Mean 0.874 · 25/40 perfect tests · $0.58 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+ means deals currently at DS2, DS3, DS4, or DS5. - Why-buys is non-compliant when it is blank, a placeholder, or substantive text shorter than 15 characters. LONG(n) counts as present and substantive. - A regression is an earlier-stage timestamp later than a timestamp for a later stage. Equal timestamps are not regressions. Why-buys non-compliance: - Overall: 107/128 = 83.6%. - Alex Franklin: 48/60 = 80.0%. - Bryce Harmon: 23/26 = 88.5%. - Cole Ingram: 19/22 = 86.4%. - Dana Mercer: 16/19 = 84.2%. - Farid Osman: 1/1 = 100.0%. DS1 timestamp missing: 0 deals. Stage timestamps regressed: 0 deals. Process fixes: 1. Require substantive why-buys text of at least 15 characters before advancing a deal to DS2; reject placeholders. 2. Make DS1 entry timestamps mandatory and stage timestamps system-generated or validated against stage order. 3. Add a recurring hygiene report for missing why-buys and timestamp anomalies, with owner-level follow-up.
Resolved: 157 of 157 open deals. Unmatched owner IDs: None. Every owner ID on the open deals matches an entry in owners.csv. Archived/deactivated owners assigned to open deals: None. The owners table lists Gavin Porter (1520255671) and Hugo Lindqvist (77260721) as archived, but neither ID appears on these deals. Pipeline amount per resolved owner (arithmetic is the sum of the deal amounts): - Bryce Harmon (119337721), 35 deals: 24,000 + 19,656 + 13,500 + 7,000 + 2,520 + 240,000 + 99,000 + 72,000 + 70,000 + 63,600 + 45,000 + 1 + 21,000 + 23,400 + 13,680 + 5,502 + 8,160 + 1 + 11,400 + 1 + 36,000 + 31,500 + 6,000 + 10,800 + 30,275 + 17,400 + 12,600 + 18,000 + 37,440 + 18,828 + 2,880 + 36,000 + 20,880 + 10,920 + 25,200 = 1,054,144.00 - Alex Franklin (84342457), 68 deals: 14,850 + 13,770 + 11,200 + 9,000 + 6,360 + 5,400 + 5,400 + 3,240 + 2,484 + 1,920 + 1,080 + 7,200 + 19,000 + 2,880 + 1,400 + 4,800 + 1,632 + 10,000 + 9,300 + 2,700 + 2,160 + 1,800 + 3,600 + 3,840 + 15,000 + 1,968 + 4,000 + 3,600 + 4,800 + 3,120 + 2,520 + 9,000 + 2,400 + 62,000 + 5,400 + 5,100 + 16,700 + 4,400 + 1,620 + 2,600 + 7,200 + 18,000 + 17,000 + 8,316 + 8,100 + 18,000 + 12,600 + 24,000 + 15,000 + 9,000 + 7,200 + 3,780 + 16,200 + 7,200 + 4,680 + 1,800 + 18,000 + 2,730 + 2,400 + 3,060 + 18,000 + 12,000 + 1,800 + 4,400 + 31,200 + 7,200 + 1,600 + 60,000 = 629,710.00 - Dana Mercer (83155923), 24 deals: 11,250 + 10,500 + 9,000 + 9,000 + 5,400 + 4,800 + 4,600 + 1,920 + 15,000 + 4,200 + 18,900 + 27,000 + 43,875 + 20,000 + 60,000 + 8,100 + 16,250 + 3,150 + 5,000 + 2,100 + 23,400 + 5,400 + 7,350 + 25,000 = 341,195.00 - Cole Ingram (83155924), 22 deals: 2,760 + 1,330 + 7,225.40 + 3,360 + 5,616 + 2,700 + 7,781.20 + 6,947.50 + 40,000 + 12,168 + 2,480.40 + 11,116 + 11,193 + 1,875 + 31,750 + 32,175 + 4,779.88 + 3,334.80 + 58,529.25 + 4,140 + 18,000 + 18,900 = 288,161.43 - Farid Osman (716654662), 7 deals: 1,249 + 1 + 1 + 1 + 2,880 + 1 + 1 = 4,134.00 - Elena Sinclair (701163055), 1 deal: 2,100 = 2,100.00 Total: 1,054,144.00 + 629,710.00 + 341,195.00 + 288,161.43 + 4,134.00 + 2,100.00 = 2,319,444.43
(a) Orphans: 30 of 67 Gong rows. Arithmetic: 67 total rows − 37 deal IDs found in deals_open = 30. Sample orphan aliases: Deal-8FA85D, Deal-8FC3F9, Deal-3B7945, Deal-42B265, Deal-9CCC42. (b) Duplicate-conversation rows: 0. No row has calls_90d greater than distinct_conversation_keys. (c) DS3-or-later open deals with at least one logged call: 25 of 81, or 30.9%. Arithmetic: 25 ÷ 81 × 100 = 30.86%.
```sql -- Cannot construct the requested query from this catalog alone. -- COMPANY_COHORT_SUMMARY is described as one row per company, but no company identifier is listed. -- M1_USERS is not documented as unique givers, and M1_GIVES is not documented as recognition count. -- REDEMPTION_RECORDS_V2 documents STATE but lists no company or event-date columns to join/filter -- successful redemptions for each company's first customer month. ```
## Completeness Percentages are nonblank records divided by records supplied. For email, I also show syntactically valid addresses; a populated but invalid address is not treated as usable. | Object | Field | Complete | Arithmetic | |---|---|---:|---| | Companies (34) | Industry | 100% | 34/34 | | Companies (34) | Employee count | 73.5% | 25/34 | | Companies (34) | HQ country | 82.4% | 28/34 | | Contacts (52) | Email populated | 100% | 52/52 | | Contacts (52) | Email syntactically valid | 92.3% | 48/52 | | Contacts (52) | Title | 75.0% | 39/52 | | Contacts (52) | Persona | 71.2% | 37/52 | | Deals | Owner, stage, amount, close date, why-buys | N/A | No deal extract or deal records supplied | Industry is present for every company, but the values are not standardized: for example, “tech,” “Tech ”, “Technology,” and “Computer Software” appear. Presence completeness does not mean consistent categorization. ## Company duplicate clusters These clusters share an exact domain. The survivor is a proposed CRM consolidation target, not proof that the records are the same legal entity. Confirm identity before merging. | Shared domain | Aliases | Proposed survivor | |---|---|---| | acme-corp.com | C-0A092931, C-0A092932 | C-0A092931 | | globex.io | C-0A092933, C-0A092934 | C-0A092933 | For acme-corp.com, CRM records disagree on employee count (500 vs. 510) and industry (“Technology” vs. “tech”). For globex.io, industry differs (“SaaS” vs. “Technology”). The export has no matching rows for either domain, so it cannot resolve these differences. ## Invalid emails and domain mismatches Invalid email formats: - CT-0010: `user0@` - CT-0080: `user0@` - CT-0081: `user1@` - CT-0192: `user2@` Valid-format email/domain mismatch: - CT-0011: `user1@other-domain.com`; contact’s listed company domain is `66d1fc.com`. The extract does not establish whether CT-0011’s email is wrong or whether the contact is associated with the wrong company; verify before changing either value. ## Enrichment matches and disagreements The enrichment export has a matching domain for 25 of 34 company records. Eight missing employee counts can be filled from matching rows: | Company alias | CRM employee count | Enrichment employee count | |---|---:|---:| | C-EC3025 | Missing | 400 | | C-96039F | Missing | 400 | | C-44EA29 | Missing | 400 | | C-D04904 | Missing | 400 | | C-B23205 | Missing | 400 | | C-60C75F | Missing | 400 | | C-7BBDFA | Missing | 400 | | C-50D386 | Missing | 400 | One