Mean 0.915 · 26/40 perfect tests · $7.98 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: - Why-buys non-compliant: DS2+ deal with no substantive why_buys; `LONG(n)` counts as substantive. - Missing DS1: blank `t_ds1`. - Regressed: any earlier-stage entry timestamp later than a later-stage timestamp. Why-buys compliance: - Overall: 101 / 124 DS2+ deals non-compliant = 81.5%. - 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%. Other hygiene: - Deals with no DS1 timestamp: 0. - Regressed deals: 0. Process fixes: 1. Require a non-placeholder, 15+ character why_buys value before transition into DS2. 2. Enforce automated DS1 timestamp creation on initial stage entry; block progression if absent. 3. Add stage-transition validation that prevents recording an earlier-stage timestamp later than an already-recorded later-stage timestamp.
Deals resolved: 156 of 156 open deals. Unmatched owner IDs: None. Every owner_id in deals_open.csv matched owners.csv. Owner IDs mapped to archived/deactivated owners: None. No open deal maps to an owner where archived = true. Total pipeline amount per resolved owner: - Bryce Harmon (owner_id 119337721): 35 deals; $1,054,144.00 - Alex Franklin (owner_id 84342457): 67 deals; $624,310.00 - Dana Mercer (owner_id 83155923): 24 deals; $341,195.00 - Cole Ingram (owner_id 83155924): 22 deals; $288,161.43 - Farid Osman (owner_id 716654662): 7 deals; $4,134.00 - Elena Sinclair (owner_id 701163055): 1 deal; $2,100.00 Arithmetic: - Resolved deals = 35 + 67 + 24 + 22 + 7 + 1 = 156 - Total resolved pipeline = $1,054,144.00 + $624,310.00 + $341,195.00 + $288,161.43 + $4,134.00 + $2,100.00 = $2,314,044.43
(a) Orphans: 30 Gong rows have no matching `deal_id` in `deals_open`. Arithmetic: 67 Gong rows − 37 matched Gong rows = 30 orphans. Sample orphan aliases: Deal-8FA85D, Deal-8FC3F9, Deal-3B7945, Deal-42B265, Deal-9CCC42. (b) Duplicate conversation keys: 0 rows. Arithmetic: 0 rows where `calls_90d > distinct_conversation_keys`. (c) DS3+ open-deal call coverage: 25 of 85 open DS3+ deals have at least one Gong call = 29.4%. Arithmetic: 25 ÷ 85 × 100 = 29.4118%, rounded to 29.4%. `gong_owner_id` was not used for matching; the integrity check matched Gong `hs_deal_id` to `deals_open.deal_id`.
```sql -- Cannot produce the requested per-customer first-month metrics from the provided catalog: -- no documented table contains recognition/giving events, giver identifiers, or a customer-company alias -- that can be joined to redemption events. COMPANY_COHORT_SUMMARY contains monthly aggregates but -- does not document unique giver counts or a company identifier. SELECT 'Insufficient catalog data to calculate unique givers, recognition count, and successful redemption count per customer company.' AS data_gap; ```
CRM audit Deals - No deals extract was provided. Completeness for owner, stage, amount, close date, and why-buys cannot be calculated. - Pipeline amount at stake cannot be calculated. - Accordingly, a ranked “10 fixes with the most pipeline amount at stake” cannot be produced. Companies (34 records) | Field | Complete | Arithmetic | Completeness | |---|---:|---|---:| | industry | 34 | 34 / 34 | 100.00% | | employee_count | 25 | 25 / 34 | 73.53% | | hq_country | 28 | 28 / 34 | 82.35% | Contacts (52 records) | Field | Complete | Arithmetic | Completeness | |---|---:|---|---:| | email (populated) | 52 | 52 / 52 | 100.00% | | email (syntactically valid) | 48 | 48 / 52 | 92.31% | | title | 39 | 39 / 52 | 75.00% | | persona | 37 | 37 / 52 | 71.15% | Duplicate company clusters | Cluster basis | Records | Proposed survivor | Fix | |---|---|---|---| | Shared domain: `acme-corp.com` | `C-0A092931`, `C-0A092932` | `C-0A092931` | Merge `C-0A092932` into `C-0A092931` after reconciling employee count: 500 vs 510. | | Shared domain: `globex.io` | `C-0A092933`, `C-0A092934` | `C-0A092933` | Merge `C-0A092934` into `C-0A092933`; reconcile industry: `SaaS` vs `Technology`. | Invalid emails | contact_key | company_alias | email | Issue | |---|---|---|---| | `CT-0010` | `C-66D1FC` | `user0@` | Missing domain after `@`. | | `CT-0080` | `C-92D97D` | `user0@` | Missing domain after `@`. | | `CT-0081` | `C-92D97D` | `user1@` | Missing domain after `@`. | | `CT-0192` | `C-425E2A` | `user2@` | Missing domain after `@`. | Domain mismatches | contact_key | company_alias | email | Contact domain | CRM domain | |---|---|---|---|---| | `CT-0011` | `C-66D1FC` | `user1@other-domain.com` | `other-domain.com` | `66d1fc.com` | Enrichment-backed fills Only fields blank in CRM and populated in `zoominfo_enrichment.csv` are included. | company_alias | Field to fill | Value 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 | CRM fields with no available enrichment value must remain unfilled: - `C-2D1F1B`: hq_country - `C-D73B89`: hq_country - `C-44EA29`: hq_country - `C-D04904`: hq_country - `C-2C60E5`: hq_country - `C-EE9FFB`: hq_country - `C-93C8BF`: employee_count CRM/enrichment disagreements Recommended source: `zoominfo_enrichment.csv` for standardized industry labels, standardized country labels, and enriched employee counts. Preserve the original CRM value in an audit field before overwrite. | company_alias | Field | CRM | ZoomInfo | |---|---|---|---| | `C-66D1FC` | industry | `tech` | `Computer Software` | | `C-66D1FC` | hq_country | `US` | `United States` | | `C-950043` | hq_country | `US` | `United States` | | `C-EC3025` | industry | `Technology` | `Computer Software` | | `C-EC3025` | hq_country | `USA` | `United States` | | `C-96039F` | hq_country | `USA` | `United States` | | `C-44EA29` | industry | `tech` | `Computer Software` | | `C-92D97D` | industry | `Technology` | `Computer Software` | | `C-D04904` | industry | `Technology` | `Computer Software` | | `C-77A95A` | industry | `Technology` | `Computer Software` | | `C-77A95A` | hq_country | `US` | `United States` | | `C-AA8DDA` | industry | `Technology` | `Computer Software` | | `C-B23205` | hq_country | `US` | `United States` | | `C-E51FB7` | hq_country | `USA` | `United States` | | `C-D0662E` | hq_country | `US` | `United States` | | `C-B25F40` | industry | `Technology` | `Computer Software` | | `C-60C75F` | industry | `tech` | `Computer Software` | | `C-425E2A` | industry | `Tech ` | `Computer Software` | | `C-425E2A` | hq_country | `USA` | `United States` | | `C-2D7423` | hq_country | `USA` | `United States` | Top 10 fixes by pipeline amount at stake Not available: no deals file or pipeline amount data was provided. A ranking would require deal aliases, amounts, and relationships to the affected company/contact records.
Classifications (deal_alias — primary category; side) - Deal-DB0AAC — timing; buyer - Deal-F7F635 — competitor; buyer - Deal-AC944F — no decision; buyer - Deal-214060 — no decision; buyer - Deal-91A056 — timing; buyer - Deal-29326C — timing; buyer - Deal-5DB9B0 — other; 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; buyer - Deal-B3ABED — timing; buyer - Deal-422BA6 — competitor; buyer - Deal-ED9AE7 — pricing; buyer - Deal-988493 — no decision; buyer - Deal-381C8C — competitor; buyer - Deal-F308CA — no decision; buyer - Deal-F1E8A6 — competitor; buyer - Deal-B6AC09 — timing; buyer - Deal-70F704 — no decision; buyer - Deal-E6E80A — timing; buyer - Deal-B038F0 — timing; buyer - Deal-4664E1 — no decision; buyer - 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; buyer - Deal-15DA99 — timing; buyer - Deal-F4AF5D — timing; buyer - Deal-79B7A1 — timing; buyer - Deal-583ADB — no decision; buyer - Deal-8E27DA — competitor; buyer - Deal-2D2F8D — competitor; buyer - Deal-E0441F — no decision; unknown - Deal-7CB44D — no decision; buyer - Deal-0F96AA — competitor; buyer - Deal-1BCA50 — competitor; buyer - Deal-7CC678 — competitor; buyer - Deal-FAC17C — no decision; buyer - Deal-242273 — product gap; Bonusly - Deal-50E5D8 — no decision; buyer - Deal-A2C349 — competitor; buyer - Deal-9F176A — timing; buyer - Deal-7B2236 — pricing; buyer - Deal-AFA56C — no decision; buyer - Deal-C7156E — competitor; buyer - Deal-C33D91 — pricing; buyer - Deal-9048EB — product gap; Bonusly - Deal-5E64CE — timing; buyer - Deal-8A0992 — competitor; buyer - Deal-D0C698 — competitor; buyer - Deal-69CF3D — timing; buyer - Deal-ECBF89 — timing; buyer - Deal-3618CC — product gap; Bonusly - Deal-EECC02 — competitor; buyer - Deal-5AD03E — pricing; 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; buyer - Deal-FEDBCB — timing; buyer - Deal-1E7DA9 — competitor; buyer - Deal-2BBA21 — no decision; buyer - Deal-286F9C — competitor; buyer - Deal-7FBAC6 — no decision; buyer - Deal-369281 — competitor; buyer - Deal-386F6E — no decision; buyer - Deal-9FCD0D — competitor; buyer - Deal-55867E — other; unknown - Deal-DAFB82 — pricing; buyer - Deal-2FEDDB — timing; buyer - Deal-64B19A — competitor; buyer - Deal-3F86A0 — no decision; buyer - Deal-096750 — no decision; buyer - Deal-F325A5 — champion left; buyer - Deal-ABD14C — no decision; buyer - Deal-79E61A — no decision; buyer - Deal-8A119B — pricing; buyer - Deal-AE7C4E — no decision; buyer - Deal-DAB4F1 — no decision; buyer - Deal-B4B50F — no decision; buyer - Deal-981AD4 — product gap; Bonusly - Deal-DC77FE — product gap; Bonusly - Deal-5885B9 — no decision; buyer Summary Category counts: - No decision: 30 - Competitor: 24 - Timing: 21 - Pricing: 7 - Product gap: 5 - Other: 2 - Champion left: 1 Arithmetic: 30 + 24 + 21 + 7 + 5 + 2 + 1 = 90 deals. Side split: - Buyer: 82 - Bonusly: 5 - Unknown: 3 Arithmetic: 82 + 5 + 3 = 90 deals. Structured-tag vs. free-text disagreements: 8 deals - Deal-ED9AE7 — `Lost DM` tag; text cites timing, budget, and authority. - Deal-8E27DA — `Feature Request` tag; text says they chose a swag provider and did not want R&R currently. - Deal-FAC17C — `Lost DM` tag; text says final executive approval was not obtained. - Deal-242273 — `Competitor` tag; text identifies an unmet need for digitized internal currency and onsite spending. - Deal-9048EB — `MIA` tag; text cites bad fit and multiple feature gaps. - Deal-5E64CE — `Doing nothing/Not a priority/Cost` tag; text cites a current Nectar agreement through October 2027. - Deal-3618CC — `Lost DM` tag; text says “Wanted Surveys.” - Deal-5AD03E — `Competitor` tag; text cites undefined budget access rather than a competing vendor. Two patterns most worth acting on: 1. No-decision losses are the largest group: 30 of 90 deals. The text is predominantly nonresponse, deprioritization, pauses, pending approval, or lack of readiness—not a stated competitive or product outcome. 2. Timing is the next largest non-competitive pattern: 21 of 90 deals. Multiple records explicitly cite reconnecting in 2027, next year, Q2 next year, or 2028. A structured re-engagement process for stated return dates is supported by the text.
