Mean 0.920 · 26/40 perfect tests · $1.04 total · modalities in: text, image, file · 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 (using only the provided fields)
1) DS2+ deals: deals where stage ∈ {DS2, DS3, DS4, DS5}.
2) Why-buys compliance (DS2+ only):
- Non-compliant if why_buys is blank/empty, OR (not matching) LONG(n) with n numeric, AND why_buys length < 15 characters after trimming, OR why_buys is a placeholder such as “tbd” or “n/a” or “see notes”.
- Compliant if why_buys is LONG(n) OR (not a placeholder) and has length ≥ 15 characters.
3) DS1 timestamp missing: t_ds1 is blank/empty/NaN.
4) Regressed timestamp entry: among the non-empty timestamps t_ds1..t_ds5, if any earlier-stage timestamp date is later than a later-stage timestamp date.
Hygiene results
A) Why-buys non-compliance (DS2+)
- Total DS2+ deals = 89
- Non-compliant why_buys = 72
- Percent non-compliant = 72 / 89 * 100 = 80.8989% (≈ 80.9%)
B) Why-buys non-compliance by owner (DS2+)
- Dana Mercer: 7 / 9 * 100 = 77.7778% (≈ 77.8%)
- Cole Ingram: 2 / 2 * 100 = 100.0000%
- Alex Franklin: 47 / 59 * 100 = 79.6610% (≈ 79.7%)
- Bryce Harmon: 15 / 18 * 100 = 83.3333% (≈ 83.3%)
- Farid Osman: 1 / 1 * 100 = 100.0000%
C) DS1 timestamp + stage-regression counts
- Deals with no DS1 timestamp (t_ds1 missing) = 0
- Deals with regressed stage timestamps = 0
Three process fixes
1) Add a DS2+ transition gate requiring why_buys to be valid per the rule: either LONG(n) OR trimmed length ≥ 15, and explicitly disallow placeholders (tbd, n/a, see notes).
2) Make DS1 timestamp (t_ds1) mandatory at/ before moving into DS2+ (or block DS2+ transitions when t_ds1 is missing).
3) Implement automated monotonic timestamp validation across t_ds1..t_ds5 updates (flag/block any update that would create a regression).
Number of deals resolved (owner_id found in owners table): 156 Deals whose owner_id has no match in owners table: None (0) Owner ids that map to archived/deactivated owners (names): None (no resolved deals belong to an archived/deactivated owner_id) Total pipeline amount per resolved owner (arithmetic: total = sum(amount) over all resolved deals for that owner_id): - Bryce Harmon (owner_id 119337721): 1,054,144 - Dana Mercer (owner_id 83155923): 341,195 - Alex Franklin (owner_id 84342457): 624,310 - Cole Ingram (owner_id 83155924): 288,161.43 - Farid Osman (owner_id 716654662): 4,134 - Elena Sinclair (owner_id 701163055): 2,100
Integrity check (based on the deals_open rows that were successfully parsed from the provided snippet in my run; I could not ingest the entire deals_open.csv payload end-to-end here, so these results may be incomplete). (a) Orphans: gong rows whose hs_deal_id has NO match in deals_open - Orphans count = 30 - Sample orphan deal_alias values (from gong_calls_by_deal_90d.csv, i.e., gong rows that did not find a matching deal_id in the parsed deals_open set): - Deal-8FA85D - Deal-8FC3F9 - Deal-3B7945 - Deal-42B265 - Deal-9CCC42 - Deal-36EA09 - Deal-9A43B4 - Deal-605F3C - Deal-E2D34B - Deal-76821A (b) Duplicate conversation keys Definition you gave: rows where calls_90d exceeds distinct_conversation_keys. - Duplicate rows count = 0 - Arithmetic: 0 rows where calls_90d > distinct_conversation_keys (so there are no examples to list) (c) Among open deals at stage DS3 or later: share that have at least one logged call - Total open DS3+ deals (parsed) = 85 - DS3+ open deals that have at least one logged call (i.e., their deal_id appears in gong_calls_by_deal_90d.csv) = 25 - Share arithmetic: 25 / 85 = 0.2941176471 = 29.41% If you can provide deals_open.csv and gong_calls_by_deal_90d.csv as actual files (or confirm I should re-run with a different ingest method), I can recompute these metrics using the full tables with no truncation.
```sql
-- Using the pre-aggregated self-serve company cohort table to return Month-1 metrics
-- (unique givers, recognition/gives, and redemptions) without applying any “exclude deleted givers” filter.
SELECT
s.*,
DATE_TRUNC('month', s.SIGNUP_DATE) AS first_calendar_month_as_customer,
/* arithmetic: unique givers (M1) = M1_USERS */
s.M1_USERS AS unique_givers,
/* arithmetic: recognition count (M1) = M1_GIVES */
s.M1_GIVES AS recognition_count,
/* arithmetic: successful redemption count (M1) = M1_REDEMPTIONS */
s.M1_REDEMPTIONS AS successful_redemption_count
FROM PRODUCTION.PLG.COMPANY_COHORT_SUMMARY AS s
WHERE s.SIGNUP_DATE IS NOT NULL;
```
DEALS AUDIT (required fields: owner, stage, amount, close date, why-buys) - Missing input: I do not see a deals export/file in the data you provided. - Result: I cannot compute completeness % per deal field, cannot list invalid/missing deals fields, and cannot rank “pipeline amount at stake” because no deal amounts are provided. COMPANIES AUDIT (companies.csv) Row count (companies): 34 1) Completeness percent per company field A) industry - Non-empty: 34 - Missing: 0 - Completeness = 34/34 = 100% B) employee_count - Non-empty: 25 - Missing: 34 - 25 = 9 - Completeness = 25/34 = 0.7353 = 73.53% Missing employee_count company_alias (CRM is blank): - C-EC3025 (ec3025.com) - C-96039F (96039f.com) - C-44EA29 (44ea29.com) - C-D04904 (d04904.com) - C-B23205 (b23205.com) - C-60C75F (60c75f.com) - C-7BBDFA (7bbdfa.com) - C-50D386 (50d386.com) - C-93C8BF (93c8bf.com) C) hq_country - Non-empty: 28 - Missing: 34 - 28 = 6 - Completeness = 28/34 = 0.8235 = 82.35% Missing hq_country company_alias (CRM is blank): - C-2D1F1B (2d1f1b.com) - C-D73B89 (d73b89.com) - C-44EA29 (44ea29.com) - C-D04904 (d04904.com) - C-2C60E5 (2c60e5.com) - C-EE9FFB (ee9ffb.com) 2) Duplicate company clusters (shared domain) + survivor per cluster Cluster A: acme-corp.com - Members: - C-0A092931 (Technology, 500, US) - C-0A092932 (tech, 510, USA) - Survivor recommendation: C-0A092931 - Rationale (based only on CRM data): same completeness, and more normalized industry/country string form (“Technology” and “US” vs “tech” and “USA”). - Disagreement to resolve: employee_count (500 vs 510) and hq_country formatting (US vs USA). - Enrichment note: no acme-corp.com row exists in zoominfo_enrichment.csv, so I cannot recommend based on enrichment. Cluster B: globex.io - Members: - C-0A092933 (SaaS, 200, US) - C-0A092934 (Technology, 200, US) - Survivor recommendation: C-0A092933 - Rationale (based only on CRM data): both complete; industry values disagree (SaaS vs Technology), so you should pick one as the canonical industry and merge the other. I’m choosing C-0A092933 as the canonical alias for clustering/simplification. - Enrichment note: no globex.io row exists in zoominfo_enrichment.csv, so this cannot be validated against enrichment. 3) Filling missing company fields using zoominfo_enrichment.csv (match by domain only) Enrichment file match domains are limited to what appears in zoominfo_enrichment.csv. A) employee_count fill candidates where enrichment has a matching row 1. C-EC3025 (ec3025.com) - CRM employee_count: blank - Enrichment zi_employee_count: 400 - Recommended fill: employee_count = 400 (from enrichment) - Disagreements to resolve: - industry: CRM “Technology” vs enrichment “Computer Software” - hq_country: CRM “USA” vs enrichment “United States” - Recommendation: for employee_count, use enrichment (400). For industry + hq_country, you must choose a source-of-truth because enrichment disagrees with CRM. 2. C-96039F (96039f.com) - CRM employee_count: blank - Enrichment zi_employee_count: 400 - Recommended fill: employee_count = 400 - Disagreements: - hq_country: CRM “USA” vs enrichment “United States” - industry: matches (Finance vs Finance) 3. C-D04904 (d04904.com) - CRM employee_count: blank - Enrichment zi_employee_count: 400 - Recommended fill: employee_count = 400 - Disagreement: - industry: CRM “Technology” vs enrichment “Computer Software” - hq_country: - CRM: blank - Enrichment: blank - So: cannot fill hq_country from enrichment. 4. C-B23205 (b23205.com) - CRM employee_count: blank - Enrichment zi_employee_count: 400 - Recommended fill: employee_count = 400 - Disagreement: - hq_country: CRM “US” vs enrichment “United States” - industry matches (Healthcare) 5. C-60C75F (60c75f.com) - CRM employee_count: blank - Enrichment zi_employee_count: 400 - Recommended fill: employee_count = 400 - Disagreement: - industry: CRM “tech” vs enrichment “Computer Software” - hq_country: CRM “United States” matches enrichment “United States” 6. C-7BBDFA (7bbdfa.com) - CRM employee_count: blank - Enrichment zi_employee_count: 400 - Recommended fill: employee_count = 400 - hq_country matches (Canada) - industry matches (health care) 7. C-50D386 (50d386.com) - CRM employee_count: blank - Enrichment zi_employee_count: 400 - Recommended fill: employee_count = 400 - hq_country matches (Canada) - industry matches (health care) B) employee_count cannot be filled from enrichment (no matching enrichment row) - C-44EA29 (44ea29.com): no enrichment row present in zoominfo_enrichment.csv - C-93C8BF (93c8bf.com): no enrichment row present in zoominfo_enrichment.csv C) hq_country fill status for rows where CRM hq_country is missing For the company aliases with missing hq_country in CRM: - C-2D1F1B (2d1f1b.com): enrichment exists but zi_hq_country is blank → cannot fill - C-D73B89 (d73b89.com): enrichment exists but zi_hq_country is blank → cannot fill - C-D04904 (d04904.com): enrichment exists but zi_hq_country is blank → cannot fill - C-2C60E5 (2c60e5.com): enrichment exists but zi_hq_country is blank → cannot fill - C-44EA29 (44ea29.com): no enrichment row → cannot fill - C-EE9FFB (ee9ffb.com): no enrichment row → cannot fill CONTACTS AUDIT (contacts.csv) Row count (contacts): 52 1) Completeness percent per contact field (non-empty values) A) email - Non-empty emails (string not blank): 52/52 = 100% - Note: this does NOT guarantee validity; invalid email formats are listed below. B) title - Non-empty titles: 39 - Missing titles: 52 - 39 = 13 - Completeness = 39/52 = 75.00% C) persona - Non-empty personas: 38 - Missing personas: 52 - 38 = 14 - Completeness = 38/52 = 73.08% 2) Invalid emails (email missing domain part) These have an email value of the form “user@” with no domain. - CT-0010 (company_alias C-66D1FC): user0@ - CT-0080 (company_alias C-92D97D): user0@ - CT-0081 (company_alias C-92D97D): user1@ - CT-0192 (company_alias C-425E2A): user2@ 3) Domain mismatches (email domain vs contacts.csv domain column) - CT-0010: email domain = blank vs domain column = 66d1fc.com - CT-0011: email domain = other-domain.com vs domain column = 66d1fc.com - CT-0080: email domain = blank vs domain column = 92d97d.com - CT-0081: email domain = blank vs domain column = 92d97d.com - CT-0192: email domain = blank vs domain column = 425e2a.com 4) Recommended fixes to enforce on contacts (based on audit) - Enforce email format validation: require non-empty domain part after “@”. - Enforce email-domain consistency: email domain must match contacts.csv domain for that contact row. - Enforce required fields for downstream use: - title missing on 13/52 contacts (75.00% completeness) - persona missing on 14/52 contacts (73.08% completeness) 10 fixes with the most pipeline amount at stake (cannot rank by pipeline amount with provided data) - Cannot compute “pipeline amount at stake” because no deals export (including deal amounts) is provided. - Below are the 10 highest-impact data fixes ranked by the largest measurable data-quality gaps in the inputs (companies + contacts). 1) Fill employee_count for the 7 company aliases where enrichment has a match and CRM employee_count is blank: - C-EC3025, C-96039F, C-D04904, C-B23205, C-60C75F, C-7BBDFA, C-50D386 2) Enrichment-backed source-of-truth decisions for ec3025.com / 96039f.com / b23205.com / 60c75f.com where CRM vs enrichment disagree: - C-EC3025 (industry + hq_country) - C-96039F (hq_country) - C-B23205 (hq_country) - C-60C75F (industry) 3) Stop duplicates from splitting reporting by merging company clusters on shared domain: - Merge acme-corp.com cluster: C-0A092931 + C-0A092932 (choose C-0A092931 as survivor) - Merge globex.io cluster: C-0A092933 + C-0A092934 (choose C-0A092933 as survivor) 4) Fix invalid email blanks (missing domain after @) for 4 contacts: - CT-0010, CT-0080, CT-0081, CT-0192 5) Fix domain mismatches for 5 contacts: - CT-0010, CT-0011, CT-0080, CT-0081, CT-0192 - Recommendation: either correct the email domain or correct the contacts.csv domain field so they align. 6) Enforce title requiredness (missing on 13/52 contacts; 75.00% completeness) - Populate title for the contacts where title is blank. 7) Enforce persona requiredness (missing on 14/52 contacts; 73.08% completeness) - Populate persona for the contacts where persona is blank. 8) Address employee_count gaps that cannot be filled from enrichment due to missing enrichment rows: - C-44EA29 (44ea29.com) - C-93C8BF (93c8bf.com) 9) Address hq_country gaps that cannot be filled from enrichment because enrichment zi_hq_country is blank or enrichment row missing: - C-2D1F1B, C-D73B89, C-D04904, C-2C60E5, C-44EA29, C-EE9FFB 10) Deals completeness is currently un-auditable and should be added to the extract: - Provide deals export with fields: owner, stage, amount, close date, why-buys - Without it, I cannot produce completeness % per deal field or compute pipeline amount at stake.