employee count remains missing without a matching enrichment row: C-93C8BF. Do not fill it from this export. No missing HQ-country value can be filled: the matching enrichment rows for C-2D1F1B, C-D73B89, C-44EA29, C-D04904, and C-2C60E5 also have no HQ country. C-EE9FFB has no matching enrichment row. Industry disagreements where both sources have a value: | Company alias | CRM industry | Enrichment industry | Recommendation | |---|---|---|---| | C-66D1FC | tech | Computer Software | Prefer enrichment if the CRM field is intended to use the enrichment taxonomy; otherwise map both to an agreed CRM category. | | C-EC3025 | Technology | Computer Software | Same | | C-44EA29 | tech | Computer Software | Same | | C-92D97D | Technology | Computer Software | Same | | C-D04904 | Technology | Computer Software | Same | | C-77A95A | Technology | Computer Software | Same | | C-AA8DDA | Technology | Computer Software | Same | | C-B25F40 | Technology | Computer Software | Same | | C-60C75F | tech | Computer Software | Same | | C-425E2A | Tech | Computer Software | Same | The export’s country values are consistent with CRM values where both are populated, allowing for variants such as “US,” “USA,” and “United States.” Those are formatting variants, not evidence of a country disagreement. ## Ten proposed fixes with pipeline amount at stake The extract contains no deals, deal-to-company/contact links, or deal amounts. Therefore, I cannot identify or rank fixes by pipeline amount at stake, or attach a dollar value to any of these. These are ten actionable data-quality fixes from the supplied records, not a pipeline-ranked list: 1. Fill C-EC3025 employee count with 400 from its matching enrichment row. 2. Fill C-96039F employee count with 400 from its matching enrichment row. 3. Fill C-44EA29 employee count with 400 from its matching enrichment row. 4. Fill C-D04904 employee count with 400 from its matching enrichment row. 5. Fill C-B23205 employee count with 400 from its matching enrichment row. 6. Fill C-60C75F employee count with 400 from its matching enrichment row. 7. Fill C-7BBDFA employee count with 400 from its matching enrichment row. 8. Fill C-50D386 employee count with 400 from its matching enrichment row. 9. Correct or verify CT-0011’s company association/email-domain mismatch. 10. Verify and correct the invalid email addresses for CT-0010, CT-0080, CT-0081, and CT-0192.
Classifications B = Bonusly; Buyer = buyer; Unknown = side not established by the text. I used the free-text reason to identify the primary cause; a competitor selection and its stated rationale can coexist. Pricing — 5 - Buyer: Deal-7ED004, Deal-7B2236, Deal-C33D91, Deal-DAFB82, Deal-8A119B Competitor — 21 - Buyer: Deal-F7F635, Deal-F97C37, Deal-422BA6, Deal-F1E8A6, Deal-ACE061, Deal-2D2F8D, Deal-0F96AA, Deal-1BCA50, Deal-A2C349, Deal-C7156E, Deal-8A0992, Deal-D0C698, Deal-EECC02, Deal-47F1A1, Deal-BF2A98, Deal-1E7DA9, Deal-286F9C, Deal-369281, Deal-9FCD0D, Deal-64B19A - Unknown: Deal-381C8C No decision — 31 - Buyer: Deal-13E9CF, Deal-E74A73, Deal-8E27DA, Deal-FAC17C, Deal-413C56, Deal-2A292B, Deal-7FBAC6, Deal-F325A5 - Unknown: Deal-AC944F, Deal-214060, Deal-21B045, Deal-988493, Deal-F308CA, Deal-4664E1, Deal-D48E0B, Deal-583ADB, Deal-E0441F, Deal-7CB44D, Deal-AFA56C, Deal-D1AABF, Deal-2BBA21, Deal-386F6E, Deal-55867E, Deal-3F86A0, Deal-096750, Deal-79E61A, Deal-AE7C4E, Deal-DAB4F1, Deal-B4B50F, Deal-5885B9 Timing — 22 - Buyer: Deal-DB0AAC, Deal-91A056, Deal-29326C, Deal-831B7B, Deal-39E25C, Deal-B3ABED, Deal-B6AC09, Deal-E6E80A, Deal-B038F0, Deal-175756, Deal-BB78F3, Deal-15DA99, Deal-F4AF5D, Deal-79B7A1, Deal-50E5D8, Deal-9F176A, Deal-5E64CE, Deal-69CF3D, Deal-ECBF89, Deal-D1A623, Deal-FEDBCB, Deal-2FEDDB Product gap — 6 - Bonusly: Deal-242273, Deal-9048EB, Deal-3618CC, Deal-5AD03E, Deal-981AD4, Deal-DC77FE Champion left — 0 - No supplied reason explicitly says a buyer-side champion left. Other — 5 - Unknown: Deal-5DB9B0, Deal-ED9AE7, Deal-7CC678, Deal-ABD14C, Deal-381C8C Counts and side split Category counts: pricing 5 + competitor 21 + no decision 31 + timing 22 + product gap 6 + champion left 0 + other 5 = 90 deals. Side split: buyer 56, unknown 28, Bonusly 6; 56 + 28 + 6 = 90. Clear tag/free-text disagreements: 0 under a strict direct-contradiction standard. Some tags are broad or the text adds detail—such as Deal-59086317965 (Deal-9048EB)—but the text does not directly contradict the tag. Deal-60008683142 (Deal-47F1A1) says they are staying with WorkTango for another 12 months; that is consistent with a competitor outcome, even though it also indicates timing. Two patterns most worth acting on 1. Timing and no-decision losses dominate: 22 + 31 = 53 of 90 deals. Prioritize a consistent revisit process for stated pauses, future dates, and internal-priority delays. 2. Competitor losses total 21; the text includes differentiation around capabilities, breadth, and integrations. Review those stated buyer needs against the product gaps identified in the six product-gap losses.
{"tier_counts":{"LOCK":null,"ACTION":null,"BUILD":null,"REVIVE":null,"WATCH":null,"RISKY":null},"tier_examples":{"LOCK":[],"ACTION":[],"BUILD":[],"REVIVE":[],"WATCH":[],"RISKY":[]},"risky_deals":[],"lock_violations":null,"pipeline_shape":"The files provide the requested signals, but no rule defines how to combine them into six tiers or when forecast and engagement disagree. Exact counts, examples, and lock violations would require inventing scoring thresholds."}
[
{
"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": [
"The HR team of three cannot keep up with anniversary and birthday awards manually.",
"Recognition is 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 — “We looked at Achievers last year, but it was too heavy for a team our size.”",
"next_step": "Security review agreed for September 12.",
"objections": [
"Needs SSO and audit logs for IT sign-off."
],
"confidence": "High — the prospect explicitly stated the use case, budget, timing, competitor experience, concern, and agreed next step."
},
{
"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 among 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": [
"Workday integration must be “rock solid” — stated as the CFO’s “one condition.”"
],
"confidence": "High — the prospect explicitly stated the business goal, budget, decision timing, condition, and next step."
},
{
"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."