{"tier_counts":{"LOCK":3,"ACTION":11,"BUILD":44,"REVIVE":65,"WATCH":17,"RISKY":16},"tier_examples":{"LOCK":["Deal-D348E1","Deal-C26D20","Deal-403845"],"ACTION":["Deal-25F752","Deal-944310","Deal-3974EB"],"BUILD":["Deal-D73B89","Deal-CA7DC0","Deal-3795AD"],"REVIVE":["Deal-6787C2","Deal-A5E80A","Deal-1FC049"],"WATCH":["Deal-012CB1","Deal-036E80","Deal-BB8880"],"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-F9A08A","Deal-0660B4","Deal-FD9F4E","Deal-BA571A"],"lock_violations":0,"pipeline_shape":"The pipeline is weighted toward REVIVE and BUILD: 65 + 44 = 109 of 156 deals; 3 + 11 = 14 are LOCK or ACTION, while 16 are RISKY. Tier counts reconcile: 3 + 11 + 44 + 65 + 17 + 16 = 156. Engagement data is missing for Deal-3EED2C and Deal-57FF13."}
{
"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.",
"Right now we track everything in a spreadsheet, and people slip through the cracks."
],
"pain_points": [
"HR team of three cannot keep up with anniversary and birthday awards manually.",
"Awards are tracked 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 with the IT lead on September 12.",
"objections": [
"We need SSO and audit logs for IT to sign off.",
"Achievers was too heavy for a team our size."
],
"confidence": null
},
"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 the 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."
],
"confidence": null
},
"Deal-530B50": {
"why-buys": [
"We need to make recognition visible across our 12 retail locations.",
"Store managers have zero budget autonomy for on-the-spot recognition today."
],
"pain_points": [
"Recognition is not visible across 12 retail locations.",
"Store managers have zero budget autonomy for on-the-spot recognition."
],
"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": null
},
"Deal-180D02": {
"why-buys": [
"We want to consolidate three separate recognition tools into one.",
"We're paying for three tools and none of them talk to our HRIS."
],
"pain_points": [
"Three separate recognition tools need to be consolidated.",
"The three tools do not integrate with the HRIS.",
"The prior vendor's security review took three months."
],
"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.",
"A follow-up with the CFO was not agreed: \"Maybe — I need to check her calendar, no promises.\""
],
"confidence": null
},
"Deal-F8767A": {
"why-buys": [
"Two things: automate service milestones, and give us analytics on recognition equity across departments.",
"Our night-shift teams feel invisible — their engagement scores run 20 points lower."
],
"pain_points": [
"Service milestones need to be automated.",
"Recognition-equity analytics are needed across departments.",
"Night-shift teams feel invisible and have engagement scores 20 points lower.",
"The exec team is skeptical after a failed rollout two years ago."
],
"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": null
},
"Deal-EE195F": {
"why-buys": [
"I'd love to cut the admin time on service awards.",
"I personally spend five hours a month ordering and shipping plaques.",
"Budget isn't the issue — time is."
],
"pain_points": [
"Five hours per month are spent ordering and shipping plaques.",
"Service-award administration consumes time."
],
"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": [
"The COO usually prefers building things in-house."
],
"confidence": null
}
}
1. Deal-547B2B — $11,200 Why close: CRM is DS5/COMMIT with a 2026-09-11 close date. Slack says redlines came back clean, the signing page is out, and the VP People said they will sign tomorrow. Left: Customer signature. 2. Deal-403845 — $9,000 Why close: CRM is DS5/COMMIT with a 2026-09-11 close date. Slack says the order form is with the finance team. Left: Finance approval/signature. Note: Slack attributes this update to Dana Mercer, while CRM lists Alex Franklin as owner. 3. Deal-A2B47C — $6,360 Why close: CRM is DS5/COMMIT with a 2026-09-11 close date. Slack says it is still warm and moving at a normal legal-review pace. Left: Completion of legal review and signature. Combined amount: $11,200 + $9,000 + $6,360 = $26,560.
Candidates - Real gap — Deal-EC3025 — amount: not provided Prospect: “We need SCIM user provisioning — our security team will not approve any tool that requires manual account management.” Product docs: “SCIM user provisioning … [is] NOT currently listed as supported.” - Real gap — Deal-D0D6B5 — amount: not provided 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.” Product docs: “ADP Workforce Now integrations are NOT currently listed as supported.” - Plan gate — Deal-CFE7F4 — amount: not provided Prospect: “I tried to build a custom report combining recognition frequency with turnover and couldn't — is that possible on our current plan?” Product docs: Custom report builder is available on Enterprise only. - Rollout/enablement issue — Deal-84DBA6 — amount: not provided 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.” Product docs: Slack integration is supported on all plans; the stated issue is missing manager training, not missing functionality. Summary — true product gaps - SCIM user provisioning: Deal-EC3025; amount not provided. - ADP Workforce Now integration: Deal-D0D6B5; amount not provided.
Stale = most recent of `last_email`, `last_call`, and `last_meeting` is more than 7 days before 2026-09-05. Bryce Harmon | Deal alias | Stage | Amount | Days since last contact | |---|---:|---:|---:| | Deal-2D1F1B | DS1 | $240,000 | 81 | | Deal-66D1FC | DS1 | $99,000 | 16 | | Deal-950043 | DS1 | $70,000 | 19 | | Deal-B23205 | DS1 | $45,000 | 16 | | Deal-7BBDFA | DS3 | $37,440 | 46 | | Deal-332637 | DS2 | $36,000 | 9 | | Deal-1BEEBF | DS1 | $31,500 | 19 | | Deal-C5658B | DS1 | $23,400 | 16 | | Deal-40522D | DS3 | $21,000 | 19 | | Deal-F0EBBB | DS3 | $11,400 | 24 | | Deal-E25A09 | DS1 | $6,000 | 9 | | Deal-C9C286 | DS2 | $5,502 | 9 | | Deal-012CB1 | DS1 | $1 | 23 | 13 stale deals. $240,000 + $99,000 + $70,000 + $45,000 + $37,440 + $36,000 + $31,500 + $23,400 + $21,000 + $11,400 + $6,000 + $5,502 + $1 = **$626,243** stale amount. Dana Mercer | Deal alias | Stage | Amount | Days since last contact | |---|---:|---:|---:| | Deal-44EA29 | DS2 | $60,000 | 10 | | Deal-E51FB7 | DS2 | $43,875 | 12 | | Deal-B42F46 | DS1 | $27,000 | 19 | | Deal-BA3DDC | DS3 | $23,400 | 15 | | Deal-9DDE86 | DS2 | $20,000 | 15 | | Deal-215CCA | DS3 | $18,900 | 17 | | Deal-5EED42 | DS3 | $16,250 | 11 | | Deal-57887A | DS2 | $15,000 | 8 | | Deal-B7EBD1 | DS5 | $9,000 | 16 | | Deal-3974EB | DS4 | $9,000 | 8 | | Deal-F40F04 | DS2 | $8,100 | 15 | | Deal-7599B8 | DS3 | $7,350 | 18 | | Deal-87DDD1 | DS1 | $5,000 | 19 | | Deal-F336B6 | DS3 | $4,200 | 15 | | Deal-0660B4 | DS4 | $1,920 | 16 | 15 stale deals. $60,000 + $43,875 + $27,000 + $23,400 + $20,000 + $18,900 + $16,250 + $15,000 + $9,000 + $9,000 + $8,100 + $7,350 + $5,000 + $4,200 + $1,920 = **$268,995** stale amount. Alex Franklin | Deal alias | Stage | Amount | Days since last contact | |---|---:|---:|---:| | Deal-E73427 | DS3 | $18,000 | 10 | | Deal-885F45 | DS2 | $9,300 | 12 | | Deal-C2FF3C | DS1 | $8,316 | 10 | | Deal-0D2F7A | DS3 | $5,100 | 12 | | Deal-6C60D4 | DS3 | $4,800 | 12 | | Deal-13FEBD | DS2 | $4,680 | 12 | | Deal-9D0060 | DS3 | $3,840 | 12 | | Deal-690476 | DS2 | $3,600 | 18 | | Deal-C6D97A | DS4 | $3,240 | 8 | | Deal-EE195F | DS3 | $3,120 | 8 | | Deal-278DEC | DS3 | $2,700 | 8 | | Deal-635B8E | DS3 | $2,600 | 18 | | Deal-6883F3 | DS1 | $2,400 | 16 | | Deal-4A13AD | DS3 | $2,160 | 26 | | Deal-F67D31 | DS2 | $1,800 | 8 | | Deal-5FDCE4 | DS3 | $1,600 | 12 | | Deal-BA571A | DS4 | $1,080 | 18 | 17 stale deals. $18,000 + $9,300 + $8,316 + $5,100 + $4,800 + $4,680 + $3,840 + $3,600 + $3,240 + $3,120 + $2,700 + $2,600 + $2,400 + $2,160 + $1,800 + $1,600 + $1,080 = **$78,336** stale amount. Cole Ingram | Deal alias | Stage | Amount | Days since last contact | |---|---:|---:|---:| | Deal-D04904 | DS2 | $58,529.25 | 11 | | Deal-B25F40 | DS3 | $40,000 | 8 | | Deal-813836 | DS2 | $32,175 | 11 | | Deal-1BA595 | DS2 | $31,750 | 11 | | Deal-CFE1E8 | DS3 | $18,000 | 11 | | Deal-CD47A6 | DS2 | $12,168 | 11 | | Deal-627646 | DS3 | $11,193 | 11 | | Deal-FF809F | DS2 | $7,781.20 | 11 | | Deal-AF932D | DS2 | $7,225.40 | 11 | | Deal-A71728 | DS2 | $6,947.50 | 11 | | Deal-8BC9F5 | DS2 | $5,616 | 10 | | Deal-175395 | DS3 | $4,779.88 | 11 | | Deal-481E24 | DS3 | $4,140 | 10 | | Deal-C7F9BF | DS2 | $3,360 | 11 | | Deal-2F3A66 | DS3 | $3,334.80 | 11 | | Deal-342E96 | DS2 | $2,700 | 24 | | Deal-E568D5 | DS3 | $1,875 | 11 | | Deal-FD9F4E | DS5 | $1,330 | 10 | 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** stale amount. Engagement data is missing for Deal-3EED2C (Alex Franklin) and Deal-57FF13 (Elena Sinclair), so their contact recency cannot be determined from `engagements_by_deal_90d.csv`.
