Mean 0.937 · 31/40 perfect tests · $7.97 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 - DS2+ non-compliant: stage DS2–DS5 with why_buys under 15 characters, blank, or a placeholder. LONG(n) is compliant. - Missing DS1: t_ds1 is blank. - Regressed: an earlier-stage timestamp is later than a later-stage timestamp. Results - Overall: 101 / 124 DS2+ deals = 81.5% non-compliant. - Bryce Harmon: 15 / 18 = 83.3% - Dana Mercer: 18 / 22 = 81.8% - Alex Franklin: 48 / 61 = 78.7% - Cole Ingram: 19 / 22 = 86.4% - Farid Osman: 1 / 1 = 100.0% - Gavin Porter, Hugo Lindqvist, and Elena Sinclair: N/A — 0 DS2+ deals. - No DS1 timestamp: 0 deals. - Regressed: 0 deals. Process fixes 1. Block advancement to DS2+ unless why_buys passes the 15-character and placeholder validation. 2. Add a required DS1-entry timestamp automation when a deal first enters DS1. 3. Add timestamp-order validation and a regression alert for any earlier-stage timestamp later than a later-stage timestamp.
Deals resolved: 156 of 156 Unmatched owner IDs: None Archived/deactivated owners assigned to open deals: None - Archived owners Gavin Porter (1520255671) and Hugo Lindqvist (77260721) have no open deals in the extract. Pipeline amount per resolved owner: - Bryce Harmon (119337721): 35 deals = $1,054,144.00 - Alex Franklin (84342457): 67 deals = $624,310.00 - Dana Mercer (83155923): 24 deals = $341,195.00 - Cole Ingram (83155924): 22 deals = $288,161.43 - Farid Osman (716654662): 7 deals = $4,134.00 - Elena Sinclair (701163055): 1 deal = $2,100.00 Arithmetic: 35 + 67 + 24 + 22 + 7 + 1 = 156 resolved deals $1,054,144.00 + $624,310.00 + $341,195.00 + $288,161.43 + $4,134.00 + $2,100.00 = $2,314,044.43 total pipeline.
(a) Orphan Gong rows: 30 Arithmetic: 67 Gong rows − 37 deal-ID matches in deals_open = 30 orphans. Sample orphan aliases: - Deal-8FA85D - Deal-8FC3F9 - Deal-3B7945 - Deal-42B265 - Deal-9CCC42 (b) Duplicate conversation keys: 0 rows Every Gong row has calls_90d = distinct_conversation_keys. (c) DS3-or-later call coverage: 29.4% Arithmetic: 25 DS3+ open deals with at least one logged call ÷ 85 total DS3+ open deals × 100 = 29.4%. gong_owner_id was not used for matching because it is a Gong user ID, not a HubSpot owner ID.
```sql
-- Cannot construct the requested query from the provided catalog:
-- 1. No customer-company identifier is documented for PRODUCTION.PLG.COMPANY_COHORT_SUMMARY.
-- 2. No recognition-event table or giver identifier is documented, so unique givers cannot be calculated.
-- 3. No company identifier or event timestamp is documented for
-- PRODUCTION.DEPRECATED_RECOGNITION.REDEMPTION_RECORDS_V2, so successful
-- redemptions cannot be attributed to a company or its first customer month.
-- 4. M1_USERS cannot be assumed to mean unique givers.
SELECT
'Insufficient catalog data to produce the requested per-company metrics without inventing columns or definitions.' AS ERROR;
```
CRM completeness audit Deals No deals extract was provided. Therefore: - Owner completeness: unavailable - Stage completeness: unavailable - Amount completeness: unavailable - Close-date completeness: unavailable - Why-buys completeness: unavailable - Pipeline amount at stake: unavailable No deal-level remediation can be ranked by pipeline amount without deal records containing company/deal aliases and amounts. Companies 34 company records. | Field | Complete | Arithmetic | Completeness | |---|---:|---:|---:| | Industry | 34 | 34 ÷ 34 × 100 | 100.0% | | Employee count | 25 | 25 ÷ 34 × 100 | 73.5% | | HQ country | 28 | 28 ÷ 34 × 100 | 82.4% | Enrichment-backed fills Only these missing values have matching, populated enrichment values: | Company | Field | Fill from enrichment | |---|---|---| | C-EC3025 | employee_count | 400 | | C-96039F | employee_count | 400 | | C-44EA29 | employee_count | 400 | | C-D04904 | employee_count | 400 | | C-B23205 | employee_count | 400 | | C-60C75F | employee_count | 400 | | C-7BBDFA | employee_count | 400 | | C-50D386 | employee_count | 400 | After these fills, employee-count completeness becomes: - (25 + 8) ÷ 34 × 100 = 97.1% Missing values that cannot be filled - C-93C8BF: employee_count missing; no matching enrichment row. - C-2D1F1B: hq_country missing; matching enrichment value is also blank. - C-D73B89: hq_country missing; matching enrichment value is also blank. - C-44EA29: hq_country missing; matching enrichment value is also blank. - C-D04904: hq_country missing; matching enrichment value is also blank. - C-2C60E5: hq_country missing; matching enrichment value is also blank. - C-EE9FFB: hq_country missing; no matching enrichment row. CRM/enrichment disagreements Industry taxonomy disagreements: | Company | CRM | Enrichment | Recommendation | |---|---|---|---| | C-66D1FC | tech | Computer Software | Use enrichment’s more specific taxonomy. | | C-EC3025 | Technology | Computer Software | Use enrichment’s more specific taxonomy. | | C-44EA29 | tech | Computer Software | Use enrichment’s more specific taxonomy. | | C-92D97D | Technology | Computer Software | Use enrichment’s more specific taxonomy. | | C-D04904 | Technology | Computer Software | Use enrichment’s more specific taxonomy. | | C-77A95A | Technology | Computer Software | Use enrichment’s more specific taxonomy. | | C-AA8DDA | Technology | Computer Software | Use enrichment’s more specific taxonomy. | | C-B25F40 | Technology | Computer Software | Use enrichment’s more specific taxonomy. | | C-60C75F | tech | Computer Software | Use enrichment’s more specific taxonomy. | | C-425E2A | Tech | Computer Software | Use enrichment’s more specific taxonomy. | Country representation disagreements: | Company | CRM | Enrichment | Recommendation | |---|---|---|---| | C-66D1FC | US | United States | Same country; normalize to one controlled value. | | C-950043 | US | United States | Same country; normalize to one controlled value. | | C-EC3025 | USA | United States | Same country; normalize to one controlled value. | | C-96039F | USA | United States | Same country; normalize to one controlled value. | | C-77A95A | US | United States | Same country; normalize to one controlled value. | | C-B23205 | US | United States | Same country; normalize to one controlled value. | | C-E51FB7 | USA | United States | Same country; normalize to one controlled value. | | C-D0662E | US | United States | Same country; normalize to one controlled value. | | C-425E2A | USA | United States | Same country; normalize to one controlled value. | | C-2D7423 | USA | United States | Same country; normalize to one controlled value. | There are no employee-count disagreements where both sources contain a value. Duplicate company clusters No company-name field was provided, so name-variant matching cannot be performed. Two shared-domain clusters exist: 1. acme-corp.com - C-0A092931: Technology, 500, US - C-0A092932: tech, 510, USA - Provisional survivor: C-0A092931, because its industry value is standardized and its alias appears first. - Conflict requiring validation before merge: employee count 500 versus 510. - Normalize US/USA during consolidation. 2. globex.io - C-0A092933: SaaS, 200, US - C-0A092934: Technology, 200, US - Provisional survivor: C-0A092933, because “SaaS” is more specific than “Technology.” - Employee count and HQ country agree. Contacts 52 contact records. | Field | Complete | Arithmetic | Completeness | |---|---:|---:|---:| | Email populated | 52 | 52 ÷ 52 × 100 | 100.0% | | Email syntactically valid | 48 | 48 ÷ 52 × 100 | 92.3% | | Title | 39 | 39 ÷ 52 × 100 | 75.0% | | Persona | 37 | 37 ÷ 52 × 100 | 71.2% | Invalid emails - CT-0010: `user0@` - CT-0080: `user0@` - CT-0081: `user1@` - CT-0192: `user2@` Domain mismatch - CT-0011, company C-66D1FC: email `user1@other-domain.com`; expected company/contact domain `66d1fc.com`. 10 fixes with the most pipeline amount at stake Pipeline-based ranking is impossible because no deals or amounts were provided. The following fixes are therefore unranked; pipeline amount at stake is unknown for each: 1. Provide the deals extract with deal alias, company alias, owner, stage, amount, close date, and why-buys. 2. Merge the acme-corp.com duplicate cluster after validating the 500-versus-510 employee-count conflict. 3. Merge the globex.io duplicate cluster using C-0A092933 as the provisional survivor. 4. Apply the eight enrichment-backed employee-count fills listed above. 5. Repair the four invalid emails: CT-0010, CT-0080, CT-0081, and CT-0192. 6. Resolve the domain mismatch for CT-0011 and verify whether the contact belongs to C-66D1FC. 7. Backfill the 13 missing contact titles. 8. Backfill the 15 missing contact personas. 9. Standardize industry values to a controlled taxonomy, using enrichment for the ten listed disagreements. 10. Standardize `US`, `USA`, and `United States` to one controlled HQ-country value.