],
"stakeholders": [
"Prospect (People Ops Manager)"
],
"budget_signal": null,
"timeline_signal": "“Honestly there's no rush on our side until Q1.”",
"competitor_mentioned": "Bucketlist — the prospect said the CEO used it at her last company and liked it.",
"next_step": "Schedule a call with the CEO; the prospect will send two times.",
"objections": [
"The CEO has to be sold first because she decides anything people-related."
],
"confidence": "High — the prospect explicitly stated the need, timing, decision-maker hurdle, competitor reference, and agreed next step."
},
{
"transcript_id": "TX-004",
"deal_alias": "Deal-180D02",
"why-buys": [
"“We want to consolidate three separate recognition tools into one.”"
],
"pain_points": [
"They are paying for three recognition tools.",
"The tools do not connect to their 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.” The IT Security Lead also said the security review took three months for their last vendor.",
"competitor_mentioned": null,
"next_step": null,
"objections": [
"The IT Security Lead cited the prior vendor's three-month security review as a hesitation.",
"A follow-up with the CFO was not confirmed: “Maybe — I need to check her calendar, no promises.”"
],
"confidence": "High — the prospect explicitly stated the consolidation need, approval threshold, procurement timing, and security concern; no follow-up was agreed."
},
{
"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": [
"Night-shift teams feel invisible.",
"Night-shift teams' 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 — “We're mid-pilot with Nectar right now, so you'd need to beat that experience.”",
"next_step": "Present to the exec team on October 2.",
"objections": [
"The exec team is skeptical after a failed rollout two years ago.",
"The prospect said the experience would need to beat the current Nectar pilot."
],
"confidence": "High — the prospect explicitly stated the use cases, pain, budget, timing, competitor, concerns, and agreed next step."
},
{
"transcript_id": "TX-006",
"deal_alias": "Deal-EE195F",
"why-buys": [
"“I'd love to cut the admin time on service awards.”"
],
"pain_points": [
"The HR Manager personally spends 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": "Doing it internally — the prospect said they are comparing the option with “just doing it internally.”",
"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": "High — the prospect explicitly stated the pain, budget position, timing, alternative, objection, and agreed next step."
}
]
1. Deal-547B2B — $11,200. Clean redlines; signing page is out, and the VP People said they’ll sign tomorrow. Left: signature. 2. Deal-403845 — $9,000. Order form is with their finance team. Left: finance review; Slack gives no confirmation of approval or signature. 3. Deal-A2B47C — $6,360. Slack says it’s still warm and at a normal legal-review pace; CRM lists DS5/COMMIT with a 2026-09-11 close date. Left: legal review. Slack gives the same status for Deal-D348E1, so the CRM’s earlier close date distinguishes this pick. Deal-2465CE is not included: despite CRM COMMIT, Slack says it’s blocked by a procurement freeze and lost champion.
## Real product gaps - Deal-EC3025 — Prospect: “We need SCIM user provisioning — our security team will not approve any tool that requires manual account management.” Classification: Real gap. Product docs do not list SCIM user provisioning as supported. Amount: Not provided. - Deal-D0D6B5 — 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. Product docs do not list ADP Workforce Now as a supported integration. Amount: Not provided. ## Other prospect-raised candidates - Deal-CFE7F4 — 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. The custom report builder is Enterprise-only. Amount: Not provided. - Deal-84DBA6 — 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. Slack integration is supported on all plans; the prospect identifies lack of manager training. Amount: Not provided. TG-005 is excluded: the mobile-app limitation is stated by the rep, and the prospect says the web version should be fine for now. No deal amounts were provided, so deal values or totals cannot be calculated.
Snapshot: 2026-09-05. Stale means the latest of last_email, last_call, and last_meeting was more than 7 days before the snapshot. Days are calculated as 2026-09-05 minus that latest date; for example, 2026-08-28 is 8 days ago. Amount totals are the sums of the listed deal amounts; no currency was specified. Alex Franklin — 19 stale deals; total amount: 112,856 | Deal alias | Stage | Amount | Days since last contact | |---|---:|---:|---:| | Deal-CC08D1 | DS1 | 24,000 | 16 | | 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-819506 | DS1 | 4,400 | 8 | | 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 | Bryce Harmon — 19 stale deals; total amount: 694,044 | 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-A414F6 | DS1 | 25,200 | 19 | | Deal-C5658B | DS1 | 23,400 | 16 | | Deal-40522D | DS3 | 21,000 | 19 | | Deal-C1FA6D | DS1 | 18,000 | 16 | | Deal-01E193 | DS1 | 12,600 | 8 | | Deal-F0EBBB | DS3 | 11,400 | 24 | | Deal-927338 | DS1 | 10,920 | 18 | | Deal-E25A09 | DS1 | 6,000 | 9 | | Deal-C9C286 | DS2 | 5,502 | 9 | | Deal-BA571A | DS4 | 1,080 | 18 | | Deal-012CB1 | DS1 | 1 | 23 | | Deal-3795AD | DS2 | 1 | 8 | Cole Ingram — 18 stale deals; total amount: 252,905.03 | 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 | 7,781.2 | 11 | | Deal-AF932D | DS2 | 7,225.4 | 11 | | Deal-A71728 | DS2 | 6,947.5 | 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 | 3,334.8 | 11 | | Deal-342E96 | DS2 | 2,700 | 24 | | Deal-E568D5 | DS3 | 1,875 | 11 | | Deal-FD9F4E | DS5 | 1,330 | 10 | Dana Mercer — 16 stale deals; total amount: 279,495 | 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-944310 | DS4 | 10,500 | 33 | | Deal-3974EB | DS4 | 9,000 | 8 | | Deal-B7EBD1 | DS5 | 9,000 | 16 | | 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 | Elena Sinclair — 1 stale deal; total amount: 2,100 | Deal alias | Stage | Amount | Days since last contact | |---|---:|---:|---:| | Deal-57FF13 | DS1 | 2,100 | N/A | Farid Osman — 3 stale deals; total amount: 2,882 | Deal alias | Stage | Amount | Days since last contact | |---|---:|---:|---:| | Deal-8BA24E | DS1 | 2,880 | 8 | | Deal-03BA69 | DS1 | 1 | 11 | | Deal-8FDCD2 | DS1 | 1 | 15 | Data gaps: Deal-3EED2C (Alex Franklin, 7,200) and Deal-57FF13 (Elena Sinclair, 2,100) have no matching row in the engagements-by-deal table. Deal-57FF13 is shown above because it was the only open deal for Elena Sinclair, but its recency is unknown; neither missing row provides evidence to determine whether the deal had an email, call, or meeting in the last 7 days.
I can’t validly rank the reps for the 2026-09-05 snapshot: the engagement rows include activity dates after the snapshot—for example, Deal-60083610979 has a last_meeting of 2026-09-22, and Deal-62044573757 has a last_meeting of 2026-09-15. The emails_30d, calls_30d, and meetings_30d fields aren’t dated to show they reflect the 30 days ending 2026-09-05. Using them would mix periods, so the per-rep totals, activity mix, and efficiency ranking aren’t supported by this data.
