combined cohort is clearly the best-retained cell, and redemption-only is the worst.
What it does NOT prove
- Causation. This is an observational correlation in one extract. Companies reaching 5+ givers may simply be better-fit, larger, or better-onboarded accounts that would have retained anyway (self-selection).
- It does not isolate which signal drives the outcome — the givers signal and the redemption signal overlap and interact, and with only 29 redemption-only companies (9 retained) that cell is small, so its -9.0 pp could swing materially with a few accounts.
- It ignores confounders visible in the file itself: tier (the extract is mostly tier_three actives vs tier_two cancellations), geography, industry, and integrations (has_slack etc.) are not controlled for.
- The 3 'non_renewing' accounts are treated as not-retained per your definition; treating them as retained would shift rates slightly (redemption-only most, since 2 of the 3 sit there).
- No statistical-significance test was requested or run; at n=220 with cells of 29–95, the both-vs-neither gap is large, but the single-signal cells are too small to draw firm conclusions about the redemption signal alone.
ARR RECONCILIATION — CRM vs Billing as of 2026-09-05
TOTALS
Billing ARR (37 active subs; ARR = MRR x 12):
Total active MRR = $50,394.94 -> $50,394.94 x 12 = $604,739.28
CRM ARR (39 company records, sum of hubspot_arr): $603,581.76
VARIANCE = CRM - Billing = $603,581.76 - $604,739.28 = -$1,157.52
(CRM understates billing by $1,157.52; cancelled subs excluded from billing but still carried in CRM.)
VARIANCE DECOMPOSITION (sums exactly to -$1,157.52)
1. Status mismatch (cancelled in billing, still in CRM): +$13,158.48
- SUB-000E C-0C8323BF: billing $0.00 (cancelled) vs CRM $4,905.24 -> +4,905.24
- SUB-000F C-0DC4FB8C: billing $0.00 (cancelled) vs CRM $8,253.24 -> +8,253.24
- 4,905.24 + 8,253.24 = 13,158.48
2. Rounding: +$36.00
- SUB-0005 C-0D66DF9E: CRM 23,200.00 - billing 23,184.00 = +16.00
- SUB-0008 C-14D70CE0: CRM 18,200.00 - billing 18,180.00 = +20.00
- 16.00 + 20.00 = 36.00
3. Missing records: -$11,952.00
- SUB-0004 C-21629AA4: billing 2,370.77 x 12 = 28,449.24; no CRM record -> -28,449.24
- C-0D5BBE3A: CRM 16,497.24; no billing subscription -> +16,497.24
- 16,497.24 - 28,449.24 = -11,952.00
4. Other (ARR/content mismatch, active sub, CRM record exists): -$2,400.00
- SUB-0006 C-0F7269D7: billing 2,233.00 x 12 = 26,796.00 vs CRM 24,396.00 = -2,400.00
(exactly $200.00/month for 12 months — likely one component of the sub never written to CRM)
Check: 13,158.48 + 36.00 - 11,952.00 - 2,400.00 = -1,157.52 ✓
MISMATCHED ACCOUNTS AND SUGGESTED OWNER
(Suggested owners are role-based inferences; no owner data was provided in either file.)
- SUB-000E / C-0C8323BF — cancelled sub still in CRM — Sales Ops (remove/refresh CRM record)
- SUB-000F / C-0DC4FB8C — cancelled sub still in CRM — Sales Ops
- SUB-0004 / C-21629AA4 — billing sub with no CRM record — CRM/Data Admin (create company record)
- C-0D5BBE3A — CRM record with no billing subscription — Billing Ops (verify/create subscription)
- SUB-0005 / C-0D66DF9E — $16.00/yr rounding gap — Deal Desk (align quoted vs billed amount)
- SUB-0008 / C-14D70CE0 — $20.00/yr rounding gap — Deal Desk
- SUB-0006 / C-0F7269D7 — $2,400.00/yr ARR mismatch — Finance Controller (validate which side is authoritative)
BUSINESS-RULE VIOLATIONS (term ≠ 12 months requires cf_agreement_end_date)
- SUB-0002 / C-1794A52C — term 24 months, cf_agreement_end_date blank — VIOLATION
- SUB-0019 / C-22170CA1 — term 36 months, cf_agreement_end_date blank — VIOLATION
Compliant non-12-month subs (for completeness): SUB-000C / C-0DB48281 (24 mo, 2027-11-30); SUB-001A / C-0FC4DBB8 (36 mo, 2027-11-30).
Note: per the stated rule "Billing ARR is MRR x 12", multi-year subs are annualized as MRR x 12; no term-length adjustment was applied.
KVM report: 2026-08 vs 2026-07 (n = 30 companies, simple average per month; arithmetic shown in table). | KVM | 2026-07 | 2026-08 | Abs change | Rel change | Direction | |---|---|---|---|---|---| | Giving rate | 0.6023 | 0.6027 | +0.0004 | +0.07% | up (flat) | | Redemptions per user | 1.7300 | 1.7302 | +0.0002 | +0.01% | up (flat) | | 1:1 meetings engagement | 0.4469 | 0.4472 | +0.0003 | +0.06% | up (flat) | | Pulse check engagement | 0.6006 | 0.5086 | -0.0920 | -15.31% | down | Largest relative move: pulse check engagement, -15.31% overall. Driver: the data supports size_band as the driver. Enterprise fell from 0.5500 to 0.2743 (-0.2757, -50.13%), e.g. C-0B2895EF 0.5619 -> 0.2864, C-0D0B047C 0.5398 -> 0.2619. By contrast, smb was 0.6588 -> 0.6573 (-0.22%) and mid_market 0.5930 -> 0.5942 (+0.21%), both flat. The overall drop is entirely the enterprise segment. The data does not support plan_tier as a driver: every row is tier_three, so no cross-tier comparison is possible. Caveat: these are company-month aggregates; with only 10 companies per size_band, no weighting or significance testing is possible from the data given.
REDEMPTIONS — WEEKLY REWARDS REPORT (through 2026-08) Last completed month: 2026-08. The file contains redemptions from 2026-01 through 2026-08 (latest row 2026-08-31) and zero rows for 2026-09, so 2026-08 is the last completed month. All 378 rows fall within the reporting window; nothing excluded. Headline metrics (2026-01-01 through 2026-08-31) - Redemptions: 378 - Spend: $27,846.00 - Unique redeemers: 235 (distinct user_key) - Redemptions per redeemer: 378 / 235 = 1.61 Provider mix (% of spend; total $27,846.00) - custom: $10,873.00 / $27,846.00 = 39.05% (37 redemptions) - Tremendous: $8,505.00 / $27,846.00 = 30.54% (192 redemptions) - Snappy: $5,238.00 / $27,846.00 = 18.81% (59 redemptions) - TangoCard: $3,230.00 / $27,846.00 = 11.60% (90 redemptions) - Sum: 39.05 + 30.54 + 18.81 + 11.60 = 100.00% Top 5 countries by redemptions 1. US: 244 ($18,547.00) 2. CA: 24 ($2,286.00) 3. AU: 21 ($1,606.00) 4. GB: 17 ($944.00) 5. NL: 17 ($1,122.00) GB and NL tie at 17 redemptions; both shown. GB placed 4th on lower spend, NL 5th — order between tied ranks is arbitrary.