Classification — 90 deals Pricing (5) - Deal-7ED004 — buyer - Deal-7B2236 — Bonusly - Deal-C33D91 — buyer - Deal-DAFB82 — buyer - Deal-8A119B — buyer Competitor (22) - Deal-F7F635 — unknown - Deal-422BA6 — Bonusly - Deal-381C8C — unknown - Deal-F1E8A6 — unknown - Deal-DDAB52 — Bonusly - Deal-ACE061 — unknown - Deal-2D2F8D — unknown - Deal-0F96AA — unknown - Deal-1BCA50 — Bonusly - Deal-7CC678 — unknown - Deal-A2C349 — buyer - Deal-C7156E — unknown - Deal-8A0992 — buyer - Deal-D0C698 — buyer - Deal-EECC02 — unknown - Deal-47F1A1 — buyer - Deal-BF2A98 — buyer - Deal-1E7DA9 — unknown - Deal-286F9C — Bonusly - Deal-369281 — buyer - Deal-9FCD0D — buyer - Deal-64B19A — buyer No decision (31) - Deal-AC944F — unknown - Deal-214060 — unknown - Deal-13E9CF — buyer - Deal-21B045 — unknown - Deal-ED9AE7 — buyer - Deal-988493 — unknown - Deal-F308CA — unknown - Deal-70F704 — unknown - Deal-4664E1 — unknown - Deal-E74A73 — buyer - Deal-D48E0B — unknown - Deal-583ADB — unknown - Deal-E0441F — unknown - Deal-7CB44D — unknown - Deal-FAC17C — buyer - Deal-50E5D8 — buyer - Deal-AFA56C — unknown - Deal-413C56 — buyer - Deal-2A292B — buyer - Deal-D1AABF — unknown - Deal-2BBA21 — unknown - Deal-7FBAC6 — buyer - Deal-386F6E — unknown - Deal-3F86A0 — unknown - Deal-096750 — unknown - Deal-ABD14C — buyer - Deal-79E61A — unknown - Deal-AE7C4E — unknown - Deal-DAB4F1 — unknown - Deal-B4B50F — unknown - Deal-5885B9 — unknown Timing (22) - Deal-DB0AAC — buyer - Deal-91A056 — buyer - Deal-29326C — buyer - Deal-831B7B — buyer - Deal-39E25C — buyer - Deal-B3ABED — buyer - Deal-B6AC09 — buyer - Deal-E6E80A — buyer - Deal-B038F0 — buyer - Deal-175756 — buyer - Deal-BB78F3 — buyer - Deal-15DA99 — buyer - Deal-F4AF5D — buyer - Deal-79B7A1 — buyer - Deal-9F176A — buyer - Deal-5E64CE — buyer - Deal-69CF3D — buyer - Deal-ECBF89 — buyer - Deal-D1A623 — buyer - Deal-FEDBCB — buyer - Deal-55867E — unknown - Deal-2FEDDB — buyer Product gap (8) - Deal-F97C37 — Bonusly - Deal-8E27DA — buyer - Deal-242273 — Bonusly - Deal-9048EB — Bonusly - Deal-3618CC — Bonusly - Deal-5AD03E — Bonusly - Deal-981AD4 — Bonusly - Deal-DC77FE — Bonusly Champion left (1) - Deal-F325A5 — buyer Other (1) - Deal-5DB9B0 — unknown Category counts - No decision: 31 - Timing: 22 - Competitor: 22 - Product gap: 8 - Pricing: 5 - Champion left: 1 - Other: 1 - Total: 31 + 22 + 22 + 8 + 5 + 1 + 1 = 90 Side split - Buyer: 44 - Unknown: 34 - Bonusly: 12 - Total: 44 + 34 + 12 = 90 Clear tag/reason disagreements: 3 - Deal-70F704: tagged “Lost DM”; reason says MIA. - Deal-3618CC: tagged “Lost DM”; reason says “Wanted Surveys.” - Deal-5AD03E: tagged “Competitor”; reason identifies budget-access functionality rather than a competitor. Two patterns most worth acting on 1. No decision and timing dominate: 31 + 22 = 53 of 90 losses. Repeated reasons include unresponsiveness, deprioritization, delayed approval, and plans to revisit in 2027 or later. This supports tighter qualification, documented approval paths, and structured recycling dates. 2. Competitive selection and product gaps account for 22 + 8 = 30 losses. Specific differentiators include broader offerings, ADP integration, surveys, customization, internal-points support, localization, and budget-access functionality. These should drive competitive enablement and product-gap prioritization.
{"tier_counts":{"LOCK":3,"ACTION":15,"BUILD":54,"REVIVE":19,"WATCH":27,"RISKY":38},"tier_examples":{"LOCK":["Deal-D348E1","Deal-C26D20","Deal-403845"],"ACTION":["Deal-25F752","Deal-944310","Deal-C6FE92"],"BUILD":["Deal-3974EB","Deal-6787C2","Deal-1FC049"],"REVIVE":["Deal-2D1F1B","Deal-66D1FC","Deal-950043"],"WATCH":["Deal-C9C286","Deal-332637","Deal-E25A09"],"RISKY":["Deal-E53952","Deal-5408B0","Deal-9AAE5F"]},"risky_deals":["Deal-E53952","Deal-5408B0","Deal-9AAE5F","Deal-547B2B","Deal-B7EBD1","Deal-A2B47C","Deal-2465CE","Deal-C61CF7","Deal-62D607","Deal-584EE5","Deal-C6D97A","Deal-7B3B0F","Deal-A5E80A","Deal-F9A08A","Deal-0660B4","Deal-FD9F4E","Deal-499BF6","Deal-BA571A","Deal-FC22A3","Deal-7BBDFA","Deal-60C2C2","Deal-4A13AD","Deal-8AD4A5","Deal-15D24F","Deal-9D0060","Deal-690476","Deal-635B8E","Deal-ED725A","Deal-55164C","Deal-3BA5EA","Deal-5FDCE4","Deal-F336B6","Deal-215CCA","Deal-5EED42","Deal-BA3DDC","Deal-7599B8","Deal-F9A3C1","Deal-FA32A0"],"lock_violations":0,"pipeline_shape":"156 total deals = 3 LOCK + 15 ACTION + 54 BUILD + 19 REVIVE + 27 WATCH + 38 RISKY. The pipeline is concentrated in BUILD and RISKY, while only 3 deals qualify as LOCK; 101 of 156 deals have zero meetings_30d, limiting near-term confidence."}
[
{
"transcript_id": "TX-001",
"deal_alias": "Deal-CFE7F4",
"why-buys": [
"The big win for us would be automating anniversary and birthday awards — our HR team of three cannot keep up with it manually."
],
"pain_points": [
"Our HR team of three cannot keep up with it manually.",
"Right now we track everything in a spreadsheet, and people slip through the cracks."
],
"stakeholders": [
"Prospect (VP People)",
"Prospect (HR Admin)"
],
"budget_signal": "We have about $40k earmarked for engagement tools this fiscal year.",
"timeline_signal": "Ideally we would have this live before open enrollment in November.",
"competitor_mentioned": "Achievers",
"next_step": "Security review on September 12.",
"objections": [
"We looked at Achievers last year, but it was too heavy for a team our size.",
"One concern: we need SSO and audit logs for IT to sign off."
],
"confidence": "high"
},
{
"transcript_id": "TX-002",
"deal_alias": "Deal-70BB30",
"why-buys": [
"We want to tie recognition to retention for our hourly workforce — regretted turnover there is over 30%."
],
"pain_points": [
"Regretted turnover for our hourly workforce is over 30%."
],
"stakeholders": [
"Prospect (Head of Total Rewards)",
"Prospect (CFO)"
],
"budget_signal": "Finance has approved a $25k pilot budget for this quarter.",
"timeline_signal": "We want a decision by end of September.",
"competitor_mentioned": null,
"next_step": "Send the pilot agreement; the prospect will route it to legal this week.",
"objections": [
"Integration with Workday has to be rock solid — that's my one condition."
],
"confidence": "high"
},
{
"transcript_id": "TX-003",
"deal_alias": "Deal-530B50",
"why-buys": [
"We need to make recognition visible across our 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": "Honestly there's no rush on our side until Q1.",
"competitor_mentioned": "Bucketlist",
"next_step": "Schedule a call with the CEO; the prospect will send two times.",
"objections": [
"The CEO has to be sold first — she decides anything people-related."
],
"confidence": "high"
},
{
"transcript_id": "TX-004",
"deal_alias": "Deal-180D02",
"why-buys": [
"We want to consolidate three separate recognition tools into one."
],
"pain_points": [
"We're paying for three tools and none of them talk to our HRIS."
],
"stakeholders": [
"Prospect (VP People)",
"Prospect (IT Security Lead)"
],
"budget_signal": "If it's under $15k annually, I can approve it without going to the board.",
"timeline_signal": "Our procurement cycle runs six to eight weeks minimum.",
"competitor_mentioned": null,
"next_step": null,
"objections": [
"The security review took three months for our last vendor — that's my hesitation."
],
"confidence": "high"
},
{
"transcript_id": "TX-005",
"deal_alias": "Deal-F8767A",
"why-buys": [
"Two things: automate service milestones, and give us analytics on recognition equity across departments."
],
"pain_points": [
"Our night-shift teams feel invisible — their engagement scores run 20 points lower."
],
"stakeholders": [
"Prospect (HR Director)",
"Prospect (People Ops Coordinator)"
],
"budget_signal": "We have $12k approved under our engagement line.",
"timeline_signal": "We need this running before our January all-hands.",
"competitor_mentioned": "Nectar",
"next_step": "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": "high"
},
{
"transcript_id": "TX-006",
"deal_alias": "Deal-EE195F",
"why-buys": [
"I'd love to cut the admin time on service awards."
],
"pain_points": [
"I personally spend five hours a month ordering and shipping plaques."
],
"stakeholders": [
"Prospect (HR Manager)"
],
"budget_signal": "Budget isn't the issue — time is.",
"timeline_signal": "Our fiscal year resets in February, so a Q1 start is realistic.",
"competitor_mentioned": null,
"next_step": "Send the one-page overview; the prospect will forward it to the COO this week.",
"objections": [
"Our COO usually prefers building things in-house."
],
"confidence": "high"
}
]
1. Deal-547B2B — $11,200 Why close: DS5/COMMIT; redlines are clean, signing page is out, and the VP People said they would sign the next day. What is left: Signature only. 2. Deal-403845 — $9,000 Why close: DS5/COMMIT; Slack says the order form is with the buyer’s finance team. What is left: Finance review/approval and signature. Note: CRM lists Alex Franklin as owner, while Dana Mercer described it as on her side. 3. Deal-A2B47C — $6,360 Why close: DS5/COMMIT with a 2026-09-11 close date; Slack confirms it is still warm and progressing at a normal legal-review pace. What is left: Complete legal review and obtain signature. Combined amount: $11,200 + $9,000 + $6,360 = $26,560.
Candidate classifications 1. Deal-EC3025 — Amount: missing from provided data Prospect: “We need SCIM user provisioning — our security team will not approve any tool that requires manual account management.” Classification: Real gap Basis: Product docs state SCIM user provisioning is not currently listed as supported. 2. Deal-D0D6B5 — Amount: missing from provided data Prospect: “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 Basis: Product docs state ADP Workforce Now integration is not currently listed as supported. 3. Deal-CFE7F4 — Amount: missing from provided data Prospect: “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 Basis: The custom report builder is supported on Enterprise only. 4. Deal-84DBA6 — Amount: missing from provided data Prospect: “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 Basis: Slack integration is supported on all plans; the stated issue is lack of manager training. Real-gap summary - Deal-EC3025: SCIM user provisioning — amount missing. - Deal-D0D6B5: ADP Workforce Now integration — amount missing. Arithmetic: 4 prospect-raised candidates − 1 plan gate − 1 rollout/enablement issue = 2 real gaps. Excluded: The native-mobile-app statement for Deal-36C33F came from Alex Franklin, not the prospect, so it does not count as prospect voice.