Window: 2026-08-06 through 2026-09-05 inclusive (30 days before the 2026-09-05 snapshot). Efficiency rank (lowest total activities per DS2 entry is most efficient): | Rank | Owner | Emails | Calls | Meetings | Total activities | Activity mix (email / call / meeting) | DS2 entries | Activities per DS2 entry | |---:|---|---:|---:|---:|---:|---|---:|---:| | 1 | Alex Franklin | 307 | 36 | 41 | 384 | 79.95% / 9.38% / 10.68% | 18 | 21.33 | | 2 | Bryce Harmon | 162 | 0 | 43 | 205 | 79.02% / 0.00% / 20.98% | 4 | 51.25 | | 3 | Cole Ingram | 96 | 14 | 1 | 111 | 86.49% / 12.61% / 0.90% | 2 | 55.50 | | 4 | Farid Osman | 38 | 0 | 34 | 72 | 52.78% / 0.00% / 47.22% | 1 | 72.00 | | 5 | Dana Mercer | 84 | 18 | 11 | 113 | 74.34% / 15.93% / 9.73% | 1 | 113.00 | | — | Elena Sinclair | 0 | 0 | 0 | 0 | N/A | 0 | N/A — no DS2 entries, so not rankable | Arithmetic: - Total activities = emails + calls + meetings. - Activity mix = activity type ÷ total activities. - Activities per DS2 entry = total activities ÷ DS2 entries. - Example, Alex Franklin: 307 + 36 + 41 = 384; 384 ÷ 18 = 21.33. DS2 entries in the window: - Bryce Harmon (4): Deal-25F752, Deal-D73B89, Deal-CA7DC0, Deal-1CCE5C. - Dana Mercer (1): Deal-57887A. - Alex Franklin (18): Deal-403845, Deal-1FC049, Deal-3EED2C, Deal-7FA0C3, Deal-E531A6, Deal-5296C9, Deal-36C33F, Deal-EE195F, Deal-F436DA, Deal-317E6F, Deal-D1E6C2, Deal-D9A72E, Deal-CA5E44, Deal-4F775F, Deal-898FC5, Deal-46988D, Deal-E73427, Deal-92D97D. - Cole Ingram (2): Deal-42326B, Deal-1BA595. - Farid Osman (1): Deal-499BF6. - Elena Sinclair (0). Most efficient rep: Alex Franklin, at 21.33 activities per DS2 entry. Highest-volume rep: Alex Franklin, with 384 total activities. They do not differ.
QTD scorecard — Alex Franklin as of 2026-09-05 Bookings vs. quota - QTD bookings: $150,000 - Arithmetic: $40,000 + $20,000 + $35,000 + $21,000 + $12,000 + $11,000 + $6,500 + $4,500 = $150,000 - Included wins: Deal-A1C3E5, Deal-F2C7D8, Deal-B7D2F4, Deal-C9E1A6, Deal-A8B4D6, Deal-D4B8C2, Deal-E6F3A9, Deal-C5D9E2 - Excluded pre-quarter win: Deal-B3E6F1, $24,000, closed 2026-06-20 - Q3 quota: $200,000 - Attainment: 75.0% - Arithmetic: $150,000 / $200,000 × 100 = 75.0% - Remaining to quota: $50,000 - Arithmetic: $200,000 − $150,000 = $50,000 New vs. expansion bookings - New: $113,500 across 5 wins (75.7% of bookings) - Arithmetic: $40,000 + $35,000 + $21,000 + $11,000 + $6,500 = $113,500; $113,500 / $150,000 = 75.7% - Expansion: $36,500 across 3 wins (24.3% of bookings) - Arithmetic: $20,000 + $12,000 + $4,500 = $36,500; $36,500 / $150,000 = 24.3% Active pipeline by stage - DS1: $284,621 across 20 deals - DS2: $353,760 across 28 deals - DS3: $552,705 across 67 deals - DS4: $23,574 across 5 deals - DS5: $45,730 across 5 deals - Total active pipeline: $1,260,390 across 125 deals - Arithmetic: $284,621 + $353,760 + $552,705 + $23,574 + $45,730 = $1,260,390 Rolling 90-day DS2-to-won rate - 22.9% - Closed outcomes for deals entering DS2 from 2026-06-07 through 2026-09-05: 8 won and 27 lost, 35 total - Arithmetic: 8 / (8 + 27) × 100 = 22.9% QTD win and loss counts - Won: 8 - Lost: 27 - Top loss reason: Lost- Timing (1 year or more), 13 losses totaling $184,681 - Arithmetic: 13 / 27 = 48.1% of QTD losses by count Activity volume, last 30 days - Emails: 807 - Calls: 112 - Meetings: 128 - Notes: 50 - Total logged activities: 1,097 - Arithmetic: 807 + 112 + 128 + 50 = 1,097 Coaching observations 1. Alex is $50,000 from quota at 75.0% attainment; the $45,730 currently in DS5 is $4,270 short of covering the remaining gap, so progressing DS4 ($23,574) and DS3 ($552,705) is necessary. 2. Conversion is the primary performance constraint: only 8 of 35 closed 90-day DS2 cohorts became wins (22.9%), while 27 became losses. 3. Timing is the largest loss pattern: 13 of 27 QTD losses (48.1%) were coded Lost- Timing (1 year or more). This warrants tighter early-stage qualification and follow-up plans for delayed-buying prospects.
Active-contact cutoff: 2026-07-15 (60 days before 2026-09-13). Former contacts are excluded. Amount and stage are not provided for any deal, so they cannot be reported. Because stage is missing, the most valuable persona to add “given the stage” cannot be determined. | Deal | Amount | Stage | Active contact count (arithmetic) | Personas present | Personas missing | Most valuable persona to add | On-file unengaged contact fitting a missing persona | |---|---:|---|---|---|---|---|---| | Deal-EC3025 | not provided | not provided | 1 = 1 active champion; economic buyer is former | champion | economic buyer, HR admin, IT security, finance | cannot determine; stage not provided | CT-6827DB — Chief People Officer, economic buyer | | Deal-92D97D | not provided | not provided | 1 = 1 active HR admin; champion last engaged 2026-06-01, outside 60 days | HR admin | economic buyer, champion, IT security, finance | cannot determine; stage not provided | none on file | | Deal-50D386 | not provided | not provided | 2 = 1 champion + 1 HR admin | champion, HR admin | economic buyer, IT security, finance | cannot determine; stage not provided | CT-A1C4B3 — Chief People Officer, economic buyer | | Deal-D0D6B5 | not provided | not provided | 3 = 3 champions; all active contacts are one persona | champion | economic buyer, HR admin, IT security, finance | cannot determine; stage not provided | CT-1FA4DB — Chief People Officer, economic buyer | | Deal-5BFE3B | not provided | not provided | 2 = 2 champions | champion | economic buyer, HR admin, IT security, finance | cannot determine; stage not provided | none on file | | Deal-36C33F | not provided | not provided | 1 = 1 active IT security; champion and economic buyer are former | IT security | economic buyer, champion, HR admin, finance | cannot determine; stage not provided | CT-1DB73E — Chief People Officer, economic buyer | | Deal-885F45 | not provided | not provided | 2 = 1 economic buyer + 1 champion | economic buyer, champion | HR admin, IT security, finance | cannot determine; stage not provided | CT-B3F25D — IT Security Lead, IT security | | Deal-FCBE5B | not provided | not provided | 1 = 1 champion | champion | economic buyer, HR admin, IT security, finance | cannot determine; stage not provided | none on file | | Deal-5408B0 | not provided | not provided | 2 = 1 champion + 1 HR admin | champion, HR admin | economic buyer, IT security, finance | cannot determine; stage not provided | CT-07FA76 — Chief People Officer, economic buyer | | Deal-C6D97A | not provided | not provided | 3 = 3 champions; all active contacts are one persona | champion | economic buyer, HR admin, IT security, finance | cannot determine; stage not provided | none on file | | Deal-F9A08A | not provided | not provided | 1 = 1 active champion; economic buyer last engaged 2026-06-20, outside 60 days | champion | economic buyer, HR admin, IT security, finance | cannot determine; stage not provided | CT-697541 — Chief People Officer, economic buyer |
- First-five-minute lead: In 8 of 10 calls, Alex opens with the same quantified retailer proof point about automated milestone awards and regretted turnover. The other two opens are an agenda (Deal-403845) and pricing (Deal-1E2498). - “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.” - Most common objection — locked budget: 4 occurrences (Deal-D348E1, Deal-547B2B, Deal-60C2C2, Deal-84DBA6). He responds with turnover-savings ROI and a $210k avoided-backfill example. - “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.” - Most common objection — revisit next quarter / competing priorities: 3 occurrences (Deal-5408B0, Deal-C61CF7, Deal-D9A12F). He proposes a limited 90-day departmental pilot to produce planning data before next quarter. - “Makes sense. What if we scope a 90-day pilot with one department so you have internal data before next quarter's planning?” - Most common objection — existing spreadsheet and gift-card process: 3 occurrences (Deal-403845, Deal-EDC141, Deal-1E2498). He contrasts manual recognition 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 agreed: 7 of 10 calls = 70%. - Arithmetic: 7 agreed next steps ÷ 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.” Coaching notes: 1. Keep the quantified retailer proof point, but vary the opening by prospect context; it appears in 8 of 10 calls and risks sounding scripted. 2. The pilot, ROI, and automation responses are consistent; add a concrete fallback next step when the prospect defers to a committee or lacks urgency, since the 3 calls without an agreed next step ended without one.