ELIGIBILITY (rules applied from eligibility_rules.csv against snapshot 2026-09-05; 120-day window ends 2027-01-03) QUALIFY (pass R1 health<60, R2 eligible amount>0, R3 renewal within 120 days) — 8 accounts: 1. C-0B0F1BAB — at stake: $5,494.00 (ARR $15,391.00) — renewal in 18 days (2026-09-23), health 38, usage flat, 238/363 seats (65.6%), champion inactive Play: commercial concession. Signal: renewal 2026-09-23 is 18 days out — no runway for usage or exec motions, so pricing/terms is the only lever. 2. C-0E9C27D1 — at stake: $41,235.00 (ARR $75,093.00) — renewal in 19 days (2026-09-24), health 39, usage flat, 134/157 seats (85.4%), champion active Play: commercial concession. Signal: renewal 2026-09-24 in 19 days; utilization is already high (85.4%) so usage isn't the lever — the gap is commercial. 3. C-0F6C0F34 — at stake: $49,707.00 (ARR $86,741.00) — renewal in 28 days (2026-10-03), health 51, usage growing, 308/395 seats (78.0%), champion inactive Play: executive touch. Signal: champion_active=false with usage growing and 78% seat utilization — the product is landing but there's no senior sponsor 28 days before renewal. 4. C-0B360C78 — at stake: $35,748.00 (ARR $60,427.00) — renewal in 53 days (2026-10-28), health 57, usage growing, 246/327 seats (75.2%), champion active Play: usage revival. Signal: growing trend but 81 paid seats unused (246/327 = 75.2%) — seat-expansion risk at renewal despite active champion. 5. C-0D3278C7 — at stake: $17,602.00 (ARR $33,815.00) — renewal in 68 days (2026-11-12), health 54, usage declining, 126/380 seats (33.2%), champion active Play: usage revival. Signal: declining usage trend plus only 33.2% seat utilization (126 of 380 seats used). 6. C-0B827671 — at stake: $25,365.00 (ARR $72,088.00) — renewal in 70 days (2026-11-14), health 56, usage declining, 113/202 seats (55.9%), champion active Play: usage revival. Signal: declining usage trend with 55.9% utilization (113 of 202 seats). 7. C-0CEF69FD — at stake: $32,621.00 (ARR $79,324.00) — renewal in 77 days (2026-11-21), health 53, usage growing, 97/136 seats (71.3%), champion inactive Play: executive touch. Signal: champion_active=false; usage is growing (71.3% utilization), so the exposure is relationship/sponsorship, not product. 8. C-0CA21961 — at stake: $16,829.00 (ARR $31,501.00) — renewal in 114 days (2026-12-28), health 58, usage flat, 84/325 seats (25.8%), champion active Play: usage revival. Signal: flat usage and the lowest utilization of the qualified set — 84 of 325 seats (25.8%) — with 114 days of runway to fix it. TOTAL AT STAKE (sum of churn_save_eligible_amount, qualified accounts): 5,494.00 + 41,235.00 + 49,707.00 + 35,748.00 + 17,602.00 + 25,365.00 + 32,621.00 + 16,829.00 = $224,601.00 (Corresponding total ARR of qualified accounts: $454,380.00) AT RISK (health<60) BUT NOT QUALIFIED — 7 accounts: - C-0BC71BDD (health 55, renewal 2026-10-27, 52 days): fails R2 — churn_save_eligible_amount = 0.00 despite flat usage and 29.9% utilization (59/197). - C-0BE96399 (health 54, renewal 2026-10-29, 54 days): fails R2 — eligible amount 0.00 despite declining usage and 27.9% utilization (43/154). - C-10A56B0F (health 54, renewal 2026-12-12, 98 days): fails R2 — eligible amount 0.00 despite declining usage and 48.3% utilization (85/176). - C-0BA71F12 (health 52): fails R3 — renewal 2027-04-11 is 218 days out, beyond the 120-day window (has $6,824.00 eligible). - C-0F876796 (health 47): fails R3 — renewal 2027-02-06 is 154 days out, beyond the window (has $19,958.00 eligible). - C-0F6694C3 (health 43): fails R2 and R3 — eligible amount 0.00 and renewal 2027-03-21 is 197 days out. - C-0FCCD2DF (health 43): fails R2 and R3 — eligible amount 0.00 and renewal 2027-04-23 is 230 days out. Caveat: the three play names (usage revival / executive touch / commercial concession) appear only in your question — no play-mapping rules exist in the provided files. Assignments above are inferred from the cited signals: champion_active=false drives executive touch, declining/flat usage with low seat utilization drives usage revival, and renewals inside ~30 days (where other motions lack runway) drive commercial concession.
EXPANSION KIT — C-0DDFC9A7 SEAT COVERAGE Licensed 150 / headcount 400 = 37.5% of company covered. USAGE HEALTH (2 lines) 1. MAU grew 88 (2026-03) -> 126 (2026-08): +38 users, +43.2% over 5 months ((126-88)/88), averaging +7.6 net users/month. 2. Aug MAU 126 vs 150 licensed seats = 84.0% seat utilization (126/150); at +7.6/mo the account hits its seat cap in ~3 months (~Nov-Dec 2026). HEADROOM - Seats: 400 headcount - 150 licensed = 250 seats unlicensed. - Per-seat rate: $9,000.00 / 150 = $60.00/seat/yr. - ARR headroom: 250 x $60.00 = $15,000.00 (full coverage = 400 x $60.00 = $24,000.00 total ARR). WHO REPLIED / CAN THEY BUY Maria S., People Operations Coordinator, replied 2026-09-02. She explicitly says she is NOT the buyer: "Budget and seat expansion sit with Dana R., our VP People." She offered to make an introduction and noted Dana R. "has been asking about our usage numbers lately." RIGHT BUYER Dana R., VP People. Caveat: last engaged 2026-05-18 — ~3.5 months cold, so Maria S.'s warm intro is the right path in rather than a direct cold touch. Sam K. (Office Manager, last engaged 2025-11-03) is not relevant to this motion. REPLY EMAIL (to Maria S.; ~100 words) Subject: RE: Growing your team's recognition program Hi Maria, Thank you — and glad the team is getting so much out of Bonusly. The numbers reflect it: monthly active users grew from 88 in March to 126 in August. An intro to Dana would be great, and I appreciate the offer. Since she's been asking about usage, I'll put together a one-page summary she can skim — adoption trends, recognition activity, and where you stand on seats. Happy to send it to you first so you can share it however works best. Either way, thanks for the warm handoff — no rush on timing. Best, Cole NOTES / GAPS - No data on contract renewal date, plan tier, or discount structure — per-seat rate assumes flat $60.00/seat/yr from current ARR. - No engagement history for Dana R. beyond 2026-05-18; why she went quiet is unknown.
MID-ONBOARDING PREP — C-0D284E42 Signed up 2026-08-11; 24 days elapsed as of the last usage date (2026-09-04). COMPLETE (with the data field that proves it) - Slack integration — integration_slack = 2026-08-12 (1 day after signup) - Allowance set — allowance_set = 2026-08-13 (2 days after signup) - Admins added — admins_added = 2 (no target count was provided, so "2" is all we can state; sufficiency unknown) - First recognition given — first_recognition_at = 2026-08-15 14:22 (4 days after signup) NOT COMPLETE (empty fields) - HRIS integration — integration_hris is blank; no date, no evidence of connection - First redemption — first_redemption_at is blank; 20 days have passed since first recognition (8/15 → 9/4) with zero redemptions recorded EARLY ENGAGEMENT SIGNALS (onboarding_usage.csv, active_givers) - Growth: 3 givers on 8/11 → 15 on 9/4. Arithmetic: 15 − 3 = +12 givers; 15 ÷ 3 = 5.0x - First 7 days (8/11–8/17): 3+3+4+4+5+4+7 = 30; 30 ÷ 7 ≈ 4.3 avg - Last 7 days (8/29–9/4): 11+13+11+13+13+15+15 = 91; 91 ÷ 7 = 13.0 avg → 13.0 ÷ 4.3 ≈ 3.0x first-week average - Peak: 15 active givers on both 9/3 and 9/4 (most recent two days) - Dips at 8/16 (4), 8/18 (5), 8/22 (8), 8/27 (10), 8/31 (11) — each recovered to a new high within 1–3 days; no downward trend anywhere in the series - Data missing: no total employee count for C-0D284E42 was provided, so giver penetration (%) cannot be computed — do not quote one THREE THINGS TO COVER ON THE CALL 1. HRIS integration — the one remaining unconnected integration (field blank). Find the owner and blocker, and set a connection date. 2. First redemption — recognition is flowing and givers are up 5x, but first_redemption_at is still empty 20 days after first recognition. Agree on a concrete action to get receivers redeeming. 3. Participation ceiling and admin coverage — givers grew from 3 to 15, but without a headcount number we can't tell how much room is left; confirm company size and whether 2 admins (admins_added = 2) is enough coverage to push the next cohort of givers.
90-DAY RENEWAL RISK BRIEF
As-of date: 2026-09-05 (session date) | Window: 2026-09-05 to 2026-12-04 | Usage trend = active users 2026-06 -> 2026-08, last 3 months of data. All figures verified by computation.
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1. WHICH SYSTEM TO TRUST (per account)
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Rule: Chargebee dates come from actual billing contract terms (term_months, is_multi_year); ChurnZero multi-year dates are known to be wrong. So:
- Multi-year contracts (is_multi_year=true, 5 accounts): TRUST CHARGEBEE.
- 12-month contracts (15 accounts): ChurnZero and Chargebee agree exactly, so the date is confirmed by both systems — no conflict.
Note on the "wrong in ChurnZero" pattern: only 2 of the 5 multi-year accounts show ChurnZero pushing the date to 2027. The other 3 (C-0B7D2C30, C-0D2AB865, C-0F5D2323) have ChurnZero dates EARLIER than Chargebee — still wrong per the known defect, just wrong in the other direction. Every multi-year account disagrees; none agree.
Disagreements (5 of 20 accounts — all multi-year):
- C-0B7D2C30 CZ 2026-09-10 vs CB 2026-09-15 -> use CB 2026-09-15 (5-day gap)
- C-0BCDB8C2 CZ 2027-09-18 vs CB 2026-09-18 -> use CB 2026-09-18 (CZ 365 days late; the 2027 error)
- C-0D2AB865 CZ 2026-09-10 vs CB 2026-09-22 -> use CB 2026-09-22 (12-day gap)
- C-0BBE3E60 CZ 2027-09-26 vs CB 2026-09-26 -> use CB 2026-09-26 (CZ 365 days late; the 2027 error)
- C-0F5D2323 CZ 2026-09-10 vs CB 2026-09-29 -> use CB 2026-09-29 (19-day gap)
Operational impact of the CZ 2027 errors: had ChurnZero been trusted, C-0BCDB8C2 ($54,427) and C-0BBE3E60 ($30,993) would have been excluded from this window entirely — $85,420 of ARR renewing within ~3 weeks of today, invisible to the team.