DEAL CLASSIFICATIONS (deal_alias, primary_category, side) Deal-DB0AAC,timing,buyer Deal-F7F635,competitor,buyer Deal-AC944F,no decision,unknown Deal-214060,no decision,unknown Deal-91A056,timing,buyer Deal-29326C,timing,buyer Deal-5DB9B0,product gap,unknown Deal-831B7B,timing,buyer Deal-F97C37,competitor,buyer Deal-13E9CF,no decision,buyer Deal-39E25C,timing,buyer Deal-7ED004,pricing,buyer Deal-21B045,no decision,unknown Deal-B3ABED,timing,buyer Deal-422BA6,competitor,buyer Deal-ED9AE7,no decision,buyer Deal-988493,no decision,unknown Deal-381C8C,competitor,buyer Deal-F308CA,no decision,unknown Deal-F1E8A6,competitor,buyer Deal-B6AC09,timing,buyer Deal-E6E80A,timing,buyer Deal-B038F0,timing,buyer Deal-4664E1,no decision,unknown Deal-175756,timing,buyer Deal-E74A73,no decision,buyer Deal-DDAB52,competitor,buyer Deal-ACE061,competitor,buyer Deal-BB78F3,timing,buyer Deal-D48E0B,no decision,unknown Deal-15DA99,timing,buyer Deal-F4AF5D,timing,buyer Deal-79B7A1,timing,buyer Deal-583ADB,no decision,unknown Deal-8E27DA,product gap,Bonusly Deal-2D2F8D,competitor,buyer Deal-E0441F,no decision,unknown Deal-7CB44D,no decision,unknown Deal-0F96AA,competitor,buyer Deal-1BCA50,competitor,buyer Deal-7CC678,competitor,buyer Deal-FAC17C,no decision,buyer Deal-242273,competitor,buyer Deal-50E5D8,no decision,buyer Deal-A2C349,competitor,buyer Deal-9F176A,timing,buyer Deal-7B2236,no decision,buyer Deal-AFA56C,no decision,unknown Deal-C7156E,competitor,buyer Deal-C33D91,pricing,buyer Deal-9048EB,no decision,unknown Deal-5E64CE,no decision,buyer Deal-8A0992,competitor,buyer Deal-DAFB82,pricing,buyer Deal-D0C698,competitor,buyer Deal-69CF3D,timing,buyer Deal-ECBF89,timing,buyer Deal-3618CC,no decision,buyer Deal-EECC02,competitor,buyer Deal-5AD03E,competitor,buyer Deal-D1A623,timing,buyer Deal-413C56,no decision,buyer Deal-47F1A1,competitor,buyer Deal-BF2A98,competitor,buyer Deal-2A292B,no decision,buyer Deal-D1AABF,no decision,unknown Deal-FEDBCB,no decision,buyer Deal-1E7DA9,competitor,buyer Deal-2BBA21,no decision,unknown Deal-286F9C,competitor,buyer Deal-7FBAC6,no decision,buyer Deal-369281,competitor,buyer Deal-386F6E,no decision,unknown Deal-9FCD0D,competitor,buyer Deal-55867E,timing,buyer Deal-2FEDDB,no decision,buyer Deal-64B19A,competitor,buyer Deal-3F86A0,no decision,unknown Deal-096750,no decision,unknown Deal-F325A5,no decision,buyer Deal-ABD14C,no decision,buyer Deal-79E61A,no decision,unknown Deal-8A119B,pricing,buyer Deal-AE7C4E,no decision,unknown Deal-DAB4F1,no decision,unknown Deal-B4B50F,no decision,unknown Deal-981AD4,product gap,Bonusly Deal-DC77FE,competitor,buyer Deal-5885B9,no decision,unknown SUMMARY 1) Category counts - competitor: 26 - no decision: 38 - timing: 19 - pricing: 4 - product gap: 3 Arithmetic check: 26 + 38 + 19 + 4 + 3 = 90 total deals 2) Side split - buyer: 64 - unknown: 24 - Bonusly: 2 Arithmetic check: 64 + 24 + 2 = 90 total deals 3) closed_lost_tag vs closed_lost_reason_text disagreements (clear mismatch) - 15 deals Deal aliases: Deal-F97C37, Deal-13E9CF, Deal-B3ABED, Deal-ED9AE7, Deal-70F704, Deal-E74A73, Deal-FAC17C, Deal-50E5D8, Deal-7B2236, Deal-5E64CE, Deal-3618CC, Deal-5AD03E, Deal-FEDBCB, Deal-7FBAC6, Deal-DC77FE 4) Two patterns most worth acting on (from the free-text reasons) - “no contact/unresponsive” language: 24 deals - “timing/pause/revisit” language: 21 deals
{"tier_counts":{"WATCH":38,"RISKY":15,"ACTION":3,"REVIVE":1},"tier_examples":{"WATCH":["Deal-25F752","Deal-D348E1","Deal-944310"],"RISKY":["Deal-E53952","Deal-5408B0","Deal-C26D20"],"ACTION":["Deal-403845","Deal-A5E80A","Deal-1FC049"],"REVIVE":["Deal-93C8BF"]},"risky_deals":["Deal-E53952","Deal-5408B0","Deal-C26D20","Deal-9AAE5F","Deal-547B2B","Deal-B7EBD1","Deal-A2B47C","Deal-2465CE","Deal-62D607","Deal-584EE5","Deal-F9A08A","Deal-0660B4","Deal-C6FE92","Deal-FC22A3","Deal-7BBDFA"],"lock_violations":0,"pipeline_shape":"Recency arithmetic used: recency_days = anchor_date(2026-09-22) - last_engagement_date (last_meeting if present else last_call/last_email). Engagement thresholds used: LOCK recency<=14; BUILD/REVIVE recency<=21; recency>30 considered weak evidence. NOTE: tiers/counts were computed only over the embedded subset of CSV rows available in this run (not the full pasted files), so total counts may not match the full pipeline."}
[
{
"transcript_id": "TX-001",
"deal_alias": "Deal-CFE7F4",
"why-buys": [
"Automating anniversary and birthday awards because the HR team of three cannot keep up manually.",
"Tracking awards in a way that prevents people slipping through the cracks."
],
"pain_points": [
"Cannot keep up with manually managing anniversary and birthday awards.",
"Tracking everything in a spreadsheet leads to people slipping through the cracks.",
"Needs SSO and audit logs for IT to sign off."
],
"stakeholders": [
"Prospect (VP People)",
"Prospect (HR Admin)"
],
"budget_signal": {
"amount": 40000,
"currency": "USD",
"context": "earmarked for engagement tools this fiscal year"
},
"timeline_signal": [
"Ideally live before open enrollment in November.",
"Security review on September 12."
],
"competitor_mentioned": [
"Achievers"
],
"next_step": [
"Security review with the IT lead on September 12."
],
"objections": [
"Need SSO and audit logs for IT to sign off."
],
"confidence": {
"score": 1.0,
"arithmetic": "8/8 fields present (why-buys, pain points, stakeholders, budget signal, timeline signal, competitor mentioned, next step, objections)"
}
},
{
"transcript_id": "TX-002",
"deal_alias": "Deal-70BB30",
"why-buys": [
"Tie recognition to retention for the hourly workforce."
],
"pain_points": [
"Regretted turnover is over 30% for the hourly workforce."
],
"stakeholders": [
"Prospect (Head of Total Rewards)",
"Prospect (CFO)"
],
"budget_signal": {
"amount": 25000,
"currency": "USD",
"context": "finance approved a $25k pilot budget for this quarter"
},
"timeline_signal": [
"Decision by end of September."
],
"competitor_mentioned": null,
"next_step": [
"Send the pilot agreement and have it routed to legal this week."
],
"objections": [
"Integration with Workday has to be rock solid (one condition)."
],
"confidence": {
"score": 0.875,
"arithmetic": "7/8 fields present (missing competitor_mentioned)"
}
},
{
"transcript_id": "TX-003",
"deal_alias": "Deal-530B50",
"why-buys": [
"Make recognition visible across 12 retail locations."
],
"pain_points": [
"Store managers have zero budget autonomy for on-the-spot recognition today."
],
"stakeholders": [
"Prospect (People Ops Manager)"
],
"budget_signal": null,
"timeline_signal": [
"No rush until Q1."
],
"competitor_mentioned": [
"Bucketlist"
],
"next_step": [
"Schedule a call with the CEO (prospect will send two times)."
],
"objections": [
"The CEO has to be sold first and decides anything people-related."
],
"confidence": {
"score": 0.875,
"arithmetic": "7/8 fields present (missing budget_signal)"
}
},
{
"transcript_id": "TX-004",
"deal_alias": "Deal-180D02",
"why-buys": [
"Consolidate three separate recognition tools into one.",
"Because the current tools are not able to talk to the HRIS (and they are paying for three tools)."
],
"pain_points": [
"Paying for three tools and none of them talk to the HRIS."
],
"stakeholders": [
"Prospect (VP People)",
"Prospect (IT Security Lead)"
],
"budget_signal": {
"amount": 15000,
"currency": "USD",
"context": "If it's under $15k annually, prospect can approve it without going to the board."
},
"timeline_signal": [
"Procurement cycle runs six to eight weeks minimum.",
"Security review took three months for the last vendor (hesitation context)."
],
"competitor_mentioned": null,
"next_step": null,
"objections": [
"Procurement cycle runs six to eight weeks minimum.",
"Security review took three months for the last vendor (hesitation)."
],
"confidence": {
"score": 0.75,
"arithmetic": "6/8 fields present (missing competitor_mentioned and next_step)"
}
},
{
"transcript_id": "TX-005",
"deal_alias": "Deal-F8767A",
"why-buys": [
"Automate service milestones.",
"Provide analytics on recognition equity across departments.",
"Improve visibility for night-shift teams."
],
"pain_points": [
"Night-shift teams feel invisible.",
"Engagement scores for night-shift teams run 20 points lower.",
"Exec team is skeptical after a failed rollout two years ago.",
"Currently mid-pilot with Nectar."
],
"stakeholders": [
"Prospect (HR Director)",
"Prospect (People Ops Coordinator)"
],
"budget_signal": {
"amount": 12000,
"currency": "USD",
"context": "£? not stated; 'We have $12k approved under our engagement line.'"
},
"timeline_signal": [
"Need this running before our January all-hands.",
"Exec team presentation agreed for October 2."
],
"competitor_mentioned": [
"Nectar"
],
"next_step": [
"Come present to the exec team on October 2."
],
"objections": [
"We're mid-pilot with Nectar right now, so you'd need to beat that experience.",
"Our exec team is skeptical after a failed rollout two years ago."
],
"confidence": {
"score": 1.0,
"arithmetic": "8/8 fields present (why-buys, pain points, stakeholders, budget signal, timeline signal, competitor mentioned, next step, objections)"
}
},
{
"transcript_id": "TX-006",
"deal_alias": "Deal-EE195F",
"why-buys": [
"Cut the admin time on service awards."
],
"pain_points": [
"Admin time burden: personally spend five hours a month ordering and shipping plaques.",
"Time is the issue (not budget)."
],
"stakeholders": [
"Prospect (HR Manager)"
],
"budget_signal": null,
"timeline_signal": [
"Fiscal year resets in February, so a Q1 start is realistic."
],
"competitor_mentioned": null,
"next_step": [
"Send the one-page overview and the prospect will forward it to their COO this week."
],
"objections": [
"Comparing this against just doing it internally.",
"COO usually prefers building things in-house."
],
"confidence": {
"score": 0.875,
"arithmetic": "7/8 fields present (missing budget_signal)"
}
}
]
1) Deal-547B2B — $11,200 (DS5, COMMIT; close_date 2026-09-11) Why closest: Slack says “redlines came back clean… signing page is out… VP People told me they are signing tomorrow. This one is signature-imminent.” What’s left: signature paperwork fully completed/returned for final signing. Arithmetic: 2026-09-13 − 2026-09-11 = 2 days. 2) Deal-403845 — $9,000 (DS5, COMMIT; close_date 2026-09-11) Why closest: Slack says “moving fine… the order form is with their finance team.” What’s left: their finance team review/approval of the order form, then final signature. Arithmetic: 2026-09-13 − 2026-09-11 = 2 days. 3) Deal-A2B47C — $6,360 (DS5, COMMIT; close_date 2026-09-11) Why closest: Slack says “Deal-D348E1 and Deal-A2B47C are both still warm, just normal legal-review pace.” What’s left: completion of the remaining legal review work before signature. Arithmetic: 2026-09-13 − 2026-09-11 = 2 days.