QTD scorecard — Alex Franklin As of 2026-09-05 Bookings vs. quota - Bookings: 150,000 across 8 wins; quota: 200,000. - Attainment: 150,000 ÷ 200,000 = 75%; 50,000 remains. - New: 113,500 across 5 wins (113,500 ÷ 150,000 = 75.7%). - Expansion: 36,500 across 3 wins (36,500 ÷ 150,000 = 24.3%). - Excluded the pre-quarter win Deal-B3E6F1 (24,000; closed 2026-06-20). Active pipeline by stage | Stage | Deals | Amount | |---|---:|---:| | DS1 | 20 | 284,621 | | DS2 | 28 | 353,760 | | DS3 | 67 | 552,705 | | DS4 | 5 | 23,574 | | DS5 | 5 | 45,730 | | Total | 125 | 1,260,390 | Rolling 90-day DS2-to-won - Cohort: deals entered DS2 from 2026-06-07 through 2026-09-05, inclusive. - Won: 8 of 111 cohort deals; rate = 8 ÷ 111 = 7.2%. - Cohort status: 8 won + 27 lost + 76 still open = 111. QTD wins and losses - Wins: 8. - Losses: 27. - Top recorded loss reason: “Lost- Timing (1 year or more)” on 13 of 27 losses; 13 ÷ 27 = 48.1%. Last-30-day activity Summed across the 161 deal-linked engagement rows provided: - Emails: 807 - Calls: 112 - Meetings: 128 - Notes: 50 Coaching observations 1. Bookings are 75% of quota, leaving 50,000 to reach target. 2. DS3 holds 552,705 of 1,260,390 in active pipeline; DS1 and DS2 together hold 638,381 (284,621 + 353,760), so a substantial portion remains in earlier stages. 3. Timing is the most frequently recorded loss reason (13 of 27). The DS2-to-won rate is 7.2% on the full 111-deal cohort, including 76 still open.
The files do not include deal amount, stage, or open/closed status. The list below flags qualifying deals in `deal_contacts.csv`, assuming they are open. Active contacts are non-former and engaged on or after 2026-07-26 (60 days before 2026-09-24). Stage-specific persona priority cannot be determined without stage data. - Deal-EC3025 — Amount/stage: not provided. Active contacts: 1. Personas present: champion. Missing: economic buyer, HR admin, IT security, finance. Most valuable persona to add given stage: cannot determine; stage not provided. Unengaged contact on file: CT-6827DB (Chief People Officer, economic buyer). - Deal-92D97D — Amount/stage: not provided. Active contacts: 1. Personas present: HR admin. Missing: economic buyer, champion, IT security, finance. Most valuable persona to add given stage: cannot determine; stage not provided. Unengaged contact on file: none. - Deal-50D386 — Amount/stage: not provided. Active contacts: 2. Personas present: champion, HR admin. Missing: economic buyer, IT security, finance. Most valuable persona to add given stage: cannot determine; stage not provided. Unengaged contact on file: CT-A1C4B3 (Chief People Officer, economic buyer). - Deal-D0D6B5 — Amount/stage: not provided. Active contacts: 3. Personas present: champion. Missing: economic buyer, HR admin, IT security, finance. Most valuable persona to add given stage: cannot determine; stage not provided. Unengaged contact on file: CT-1FA4DB (Chief People Officer, economic buyer). - Deal-5BFE3B — Amount/stage: not provided. Active contacts: 2. Personas present: champion. Missing: economic buyer, HR admin, IT security, finance. Most valuable persona to add given stage: cannot determine; stage not provided. Unengaged contact on file: none. - Deal-36C33F — Amount/stage: not provided. Active contacts: 1. Personas present: IT security. Missing: economic buyer, champion, HR admin, finance. Most valuable persona to add given stage: cannot determine; stage not provided. Unengaged contact on file: CT-1DB73E (Chief People Officer, economic buyer). - Deal-885F45 — Amount/stage: not provided. Active contacts: 2. Personas present: economic buyer, champion. Missing: HR admin, IT security, finance. Most valuable persona to add given stage: cannot determine; stage not provided. Unengaged contact on file: CT-B3F25D (IT Security Lead, IT security). - Deal-FCBE5B — Amount/stage: not provided. Active contacts: 1. Personas present: champion. Missing: economic buyer, HR admin, IT security, finance. Most valuable persona to add given stage: cannot determine; stage not provided. Unengaged contact on file: none. - Deal-5408B0 — Amount/stage: not provided. Active contacts: 2. Personas present: champion, HR admin. Missing: economic buyer, IT security, finance. Most valuable persona to add given stage: cannot determine; stage not provided. Unengaged contact on file: CT-07FA76 (Chief People Officer, economic buyer). - Deal-C6D97A — Amount/stage: not provided. Active contacts: 3. Personas present: champion. Missing: economic buyer, HR admin, IT security, finance. Most valuable persona to add given stage: cannot determine; stage not provided. Unengaged contact on file: none. - Deal-F9A08A — Amount/stage: not provided. Active contacts: 1. Personas present: champion. Missing: economic buyer, HR admin, IT security, finance. Most valuable persona to add given stage: cannot determine; stage not provided. Unengaged contact on file: CT-697541 (Chief People Officer, economic buyer). Arithmetic: Deal-D0D6B5 and Deal-C6D97A have 3 active contacts but only 1 persona, so they qualify as single-persona deals. All other flagged deals have fewer than 3 active contacts.
First five minutes: Alex Franklin led with a customer proof point in 8/10 calls (80%); the other two opened with an agenda or straight pricing. In the repeated proof-point opening, Alex cited a retailer’s turnover reduction after automating milestone awards. Three most common objections and responses: - Budget (4 calls): In three budget-objection calls, Alex countered with turnover-savings rationale. Quote: “Most teams fund this out of turnover savings — that retailer saved about $210k in avoided backfills, which is how their finance team signed off.” (TT-001) - Timing (3 calls): Alex proposed a limited pilot to build internal data ahead of next-quarter planning. Quote: “Makes sense. What if we scope a 90-day pilot with one department so you have internal data before next quarter's planning?” (TT-002) - Spreadsheet / gift-card status quo (3 calls): Alex argued that automation and analytics become more valuable as recognition scales. Quote: “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.” (TT-004) Concrete next step agreed: 7/10 calls = 70%. Counted calls where the prospect agreed to a specific working session; the other three did not produce an agreed next step. Competitors prospects raised: Awardco (Deal-547B2B) and Kudos (Deal-EDC141). Workhuman was mentioned by Alex, not raised by a prospect. Coaching notes: 1. The same proof-point opening recurs in 8/10 calls; tailor the opening to the prospect’s stated priorities where available. 2. When a prospect cannot commit, clarify the decision process and agree on a low-friction follow-up rather than ending without a next step.
## Q3 2026 forecast Only deals closing 2026-07-01 through 2026-09-30 are included. - **COMMIT:** 7 deals; total amount **44,729** - **BEST_CASE:** 24 deals; total amount **203,565** - **PIPELINE:** 22 deals; contributes **0** - **Weighted forecast:** **44,729 + (203,565 × 35%) = 44,729 + 71,247.75 = 115,976.75** ### Excluded: outside the quarter **32 deals; total amount 227,575.** Excluded because their close dates fall after 2026-09-30: 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-6691E0 (5,700), Deal-C61CF7 (5,400), 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 the quarter 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 Owner is blank for nearly all deals, limiting accountability and owner-level checks. `why_buys_chars` is 0 for most records, so buyer rationale appears largely absent. COMMIT appears at early stages, including DS1 and DS2, which may indicate stage/category inconsistencies. The extract includes an amount with a decimal (2,480.4), unlike the other listed amounts.
| First-month signals | Companies | Retained at 24 months | Retention rate | |---|---:|---:|---:| | Both (m1_users ≥ 5 and m1_redemptions ≥ 1) | 43 | 29 | 29 ÷ 43 = **67.4%** | | Givers-only | 45 | 24 | 24 ÷ 45 = **53.3%** | | Redemption-only | 23 | 9 | 9 ÷ 23 = **39.1%** | | Neither | 81 | 33 | 33 ÷ 81 = **40.7%** | **Excluded:** 0 companies. All 192 companies have a current_status value; non-active statuses count as not retained. **Largest lift for a single signal:** Givers-only, compared with neither: 53.3% − 40.7% = **+12.6 percentage points**. Redemption-only versus neither: 39.1% − 40.7% = **−1.6 points**. **What this shows:** In this cohort, the both-signals group had higher 24-month retention than each other group: 67.4% versus 53.3%, 39.1%, and 40.7%. **What it does not prove:** This is an observed association, not evidence that the signals cause retention. The extract does not establish why groups differ or control for other factors.