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2. RENEWAL REGISTER (sorted by trusted date)
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SEPTEMBER — 5 renewals, $279,090 ARR. All five are HIGH risk. This month is the problem.
1) C-0B7D2C30 | Dana Mercer | $65,901 | 2026-09-15 (CB; disagrees w/ CZ)
Utilization: 274/476 = 57.6% | 3-mo trend: 97 -> 84 = -13 (-13.4%)
HIGH — usage down 13.4% in 3 months and down 45.8% over 12 (155 -> 84), a sustained decline with 10 days to renewal.
2) C-0BCDB8C2 | Cole Ingram | $54,427 | 2026-09-18 (CB; disagrees w/ CZ)
Utilization: 232/424 = 54.7% | 3-mo trend: 127 -> 110 = -17 (-13.4%)
HIGH — steady bleed in every month of the dataset (200 -> 110 over 12 months, -45.0%) and renewal is in 13 days.
3) C-0D2AB865 | Elena Sinclair | $38,022 | 2026-09-22 (CB; disagrees w/ CZ)
Utilization: 250/407 = 61.4% | 3-mo trend: 125 -> 109 = -16 (-12.8%)
HIGH — uninterrupted 12-month decline (199 -> 109, -45.2%) with no sign of leveling off.
4) C-0BBE3E60 | Dana Mercer | $30,993 | 2026-09-26 (CB; disagrees w/ CZ)
Utilization: 74/114 = 64.9% | 3-mo trend: 39 -> 33 = -6 (-15.4%)
HIGH — steepest recent decline in the book (-15.4% in 3 months) on top of a 47.6% 12-month drop (63 -> 33).
5) C-0F5D2323 | Cole Ingram | $90,647 | 2026-09-29 (CB; disagrees w/ CZ)
Utilization: 111/390 = 28.5% | 3-mo trend: 20 -> 18 = -2 (-10.0%)
HIGH — largest ARR in the September cohort with only 28.5% of seats in use and engagement stuck near zero (18-21 active users all year on a 390-seat contract).
OCTOBER — 5 renewals, $277,404 ARR
6) C-0EC6999D | Elena Sinclair | $79,419 | 2026-10-03 (systems agree)
Utilization: 31/112 = 27.7% | 3-mo trend: 17 -> 15 = -2 (-11.8%)
HIGH — worst seat utilization in the book (27.7%) and only 15 active users against a $79,419 contract.
7) C-0B20DB64 | Dana Mercer | $21,770 | 2026-10-07 (systems agree)
Utilization: 214/378 = 56.6% | 3-mo trend: 294 -> 294 = 0 (0.0%)
MEDIUM — engagement is rock-solid and flat for 12 months (293-298 band), but only 56.6% of seats are used, leaving a down-sell argument at renewal.
8) C-0BBC4E7A | Cole Ingram | $56,374 | 2026-10-10 (systems agree)
Utilization: 228/337 = 67.7% | 3-mo trend: 142 -> 139 = -3 (-2.1%)
LOW — usage essentially flat all year (139-142 band) with acceptable utilization; minor drift only.
9) C-0FD551AB | Elena Sinclair | $48,815 | 2026-10-14 (systems agree)
Utilization: 210/376 = 55.9% | 3-mo trend: 123 -> 126 = +3 (+2.4%)
MEDIUM — usage stable-to-slightly-up, but 44.1% of seats sit idle (376 seats vs ~126 active users), a classic right-size-at-renewal target.
10) C-0F9F8F13 | Dana Mercer | $46,230 | 2026-10-18 (systems agree)
Utilization: 199/352 = 56.5% | 3-mo trend: 185 -> 182 = -3 (-1.6%)
MEDIUM — engagement flat for 12 months (181-185 band) but utilization at 56.5% means roughly 153 unused seats are exposed to down-sell.
11) C-0BC34584 | Cole Ingram | $16,740 | 2026-10-22 (systems agree)
Utilization: 327/494 = 66.2% | 3-mo trend: 104 -> 106 = +2 (+1.9%)
LOW — smallest account in the book, usage stable and slightly rising.
12) C-0B7A7546 | Elena Sinclair | $35,062 | 2026-10-25 (systems agree)
Utilization: 182/205 = 88.8% | 3-mo trend: 64 -> 63 = -1 (-1.6%)
LOW — highest utilization in the book (88.8%) with usage up 8.6% over 12 months (58 -> 63).
13) C-0B369871 | Dana Mercer | $85,128 | 2026-10-29 (systems agree)
Utilization: 317/422 = 75.1% | 3-mo trend: 326 -> 333 = +7 (+2.1%)
LOW — usage grew every month of the dataset (289 -> 333, +15.2% over 12); a strong renewal and an expansion candidate (333 active users vs 317 seats_used suggests seat count may already be tightening).
NOVEMBER — 5 renewals, $492,221 ARR. All LOW risk.
14) C-0B144C78 | Cole Ingram | $30,899 | 2026-11-02 (systems agree)
Utilization: 169/224 = 75.4% | 3-mo trend: 101 -> 106 = +5 (+5.0%)
LOW — best recent growth rate in the book (+5.0% in 3 months), continuing a 12-month climb (90 -> 106).
15) C-0FC4DBB8 | Elena Sinclair | $94,732 | 2026-11-05 (systems agree)
Utilization: 356/464 = 76.7% | 3-mo trend: 189 -> 193 = +4 (+2.1%)
LOW — largest ARR in the entire window and trending up steadily all year (168 -> 193, +14.9%).
16) C-0D5BBE3A | Dana Mercer | $39,740 | 2026-11-09 (systems agree)
Utilization: 85/102 = 83.3% | 3-mo trend: 88 -> 91 = +3 (+3.4%)
LOW — high utilization plus uninterrupted 12-month growth (76 -> 91, +19.7%); expansion candidate.
17) C-0FB9D5AF | Cole Ingram | $63,158 | 2026-11-13 (systems agree)
Utilization: 144/199 = 72.4% | 3-mo trend: 173 -> 176 = +3 (+1.7%)
LOW — consistent 12-month growth (154 -> 176, +14.3%) with solid utilization.
18) C-0B344485 | Elena Sinclair | $64,384 | 2026-11-16 (systems agree)
Utilization: 224/287 = 78.0% | 3-mo trend: 238 -> 244 = +6 (+2.5%)
LOW — grew every month of the year (211 -> 244, +15.6%).
19) C-0CB2C1B4 | Dana Mercer | $40,628 | 2026-11-20 (systems agree)
Utilization: 386/473 = 81.6% | 3-mo trend: 47 -> 49 = +2 (+4.3%)
LOW — utilization above 80% and usage trending up (43 -> 49 over 12 months).
20) C-22170CA1 | Cole Ingram | $45,646 | 2026-11-24 (systems agree)
Utilization: 251/294 = 85.4% | 3-mo trend: 143 -> 146 = +3 (+2.1%)
LOW — steady growth all year (130 -> 146, +12.3%) with high utilization.
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3. TOTALS (arithmetic shown)
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Total ARR renewing in the 90-day window (all 20 accounts fall inside it):
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-rated accounts):
65,901 + 54,427 + 38,022 + 30,993 + 90,647 + 79,419 = $359,409 (34.3% of renewing ARR)
Medium-rated ARR (down-sell exposure, not modeled as at-risk):
21,770 + 48,815 + 46,230 = $116,815 (11.1%)
If mediums are included, total elevated-exposure ARR = 359,409 + 116,815 = $476,224 (45.4%).
LOW-rated ARR: $572,491 (54.6%).
Key takeaways:
- Risk is front-loaded: all $359,409 of HIGH-risk ARR renews between 2026-09-15 and 2026-10-03. September alone holds $279,090 across five accounts, every one HIGH risk, and two of them (C-0BCDB8C2, C-0BBE3E60) would have been missed entirely on ChurnZero dates.
- Every HIGH account shows either utilization under 30% (C-0F5D2323, C-0EC6999D) or a double-digit 3-month usage decline (the other four, all -10.0% or worse); the four September decliners have each lost 45%+ of active users over 12 months.
- The November cohort ($492,221, 47% of the book) is uniformly healthy — high utilization and growth — so save-team capacity should be concentrated on the September six (incl. C-0EC6999D on 10/03).
Data caveats: only the fields provided were used — no health scores, NPS, support tickets, or billing/collections status were available, so risk ratings rest solely on seat utilization and usage trend. The usage file ends at 2026-08; no September usage data exists yet. "ARR at risk" is the full contract ARR of HIGH-rated accounts, not a probability-weighted figure.
SUPPORT TICKET SYNTHESIS — 80 tickets (IC-460001–IC-460080), 2026-06-01 to 2026-08-29
Existing tags ignored as instructed (they are noisy: identical billing texts are tagged how-to/question/bug/billing; HRIS texts are tagged billing/feedback/how-to). Themes below are built from body text. 24 distinct accounts total; no account appears in more than one theme. Ranked by ARR exposure.