Candidate product gaps from active deals (prospect voice only) 1) Deal: Deal-EC3025 | Amount: not provided Prospect line: “We need SCIM user provisioning — our security team will not approve any tool that requires manual account management.” Classification: Real gap Product-doc basis: “SCIM user provisioning and ADP Workforce Now integrations are NOT currently listed as supported capabilities.” 2) Deal: Deal-D0D6B5 | Amount: not provided Prospect line: “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 Product-doc basis: “SCIM user provisioning and ADP Workforce Now integrations are NOT currently listed as supported capabilities.” 3) Deal: Deal-CFE7F4 | Amount: not provided Prospect line: “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 Product-doc basis: “Custom report builder … Enterprise: yes” (and “Core/Pro: —”) 4) Deal: Deal-84DBA6 | Amount: not provided Prospect line: “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 Product-doc basis: Slack integration is supported (“Slack and Microsoft Teams integration: yes … Core/Pro/Enterprise”). The blocker described is training/enablement, not lack of Slack integration support. Arithmetic (from the 4 candidates above) - Real gaps: 2 (Deal-EC3025, Deal-D0D6B5) - Plan gate: 1 (Deal-CFE7F4) - Rollout/enablement issue: 1 (Deal-84DBA6) - Check: 2 + 1 + 1 = 4 total candidates Summary (only real gaps) - Deal-EC3025: “We need SCIM user provisioning — our security team will not approve any tool that requires manual account management.” (Amount: not provided) - Deal-D0D6B5: “Our payroll runs on ADP Workforce Now and I don't see ADP anywhere in your integration list — that's a dealbreaker for us.” (Amount: not provided)
Stale criteria (snapshot date 2026-09-05): keep deals where max(last_email,last_call,last_meeting) is older than 2026-08-29 (i.e., last_contact_date <= 2026-08-28). days since last contact = (2026-09-05 - last_contact_date).days Owner: Bryce Harmon (13 stale deals) Total stale amount = 240000 + 99000 + 70000 + 45000 + 37440 + 36000 + 31500 + 23400 + 21000 + 11400 + 6000 + 5502 + 1 = 626243 - Deal-2D1F1B | Stage DS1 | Amount 240000 | days since last contact: (2026-09-05 - 2026-06-16)=81 - Deal-66D1FC | Stage DS1 | Amount 99000 | days since last contact: (2026-09-05 - 2026-08-20)=16 - Deal-950043 | Stage DS1 | Amount 70000 | days since last contact: (2026-09-05 - 2026-08-17)=19 - Deal-B23205 | Stage DS1 | Amount 45000 | days since last contact: (2026-09-05 - 2026-08-20)=16 - Deal-7BBDFA | Stage DS3 | Amount 37440 | days since last contact: (2026-09-05 - 2026-07-21)=46 - Deal-332637 | Stage DS2 | Amount 36000 | days since last contact: (2026-09-05 - 2026-08-27)=9 - Deal-1BEEBF | Stage DS1 | Amount 31500 | days since last contact: (2026-09-05 - 2026-08-17)=19 - Deal-C5658B | Stage DS1 | Amount 23400 | days since last contact: (2026-09-05 - 2026-08-20)=16 - Deal-40522D | Stage DS3 | Amount 21000 | days since last contact: (2026-09-05 - 2026-08-17)=19 - Deal-F0EBBB | Stage DS3 | Amount 11400 | days since last contact: (2026-09-05 - 2026-08-12)=24 - Deal-E25A09 | Stage DS1 | Amount 6000 | days since last contact: (2026-09-05 - 2026-08-27)=9 - Deal-C9C286 | Stage DS2 | Amount 5502 | days since last contact: (2026-09-05 - 2026-08-27)=9 - Deal-012CB1 | Stage DS1 | Amount 1 | days since last contact: (2026-09-05 - 2026-08-13)=23 Owner: Alex Franklin (18 stale deals) Total stale amount = 24000 + 18000 + 9300 + 8316 + 5100 + 4800 + 4680 + 3840 + 3600 + 3120 + 2700 + 2600 + 2520 + 2400 + 2160 + 1800 + 1600 + 1080 = 101616 - Deal-CC08D1 | Stage DS1 | Amount 24000 | days since last contact: (2026-09-05 - 2026-08-20)=16 - Deal-E73427 | Stage DS3 | Amount 18000 | days since last contact: (2026-09-05 - 2026-08-26)=10 - Deal-885F45 | Stage DS2 | Amount 9300 | days since last contact: (2026-09-05 - 2026-08-24)=12 - Deal-C2FF3C | Stage DS1 | Amount 8316 | days since last contact: (2026-09-05 - 2026-08-24)=12 - Deal-0D2F7A | Stage DS3 | Amount 5100 | days since last contact: (2026-09-05 - 2026-08-24)=12 - Deal-6C60D4 | Stage DS3 | Amount 4800 | days since last contact: (2026-09-05 - 2026-08-24)=12 - Deal-13FEBD | Stage DS2 | Amount 4680 | days since last contact: (2026-09-05 - 2026-08-24)=12 - Deal-9D0060 | Stage DS3 | Amount 3840 | days since last contact: (2026-09-05 - 2026-08-24)=12 - Deal-690476 | Stage DS2 | Amount 3600 | days since last contact: (2026-09-05 - 2026-08-18)=18 - Deal-EE195F | Stage DS3 | Amount 3120 | days since last contact: (2026-09-05 - 2026-08-28)=8 - Deal-278DEC | Stage DS3 | Amount 2700 | days since last contact: (2026-09-05 - 2026-08-28)=8 - Deal-635B8E | Stage DS3 | Amount 2600 | days since last contact: (2026-09-05 - 2026-08-24)=12? (2026-09-05 - 2026-08-18)=18 - Deal-F436DA | Stage DS3 | Amount 2520 | days since last contact: (2026-09-05 - 2026-08-20)=16 - Deal-6883F3 | Stage DS1 | Amount 2400 | days since last contact: (2026-09-05 - 2026-08-20)=16 - Deal-4A13AD | Stage DS3 | Amount 2160 | days since last contact: (2026-09-05 - 2026-08-10)=26 - Deal-F67D31 | Stage DS2 | Amount 1800 | days since last contact: (2026-09-05 - 2026-08-28)=8 - Deal-5FDCE4 | Stage DS3 | Amount 1600 | days since last contact: (2026-09-05 - 2026-08-24)=12 - Deal-BA571A | Stage DS4 | Amount 1080 | days since last contact: (2026-09-05 - 2026-08-18)=18 Owner: Dana Mercer (4 stale deals) Total stale amount = 9000 + 9000 + 3240 + 1920 = 23160 - Deal-B7EBD1 | Stage DS5 | Amount 9000 | days since last contact: (2026-09-05 - 2026-08-20)=16 - Deal-3974EB | Stage DS4 | Amount 9000 | days since last contact: (2026-09-05 - 2026-08-28)=8 - Deal-C6D97A | Stage DS4 | Amount 3240 | days since last contact: (2026-09-05 - 2026-08-28)=8 - Deal-0660B4 | Stage DS4 | Amount 1920 | days since last contact: (2026-09-05 - 2026-08-20)=16 Owner: Cole Ingram (1 stale deal) Total stale amount = 1330 = 1330 - Deal-FD9F4E | Stage DS5 | Amount 1330 | days since last contact: (2026-09-05 - 2026-08-26)=10
Snapshot date: 2026-09-05 30-day window for DS2 entry (t_ds2): 2026-08-06 through 2026-09-05 (inclusive) Data used: - DS2 entry count per owner = deals_open.csv rows where t_ds2 is in the window - Email/call/meeting totals per owner = engagements_by_deal_90d (emails_30d, calls_30d, meetings_30d) for those same DS2-in-window deals Owner aliases (from deals_open + owners.csv): - 119337721 = Bryce Harmon - 83155923 = Dana Mercer - 84342457 = Alex Franklin - 83155924 = Cole Ingram - 716654662 = Farid Osman 1) Bryce Harmon (owner_id 119337721) DS2 deals in window (t_ds2): - Deal-25F752 (t_ds2=2026-08-10) - Deal-CA7DC0 (t_ds2=2026-08-12) - Deal-D73B89 (t_ds2=2026-09-03) Engagement totals (last-30d): - Emails = 8 + 5 + 14 = 27 - Calls = 0 + 0 + 0 = 0 - Meetings = 3 + 1 + 2 = 6 - Total activities = 27 + 0 + 6 = 33 Activity mix (share of total activities = 33): - Emails = 27/33 = 81.82% - Calls = 0/33 = 0.00% - Meetings = 6/33 = 18.18% DS2 entries = 3 Efficiency ratio (activities per DS2 entry) = 33 / 3 = 11.00 2) Dana Mercer (owner_id 83155923) DS2 deals in window: - Deal-7FA0C3 (t_ds2=2026-08-07) Engagement totals: - Emails = 3 - Calls = 0 - Meetings = 1 - Total activities = 3 + 0 + 1 = 4 Activity mix (share of total activities = 4): - Emails = 3/4 = 75.00% - Calls = 0/4 = 0.00% - Meetings = 1/4 = 25.00% DS2 entries = 1 Efficiency ratio = 4 / 1 = 4.00 3) Alex Franklin (owner_id 84342457) DS2 deals in window: - Deal-1FC049 (t_ds2=2026-09-03) - Deal-36C33F (t_ds2=2026-08-11) - Deal-F436DA (t_ds2=2026-08-19) - Deal-D1E6C2 (t_ds2=2026-08-11) Engagement totals: - Emails = 2 + 4 + 5 + 2 = 13 - Calls = 0 + 0 + 0 + 0 = 0 - Meetings = 0 + 0 + 0 + 2 = 2 - Total activities = 13 + 0 + 2 = 15 Activity mix (share of total activities = 15): - Emails = 13/15 = 86.67% - Calls = 0/15 = 0.00% - Meetings = 2/15 = 13.33% DS2 entries = 4 Efficiency ratio = 15 / 4 = 3.75 4) Cole Ingram (owner_id 83155924) DS2 deals in window: - Deal-1BA595 (t_ds2=2026-08-12) Engagement totals: - Emails = 10 - Calls = 0 - Meetings = 0 - Total activities = 10 + 0 + 0 = 10 Activity mix (share of total activities = 10): - Emails = 10/10 = 100.00% - Calls = 0/10 = 0.00% - Meetings = 0/10 = 0.00% DS2 entries = 1 Efficiency ratio = 10 / 1 = 10.00 5) Farid Osman (owner_id 716654662) DS2 deals in window: - Deal-499BF6 (t_ds2=2026-08-26) Engagement totals: - Emails = 17 - Calls = 0 - Meetings = 4 - Total activities = 17 + 0 + 4 = 21 Activity mix (share of total activities = 21): - Emails = 17/21 = 80.95% - Calls = 0/21 = 0.00% - Meetings = 4/21 = 19.05% DS2 entries = 1 Efficiency ratio = 21 / 1 = 21.00 Ranking by efficiency (lowest activities per DS2 entry = most efficient) 1) Alex Franklin — ratio 3.75 (15 total activities / 4 DS2 entries) 2) Dana Mercer — ratio 4.00 (4 / 1) 3) Cole Ingram — ratio 10.00 (10 / 1) 4) Bryce Harmon — ratio 11.00 (33 / 3) 5) Farid Osman — ratio 21.00 (21 / 1) Most efficient rep: Alex Franklin (activities/DS2 entry = 3.75) Highest-volume rep (most DS2 entries): Bryce Harmon (DS2 entries = 3) They differ: Yes.
QTD scorecard (AE: Alex Franklin) as of 2026-09-05 Quarter used: 2026-07-01 through 2026-09-05 1) Bookings vs quota (CLOSED_WON only; exclude CLOSED_WON before 2026-07-01) - Bookings (QTD) = sum(amount for CLOSED_WON deals with close_date in [2026-07-01, 2026-09-05]) = 150,000 - Quota (2026-Q3) = 200,000 - Attainment % = bookings / quota * 100 = 150,000 / 200,000 * 100 = 75.00% 2) New vs expansion split (by deal_type; bookings only, QTD) - New bookings (deal_type = new) = 113,500 - Expansion bookings (deal_type = expansion) = 36,500 - Check: 113,500 + 36,500 = 150,000 Percent split (grounded in the above bookings arithmetic): - New % = 113,500 / 150,000 * 100 = 75.67% - Expansion % = 36,500 / 150,000 * 100 = 24.33% 3) Active pipeline by stage (OPEN deals only; amount) - DS1: 284,621 - DS2: 353,760 - DS3: 552,705 - DS4: 23,574 - DS5: 45,730 Total active pipeline (sum) = 284,621 + 353,760 + 552,705 + 23,574 + 45,730 = 1,260,390 4) Rolling 90-day DS2-to-won rate (as-of 2026-09-05) Definition used from data: - Denominator = count of deals with entered_ds2 in the last 90 days: entered_ds2 in [2026-06-07, 2026-09-05] - Numerator = count of CLOSED_WON deals whose entered_ds2 is in that same window From the file calculations: - Denominator (DS2-entered deals in window) = 111 - Numerator (CLOSED_WON with DS2 entry in window) = 8 DS2-to-won rate % = num / denom * 100 = 8 / 111 * 100 = 7.21% 5) Win and loss counts (QTD; closed deals only) - Wins = count(CLOSED_WON, close_date in [2026-07-01, 2026-09-05]) = 8 - Losses = count(CLOSED_LOST, close_date in [2026-07-01, 2026-09-05]) = 27 - Total closed (wins + losses) = 8 + 27 = 35 Top loss reason (QTD, CLOSED_LOST close_date in window) - Top loss reason = “Lost- Timing (1 year or more)” - Count = 13 6) Activity volume by type in the last 30 days (from ae_engagements.csv *_30d columns) (These are summed across the deals present in ae_deals.csv for this AE.) - Emails (30d) = 807 - Calls (30d) = 112 - Meetings (30d) = 128 - Notes (30d) = 50 7) Coaching observations (grounded in the numbers above) 1) Bookings are below quota (but not drastically) - 150,000 / 200,000 * 100 = 75.00% - Short by 200,000 − 150,000 = 50,000 bookings versus quota. 2) DS2 conversion in the rolling window is weak - Rolling DS2-to-won = 8 / 111 * 100 = 7.21% - With 111 deals entering DS2 in the last 90 days, only 8 reach CLOSED_WON in that window (low conversion). 3) Loss concentration + pipeline concentration are both pointing to DS3 execution - Pipeline (OPEN) is heavily weighted to DS3: 552,705 out of 1,260,390 - DS3 share = 552,705 / 1,260,390 * 100 = 43.85% - Top loss reason is “Lost- Timing (1 year or more)” with 13 losses, aligning with a likely “stalled/extended cycle” pattern.
Reference date used (anchor for “last 60 days”) = max(last_engaged_date in deal_contacts.csv) = 2026-09-03 60 days prior = 2026-09-03 - 60 days = 2026-07-05 Active contact filter used = (last_engaged_date >= 2026-07-05) AND (is_former == false) Important missing data in provided files: - “Open deal” status is not provided (no open/closed field). Below are all deals in deal_contacts.csv that meet the threading criteria under the “Active contact” definition above. - Deal amount and deal stage are not provided in the provided files, so they are marked as “missing”. ------------------------------------------------------------ Flagged deal: Deal-F9A08A - amount: missing - stage: missing - active contact count: 1 - personas present: [champion] - personas missing: [economic buyer, HR admin, IT security, finance] - most valuable persona to add given the stage: missing (deal stage not provided) - on-file unengaged contact(s) at company C-0D15DF that fits missing personas: - CT-697541 (economic buyer, Chief People Officer) ------------------------------------------------------------ Flagged deal: Deal-5BFE3B - amount: missing - stage: missing - active contact count: 2 - personas present: [champion] - personas missing: [economic buyer, HR admin, IT security, finance] - most valuable persona to add given the stage: missing (deal stage not provided) - on-file unengaged contact(s) at company C-535D36 that fits missing personas: none on file ------------------------------------------------------------ Flagged deal: Deal-92D97D - amount: missing - stage: missing - active contact count: 1 - personas present: [HR admin] - personas missing: [economic buyer, champion, IT security, finance] - most valuable persona to add given the stage: missing (deal stage not provided) - on-file unengaged contact(s) at company C-E23238 that fits missing personas: none on file ------------------------------------------------------------ Flagged deal: Deal-D0D6B5 - amount: missing - stage: missing - active contact count: 3 - personas present: [champion] - personas missing: [economic buyer, HR admin, IT security, finance] - most valuable persona to add given the stage: missing (deal stage not provided) - on-file unengaged contact(s) at company C-32918E that fits missing personas: - CT-1FA4DB (economic buyer, Chief People Officer) ------------------------------------------------------------ Flagged deal: Deal-5408B0 - amount: missing - stage: missing - active contact count: 2 - personas present: [HR admin, champion] - personas missing: [economic buyer, IT security, finance] - most valuable persona to add given the stage: missing (deal stage not provided) - on-file unengaged contact(s) at company C-2AE3AA that fits missing personas: - CT-07FA76 (economic buyer, Chief People Officer) ------------------------------------------------------------ Flagged deal: Deal-885F45 - amount: missing - stage: missing - active contact count: 2 - personas present: [economic buyer, champion] - personas missing: [HR admin, IT security, finance] - most valuable persona to add given the stage: missing (deal stage not provided) - on-file unengaged contact(s) at company C-5E8EFB that fits missing personas: - CT-B3F25D (IT security, IT Security Lead) ------------------------------------------------------------ Flagged deal: Deal-EC3025 - amount: missing - stage: missing - active contact count: 1 - personas present: [champion] - personas missing: [economic buyer, HR admin, IT security, finance] - most valuable persona to add given the stage: missing (deal stage not provided) - on-file unengaged contact(s) at company C-FDD0C7 that fits missing personas: - CT-6827DB (economic buyer, Chief People Officer) ------------------------------------------------------------ Flagged deal: Deal-50D386 - amount: missing - stage: missing - active contact count: 2 - personas present: [HR admin, champion] - personas missing: [economic buyer, IT security, finance] - most valuable persona to add given the stage: missing (deal stage not provided) - on-file unengaged contact(s) at company C-EB10E4 that fits missing personas: - CT-A1C4B3 (economic buyer, Chief People Officer) ------------------------------------------------------------ Flagged deal: Deal-C6D97A - amount: missing - stage: missing - active contact count: 3 - personas present: [champion] - personas missing: [economic buyer, HR admin, IT security, finance] - most valuable persona to add given the stage: missing (deal stage not provided) - on-file unengaged contact(s) at company C-5A8FC2 that fits missing personas: none on file ------------------------------------------------------------ Flagged deal: Deal-FCBE5B - amount: missing - stage: missing - active contact count: 1 - personas present: [champion] - personas missing: [economic buyer, HR admin, IT security, finance] - most valuable persona to add given the stage: missing (deal stage not provided) - on-file unengaged contact(s) at company C-737030 that fits missing personas: none on file ------------------------------------------------------------ Flagged deal: Deal-36C33F - amount: missing - stage: missing - active contact count: 1 - personas present: [IT security] - personas missing: [economic buyer, champion, HR admin, finance] - most valuable persona to add given the stage: missing (deal stage not provided) - on-file unengaged contact(s) at company C-077A0E that fits missing personas: - CT-1DB73E (economic buyer, Chief People Officer)
First 5 minutes (what Alex Franklin leads with) - Common opener in 8/10 calls (TT-001, TT-002, TT-003, TT-005, TT-006, TT-007, TT-008, TT-010): “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: - TT-004: agenda-first (“I put together a short agenda — security review first, then pricing.”) - TT-009: pricing-first (“You asked for straight pricing last time, so let's start there.”) Three most common objections (counts) + how they handle them 1) “Budget is locked / can’t add a new line item” — 4/10 calls (TT-001, TT-003, TT-006, TT-010) - Handling: “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.” 2) “Revisit next quarter / open enrollment starts in October and we’re underwater” — 3/10 calls (TT-002, TT-005, TT-008) - Handling: “Makes sense. What if we scope a 90-day pilot with one department so you have internal data before next quarter's planning?” 3) “We already do recognition with a spreadsheet and quarterly gift cards — why change?” — 3/10 calls (TT-004, TT-007, TT-009) - Handling: “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.” Concrete next step agreed (rate) - Calls with explicit agreement to a next step (the “Yes, Thursday at 2pm works — send the invite…” line): 7 calls - Agreed: TT-001, TT-002, TT-003, TT-005, TT-006, TT-008, TT-009 - Not agreed (no such agreement line present): TT-004, TT-007, TT-010 - Arithmetic: 7/10 = 0.70 = 70% - Quote (agreement line): “Yes, Thursday at 2pm works — send the invite and I'll bring our HRIS manager.” Every competitor a prospect raised - Awardco (TT-003) - Kudos (TT-007) Coaching notes (2) 1) In calls where no next-step agreement appears in the transcript (TT-004, TT-007, TT-010), consider using the same explicit “lock the next step” close pattern earlier, since many agreements show up right after that framing (e.g., TT-001/TT-002/TT-003). 2) For the repeated “budget locked” objection, you consistently anchor on a funding logic tied to avoided backfills (the $210k example). Keep that linkage tight—and when possible, connect it to the prospect’s stated constraint (e.g., line-item freeze) rather than only to the savings story.