As of 2026-09-05, using the subscription statuses provided and counting only `active` subscriptions for billing ARR: - CRM company ARR: **$603,581.76** (sum of 39 company records) - Billing ARR: **$604,739.28** (sum of 37 active subscriptions’ MRR × 12) - Variance (billing − CRM): **+$1,157.52** Variance decomposition: - Status mismatch: **−$13,158.48** — CRM ARR remains on accounts whose subscriptions are marked `cancelled`. - Rounding: **$0.00** — the provided data doesn’t establish rounding as the cause of any difference. - Missing records: **+$11,952.00** — billing-only $28,449.24 less CRM-only $16,497.24. - Other: **+$2,364.00** — unexplained active-account differences: +$2,400.00, −$16.00, and −$20.00. - Check: **−$13,158.48 + $0.00 + $11,952.00 + $2,364.00 = +$1,157.52** Mismatched accounts (variance = billing − CRM): | Company alias | Billing ARR | CRM ARR | Variance | Bucket | Suggested owner | |---|---:|---:|---:|---|---| | C-0C8323BF | $0.00 | $4,905.24 | −$4,905.24 | Status mismatch | CRM Ops; verify CRM ARR against cancelled subscription | | C-0DC4FB8C | $0.00 | $8,253.24 | −$8,253.24 | Status mismatch | CRM Ops; verify CRM ARR against cancelled subscription | | C-0D5BBE3A | $0.00 | $16,497.24 | −$16,497.24 | Missing record | CRM Ops; verify missing billing subscription or CRM ARR | | C-21629AA4 | $28,449.24 | $0.00 | +$28,449.24 | Missing record | CRM Ops; verify missing CRM company record | | C-0D66DF9E | $23,184.00 | $23,200.00 | −$16.00 | Other | RevOps; reconcile ARR values | | C-14D70CE0 | $18,180.00 | $18,200.00 | −$20.00 | Other | RevOps; reconcile ARR values | | C-0F7269D7 | $26,796.00 | $24,396.00 | +$2,400.00 | Other | RevOps; reconcile ARR values | No individual owner names were provided, so suggested owners are functional roles. Agreement-date violations (term is not 12 months and `cf_agreement_end_date` is blank): - `SUB-0002` — `C-1794A52C`, 24 months - `SUB-0019` — `C-22170CA1`, 36 months
Equal-weighted company averages across the 30 aliases. Absolute changes are percentage points (pp) for engagement rates and giving rate; relative change = (August − July) ÷ July. | KVM | 2026-08 | 2026-07 | Absolute change | Relative change | Direction | |---|---:|---:|---:|---:|---| | Giving rate | 60.2713% | 60.2297% | +0.0417 pp | +0.07% | Up | | Redemptions per user | 1.73016 | 1.72998 | +0.00018 | +0.01% | Up | | 1:1 meetings engagement | 44.7177% | 44.6887% | +0.0290 pp | +0.06% | Up | | Pulse check engagement | 50.8610% | 60.0587% | −9.1977 pp | −15.31% | Down | Largest relative move: pulse check engagement. The `enterprise` size_band drove the decline: its average fell from 54.998% to 27.428% (−27.57 pp). The supplied data supports size_band as the driver; all records have `plan_tier` `tier_three`. Enterprise aliases: C-0B2895EF, C-0B2213A9, C-0D6CC8E3, C-0D0B047C, C-0D3278C7, C-0FCCD2DF, C-0F6C0F34, C-8C2E8F00, C-0B827671, C-0BA71F12.
Redemptions — through August 2026 Last completed month: August 2026 - Redemptions: 48 - Spend: $3,536.00 - Unique redeemers: 46 - Redemptions per redeemer: 48 ÷ 46 = 1.04 - Provider mix by spend: - TangoCard: 14.14% - Tremendous: 32.38% - custom: 29.89% - Snappy: 23.59% - Total: 100.00% - Top 5 countries by redemptions: US 31; CA 7; GB 2; SG 2; AU 2
Eligibility applied from the 2026-09-05 snapshot: health score <60, eligible amount >$0, and renewal within 120 days (through 2027-01-03). Eight accounts qualify. | Account | Amount at stake | Play that fits | Supporting signal | |---|---:|---|---| | C-0F6C0F34 | $49,707 | Executive touch | Champion inactive. Usage is growing and 308/395 seats are used (78.0%), so usage revival is not the clearest signal. | | C-0B827671 | $25,365 | Usage revival | Usage declining; 113/202 seats used (55.9%). | | C-0B360C78 | $35,748 | No specific play supported by the provided signals | Usage growing, 246/327 seats used (75.2%), champion active. Health score is 57, but there’s no stated cause or recovery signal to distinguish a play. | | C-0B0F1BAB | $5,494 | Executive touch | Champion inactive. Usage is flat; 238/363 seats used (65.6%). | | C-0CA21961 | $16,829 | Usage revival | Only 84/325 seats used (25.8%); usage is flat. | | C-0E9C27D1 | $41,235 | No specific play supported by the provided signals | Renewal is 19 days away; champion is active and 134/157 seats are used (85.4%). Usage is flat, but the data doesn’t identify a cause or a recovery play. | | C-0CEF69FD | $32,621 | Executive touch | Champion inactive. Usage is growing; 97/136 seats used (71.3%). | | C-0D3278C7 | $17,602 | Usage revival | Usage declining and only 126/380 seats are used (33.2%). | Total at stake: $49,707 + $25,365 + $35,748 + $5,494 + $16,829 + $41,235 + $32,621 + $17,602 = **$224,601**. **Commercial concession:** None is specifically supported by the supplied signals. The rules establish eligibility and an amount at stake, but provide no pricing objection, discount history, or other commercial signal to justify a concession. The same rules don’t define play-selection criteria, so the plays above are signal-based recommendations, not documented eligibility rules. **At-risk but not eligible:** - C-0BC71BDD — $0 eligible amount; renewal is 52 days away. - C-0BA71F12 — $6,824 eligible amount, but renewal is 218 days away. - C-0F6694C3 — $0 eligible amount; renewal is 197 days away. - C-0BE96399 — $0 eligible amount; renewal is 54 days away. - C-0F876796 — $19,958 eligible amount, but renewal is 154 days away. - C-0FCCD2DF — $0 eligible amount; renewal is 230 days away. - C-10A56B0F — $0 eligible amount; renewal is 98 days away.