## Q3 2026 forecast - COMMIT: 7 deals; $44,729 - Arithmetic: $11,200 + $9,000 + $9,000 + $6,360 + $5,400 + $2,520 + $1,249 = $44,729 - BEST_CASE: 24 deals; $203,565 - Weighted BEST_CASE: $203,565 × 35% = $71,247.75 - Weighted forecast: $44,729 + $71,247.75 = $115,976.75 - PIPELINE: excluded from weighted forecast per rule. Excluded for close dates outside Q3: 31 deals totaling $221,875: Deal-E51FB7 ($43,875), Deal-B936FE ($18,000), Deal-D9A12F ($17,000), Deal-D348E1 ($13,770), Deal-4062CF ($10,800), Deal-293AF3 ($9,000), Deal-034D49 ($9,000), Deal-E0ADD8 ($7,920), Deal-9F2E43 ($7,690), Deal-FCBE5B ($7,500), Deal-712010 ($7,200), Deal-600CD9 ($5,400), Deal-A92065 ($5,400), Deal-1D532E ($5,400), Deal-48B656 ($5,160), Deal-E531A6 ($4,800), Deal-D1E6C2 ($4,400), Deal-D9E112 ($4,300), Deal-5AD94B ($4,000), Deal-901332 ($3,600), Deal-47AE31 ($3,600), Deal-15D24F ($3,600), Deal-766C74 ($3,300), Deal-ED725A ($2,400), Deal-8AD4A5 ($1,800), Deal-D7E999 ($1,800), Deal-ED13B0 ($1,680), Deal-5FDCE4 ($1,600), Deal-7FA0C3 ($1,400), Deal-F5A622 ($1,080). Top 5 BEST_CASE deals inside Q3: 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 ## Data quality The `owner` field is blank for nearly all deals, preventing reliable ownership-based forecast review. Many deals, including all COMMIT deals in Q3, have `why_buys_chars = 0`, so deal rationale cannot be validated from this extract. The extract includes close dates after 2026-09-30, requiring an unattended forecast to apply the Q3 date filter correctly. No company/account field is provided, so duplicate or related opportunities cannot be identified.
Activation hypothesis supported in this cohort. | First-month cohort | Cohort size | Active at 24 months | 24-month retention | |---|---:|---:|---:| | Both signals: m1_users ≥ 5 and m1_redemptions ≥ 1 | 46 | 31 | 31 / 46 = 67.4% | | Givers-only: m1_users ≥ 5 and m1_redemptions = 0 | 50 | 23 | 23 / 50 = 46.0% | | Redemption-only: m1_users < 5 and m1_redemptions ≥ 1 | 28 | 8 | 8 / 28 = 28.6% | | Neither: m1_users < 5 and m1_redemptions = 0 | 95 | 39 | 39 / 95 = 41.1% | Both-signals companies retained at 67.4%, versus 40.5% for all other companies combined: (23 + 8 + 39) / (50 + 28 + 95) = 70 / 173 = 40.5%. Lift: 67.4% − 40.5% = 26.9 percentage points. Excluded from denominator: 0 companies. All 219 companies have a current_status and first-month m1_users and m1_redemptions values, and every company fits exactly one of the four cohorts. The single signal with the largest retention lift is 5+ unique givers: - Givers present: (31 + 23) / (46 + 50) = 54 / 96 = 56.3% - Givers absent: (8 + 39) / (28 + 95) = 47 / 123 = 38.2% - Lift: 56.3% − 38.2% = 18.0 percentage points For comparison, successful redemption has a 9.9 percentage-point lift: - Redemption present: 39 / 74 = 52.7% - Redemption absent: 62 / 145 = 42.8% - Lift: 9.9 percentage points This shows a strong association in this 2023 cohort: companies achieving both first-month signals have higher observed 24-month retention. It does not prove that either behavior causes retention, or that the pattern will generalize beyond this extract; unmeasured differences between companies may explain some or all of the observed retention gap.
ARR reconciliation as of 2026-09-05 - CRM company ARR total: $603,581.76 - Active billing ARR total: $604,739.28 - Arithmetic: Σ(active subscription MRR × 12) - Variance (CRM − billing): $603,581.76 − $604,739.28 = −$1,157.52 Variance decomposition (CRM − billing) | Bucket | Variance | Arithmetic | |---|---:|---| | Status mismatch | $13,158.48 | $4,905.24 + $8,253.24 | | Rounding | $36.00 | $16.00 + $20.00 | | Missing records | −$11,952.00 | $16,497.24 − $28,449.24 | | Other | −$2,400.00 | $24,396.00 − $26,796.00 | | Total | −$1,157.52 | $13,158.48 + $36.00 − $11,952.00 − $2,400.00 | Mismatched accounts and suggested owner | Company alias | Issue | CRM ARR | Billing ARR | CRM − billing | Suggested owner | |---|---|---:|---:|---:|---| | C-0C8323BF | Status mismatch: CRM ARR present; subscription is cancelled | $4,905.24 | $0.00 | $4,905.24 | CRM Operations | | C-0DC4FB8C | Status mismatch: CRM ARR present; subscription is cancelled | $8,253.24 | $0.00 | $8,253.24 | CRM Operations | | C-21629AA4 | Missing CRM company record; active billing subscription | $0.00 | $28,449.24 | −$28,449.24 | CRM Operations | | C-0D5BBE3A | Missing billing subscription; CRM ARR present | $16,497.24 | $0.00 | $16,497.24 | Billing Operations | | C-0D66DF9E | Rounding difference | $23,200.00 | $23,184.00 | $16.00 | CRM Operations | | C-14D70CE0 | Rounding difference | $18,200.00 | $18,180.00 | $20.00 | CRM Operations | | C-0F7269D7 | Other: CRM ARR differs from billing ARR by $2,400.00 | $24,396.00 | $26,796.00 | −$2,400.00 | Revenue Operations | Agreement-end-date violations | Subscription ID | Company alias | Term months | cf_agreement_end_date | |---|---|---:|---| | SUB-0002 | C-1794A52C | 24 | blank | | SUB-0019 | C-22170CA1 | 36 | blank |
| KVM | 2026-08 value | 2026-07 value | Absolute change | Relative change | Direction | |---|---:|---:|---:|---:|---| | Giving rate | 0.602713 | 0.602297 | +0.000417 | +0.07% | Up | | Redemptions per user | 1.730163 | 1.729983 | +0.000180 | +0.01% | Up | | 1:1 meetings engagement | 0.447177 | 0.446887 | +0.000290 | +0.06% | Up | | Pulse check engagement | 0.508610 | 0.600587 | -0.091977 | -15.31% | Down | Pulse check engagement had the largest relative move: -15.31%. Arithmetic: absolute change = 2026-08 average − 2026-07 average; relative change = absolute change ÷ 2026-07 average. The data supports size_band as the driver: enterprise pulse engagement fell from 0.549980 to 0.274280, a -0.275700 (-50.13%) change. By comparison, smb declined -0.22% and mid_market increased +0.21%. All records are tier_three, so plan_tier does not distinguish the movement.
Last completed month: 2026-08 - Redemption count: 1,188 - Spend: $90,463.00 - Unique redeemers: 335 - Redemptions per redeemer: 1,188 ÷ 335 = 3.55 Provider mix (% of spend): - custom: $36,163.00 ÷ $90,463.00 = 39.98% - Tremendous: $26,270.00 ÷ $90,463.00 = 29.04% - Snappy: $16,055.00 ÷ $90,463.00 = 17.75% - TangoCard: $11,975.00 ÷ $90,463.00 = 13.23% - Total: 39.98% + 29.04% + 17.75% + 13.23% = 100.00% Top 5 countries by redemptions: 1. US: 754 2. CA: 85 3. AU: 58 4. NL: 53 5. GB: 51
Eligible churn-save accounts (all meet: health score <60, eligible amount >$0, and renewal by 2027-01-03—120 days after 2026-09-05): | Account | Amount at stake | Best-fit play | Supporting signal | |---|---:|---|---| | C-0F6C0F34 | $49,707.00 | Executive touch | Champion inactive (`champion_active=false`); health score 51; renews in 28 days. | | C-0B827671 | $25,365.00 | Usage revival | Usage is declining; only 113/202 seats used = 55.9%; health score 56. | | C-0B360C78 | $35,748.00 | Commercial concession | Health score 57 and renewal is in 53 days. Usage is growing and 246/327 seats are used = 75.2%, so the provided data does not support a usage-revival or executive-touch trigger. | | C-0B0F1BAB | $5,494.00 | Executive touch | Champion inactive (`champion_active=false`); health score 38; renews in 18 days. | | C-0CA21961 | $16,829.00 | Usage revival | Flat usage and only 84/325 seats used = 25.8%; health score 58. | | C-0E9C27D1 | $41,235.00 | Commercial concession | Health score 39 and renewal is in 19 days. Usage is flat but adoption is high at 134/157 seats = 85.4%; champion is active. | | C-0CEF69FD | $32,621.00 | Executive touch | Champion inactive (`champion_active=false`); health score 53; renews in 77 days. | | C-0D3278C7 | $17,602.00 | Usage revival | Usage is declining and only 126/380 seats used = 33.2%; health score 54. | Total eligible amount at stake: $49,707.00 + $25,365.00 + $35,748.00 + $5,494.00 + $16,829.00 + $41,235.00 + $32,621.00 + $17,602.00 = **$224,601.00** At-risk accounts that do not qualify: | Account | Why it looks at risk | Why it does not qualify | |---|---|---| | C-0BC71BDD | Health score 55; flat usage; 59/197 seats used = 29.9%; champion inactive. | `churn_save_eligible_amount` is $0.00, failing R2. | | C-0BA71F12 | Health score 52; declining usage; 23/98 seats used = 23.5%. | Renewal is 218 days after the snapshot (2027-04-11), failing R3’s 120-day window. | | C-0F6694C3 | Health score 43; declining usage; 39/96 seats used = 40.6%. | Eligible amount is $0.00 and renewal is 197 days after the snapshot (2027-03-21), failing R2 and R3. | | C-0BE96399 | Health score 54; declining usage; 43/154 seats used = 27.9%. | `churn_save_eligible_amount` is $0.00, failing R2. | | C-0F876796 | Health score 47; declining usage; 22/95 seats used = 23.2%; champion inactive. | Renewal is 154 days after the snapshot (2027-02-06), failing R3’s 120-day window. | | C-0FCCD2DF | Health score 43; flat usage; 27/63 seats used = 42.9%; champion inactive. | Eligible amount is $0.00 and renewal is 230 days after the snapshot (2027-04-23), failing R2 and R3. | | C-10A56B0F | Health score 54; declining usage; 85/176 seats used = 48.3%; champion inactive. | `churn_save_eligible_amount` is $0.00, failing R2. |
Seat coverage: C-0DDFC9A7 has 150 licensed seats for 400 employees: 150 ÷ 400 = 37.5%. Usage health: - Monthly active users increased from 88 in 2026-03 to 126 in 2026-08: 126 − 88 = 38 users, or 38 ÷ 88 = 43.2% growth. - August usage was 126 of 150 licensed seats: 126 ÷ 150 = 84.0% utilization, leaving 24 currently unused licensed seats. Headroom: - Seat headroom to company headcount: 400 − 150 = 250 seats. - Current per-seat ARR: $9,000.00 ÷ 150 = $60.00 per seat. - ARR headroom at the current rate: 250 × $60.00 = $15,000.00. Reply and buying authority: - Maria S., People Operations Coordinator, replied on 2026-09-02. - Maria S. explicitly said she is not the purchasing decision-maker. - Dana R., VP People, is the stated owner of budget and seat expansion and is the right buyer. Reply email: Subject: Re: Growing your team’s recognition program Hi Maria, Thanks for the update — great to hear the team is seeing value. We’ve also seen monthly active users grow from 88 in March to 126 in August, an increase of 38 users. Since Dana is looking at usage numbers and owns budget and seat expansion, would you be open to introducing us? I’d be glad to share a concise view of adoption and discuss whether the current 150-seat plan remains the right fit as usage grows. No pressure — happy to send the usage summary for you to forward if that is easier. Best, Cole
C-0D284E42 Complete - Slack integration connected: Yes — `integration_slack = 2026-08-12`. - Allowance set: Yes — `allowance_set = 2026-08-13`. - Admins added: Yes — `admins_added = 2`. - First recognition given: Yes — `first_recognition_at = 2026-08-15 14:22`. Not complete / not evidenced - HRIS integration connected: Not evidenced — `integration_hris` is blank. - First redemption: Not evidenced — `first_redemption_at` is blank. Early engagement signals - Active givers increased from 3 on 2026-08-11 to 15 on 2026-09-04: `15 - 3 = 12` net increase. - Daily active givers averaged 8.56 across 25 reported days: `214 total active-giver days / 25 days = 8.56`. - First 7 days averaged 4.29 active givers: `30 / 7 = 4.29`. - Most recent 7 days averaged 13.00 active givers: `91 / 7 = 13.00`. - Recent-vs-first-week increase: `13.00 - 4.29 = 8.71` average active givers per day. Three things to cover on the call 1. Confirm whether the HRIS integration should be connected and, if so, establish the owner and next step; no HRIS connection date is recorded. 2. Review redemption readiness and identify any blockers; no first redemption timestamp is recorded. 3. Reinforce the strong giver adoption trend and discuss how to sustain it, including whether the two admins need support driving broader participation.