Q3 2026 forecast window: 2026-07-01 to 2026-09-30 (close_date inclusive) 1) Totals inside the quarter - COMMIT total = 44,729.0 (7 deals) - BEST_CASE total = 203,565.0 (24 deals) - PIPELINE total = 201,637.4 (23 deals) - Pipeline deals count = 0 (per your rule; PIPELINE is excluded from the weighted forecast) 2) Arithmetic (weighted forecast) - Weighted forecast = COMMIT + 0.35 * BEST_CASE - Weighted forecast = 44,729.0 + 0.35 * 203,565.0 - 0.35 * 203,565.0 = 71,247.75 - Weighted forecast = 44,729.0 + 71,247.75 = 115,976.75 3) Deal counts by category inside the quarter (close_date within Q3) - COMMIT: 7 - BEST_CASE: 24 - PIPELINE: 23 4) Excluded deals (close_date outside the quarter) - Excluded count = 32 deals - Excluded total amount = 227,575.0 5) Top 5 BEST_CASE deals by amount inside the quarter 1. Deal-2D7423 — 38,935.0 (close_date 2026-09-30) 2. Deal-25F752 — 24,000.0 (close_date 2026-09-25) 3. Deal-E53952 — 19,656.0 (close_date 2026-09-30) 4. Deal-5EED42 — 16,250.0 (close_date 2026-09-30) 5. Deal-FA32A0 — 11,116.0 (close_date 2026-09-25) ## Data quality This extract has multiple stage/forecast_category inconsistencies: two deals are labeled forecast_category=COMMIT but are not in DS5 (Deal-A5E80A has stage DS1; Deal-499BF6 has stage DS2). 49 of 54 Q3 deals have why_buys_chars = 0, indicating missing/blank qualification signals. One Q3 amount is non-integer (Deal-42326B has amount 2,480.4), which can cause downstream formatting/rounding issues if unattended. Finally, 32 of the total deals in the pull are excluded due to close_date outside Q3, so small close_date edits could materially change totals and rankings.
Test definition (within this extract)
- Cohort signals in first calendar month (signup_month in file)
- Givers-only: m1_users >= 5 AND m1_redemptions == 0
- Redemption-only: m1_users < 5 AND m1_redemptions >= 1
- Both signals: m1_users >= 5 AND m1_redemptions >= 1
- Neither: m1_users < 5 AND m1_redemptions == 0
- “Retained at 24 months” = current_status == 'active'
- Denominator = all companies whose CSV row parsed cleanly into the required fields
Denominator exclusions
- Total company rows in the provided CSV extract (excluding header) = 220
- Well-formed rows used in calculations = 218
- Excluded from denominator = 2
- Why: 2 rows had incorrect column counts in the provided CSV text, so required fields could not be parsed reliably:
- C-0F4F2729
- C-0E508AB1
- Arithmetic: 220 - 218 = 2 excluded
2x2 cohort results (cohort size, 24-month retention rate)
- Both signals (m1_users >= 5 AND m1_redemptions >= 1)
- N = 46
- Retained = 31/46 = 0.673913… = 67.39%
- Givers-only (m1_users >= 5 AND m1_redemptions == 0)
- N = 49
- Retained = 23/49 = 0.469387… = 46.94%
- Redemption-only (m1_users < 5 AND m1_redemptions >= 1)
- N = 29
- Retained = 9/29 = 0.310345… = 31.03%
- Neither (m1_users < 5 AND m1_redemptions == 0)
- N = 94
- Retained = 38/94 = 0.404255… = 40.43%
Retention lift (single-signal vs neither)
- Neither baseline = 0.404255… (38/94)
- Givers-only lift = 0.469388… - 0.404255… = +0.065132… = +6.51 percentage points
- Redemption-only lift = 0.310345… - 0.404255… = -0.093910… = -9.39 percentage points
- Single signal with largest retention lift: givers-only (+6.51 pp)
What this does and does not prove
- Does indicate: In this cohort, companies with m1_users >= 5 and no successful redemption (givers-only) retain better at 24 months than the “neither” group. Companies with successful redemptions but <5 givers (redemption-only) retain worse than “neither.”
- Does not prove: a causal effect of “activation signals.” This is an association within the provided extract, with limited signals (only thresholded m1_users and m1_redemptions) and with 2 malformed rows excluded from the denominator (C-0F4F2729, C-0E508AB1).
Reconciliation (as-of 2026-09-05) using ONLY the provided snapshot files (no “as-of date” field exists in either CSV). Definitions - CRM ARR total = sum(company_arr.hubspot_arr) across all company_alias rows provided - Billing ARR total = sum over subscriptions with status = active of (mrr * 12) - Variance = CRM ARR total − Billing ARR total 1) Totals + variance - CRM ARR total = 603,581.76 - Billing ARR total (active subs only) = 604,739.28 - Variance (CRM − Billing) = 603,581.76 − 604,739.28 = -1,157.52 2) Variance decomposition (must sum exactly to variance) Let bucket sums be in terms of (CRM ARR − Billing ARR) per account. - status mismatch = 13,158.48 (CRM includes ARR for companies whose subscription(s) shown are cancelled; billing ARR excludes them because billing uses active subscriptions) - rounding = 36.00 (small ARR deltas between CRM and billing ARR) - missing records = 16,497.24 (CRM has ARR but there is no active subscription for the account, so Billing ARR = 0) - other = -30,849.24 (remaining deltas, including cases where billing exists but CRM ARR is 0) Check: 13,158.48 + 36.00 + 16,497.24 + (-30,849.24) = -1,157.52 (exact) 3) Mismatched accounts (only those where CRM ARR − Billing ARR != 0) Suggested owner: (generic by bucket type; the CSV does not provide account ownership fields) A) status mismatch (suggested owner: Billing Ops) - C-0C8323BF - CRM hubspot_arr: 4,905.24 - Billing ARR: 0.00 (no active subscription; only cancelled shown) - Diff (CRM − Billing): +4,905.24 - C-0DC4FB8C - CRM hubspot_arr: 8,253.24 - Billing ARR: 0.00 (no active subscription; only cancelled shown) - Diff (CRM − Billing): +8,253.24 B) missing records (suggested owner: CRM Ops / Data Steward) - C-0D5BBE3A - CRM hubspot_arr: 16,497.24 - Billing ARR: 0.00 (no active subscription) - Diff (CRM − Billing): +16,497.24 C) rounding (suggested owner: RevOps / Finance) - C-0D66DF9E - CRM hubspot_arr: 23,200.00 - Billing ARR: 23,184.00 (mrr 1,932.00 * 12) - Diff (CRM − Billing): +16.00 - C-14D70CE0 - CRM hubspot_arr: 18,200.00 - Billing ARR: 18,180.00 (mrr 1,515.00 * 12) - Diff (CRM − Billing): +20.00 D) other (suggested owner: RevOps / Finance) - C-0F7269D7 - CRM hubspot_arr: 24,396.00 - Billing ARR: 26,796.00 (mrr 2,233.00 * 12) - Diff (CRM − Billing): -2,400.00 - C-21629AA4 - CRM hubspot_arr: 0.00 (not present in company_arr.csv) - Billing ARR: 28,449.24 (mrr 2,370.77 * 12) - Diff (CRM − Billing): -28,449.24 4) Business rule violations (term_months != 12 must have cf_agreement_end_date populated) Rule checked on chargebee_subscriptions.csv exactly as provided. Violations: - SUB-0002, C-1794A52C - term_months: 24 - cf_agreement_end_date: missing/blank - SUB-0019, C-22170CA1 - term_months: 36 - cf_agreement_end_date: missing/blank
2026-08 vs 2026-07 (overall mean across all rows) giving_rate: abs = 0.6027133 − 0.6022967 = +0.0004167; rel = 0.0004167 / 0.6022967 = +0.0006918 = +0.07% (up) redemptions_per_user: abs = 1.7301633 − 1.7299833 = +0.0001800; rel = 0.0001800 / 1.7299833 = +0.0001040 = +0.01% (up) one_to_one_engagement: abs = 0.4471767 − 0.4468867 = +0.0002900; rel = 0.0002900 / 0.4468867 = +0.0006489 = +0.06% (up) pulse_engagement: abs = 0.5086100 − 0.6005867 = −0.0919767; rel = −0.0919767 / 0.6005867 = −0.1531447 = −15.31% (down) Largest relative move: pulse_engagement (−15.31%), driven by size_band = enterprise where pulse_engagement abs = 0.27428 − 0.54998 = −0.27570; rel = −0.27570 / 0.54998 = −0.50129 = −50.13% (down). +--------------------------+----------+----------+-----------+-----------+-----------+ | KVM | 2026-07 | 2026-08 | absΔ | relΔ | direction | +--------------------------+----------+----------+-----------+-----------+-----------+ | giving_rate | 0.6023 | 0.6027 | +0.0004 | +0.07% | up | | redemptions_per_user | 1.7300 | 1.7302 | +0.0002 | +0.01% | up | | one_to_one_engagement | 0.4469 | 0.4472 | +0.0003 | +0.06% | up | | pulse_engagement | 0.6006 | 0.5086 | -0.0920 | -15.31% | down | +--------------------------+----------+----------+-----------+-----------+-----------+
Last completed month: 2026-08 (i.e., through 2026-08-31) Redemption count - 377 Spend (USD) - Total spend = 10,873.00 (custom) + 8,540.00 (Tremendous) + 5,128.00 (Snappy) + 3,230.00 (TangoCard) = 27,771.00 Unique redeemers - 234 Redemptions per redeemer - 377 / 234 = 1.611111... ≈ 1.61 Provider mix (as % of spend; must sum to 100%) Total spend denominator = 27,771.00 - custom: 10,873.00 / 27,771.00 * 100 = 39.15% - Tremendous: 8,540.00 / 27,771.00 * 100 = 30.75% - Snappy: 5,128.00 / 27,771.00 * 100 = 18.47% - TangoCard: 3,230.00 / 27,771.00 * 100 = 11.63% - Check: 39.15% + 30.75% + 18.47% + 11.63% = 100.00% Top 5 countries by redemptions (by count of redemption_id) - US: 244 - CA: 24 - AU: 21 - GB: 17 - NL: 16
Snapshot date used from rules: 2026-09-05 Eligibility rules: - R1: health_score < 60 - R2: churn_save_eligible_amount > 0 - R3: renewal_date within 120 days of snapshot Qualified for churn-save offer (R1+R2+R3) - C-0B0F1BAB - health_score=38 (R1 passes) - churn_save_eligible_amount=5,494.00 (R2 passes) - days_to_renewal = (2026-09-23 - 2026-09-05) = 18 days (R3 passes) - usage_trend_3m=flat; seats=363; seats_used=238; champion_active=false - Play assignment: NOT DETERMINABLE from provided data (play-to-signal eligibility rules not included in the inputs). Signal justifying offer eligibility: R1 health_score and R2 amount and R3 renewal window. - C-0E9C27D1 - health_score=39 (R1 passes) - churn_save_eligible_amount=41,235.00 (R2 passes) - days_to_renewal = (2026-09-24 - 2026-09-05) = 19 days (R3 passes) - usage_trend_3m=flat; seats=157; seats_used=134; champion_active=true - Play assignment: NOT DETERMINABLE from provided data. - Signal justifying offer eligibility: R1/R2/R3. - C-0F6C0F34 - health_score=51 (R1 passes) - churn_save_eligible_amount=49,707.00 (R2 passes) - days_to_renewal = (2026-10-03 - 2026-09-05) = 28 days (R3 passes) - usage_trend_3m=growing; seats=395; seats_used=308; champion_active=false - Play assignment: NOT DETERMINABLE from provided data. - Signal justifying offer eligibility: R1/R2/R3. - C-0B360C78 - health_score=57 (R1 passes) - churn_save_eligible_amount=35,748.00 (R2 passes) - days_to_renewal = (2026-10-28 - 2026-09-05) = 53 days (R3 passes) - usage_trend_3m=growing; seats=327; seats_used=246; champion_active=true - Play assignment: NOT DETERMINABLE from provided data. - Signal justifying offer eligibility: R1/R2/R3. - C-0D3278C7 - health_score=54 (R1 passes) - churn_save_eligible_amount=17,602.00 (R2 passes) - days_to_renewal = (2026-11-12 - 2026-09-05) = 68 days (R3 passes) - usage_trend_3m=declining; seats=380; seats_used=126; champion_active=true - Play assignment: NOT DETERMINABLE from provided data. - Signal justifying offer eligibility: R1/R2/R3. - C-0B827671 - health_score=56 (R1 passes) - churn_save_eligible_amount=25,365.00 (R2 passes) - days_to_renewal = (2026-11-14 - 2026-09-05) = 70 days (R3 passes) - usage_trend_3m=declining; seats=202; seats_used=113; champion_active=true - Play assignment: NOT DETERMINABLE from provided data. - Signal justifying offer eligibility: R1/R2/R3. - C-0CEF69FD - health_score=53 (R1 passes) - churn_save_eligible_amount=32,621.00 (R2 passes) - days_to_renewal = (2026-11-21 - 2026-09-05) = 77 days (R3 passes) - usage_trend_3m=growing; seats=136; seats_used=97; champion_active=false - Play assignment: NOT DETERMINABLE from provided data. - Signal justifying offer eligibility: R1/R2/R3. - C-0CA21961 - health_score=58 (R1 passes) - churn_save_eligible_amount=16,829.00 (R2 passes) - days_to_renewal = (2026-12-28 - 2026-09-05) = 114 days (R3 passes) - usage_trend_3m=flat; seats=325; seats_used=84; champion_active=true - Play assignment: NOT DETERMINABLE from provided data. - Signal justifying offer eligibility: R1/R2/R3. Total amount at stake (sum of churn_save_eligible_amount for qualified accounts) Arithmetic: - 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 At-risk but NOT qualified (fail at least one eligibility rule) (At risk = R1 only: health_score < 60) - C-0BC71BDD - health_score=55 (R1 passes) - churn_save_eligible_amount=0.00 (R2 fails: must be > 0) - days_to_renewal = (2026-10-27 - 2026-09-05) = 52 days (R3 would pass, but R2 blocks) - usage_trend_3m=flat; champion_active=false - C-0BA71F12 - health_score=52 (R1 passes) - churn_save_eligible_amount=6,824.00 (R2 passes) - days_to_renewal = (2027-04-11 - 2026-09-05) = 218 days (R3 fails: must be <= 120) - usage_trend_3m=declining; champion_active=true - C-0F6694C3 - health_score=43 (R1 passes) - churn_save_eligible_amount=0.00 (R2 fails) - days_to_renewal = (2027-03-21 - 2026-09-05) = 197 days (R3 fails) - usage_trend_3m=declining; champion_active=true - C-0BE96399 - health_score=54 (R1 passes) - churn_save_eligible_amount=0.00 (R2 fails) - days_to_renewal = (2026-10-29 - 2026-09-05) = 54 days (R3 would pass) - usage_trend_3m=declining; champion_active=true - C-0F876796 - health_score=47 (R1 passes) - churn_save_eligible_amount=19,958.00 (R2 passes) - days_to_renewal = (2027-02-06 - 2026-09-05) = 154 days (R3 fails) - usage_trend_3m=declining; champion_active=false - C-0FCCD2DF - health_score=43 (R1 passes) - churn_save_eligible_amount=0.00 (R2 fails) - days_to_renewal = (2027-04-23 - 2026-09-05) = 230 days (R3 fails) - usage_trend_3m=flat; champion_active=false - C-10A56B0F - health_score=54 (R1 passes) - churn_save_eligible_amount=0.00 (R2 fails) - days_to_renewal = (2026-12-12 - 2026-09-05) = 98 days (R3 would pass, but R2 blocks) - usage_trend_3m=declining; champion_active=false Missing input needed for the “which play fits each” part You asked for: “which play fits each (usage revival, executive touch, commercial concession) and cite the signal that justifies each play.” The provided inputs only define eligibility rules R1–R3 for offer qualification. They do NOT define play assignment rules (which signals map to “usage revival” vs “executive touch” vs “commercial concession”). If you share the documented play eligibility rules (or the mapping table), I can assign a play to each qualified account and cite the exact signal(s) from churnzero_accounts.csv.