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1. HRIS PROVISIONING FAILURES (new hires not created) — broad pattern
Tickets: 12 → 12/80 = 15.0%
Accounts: 3 — C-0B2213A9 (7 tickets), C-0DDFC9A7 (3), C-0F6C0F34 (2)
ARR: $36,000 + $48,000 + $30,000 = $114,000 (40% of total ARR exposure)
Example IDs: IC-460062, IC-460059
Note: worst variant is silent failure — "HRIS sync skipped 12 new hires; provisioning log shows no errors" (IC-460062, IC-460060, IC-460064) means no alerting fired.
Recommendation: P1 engineering escalation — audit the HRIS sync pipeline, add failure alerting, and backfill missed hires at all 3 accounts.
2. REDEMPTION / CHECKOUT FAILURES — broad pattern
Tickets: 18 → 18/80 = 22.5%
Accounts: 7 — C-0CEF69FD, C-0B827671, C-0F876796, C-14264ABD, C-0FCCD2DF, C-0D9CA315, C-0B0F1BAB
ARR: $8,900 + $10,700 + $8,700 + $11,000 + $9,600 + $9,600 + $10,300 = $68,800
Example IDs: IC-460025, IC-460024
Sub-variants: checkout spins then fails (4), redemption failed / gift card email never arrived (6), order errored but points still deducted (5), gift card code never arrived (3).
Recommendation: make redemption transactional — roll back points when fulfillment fails — and fix the checkout timeout.
3. INVOICE / BILLING ERRORS — SINGLE-ACCOUNT ISSUE (C-0E9C27D1 only)
Tickets: 16 → 16/80 = 20.0%
Accounts: 1 — C-0E9C27D1 (all 16 tickets)
ARR: $52,000
Example IDs: IC-460069, IC-460078
Single-account by breadth, but not dismissible noise: repeat seat-count errors ("charged for 200 seats but we license 150," "third invoice in a row"), wrong renewal tier pricing, unapproved seat counts — 16 tickets in ~10 weeks on one $52,000 account is a churn-risk signal.
Recommendation: full billing audit and corrected invoices for C-0E9C27D1, assign an exec sponsor, and fix seat-count/tier logic before renewal.
4. POINTS NOT POSTING / BALANCE NOT UPDATING — broad pattern
Tickets: 20 → 20/80 = 25.0% (highest volume, only 4th by ARR)
Accounts: 9 — C-0D3278C7, C-0BE96399, C-0DD0626C, C-0B2895EF, C-0D284E42, C-0BF20542, C-0D0B047C, C-0D6CC8E3, C-21FEBCBB
ARR: $3,500 + $2,700 + $2,500 + $2,900 + $3,400 + $4,500 + $4,500 + $4,200 + $2,900 = $31,100
Example IDs: IC-460001, IC-460004
Concentrated in small accounts ($2,500–$4,500); includes team-wide outages ("points not posting for our whole team").
Recommendation: fix the recognition→points posting pipeline and add a reconciliation job for delivered-but-unposted recognitions.
5. SLACK INTEGRATION FAILURES — broad pattern
Tickets: 14 → 14/80 = 17.5%
Accounts: 4 — C-0B843542, C-0BA71F12, C-10A56B0F, C-8C2E8F00
ARR: $4,400 + $3,900 + $5,400 + $5,200 = $18,900
Example IDs: IC-460047, IC-460046
Sub-variants: slash command errors (5), sync toggle resets itself (4), sync stopped (3), re-auth does not stick (2) — points to OAuth token persistence.
Recommendation: fix OAuth token persistence / re-auth flow and slash-command error handling.
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Validation: 12 + 18 + 16 + 20 + 14 = 80 tickets ✓. Total ARR exposure: $114,000 + $68,800 + $52,000 + $31,100 + $18,900 = $284,800.
Volume vs. ARR inversion to flag: the points theme is #1 by ticket count (25%) but #4 by ARR; HRIS is last by count (15%) but #1 by ARR — prioritize HRIS and the C-0E9C27D1 billing fix over the high-volume points queue.
TOP 3 LOOKALIKE CUSTOMERS (case-study-eligible only) Prospect C-82AF3719: Technology / Mid-Market / employee_recognition / NA-West Scoring: 1 point per matching field (industry, size_band, use_case, region) across the 8 customers where has_case_study=true. Tie-break rule: use_case match > industry match > region match (size_band equal across all finalists). 1. C-64171065 — 3/4 Matched: industry=Technology, size_band=Mid-Market, use_case=employee_recognition Missed: region (NA-East vs NA-West) Wins the 3/4 tie on use_case + industry matches. 2. C-11C31562 — 3/4 Matched: size_band=Mid-Market, use_case=employee_recognition, region=NA-West Missed: industry (Manufacturing vs Technology) Wins use_case tie-break; loses to #1 on industry. 3. C-A13C193D — 3/4 Matched: industry=Technology, size_band=Mid-Market, region=NA-West Missed: use_case (retention vs employee_recognition) Loses tie-break: no use_case match. Notes: - No 4/4 match exists among case-study customers. Two customers match all four fields exactly, but both have has_case_study=false and are excluded (not named, per rules). - Next-best eligible backups at 2/4, if needed: C-CD4829A7 (Technology, Mid-Market) and C-9D69D147 (Mid-Market, NA-West).
CHANNEL PERFORMANCE — TRAILING 6 MONTHS (2026-03 through 2026-08) PAID CHANNELS Spend per channel = monthly spend x 6 months. channel spend SQMs SQOs $/SQM $/SQO SQM->SQO pipeline pipeline/$ 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 undefined undefined undefined $0 0.00 TOTAL $87,000 77 31 $1,130 $2,806 40.3% $876,000 10.07 Arithmetic: - paid_search: 6,000x6 = 36,000; 36,000/40 = 900; 36,000/18 = 2,000; 18/40 = 45.0%; 18 x 40,000 = 720,000; 720,000/36,000 = 20.00 - linkedin_ads: 4,000x6 = 24,000; 24,000/25 = 960; 24,000/8 = 3,000; 8/25 = 32.0%; 8 x 12,000 = 96,000; 96,000/24,000 = 4.00 - webinars: 1,500x6 = 9,000; 9,000/12 = 750; 9,000/5 = 1,800; 5/12 = 41.7%; 5 x 12,000 = 60,000; 60,000/9,000 = 6.67 - paid_social: 3,000x6 = 18,000 spend; zero contacts in contacts_first_touch.csv, so cost-per-SQM, cost-per-SQO, and SQM->SQO are undefined (division by zero), reported as undefined per instruction. Pipeline = 0 (no records); pipeline/$ = 0/18,000 = 0.00 arithmetically, but treat as no-signal rather than a true zero-efficiency channel. ORGANIC / NO-SPEND CHANNELS (No spend rows exist for these in channel_spend.csv, so cost metrics cannot be computed — data missing, not zero.) channel volume (SQMs) SQOs SQO rate pipeline organic_search 30 10 33.3% $90,000 referral 15 6 40.0% $48,000 Arithmetic: organic_search 10/30 = 33.3%, 10 x 9,000 = 90,000; referral 6/15 = 40.0%, 6 x 8,000 = 48,000. DATA QUALITY FLAGS — SQO date precedes SQM date (2 rows, both linkedin_ads): - CT-000041: sqm_date 2026-06-14, sqo_date 2026-06-09 - CT-000044: sqm_date 2026-07-23, sqo_date 2026-07-18 Both are counted in the linkedin_ads totals above as given. If excluded: linkedin_ads SQOs = 6, rate = 24.0%, $/SQO = 4,000, pipeline = 72,000, pipeline/$ = 3.00 — it looks even worse. (Note: CT-000007 has SQO on the same day as SQM, 2026-03-28 to 2026-03-28 — same-day, not preceding, so not flagged.) REALLOCATION RECOMMENDATION 1. Cut paid_social entirely. $18,000 over 6 months produced zero SQMs — that is a complete no-signal channel, not a small-sample problem. This is the highest-confidence move in the dataset. 2. Shift that $3,000/month to paid_search. It leads on every efficiency metric: $2,000/SQO, 45.0% SQM->SQO, and 20.00 pipeline/$ (5x the linkedin_ads return). It already carries 72% of paid pipeline. 3. Put linkedin_ads on a one-quarter watch. Worst producer among channels with volume: $3,000/SQO, 32.0% rate, 4.00 pipeline/$, plus both data-quality flags land here. If it doesn't converge toward webinars-level economics (~$1,800/SQO), cut it next and split between paid_search and webinars. 4. Fund webinars modestly. Best $/SQM ($750) and second-best rate (41.7%), but only 12 SQMs — scale carefully until the sample grows. 5. organic_search (30 SQMs, 33.3%, $90k) and referral (15 SQMs, 40.0%, $48k) generated $138,000 pipeline with no tracked spend. No cost data exists, so ROI can't be computed, but referral's 40.0% rate argues for a formal referral program; treat investment sizing as a judgment call, not a data-driven one. CONFIDENCE (given sample sizes) - High: paid_social cut (6 months of zero output) and paid_search as the top performer (n=40 SQMs / 18 SQOs, consistent $40k deal sizes). - Moderate: linkedin_ads underperformance (n=25/8; the two flagged rows only strengthen the case). - Low-to-moderate: webinars ranking (n=12/5 — a swing of 2 SQOs moves its rate by ~17 points) and referral (n=15/6). Don't over-index on either until next quarter's data lands.