Stale = most recent logged email/call/meeting is more than 7 days before 2026-09-05. Future meeting dates are excluded because they were not logged contacts as of the snapshot. Bryce Harmon - Deal-2D1F1B | DS1 | $240,000 | 81 days - Deal-66D1FC | DS1 | $99,000 | 16 days - Deal-950043 | DS1 | $70,000 | 19 days - Deal-B23205 | DS1 | $45,000 | 16 days - Deal-7BBDFA | DS3 | $37,440 | 46 days - Deal-332637 | DS2 | $36,000 | 9 days - Deal-1BEEBF | DS1 | $31,500 | 19 days - Deal-A414F6 | DS1 | $25,200 | 19 days - Deal-C5658B | DS1 | $23,400 | 16 days - Deal-40522D | DS3 | $21,000 | 19 days - Deal-C1FA6D | DS1 | $18,000 | 16 days - Deal-01E193 | DS1 | $12,600 | 8 days - Deal-F0EBBB | DS3 | $11,400 | 24 days - Deal-927338 | DS1 | $10,920 | 18 days - Deal-E25A09 | DS1 | $6,000 | 9 days - Deal-C9C286 | DS2 | $5,502 | 9 days - Deal-012CB1 | DS1 | $1 | 23 days - Deal-3795AD | DS2 | $1 | 8 days Owner total: 18 stale deals $240,000 + $99,000 + $70,000 + $45,000 + $37,440 + $36,000 + $31,500 + $25,200 + $23,400 + $21,000 + $18,000 + $12,600 + $11,400 + $10,920 + $6,000 + $5,502 + $1 + $1 = $692,964 Dana Mercer - Deal-44EA29 | DS2 | $60,000 | 10 days - Deal-E51FB7 | DS2 | $43,875 | 12 days - Deal-B42F46 | DS1 | $27,000 | 19 days - Deal-BA3DDC | DS3 | $23,400 | 15 days - Deal-9DDE86 | DS2 | $20,000 | 15 days - Deal-215CCA | DS3 | $18,900 | 17 days - Deal-5EED42 | DS3 | $16,250 | 11 days - Deal-57887A | DS2 | $15,000 | 8 days - Deal-944310 | DS4 | $10,500 | 33 days - Deal-B7EBD1 | DS5 | $9,000 | 16 days - Deal-3974EB | DS4 | $9,000 | 8 days - Deal-F40F04 | DS2 | $8,100 | 15 days - Deal-7599B8 | DS3 | $7,350 | 18 days - Deal-87DDD1 | DS1 | $5,000 | 19 days - Deal-F336B6 | DS3 | $4,200 | 15 days - Deal-0660B4 | DS4 | $1,920 | 16 days Owner total: 16 stale deals $60,000 + $43,875 + $27,000 + $23,400 + $20,000 + $18,900 + $16,250 + $15,000 + $10,500 + $9,000 + $9,000 + $8,100 + $7,350 + $5,000 + $4,200 + $1,920 = $279,495 Alex Franklin - Deal-E73427 | DS3 | $18,000 | 10 days - Deal-885F45 | DS2 | $9,300 | 12 days - Deal-C2FF3C | DS1 | $8,316 | 10 days - Deal-0D2F7A | DS3 | $5,100 | 12 days - Deal-6C60D4 | DS3 | $4,800 | 12 days - Deal-13FEBD | DS2 | $4,680 | 12 days - Deal-819506 | DS1 | $4,400 | 8 days - Deal-9D0060 | DS3 | $3,840 | 12 days - Deal-690476 | DS2 | $3,600 | 18 days - Deal-C6D97A | DS4 | $3,240 | 8 days - Deal-EE195F | DS3 | $3,120 | 8 days - Deal-278DEC | DS3 | $2,700 | 8 days - Deal-635B8E | DS3 | $2,600 | 18 days - Deal-6883F3 | DS1 | $2,400 | 16 days - Deal-4A13AD | DS3 | $2,160 | 26 days - Deal-F67D31 | DS2 | $1,800 | 8 days - Deal-5FDCE4 | DS3 | $1,600 | 12 days - Deal-BA571A | DS4 | $1,080 | 18 days Owner total: 18 stale deals $18,000 + $9,300 + $8,316 + $5,100 + $4,800 + $4,680 + $4,400 + $3,840 + $3,600 + $3,240 + $3,120 + $2,700 + $2,600 + $2,400 + $2,160 + $1,800 + $1,600 + $1,080 = $82,736 Cole Ingram - Deal-D04904 | DS2 | $58,529.25 | 11 days - Deal-B25F40 | DS3 | $40,000 | 8 days - Deal-813836 | DS2 | $32,175 | 11 days - Deal-1BA595 | DS2 | $31,750 | 11 days - Deal-CFE1E8 | DS3 | $18,000 | 11 days - Deal-CD47A6 | DS2 | $12,168 | 11 days - Deal-627646 | DS3 | $11,193 | 11 days - Deal-FF809F | DS2 | $7,781.20 | 11 days - Deal-AF932D | DS2 | $7,225.40 | 11 days - Deal-A71728 | DS2 | $6,947.50 | 11 days - Deal-8BC9F5 | DS2 | $5,616 | 10 days - Deal-175395 | DS3 | $4,779.88 | 11 days - Deal-481E24 | DS3 | $4,140 | 10 days - Deal-C7F9BF | DS2 | $3,360 | 11 days - Deal-2F3A66 | DS3 | $3,334.80 | 11 days - Deal-342E96 | DS2 | $2,700 | 24 days - Deal-E568D5 | DS3 | $1,875 | 11 days - Deal-FD9F4E | DS5 | $1,330 | 10 days Owner total: 18 stale deals $58,529.25 + $40,000 + $32,175 + $31,750 + $18,000 + $12,168 + $11,193 + $7,781.20 + $7,225.40 + $6,947.50 + $5,616 + $4,779.88 + $4,140 + $3,360 + $3,334.80 + $2,700 + $1,875 + $1,330 = $252,905.03 Farid Osman - Deal-8BA24E | DS1 | $2,880 | 8 days - Deal-8FDCD2 | DS1 | $1 | 15 days Owner total: 2 stale deals $2,880 + $1 = $2,881 Missing engagement data - Deal-3EED2C | Alex Franklin | DS2 | $7,200 - Deal-57FF13 | Elena Sinclair | DS1 | $2,100 These two deals have no row in `engagements_by_deal_90d.csv`, so days since last contact and stale status cannot be determined from the provided data.
Window: 2026-08-06 through 2026-09-05, inclusive. | Rank | Owner | Emails | Calls | Meetings | Activity mix | DS2 entries | Activities per DS2 | |---:|---|---:|---:|---:|---|---:|---:| | 1 | Alex Franklin | 307 | 36 | 41 | 79.9% / 9.4% / 10.7% | 18 | 384 ÷ 18 = **21.33** | | 2 | Bryce Harmon | 162 | 0 | 43 | 79.0% / 0.0% / 21.0% | 4 | 205 ÷ 4 = **51.25** | | 3 | Cole Ingram | 96 | 14 | 1 | 86.5% / 12.6% / 0.9% | 2 | 111 ÷ 2 = **55.50** | | 4 | Farid Osman | 38 | 0 | 34 | 52.8% / 0.0% / 47.2% | 1 | 72 ÷ 1 = **72.00** | | 5 | Dana Mercer | 84 | 18 | 11 | 74.3% / 15.9% / 9.7% | 1 | 113 ÷ 1 = **113.00** | | — | Elena Sinclair | Not computable | Not computable | Not computable | Not computable | 0 | Undefined | | — | Gavin Porter (archived) | 0 | 0 | 0 | Undefined | 0 | Undefined | | — | Hugo Lindqvist (archived) | 0 | 0 | 0 | Undefined | 0 | Undefined | Arithmetic examples: - Alex Franklin: 307 + 36 + 41 = 384; email share = 307 ÷ 384 = 79.9%. - Bryce Harmon: 162 + 0 + 43 = 205; meeting share = 43 ÷ 205 = 21.0%. - Cole Ingram: 96 + 14 + 1 = 111. - Farid Osman: 38 + 0 + 34 = 72. - Dana Mercer: 84 + 18 + 11 = 113. Most efficient: **Alex Franklin**, at 21.33 recorded activities per DS2 entry. Highest volume: **Alex Franklin**, with 384 recorded activities. They do not differ. Data limitation: `Deal-3EED2C` for Alex Franklin and `Deal-57FF13` for Elena Sinclair have no matching engagement records. Therefore, Alex Franklin’s activity total and efficiency ratio use only recorded engagement data and may be incomplete; Elena Sinclair’s activity totals and mix cannot be computed.
Alex Franklin — QTD Scorecard as of 2026-09-05 Bookings vs. quota - QTD bookings: $150,000 - Quota: $200,000 - Attainment: $150,000 ÷ $200,000 = 75.0% - Remaining gap: $200,000 − $150,000 = $50,000 - 8 closed-won deals included. - Deal-B3E6F1 ($24,000; closed 2026-06-20) excluded because it predates Q3. Bookings mix - New: 5 wins totaling $113,500 - $113,500 ÷ $150,000 = 75.7% of bookings - Expansion: 3 wins totaling $36,500 - $36,500 ÷ $150,000 = 24.3% of bookings - Check: $113,500 + $36,500 = $150,000 Active pipeline - DS1: 20 deals | $284,621 - DS2: 28 deals | $353,760 - DS3: 67 deals | $552,705 - DS4: 5 deals | $23,574 - DS5: 5 deals | $45,730 - Total: 125 deals | $1,260,390 Rolling 90-day DS2-to-won rate - Window: 2026-06-08 through 2026-09-05 - Deals entering DS2: 111 - Won: 8 - Rate: 8 ÷ 111 = 7.2% - Of this cohort, 27 were lost and 76 remained open as of the snapshot. QTD outcomes - Wins: 8 - Losses: 27 - Top loss reason: “Lost- Timing (1 year or more)” — 13 losses - Share of losses: 13 ÷ 27 = 48.1% Last-30-day activity - Emails: 807 - Calls: 112 - Meetings: 128 - Notes: 50 - Total activities: 807 + 112 + 128 + 50 = 1,097 Coaching observations 1. Alex is at 75.0% attainment with a $50,000 gap. DS4 and DS5 contain $69,304 combined ($23,574 + $45,730), equal to 138.6% of the remaining quota gap, so late-stage execution is the clearest path to quota. 2. The rolling DS2-to-won rate is 7.2%, while 76 of 111 DS2 entrants remain open. Prioritize qualification and progression within this cohort rather than relying on the large $1.26M headline pipeline. 3. Timing caused 48.1% of QTD losses. Tightening timing qualification and establishing compelling events earlier could address the largest identifiable loss pattern.