Company alias: C-0DDFC9A7 1) Seat coverage (licensed over headcount) Licensed seats / headcount = 150 / 400 = 0.375 = 37.5% 2) Usage health (2 lines) Users grew from 2026-03: 88 to 2026-08: 126 → +38 users (126-88=38), which is 38/88=43.18% growth. Maria S. also said “monthly active users keep climbing” and that the recognition feed is busy every week (qualitative usage health). 3) Headroom in seats + ARR at current per-seat rate Per-seat rate (current_arr ÷ licensed_seats) = 9000.00 ÷ 150 = 60.00 ARR/seat. Additional seats needed to cover headcount = headcount - licensed_seats = 400 - 150 = 250 seats. ARR potential at this per-seat rate = 250 × 60.00 = 15,000.00 ARR. 4) Who replied + whether they can buy Replied: Maria S. (People Operations Coordinator). Can they buy? No—she said budget/seat expansion decisions sit with Dana R., VP People. 5) Right buyer from our contacts Dana R., VP People (last_engaged: 2026-05-18). Reply email (under 150 words) Subject: Re: Growing your team's recognition program Hi Maria, thanks for the note—and I’m glad to hear adoption keeps climbing. Your team’s monthly active users went from 88 in 2026-03 to 126 in 2026-08, which lines up with what you’re seeing in the recognition feed. You mentioned Dana is the right person for budget and seat expansion. If you’re comfortable, could you introduce us? If helpful, I can share a quick snapshot of current usage and the seat headroom based on your headcount and licensed seats. Either way, appreciate your help. Best, Cole
CSM mid-onboarding prep — company alias: C-0D284E42
1) What is complete (based on populated fields in onboarding_account.csv)
- Integrations connected
- Slack integration: COMPLETE (integration_slack = 2026-08-12)
- HRIS integration: NOT complete (integration_hris is blank)
- Allowance set
- NOT complete (allowance_set is blank)
- Admins added
- COMPLETE (admins_added = 2)
- First recognition given
- COMPLETE (first_recognition_at = 2026-08-15 14:22)
- First redemption
- NOT complete (first_redemption_at is blank)
2) What is not complete
- HRIS integration (integration_hris missing)
- Allowance set (allowance_set missing)
- First redemption (first_redemption_at missing)
3) Early engagement signals (from onboarding_usage.csv)
- Active givers growth over the observed period:
- From signup date 2026-08-11: active_givers = 3
- Latest available date 2026-09-04: active_givers = 15
- Arithmetic: 15 - 3 = 12 more active givers (over the provided date range)
- Around Slack integration and first recognition:
- Slack integration date 2026-08-12: active_givers = 3 vs 2026-08-11 = 3
- Arithmetic: 3 - 3 = 0 change on 08-12
- First recognition date 2026-08-15: active_givers = 5
- Arithmetic: 5 - 3 = 2 more active givers vs 08-11
- Peak engagement in the provided data:
- Peak active_givers = 15 on 2026-09-03 and 2026-09-04
4) Three things to cover on the mid-onboarding call
1. Close the gap on integrations: confirm status and next steps to get HRIS integration completed (integration_hris is blank).
2. Get allowance completed: allowance_set is blank—confirm who sets it and the needed info/timing (can’t determine amount from the data provided).
3. Drive the first redemption: first_recognition_at exists (2026-08-15 14:22) but first_redemption_at is blank—identify what’s blocking the first redemption and set an immediate plan to complete it.
90-day renewal risk brief (date window: 2026-09-13 through 2026-12-11; “company” shown as account_alias because no company names are provided) Risk rules used (based only on provided data): - High if (seats_used/seats) < 0.70 OR Jun→Aug active_users change % <= -10.0% - Medium if not High and ((seats_used/seats) < 0.85 OR Jun→Aug change % <= 0%) - Low otherwise 1) Company: C-0B7D2C30 | CSM: Dana Mercer | ARR: 65901.00 - Date used (trusted): 2026-09-15 (Chargebee; is_multi_year=true → ChurnZero known wrong) - Seat utilization: 274/476 = 0.576 - 3-month usage trend (active_users): 2026-06=97 → 2026-07=94 → 2026-08=84 - Jun→Aug: 97 + (-13) = 84; percent = -13.4% (=-13/97) - Risk rating: High - Evidence (one sentence): Seat utilization is low (274/476=0.576) and usage declined 97→84 (-13.4%) from 2026-06 to 2026-08. - Disagreement flagged: CZ 2026-09-10 vs Chargebee 2026-09-15. 2) Company: C-0BCDB8C2 | CSM: Cole Ingram | ARR: 54427.00 - Date used (trusted): 2026-09-18 (Chargebee; is_multi_year=true → ChurnZero known wrong) - Seat utilization: 232/424 = 0.547 - 3-month usage trend (active_users): 2026-06=127 → 2026-07=118 → 2026-08=110 - Jun→Aug: 127 + (-17) = 110; percent = -13.4% (=-17/127) - Risk rating: High - Evidence (one sentence): Seat utilization is low (232/424=0.547) and usage declined 127→110 (-13.4%). - Disagreement flagged: CZ 2027-09-18 vs Chargebee 2026-09-18. 3) Company: C-0D2AB865 | CSM: Elena Sinclair | ARR: 38022.00 - Date used (trusted): 2026-09-22 (Chargebee; is_multi_year=true → ChurnZero known wrong) - Seat utilization: 250/407 = 0.614 - 3-month usage trend (active_users): 2026-06=125 → 2026-07=117 → 2026-08=109 - Jun→Aug: 125 + (-16) = 109; percent = -12.8% (=-16/125) - Risk rating: High - Evidence (one sentence): Seat utilization is low (250/407=0.614) and usage declined 125→109 (-12.8%). - Disagreement flagged: CZ 2026-09-10 vs Chargebee 2026-09-22. 4) Company: C-0BBE3E60 | CSM: Dana Mercer | ARR: 30993.00 - Date used (trusted): 2026-09-26 (Chargebee; is_multi_year=true → ChurnZero known wrong) - Seat utilization: 74/114 = 0.649 - 3-month usage trend (active_users): 2026-06=39 → 2026-07=35 → 2026-08=33 - Jun→Aug: 39 + (-6) = 33; percent = -15.4% (=-6/39) - Risk rating: High - Evidence (one sentence): Seat utilization is low (74/114=0.649) and usage declined 39→33 (-15.4%). - Disagreement flagged: CZ 2027-09-26 vs Chargebee 2026-09-26. 5) Company: C-0F5D2323 | CSM: Cole Ingram | ARR: 90647.00 - Date used (trusted): 2026-09-29 (Chargebee; is_multi_year=true → ChurnZero known wrong) - Seat utilization: 111/390 = 0.285 - 3-month usage trend (active_users): 2026-06=20 → 2026-07=21 → 2026-08=18 - Jun→Aug: 20 + (-2) = 18; percent = -10.0% (=-2/20) - Risk rating: High - Evidence (one sentence): Seat utilization is very low (111/390=0.285) and usage fell 20→18 (-10.0%). - Disagreement flagged: CZ 2026-09-10 vs Chargebee 2026-09-29. 6) Company: C-0EC6999D | CSM: Elena Sinclair | ARR: 79419.00 - Date used (trusted): 2026-10-03 (ChurnZero; is_multi_year=false; no disagreement in dates) - Seat utilization: 31/112 = 0.277 - 3-month usage trend (active_users): 2026-06=17 → 2026-07=16 → 2026-08=15 - Jun→Aug: 17 + (-2) = 15; percent = -11.8% (=-2/17) - Risk rating: High - Evidence (one sentence): Seat utilization is low (31/112=0.277) and usage declined 17→15 (-11.8%). 7) Company: C-0B20DB64 | CSM: Dana Mercer | ARR: 21770.00 - Date used (trusted): 2026-10-07 (ChurnZero; is_multi_year=false; no disagreement in dates) - Seat utilization: 214/378 = 0.566 - 3-month usage trend (active_users): 2026-06=294 → 2026-07=298 → 2026-08=294 - Jun→Aug: 294 + (0) = 294; percent = 0.0% (=0/294) - Risk rating: High - Evidence (one sentence): Seat utilization is low (214/378=0.566), and usage is flat overall from 2026-06 to 2026-08 (294→294, 0.0%). 8) Company: C-0BBC4E7A | CSM: Cole Ingram | ARR: 56374.00 - Date used (trusted): 2026-10-10 (ChurnZero; is_multi_year=false; no disagreement in dates) - Seat utilization: 228/337 = 0.677 - 3-month usage trend (active_users): 2026-06=142 → 2026-07=141 → 2026-08=139 - Jun→Aug: 142 + (-3) = 139; percent = -2.1% (=-3/142) - Risk rating: High - Evidence (one sentence): Seat utilization is low (228/337=0.677), and usage decreased 142→139 (-2.1%) over the 3-month window. 9) Company: C-0FD551AB | CSM: Elena Sinclair | ARR: 48815.00 - Date used (trusted): 2026-10-14 (ChurnZero; is_multi_year=false; no disagreement in dates) - Seat utilization: 210/376 = 0.559 - 3-month usage trend (active_users): 2026-06=123 → 2026-07=122 → 2026-08=126 - Jun→Aug: 123 + (3) = 126; percent = 2.4% (=3/123) - Risk rating: High - Evidence (one sentence): Seat utilization is low (210/376=0.559) even though usage increased 123→126 (+2.4%). 10) Company: C-0F9F8F13 | CSM: Dana Mercer | ARR: 46230.00 - Date used (trusted): 2026-10-18 (ChurnZero; is_multi_year=false; no disagreement in dates) - Seat utilization: 199/352 = 0.565 - 3-month usage trend (active_users): 2026-06=185 → 2026-07=185 → 2026-08=182 - Jun→Aug: 185 + (-3) = 182; percent = -1.6% (=-3/185) - Risk rating: High - Evidence (one sentence): Seat utilization is low (199/352=0.565) and usage declined 185→182 (-1.6%). 11) Company: C-0BC34584 | CSM: Cole Ingram | ARR: 16740.00 - Date used (trusted): 2026-10-22 (ChurnZero; is_multi_year=false; no disagreement in dates) - Seat utilization: 327/494 = 0.662 - 3-month usage trend (active_users): 2026-06=104 → 2026-07=104 → 2026-08=106 - Jun→Aug: 104 + (2) = 106; percent = 1.9% (=2/104) - Risk rating: High - Evidence (one sentence): Seat utilization is low (327/494=0.662) even though usage increased 104→106 (+1.9%). 12) Company: C-0B7A7546 | CSM: Elena Sinclair | ARR: 35062.00 - Date used (trusted): 2026-10-25 (ChurnZero; is_multi_year=false; no disagreement in dates) - Seat utilization: 182/205 = 0.888 - 3-month usage trend (active_users): 2026-06=64 → 2026-07=65 → 2026-08=63 - Jun→Aug: 64 + (-1) = 63; percent = -1.6% (=-1/64) - Risk rating: Medium - Evidence (one sentence): Seat utilization is strong (182/205=0.888) but usage declined 64→63 (-1.6%), driving Medium risk. 13) Company: C-0B369871 | CSM: Dana Mercer | ARR: 85128.00 - Date used (trusted): 2026-10-29 (ChurnZero; is_multi_year=false; no disagreement in dates) - Seat utilization: 317/422 = 0.751 - 3-month usage trend (active_users): 2026-06=326 → 2026-07=330 → 2026-08=333 - Jun→Aug: 326 + (7) = 333; percent = 2.1% (=7/326) - Risk rating: Medium - Evidence (one sentence): Seat utilization is below 0.85 (317/422=0.751), even though usage rose 326→333 (+2.1%). 14) Company: C-0B144C78 | CSM: Cole Ingram | ARR: 30899.00 - Date used (trusted): 2026-11-02 (ChurnZero; is_multi_year=false; no disagreement in dates) - Seat utilization: 169/224 = 0.754 - 3-month usage trend (active_users): 2026-06=101 → 2026-07=101 → 2026-08=106 - Jun→Aug: 101 + (5) = 106; percent = 5.0% (=5/101) - Risk rating: Medium - Evidence (one sentence): Seat utilization is below 0.85 (169/224=0.754) which drives Medium risk, despite usage increasing 101→106 (+5.0%). 15) Company: C-0FC4DBB8 | CSM: Elena Sinclair | ARR: 94732.00 - Date used (trusted): 2026-11-05 (ChurnZero; is_multi_year=false; no disagreement in dates) - Seat utilization: 356/464 = 0.767 - 3-month usage trend (active_users): 2026-06=189 → 2026-07=191 → 2026-08=193 - Jun→Aug: 189 + (4) = 193; percent = 2.1% (=4/189) - Risk rating: Medium - Evidence (one sentence): Seat utilization is below 0.85 (356/464=0.767), driving Medium risk even as usage increased 189→193 (+2.1%). 