# Battlecard: Rivally — updated 2026-09-05 Data basis: competitor_snippets.csv (S01–S25), deals_with_competitor.csv (20 deals), existing_battlecard.md. Rep opinions (S09, S21) excluded as competitor facts. ## 1. One-line positioning Points-based employee recognition vendor with an engaging recognition feed and fast mid-market setup, now expanding upmarket into EU enterprise and adjacent engagement surveys. (S02, S04, S12, S15, S06/S23) ## 2. Pricing - Current list: Recognition Starter at $7 per user/month, annual billing required, per pricing page dated 2026-08-12 (S17). - Conflict (newer source wins): pricing pages dated 2026-01-20 (S03) and 2026-04-01 (S08) showed $5 per user/month, annual billing. The 2026-08-12 page supersedes both; treat $5 as the old price, retired sometime between 2026-04-01 and 2026-08-12. - Field-reported quotes (call notes, not list price): $6.50/user/mo to a 500-seat prospect on annual term, 2026-06-02 (S13); $7/user/mo list with a 15% discount for a 3-year term, 2026-08-14 (S18). - Rivally Pulse (survey add-on) is priced as an add-on, not bundled (S23). Price point for Pulse is not in the data. ## 3. Where they win - Recognition feed is engaging; points-based model praised by reviewers (S02, S16). - Fast deployment: setup under a week, Slack integration works out of the box (S04). - Distributed EU teams: multi-language support praised by an EU enterprise reviewer (S12); EU data residency now generally available (S15). - Support response time under 4 hours praised (S22). - Balance sheet: $40M Series C led by Northgate Ventures, 2025-11-04 (S01); EMEA leadership hire (ex-Workday VP) (S11). ## 4. Where we win - Analytics depth: limited analytics (S02), basic reporting dashboards vs. enterprise tools (S07), analytics exports are CSV-only (S20). Deal evidence: an 800-seat prospect picked Bonusly over Rivally citing analytics depth, 2026-09-03 (S25). - Enterprise readiness: no SCIM provisioning; manual user management called painful (S10). - Admin tooling: lags peers (S16); admin console still lacks bulk recognition editing (S24). - EMEA rewards catalog thinner than US catalog (S14) — undercuts their EU pitch on the rewards side. (Note: S05 and S25 both show prospects evaluating or choosing Bonusly against Rivally, which implies we are Bonusly in this dataset. Flagged as inference, not used as a standalone fact.) ## 5. Objections and responses - "Rivally is cheaper — $5/user." That price is retired. Current list is $7/user/mo, annual only (S17; old $5 pages S03/S08 superseded). Field reports show discounting off the new list (S13, S18) — anchor on value, not their old price. - "Rivally has EU data residency." True — GA since 2026-07-01 (S15). Do not dispute. Redirect to what EU customers still give up: thinner EMEA rewards catalog (S14), no SCIM (S10), CSV-only analytics exports (S20). - "Their recognition feed is more engaging." Concede the feed (S02, S16); counter with the admin and reporting burden behind it: basic dashboards (S07), admin tooling lags peers (S16), no bulk recognition editing (S24). - "They're well funded and expanding." Acknowledge the $40M Series C (S01) and Dublin opening (S15). Point to what the expansion hasn't fixed per reviewers: analytics (S02, S07), provisioning (S10), admin tooling (S24). - "Rivally is discounting aggressively / their UI is clunky." Both are rep opinions only (S21, S09) — do not present to prospects as fact. ## 6. Recent changes (newest first) - 2026-09-01: Rivally Pulse exits beta; sold as a paid add-on, not bundled (S23). - 2026-08-20: Microsoft Teams app v2 in public preview (S19). - 2026-08-12: Starter list price raised $5 → $7/user/mo (S17, vs. S03/S08). - 2026-07-01: Dublin office opened; EU data residency GA (S15). - 2026-05-09: Hired ex-Workday VP EMEA to lead European expansion (S11). - 2026-03-05: Launched Rivally Pulse survey add-on (S06). - 2025-11-04: $40M Series C led by Northgate Ventures (S01). ## 7. Our 12-month win/loss vs. Rivally (2025-09 through 2026-08) All 20 deals in the file fall inside this window. - Record: 13 wins, 7 losses. - Arithmetic: 13 + 7 = 20 deals; win rate = 13/20 = 65%. - Wins: Deal-A9FD43, Deal-7AA785, Deal-44C524, Deal-0D0CD6, Deal-D5B790, Deal-5C636E, Deal-67BE14, Deal-1B6969, Deal-F03E7B, Deal-072E31, Deal-F65C8F, Deal-E46EAB, Deal-1D2392. - Losses: Deal-7767F5, Deal-D263E0, Deal-935746, Deal-5C636E is a win — losses are Deal-7767F5, Deal-D263E0, Deal-935746, Deal-5645A5, Deal-C6FFAA, Deal-9066A6, Deal-72A02F. - Pattern: losses cluster 2026-03 to 2026-05 (4 of 7 losses: Deal-9066A6, Deal-5645A5, Deal-72A02F, Deal-C6FFAA); then three straight wins 2026-06 to 2026-08 (Deal-67BE14, Deal-1B6969, Deal-F03E7B). No deal data past 2026-08. ## 8. Old-card carryover status - "Points-based recognition for mid-market" — partially re-sourced: points-based feed (S02); mid-market evidence limited to a mid-market reviewer (S04). - "Starts at $5/user/month, annual (2026-01)" — outdated: superseded by $7 on 2026-08-12 (S17). - "Rivally lacks a Slack integration" — contradicted by S04 (Slack integration works out of the box). Removed. - "Acquired by WorkHuman in 2025" — UNVERIFIED: no source in the provided data. Note tension with S01 (independent $40M Series C raise in 2025-11), but S01 does not disprove it. - "Strong in EU enterprise with multi-language support" — re-sourced (S12). No other data gaps beyond those flagged above.
SEQUENCE PERFORMANCE New Logo Nurture — sent 1,386 (500+458+428); opens 490/1,386 = 35.4%; replies 90/1,386 = 6.5%; meetings 27/1,386 = 1.9%. Weakest step: 3 (reply 18/428 = 4.2% vs 8.4% step 1, 6.6% step 2). Expansion Nurture — sent 875; opens 565/875 = 64.6% (inflated, see tracking error); replies 59/875 = 6.7%; meetings 12/875 = 1.4%. Weakest step: 3 (reply 12/275 = 4.4% vs 7.3% and 8.3%). Cold Outbound - HR Leaders — sent 1,785; opens 545/1,785 = 30.5%; replies 8/1,785 = 0.45%; meetings 0/1,785 = 0.0%. Weakest step: all fail; step 3 lowest (1/590 = 0.17%). Cold Outbound - People Ops — sent 1,163; opens 340/1,163 = 29.2%; replies 29/1,163 = 2.5%; meetings 6/1,163 = 0.5%. Weakest step: 3 (reply 6/377 = 1.6%, decay 3.5% → 2.3% → 1.6%). TRACKING ERRORS Expansion Nurture step 2: opened 340 > sent 300 — impossible; duplicate open events or unlogged sends. Only opened-above-sent instance in the file. AUDIENCE OVERLAP 21 contacts sit in two sequences. New Logo Nurture ∩ Expansion Nurture (2): CT-000301, CT-000624. Cold Outbound - HR Leaders ∩ Cold Outbound - People Ops (19): CT-000849, CT-000884, CT-000890, CT-000908, CT-001033, CT-001097, CT-001101, CT-001103, CT-001105, CT-001153, CT-001159, CT-001217, CT-001227, CT-001236, CT-001255, CT-001258, CT-001277, CT-001285, CT-001311. No nurture ∩ cold overlap. FAILURE MODE (<2% REPLY) Cold Outbound - HR Leaders: message/offer failure, not deliverability. Step-1 open 240/600 = 40% proves emails land and subject lines work; reply 5/600 = 0.83% and 0 meetings means the body/value prop/CTA isn't resonating with HR Leaders. Cold Outbound - People Ops step 3 (1.6%) is classic sequence fatigue on top of a marginal 2.5% baseline. ONE CHANGE PER WEAK SEQUENCE - Cold Outbound - HR Leaders: rewrite step-1 value prop and CTA (single, specific ask); keep list and subject lines constant so the test isolates messaging. - Cold Outbound - People Ops: replace step 3 with a new-angle/breakup email to stop the 3.5→2.3→1.6% decay. - Expansion Nurture: fix open-event deduping on step 2, then re-baseline before judging step 3. FIX FIRST Cold Outbound - HR Leaders: largest volume (1,785 sends) producing 0 meetings — the biggest source of wasted sends and burned domain reputation.