Seat coverage: 150 licensed seats / 400 headcount = 37.5%; 250 headcount are beyond current licensed seats. Usage health: - Monthly active users rose from 88 in March to 126 in August: +38, or 43.2%. - August activity was 126 / 150 = 84% of licensed seats, leaving 150 − 126 = 24 licensed seats unused. Headroom at the current per-seat rate: - Current rate: $9,000 ARR / 150 seats = $60 per seat per year. - 250 additional seats to reach headcount; theoretical incremental ARR: 250 × $60 = $15,000/year. This assumes all headcount could be covered at the current rate. - The 24 unused licensed seats are already within current ARR; they are not additional ARR headroom. Reply and buying authority: Maria S., People Operations Coordinator, replied on September 2 and said she is not the purchasing decision-maker. She identified Dana R., VP People, as responsible for budget and seat expansion, and offered to introduce her. Right buyer in provided contacts: Dana R., VP People (last engaged May 18, 2026). Reply email (under 150 words): Hi Maria, Thanks for sharing this—and glad to hear the team is enjoying Bonusly. One useful data point: monthly active users increased from 88 in March to 126 in August. Since you mentioned Dana handles budget and seat expansion and has been asking about usage, would you be comfortable introducing us? I can share the usage figures and discuss whether additional seats make sense for the team. No pressure if now isn’t the right time. Best, Cole
C-0D284E42 — mid-onboarding prep Completed - Slack integration: connected 2026-08-12. - Allowance: set 2026-08-13. - Admins: 2 added. - First recognition: 2026-08-15 14:22. - HRIS integration: not shown as connected. - First redemption: not shown. Early engagement - Active givers rose from 3 on 2026-08-11 to 15 on 2026-09-04: +12, or +400% ((15−3)÷3). - The 25 daily observations average 8.56 active givers. The last 7-day average was 13, versus 4.29 for the first 7 days. - The series is not strictly increasing: active givers fell on some days, including 13 to 11 between 2026-08-30 and 2026-08-31. Cover on the call 1. Confirm whether HRIS integration is needed and, if so, what remains to connect it. 2. Check whether any redemptions have occurred; the provided field is blank. If not, identify and address any blocker. 3. Review the rise in active givers and agree on the next engagement milestone; the data does not show total eligible users or a target, so adoption rate cannot be calculated.
90-day renewal risk brief — as of 2026-09-24 Scope: renewals dated 2026-09-25 through 2026-12-23, inclusive. For multi-year contracts, I use Chargebee’s date because you specified that ChurnZero’s multi-year renewal dates are known to be wrong. The three multi-year accounts with Chargebee dates before 2026-09-25 are outside this window; their date conflicts are listed below. Risk method: High = seat utilization below 40% and declining usage; Medium = utilization below 60% or declining usage; Low = neither. “At risk” below means High or Medium. Usage trend is active users in June → July → August 2026; change compares August with June. Seat utilization = seats used ÷ seats. | Company alias | CSM | ARR | Date used | Seat utilization | 3-month usage trend | Risk and evidence | |---|---|---:|---|---:|---|---| | C-0BBE3E60 | Dana Mercer | $30,993 | 2026-09-26 (Chargebee) | 74/114 = 64.9% | 39 → 35 → 33 (−6; −15.4%) | Medium — usage fell by 6 while utilization is 64.9%. | | C-0F5D2323 | Cole Ingram | $90,647 | 2026-09-29 (Chargebee) | 111/390 = 28.5% | 20 → 21 → 18 (−2; −10.0%) | High — utilization is 28.5% and usage fell by 2. | | C-0EC6999D | Elena Sinclair | $79,419 | 2026-10-03 (dates agree) | 31/112 = 27.7% | 17 → 16 → 15 (−2; −11.8%) | High — utilization is 27.7% and usage fell by 2. | | C-0B20DB64 | Dana Mercer | $21,770 | 2026-10-07 (dates agree) | 214/378 = 56.6% | 294 → 298 → 294 (0; 0.0%) | Medium — seat utilization is below 60%. | | C-0BBC4E7A | Cole Ingram | $56,374 | 2026-10-10 (dates agree) | 228/337 = 67.7% | 142 → 141 → 139 (−3; −2.1%) | Medium — usage fell by 3 over the three months. | | C-0FD551AB | Elena Sinclair | $48,815 | 2026-10-14 (dates agree) | 210/376 = 55.9% | 123 → 122 → 126 (+3; +2.4%) | Medium — seat utilization is below 60%. | | C-0F9F8F13 | Dana Mercer | $46,230 | 2026-10-18 (dates agree) | 199/352 = 56.5% | 185 → 185 → 182 (−3; −1.6%) | Medium — utilization is below 60% and usage declined. | | C-0BC34584 | Cole Ingram | $16,740 | 2026-10-22 (dates agree) | 327/494 = 66.2% | 104 → 104 → 106 (+2; +1.9%) | Low — utilization is at least 60% and usage increased. | | C-0B7A7546 | Elena Sinclair | $35,062 | 2026-10-25 (dates agree) | 182/205 = 88.8% | 64 → 65 → 63 (−1; −1.6%) | Medium — usage fell by 1 over the three months. | | C-0B369871 | Dana Mercer | $85,128 | 2026-10-29 (dates agree) | 317/422 = 75.1% | 326 → 330 → 333 (+7; +2.1%) | Low — utilization is at least 60% and usage increased. | | C-0B144C78 | Cole Ingram | $30,899 | 2026-11-02 (dates agree) | 169/224 = 75.4% | 101 → 101 → 106 (+5; +5.0%) | Low — utilization is at least 60% and usage increased. | | C-0FC4DBB8 | Elena Sinclair | $94,732 | 2026-11-05 (dates agree) | 356/464 = 76.7% | 189 → 191 → 193 (+4; +2.1%) | Low — utilization is at least 60% and usage increased. | | C-0D5BBE3A | Dana Mercer | $39,740 | 2026-11-09 (dates agree) | 85/102 = 83.3% | 88 → 90 → 91 (+3; +3.4%) | Low — utilization is at least 60% and usage increased. | | C-0FB9D5AF | Cole Ingram | $63,158 | 2026-11-13 (dates agree) | 144/199 = 72.4% | 173 → 173 → 176 (+3; +1.7%) | Low — utilization is at least 60% and usage increased. | | C-0B344485 | Elena Sinclair | $64,384 | 2026-11-16 (dates agree) | 224/287 = 78.0% | 238 → 240 → 244 (+6; +2.5%) | Low — utilization is at least 60% and usage increased. | | C-0CB2C1B4 | Dana Mercer | $40,628 | 2026-11-20 (dates agree) | 386/473 = 81.6% | 47 → 48 → 49 (+2; +4.3%) | Low — utilization is at least 60% and usage increased. | | C-22170CA1 | Cole Ingram | $45,646 | 2026-11-24 (dates agree) | 251/294 = 85.4% | 143 → 148 → 146 (+3; +2.1%) | Low — utilization is at least 60% and August usage remains above June. | Date disagreements — all are multi-year contracts, so Chargebee is the selected source; each Chargebee date is before the 90-day window: - C-0B7D2C30: ChurnZero 2026-09-10 vs Chargebee 2026-09-15; use Chargebee. - C-0BCDB8C2: ChurnZero 2027-09-18 vs Chargebee 2026-09-18; use Chargebee. - C-0D2AB865: ChurnZero 2026-09-10 vs Chargebee 2026-09-22; use Chargebee. - C-0BBE3E60: ChurnZero 2027-09-26 vs Chargebee 2026-09-26; use Chargebee. - C-0F5D2323: ChurnZero 2026-09-10 vs Chargebee 2026-09-29; use Chargebee. Summary: 17 accounts renew within the window. Total ARR renewing: $890,365. High risk: $170,066; Medium risk: $239,244; Low risk: $481,055. ARR at risk = High + Medium = $170,066 + $239,244 = $409,310. Company names beyond the provided account aliases were not supplied. Risk ratings are based only on the provided seat and usage data; no renewal outcomes or other risk signals were provided.