16) Company: C-0D5BBE3A | CSM: Dana Mercer | ARR: 39740.00 - Date used (trusted): 2026-11-09 (ChurnZero; is_multi_year=false; no disagreement in dates) - Seat utilization: 85/102 = 0.833 - 3-month usage trend (active_users): 2026-06=88 → 2026-07=90 → 2026-08=91 - Jun→Aug: 88 + (3) = 91; percent = 3.4% (=3/88) - Risk rating: Medium - Evidence (one sentence): Seat utilization is slightly below 0.85 (85/102=0.833), resulting in Medium risk while usage climbs 88→91 (+3.4%). 17) Company: C-0FB9D5AF | CSM: Cole Ingram | ARR: 63158.00 - Date used (trusted): 2026-11-13 (ChurnZero; is_multi_year=false; no disagreement in dates) - Seat utilization: 144/199 = 0.724 - 3-month usage trend (active_users): 2026-06=173 → 2026-07=173 → 2026-08=176 - Jun→Aug: 173 + (3) = 176; percent = 1.7% (=3/173) - Risk rating: Medium - Evidence (one sentence): Seat utilization is below 0.85 (144/199=0.724), driving Medium risk even though usage increased 173→176 (+1.7%). 18) Company: C-0B344485 | CSM: Elena Sinclair | ARR: 64384.00 - Date used (trusted): 2026-11-16 (ChurnZero; is_multi_year=false; no disagreement in dates) - Seat utilization: 224/287 = 0.780 - 3-month usage trend (active_users): 2026-06=238 → 2026-07=240 → 2026-08=244 - Jun→Aug: 238 + (6) = 244; percent = 2.5% (=6/238) - Risk rating: Medium - Evidence (one sentence): Seat utilization is below 0.85 (224/287=0.780), producing Medium risk despite usage rising 238→244 (+2.5%). 19) Company: C-0CB2C1B4 | CSM: Dana Mercer | ARR: 40628.00 - Date used (trusted): 2026-11-20 (ChurnZero; is_multi_year=false; no disagreement in dates) - Seat utilization: 386/473 = 0.816 - 3-month usage trend (active_users): 2026-06=47 → 2026-07=48 → 2026-08=49 - Jun→Aug: 47 + (2) = 49; percent = 4.3% (=2/47) - Risk rating: Medium - Evidence (one sentence): Seat utilization is below 0.85 (386/473=0.816), so risk is Medium even though usage increased 47→49 (+4.3%). 20) Company: C-22170CA1 | CSM: Cole Ingram | ARR: 45646.00 - Date used (trusted): 2026-11-24 (ChurnZero; is_multi_year=false; no disagreement in dates) - Seat utilization: 251/294 = 0.854 - 3-month usage trend (active_users): 2026-06=143 → 2026-07=148 → 2026-08=146 - Jun→Aug: 143 + (3) = 146; percent = 2.1% (=3/143) - Risk rating: Low - Evidence (one sentence): Seat utilization is high enough (251/294=0.854) and usage increases overall 143→146 (+2.1%), resulting in Low risk. Totals (90-day window) - Total ARR renewing: 1,048,715.00 - ARR at risk (High + Medium): 1,003,069.00 - Arithmetic check: at-risk = total − Low = 1,048,715.00 − 45,646.00 = 1,003,069.00 Disagreements summary (flagged; Chargebee trusted because is_multi_year=true and ChurnZero known wrong) - C-0B7D2C30: CZ 2026-09-10 vs Chargebee 2026-09-15 - C-0BCDB8C2: CZ 2027-09-18 vs Chargebee 2026-09-18 - C-0D2AB865: CZ 2026-09-10 vs Chargebee 2026-09-22 - C-0BBE3E60: CZ 2027-09-26 vs Chargebee 2026-09-26 - C-0F5D2323: CZ 2026-09-10 vs Chargebee 2026-09-29
Total tickets in file (used as denominator for shares): N = 79 1) HRIS provisioning & sync failures - Count: 10 - Share: 10/79 = 0.126582… = 12.7% - Distinct accounts: 3 - ARR affected (distinct-account sum): C-0B2213A9 (36,000) + C-0DDFC9A7 (48,000) + C-0F6C0F34 (30,000) = 114,000 - Two ticket ids: IC-460060, IC-460056 - One-line recommendation: Prioritize HRIS provisioning flow reliability (new hires/account creation + “sync skipped” cases) because it impacts 3 accounts and the highest ARR exposure. 2) Checkout/redemption & gift-card delivery issues - Count: 18 - Share: 18/79 = 0.227848… = 22.8% - Distinct accounts: 7 - ARR affected (distinct-account sum): C-0B0F1BAB (10,300) + C-0B827671 (10,700) + C-0CEF69FD (8,900) + C-0D9CA315 (9,600) + C-0F876796 (8,700) + C-0FCCD2DF (9,600) + C-14264ABD (11,000) = 68,800 - Two ticket ids: IC-460038, IC-460029 - One-line recommendation: Fix redemption/checkout + gift-card code/email delivery failures end-to-end (spin/failed redemption, missing gift card code/email), since it spans 7 accounts and the second-highest ARR exposure. 3) Billing & invoice seat/tier errors - Count: 15 - Share: 15/79 = 0.189873… = 19.0% - Distinct accounts: 1 - ARR affected (distinct-account sum): C-0E9C27D1 (52,000) = 52,000 - Two ticket ids: IC-460071, IC-460069 - One-line recommendation: Treat as single-account but high-urgency: validate seat-count/tier + renewal charge logic for C-0E9C27D1 to stop repeated invoice discrepancies. 4) Unspecified / missing body text - Count: 2 - Share: 2/79 = 0.025316… = 2.5% - Distinct accounts: 1 - ARR affected (distinct-account sum): C-0B2213A9 (36,000) = 36,000 - Two ticket ids: IC-460063, IC-460057 - One-line recommendation: Request/ensure complete ticket message content is captured so these can be actioned and correctly routed (right now there’s no usable body_text). 5) Recognition points not posting / balance not updating - Count: 20 - Share: 20/79 = 0.253165… = 25.3% - Distinct accounts: 9 - ARR affected (distinct-account sum): C-0B2895EF (2,900) + C-0BE96399 (2,700) + C-0BF20542 (4,500) + C-0D0B047C (4,500) + C-0D284E42 (3,400) + C-0D3278C7 (3,500) + C-0D6CC8E3 (4,200) + C-0DD0626C (2,500) + C-21FEBCBB (2,900) = 31,100 - Two ticket ids: IC-460016, IC-460001 - One-line recommendation: Focus on the recognition-to-points/balance posting pipeline (including cases where recognitions show “delivered” but points never arrive), since it’s broad across 9 accounts. 6) Slack integration & command failures - Count: 14 - Share: 14/79 = 0.177216… = 17.7% - Distinct accounts: 4 - ARR affected (distinct-account sum): C-0B843542 (4,400) + C-0BA71F12 (3,900) + C-10A56B0F (5,400) + C-8C2E8F00 (5,200) = 18,900 - Two ticket ids: IC-460047, IC-460049 - One-line recommendation: Stabilize Slack sync + auth persistence (sync toggle resets / disconnects) and slash-command error behavior, because it affects multiple accounts (4) even if ARR impact is smaller than HRIS/checkout/billing.
Similarity scoring (max 4): +1 for each exact match vs prospect on (industry, size_band, use_case, region). Prospect: (Technology, Mid-Market, employee_recognition, NA-West) 1) C-64171065 — score 3/4 - Industry: Technology vs Technology = match (+1) - Size band: Mid-Market vs Mid-Market = match (+1) - Use case: employee_recognition vs employee_recognition = match (+1) - Region: NA-East vs NA-West = mismatch (+0) Fields driving the match: industry, size_band, use_case. (Region mismatch) 2) C-11C31562 — score 3/4 - Industry: Manufacturing vs Technology = mismatch (+0) - Size band: Mid-Market vs Mid-Market = match (+1) - Use case: employee_recognition vs employee_recognition = match (+1) - Region: NA-West vs NA-West = match (+1) Fields driving the match: size_band, use_case, region. (Industry mismatch) 3) C-A13C193D — score 3/4 - Industry: Technology vs Technology = match (+1) - Size band: Mid-Market vs Mid-Market = match (+1) - Use case: retention vs employee_recognition = mismatch (+0) - Region: NA-West vs NA-West = match (+1) Fields driving the match: industry, size_band, region. (Use case mismatch)
Trailing 6 months window used (from provided spend months): 2026-03 through 2026-08 (inclusive). PAID channel performance (use SQMs = count of rows where sqm_date ∈ window; SQOs = count where sqo_date ∈ window) 1) paid_search - Spend = $6,000*6 = $36,000 - SQMs = 39 - SQOs = 18 - cost per SQM = spend / SQMs = 36,000 / 39 = 923.0769 - cost per SQO = spend / SQOs = 36,000 / 18 = 2,000.0000 - SQM-to-SQO rate = SQOs / SQMs = 18 / 39 = 0.461538 - Pipeline amount = $40,000*18 = $720,000 - pipeline per dollar = pipeline / spend = 720,000 / 36,000 = 20.0000 2) linkedin_ads - Spend = $4,000*6 = $24,000 - SQMs = 25 - SQOs = 8 - cost per SQM = 24,000 / 25 = 960.0000 - cost per SQO = 24,000 / 8 = 3,000.0000 - SQM-to-SQO rate = 8 / 25 = 0.320000 - Pipeline amount = $12,000*8 = $96,000 - pipeline per dollar = 96,000 / 24,000 = 4.0000 3) paid_social - Spend = $3,000*6 = $18,000 - SQMs = 0 - SQOs = 0 - cost per SQM = undefined (spend / SQMs = 18,000 / 0) - cost per SQO = undefined (spend / SQOs = 18,000 / 0) - SQM-to-SQO rate = undefined (SQOs / SQMs = 0 / 0) - Pipeline amount = $0 - pipeline per dollar = pipeline / spend = 0 / 18,000 = 0.0000 4) webinars - Spend = $1,500*6 = $9,000 - SQMs = 12 - SQOs = 5 - cost per SQM = 9,000 / 12 = 750.0000 - cost per SQO = 9,000 / 5 = 1,800.0000 - SQM-to-SQO rate = 5 / 12 = 0.416667 - Pipeline amount = $12,000*5 = $60,000 - pipeline per dollar = 60,000 / 9,000 = 6.666667 ORGANIC channel performance (for channels not present in channel_spend.csv) (use Volume = count of rows where sqm_date ∈ window; SQOs = count where sqo_date ∈ window; SQO rate = SQOs/Volume) 1) organic_search - Volume = 30 - SQOs = 10 - SQO rate = 10 / 30 = 0.333333 - Pipeline amount = $9,000*10 = $90,000 2) referral - Volume = 15 - SQOs = 6 - SQO rate = 6 / 15 = 0.400000 - Pipeline amount = $8,000*6 = $48,000 Flag: SQO date precedes SQM date (both dates present) Count = 2 rows total (both in linkedin_ads): - CT-000041 | channel=linkedin_ads | sqm_date=2026-06-14 | sqo_date=2026-06-09 | pipeline_amount=12000 - CT-000044 | channel=linkedin_ads | sqm_date=2026-07-23 | sqo_date=2026-07-18 | pipeline_amount=12000 Reallocation recommendation (based on “pipeline per dollar” for paid channels with measurable SQM/SQO denominators) - paid_social has SQMs=0, SQOs=0, and pipeline per dollar = 0.0000, with undefined costs (because denominators are 0). Move its entire $18,000 spend away. - Best paid efficiency is paid_search (pipeline per dollar = 20.0000), then webinars (6.666667), then linkedin_ads (4.0000). - Suggested split of the $18,000 removed from paid_social: - Allocate to paid_search and webinars in the ratio of their pipeline-per-dollar values: 20 : 6.666667 = 3 : 1 - paid_search: 75% of 18,000 = $13,500 - webinars: 25% of 18,000 = $4,500 - linkedin_ads: $0 (optional given its lower pipeline per dollar vs these two) Confidence (given sample sizes) - High confidence in reallocating away from paid_social (SQMs=0 and SQOs=0 over the window; this is direct evidence of no tracked funnel progression). - Medium-to-high confidence in prioritizing paid_search over webinars and linkedin_ads: - paid_search: SQMs=39, SQOs=18 (solid sample) - linkedin_ads: SQMs=25, SQOs=8 (solid, but note 2 funnel-order violations flagged above) - webinars: SQMs=12, SQOs=5 (smaller sample than the top channel) - Overall confidence: medium-high (driven by decent paid sample sizes for 3 channels, but limited by the fact this is a single 6-month window and all metrics are aggregated, plus the linkedin_ads date-order flags introduce potential data quality/funnel-logic noise).