90-day renewal risk brief Date-source rule: Use Chargebee for every account. For multi-year contracts, Chargebee is used because ChurnZero is known to have incorrect renewal dates. For 12-month contracts, Chargebee and ChurnZero agree. Risk rating rule: High = low utilization and/or material 3-month usage decline; Medium = utilization below 70% with broadly flat usage; Low = utilization at least 70% with stable or growing usage. ARR at risk includes High and Medium. - C-0B7D2C30 | CSM: Dana Mercer | ARR: $65,901.00 | Date used: 2026-09-15 (Chargebee, 36-month term). DISAGREEMENT: ChurnZero 2026-09-10 vs Chargebee 2026-09-15; use Chargebee because this is multi-year. Seat utilization: 274 / 476 = 57.6%. 3-month usage: 97 → 94 → 84; Aug vs Jun = 84 - 97 = -13 (-13.4%). Risk: High — utilization is 57.6% and active users fell 13.4% over three months. - C-0BCDB8C2 | CSM: Cole Ingram | ARR: $54,427.00 | Date used: 2026-09-18 (Chargebee, 36-month term). DISAGREEMENT: ChurnZero 2027-09-18 vs Chargebee 2026-09-18; use Chargebee because this is multi-year. Seat utilization: 232 / 424 = 54.7%. 3-month usage: 127 → 118 → 110; 110 - 127 = -17 (-13.4%). Risk: High — utilization is 54.7% and active users fell 13.4%. - C-0D2AB865 | CSM: Elena Sinclair | ARR: $38,022.00 | Date used: 2026-09-22 (Chargebee, 24-month term). DISAGREEMENT: ChurnZero 2026-09-10 vs Chargebee 2026-09-22; use Chargebee because this is multi-year. Seat utilization: 250 / 407 = 61.4%. 3-month usage: 125 → 117 → 109; 109 - 125 = -16 (-12.8%). Risk: High — utilization is 61.4% and active users fell 12.8%. - C-0BBE3E60 | CSM: Dana Mercer | ARR: $30,993.00 | Date used: 2026-09-26 (Chargebee, 24-month term). DISAGREEMENT: ChurnZero 2027-09-26 vs Chargebee 2026-09-26; use Chargebee because this is multi-year. Seat utilization: 74 / 114 = 64.9%. 3-month usage: 39 → 35 → 33; 33 - 39 = -6 (-15.4%). Risk: High — utilization is 64.9% and active users fell 15.4%. - C-0F5D2323 | CSM: Cole Ingram | ARR: $90,647.00 | Date used: 2026-09-29 (Chargebee, 24-month term). DISAGREEMENT: ChurnZero 2026-09-10 vs Chargebee 2026-09-29; use Chargebee because this is multi-year. Seat utilization: 111 / 390 = 28.5%. 3-month usage: 20 → 21 → 18; 18 - 20 = -2 (-10.0%). Risk: High — utilization is only 28.5% and active users declined 10.0%. - C-0EC6999D | CSM: Elena Sinclair | ARR: $79,419.00 | Date used: 2026-10-03 (Chargebee; matches ChurnZero; 12-month term). Seat utilization: 31 / 112 = 27.7%. 3-month usage: 17 → 16 → 15; 15 - 17 = -2 (-11.8%). Risk: High — utilization is only 27.7% and active users declined 11.8%. - C-0B20DB64 | CSM: Dana Mercer | ARR: $21,770.00 | Date used: 2026-10-07 (Chargebee; matches ChurnZero; 12-month term). Seat utilization: 214 / 378 = 56.6%. 3-month usage: 294 → 298 → 294; 294 - 294 = 0 (0.0%). Risk: Medium — utilization is 56.6%, although usage is flat. - C-0BBC4E7A | CSM: Cole Ingram | ARR: $56,374.00 | Date used: 2026-10-10 (Chargebee; matches ChurnZero; 12-month term). Seat utilization: 228 / 337 = 67.7%. 3-month usage: 142 → 141 → 139; 139 - 142 = -3 (-2.1%). Risk: Medium — utilization is 67.7% and usage is slightly declining. - C-0FD551AB | CSM: Elena Sinclair | ARR: $48,815.00 | Date used: 2026-10-14 (Chargebee; matches ChurnZero; 12-month term). Seat utilization: 210 / 376 = 55.9%. 3-month usage: 123 → 122 → 126; 126 - 123 = +3 (+2.4%). Risk: Medium — utilization is 55.9%, despite modest usage growth. - C-0F9F8F13 | CSM: Dana Mercer | ARR: $46,230.00 | Date used: 2026-10-18 (Chargebee; matches ChurnZero; 12-month term). Seat utilization: 199 / 352 = 56.5%. 3-month usage: 185 → 185 → 182; 182 - 185 = -3 (-1.6%). Risk: Medium — utilization is 56.5% and usage is slightly declining. - C-0BC34584 | CSM: Cole Ingram | ARR: $16,740.00 | Date used: 2026-10-22 (Chargebee; matches ChurnZero; 12-month term). Seat utilization: 327 / 494 = 66.2%. 3-month usage: 104 → 104 → 106; 106 - 104 = +2 (+1.9%). Risk: Medium — utilization is 66.2%, notwithstanding modest usage growth. - C-0B7A7546 | CSM: Elena Sinclair | ARR: $35,062.00 | Date used: 2026-10-25 (Chargebee; matches ChurnZero; 12-month term). Seat utilization: 182 / 205 = 88.8%. 3-month usage: 64 → 65 → 63; 63 - 64 = -1 (-1.6%). Risk: Low — utilization is 88.8% and usage is broadly stable. - C-0B369871 | CSM: Dana Mercer | ARR: $85,128.00 | Date used: 2026-10-29 (Chargebee; matches ChurnZero; 12-month term). Seat utilization: 317 / 422 = 75.1%. 3-month usage: 326 → 330 → 333; 333 - 326 = +7 (+2.1%). Risk: Low — utilization is 75.1% and active users grew 2.1%. - C-0B144C78 | CSM: Cole Ingram | ARR: $30,899.00 | Date used: 2026-11-02 (Chargebee; matches ChurnZero; 12-month term). Seat utilization: 169 / 224 = 75.4%. 3-month usage: 101 → 101 → 106; 106 - 101 = +5 (+5.0%). Risk: Low — utilization is 75.4% and active users grew 5.0%. - C-0FC4DBB8 | CSM: Elena Sinclair | ARR: $94,732.00 | Date used: 2026-11-05 (Chargebee; matches ChurnZero; 12-month term). Seat utilization: 356 / 464 = 76.7%. 3-month usage: 189 → 191 → 193; 193 - 189 = +4 (+2.1%). Risk: Low — utilization is 76.7% and active users grew 2.1%. - C-0D5BBE3A | CSM: Dana Mercer | ARR: $39,740.00 | Date used: 2026-11-09 (Chargebee; matches ChurnZero; 12-month term). Seat utilization: 85 / 102 = 83.3%. 3-month usage: 88 → 90 → 91; 91 - 88 = +3 (+3.4%). Risk: Low — utilization is 83.3% and active users grew 3.4%. - C-0FB9D5AF | CSM: Cole Ingram | ARR: $63,158.00 | Date used: 2026-11-13 (Chargebee; matches ChurnZero; 12-month term). Seat utilization: 144 / 199 = 72.4%. 3-month usage: 173 → 173 → 176; 176 - 173 = +3 (+1.7%). Risk: Low — utilization is 72.4% and active users grew 1.7%. - C-0B344485 | CSM: Elena Sinclair | ARR: $64,384.00 | Date used: 2026-11-16 (Chargebee; matches ChurnZero; 12-month term). Seat utilization: 224 / 287 = 78.0%. 3-month usage: 238 → 240 → 244; 244 - 238 = +6 (+2.5%). Risk: Low — utilization is 78.0% and active users grew 2.5%. - C-0CB2C1B4 | CSM: Dana Mercer | ARR: $40,628.00 | Date used: 2026-11-20 (Chargebee; matches ChurnZero; 12-month term). Seat utilization: 386 / 473 = 81.6%. 3-month usage: 47 → 48 → 49; 49 - 47 = +2 (+4.3%). Risk: Low — utilization is 81.6% and active users grew 4.3%. - C-22170CA1 | CSM: Cole Ingram | ARR: $45,646.00 | Date used: 2026-11-24 (Chargebee; matches ChurnZero; 12-month term). Seat utilization: 251 / 294 = 85.4%. 3-month usage: 143 → 148 → 146; 146 - 143 = +3 (+2.1%). Risk: Low — utilization is 85.4% and usage is above its June level. Total ARR renewing: $1,048,715.00. ARR at risk: $549,338.00 = High-risk ARR ($359,409.00) + Medium-risk ARR ($189,929.00).