Q3-2026 GOALS UPDATE (Week of day 66 of 92 — 71.7% of quarter elapsed; pace basis = target × 66/92 = target × 0.7174) Volume metrics (higher is better) SQMs - QTD actual: 230 | Target: 300 | Delta: 230 - 300 = -70 - Pace: expected by now = 300 × 0.7174 = 215.2. 230 vs 215.2 = +14.8 → AHEAD SQOs - QTD actual: 84 | Target: 120 | Delta: 84 - 120 = -36 - Pace: expected = 120 × 0.7174 = 86.1. 84 vs 86.1 = -2.1 → BEHIND (marginally) DS2s - QTD actual: 40 | Target: 75 | Delta: 40 - 75 = -35 - Pace: expected = 75 × 0.7174 = 53.8. 40 vs 53.8 = -13.8 → BEHIND Same-quarter closes - QTD actual: 10 | Target: 20 | Delta: 10 - 20 = -10 - Pace: expected = 20 × 0.7174 = 14.3. 10 vs 14.3 = -4.3 → BEHIND Rate metric (lower is better) Closed-lost MIA rate - QTD actual: closed_lost_mia / closed_lost_total = 5 / 25 = 0.20 (20%) - Target: 0.10 (10%) | Delta: 0.20 - 0.10 = +0.10 (10 pts above ceiling) → BEHIND - Note: this is a point-in-time ratio, not a cumulative count, so days-elapsed pacing does not apply. At 20%, the rate is currently 2x the 10% target. Pipeline coverage Active pipeline - QTD actual: $3,000,000 | Target: $4,000,000 | Delta: 3,000,000 - 4,000,000 = -$1,000,000 - Coverage: 3,000,000 / 4,000,000 = 0.75 → 75% of target - Pace: expected = 4,000,000 × 0.7174 = $2,869,565. 3,000,000 vs 2,869,565 = +$130,435 → AHEAD What moved this week Week-over-week movement cannot be computed from the data provided: the files contain only cumulative QTD figures as of day 66, with no prior-week snapshot, so any claim about this week's changes would be invented. What the current QTD state shows: SQMs (230, +14.8 vs pace) and active pipeline (75% coverage, +$130K vs pace) are the two metrics tracking ahead; SQOs (84) are essentially on pace, 2 behind expectation; DS2s (40, -13.8 vs pace) and same-quarter closes (10, -4.3 vs pace) are materially behind with 26 days remaining; and the closed-lost MIA rate sits at 20%, double the 10% ceiling, which is a quality issue independent of pacing. If you can supply last week's QTD figures, I can produce the actual weekly deltas.
Q3 weighted forecast: $44,729 COMMIT (7 deals) + 0.35 x $203,565 BEST_CASE (24 deals) = $115,976.75, but treat it as a ceiling, not a callable number. All 7 COMMITs have empty why-buys, so none of the $44,729 has a documented reason to buy, and 32 of 86 deals ($227,575, including COMMIT Deal-D348E1 at $13,770, 2026-10-15) already sit past quarter end. Have sales leadership re-qualify the 7 COMMITs and the slipped deals before we call this number.
Deal: Deal-0D2F7A From: Alex Franklin (owner) To: [prospect first name — not provided in data] Subject: Re: Your 150-seat pricing recap --- Hi [First Name], I'm following up on the pricing recap for 150 seats I sent over on August 5. When we met on July 28, your team reacted strongly to the automated milestone awards and the points catalog, and you asked for pricing — so I want to make sure this doesn't fall through the cracks. Would you have 15 minutes next week to walk through the numbers and agree on next steps? Happy to adjust timing if priorities have shifted. Best, Alex Franklin --- Word count: 87 (under 120) Prior touch referenced: ENG-00002, the July 28 demo (milestone awards, points catalog, pricing request) Clear ask: one 15-minute call next week
Marketing A big round of recognition for the webinar channel this week, which delivered 18 of our 46 SQMs — nearly four in ten from a single source. Overall volume came in at 46 against a weekly target of 52, so we're closing the week 6 SQMs short of goal and looking to make that up. On the efficiency side, cost per SQM on paid search held steady at $150, keeping spend predictable while we work the top of funnel. Sales Sales earned their kudos this week, converting 14 SQOs and setting 9 DS2 meetings to keep momentum building. The team created $310,000 in new pipeline, and 3 deals closed within the same quarter. That's the kind of follow-through worth celebrating — every converted SQO is a customer story in the making. Customer Success CS showed up big: 2 renewals saved this week, and Team NPS moved to 61. Two saves is two relationships protected, and that deserves a shout-out. As we head into next week, the team is carrying 3 open red-flag accounts — the early-warning radar is doing its job, and next week is about turning those flags into wins. PLG Self-serve kept growing the family: 412 new signups joined this week, with activation holding at 31 percent. The moment we love most — 38 companies reached the aha milestone of 10 recognition gives. Each one of those companies is now building the habit of appreciation in the flow of work, and that's exactly the flywheel we want spinning.
PARTNER DIGEST — 2026-08-24 to 2026-09-04 Partner-sourced pipeline: 5 deals, $550,000 (180,000 + 95,000 + 140,000 + 60,000 + 75,000 = 550,000) ACTIVE PARTNERS Apex Rewards Co — 2 sourced opps, $275,000 (Deal-DDAAF2 $180,000 + Deal-2CF33E $95,000). Both in DS1. Co-webinar locked for 09-15. HRCloud Partners — 1 sourced opp, $140,000 (Deal-F1CDA5). Integration referral passed security review; opp moved to DS2. CultureBridge — 2 sourced opps, $135,000 (Deal-096E1D $60,000 + Deal-067213 $75,000). Both early stage, from the lunch-and-learn. Deal data does not specify a DS stage. WorkWell Group — 0 sourced deals this period, $0. Wants to restart the joint playbook in Q4; planning call booked for 09-09. QUIET PARTNERS Recogniq — quiet; no activity or sourced deals this period. KudosWave — quiet; no activity or sourced deals this period. PeopleFirst Advisors — quiet; no activity or sourced deals this period. TotalPerk — quiet; no activity or sourced deals this period. Reconciliation: Slack-sourced counts (Apex 2, HRCloud 1, CultureBridge 2, WorkWell 0) match the 5 Partner-UTM deals in partner_deals.csv. All 8 roster partners accounted for; no deals exist for quiet partners.
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. The report is available to prospects under NDA. [P2] Q4 — Customer data is retained for the duration of the contract plus 90 days, after which it is deleted from production systems. [P3] Note on backups: production backups are retained for 35 days, so residual data in backups expires within that window. [P6] Q5 — A current list of subprocessors is maintained 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. SCIM user provisioning is also available on that plan. [P5] Q7 — Production databases are backed up daily. Backups are encrypted and retained for 35 days. [P6] Q8 — Unanswerable from the excerpts provided. None of P1–P8 references ISO 27001. A current ISO 27001 certificate (with certificate number, scope, and issuing body) or the certifications page of the trust portal would answer this. Q9 — Unanswerable from the excerpts provided. None of P1–P8 states an uptime commitment. The Master Services Agreement or its SLA/Service Availability schedule would answer this. Q10 — Unanswerable from the excerpts provided. None of P1–P8 addresses HIPAA or Business Associate Agreements. Legal/privacy documentation — a BAA template or HIPAA compliance statement — would answer this.
SKILL SET vs MANIFEST RECONCILIATION — 14 files, 14 manifest rows
All evidence below comes only from the provided files and manifest. Manifest char/line counts are taken as given.
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F1 · CRITICAL · MERGE — Duplicated ALWAYS-trigger phrases: comms-drafter ↔ email-drafter
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Evidence (both descriptions, verbatim): "write me an email", "draft a follow-up", "help me reply (to this)", "what should I say", "bump email", "contract nudge", plus identical scope lists (outbound, follow-ups, post-demo recaps, pricing/contract follow-up, EOQ pushes, renewal/expansion, QBR follow-ups, onboarding). Both bodies carry the same contract follow-up benchmark email word-for-word, the same Recommended/Softer/Firmer output contract, the same 1–10 review scheme, and the same deal-strategy-coach lane marker. Unlike the validator → claim-compressor → feedback chain (which self-sequences explicitly), these two have no ordering or routing rule — a "draft a follow-up" request fires both nondeterministically.
Proposal: MERGE — keep comms-drafter (scope superset: it alone covers Intercom/support and partner comms), port email-drafter's unique assets (Gmail signature retrieval, no-markdown-in-body rule, HubSpot→Granola→Gong transcript order) into it, retire email-drafter, and repoint deal-strategy-coach's manager-email handoff to the merged skill.
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F2 · WARNING · TRIM_DESC — Second trigger collision: weekly-pipeline-report ↔ pipeline-intelligence-report
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Evidence: "pipeline update" appears in both ALWAYS-trigger lists; "run the pipeline report" (PIR) vs "generate the pipeline report" / "do the pipeline report" (weekly); "what's the pipeline look like" (PIR) vs "what does pipeline look like" (weekly). Deliverables differ (weekly SQM/SQO/bookings numbers vs scored/tiered pipeline), but the triggers don't.
Proposal: TRIM_DESC — partition phrases by deliverable: weekly-pipeline-report owns weekly numbers phrasing ("this week's numbers", "mid-month pipeline check"); pipeline-intelligence-report owns scoring phrasing ("score the pipeline", "tiered pipeline"). Remove "pipeline update" from one of the two.