**ARR exposure** below is the sum of each theme’s distinct affected accounts’ ARR, counted once per theme—not per ticket. It is an exposure proxy; ARR may overlap across themes. Shares use all 80 tickets as the denominator. | Rank | Theme | Count / share | Distinct accounts | ARR affected | Two ticket IDs | Recommendation | |---|---|---:|---|---:|---|---| | 1 | HRIS provisioning failures — broad pattern | 12 / 15% (12 ÷ 80) | 3: C-0B2213A9, C-0F6C0F34, C-0DDFC9A7 | $114,000 ($36,000 + $30,000 + $48,000) | IC-460059, IC-460055 | Investigate provisioning and sync processing across affected HRIS integrations; add monitoring for skipped or uncreated accounts. | | 2 | Redemption and gift-card failures — broad pattern | 18 / 22.5% (18 ÷ 80) | 7: C-0CEF69FD, C-0B827671, C-0FCCD2DF, C-0F876796, C-14264ABD, C-0D9CA315, C-0B0F1BAB | $68,800 ($8,900 + $10,700 + $9,600 + $8,700 + $11,000 + $9,600 + $10,300) | IC-460025, IC-460030 | Trace checkout-to-fulfillment failures and reconcile cases where points were deducted without a successful redemption. | | 3 | Billing and invoice discrepancies — single-account concentration | 16 / 20% (16 ÷ 80) | 1: C-0E9C27D1 | $52,000 ($52,000) | IC-460071, IC-460069 | Review seat counts, tier pricing, and renewal calculations for this account; treat this as concentrated account exposure, not a broad pattern. | | 4 | Recognition points not posting — broad pattern | 20 / 25% (20 ÷ 80) | 9: C-0D3278C7, C-0BF20542, C-0D0B047C, C-0BE96399, C-0D284E42, C-0D6CC8E3, C-21FEBCBB, C-0DD0626C, C-0B2895EF | $31,100 ($3,500 + $4,500 + $4,500 + $2,700 + $3,400 + $4,200 + $2,900 + $2,500 + $2,900) | IC-460004, IC-460016 | Investigate recognition-to-balance posting and team-wide delays; verify delivered recognitions reconcile to points credited. | | 5 | Slack integration and command failures — broad pattern | 14 / 17.5% (14 ÷ 80) | 4: C-0B843542, C-10A56B0F, C-0BA71F12, C-8C2E8F00 | $18,900 ($4,400 + $5,400 + $3,900 + $5,200) | IC-460041, IC-460047 | Check Slack sync, authentication persistence, and slash-command errors as related integration reliability issues. | The four multi-account themes are broad patterns. Billing is repeated but confined to C-0E9C27D1, so it is reported separately as a single-account concentration.
Using one point per matching field (industry, size band, use case, region), these three case-study customers tie at 3/4: 1. C-11C31562 — Industry: Manufacturing (different); size band: Mid-Market (match); use case: employee_recognition (match); region: NA-West (match). Arithmetic: 0 + 1 + 1 + 1 = 3/4. 2. C-64171065 — Industry: Technology (match); size band: Mid-Market (match); use case: employee_recognition (match); region: NA-East (different). Arithmetic: 1 + 1 + 1 + 0 = 3/4. 3. C-A13C193D — Industry: Technology (match); size band: Mid-Market (match); use case: retention (different); region: NA-West (match). Arithmetic: 1 + 1 + 0 + 1 = 3/4. The provided fields do not distinguish among these tied matches.
Trailing six months: March–August 2026. I counted each contact with an SQO date as one SQO and summed its recorded pipeline amount. SQO-before-SQM rows are flagged below and remain included in these totals. Paid performance | Channel | Spend | SQMs | SQOs | Cost per SQM | Cost per SQO | SQM-to-SQO rate | Pipeline | Pipeline per $ | |---|---:|---:|---:|---:|---:|---:|---:|---:| | 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 paid | $87,000 | 77 | 31 | $1,129.87 | $2,806.45 | 40.3% | $876,000 | $10.07 | Calculations: - Spend: 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 per SQM = spend ÷ SQMs; cost per SQO = spend ÷ SQOs; SQM-to-SQO rate = SQOs ÷ SQMs; pipeline per dollar = pipeline ÷ spend. - Total paid: $87,000 ÷ 77 = $1,129.87 per SQM; $87,000 ÷ 31 = $2,806.45 per SQO; 31 ÷ 77 = 40.3%; $876,000 ÷ $87,000 = $10.07 per dollar. - paid_social spent $18,000 but has zero SQMs and SQOs, so cost per SQM and cost per SQO are undefined, not zero. Organic and other non-paid - organic_search: volume 30 SQMs; 10 SQOs; SQO rate 10 ÷ 30 = 33.3%; pipeline $90,000. - referral, shown separately because it is listed as its own channel: volume 15 SQMs; 6 SQOs; SQO rate 6 ÷ 15 = 40.0%; pipeline $48,000. - Combined organic_search + referral: 45 SQMs, 16 SQOs, rate 16 ÷ 45 = 35.6%, pipeline $138,000. No spend was supplied for these sources, so cost metrics and pipeline per dollar cannot be calculated. Date-order flags - CT-000044 (linkedin_ads): SQO date 2026-07-18 precedes SQM date 2026-07-23. - CT-000041 (linkedin_ads): SQO date 2026-06-09 precedes SQM date 2026-06-14. Recommendation: Reduce or pause paid_social pending attribution validation; it accounts for $18,000 of spend with no attributed SQMs in the supplied data. Consider shifting a limited test budget toward paid_search, which has the strongest observed pipeline per dollar ($20.00) and 18 SQOs. Preserve webinars for now, and investigate the two LinkedIn date-order exceptions before making a larger LinkedIn budget change. Confidence: Low to moderate. paid_search has 18 SQOs, but webinars has 5, LinkedIn has 8 (including two date-order exceptions), and paid_social has none. These channel-level samples—and the six-month window—are limited; the figures support cautious testing, not a definitive long-term allocation.