Cannot definitively identify “every open deal” because open/closed status, amount, stage, and the analysis date are not provided. Therefore, stage-specific persona recommendations cannot be determined. Using 2026-09-13 as the reference date, the 60-day cutoff is: 2026-09-13 − 60 days = 2026-07-15 Assuming every listed deal is open, 11 deals are flagged: | Deal | Classification | Amount | Stage | Active contacts | Personas present | Personas missing | Most valuable persona to add | On-file unengaged fit | |---|---|---:|---|---:|---|---|---|---| | Deal-EC3025 | Single-threaded | Not provided | Not provided | 1 | champion | economic buyer, HR admin, IT security, finance | Cannot determine without stage | CT-6827DB — Chief People Officer — economic buyer | | Deal-92D97D | Single-threaded | Not provided | Not provided | 1 | HR admin | economic buyer, champion, IT security, finance | Cannot determine without stage | None on file | | Deal-50D386 | Under-threaded | Not provided | Not provided | 2 | champion, HR admin | economic buyer, IT security, finance | Cannot determine without stage | CT-A1C4B3 — Chief People Officer — economic buyer | | Deal-D0D6B5 | Under-threaded: all 3 contacts share one persona | Not provided | Not provided | 3 | champion | economic buyer, HR admin, IT security, finance | Cannot determine without stage | CT-1FA4DB — Chief People Officer — economic buyer | | Deal-5BFE3B | Under-threaded: all contacts share one persona | Not provided | Not provided | 2 | champion | economic buyer, HR admin, IT security, finance | Cannot determine without stage | None on file | | Deal-36C33F | Single-threaded | Not provided | Not provided | 1 | IT security | economic buyer, champion, HR admin, finance | Cannot determine without stage | CT-1DB73E — Chief People Officer — economic buyer | | Deal-885F45 | Under-threaded | Not provided | Not provided | 2 | economic buyer, champion | HR admin, IT security, finance | Cannot determine without stage | CT-B3F25D — IT Security Lead — IT security | | Deal-FCBE5B | Single-threaded | Not provided | Not provided | 1 | champion | economic buyer, HR admin, IT security, finance | Cannot determine without stage | None on file | | Deal-5408B0 | Under-threaded | Not provided | Not provided | 2 | champion, HR admin | economic buyer, IT security, finance | Cannot determine without stage | CT-07FA76 — Chief People Officer — economic buyer | | Deal-C6D97A | Under-threaded: all 3 contacts share one persona | Not provided | Not provided | 3 | champion | economic buyer, HR admin, IT security, finance | Cannot determine without stage | None on file | | Deal-F9A08A | Single-threaded | Not provided | Not provided | 1 | champion | economic buyer, HR admin, IT security, finance | Cannot determine without stage | CT-697541 — Chief People Officer — economic buyer | Count arithmetic: - Single-threaded: 5 - Under-threaded: 6 - Total flagged: 5 + 6 = 11 Deal-F9A08A’s economic buyer engagement dated 2026-06-20 is older than the assumed cutoff, so that contact is not active. Former contacts were excluded regardless of engagement date.
What they lead with in the first five minutes - 8/10 calls: a quantified retailer success story focused on turnover reduction and automated milestone awards: Deal-D348E1, Deal-5408B0, Deal-547B2B, Deal-C61CF7, Deal-60C2C2, Deal-EDC141, Deal-D9A12F, and Deal-84DBA6. “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.” - 1/10: security and pricing agenda in Deal-403845. - 1/10: direct pricing in Deal-1E2498. - Deal-C61CF7 also included an unsolicited Workhuman comparison at minute 2. Three most common objections and handling 1. Budget locked — 4/10 calls: Deal-D348E1, Deal-547B2B, Deal-60C2C2, Deal-84DBA6. - Alex reframes the purchase around turnover savings and finance-approved avoided-backfill economics. - “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. Timing/bandwidth; revisit next quarter — 3/10 calls: Deal-5408B0, Deal-C61CF7, Deal-D9A12F. - Alex reduces scope to a 90-day, single-department pilot that can generate evidence before planning. - “Makes sense. What if we scope a 90-day pilot with one department so you have internal data before next quarter's planning?” 3. Existing spreadsheet and gift-card process — 3/10 calls: Deal-403845, Deal-EDC141, Deal-1E2498. - Alex contrasts the status quo with automated milestones and recognition analytics. - “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 rate - Agreed: 7 calls — Deal-D348E1, Deal-5408B0, Deal-547B2B, Deal-C61CF7, Deal-60C2C2, Deal-D9A12F, Deal-1E2498. - Not agreed: 3 calls — Deal-403845, Deal-EDC141, Deal-84DBA6. - Arithmetic: 7 agreed ÷ 10 calls × 100 = 70%. - “Yes, Thursday at 2pm works — send the invite and I'll bring our HRIS manager.” Competitors raised by prospects - Awardco — Deal-547B2B: “We're also in late talks with Awardco — their rewards catalog looks bigger than yours.” - Kudos — Deal-EDC141: “How are you different from Kudos? Our CEO used them at her last company.” Workhuman was raised by Alex, not a prospect. Coaching notes 1. Preserve the concrete next-step ask, but use it consistently: all seven calls containing that ask secured a meeting, while Deal-403845, Deal-EDC141, and Deal-84DBA6 ended without one. 2. Add discovery before repeating the retailer story or standard rebuttal; 8/10 openings and all responses to each common objection use nearly identical language, limiting personalization to the prospect’s specific business case.
# Q3 2026 forecast - COMMIT: $44,729 across 7 deals Arithmetic: $11,200 + $9,000 + $9,000 + $6,360 + $5,400 + $2,520 + $1,249 = $44,729 - BEST_CASE: $203,565 across 24 deals - PIPELINE: 23 deals; weighted contribution = $0 - Weighted forecast: $44,729 + (35% × $203,565) = $44,729 + $71,247.75 = **$115,976.75** ## Top 5 BEST_CASE deals 1. Deal-2D7423 — $38,935 2. Deal-25F752 — $24,000 3. Deal-E53952 — $19,656 4. Deal-5EED42 — $16,250 5. Deal-FA32A0 — $11,116 ## Excluded for being outside Q3 32 deals totaling **$227,575**, all closing between 2026-10-01 and 2026-10-15. Arithmetic: $43,875 + $18,000 + $17,000 + $13,770 + $10,800 + $9,000 + $9,000 + $7,920 + $7,690 + $7,500 + $7,200 + $5,700 + $5,400 + $5,400 + $5,400 + $5,160 + $4,800 + $4,400 + $4,300 + $4,000 + $3,600 + $3,600 + $3,600 + $3,300 + $2,400 + $1,800 + $1,800 + $1,680 + $1,600 + $1,400 + $1,080 + $5,400 = $227,575. Excluded deals: Deal-E51FB7, Deal-B936FE, Deal-D9A12F, Deal-D348E1, Deal-4062CF, Deal-293AF3, Deal-034D49, Deal-E0ADD8, Deal-9F2E43, Deal-FCBE5B, Deal-712010, Deal-6691E0, Deal-C61CF7, Deal-600CD9, Deal-A92065, Deal-1D532E, Deal-48B656, Deal-E531A6, Deal-D1E6C2, Deal-D9E112, Deal-5AD94B, Deal-901332, Deal-47AE31, Deal-15D24F, Deal-766C74, Deal-ED725A, Deal-8AD4A5, Deal-D7E999, Deal-ED13B0, Deal-5FDCE4, Deal-7FA0C3, and Deal-F5A622. ## Data quality Owner is missing for 85 of 86 deals, preventing reliable owner-level accountability. Most deals have `why_buys_chars` equal to zero, including many COMMIT and BEST_CASE deals, so forecast categories lack documented justification. Stage and forecast category are inconsistent in cases such as DS1 COMMIT Deal-A5E80A and DS5 BEST_CASE Deal-C61CF7. The 2026-09-05 extract also contains open deals with close dates before the extraction date, indicating potentially stale records.
| Activation cohort | Retained / cohort | 24-month retention | |---|---:|---:| | Both signals | 31 / 47 | 31 ÷ 47 = **66.0%** | | Givers-only | 23 / 49 | 23 ÷ 49 = **46.9%** | | Redemption-only | 9 / 29 | 9 ÷ 29 = **31.0%** | | Neither | 38 / 95 | 38 ÷ 95 = **40.0%** | **Excluded:** 0 companies. All 220 companies had the required `m1_users`, `m1_redemptions`, and `current_status` values. Only `active` was counted as retained; `cancelled` and `non_renewing` were counted as not retained. **Largest single-signal lift:** 5+ unique givers. Givers-only retention exceeded neither by **46.9% − 40.0% = 6.9 percentage points**. Redemption-only produced **31.0% − 40.0% = −9.0 points**. **Conclusion:** The extract supports the activation hypothesis descriptively: companies with both signals had the highest retention, exceeding givers-only by **19.0 points**, redemption-only by **34.9 points**, and neither by **26.0 points**. This proves an association within this cohort. It does **not** prove that these activation behaviors caused retention; no controls, randomization, significance testing, or adjustment for confounding factors were provided.
Reconciliation basis: active billing subscriptions only; cancelled subscriptions contribute $0 billing ARR. Variance = CRM ARR − billing ARR. Totals - CRM ARR: $603,581.76 - Billing ARR: $604,739.28 - Variance: $603,581.76 − $604,739.28 = −$1,157.52 Variance decomposition - Status mismatch: +$13,158.48 - C-0C8323BF: +$4,905.24 - C-0DC4FB8C: +$8,253.24 - Rounding: +$36.00 - C-0D66DF9E: +$16.00 - C-14D70CE0: +$20.00 - Missing records: −$11,952.00 - C-21629AA4 missing from CRM: −$28,449.24 - C-0D5BBE3A missing from billing: +$16,497.24 - Other: −$2,400.00 - C-0F7269D7: −$2,400.00 Check: $13,158.48 + $36.00 − $11,952.00 − $2,400.00 = −$1,157.52 Mismatched accounts | Company alias | CRM ARR | Billing ARR | Variance | Bucket | Suggested owner | |---|---:|---:|---:|---|---| | C-0C8323BF | $4,905.24 | $0.00 | +$4,905.24 | Status mismatch: billing cancelled | Revenue Operations | | C-0DC4FB8C | $8,253.24 | $0.00 | +$8,253.24 | Status mismatch: billing cancelled | Revenue Operations | | C-0D66DF9E | $23,200.00 | $23,184.00 | +$16.00 | Rounding | Revenue Operations | | C-14D70CE0 | $18,200.00 | $18,180.00 | +$20.00 | Rounding | Revenue Operations | | C-21629AA4 | Missing | $28,449.24 | −$28,449.24 | Missing CRM record | Revenue Operations | | C-0D5BBE3A | $16,497.24 | Missing | +$16,497.24 | Missing billing record | Billing Operations | | C-0F7269D7 | $24,396.00 | $26,796.00 | −$2,400.00 | Other ARR discrepancy | Revenue Operations | No named account owners were provided; suggested owners are functional teams. Agreement-end-date violations - SUB-0002 / C-1794A52C: 24-month term; `cf_agreement_end_date` missing. - SUB-0019 / C-22170CA1: 36-month term; `cf_agreement_end_date` missing.
| KVM | 2026-08 value | 2026-07 prior | Absolute change | Relative change | Direction | |---|---:|---:|---:|---:|---| | Giving rate | 60.2713% | 60.2297% | +0.0417 pp | +0.0692% | Up | | Redemptions per user | 1.730163 | 1.729983 | +0.000180 | +0.0104% | Up | | 1:1 meetings engagement | 44.7177% | 44.6887% | +0.0290 pp | +0.0649% | Up | | Pulse check engagement | 50.8610% | 60.0587% | −9.1977 pp | −15.3145% | Down | Arithmetic: each monthly value is the unweighted mean across 30 companies. Absolute change = August − July. Relative change = absolute change ÷ July × 100. Pulse check engagement had the largest relative move: −15.3145%. The **enterprise** `size_band` drove it, falling from 54.9980% to 27.4280%: −27.5700 pp, or `(−27.5700 ÷ 54.9980) × 100 = −50.1291%`. SMB declined 0.2247%; mid_market increased 0.2108%. All records are `tier_three`, so plan-tier comparison is unsupported.