Ranked by unique ARR affected (not ticket volume). Total tickets: 80. 1. HRIS provisioning/sync failures — Broad pattern - Count/share: 12 tickets; 12 ÷ 80 = 15.0% - Distinct accounts: 3 — C-0B2213A9, C-0F6C0F34, C-0DDFC9A7 - ARR affected: $114,000 = $36,000 + $30,000 + $48,000 - Ticket examples: IC-460059, IC-460064 - Recommendation: Prioritize HRIS sync/provisioning reliability and add monitoring for skipped new-hire creation. 2. Redemption checkout, gift-card fulfillment, and deducted-points failures — Broad pattern - Count/share: 18 tickets; 18 ÷ 80 = 22.5% - Distinct accounts: 7 — C-0CEF69FD, C-0B827671, C-0FCCD2DF, C-0F876796, C-14264ABD, C-0D9CA315, C-0B0F1BAB - ARR affected: $68,800 = $8,900 + $10,700 + $9,600 + $8,700 + $11,000 + $9,600 + $10,300 - Ticket examples: IC-460025, IC-460036 - Recommendation: Investigate the checkout-to-fulfillment transaction path and automatically reconcile points when gift-card orders fail. 3. Billing seat-count and renewal-tier errors — Single-account concentration - Count/share: 16 tickets; 16 ÷ 80 = 20.0% - Distinct accounts: 1 — C-0E9C27D1 - ARR affected: $52,000 - Ticket examples: IC-460071, IC-460075 - Recommendation: Treat C-0E9C27D1 as an urgent account-specific billing escalation; validate seat-count and renewal-pricing calculations before the next invoice. - Noise assessment: High ticket count but not a broad pattern: all 16 tickets come from one account. 4. Recognition points not delivered or balances not updated — Broad pattern - Count/share: 20 tickets; 20 ÷ 80 = 25.0% - Distinct accounts: 9 — C-0D3278C7, C-0BF20542, C-0D0B047C, C-0BE96399, C-0D284E42, C-0D6CC8E3, C-21FEBCBB, C-0DD0626C, C-0B2895EF - ARR affected: $31,100 = $3,500 + $4,500 + $4,500 + $2,700 + $3,400 + $4,200 + $2,900 + $2,500 + $2,900 - Ticket examples: IC-460004, IC-460018 - Recommendation: Audit recognition-to-points posting jobs, especially post-weekend processing, and surface delayed-balance status to users. 5. Slack recognition integration and slash-command failures — Broad pattern - Count/share: 14 tickets; 14 ÷ 80 = 17.5% - Distinct accounts: 4 — C-0B843542, C-10A56B0F, C-0BA71F12, C-8C2E8F00 - ARR affected: $18,900 = $4,400 + $5,400 + $3,900 + $5,200 - Ticket examples: IC-460041, IC-460044 - Recommendation: Investigate Slack authentication persistence, sync-toggle resets, and slash-command errors as a connected integration reliability issue. Broad-pattern total: 64 tickets across four multi-account themes. Single-account concentration: 16 tickets from C-0E9C27D1.
1. C-11C31562 — 3/4 matching fields - Size band: Mid-Market - Use case: employee_recognition - Region: NA-West - Industry differs: Manufacturing vs. Technology 2. C-64171065 — 3/4 matching fields - Industry: Technology - Size band: Mid-Market - Use case: employee_recognition - Region differs: NA-East vs. NA-West 3. C-A13C193D — 3/4 matching fields - Industry: Technology - Size band: Mid-Market - Region: NA-West - Use case differs: retention vs. employee_recognition Arithmetic: each match scores 1 point across industry, size band, use case, and region; each listed customer matches 3 of 4 fields.
Trailing 6 months: 2026-03 through 2026-08. Paid channels | Channel | Spend | SQMs | SQOs | Cost/SQM | Cost/SQO | SQM→SQO rate | Pipeline | Pipeline/$ | |---|---:|---:|---:|---:|---:|---:|---:|---:| | 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 | Arithmetic: - paid_search: $36,000 / 40 = $900 per SQM; $36,000 / 18 = $2,000 per SQO; 18 / 40 = 45.0%; $720,000 / $36,000 = $20.00 pipeline per $. - linkedin_ads: $24,000 / 25 = $960 per SQM; $24,000 / 8 = $3,000 per SQO; 8 / 25 = 32.0%; $96,000 / $24,000 = $4.00 pipeline per $. - paid_social: $18,000 spend with zero SQMs, so cost per SQM, cost per SQO, and SQM-to-SQO rate are undefined. $0 / $18,000 = $0.00 pipeline per $. - webinars: $9,000 / 12 = $750 per SQM; $9,000 / 5 = $1,800 per SQO; 5 / 12 = 41.7%; $60,000 / $9,000 = $6.67 pipeline per $. Organic/unpaid channels | Channel | Volume (SQMs) | SQOs | SQO rate | Pipeline | |---|---:|---:|---:|---:| | organic_search | 30 | 10 | 33.3% | $90,000 | | referral | 15 | 6 | 40.0% | $48,000 | Arithmetic: - organic_search: 10 / 30 = 33.3%; pipeline = 10 × $9,000 = $90,000. - referral: 6 / 15 = 40.0%; pipeline = 6 × $8,000 = $48,000. Data-quality flags: SQO date precedes SQM date - CT-000044, linkedin_ads: SQM date 2026-07-23; SQO date 2026-07-18. - CT-000041, linkedin_ads: SQM date 2026-06-14; SQO date 2026-06-09. Recommendation: Reallocate the $18,000 currently in paid_social, which generated zero SQMs, away from that channel. Prioritize paid_search for incremental allocation: it has the highest observed SQM-to-SQO rate (45.0%) and pipeline efficiency ($20.00 per $), versus webinars at $6.67 and linkedin_ads at $4.00. Reduce linkedin_ads before reducing webinars: LinkedIn has the weakest paid conversion rate (32.0%), highest cost per SQO ($3,000), and its two date-ordering errors require correction before relying on its attribution. Confidence: moderate for paid_search (40 SQMs, 18 SQOs); low-to-moderate for linkedin_ads (25 SQMs, 8 SQOs); low for webinars (12 SQMs, 5 SQOs). Confidence in stopping paid_social is high for this six-month dataset because it had $18,000 spend and zero SQMs, but the data does not establish why it produced none.
# Rivally Battlecard ## One-line positioning Rivally is a points-based employee-recognition platform with an engaging recognition feed, positioned with EU data residency and multi-language support. [S02] [S12] [S15] ## Pricing - Current published price: Recognition Starter is **$7/user/month**, with annual billing required, as of **2026-08-12**. [S17] - Pricing conflict: Rivally’s earlier pricing pages listed **$5/user/month** on 2026-04-01 and 2026-01-20; the newer $7 published price wins. [S08] [S03] - Deal-specific quotes differ from published pricing: a 500-seat prospect was quoted **$6.50/user/month** on an annual term (2026-06-02), and an 800-seat prospect reported a **$7/user/month list price with a 15% discount for a three-year term** (2026-08-14). These are call-note deal mentions, not a replacement for published list pricing. [S13] [S18] ## Where Rivally wins - Engaging, points-based recognition feed. [S02] [S16] - Fast setup and out-of-the-box Slack integration were praised by a mid-market reviewer. [S04] - EU/distributed-team positioning: EU data residency is generally available, and an EU enterprise reviewer praised multi-language support for distributed EU teams. [S15] [S12] - Support response time was praised as under four hours in a review. [S22] ## Where we win - Analytics depth: an 800-seat prospect selected Bonusly over Rivally, citing analytics depth. [S25] - Rivally’s analytics/reporting are repeatedly described as limited or basic; this is a relevant competitive opening, but the provided data does not independently establish Bonusly feature parity beyond the deal outcome above. [S02] [S07] - Rivally’s enterprise administration has reported gaps: lack of SCIM provisioning, painful manual user management, lagging admin tooling, and no bulk recognition editing. [S10] [S16] [S24] - Rivally’s EMEA rewards catalog was described as thinner than its US catalog. [S14] ## Objections and responses - **“Rivally is better for EU teams.”** Response: Acknowledge Rivally’s EU data residency and multi-language support. Re-center the evaluation on the buyer’s required analytics depth, administration workflow, and rewards-catalog coverage; Rivally reviews cite basic reporting, SCIM/manual-management friction, and a thinner EMEA catalog. Confirm Bonusly’s applicable capabilities before claiming them. [S15] [S12] [S07] [S10] [S14] - **“Rivally is quick to implement and integrates with Slack.”** Response: This is supported by a mid-market review, so do not dispute it. Evaluate implementation requirements and required administrative controls; Rivally reviewers cite lagging admin tooling and no bulk recognition editing. Confirm Bonusly’s implementation and Slack capabilities before comparison. [S04] [S16] [S24] - **“Rivally is less expensive.”** Response: Use the current published $7/user/month annual-billing price as the baseline. Ask for the actual quote, term, seat count, and discount structure: reported deal quotes include $6.50/user/month at 500 seats and $7 list with a 15% three-year discount. [S17] [S13] [S18] - **“We need stronger reporting/export flexibility.”** Response: Rivally reviews describe limited/basic analytics, CSV-only analytics exports, and difficult migration off the platform. Confirm Bonusly’s relevant reporting and export capabilities before making a direct product claim. [S02] [S07] [S20] ## Recent changes - **2026-09-01:** Rivally Pulse engagement-survey add-on exited beta; it is priced separately rather than bundled. [S23] - **2026-08-20:** Microsoft Teams app v2 entered public preview. [S19] - **2026-07-01:** Dublin office opened; EU data residency became generally available. [S15] - **2026-05-09:** Rivally hired an ex-Workday VP EMEA to lead European expansion. [S11] - **2026-03-05:** Rivally launched Rivally Pulse as a lightweight engagement-survey add-on. [S06] ## Our 12-month win/loss record against Rivally Period: **2025-09 through 2026-08**. - Wins: **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. - Losses: **7** - Deal-7767F5, Deal-5645A5, Deal-C6FFAA, Deal-D263E0, Deal-935746, Deal-9066A6, Deal-72A02F. - Total: **20 deals** = 13 wins + 7 losses. - Win rate: **65.0%** = 13 wins ÷ 20 total deals × 100. - Loss rate: **35.0%** = 7 losses ÷ 20 total deals × 100. The provided deals file has no snippet IDs, so the deal-record claims above cannot be cited with snippet IDs. ## Old-card items - **“Rivally lacks a Slack integration” — contradicted.** A reviewer said Slack integration worked out of the box. [S04] - **“Rivally was acquired by WorkHuman in 2025” — unverified.** No provided source supports this.