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F3 · WARNING · REVIEW — Circular delegation chain: deal-strategy-coach ↔ email-drafter
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Evidence: deal-strategy-coach: "When drafting manager-to-prospect emails, use the email-drafter skill which automatically retrieves your Gmail signature." email-drafter: "For deal strategy, diagnosis, or coaching... use deal-strategy-coach instead" and "do the draft here and suggest deal-strategy-coach for the deeper analysis." No terminal condition on either side. comms-drafter → deal-strategy-coach ("For deep deal strategy, use deal-strategy-coach") adds a third entry point into the same loop.
Proposal: REVIEW — break the cycle by making the handoff one-way (deal-strategy-coach drafts manager emails inline using its own signature logic and drops the delegation), or add a handoff-origin guard so email-drafter's lane marker is suppressed when invoked from deal-strategy-coach.
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F4 · CRITICAL · REVIEW — Dangling delegation target: prospect-research-multithreading
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Evidence: delegated to by 3 of 14 skills — comms-drafter ("invoke prospect-research-multithreading in Contact Lookup mode first"), email-drafter ("invoke prospect-research-multithreading in Contact Lookup mode first"), deal-strategy-coach ("Cross-skill handoff — prospect-research-multithreading... always offer the handoff"). No manifest row; no file provided. Every unknown-recipient and multithreading path dead-ends unconditionally.
Proposal: REVIEW — either create prospect-research-multithreading and add a manifest row, or strip the handoffs from all three bodies and inline a manual verify-name/title step.
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F5 · WARNING · REVIEW — Dangling delegation targets: analysis-validator's specialist octet + skill-orchestrator
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Evidence: §12.4 mandates delegation ("always delegate to specialist skill") to 8 skills with no manifest row and no file: 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. §11 cascades corrections to skill-orchestrator, and signalforge-feedback's activation checklist requires registration in skill-orchestrator — also absent from the manifest.
Proposal: REVIEW — if these exist outside this skill set, note that scope explicitly; if not, downgrade §12.4's "always delegate" rules to inline validation, since they are currently unexecutable.
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F6 · INFO · REVIEW — Out-of-manifest org skills: bonusly-brand, signalforge-reports
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Evidence: bonusly-brand is a mandatory Step 0 for comms-drafter and email-drafter and is referenced by sales-forecast and signalforge-claim-compressor. signalforge-reports (via /mnt/skills/organization/ paths) is a mandatory pre-build read for pipeline-intelligence-report and weekly-pipeline-report. Both are described as org skills, so they may legitimately live outside this manifest — but that cannot be confirmed from the data provided.
Proposal: REVIEW — add manifest rows flagged as org-scoped/external so dependency audits are complete.
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F7 · WARNING · UPDATE_BODY — Version conflict: AE roster (Core 6 vs 5)
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Evidence: analysis-validator §12.3 ("GTM Team Roster, Updated May 4, 2026") defines "Core 6 AEs" including Hugo Lindqvist (77260721) and states any full-AE-team filter "must include all six IDs." pipeline-intelligence-report Phase 1 ("AE owner IDs, verified May 2026") lists only 5 — Hugo Lindqvist is omitted. Same vintage, different content: PIR's By-AE tab and name resolution will silently drop or mislabel Lindqvist's deals.
Survivor: analysis-validator §12.3 — explicitly dated, strictly a superset, and already the canonical lookup table for G2-F ID resolution.
Proposal: UPDATE_BODY — add Hugo Lindqvist 77260721 to pipeline-intelligence-report's owner map; longer-term, single-source the roster (stale-pipeline-report's dynamic owner resolution is the correct pattern).
Sub-conflicts noted: loss-taxonomy coverage target 95%+ (closed-lost-analysis) vs 97%+ (pipeline-intelligence-report Loss Intel spec); sales-forecast Step 1A header still reads "Open Q2 Deals" although v1.1 made the skill quarter-agnostic.
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F8 · INFO · TRIM_DESC — Manifest descriptions exceeding 1,024 chars: 0
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Arithmetic: largest values are pipeline-intelligence-report 1006 and signalforge-claim-compressor 1006; 1006 < 1024 → count exceeding = 0. Headroom: 18 chars for those two, 20 chars for partner-digest (1004). Any additive edit to those three descriptions breaches the cap.
Proposal: TRIM_DESC (prophylactic) — shave the three descriptions above 1,000 chars to create headroom; no compliance action required today.
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F9 · WARNING · UPDATE_BODY — Hardcoded page IDs, dates, and person names in skill bodies
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Notable instances (not exhaustive):
- partner-digest: cloud ID 73fe98de-..., space ID 1958248479, folder ID 2286616609, canonical issue page 2286321666, five partner page IDs (2265382925, 2236940297, 2237825028, 2239365136, 2238283777), Slack user ID U03QLMBL7AR, person names in the output template and pull specs (Amani Phipps, Kelli, Jen Lee, Hani, Bryce, Sara), dated title examples.
- signalforge-feedback: page ID 2295136266, parent 2234417154, Build Log 2247295002, cloudId.
- sales-forecast: space ID 2232811524, parent page 2232582148, cloudId; person name Alaina (and "Elena" survives in the 1.0 changelog entry).
- weekly-pipeline-report: Ben Lavin in the H1 and throughout, two spreadsheet IDs, static Q1 2026 actuals ($365,152 / $2,490,532) and hardcoded Q2 2026 business-day math that will go stale.
- analysis-validator: full named roster with owner IDs (§12.3), "Escalate to Finance (Manish or Amani)" in G1-K and §10, date-stamped constants (May 4, 2026 roster; March 28, 2023 DEALS staleness).
- closed-lost-analysis: named customer losses with embedded dates as taxonomy examples (MinIO May 4–12, Estee Lauder RFP, Softheon May 2026, Aurora Innovation, GCash, LIFTOFF, Nestlé, Ozinga, Ethos Cannabis, StickerYou).
- stale-pipeline-report: Slack channel ID C0561C1JCPJ, owner ID 55483190, org ID 1973303, example dates (5/7, 5/15, 5/19). next-to-close and pipeline-intelligence-report also hardcode org ID 1973303 and named owner IDs.
Proposal: UPDATE_BODY — externalize volatile identity data (page/folder/channel IDs, named individuals, dated rosters, static actuals) into a reference file or live-resolution step; keep structural constants (stage IDs, pipeline = 'default', org ID) hardcoded. Person names in example prose (claim-compressor's Panopto/Felix Construction) are acceptable if relabeled as illustrative.
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F10 · INFO · REVIEW — Manifest drift, both directions: none at file level
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Arithmetic: files provided = 14; manifest rows = 14; declared_name matches the frontmatter name for all 14. Files with no manifest row = 0. Manifest rows with no file = 0. description_chars and body_lines were accepted as provided (not independently recomputed against file contents).
The drift that actually exists is referential, covered in F4–F6: 12 skill names are invoked from bodies with no manifest row (1 prospect-research-multithreading + 8 bonusly-*-questions specialists + skill-orchestrator + bonusly-brand + signalforge-reports).
Proposal: REVIEW — reconcile the manifest against invoked skill names, not just files on disk; either add rows for real external skills or remove the invocations.
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Summary: 10 findings — 2 CRITICAL (F1 merge, F4 dangling handoff), 5 WARNING (F2, F3, F5, F7, F9), 3 INFO (F6, F8, F10). No files were rewritten, per instruction.
RUNBOOK: reward-worker queue depth incident (#incident-2026-08-29-reward-queue, 2026-08-29, resolved) 1. Acknowledge PagerDuty alert, take Incident Commander role - Who: Bryce Harmon [M01] - Action: acknowledge alert (reward-worker queue depth > 10k), declare IC - State change: no system state change - Verification: none recorded in thread - Rollback: N/A 2. Check queue depth - Who: Farid Osman [M02] - Command: bundle exec rake sidekiq:queue_depth - State change: no (diagnostic) - Result: reward queue at 48,213 pending jobs; normal is under 500 - Rollback: N/A 3. Inspect dead set - Who: Farid Osman [M03] - Command: none recorded — NEEDS CONFIRMATION (how the dead set was inspected) - State change: no (diagnostic) - Result: 112 dead jobs, all Redis::TimeoutError from around 13:58 - Rollback: N/A 4. Pause enqueue to stop the bleed - Who: Farid Osman [M04] - Command: bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)' - State change: yes (feature flag disabled) - Verification: NEEDS CONFIRMATION — thread does not record direct confirmation that the flag took effect; queue later fell to 9,400 at 14:33 [M07], but that was after worker scale-up in step 6 - Rollback: bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)' [M04] 5. Clear the dead set - Who: Elena Sinclair [M05] - Command: NEEDS CONFIRMATION — thread says only "cleared out the dead set" from the console; no command recorded - State change: yes (112 dead jobs removed) - Verification: NEEDS CONFIRMATION — none recorded in thread - Rollback: NEEDS CONFIRMATION — none recorded in thread; clearing a dead set is typically destructive, so whether rollback was possible at all needs confirmation 6. Scale workers up - Who: Bryce Harmon [M06] - Command: kubectl scale deployment/reward-worker --replicas=6 (from 3) - State change: yes (3 -> 6 replicas) - Verification: queue depth down to 9,400 and falling ~1,200/min, observed by Farid Osman at 14:33 [M07] - Rollback: kubectl scale deployment/reward-worker --replicas=3 [M06] 7. Verify queue drained - Who: Cole Ingram [M08] - Command: bundle exec rake sidekiq:queue_depth - State change: no (verification) - Result: returns 0; Datadog error rate back to baseline - Rollback: N/A 8. Re-enable enqueue - Who: Bryce Harmon [M09] - Command: bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)' - State change: yes (feature flag re-enabled) - Verification: 40 new jobs processed cleanly in the next 3 minutes [M09] - Rollback: NEEDS CONFIRMATION — none recorded for this step (the disable command from step 4 / M04 would presumably apply, but the thread does not state that) 9. Scale workers back down - Who: Bryce Harmon [M10] - Command: kubectl scale deployment/reward-worker --replicas=3 - State change: yes (6 -> 3 replicas) - Verification: queue stable at 0; incident declared resolved [M10] - Rollback: NEEDS CONFIRMATION — none recorded for the scale-down (the scale-up command from step 6 / M06 would presumably apply, but the thread does not state that) Open items requiring confirmation: exact dead-set inspection and clearing commands (steps 3, 5), verification and rollback for dead-set clearing (step 5), direct verification that the flag disable took effect (step 4), and explicit rollback statements for steps 8 and 9.