Redemption — YTD through 2026-08 Last completed month: August 2026 (2026-08) - Redemption count: 1,188 - Spend: $90,463.00 - Unique redeemers: 335 - Redemptions per redeemer: 1,188 ÷ 335 = 3.55 Provider mix by spend: - custom: $36,163 ÷ $90,463 = 39.97% - Tremendous: $26,270 ÷ $90,463 = 29.04% - Snappy: $16,055 ÷ $90,463 = 17.75% - TangoCard: $11,975 ÷ $90,463 = 13.24% - Total: 100.00% (custom adjusted by 0.01 percentage point for rounding) Top 5 countries by redemptions: 1. US: 754 2. CA: 85 3. AU: 58 4. NL: 53 5. GB: 51
Eligibility requires all three rules: health score <60, eligible amount >$0, and renewal within 120 days of 2026-09-05. Qualified accounts | Account | Amount at stake | Best-fit play | Justifying signal | |---|---:|---|---| | C-0F6C0F34 | $49,707.00 | Executive touch | Champion inactive; usage is growing and utilization is 308/395 = 78.0%, so usage revival is not indicated. | | C-0B827671 | $25,365.00 | Usage revival | Usage declining; utilization is 113/202 = 55.9%. | | C-0B360C78 | $35,748.00 | Commercial concession | Health score is 57 despite growing usage, an active champion, and 246/327 = 75.2% utilization. No usage or champion issue is evident. | | C-0B0F1BAB | $5,494.00 | Executive touch | Champion inactive; renewal is only 18 days from the snapshot. | | C-0CA21961 | $16,829.00 | Usage revival | Only 84/325 seats used = 25.8%; usage is flat. | | C-0E9C27D1 | $41,235.00 | Commercial concession | Health score is 39 despite an active champion and 134/157 = 85.4% utilization; renewal is only 19 days away. | | C-0CEF69FD | $32,621.00 | Executive touch | Champion inactive; usage is growing, so re-establishing executive sponsorship fits better than usage revival. | | C-0D3278C7 | $17,602.00 | Usage revival | Usage declining; only 126/380 seats used = 33.2%. | Total amount at stake $49,707 + $25,365 + $35,748 + $5,494 + $16,829 + $41,235 + $32,621 + $17,602 = $224,601.00 The eligibility file does not document play-assignment rules. The plays above are best-fit classifications based only on usage trend, seat utilization, champion activity, health, and renewal timing. “Commercial concession” is used where neither usage revival nor executive touch has a supporting signal; no explicit pricing or budget signal was provided. At risk but not qualified | Account | Why it does not qualify | |---|---| | C-0BC71BDD | Health score 55 and renewal within 52 days, but eligible amount is $0. | | C-0BA71F12 | Health score 52 and eligible amount $6,824, but renewal is 218 days away—outside the 120-day window. | | C-0F6694C3 | Health score 43, but eligible amount is $0 and renewal is 197 days away. | | C-0BE96399 | Health score 54 and renewal within 54 days, but eligible amount is $0. | | C-0F876796 | Health score 47 and eligible amount $19,958, but renewal is 154 days away. | | C-0FCCD2DF | Health score 43, but eligible amount is $0 and renewal is 230 days away. | | C-10A56B0F | Health score 54 and renewal within 98 days, but eligible amount is $0. |
Account: C-0DDFC9A7 Seat coverage - 150 licensed seats ÷ 400 employees = 37.5% coverage. Usage health - Monthly active users increased every month, from 88 in March to 126 in August: 126 − 88 = 38 users, or 38 ÷ 88 = 43.2% growth. - August utilization was 126 ÷ 150 = 84.0% of licensed seats. Expansion headroom - Seats: 400 − 150 = 250 seats. - Current rate: $9,000 ARR ÷ 150 seats = $60 per seat annually. - Incremental ARR headroom: 250 × $60 = $15,000 ARR. Reply and buying authority - Maria S., People Operations Coordinator, replied. - She explicitly said she cannot make purchasing decisions. Right buyer - Dana R., VP People. Maria identified Dana as responsible for budget and seat expansion. Reply email Subject: Re: Growing your team’s recognition program Hi Maria, Thanks for the context—and great to hear the team loves Bonusly. Monthly active users have grown from 88 in March to 126 in August, so I can see why Dana has been asking about the usage numbers. An introduction would be very helpful. I’d be glad to share a concise view of current adoption and the options for expanding coverage, without creating extra work for you. If it’s easiest, feel free to connect Dana and me on this thread, and I’ll take it from there. Thanks again, Cole
C-0D284E42 — Mid-onboarding call prep Complete - Slack integration connected: 2026-08-12 - Allowance set: 2026-08-13 - Admins added: 2 - First recognition given: 2026-08-15 at 14:22 Not complete - HRIS integration: no completion date provided - First redemption: no completion date provided Early engagement signals - Active givers increased from 3 on 2026-08-11 to 15 on 2026-09-04. - Increase: 15 − 3 = 12 active givers. - Percentage increase: 12 ÷ 3 × 100 = 400%. - First seven-day average: (3 + 3 + 4 + 4 + 5 + 4 + 7) ÷ 7 = 30 ÷ 7 = 4.3 active givers. - Latest seven-day average: (11 + 13 + 13 + 15 + 15 + 15 + 15) ÷ 7 = 91 ÷ 7 = 13.0 active givers. - Average increase: 13.0 − 4.3 = 8.7 active givers. Three things to cover 1. Identify what is blocking the HRIS integration and agree on an owner and next step. 2. Review the rise in active givers and determine what is driving adoption so it can be reinforced. 3. Address the absence of a first redemption and agree on how to encourage one.
90-Day Renewal Risk Brief
Window: 2026-09-13 through 2026-12-12. Company names were not provided, so account aliases are used exactly as given.
Method:
- Seat utilization = seats_used ÷ seats.
- 3-month trend = June → July → August active users; percentage change = (August − June) ÷ June.
- High risk: utilization below 50%.
- Medium risk: utilization of 50%–69.9% or declining usage.
- Low risk: utilization at least 70% with flat/growing usage.
- Chargebee is trusted for multi-year contracts because ChurnZero multi-year dates are known to be wrong. Where dates agree, the shared date is used.
1. C-0B7D2C30 — Dana Mercer — $65,901 ARR
Date used: 2026-09-15 (Chargebee).
DISAGREEMENT: ChurnZero 2026-09-10 vs Chargebee 2026-09-15; Chargebee is trusted because this is a 36-month multi-year contract.
Seat utilization: 274 ÷ 476 = 57.6%.
Usage trend: 97 → 94 → 84; change = -13 ÷ 97 = -13.4%.
Risk: Medium — utilization is below 70% and active usage declined 13.4%.
2. C-0BCDB8C2 — Cole Ingram — $54,427 ARR
Date used: 2026-09-18 (Chargebee).
DISAGREEMENT: ChurnZero 2027-09-18 vs Chargebee 2026-09-18; Chargebee is trusted because this is a 36-month multi-year contract.
Seat utilization: 232 ÷ 424 = 54.7%.
Usage trend: 127 → 118 → 110; change = -17 ÷ 127 = -13.4%.
Risk: Medium — utilization is below 70% and active usage declined 13.4%.
3. C-0D2AB865 — Elena Sinclair — $38,022 ARR
Date used: 2026-09-22 (Chargebee).
DISAGREEMENT: ChurnZero 2026-09-10 vs Chargebee 2026-09-22; Chargebee is trusted because this is a 24-month multi-year contract.
Seat utilization: 250 ÷ 407 = 61.4%.
Usage trend: 125 → 117 → 109; change = -16 ÷ 125 = -12.8%.
Risk: Medium — utilization is below 70% and active usage declined 12.8%.
4. C-0BBE3E60 — Dana Mercer — $30,993 ARR
Date used: 2026-09-26 (Chargebee).
DISAGREEMENT: ChurnZero 2027-09-26 vs Chargebee 2026-09-26; Chargebee is trusted because this is a 24-month multi-year contract.
Seat utilization: 74 ÷ 114 = 64.9%.
Usage trend: 39 → 35 → 33; change = -6 ÷ 39 = -15.4%.
Risk: Medium — utilization is below 70% and active usage declined 15.4%.
5. C-0F5D2323 — Cole Ingram — $90,647 ARR
Date used: 2026-09-29 (Chargebee).
DISAGREEMENT: ChurnZero 2026-09-10 vs Chargebee 2026-09-29; Chargebee is trusted because this is a 24-month multi-year contract.
Seat utilization: 111 ÷ 390 = 28.5%.
Usage trend: 20 → 21 → 18; change = -2 ÷ 20 = -10.0%.
Risk: High — only 28.5% of seats are used, with active usage also declining 10.0%.
6. C-0EC6999D — Elena Sinclair — $79,419 ARR
Date used: 2026-10-03 (both systems agree).
Seat utilization: 31 ÷ 112 = 27.7%.
Usage trend: 17 → 16 → 15; change = -2 ÷ 17 = -11.8%.
Risk: High — only 27.7% of seats are used, with active usage declining 11.8%.
7. C-0B20DB64 — Dana Mercer — $21,770 ARR
Date used: 2026-10-07 (both systems agree).
Seat utilization: 214 ÷ 378 = 56.6%.
Usage trend: 294 → 298 → 294; change = 0 ÷ 294 = 0.0%.
Risk: Medium — usage is flat, but seat utilization is only 56.6%.
8. C-0BBC4E7A — Cole Ingram — $56,374 ARR
Date used: 2026-10-10 (both systems agree).
Seat utilization: 228 ÷ 337 = 67.7%.
Usage trend: 142 → 141 → 139; change = -3 ÷ 142 = -2.1%.
Risk: Medium — utilization is below 70% and active usage declined 2.1%.
9. C-0FD551AB — Elena Sinclair — $48,815 ARR
Date used: 2026-10-14 (both systems agree).
Seat utilization: 210 ÷ 376 = 55.9%.
Usage trend: 123 → 122 → 126; change = 3 ÷ 123 = +2.4%.
Risk: Medium — active usage grew 2.4%, but seat utilization remains only 55.9%.
10. C-0F9F8F13 — Dana Mercer — $46,230 ARR
Date used: 2026-10-18 (both systems agree).
Seat utilization: 199 ÷ 352 = 56.5%.
Usage trend: 185 → 185 → 182; change = -3 ÷ 185 = -1.6%.
Risk: Medium — utilization is 56.5% and active usage declined 1.6%.
11. C-0BC34584 — Cole Ingram — $16,740 ARR
Date used: 2026-10-22 (both systems agree).
Seat utilization: 327 ÷ 494 = 66.2%.
Usage trend: 104 → 104 → 106; change = 2 ÷ 104 = +1.9%.
Risk: Medium — active usage grew 1.9%, but seat utilization remains below 70%.
12. C-0B7A7546 — Elena Sinclair — $35,062 ARR
Date used: 2026-10-25 (both systems agree).
Seat utilization: 182 ÷ 205 = 88.8%.
Usage trend: 64 → 65 → 63; change = -1 ÷ 64 = -1.6%.
Risk: Medium — seat utilization is strong, but active usage declined 1.6%.
13. C-0B369871 — Dana Mercer — $85,128 ARR
Date used: 2026-10-29 (both systems agree).
Seat utilization: 317 ÷ 422 = 75.1%.
Usage trend: 326 → 330 → 333; change = 7 ÷ 326 = +2.1%.