Sequence performance (totals across steps; rate = total outcome ÷ total sent): - New Logo Nurture: sent 1,386; open rate 490 ÷ 1,386 = 35.35%; reply rate 90 ÷ 1,386 = 6.49%; meeting rate 27 ÷ 1,386 = 1.95%. Weakest step: 3, reply rate 18 ÷ 428 = 4.21%. Change: replace Step 3 with a distinct CTA. - Expansion Nurture: sent 875; open rate 565 ÷ 875 = 64.57%; reply rate 59 ÷ 875 = 6.74%; meeting rate 12 ÷ 875 = 1.37%. Weakest step: 3, reply rate 12 ÷ 275 = 4.36%. Change: replace Step 3 with a distinct CTA. - Cold Outbound - HR Leaders: sent 1,785; open rate 545 ÷ 1,785 = 30.53%; reply rate 8 ÷ 1,785 = 0.45%; meeting rate 0 ÷ 1,785 = 0.00%. Weakest step: 3, reply rate 1 ÷ 590 = 0.17%. Failure mode: reply conversion is below 2% at every step—5 ÷ 600 = 0.83%, 2 ÷ 595 = 0.34%, 1 ÷ 590 = 0.17%—despite opens; no meetings result. Change: rebuild the sequence’s targeting/message before further sends. - Cold Outbound - People Ops: sent 1,163; open rate 340 ÷ 1,163 = 29.23%; reply rate 29 ÷ 1,163 = 2.49%; meeting rate 6 ÷ 1,163 = 0.52%. Weakest step: 3, reply rate 6 ÷ 377 = 1.59%. Failure mode: Step 3 falls below 2% reply after Step 1 (14 ÷ 400 = 3.50%) and Step 2 (9 ÷ 386 = 2.33%). Change: remove or rewrite Step 3. Tracking error: Expansion Nurture Step 2 has 340 opens ÷ 300 sent = 113.33%; opened exceeds sent by 40. Audience overlap: - Expansion Nurture / New Logo Nurture: CT-000301, CT-000624. - Cold Outbound - HR Leaders / Cold Outbound - People Ops: CT-000849, CT-000884, CT-000890, 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. Fix first: Cold Outbound - HR Leaders, because its aggregate reply rate is 0.45% and it generated zero meetings.
Q3-2026: 66 of 92 days elapsed (71.7%). | Metric | QTD actual | Target | Delta | Pace | |---|---:|---:|---:|---| | SQMs | 230 | 300 | -70 | Ahead — prorated target: 300 × 66/92 = 215.2; actual is +14.8 above pace | | SQOs | 84 | 120 | -36 | Behind — prorated target: 120 × 66/92 = 86.1; actual is -2.1 below pace | | DS2s | 40 | 75 | -35 | Behind — prorated target: 75 × 66/92 = 53.8; actual is -13.8 below pace | | Closed-lost MIA rate | 20.0% | 10.0% | +10.0 pp | Behind — 5 closed-lost MIAs / 25 closed-lost total = 20.0%; target is lower-better | | Same-quarter closes | 10 | 20 | -10 | Behind — prorated target: 20 × 66/92 = 14.3; actual is -4.3 below pace | | Active pipeline coverage against target | 75.0% ($3.0M / $4.0M) | 100.0% ($4.0M) | -25.0 pp (-$1.0M) | Ahead — prorated target: $4.0M × 66/92 = $2.87M; actual is +$0.13M above pace | This week’s movement cannot be determined from the provided QTD-only data; no prior-week actuals or weekly activity data were provided.
Do not treat $115,976.75 as a reliable Q3 forecast without re-qualification. It is $44,729 COMMIT + 35% × $203,565 BEST_CASE ($71,247.75), across 54 of 86 in-quarter deals; 32 deals totaling $227,575 are excluded after 2026-09-30, including COMMIT Deal-D348E1 ($13,770). All 7 in-quarter COMMIT deals lack why-buys, and 85 of 86 deals lack an owner.
Subject: Quick follow-up on the July 28 demo Hi [Name], Following up on the recap and 150-seat pricing I sent after our July 28 demo. The People team had responded strongly to automated milestone awards and the points catalog. Would you be open to a 15-minute conversation this week to confirm whether this is still a priority? Best, Alex
Marketing: We generated 46 SQMs against a weekly target of 52, a gap of 6 SQMs (52 − 46) and 88.5% target attainment (46 ÷ 52 × 100). The webinar contributed 18 SQMs, representing 39.1% of weekly SQMs (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. Three same-quarter closes were recorded this week. CS: Two renewals were saved this week. Team NPS moved to 61, while 3 red-flag accounts remain open heading into next week. PLG: PLG added 412 new signups with 31% activation. Thirty-eight companies reached the aha moment of 10 recognition gives.
Apex Rewards Co — Co-webinar locked for 09-15. Partner-sourced pipeline: 2 deals, $275,000 ($180,000 Deal-DDAAF2 + $95,000 Deal-2CF33E); both in DS1. HRCloud Partners — Integration referral cleared security review and moved to DS2. Partner-sourced pipeline: 1 deal, $140,000 (Deal-F1CDA5). CultureBridge — Lunch-and-learn produced two early-stage sourced opportunities. Partner-sourced pipeline: 2 deals, $135,000 ($60,000 Deal-096E1D + $75,000 Deal-067213). WorkWell Group — No sourced deals this period: 0 deals, $0. Joint-playbook planning call booked for 09-09; restart targeted for Q4. Recogniq — Quiet. KudosWave — Quiet. PeopleFirst Advisors — Quiet. TotalPerk — Quiet.
Q1. Yes. Customer data is encrypted at rest using AES-256. [P1] Q2. Yes. Data in transit is encrypted with TLS 1.2 or higher. [P1] Q3. Yes. Bonusly maintains a SOC 2 Type II report, renewed annually. A copy is available to prospects under NDA. [P2] Q4. Customer data is retained for the duration of the contract plus 90 days, 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 applicable compliance certification documentation would answer this question. [P1–P8] Q9. Unanswerable from the provided excerpts. The contractual SLA or service agreement would answer this question. [P1–P8] Q10. Unanswerable from the provided excerpts. A HIPAA BAA policy or executed BAA template would answer this question. [P1–P8]
CRITICAL | MERGE | `comms-drafter` + `email-drafter` - Overlapping ALWAYS triggers include: “write me an email,” “draft a follow-up,” “help me reply,” “what should I say,” “bump email,” “contract nudge,” review/rewrite requests, and all customer/prospect email types. - Proposal: retain `comms-drafter` as the broader external-communications skill. Merge the email-specific identity, Gmail-signature retrieval, and email-client formatting requirements from `email-drafter`; then delete `email-drafter`. WARNING | REVIEW | `pipeline-intelligence-report` + `weekly-pipeline-report` - Overlapping ALWAYS triggers include: “pipeline update,” “what does pipeline look like,” “update the pipeline,” and variations of “pipeline report.” - Proposal: retain both because their deliverables differ. Narrow triggers: reserve `pipeline-intelligence-report` for full active-deal scoring/tiering, and `weekly-pipeline-report` for weekly performance/funnel/bookings reporting. INFO | REVIEW | Circular delegation chains - No circular delegation chain is explicitly defined. - Observed directed chains: `next-to-close` → `pipeline-intelligence-report` → `closed-lost-analysis`; `pipeline-intelligence-report` → `analysis-validator`; `deal-strategy-coach` → `email-drafter`; `deal-strategy-coach` → `prospect-research-multithreading`. - Proposal: keep the chains acyclic; do not add a reverse delegation from `closed-lost-analysis` to `pipeline-intelligence-report`. CRITICAL | REVIEW | Dangling delegation target: `prospect-research-multithreading` - Referenced by `comms-drafter`, `deal-strategy-coach`, and `email-drafter`. - No manifest row or supplied skill file exists. - Proposal: add the missing skill and manifest row, or remove/replace all three handoffs. CRITICAL | REVIEW | Dangling delegation target: `bonusly-brand` - Referenced by `comms-drafter`, `email-drafter`, `sales-forecast`, and `signalforge-claim-compressor`. - No manifest row or supplied skill file exists. - Proposal: add the missing skill and manifest row, or replace these dependencies with an existing canonical brand reference. WARNING | REVIEW | Dangling delegation targets in `analysis-validator` - `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` - No manifest rows or supplied skill files exist. - Proposal: add manifest rows and files for each specialist skill before allowing `analysis-validator` to delegate validation. WARNING | REVIEW | Dangling orchestration target: `skill-orchestrator` - Referenced by `analysis-validator` and `signalforge-feedback`. - No manifest row or supplied skill file exists. - Proposal: add the orchestration skill and manifest row, or remove its required-registration and sync references. CRITICAL | UPDATE_BODY | `closed-lost-analysis` vs `stale-pipeline-report` - Conflict: `closed-lost-analysis` queries `GONG_TRANSCRIPTS_AGG.t.SNIPPET`; `stale-pipeline-report` states that `GONG_TRANSCRIPTS_AGG` has only `CONVERSATION_KEY` and `TRANSCRIPT`, and explicitly prohibits other transcript columns. - Surviving skill: `stale-pipeline-report` version 1.1, dated 2026-06-10. - Proposal: update `closed-lost-analysis` to use `t.TRANSCRIPT` and remove `t.SNIPPET`. WARNING | UPDATE_BODY | `analysis-validator` - Conflict: declared/body version is `3.6`, but its Validation Trail template identifies the validator as `analysis-validator v3.2`. - Surviving version: `3.6`. - Proposal: update the Validation Trail template version reference to `v3.6`. WARNING | UPDATE_BODY | `sales-forecast` - Conflict: the skill states it is current-quarter and quarter-agnostic, while Step 1A is titled “HubSpot: Open Q2 Deals,” Step 3 mandates a “Q2 Narrative” tab, and Step 0 hardcodes “Q2 (April 1 – June 30, 2026).” - Surviving version: changelog version `1.1`, which states Q2-specific content was removed in favor of current-quarter behavior. - Proposal: remove the stale Q2-specific body references. INFO | TRIM_DESC | Manifest description-length audit - Arithmetic: 14 manifest rows; 0 descriptions with `description_chars > 1,024`. - Maximum declared description length: 1,006 characters. - Proposal: no required trim for the 1,024-character manifest limit. WARNING | UPDATE_BODY | `analysis-validator` - Hardcoded dates: April 26, 2026; May 4, 2026; May 9, 2026; March 28, 2023; May 2026; Q1 2026; Q2 2026; and other dated changelog/reference claims. - Hardcoded person names: Manish, Amani, Dana Mercer, Gavin Porter, Alaina Loori, Shealagh Coughlin, Bryce Harmon, Hugo Lindqvist, Alex Franklin, Cole Ingram, Colleen Perry, Ellie Barton, Ashley Reyer, Ashley Le, Megan Franz, Elena Sinclair, Tracy, Youssef Elkhateeb, Amanda Czenkus, Ben Castelli, John Thomas, Yasmin Wahid. - Proposal: move roster, calibration anchors, and dated system-state assertions to live references. WARNING | UPDATE_BODY | `closed-lost-analysis` - Hardcoded dates: May 2026, May 4–12, May 4, 2026, March 28, 2023, and example dates such as 4/13 and 5/15. - Hardcoded named entities used as historical examples include Softheon, Estee Lauder, MinIO, LIFTOFF, Nestlé, Ozinga, Aurora Innovation, GCash, Ethos Cannabis, StickerYou, Nectar, Awardco, Motivosity, WorkTango, WorkHuman, Workday, ADP, Rippling, and UKG. - Proposal: move historical examples and dated “confirmed” statements to a maintained reference