FAILURE SEQUENCE — datadog_logs.csv + sidekiq_jobs.csv
1. FIRST ERROR
- 2026-09-03T14:01:12Z | reward-service | ERROR | "Redis::TimeoutError: Connection to Redis at redis-primary:6379 timed out after 5s"
- Prior log lines (13:55:13Z–13:59:30Z) are all INFO across reward-service, web-app, api-gateway — no warning signs in the slice.
2. CASCADE (in order, timestamps from the slice)
1. 14:01:12Z — reward-service: Redis::TimeoutError connecting to redis-primary:6379 (5s timeout).
2. 14:01:20Z / 14:01:30Z / 14:01:40Z — reward-service: "retry exhausted for RewardGiveJob" x3.
3. 14:01:40Z–14:06:47Z — sidekiq: "RewardGiveJob failed: Redis::TimeoutError; retrying" x6 (first at 14:01:40Z says "retrying in 60s").
4. 14:02:30Z — sidekiq WARN: "Queue reward depth above 10,000" (backlog forming ~1m18s after first error).
5. 14:03:05Z — api-gateway: first "502 upstream timeout calling reward-service /gives" (1m53s after first error); repeats x5 through 14:06:52Z.
6. 14:03:30Z — web-app: first "Give form submission failed: upstream 502 from api-gateway" (2m18s after first error — first user-facing symptom); repeats x4 through 14:06:49Z.
7. 14:06:52Z–14:22:10Z — gap with no application logs; only postgres "checkpoint complete" INFO lines.
8. 14:22:10Z — reward-service: "Redis connection restored; resuming job processing" (20m58s after first error).
9. 14:24:45Z — sidekiq: "Queue reward depth below 500" (drain confirmed 2m35s after restore).
3. SERVICE AND JOB
- Service: reward-service (Redis client failing against redis-primary:6379).
- Primary job: RewardGiveJob (log messages + J-00001 through J-00012 in sidekiq_jobs.csv, all Redis::TimeoutError, failed_at 14:01:46Z–14:02:58Z; earliest is J-00005 at 14:01:46Z).
- Secondary: RecognitionDigestJob (J-00013–J-00016, failed_at 14:02:36Z–14:05:50Z, same error) — present in sidekiq_jobs.csv only; no log line names it.
- Note: sidekiq_jobs.csv failed_at values (14:01:46Z–14:01:57Z for the first six) fall between the "retry exhausted" log lines and the first sidekiq "failed; retrying" line — the logs do not link job IDs to log events.
4. DATADOG QUERY TO CONFIRM THE FIRST ERROR
Log Explorer:
service:reward-service status:error "Redis::TimeoutError"
Time range: 2026-09-03 13:55:00 UTC – 14:05:00 UTC, sort by timestamp ASCENDING.
Expected first row: 2026-09-03T14:01:12Z — "Connection to Redis at redis-primary:6379 timed out after 5s".
To prove nothing earlier exists, run service:reward-service status:error over the wider window 13:00–14:01 UTC and confirm zero results.
5. WHAT THE LOGS DO NOT SHOW
- Root cause of the Redis timeout: no redis-primary logs, no network/infra/failover events in the slice.
- Why Redis recovered at 14:22:10Z ("restored" is stated, cause is not).
- What happened 14:06:52Z–14:22:10Z (~16 min): no reward-service, sidekiq, web-app, or api-gateway entries at all.
- Job IDs, payloads, retry counts, or final disposition (retried-to-success vs. dead/discarded) for J-00001–J-00016.
- Any mention of RecognitionDigestJob in log text, despite 4 failures in sidekiq_jobs.csv.
- User/request impact: no request counts, user IDs, or error rates.
- Metrics: no latency, memory, CPU, or Redis connection-pool telemetry — the Datadog slice is logs only.
- Whether postgres was affected: its "checkpoint complete" INFO lines continue unchanged, suggesting no, but no data confirms it.
FEATURE FLAG SUMMARY (based solely on flags_export.csv + flag_code.md) 1) recognition_streaks_v2 — ON Controls: streak tracking on recognition events. Per app/models/recognition.rb, when enabled it calls StreakTracker.record(give). Targeting: segment:beta_companies — 42 companies. 2) points_budget_guardrails — ON Controls: points budget enforcement. Per app/services/budget_service.rb, when enabled it calls BudgetService.new(company).enforce!(giver, points). Targeting: all_companies — 220 companies. 3) slack_dm_nudges — ON Controls: Slack DM nudge delivery. Per app/jobs/nudge_job.rb, the job exits early unless the flag is enabled; when on, it calls SlackDm.send_nudge(user). Targeting: segment:region_na — 87 companies. 4) redeem_flow_redesign — OFF Controls: redeem flow version. Per app/controllers/redeem_controller.rb, enabled renders RedeemV2Component; otherwise renders RedeemV1Component. Since state is OFF, targeted companies get RedeemV1Component. Targeting: targeted_list — 12 companies. 5) analytics_dashboard_v3 — ON Controls: analytics dashboard version. Per app/controllers/analytics_controller.rb, when enabled it sets @dashboard = AnalyticsV3.new(company). Targeting: segment:tier_three — 65 companies. 6) ms_teams_app_v2 — OFF Controls: MS Teams app v2 install. Per app/services/teams_installer.rb, when enabled it calls TeamsAppV2.install(company). Targeting: targeted_list — 9 companies. 7) legacy_give_modal — OFF Controls: unknown — no code reference in flag_code.md. Targeting: segment:legacy_plan — 14 companies. 8) survey_boosters_q3 — ON Controls: unknown — no code reference in flag_code.md. Targeting: segment:legacy_plan — 7 companies. 9) paused_offboard_cleanup — OFF Controls: unknown — no code reference in flag_code.md. Targeting: none listed (targeting_rules blank) — 0 companies. FLAGS WITH NO CODE REFERENCE - legacy_give_modal, survey_boosters_q3, paused_offboard_cleanup — these appear in the export but have no usage in the provided code excerpt. FLAGS WITH NO TARGETS - paused_offboard_cleanup — blank targeting_rules and company_count 0. NOTES / DATA LIMITS - The export lists segments and rule types only (beta_companies, all_companies, region_na, tier_three, legacy_plan, targeted_list). No individual company names are provided, so I can't say which specific companies have any flag. - legacy_give_modal (14) and survey_boosters_q3 (7) both target segment:legacy_plan, but the data doesn't show whether those groups overlap. - company_count is per flag; the same company may appear in multiple flags, so counts are not additive to a unique-company total. ARITHMETIC (counts as given in the export) - Rows in export: 9 - ON flags: 42 + 220 + 87 + 65 + 7 = 421 company-flag assignments - OFF flags: 12 + 9 + 14 + 0 = 35 company-flag assignments - Total: 421 + 35 = 456 (sum of all company_count values)
NDA-1 — [PARTY A] and Bonusly: GREEN Driving clause: "No license, no obligation to proceed, no exclusivity." (Clause 5), supported by full standard carve-outs in Clause 3 (a–e) and Delaware governing law. Reasoning: Mutual, 2-year term with 3-year survival, complete carve-outs, familiar US governing law — nothing non-standard. NDA-2 — [PARTY B] and Bonusly: YELLOW Driving clause: "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." (Clause 4) Reasoning: Otherwise-standard mutual NDA (3-year term, full carve-outs, Delaware law) but carries an embedded employee non-solicit surviving 18 months post-term — a hiring restriction counsel should approve before signature. NDA-3 — [PARTY C] and Bonusly: RED Driving clause: "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." (Clause 2) Reasoning: Broad 3-year non-compete on Bonusly as Recipient, compounded by one-way confidentiality in favor of [PARTY C], a 5-year term, no stated confidentiality carve-outs (Clause 4 states none), and exclusive Irish governing law/jurisdiction — multiple compounding red flags requiring full legal review.