Risk: Low — utilization is 75.1% and active usage grew 2.1%.
14. C-0B144C78 — Cole Ingram — $30,899 ARR
Date used: 2026-11-02 (both systems agree).
Seat utilization: 169 ÷ 224 = 75.4%.
Usage trend: 101 → 101 → 106; change = 5 ÷ 101 = +5.0%.
Risk: Low — utilization is 75.4% and active usage grew 5.0%.
15. C-0FC4DBB8 — Elena Sinclair — $94,732 ARR
Date used: 2026-11-05 (both systems agree).
Seat utilization: 356 ÷ 464 = 76.7%.
Usage trend: 189 → 191 → 193; change = 4 ÷ 189 = +2.1%.
Risk: Low — utilization is 76.7% and active usage grew 2.1%.
16. C-0D5BBE3A — Dana Mercer — $39,740 ARR
Date used: 2026-11-09 (both systems agree).
Seat utilization: 85 ÷ 102 = 83.3%.
Usage trend: 88 → 90 → 91; change = 3 ÷ 88 = +3.4%.
Risk: Low — utilization is 83.3% and active usage grew 3.4%.
17. C-0FB9D5AF — Cole Ingram — $63,158 ARR
Date used: 2026-11-13 (both systems agree).
Seat utilization: 144 ÷ 199 = 72.4%.
Usage trend: 173 → 173 → 176; change = 3 ÷ 173 = +1.7%.
Risk: Low — utilization is 72.4% and active usage grew 1.7%.
18. C-0B344485 — Elena Sinclair — $64,384 ARR
Date used: 2026-11-16 (both systems agree).
Seat utilization: 224 ÷ 287 = 78.0%.
Usage trend: 238 → 240 → 244; change = 6 ÷ 238 = +2.5%.
Risk: Low — utilization is 78.0% and active usage grew 2.5%.
19. C-0CB2C1B4 — Dana Mercer — $40,628 ARR
Date used: 2026-11-20 (both systems agree).
Seat utilization: 386 ÷ 473 = 81.6%.
Usage trend: 47 → 48 → 49; change = 2 ÷ 47 = +4.3%.
Risk: Low — utilization is 81.6% and active usage grew 4.3%.
20. C-22170CA1 — Cole Ingram — $45,646 ARR
Date used: 2026-11-24 (both systems agree).
Seat utilization: 251 ÷ 294 = 85.4%.
Usage trend: 143 → 148 → 146; change = 3 ÷ 143 = +2.1%.
Risk: Low — utilization is 85.4% and active usage increased 2.1% overall.
Totals:
- September ARR: $65,901 + $54,427 + $38,022 + $30,993 + $90,647 = $279,990.
- October ARR: $79,419 + $21,770 + $56,374 + $48,815 + $46,230 + $16,740 + $35,062 + $85,128 = $389,538.
- November ARR: $30,899 + $94,732 + $39,740 + $63,158 + $64,384 + $40,628 + $45,646 = $379,187.
- Total ARR renewing: $279,990 + $389,538 + $379,187 = $1,048,715.
- High-risk ARR: $90,647 + $79,419 = $170,066.
- Medium-risk ARR: $414,334.
- ARR at risk: $170,066 + $414,334 = $584,400.
Total: 80 tickets. Share = theme tickets ÷ 80. ARR affected counts each distinct account once per theme. Broad patterns — ranked by ARR exposure 1. HRIS provisioning/sync failures - Count: 12 - Share: 12 ÷ 80 = 15.0% - Distinct accounts: 3 - ARR affected: $36,000 C-0B2213A9 + $30,000 C-0F6C0F34 + $48,000 C-0DDFC9A7 = $114,000 - Example tickets: IC-460059, IC-460060 - Recommendation: Prioritize HRIS sync reliability and alert on skipped provisioning or silent log failures. 2. Redemption and gift-card fulfillment failures - Count: 18 - Share: 18 ÷ 80 = 22.5% - Distinct accounts: 7 - ARR affected: $8,900 C-0CEF69FD + $10,700 C-0B827671 + $9,600 C-0FCCD2DF + $8,700 C-0F876796 + $11,000 C-14264ABD + $9,600 C-0D9CA315 + $10,300 C-0B0F1BAB = $68,800 - Example tickets: IC-460025, IC-460024 - Recommendation: Make redemption processing idempotent and automatically reconcile deducted points when fulfillment fails. 3. Points posting/balance failures - Count: 20 - Share: 20 ÷ 80 = 25.0% - Distinct accounts: 9 - ARR affected: $3,500 C-0D3278C7 + $4,500 C-0BF20542 + $4,500 C-0D0B047C + $2,700 C-0BE96399 + $3,400 C-0D284E42 + $4,200 C-0D6CC8E3 + $2,900 C-21FEBCBB + $2,500 C-0DD0626C + $2,900 C-0B2895EF = $31,100 - Example tickets: IC-460004, IC-460016 - Recommendation: Investigate recognition-to-ledger processing and add monitoring for delivered recognitions with unposted points. 4. Slack integration failures - Count: 14 - Share: 14 ÷ 80 = 17.5% - Distinct accounts: 4 - ARR affected: $4,400 C-0B843542 + $5,400 C-10A56B0F + $3,900 C-0BA71F12 + $5,200 C-8C2E8F00 = $18,900 - Example tickets: IC-460041, IC-460047 - Recommendation: Fix authentication persistence, sync-toggle resets, and slash-command failures across the Slack integration. Single-account concentration 5. Billing seat-count and tier-pricing errors - Count: 16 - Share: 16 ÷ 80 = 20.0% - Distinct accounts: 1 - ARR affected: $52,000 C-0E9C27D1 = $52,000 - Example tickets: IC-460071, IC-460078 - Recommendation: Audit C-0E9C27D1’s billing configuration and renewal history; do not generalize this as a portfolio-wide pattern without more accounts.
1. C-64171065 — 3/4 fields match - Industry: Technology ✓ - Size band: Mid-Market ✓ - Use case: employee_recognition ✓ - Region: NA-East ≠ NA-West - Public case study: true 2. C-A13C193D — 3/4 fields match - Industry: Technology ✓ - Size band: Mid-Market ✓ - Use case: retention ≠ employee_recognition - Region: NA-West ✓ - Public case study: true 3. C-11C31562 — 3/4 fields match - Industry: Manufacturing ≠ Technology - Size band: Mid-Market ✓ - Use case: employee_recognition ✓ - Region: NA-West ✓ - Public case study: true Arithmetic: each customer matches 3 of 4 similarity fields, so all three tie at 3 ÷ 4 = 75%. The data provides no weighting rule to break the tie.
Trailing 6 months: 2026-03 through 2026-08 Paid performance | Channel | Spend | SQMs | SQOs | Cost/SQM | Cost/SQO | SQM→SQO | Pipeline | Pipeline/$ | |---|---:|---:|---:|---:|---:|---:|---:|---:| | paid_search | $36,000 | 40 | 18 | $900 | $2,000 | 45.0% | $720,000 | $20.00 | | linkedin_ads | $24,000 | 25 | 8 | $960 | $3,000 | 32.0% | $96,000 | $4.00 | | paid_social | $18,000 | 0 | 0 | Undefined | Undefined | Undefined | $0 | $0.00 | | webinars | $9,000 | 12 | 5 | $750 | $1,800 | 41.7% | $60,000 | $6.67 | | Total | $87,000 | 77 | 31 | $1,129.87 | $2,806.45 | 40.3% | $876,000 | $10.07 | Arithmetic: - Spend = six monthly amounts: paid_search $6,000×6=$36,000; linkedin_ads $4,000×6=$24,000; paid_social $3,000×6=$18,000; webinars $1,500×6=$9,000. - Cost/SQM = spend ÷ SQMs. - Cost/SQO = spend ÷ SQOs. - SQM→SQO rate = SQOs ÷ SQMs. - Pipeline/$ = pipeline ÷ spend. - paid_social’s acquisition costs and conversion rate are undefined because it spent $18,000 but generated zero SQMs. Organic performance | Channel | Volume | SQOs | SQO rate | Pipeline | |---|---:|---:|---:|---:| | organic_search | 30 | 10 | 33.3% | $90,000 | | referral | 15 | 6 | 40.0% | $48,000 | | Total organic | 45 | 16 | 35.6% | $138,000 | Arithmetic: - organic_search: 10÷30=33.3%; 10×$9,000=$90,000. - referral: 6÷15=40.0%; 6×$8,000=$48,000. - Total: 16÷45=35.6%; $90,000+$48,000=$138,000. - No organic spend data was provided, so organic acquisition costs and pipeline per dollar cannot be calculated. Date-quality flags - CT-000044: SQO date 2026-07-18 precedes SQM date 2026-07-23. - CT-000041: SQO date 2026-06-09 precedes SQM date 2026-06-14. Both are included as SQOs as recorded; their dates require correction. Recommendation Reallocate paid_social’s $18,000 away from that channel because it produced zero SQMs. Prioritize paid_search, which generated the highest pipeline per dollar ($20.00) with the largest paid sample (40 SQMs, 18 SQOs). Test a smaller increase in webinars, which had the best unit costs but only 12 SQMs and 5 SQOs. Consider reducing linkedin_ads after paid_social because its $4.00 pipeline per dollar was the weakest among paid channels producing SQMs. Confidence: Moderate. paid_search has the strongest sample, but webinars and linkedin_ads have only 12 and 25 SQMs, respectively, and there is no evidence showing whether performance will remain constant as spend increases.