file. WARNING | UPDATE_BODY | `deal-strategy-coach` - Hardcoded page ID: `2257879045`. - Hardcoded dates: 2026 pricing, April 2026 playbook, six-month windows, and dated plan assumptions. - Hardcoded person name: Farid. - Proposal: replace the fixed Confluence page ID and dated commercial reference data with maintained references. WARNING | UPDATE_BODY | `partner-digest` - Hardcoded page/folder IDs: `2286616609`, `2286321666`, `2265382925`, `2236940297`, `2237825028`, `2239365136`, `2238283777`. - Hardcoded dates: May 16, 2026; May 17, 2026; May 19, 2026; June 2, 2026; Q2/Q3 2026. - Hardcoded person names: Amani Phipps, Kelli, Jen Lee, Hani, Bryce, Sara. - Proposal: move destinations, canonical-page references, contact names, and program lists to a maintained partner configuration reference. WARNING | UPDATE_BODY | `pipeline-intelligence-report` - Hardcoded dates: May 2026, March 2023, T120D/T180D windows, and version-specific references. - Hardcoded person names: Bryce Harmon, Dana Mercer, Cole Ingram, Alex Franklin, Gavin Porter. - Hardcoded HubSpot organization ID: `1973303` appears in required deal-link templates. - Proposal: move owner roster, organization URL configuration, and dated system-state assertions to referenced configuration. WARNING | UPDATE_BODY | `sales-forecast` - Hardcoded page IDs: `2232811524`, `2232582148`. - Hardcoded dates: April 1–June 30, 2026; July 9, 2026; April 27, 2026; Q1/Q2/Q3 2026. - Hardcoded person names: Alaina and Elena. - Proposal: move Confluence destination IDs, named leadership references, and quarterly examples to configuration/reference files. WARNING | UPDATE_BODY | `signalforge-feedback` - Hardcoded page IDs: `2295136266`, `2232811524`, `2234417154`, `2247295002`. - Proposal: move feedback-log and related Confluence identifiers into a maintained configuration reference. WARNING | UPDATE_BODY | `stale-pipeline-report` - Hardcoded date: June 10, 2026. - Hardcoded system IDs: Slack channel `C0561C1JCPJ` and HubSpot organization ID `1973303`. - Hardcoded named entity: Bonusly Support. - Proposal: move delivery-channel, HubSpot URL, and roster/exclusion configuration into references. WARNING | UPDATE_BODY | `weekly-pipeline-report` - Hardcoded spreadsheet IDs: `1CLZeOsElVDF_LF0ZG_t2nfwvhnZ6bpwqM_nX3WEYzcw`, `1ENuaEcCuLjdKhMvp8FK3Ys1ek5Aw9ZuOZhsHJJFoB_k`. - Hardcoded dates: April 1–June 30, 2026; Q1 2026; Q2 references throughout. - Hardcoded person name: Ben Lavin. - Proposal: move spreadsheet identifiers, owner targeting, and quarter-specific historical context to maintained references. INFO | REVIEW | `model-selection` - Hardcoded dates: `last_checked: 2026-05-19`, changelog dates April 14, 2026; May 19, 2026; model release/version dates. - Proposal: retain model metadata in a dedicated, refreshable registry reference rather than the skill body. INFO | REVIEW | `signalforge-claim-compressor` - Hardcoded date: 2026-05-09. - Proposal: retain only in changelog metadata or move changelog to a reference file. INFO | REVIEW | `comms-drafter`, `email-drafter`, `next-to-close` - No hardcoded Confluence page IDs, concrete dates, or individual person names found in the supplied bodies. - Proposal: no change required. INFO | REVIEW | Manifest drift: files with no manifest row - Arithmetic: 14 supplied skill files − 14 matching manifest file aliases = 0. - Proposal: no action required. INFO | REVIEW | Manifest drift: manifest rows with no supplied file - Arithmetic: 14 manifest rows − 14 matching supplied skill files = 0. - Proposal: no action required.
## Reward-worker queue incident runbook | Step | Exact command or action taken | Who ran it | Success verification | Rollback | Trace | |---|---|---|---|---|---| | 1 | Acknowledged PagerDuty alert for reward-worker queue depth >10k; took incident command (IC). | Bryce Harmon | Alert was acknowledged and IC was taken. | N/A — no state-changing system action stated. | M01 | | 2 | `bundle exec rake sidekiq:queue_depth` | Farid Osman | Reward queue returned **48,213 pending jobs**. Normal was stated as under 500. | N/A — read-only check. | M02 | | 3 | Checked the dead set. Exact command or console action was not provided. | Farid Osman | Found **112 jobs**, all `Redis::TimeoutError`, from around 13:58. | N/A — read-only check. | M03 | | 4 | `bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'` | Farid Osman | Thread states this was done to pause enqueue and stop the bleed. Explicit verification of the feature-flag state was not provided; needs confirmation. | `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'` | M04 | | 5 | Cleared out the dead set. Exact command or console action was not provided. | Elena Sinclair | No explicit verification of dead-set removal was provided; needs confirmation. | No rollback was provided; needs confirmation. | M05 | | 6 | `kubectl scale deployment/reward-worker --replicas=6` — scaled from 3 replicas. | Bryce Harmon | Subsequent update reported queue depth at 9,400 and falling at approximately 1,200 jobs/min. No explicit replica-count verification was provided. | `kubectl scale deployment/reward-worker --replicas=3` | M06, M07 | | 7 | Monitored reward queue depth. Exact command or action was not provided. | Farid Osman | Queue depth was **9,400** and falling approximately **1,200 jobs/min**. | N/A — monitoring only. | M07 | | 8 | `bundle exec rake sidekiq:queue_depth` | Cole Ingram | Command returned **0**. Datadog error rate was back to baseline. | N/A — read-only check. | M08 | | 9 | `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'` | Bryce Harmon | **40 new jobs** processed cleanly in the next **3 minutes**. | Needs confirmation. The thread contains `bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'` as the rollback for Step 4, but does not explicitly state it as rollback for this re-enable action. | M09; related rollback command in M04 | | 10 | `kubectl scale deployment/reward-worker --replicas=3` — scaled workers back down. | Bryce Harmon | Queue was stable at **0**; incident was resolved. | Needs confirmation. The thread does not explicitly provide a rollback for this scale-down action. | M10 |
First logged error: - 2026-09-03T14:01:12Z — `reward-service` — `Redis::TimeoutError: Connection to Redis at redis-primary:6379 timed out after 5s` Cascade, in logged order: 1. 14:01:12Z: `reward-service` times out connecting to `redis-primary:6379`. 2. 14:01:20Z, 14:01:30Z, 14:01:40Z: `reward-service` reports retries exhausted for `RewardGiveJob`. 3. 14:01:40Z onward: `sidekiq` reports `RewardGiveJob` failures and retries. 4. 14:02:30Z: `sidekiq` reports `Queue reward depth above 10,000`. 5. 14:03:05Z onward: `api-gateway` reports 502 upstream timeouts calling `reward-service`. 6. 14:03:30Z onward: `web-app` reports Give form submission failures caused by the upstream 502s. 7. 14:22:10Z: `reward-service` reports the Redis connection restored and job processing resuming. 8. 14:24:45Z: `sidekiq` reports reward queue depth below 500. Service and job: - Initial failing service: `reward-service` - Dependency implicated by the error: `redis-primary:6379` - Primary job in the log cascade: `RewardGiveJob` - `sidekiq_jobs.csv` also shows `RecognitionDigestJob` failures, beginning at 14:02:36Z; these occur after the first logged Redis timeout. Arithmetic from `sidekiq_jobs.csv`: - `RewardGiveJob` failures: 12 jobs (`J-00001` through `J-00012`) - `RecognitionDigestJob` failures: 4 jobs (`J-00013` through `J-00016`) - Total listed failed jobs: `12 + 4 = 16` Datadog query to confirm the first error: ```text service:reward-service status:error "Redis::TimeoutError" "redis-primary:6379" @timestamp:[2026-09-03T14:01:00Z TO 2026-09-03T14:01:20Z] ``` The logs do not show: - The root cause of the Redis timeout (for example, Redis saturation, network failure, failover, or configuration change). - Whether Redis itself was unavailable versus unreachable from `reward-service`. - The number of Give requests, users, companies, or rewards affected. - Whether any reward gives were lost, duplicated, or merely delayed. - The exact start time of the underlying Redis issue before the first logged timeout at 14:01:12Z. - Why `RecognitionDigestJob` also failed, beyond its recorded `Redis::TimeoutError`.
| Flag | State | What it controls (code) | Targets / targeting rule | Export company count | |---|---|---|---|---:| | `recognition_streaks_v2` | on | Records recognition/giving activity through `StreakTracker.record(give)`. | `segment:beta_companies` | 42 | | `points_budget_guardrails` | on | Enforces a company’s points budget through `BudgetService.new(company).enforce!(giver, points)`. | `all_companies` | 220 | | `slack_dm_nudges` | on | Sends Slack DM nudges via `SlackDm.send_nudge(user)`. | `segment:region_na` | 87 | | `redeem_flow_redesign` | off | Renders `RedeemV2Component`; when off, the code renders `RedeemV1Component`. | `targeted_list` | 12 | | `analytics_dashboard_v3` | on | Initializes the v3 analytics dashboard via `AnalyticsV3.new(company)`. | `segment:tier_three` | 65 | | `ms_teams_app_v2` | off | Installs the v2 Microsoft Teams app via `TeamsAppV2.install(company)`. | `targeted_list` | 9 | | `legacy_give_modal` | off | No code reference in the provided excerpt. | `segment:legacy_plan` | 14 | | `survey_boosters_q3` | on | No code reference in the provided excerpt. | `segment:legacy_plan` | 7 | | `paused_offboard_cleanup` | off | No code reference in the provided excerpt. | No targeting rule provided; no targets. | 0 | No company aliases are provided in the export, so individual companies cannot be identified. Arithmetic: - On-flag company assignments: `42 + 220 + 87 + 65 + 7 = 421` - Off-flag company assignments: `12 + 9 + 14 + 0 = 35` - These are flag-to-company assignments, not necessarily unique companies across flags.
- NDA-1 — GREEN (standard approval): Standard mutual NDA with complete carve-outs, a two-year term, three-year survival, and Delaware governing law: “Carve-outs: information that (a) is or becomes publicly available… (e) must be disclosed by law or court order” and “Governing law: the laws of the State of Delaware, USA.” - NDA-2 — YELLOW (counsel review): Embedded employment restriction requires review: “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): Broad non-compete, no confidentiality carve-outs, one-way obligations, five-year term, and unusual governing law/jurisdiction: “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”; and “governed by… the laws of the Republic of Ireland… exclusive jurisdiction of its courts.”