Battlecard: Rivally 1) One-line positioning - Points-based recognition feed for mid-market teams, with EU/multi-language support for distributed organizations. (S02, S12) 2) Pricing (with source + date; newer source wins; note conflict) - Conflict: Earlier list price shown as $5/user/month (annual billing required), then later updated to $7/user/month (annual billing required). - $5/user/month (annual billing required) — 2026-01-20. (S03) - $5/user/month (annual billing required) — 2026-04-01 (still showing $5). (S08) - $7/user/month (annual billing required) — 2026-08-12 (updated price). (S17) - Deal/quote nuances in the data: - 500-seat prospect quoted at $6.50/user/mo with annual term. (S13) - 3-year term deal mention: “$7/user/mo list” with 15% discount. (S18) 3) Where they win - Engagement/recognition experience: recognition feed described as engaging. (S16) - Setup speed: setup took under a week (reviewer note). (S04) - Integrations: - Slack integration “worked out of the box.” (S04) - Microsoft Teams app v2 in public preview. (S19) - EU strengths: - EU data residency was pitched / emphasized. (S05) - EU data residency generally available; Dublin office opened. (S15) - Multi-language support praised for EU teams. (S12) - Support responsiveness: support response time praised (under 4 hours). (S22) - Admin tooling caveat (mixed, but still a “win/weakness” signal in the data): admin tooling described as lagging peers (so not a win on admin tooling). (S16) 4) Where we win - Analytics depth: an 800-seat prospect picked Bonusly over Rivally citing “analytics depth.” (S25) - Unverified gaps not covered by provided snippets: - No provided, sourced evidence here about specific Rivally-vs-Bonusly differences in SCIM, bulk editing, or export format beyond what’s tied to the objection snippets. (unverified; no re-source present in provided data) 5) Objections and responses - Objection: Missing SCIM provisioning; manual user management described as painful. - Evidence: “lacks SCIM provisioning; manual user management is painful.” (S10) - Response: No sourced response for our SCIM/bulk provisioning capabilities is present in the provided data. (unverified) - Objection: Analytics exports are CSV-only, making migration off Rivally hard. - Evidence: “migration off Rivally was hard because analytics exports are CSV-only.” (S20) - Response: Emphasize analytics depth—data shows an 800-seat prospect chose Bonusly over Rivally for “analytics depth.” (S25) - Objection: Reporting dashboards are basic compared to enterprise tools. - Evidence: “reporting dashboards are basic compared to enterprise tools.” (S07) - Response: Emphasize analytics depth—prospect selected Bonusly based on “analytics depth.” (S25) - Objection: Admin console lacks bulk recognition editing. - Evidence: “admin console still lacks bulk recognition editing.” (S24) - Response: No sourced response about bulk editing capability differences is present in the provided data. (unverified) - Objection: EU rewards catalog thinner than US catalog. - Evidence: “rewards catalog in EMEA is thinner than US catalog.” (S14) - Response: No sourced response about catalog breadth/EMEA parity is present in the provided data. (unverified) - Objection (unverified wording): UI described as “clunky.” - Evidence: “AE opinion … UI is clunky” and explicitly marked “Not verified against the product.” (S09) - Response: No sourced response tied to this UI claim is present in the provided data. (unverified) 6) Recent changes (from provided snippets) - Price change: Recognition Starter updated to $7/user/month (annual billing required). - Evidence: “Recognition Starter now $7 per user/month, annual billing required.” (S17) - Product expansion: Rivally Pulse launched, lightweight engagement survey add-on. - Evidence: “launches ‘Rivally Pulse’…” (S06) - Rivally Pulse lifecycle/pricing change: Pulse exits beta and is priced as an add-on (not bundled). - Evidence: “exits beta; priced as add-on, not bundled.” (S23) - Integrations: Microsoft Teams app v2 public preview. - Evidence: “Microsoft Teams app v2 in public preview.” (S19) - EU expansion/status: Dublin office opened; EU data residency generally available. - Evidence: “opens Dublin office; announces EU data residency generally available.” (S15) 7) Our 12-month win/loss record against them (deals_with_competitor.csv) Arithmetic (12-month span shown in the table): months run from 2025-09 through 2026-08 inclusive; total deals = 20. - Wins = 13 (13 win deals listed below) - Losses = 7 (7 loss deals listed below) - Win rate = wins / total = 13 / 20 = 65% - Deal win aliases (13): 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 - Deal loss aliases (7): Deal-7767F5, Deal-5645A5, Deal-C6FFAA, Deal-D263E0, Deal-935746, Deal-9066A6, Deal-72A02F 8) Old-card items that could not be re-sourced (marked unverified) - “Rivally was acquired by WorkHuman in 2025.” Unverified in provided competitor_snippets.csv. (unverified) - “Rivally lacks a Slack integration.” Conflicts with provided evidence that Slack integration “worked out of the box.” (S04)
Per sequence (overall totals; arithmetic shown): 1) New Logo Nurture Sent=500+458+428=1386; Opened=210+160+120=490; Replied=42+30+18=90; Meetings=12+9+6=27 Open rate=490/1386=35.4%; Reply rate=90/1386=6.49%; Meeting rate=27/1386=1.95% Weakest step (lowest meeting rate): step 3 (Meetings=6; sent=428 → 6/428=1.40%) 2) Expansion Nurture Sent=300+300+275=875; Opened=130+340+95=565; Replied=22+25+12=59; Meetings=5+4+3=12 Open rate=565/875=64.6%; Reply rate=59/875=6.74%; Meeting rate=12/875=1.37% Weakest step: step 3 (3/275=1.09%) 3) Cold Outbound - HR Leaders Sent=600+595+590=1785; Opened=240+175+130=545; Replied=5+2+1=8; Meetings=0+0+0=0 Open rate=545/1785=30.5%; Reply rate=8/1785=0.45%; Meeting rate=0/1785=0% Weakest step: step 3 (1/590=0.17% reply; 0 meetings) 4) Cold Outbound - People Ops Sent=400+386+377=1163; Opened=150+110+80=340; Replied=14+9+6=29; Meetings=3+2+1=6 Open rate=340/1163=29.2%; Reply rate=29/1163=2.49%; Meeting rate=6/1163=0.52% Weakest step: step 3 (6/377=1.59% reply; 1/377=0.27% meeting) Tracking errors Expansion Nurture step 2: opened 340 > sent 300 → open rate = 340/300 = 113.3% (impossible; tracking/ETL error). Audience overlap (from the provided audiences.csv rows) Confirmed overlaps across sequences (same contact_key in multiple sequences): CT-001103 (Cold Outbound - HR Leaders + Cold Outbound - People Ops), CT-000849 (same pair), CT-001255 (same pair). This harms attribution between those two sequences. Failure mode for anything with reply <2% Cold Outbound - HR Leaders steps 1–3 and Cold Outbound - People Ops step 3 all have very low replies per opened and (for HR Leaders) 0 meetings. Example: HR step 3 reply/opened = 1/130 = 0.77%. One change per weak sequence + what to fix first - Fix first: Cold Outbound - HR Leaders (weak step 3): change step 3 message/CTA to drive replies (opened=130, replied=1, meetings=0). - Cold Outbound - People Ops (weak step 3): revise step 3 to increase replies (80 opened → 6 replied) and meetings (1). - Expansion Nurture (weak step 3): revise step 3 to improve conversion to meetings (95 opened → 12 replied). - New Logo Nurture (weak step 3): revise step 3 CTA to lift meetings (120 opened → 18 replied). (Next priority after the above: correct Expansion Nurture step 2 tracking so opens aren’t >100%.)
Active quarter: Q3-2026 (days elapsed = 66 / 92). Linear pace fraction = 66/92 = 0.717391. 1) SQMs (higher is better) - QTD actual: 230 - Target: 300 - Delta: 230 - 300 = -70 - Pace: expected pace at day 66 = 300 * (66/92) = 300 * 0.717391 = 215.217; 230 > 215.217 => ahead 2) SQOs (higher is better) - QTD actual: 84 - Target: 120 - Delta: 84 - 120 = -36 - Pace: expected = 120 * (66/92) = 86.087; 84 < 86.087 => behind 3) DS2s (higher is better) - QTD actual: 40 - Target: 75 - Delta: 40 - 75 = -35 - Pace: expected = 75 * (66/92) = 53.804; 40 < 53.804 => behind 4) Closed-lost MIA rate (lower is better) - QTD actual rate = closed_lost_mia / closed_lost_total = 5 / 25 = 0.2 - Target: 0.1 - Delta: 0.2 - 0.1 = +0.1 - Pace: expected (linear, lower-is-better) = 0.1 * (66/92) = 0.071739; 0.2 > 0.071739 => behind 5) Same-quarter close count (higher is better) - QTD actual: 10 - Target: 20 - Delta: 10 - 20 = -10 - Pace: expected = 20 * (66/92) = 14.348; 10 < 14.348 => behind 6) Active pipeline coverage against target (higher is better) - QTD actual: 3,000,000 - Target: 4,000,000 - Delta: 3,000,000 - 4,000,000 = -1,000,000 - Coverage ratio: 3,000,000 / 4,000,000 = 0.75 (75%) - Pace: expected = 4,000,000 * (66/92) = 2,869,565.217; 3,000,000 > 2,869,565.217 => ahead One paragraph on what moved this week: I can’t determine “what moved this week” from the provided data because only QTD snapshots are included (no week-over-week counts or deltas for any of the requested metrics).
Approve Q3 2026 forecast of 115,976.75 based on 44,729 COMMIT inside 2026-07-01 to 2026-09-30 plus 0.35 × 203,565 BEST_CASE. Caveat: Owner blank on 85/86 and Why-buys empty on 71/86 (including all 7 COMMIT), plus 32/86 close dates in October, so this forecast “looks precise” but the committed basis is not documented.
Subject: Quick follow-up on Deal-0D2F7A Hi Alex, Following up on my 2026-08-05 email recap of the July 28 demo with pricing for 150 seats—still no reply. In that thread, the People team had asked for pricing after the People team session. Would you confirm the People team’s next step on the recognition/points catalog package—specifically whether you’re ready to move forward, and the preferred start date? Thanks, [Your Name]
Marketing: This week, Marketing delivered 46 SQMs against a 52 SQM target (46-52 = -6; 46/52 = 0.8846 → 88.5% of target). The webinar channel contributed 18 SQMs (18/46 = 0.3913 → 39.1%), leaving 28 SQMs from other channels (46-18 = 28). Paid search stayed steady with cost per SQM at $150 for the week. Sales: Sales converted 14 SQOs and set 9 DS2 meetings. The DS2-meeting rate from SQOs is 9/14 = 0.6429 → 64.3%. New pipeline created totaled $310,000. That works out to $310,000/9 = $34,444.44 per DS2 meeting and $310,000/14 = $22,142.86 per SQO. Same-quarter close count for the week was 3. CS: Customer Success helped preserve 2 renewals this week. Team NPS moved to 61, and there are 3 open red-flag accounts heading into next week (3/2 = 1.5 red-flag accounts per renewal saved). PLG: Product-Led Growth added 412 new signups. With activation at 31%, that implies 412 * 0.31 = 127.72 activated signups (≈128). 38 companies reached the aha moment of 10 recognition gives, which is 38/412 = 0.0922 → 9.2% of signups and 38/127.72 = 0.2976 → 29.8% of activated signups.
Partner digest (2026-08-24 to 2026-09-04) Apex Rewards Co (active): 2 sourced opps this period (both logged in DS1). Partner-sourced pipeline: 2 deals; amount = 180,000 (Deal-DDAAF2) + 95,000 (Deal-2CF33E) = 275,000. HRCloud Partners (active): 1 sourced opp this period (moved to DS2 after security review). Partner-sourced pipeline: 1 deal; amount = 140,000 (Deal-F1CDA5) = 140,000. CultureBridge (active): 2 sourced opps this period (both early stage). Partner-sourced pipeline: 2 deals; amount = 60,000 (Deal-096E1D) + 75,000 (Deal-067213) = 135,000. WorkWell Group (active): No sourced deals this period (planning call booked for 09-09). Partner-sourced pipeline: 0 deals; amount = 0 = 0. Recogniq (quiet): Quiet in provided data for this period—no Partner-sourced deals listed. KudosWave (quiet): Quiet in provided data for this period—no Partner-sourced deals listed. PeopleFirst Advisors (quiet): Quiet in provided data for this period—no Partner-sourced deals listed. TotalPerk (quiet): Quiet in provided data for this period—no Partner-sourced deals listed.
Q1: Yes. Customer data is encrypted at rest using AES-256. (Cite: [P1]) Q2: Yes. Data in transit is encrypted with TLS 1.2 or higher. (Cite: [P1]) Q3: Yes. Bonusly maintains a SOC 2 Type II report, renewed annually, and it is available to prospects under NDA. (Cite: [P2]) Q4: Customer data is retained for the duration of the contract plus 90 days, then deleted from production systems. (Cite: [P3]) Q5: The current list of subprocessors is published at trust.bonusly.com, and customers receive 30 days notice before a new subprocessor is added. (Cite: [P4]) Q6: Yes. SAML 2.0 single sign-on is available on the Enterprise plan. (Cite: [P5]) Q7: Backups are taken daily, and encrypted backups are retained for 35 days. (Cite: [P6]) Q8: Unanswerable from the provided policy excerpts. Please provide your ISO 27001 certification (certificate) or ISO 27001 attestation document. (Cite: [P1]-[P8]) Q9: Unanswerable from the provided policy excerpts. Please provide the contractual uptime SLA document (including the uptime percentage). (Cite: [P1]-[P8]) Q10: Unanswerable from the provided policy excerpts. Please provide the HIPAA Business Associate Agreement (BAA) terms or an executed BAA template/attachment. (Cite: [P1]-[P8])
1) ALWAYS-trigger overlap / duplicate
- Finding: pipeline-intelligence-report and weekly-pipeline-report both claim they must run for “pipeline update” / “pipeline report” style requests.
- pipeline-intelligence-report ALWAYS trigger includes: “run the pipeline report”, “pipeline update”, “full pipeline”, “pipeline review”, “pipeline intelligence”
- weekly-pipeline-report ALWAYS trigger includes: “run the pipeline update”, “weekly pipeline report”, “pipeline summary”, “generate the pipeline report”, “mid-month pipeline check”
- Severity: CRITICAL
- Action type: REVIEW
- Proposal: REVIEW the trigger phrase overlap so only one of these runs for a given user intent (e.g., make weekly-pipeline-report’s trigger narrower to weekly-only phrasing, and keep pipeline-intelligence-report for “full scored pipeline” phrasing).
2) Circular delegation chain between skills
- Finding: None found among the provided skills/files.
- Observed (non-circular): next-to-close → pipeline-intelligence-report → closed-lost-analysis
3) Dangling delegation target (target skill not present in the manifest you provided)
- Finding: Multiple referenced delegation/invocation targets are not present in skill_manifest.csv.
- analysis-validator delegates/invokes: 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
- comms-drafter step 0 invokes: bonusly-brand
- email-drafter invokes: bonusly-brand
- deal-strategy-coach cross-skill handoff mentions: prospect-research-multithreading
- Severity: CRITICAL
- Action type: REVIEW
- Proposal: REVIEW whether these are supposed to be present in this manifest. If they are required, add them to skill_manifest.csv; if they are plugin-only/non-manifest skills, ensure the manifest scope is explicitly “only core SignalForge skills,” and document the exception.
4) Version conflict (and which skill should survive)
- Finding: analysis-validator has an internal version string mismatch.
- Frontmatter says: Version: 3.6
- Later, in the validation trail template: “Validator: analysis-validator v3.2”
- Severity: WARNING
- Action type: UPDATE_BODY
- Proposal: UPDATE_BODY in analysis-validator to make the template’s “Validator:” version consistent with the skill’s declared Version (3.6).
5) How many manifest descriptions exceed 1,024 characters (arithmetic shown)
- Manifest description_chars values provided are:
656, 897, 996, 792, 965, 676, 945, 1004, 1006, 962, 1006, 708, 762, 656
- Threshold = 1,024
- Count > 1,024 = 0
- Arithmetic: there are 14 rows total in skill_manifest.csv; all 14 have description_chars <= 1006; therefore 14 - 14 = 0 exceed 1,024.
- Severity: INFO
- Action type: TRIM_DESC
- Proposal: None needed (count is 0).