# Battlecard: Rivally ## One-line positioning Points-based employee recognition with an engaging social feed, quick setup, and strengths for distributed EU teams; analytics and administration are comparatively limited. [S02, S04, S12, S16] ## Pricing Current public price: Recognition Starter is $7/user/month with annual billing, effective August 12, 2026. [S17] Source conflict: - $5/user/month with annual billing on January 20, 2026. [S03] - Still $5/user/month on April 1, 2026. [S08] - A 500-seat prospect reported a $6.50/user/month annual quote on June 2, 2026. [S13] - A prospect reported a $7/user/month list price and a 15% discount for a three-year term on August 14, 2026. [S18] Use the newer public source: $7/user/month annually as of 2026-08-12. [S17] ## Where they win - Engaging points-based recognition feed. [S02, S16] - Fast implementation; one reviewer reported setup in under a week. [S04] - Slack integration reportedly works out of the box. [S04] - Strong fit for distributed EU teams, with praised multi-language support. [S12] - EU data residency became generally available in July 2026. [S15] - Support response time was praised as under four hours. [S22] ## Where we win - Analytics depth: reviewers describe Rivally’s analytics and reporting dashboards as limited or basic, and an 800-seat prospect selected Bonusly citing analytics depth. [S02, S07, S25] - Enterprise provisioning: an enterprise reviewer reported that Rivally lacks SCIM provisioning and requires painful manual user management. [S10] - Administration: reviewers report lagging admin tooling and no bulk recognition editing. [S16, S24] - Data portability: one reviewer reported difficult migration because analytics exports are CSV-only. [S20] - EMEA rewards breadth: a reviewer described Rivally’s EMEA catalog as thinner than its US catalog. [S14] ## Objections and responses **“Rivally has a more engaging recognition experience.”** Response: Reviewers do praise its recognition feed, but other reviews identify limited analytics, basic reporting, and lagging administration. [S02, S07, S16] **“Rivally is better for European teams.”** Response: Rivally has EU data residency and praised multi-language support, but its EMEA rewards catalog has been described as thinner than its US catalog. [S12, S14, S15] **“Rivally is faster to deploy.”** Response: One reviewer reported setup in under a week and an out-of-the-box Slack integration; evaluate whether that initial speed offsets manual user management where SCIM is required. [S04, S10] **“Rivally is cheaper.”** Response: Its current public annual price is $7/user/month, up from $5/user/month earlier in 2026; one prospect reported a 15% discount tied to a three-year term. [S03, S08, S17, S18] **“Rivally includes engagement surveys.”** Response: Rivally Pulse is available, but it is priced as an add-on rather than bundled. [S23] ## Recent changes - Raised a $40M Series C led by Northgate Ventures. [S01] - Launched the Rivally Pulse engagement-survey add-on in March 2026. [S06] - Hired an ex-Workday VP EMEA to lead European expansion. [S11] - Opened a Dublin office and made EU data residency generally available. [S15] - Increased Recognition Starter’s public price from $5 to $7/user/month. [S08, S17] - Put Microsoft Teams app v2 into public preview. [S19] - Moved Rivally Pulse out of beta as a separately priced add-on. [S23] ## Our 12-month win/loss record against Rivally Period: September 2025 through August 2026. - Wins: 13 - Losses: 7 - Total: 13 + 7 = 20 - Win rate: 13 ÷ 20 × 100 = **65%** - Loss rate: 7 ÷ 20 × 100 = **35%** Wins: Deal-A9FD43, Deal-7AA785, Deal-44C524, Deal-0D0CD6, Deal-D5B790, Deal-5C636E, Deal-67BE14, Deal-1B6969, Deal-F03E7B, Deal-072E31, Deal-F65C8F, Deal-E46EAB, Deal-1D2392. Losses: Deal-7767F5, Deal-5645A5, Deal-C6FFAA, Deal-D263E0, Deal-935746, Deal-9066A6, Deal-72A02F. The deal file contains no `snippet_id` field, so these win/loss claims cannot be assigned snippet citations. ## Existing-card verification - “Points-based recognition”: re-sourced. [S02] - “For mid-market”: **unverified**; no supplied source establishes this as Rivally’s market positioning. - “Starts at $5/user/month”: outdated; superseded by the $7/user/month public price. [S17] - “Rivally lacks a Slack integration”: contradicted by a review reporting that its Slack integration worked out of the box. [S04] - “Rivally was acquired by WorkHuman in 2025”: **unverified**; no supplied source supports this claim. - “Strong in EU enterprise with multi-language support”: re-sourced from an EU enterprise review. [S12]
Rates use summed step activity: total outcomes ÷ total sent. - New Logo Nurture: sent 1,386 (500+458+428); open rate 35.35% (490÷1,386); reply rate 6.49% (90÷1,386); meeting rate 1.95% (27÷1,386). Weakest: step 3, 4.21% replies (18÷428). - Expansion Nurture: sent 875; open rate 64.57% (565÷875), but invalid due to tracking error; reply rate 6.74% (59÷875); meeting rate 1.37% (12÷875). Weakest: step 3, 4.36% replies (12÷275). - Cold Outbound - HR Leaders: sent 1,785; open rate 30.53% (545÷1,785); reply rate 0.45% (8÷1,785); meeting rate 0.00% (0÷1,785). Weakest: step 3, 0.17% replies (1÷590). - Cold Outbound - People Ops: sent 1,163; open rate 29.23% (340÷1,163); reply rate 2.49% (29÷1,163); meeting rate 0.52% (6÷1,163). Weakest: step 3, 1.59% replies (6÷377). Tracking error: Expansion Nurture step 2 reports 340 opened from 300 sent (113.33%). Audit event deduplication before trusting its open rate. Audience overlap: - Expansion Nurture / New Logo Nurture: CT-000301, CT-000624. - Cold Outbound - HR Leaders / Cold Outbound - People Ops: 21 contacts—CT-000849, CT-000884, CT-000890, CT-000908, CT-001033, CT-001097, CT-001101, CT-001103, CT-001105, CT-001130, CT-001153, CT-001159, CT-001217, CT-001227, CT-001236, CT-001255, CT-001258, CT-001277, CT-001285, CT-001311, CT-001345. Under-2% failure modes and changes: - Cold Outbound - HR Leaders: all steps are under 2%; opens without replies indicate message/audience relevance failure. Change: rewrite step 1 around an HR-leader-specific pain and CTA. - Cold Outbound - People Ops step 3: declining engagement indicates sequence fatigue. Change: replace step 3 with a concise breakup email. Fix first: Cold Outbound - HR Leaders because it has the lowest overall reply rate and zero meetings.
Q3-2026: 66 of 92 days elapsed = 71.7%. | Metric | QTD actual | Target | Delta (actual − target) | Pace | |---|---:|---:|---:|---| | SQMs | 230 | 300 | -70 | Ahead | | SQOs | 84 | 120 | -36 | Behind | | DS2s | 40 | 75 | -35 | Behind | | Closed-lost MIA rate | 20.0% | ≤10.0% | +10.0 pp unfavorable | Behind | | Same-quarter close count | 10 | 20 | -10 | Behind | | Active pipeline coverage | $3,000,000 (75.0%) | $4,000,000 (100%) | -$1,000,000 / -25.0 pp | Ahead | Arithmetic: - Expected progress at this point: 66 ÷ 92 = 71.7%. - SQMs: 230 ÷ 300 = 76.7%; 76.7% > 71.7% → ahead. - SQOs: 84 ÷ 120 = 70.0%; 70.0% < 71.7% → behind. - DS2s: 40 ÷ 75 = 53.3%; 53.3% < 71.7% → behind. - Closed-lost MIA rate: 5 ÷ 25 = 20.0%; 20.0% > 10.0% ceiling → behind. - Same-quarter closes: 10 ÷ 20 = 50.0%; 50.0% < 71.7% → behind. - Pipeline coverage: $3,000,000 ÷ $4,000,000 = 75.0%; 75.0% > 71.7% → ahead. What moved this week cannot be determined because no prior-week data was provided. Current QTD results show SQMs and active pipeline ahead of elapsed-quarter pace, while SQOs, DS2s, same-quarter closes, and the closed-lost MIA rate are behind.
Plan Q3 to $115,976.75: $44,729 COMMIT + 35% × $203,565 BEST_CASE ($71,247.75), with PIPELINE at $0. This includes 54 of 86 deals: 7 COMMIT, 24 BEST_CASE, and 23 PIPELINE; 32 deals worth $227,575 close after Q3, including Deal-D348E1 at $13,770. Caveat: why-buys are blank on all 7 in-quarter COMMIT deals, so the $44,729 committed forecast lacks documented purchase rationale.
Subject: Following up on 150-seat pricing Hi there, I’m following up on the August 5 recap and pricing for 150 seats after our July 28 demo. Is this still something your team plans to evaluate? Best, Alex Franklin
Marketing: SQMs reached 46 against a target of 52, a gap of 6 (52 − 46) and 88.5% attainment (46 ÷ 52 × 100). Webinars contributed 18 SQMs, or 39.1% of the total (18 ÷ 46 × 100). Paid-search cost per SQM held at $150. Sales: The team converted 14 SQOs, set 9 DS2 meetings, and created $310,000 in new pipeline. Same-quarter closes totaled 3 for the week. CS: The team saved 2 renewals this week, while team NPS moved to 61. Looking ahead, 3 open red-flag accounts require attention next week. PLG: PLG welcomed 412 new signups, with activation at 31%. In addition, 38 companies reached the aha moment of 10 recognition gives.
Partner Digest: 2026-08-24 to 2026-09-04 - Apex Rewards Co — Co-webinar scheduled for 09-15. 2 partner-sourced DS1 deals: Deal-DDAAF2 ($180,000) + Deal-2CF33E ($95,000) = $275,000. - HRCloud Partners — Security review completed; referral moved to DS2. 1 partner-sourced deal: Deal-F1CDA5 = $140,000. - CultureBridge — Lunch-and-learn generated 2 early-stage partner-sourced deals: Deal-096E1D ($60,000) + Deal-067213 ($75,000) = $135,000. - WorkWell Group — Joint-playbook restart planned for Q4; planning call scheduled for 09-09. 0 partner-sourced deals = $0. - Recogniq — Quiet; 0 partner-sourced deals = $0. - KudosWave — Quiet; 0 partner-sourced deals = $0. - PeopleFirst Advisors — Quiet; 0 partner-sourced deals = $0. - TotalPerk — Quiet; 0 partner-sourced deals = $0. Total partner-sourced pipeline: 2 + 1 + 2 = 5 deals; $275,000 + $140,000 + $135,000 = $550,000.