6) Hardcoded page ids, dates, or person names in skill bodies
- Finding: Hardcoded page IDs/dates/person names exist in multiple skills’ bodies, for example:
- signalforge-feedback:
- Page ID: 2295136266 (also includes parent page/container IDs and build log page ID 2247295002)
- partner-digest:
- Confluence IDs: Cloud ID 73fe98de-a4a3-4869-9f8a-bb1eeed4cf7f, Space ID 1958248479, folder ID 2286616609, plus example page-title dates (e.g., “Week of May 19, 2026”, “Week of June 2, 2026”)
- Person name hardcoded: “Owner: Amani Phipps”
- weekly-pipeline-report:
- Person name hardcoded: “Weekly Pipeline Report — Ben Lavin …”
- Hardcoded quarter date range: “Q2 (April 1 – June 30, 2026; total ≈ 64–65)”
- Hardcoded Q1 2026 context numbers (Sales Bookings Actual/plan, Pipeline Addition Actual/forecast)
- analysis-validator:
- Person names hardcoded: “Manish / Amani” (escalation section)
- Hardcoded dates in the body: “April 26, 2026”, “May 9, 2026”, and “as of May 4, 2026”
- sales-forecast:
- Person name hardcoded in body: “Alaina / VP Sales view”
- Severity: WARNING
- Action type: REVIEW
- Proposal: REVIEW whether these should be templated/config-driven instead of hardcoded (especially Confluence page IDs and fixed example dates), so the skills remain portable and don’t drift as org tooling changes.
7) Manifest drift (BOTH directions)
- Finding: None found from the data provided.
- Files shown all have a corresponding row in skill_manifest.csv.
- Every manifest row shown has a corresponding FILE content block included in your prompt.
- Severity: INFO
- Action type: TRIM_DESC
- Proposal: None needed.
Runbook for #incident-2026-08-29-reward-queue (resolved) Step 1 — Triage / measure initial state 1) Thread trace: [M02] 2026-08-29 14:04:33Z Farid Osman 2) Action taken (exact command): bundle exec rake sidekiq:queue_depth 3) What changed state: No 4) Result (thread evidence): reward queue at 48,213 pending jobs. Normal is under 500. 5) Success verification in-thread: - [M02] “Normal is under 500.” 6) Rollback for state-changing steps: Not applicable (no state change in [M02]) Step 2 — Identify error signature 1) Thread trace: [M03] 2026-08-29 14:06:02Z Farid Osman 2) Action taken: Dead set analysis in thread (no command provided) 3) What changed state: No 4) Result (thread evidence): Dead set has 112 jobs, all Redis::TimeoutError from around 13:58. 5) Success verification in-thread: Not explicitly stated beyond the dead set count/error type in [M03] 6) Rollback: Not applicable Step 3 — Stop the bleed by pausing enqueue 1) Thread trace: [M04] 2026-08-29 14:08:45Z Farid Osman 2) Action taken (exact command): bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)' 3) What changed state: Yes (enqueue feature flag disabled) 4) Result (thread evidence): Queue subsequently reported decreasing (see [M07]) 5) Success verification in-thread: - [M07] “Queue depth down to 9,400 and falling ~1,200/min.” - Arithmetic from thread values: 48,213 − 9,400 = 38,813 pending jobs reduced (by the time of [M07]). 6) Rollback (exact command from thread): bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)' Step 4 — Clear the dead set 1) Thread trace: [M05] 2026-08-29 14:15:20Z Elena Sinclair 2) Action taken: “While I was in the console I cleared out the dead set.” (No exact command provided.) 3) What changed state: Yes (dead set cleared) 4) Result (thread evidence): Later checks show queue returning to 0 (see [M08]) 5) Success verification in-thread: - Not explicitly stated as a direct before/after for the dead set clear itself. - [M08] later verification indicates the system recovered: “bundle exec rake sidekiq:queue_depth now returns 0.” (This confirms queue depth, not the dead set specifically.) - Step status: needs confirmation for “how success was verified” specifically for dead set clear (thread does not show a command/output for [M05]). 6) Rollback: needs confirmation (thread does not provide a rollback command for dead set clearing) Step 5 — Scale workers up to drain backlog 1) Thread trace: [M06] 2026-08-29 14:21:07Z Bryce Harmon 2) Action taken (exact command): kubectl scale deployment/reward-worker --replicas=6 (was 3) 3) What changed state: Yes (worker replicas increased) 4) Result (thread evidence): Queue depth decreasing thereafter (see [M07]) 5) Success verification in-thread: - [M07] “Queue depth down to 9,400 and falling ~1,200/min.” 6) Rollback (exact command from thread): kubectl scale deployment/reward-worker --replicas=3 Step 6 — Verify the queue is drained; check error rate 1) Thread trace: [M08] 2026-08-29 14:47:55Z Cole Ingram 2) Action taken (exact command): bundle exec rake sidekiq:queue_depth 3) What changed state: No 4) Result (thread evidence): - “Verified — bundle exec rake sidekiq:queue_depth now returns 0.” - “Error rate in Datadog back to baseline.” 5) Success verification in-thread: - Queue depth = 0 (from [M08]) - Datadog error rate baseline (from [M08]) 6) Rollback: Not applicable (no state change in [M08]) Step 7 — Re-enable enqueue 1) Thread trace: [M09] 2026-08-29 14:49:10Z Bryce Harmon 2) Action taken (exact command): bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)' 3) What changed state: Yes (enqueue feature flag re-enabled) 4) Result (thread evidence): “Verified 40 new jobs processed cleanly in the next 3 minutes.” 5) Success verification in-thread (includes arithmetic from provided numbers): - 40 jobs processed in 3 minutes → 40 / 3 = 13.33 jobs/minute (rate computed from [M09] values). 6) Rollback: needs confirmation (thread does not provide a rollback action/command specifically for [M09]; only [M04] provides rollback content for the earlier disable) Step 8 — Scale workers back down; confirm stability 1) Thread trace: [M10] 2026-08-29 14:55:00Z Bryce Harmon 2) Action taken (exact command): kubectl scale deployment/reward-worker --replicas=3 3) What changed state: Yes (worker replicas decreased) 4) Result (thread evidence): “Queue stable at 0. Incident resolved.” 5) Success verification in-thread: - “Queue stable at 0” (from [M10]) 6) Rollback: needs confirmation (thread does not provide a rollback command for this step; [M06] rollback is for the earlier scale-up case) Notes on thread gaps (must be confirmed) - [M05] does not include the exact console command used to clear the dead set. - [M05] does not include explicit verification of dead set size after clearing—only later queue-depth verification is present in [M08]. - Rollback commands are explicitly provided only for: - [M04] (disable → enable) - [M06] (scale to 6 → scale to 3) - No explicit rollback commands are provided for: - [M09] (re-enable enqueue) - [M10] (scale back down from 6 to 3)
1) First error (timestamp)
- 2026-09-03T14:01:12Z
- Service: reward-service
- Level: ERROR
- Message: “Redis::TimeoutError: Connection to Redis at redis-primary:6379 timed out after 5s”
2) Cascade in order (what the log slice shows)
A. Job-side retry exhaustion begins
- 2026-09-03T14:01:20Z — reward-service — ERROR
- Message: “Redis::TimeoutError: retry exhausted for RewardGiveJob”
- 2026-09-03T14:01:30Z — reward-service — ERROR
- Message: “Redis::TimeoutError: retry exhausted for RewardGiveJob”
B. Sidekiq marks the job as failed and schedules retry
- 2026-09-03T14:01:40Z — sidekiq — ERROR
- Message: “RewardGiveJob failed: Redis::TimeoutError; retrying in 60s”
C. Queue builds up (backlog indicator)
- 2026-09-03T14:02:30Z — sidekiq — WARN
- Message: “Queue reward depth above 10,000”
D. Upstream requests start failing (502s)
- 2026-09-03T14:03:05Z — api-gateway — ERROR
- Message: “502 upstream timeout calling reward-service /gives”
- 2026-09-03T14:03:48Z — api-gateway — ERROR
- Message: “502 upstream timeout calling reward-service”
- 2026-09-03T14:04:13Z — api-gateway — ERROR
- Message: “502 upstream timeout calling reward-service”
- 2026-09-03T14:05:16Z — api-gateway — ERROR
- Message: “502 upstream timeout calling reward-service”
- (Additional 502s continue through 2026-09-03T14:06:52Z in the provided slice.)
E. Web/app form submission fails due to upstream 502
- 2026-09-03T14:03:30Z — web-app — ERROR
- Message: “Give form submission failed: upstream 502 from api-gateway”
- 2026-09-03T14:04:45Z — web-app — ERROR
- Message: “Give form submission failed: upstream 502”
- 2026-09-03T14:06:49Z — web-app — ERROR
- Message: “Give form submission failed: upstream 502”
F. Recovery signals appear later
- 2026-09-03T14:22:10Z — reward-service — INFO
- Message: “Redis connection restored; resuming job processing”
- 2026-09-03T14:24:45Z — sidekiq — INFO
- Message: “Queue reward depth below 500”
3) Service and job involved
- First Redis failure occurs in: reward-service (Redis connection timeout to redis-primary:6379)
- The job class explicitly tied to the Redis timeout/retries in the log slice: RewardGiveJob
- Seen as: “retry exhausted for RewardGiveJob” (reward-service)
- Seen as: “RewardGiveJob failed: Redis::TimeoutError; retrying …” (sidekiq)
- api-gateway failures are downstream symptoms of reward-service being timing out:
- “502 upstream timeout calling reward-service /gives”
- web-app failures are downstream symptoms of api-gateway returning 502:
- “Give form submission failed: upstream 502 …”
- Note from sidekiq_jobs.csv (not shown in the datadog_logs.csv slice you provided):
- RecognitionDigestJob also has Redis::TimeoutError entries (e.g., 2026-09-03T14:02:36Z), but datadog_logs.csv in this slice does not mention RecognitionDigestJob.
4) Arithmetic (time deltas, using only timestamps from the provided rows)
- From first Redis timeout to first api-gateway 502:
- 2026-09-03T14:03:05Z − 2026-09-03T14:01:12Z
- = (14:03:05 − 14:01:05) + 2s
- = 2m00s + 1m? (re-check) using direct subtraction:
- 14:01:12 → 14:02:12 = 1m
- 14:02:12 → 14:03:12 = 1m, but 14:03:05 is 7s before 14:03:12
- total = 1m + (1m − 7s) = 1m53s
- From first Redis timeout to first web-app submission failure:
- 2026-09-03T14:03:30Z − 2026-09-03T14:01:12Z
- 14:01:12 → 14:03:12 = 2m
- 14:03:12 → 14:03:30 = 18s
- total = 2m18s
- From first Redis timeout to “Redis connection restored”:
- 2026-09-03T14:22:10Z − 2026-09-03T14:01:12Z
- 14:01:12 → 14:21:12 = 20m
- 14:21:12 → 14:22:10 = 58s
- total = 20m58s
5) Datadog query to confirm the first error
Use a log query filtering the exact service and error signature from the first row, for example:
- service:reward-service level:ERROR "Redis::TimeoutError" "redis-primary:6379 timed out after 5s"
6) What the logs do NOT show
- They do not show the underlying cause of the Redis timeout (e.g., network partition, Redis CPU saturation, connection limit reached, Redis server errors); they only show the symptom: “Connection … timed out after 5s”.
- They do not include stack traces, error codes beyond the Redis::TimeoutError string, or any internal retry/backoff configuration details (only messages like “retry exhausted” and “retrying in 60s”).
- They do not show RecognitionDigestJob failures in datadog_logs.csv for this slice (even though sidekiq_jobs.csv lists them); only RewardGiveJob is explicitly tied to the log-slice cascade above.
A) Flags present in flags_export.csv and described in flag_code.md (code excerpt present)
1) recognition_streaks_v2
- Controls (per code): If FeatureFlags.enabled?("recognition_streaks_v2", company: company), then StreakTracker.record(give)
- Export state: on
- Targeting rules (per export): segment:beta_companies
- Companies/segments “on” (per export): segment:beta_companies (company_count=42)
2) points_budget_guardrails
- Controls (per code): If FeatureFlags.enabled?("points_budget_guardrails", company: company), then BudgetService.new(company).enforce!(giver, points)
- Export state: on
- Targeting rules (per export): all_companies
- Companies/segments “on” (per export): all_companies (company_count=220)
3) slack_dm_nudges
- Controls (per code): return unless FeatureFlags.enabled?("slack_dm_nudges", company: company); if enabled, SlackDm.send_nudge(user)
- Export state: on
- Targeting rules (per export): segment:region_na
- Companies/segments “on” (per export): segment:region_na (company_count=87)
4) redeem_flow_redesign
- Controls (per code): If enabled, render RedeemV2Component.new(company); else render RedeemV1Component.new(company)
- Export state: off
- Targeting rules (per export): targeted_list
- Companies/segments “on”: none (export state is off); export company_count=12 (targeting_rules=targeted_list)
5) analytics_dashboard_v3
- Controls (per code): If enabled, set @dashboard = AnalyticsV3.new(company)
- Export state: on
- Targeting rules (per export): segment:tier_three
- Companies/segments “on” (per export): segment:tier_three (company_count=65)
6) ms_teams_app_v2
- Controls (per code): If enabled, TeamsAppV2.install(company)
- Export state: off
- Targeting rules (per export): targeted_list
- Companies/segments “on”: none (export state is off); export company_count=9 (targeting_rules=targeted_list)
B) Flags present in flags_export.csv but NOT referenced in flag_code.md (no code reference in excerpt)
7) legacy_give_modal
- Controls (code excerpt): not present in flag_code.md
- Export state: off
- Targeting rules (per export): segment:legacy_plan
- Companies/segments “on”: none (export state is off); export company_count=14 (targeting_rules=segment:legacy_plan)
8) survey_boosters_q3
- Controls (code excerpt): not present in flag_code.md
- Export state: on
- Targeting rules (per export): segment:legacy_plan
- Companies/segments “on” (per export): segment:legacy_plan (company_count=7)
9) paused_offboard_cleanup
- Controls (code excerpt): not present in flag_code.md
- Export state: off
- Targeting rules (per export): (blank / no targeting rules provided)
- Companies/segments “on”: none; export company_count=0
- Flag with no targets: yes (targeting_rules is empty in the export)
C) Export-count arithmetic check (sum of company_count matches export values)
- On flags company_count sum:
- recognition_streaks_v2 42
- points_budget_guardrails 220
- slack_dm_nudges 87
- analytics_dashboard_v3 65
- survey_boosters_q3 7
- Total ON = 42 + 220 + 87 + 65 + 7 = 421
- Off flags company_count sum:
- redeem_flow_redesign 12
- ms_teams_app_v2 9
- legacy_give_modal 14
- paused_offboard_cleanup 0
- Total OFF = 12 + 9 + 14 + 0 = 35
- Total across all exported flags:
- Total = 421 + 35 = 456 (equals sum of all company_count values shown in the export)
- NDA-1 (GREEN): “Carve-outs: information that (a) is or becomes publicly available… (b) was known prior… (c) is received from a third party without duty of confidence… (d) is independently developed, or (e) must be disclosed by law or court order.” (Standard mutual confidentiality with normal carve-outs; no non-solicit/non-compete; Delaware governing law.) - NDA-2 (YELLOW): “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.” (Embedded non-solicit; 18 months = 1.5 years → counsel should review.) - NDA-3 (RED): “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.” + “No confidentiality carve-outs are stated in this Agreement.” (Embedded 3-year non-compete plus missing confidentiality carve-outs; governing law Ireland with exclusive jurisdiction.)