Q1: Yes. Customer data is encrypted at rest using AES-256. [P1] Q2: Yes. Data in transit is encrypted using TLS 1.2 or higher. [P1] Q3: Yes. Bonusly maintains a SOC 2 Type II report, renewed annually. Prospects can obtain the report under NDA. [P2] Q4: Customer data is retained for 90 days after contract termination, then deleted from production systems. [P3] Q5: The current subprocessor list is published at trust.bonusly.com. Customers receive 30 days’ notice before a new subprocessor is added. [P4] Q6: Yes. SAML 2.0 single sign-on is available on the Enterprise plan. [P5] Q7: Production databases are backed up daily. Backups are encrypted and retained for 35 days. [P6] Q8: Unanswerable from the provided excerpts. An ISO 27001 certificate or certification statement would answer this question. [P1–P8] Q9: Unanswerable from the provided excerpts. The contractual Service Level Agreement or uptime policy would provide the uptime SLA percentage. [P1–P8] Q10: Unanswerable from the provided excerpts. A HIPAA/BAA policy or standard Business Associate Agreement terms would answer this question. [P1–P8]
| Severity | Action | Finding | Proposal | |---|---|---|---| | CRITICAL | MERGE | `comms-drafter` and `email-drafter` duplicate triggers including `"write me an email"`, `"draft a follow-up"`, `"what should I say"`, `"bump email"`, `"contract nudge"`, and pasted-message review/rewrites. Their email guidance and output formats also substantially duplicate. | Merge email-specific signature retrieval and formatting requirements into `comms-drafter`; retain `comms-drafter` as the broader surviving skill and delete `email-drafter` after migration. | | CRITICAL | REVIEW | `pipeline-intelligence-report` and `weekly-pipeline-report` overlap on `"pipeline update"`, `"pipeline report"`, `"pipeline review/summary"`, and `"what's/what does the pipeline look like"`. Both claim mandatory routing for general pipeline requests. | Establish mutually exclusive triggers: scored deal-level pipeline → `pipeline-intelligence-report`; weekly performance/funnel metrics → `weekly-pipeline-report`. | | INFO | REVIEW | No circular delegation chain is demonstrated by the provided bodies. `pipeline-intelligence-report → closed-lost-analysis` is one-way. `next-to-close → pipeline-intelligence-report` is also one-way. | No change unless an omitted target skill delegates back into its caller. | | CRITICAL | REVIEW | Dangling skill targets not represented by a manifest row or provided file: `bonusly-brand`, `prospect-research-multithreading`, `signalforge-reports`, `bonusly-data-questions`, `bonusly-product-questions`, `bonusly-business-reporting-questions`, `bonusly-rewards-questions`, `bonusly-ppp-questions`, `bonusly-feature-flag-questions`, `bonusly-deal-desk-questions`, and `bonusly-datadog-questions`. | Add the missing skills and manifest rows, or remove/redirect each invocation. Do not execute dependent delegation until resolved. | | WARNING | UPDATE_BODY | `analysis-validator` declares v3.6 in its header, changelog, and footer, but its validation-trail template says `Validator: analysis-validator v3.2`. | Keep v3.6 as the surviving version and update the stale v3.2 reference. | | INFO | TRIM_DESC | Manifest descriptions exceeding 1,024 characters: `0 / 14 = 0%`. Maximum supplied value is `1,006`, appearing for `pipeline-intelligence-report` and `signalforge-claim-compressor`; `1,006 ≤ 1,024`. | No trimming required. | | WARNING | UPDATE_BODY | `analysis-validator` hardcodes dates and people: `April 26, 2026`, `May 4, 2026`, `May 9, 2026`, the May 2026 roster, `Manish`, `Amani`, and numerous named GTM employees. | Replace operational dates and personnel rosters with live references or runtime lookups; retain dates only in the changelog. | | WARNING | UPDATE_BODY | `closed-lost-analysis` hardcodes dated examples and snapshots, including `May 2026`, `May 4–12`, and company-specific historical examples. | Move historical examples into a dated reference file and keep the executable body date-agnostic. | | WARNING | UPDATE_BODY | `deal-strategy-coach` hardcodes Confluence page ID `2257879045`, `April 2026`, 2026 pricing, and the person name `Farid`. | Resolve the playbook and routing owner dynamically or through a maintained reference. | | WARNING | UPDATE_BODY | `model-selection` hardcodes registry date `2026-05-19`, model-release dates, deprecation dates, and a dated model catalog. | Keep only the update procedure in the body and load the current registry dynamically. | | WARNING | UPDATE_BODY | `partner-digest` hardcodes page/folder IDs `2286616609`, `2286321666`, `2265382925`, `2236940297`, `2237825028`, `2239365136`, and `2238283777`; dates including `May 16–19, 2026`; and people including `Amani`, `Kelli`, `Jen Lee`, `Hani`, `Bryce`, and `Sara`. | Move destination IDs, contacts, and canonical-page mappings into maintained configuration. | | WARNING | UPDATE_BODY | `pipeline-intelligence-report` hardcodes `May 2026` and AE names `Bryce Harmon`, `Dana Mercer`, `Cole Ingram`, `Alex Franklin`, and `Gavin Porter`. | Resolve owners dynamically and move historical version dates to the changelog. | | WARNING | UPDATE_BODY | `sales-forecast` hardcodes Confluence IDs `2232811524` and `2232582148`, dated examples including `April 27, 2026` and `July 9, 2026`, and the person name `Alaina`. | Move publication IDs to configuration and resolve the forecast owner dynamically. | | WARNING | UPDATE_BODY | `signalforge-claim-compressor` hardcodes changelog date `2026-05-09`. | Keep the date only if changelog history is intentionally immutable; otherwise move changelog metadata outside the executable body. | | WARNING | UPDATE_BODY | `signalforge-feedback` hardcodes page IDs `2295136266`, `2232811524`, `2234417154`, and `2247295002`. | Move Confluence destinations into configuration and validate them at runtime. | | WARNING | UPDATE_BODY | `stale-pipeline-report` hardcodes `Alaina`, example dates such as `5/7`, `5/15`, and `5/19`, plus changelog date `2026-06-10`. | Replace person-specific routing and dated examples with role-based/runtime placeholders; keep release dates only in the changelog. | | WARNING | UPDATE_BODY | `weekly-pipeline-report` hardcodes `Ben Lavin`, fixed Q2 dates `April 1 – June 30, 2026`, and static Q1 2026 figures. | Make the reporting owner and quarter dynamic; move historical benchmark figures into a dated reference. | | INFO | REVIEW | No qualifying hardcoded page IDs, literal dates, or person names were found in `comms-drafter`, `email-drafter`, or `next-to-close`. | No change. | | INFO | REVIEW | Manifest drift, files without manifest rows: `0`. Arithmetic: 14 provided skill files − 14 matched manifest files = `0`. | No action. | | INFO | REVIEW | Manifest drift, rows without files: `0`. Arithmetic: 14 manifest rows − 14 matched skill files = `0`. | No action. |
| Step | Exact command or action | Who ran it | Success verification | Rollback | Trace | |---:|---|---|---|---|---| | 1 | Acknowledge the PagerDuty alert for reward-worker queue depth > 10k and take incident command. | Bryce Harmon | Bryce stated he was acknowledging the alert and taking IC. No separate verification was recorded. | Not provided; needs confirmation if rollback of acknowledgement or IC assignment is required. | [M01] | | 2 | `bundle exec rake sidekiq:queue_depth` | Farid Osman | Command returned 48,213 pending reward jobs. Normal was stated as under 500. | N/A — read-only diagnostic. | [M02] | | 3 | Inspect the dead set. Exact command not provided. | Farid Osman | Found 112 jobs, all showing `Redis::TimeoutError` from around 13:58. | N/A — read-only diagnostic. | [M03] | | 4 | `bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'` | Farid Osman | No explicit verification that the flag was disabled was recorded; needs confirmation. | `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'` | [M04] | | 5 | Clear the dead set from the console. Exact command or console action not provided. | Elena Sinclair | No explicit verification was recorded; needs confirmation. | Not provided; needs confirmation. | [M05] | | 6 | `kubectl scale deployment/reward-worker --replicas=6` — increased from 3 replicas. | Bryce Harmon | Queue subsequently reached 9,400 and was falling approximately 1,200 jobs/min. No explicit verification of the replica count was recorded; needs confirmation. | `kubectl scale deployment/reward-worker --replicas=3` | [M06], [M07] | | 7 | Monitor the reward queue depth. Exact command not provided. | Farid Osman | Queue depth was 9,400 and falling approximately 1,200 jobs/min. | N/A — monitoring action. | [M07] | | 8 | `bundle exec rake sidekiq:queue_depth`; check the Datadog error rate. | Cole Ingram | Queue-depth command returned 0, and the Datadog error rate was back to baseline. | N/A — verification actions. | [M08] | | 9 | `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'` | Bryce Harmon | Forty new jobs processed cleanly during the next 3 minutes. | Not explicitly specified. The thread contains the disable command from Step 4, but its use as rollback here needs confirmation. | [M09], [M04] | | 10 | `kubectl scale deployment/reward-worker --replicas=3` — reduced from 6 replicas. | Bryce Harmon | Queue remained stable at 0; incident was declared resolved. | Not explicitly specified. The thread contains the scale-to-6 command from Step 6, but its use as rollback here needs confirmation. | [M10], [M06] |
First error: - 2026-09-03T14:01:12Z — `reward-service` — `Redis::TimeoutError: Connection to Redis at redis-primary:6379 timed out after 5s` Cascade, in order: 1. 14:01:12Z — `reward-service` loses Redis connectivity. 2. 14:01:20Z–14:01:40Z — Redis retries are exhausted for `RewardGiveJob`. 3. 14:01:40Z onward — `sidekiq` reports repeated `RewardGiveJob` failures and retries. 4. 14:02:30Z — reward queue depth exceeds 10,000. 5. 14:03:05Z onward — `api-gateway` returns 502 upstream timeouts while calling `reward-service`. 6. 14:03:30Z onward — `web-app` give-form submissions fail because of the upstream 502s. 7. 14:22:10Z — Redis connectivity is restored and job processing resumes. 8. 14:24:45Z — reward queue depth falls below 500. Service and jobs involved: - Initial failing service: `reward-service` - Worker service: `sidekiq` - Primary affected job: `RewardGiveJob` - `sidekiq_jobs.csv` also shows `RecognitionDigestJob` failing with `Redis::TimeoutError`, beginning at 2026-09-03T14:02:36Z. Datadog query to confirm the first error: ```text service:reward-service status:error "Redis::TimeoutError" "redis-primary:6379" ``` Time window: `2026-09-03T14:01:00Z` through `2026-09-03T14:01:20Z`. The logs do not show: - Why `redis-primary:6379` stopped responding. - Any Redis host metrics, infrastructure failure, deployment, or configuration change. - Why connectivity was restored at 14:22:10Z. - Whether every failed job ultimately succeeded or whether any jobs were lost. - Evidence that PostgreSQL caused the failure; its entries only report successful checkpoints.
| Flag | State | What it controls per code | Targeting rule | Exported company count | |---|---|---|---|---:| | `recognition_streaks_v2` | On | Records recognition gives through `StreakTracker.record(give)`. | `segment:beta_companies` | 42 | | `points_budget_guardrails` | On | Enforces giver point budgets through `BudgetService.enforce!`. | `all_companies` | 220 | | `slack_dm_nudges` | On | Sends Slack DM nudges; the job exits when the flag is disabled. | `segment:region_na` | 87 | | `redeem_flow_redesign` | Off | Selects the V2 redemption component when enabled; otherwise renders V1. | `targeted_list` | 12 | | `analytics_dashboard_v3` | On | Initializes the V3 analytics dashboard. | `segment:tier_three` | 65 | | `ms_teams_app_v2` | Off | Installs Teams App V2 when enabled. | `targeted_list` | 9 | | `legacy_give_modal` | Off | No reference in the provided code excerpt. | `segment:legacy_plan` | 14 | | `survey_boosters_q3` | On | No reference in the provided code excerpt. | `segment:legacy_plan` | 7 | | `paused_offboard_cleanup` | Off | No reference in the provided code excerpt. | No targeting rule or targets | 0 | Company names are not provided, so only segments, targeting types, and counts can be identified. Count check: `42 + 220 + 87 + 12 + 65 + 9 + 14 + 7 + 0 = 456` exported flag-company assignments. Unique company count cannot be calculated because companies may overlap across flags. On-state assignments total `42 + 220 + 87 + 65 + 7 = 421`; off-state assignments total `12 + 9 + 14 + 0 = 35`.
NDA-1 — GREEN — Standard mutual NDA: “Carve-outs: information that (a) is or becomes publicly available through no breach, (b) was known prior to disclosure, (c) is received from a third party without duty of confidence, (d) is independently developed, or (e) must be disclosed by law or court order”; standard Delaware law and term. NDA-2 — YELLOW — Counsel review required for embedded 18-month employee non-solicit/no-hire: “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.” NDA-3 — RED — Full legal review required due to the broad three-year non-compete (“Recipient shall not, directly or indirectly, engage in or provide services to any business that competes with Discloser's business”), absent confidentiality carve-outs (“No confidentiality carve-outs are stated in this Agreement”), five-year one-way term, and unusual foreign governing law (“the laws of the Republic of Ireland” with “exclusive jurisdiction of its courts”).