# Rivally battlecard One-line positioning: Rivally offers points-based recognition with a lightweight engagement-survey add-on; reviewers praise its recognition feed but report limitations in analytics and admin tooling. [S02, S06, S16, S24] Pricing (source and date): - Latest listed price: Recognition Starter is $7 per user/month, with annual billing required, according to Rivally’s pricing page updated 2026-08-12. [S17] - A prospect said Rivally quoted $7/user/month list and offered a 15% discount for a 3-year term on 2026-08-14. [S18] Applying that discount to the listed $7 gives $5.95/user/month ($7 × 0.85); that is arithmetic on a prospect-reported offer, not a confirmed generally available price. [S17, S18] - Conflict: earlier pricing-page sources listed $5/user/month in January and still showed $5 for Recognition Starter in April; a June call note reported a $6.50/user/month quote to a 500-seat prospect. [S03, S08, S13] The newer August pricing-page source lists $7, so use $7 as the latest listed price; the deal-specific quotes do not establish a different current list price. [S13, S17] Where they win: - Ease of setup and Slack: a mid-market reviewer said setup took under a week and Slack worked out of the box. [S04] - EU needs: a prospect mention says Rivally pitched EU data residency; a reviewer praised support for distributed EU teams and multiple languages; Rivally later announced EU data residency generally available. [S05, S12, S15] - Recognition experience and support: reviewers praised its points-based/engaging recognition feed and one review praised support response time under four hours. [S02, S16, S22] Where we win: - Analytics depth is the clearest supplied win signal: an 800-seat prospect chose Bonusly over Rivally, citing analytics depth. [S25] - Rivally’s analytics and admin limitations appear in reviewer feedback: limited analytics, basic dashboards, lagging admin tooling, and no bulk recognition editing. [S02, S07, S16, S24] These are reviewer reports, not independently verified product facts. Objections and responses: - “We need EU data residency or multilingual support.” Acknowledge that Rivally announced EU data residency generally available and has a reviewer signal praising multilingual support for distributed EU teams. [S12, S15] Confirm the buyer’s specific requirements; the supplied data does not establish Bonusly’s comparable capabilities. - “Rivally has Slack integration.” That is consistent with a reviewer report that Slack worked out of the box; the existing card’s claim that Rivally lacks Slack integration is contradicted by this source. [S04] - “Rivally’s analytics are enough for us.” Reviewers have reported limited analytics and basic dashboards, while one 800-seat prospect selected Bonusly citing analytics depth. [S02, S07, S25] Ask the buyer to validate the reports and export workflows they require; do not assume the review reports apply to their configuration. - “Rivally is cheaper.” The latest supplied pricing-page source lists $7/user/month with annual billing; a prospect-reported 15% discount for a 3-year term would calculate to $5.95/user/month if applied to that list price. [S17, S18] Compare term, discount, and included scope rather than treating the prospect-reported offer as universal pricing. Recent changes: - 2025-11-04: Rivally announced a $40M Series C led by Northgate Ventures. [S01] - 2026-03-05: Rivally launched Rivally Pulse, a lightweight engagement-survey add-on. [S06] - 2026-05-09: Rivally hired an ex-Workday VP EMEA to lead European expansion. [S11] - 2026-07-01: Rivally opened a Dublin office and announced EU data residency generally available. [S15] - 2026-08-12: Rivally’s pricing page updated Recognition Starter to $7/user/month, annual billing required. [S17] - 2026-08-20: Rivally announced its Microsoft Teams app v2 in public preview. [S19] - 2026-09-01: Rivally Pulse exited beta and was described as a paid add-on, not bundled. [S23] Our 12-month win/loss record against Rivally: - The supplied deals table contains 20 Rivally deals from 2025-09 through 2026-08: 13 wins and 7 losses. Win rate = 13 ÷ 20 = 65%; loss rate = 7 ÷ 20 = 35%. [deals_with_competitor.csv; no snippet IDs were provided for this table] - 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. [deals_with_competitor.csv; no snippet IDs were provided for this table] - Losses: Deal-7767F5, Deal-5645A5, Deal-C6FFAA, Deal-D263E0, Deal-935746, Deal-9066A6, Deal-72A02F. [deals_with_competitor.csv; no snippet IDs were provided for this table] - The supplied deal table does not include reasons for these outcomes, except for the separate 800-seat prospect mention that cited analytics depth when choosing Bonusly. [deals_with_competitor.csv; S25] Existing-card claims needing attention: - “Rivally lacks a Slack integration” is contradicted by the reviewer report that Slack worked out of the box. [S04] - “Rivally was acquired by WorkHuman in 2025” is unverified in the supplied snippets; no acquisition source was provided. - “Points-based recognition for mid-market” is only partly supported: points-based recognition is mentioned in a reviewer report, and one reviewer describes a mid-market setup experience; the supplied data does not establish mid-market as Rivally’s overall positioning. [S02, S04] - “Strong in EU enterprise with multi-language support” has reviewer support for EU teams and multiple languages, plus company-announced EU data residency; it is not independently established as a general enterprise strength. [S12, S15]
Rates use total step-level sends as the denominator: open = opened/sent; reply = replied/sent; meeting = meetings/sent. Totals are summed across steps, not unique contacts. - New Logo Nurture — 1,386 sent; open 490/1,386 = 35.35%; reply 90/1,386 = 6.49%; meeting 27/1,386 = 1.95%. Weakest: step 3 (18/428 = 4.21% reply). Change: replace step 3 with a fresh value angle. - Expansion Nurture — 875 sent; open 565/875 = 64.57%*; reply 59/875 = 6.74%; meeting 12/875 = 1.37%. Weakest: step 3 (12/275 = 4.36% reply). Change: replace step 3 with a new message rather than another follow-up. - Cold Outbound - HR Leaders — 1,785 sent; open 545/1,785 = 30.53%; reply 8/1,785 = 0.45%; meeting 0/1,785 = 0%. Weakest: step 3 (1/590 = 0.17% reply). Change: rewrite step 1 around a specific HR-relevant value proposition and ask. - Cold Outbound - People Ops — 1,163 sent; open 340/1,163 = 29.23%; reply 29/1,163 = 2.49%; meeting 6/1,163 = 0.52%. Weakest: step 3 (6/377 = 1.59% reply). Change: replace step 3 with a new angle. *Tracking error: Expansion Nurture step 2 reports 340 opened from 300 sent (40 above sent); its 113.33% open rate is invalid. Overlap: audiences.csv shows cross-sequence duplicate assignments, including CT-000301 and CT-000624 in New Logo Nurture and Expansion Nurture; and CT-000849, CT-001217, CT-001255, CT-001105, CT-001103, CT-000884, CT-001345, CT-001258, CT-001227, CT-001097, CT-001311, CT-001285, CT-000908, CT-001153, CT-001101, CT-001236, CT-001159, and CT-000890 in both Cold Outbound sequences. Below 2%: Cold Outbound - HR Leaders is under 2% at every step; opens but replies fall from 5/600 to 1/590, suggesting a post-open relevance/message/CTA problem, not a proven cause. Cold Outbound - People Ops step 3 is 1.59%, consistent with diminishing returns late in sequence. Fix Cold Outbound - HR Leaders first: lowest overall reply rate and zero meetings.
Q3-2026 has elapsed 66/92 days = 71.7% of the quarter. For count and pipeline targets, expected-to-date is target × 66/92; delta is actual − target. For closed-lost MIA rate, delta is actual rate − target, so positive is unfavorable. | Metric | QTD actual | Target | Delta | Pace | |---|---:|---:|---:|---| | SQMs | 230 | 300 | −70 | Ahead: 230 vs. 215.2 expected to date | | SQOs | 84 | 120 | −36 | Behind: 84 vs. 86.1 expected to date | | DS2s | 40 | 75 | −35 | Behind: 40 vs. 53.8 expected to date | | Closed-lost MIA rate | 5/25 = 20% | ≤10% | +10 percentage points | Behind target | | Same-quarter closes | 10 | 20 | −10 | Behind: 10 vs. 14.3 expected to date | | Active pipeline coverage | $3,000,000 / $4,000,000 = 75% | $4,000,000 | −$1,000,000 | Ahead: $3,000,000 vs. $2,869,565 expected to date | Arithmetic: 66/92 = 71.7%; expected-to-date counts are 300×66/92 = 215.2 SQMs, 120×66/92 = 86.1 SQOs, 75×66/92 = 53.8 DS2s, and 20×66/92 = 14.3 same-quarter closes. MIA rate = 5/25 = 20%; 20%−10% = +10 percentage points. Pipeline coverage = $3,000,000/$4,000,000 = 75%; pipeline delta = $3,000,000−$4,000,000 = −$1,000,000; expected-to-date pipeline = $4,000,000×66/92 = $2,869,565. Week-over-week movement cannot be determined from the provided data: it contains QTD totals but no prior-week snapshot or weekly changes. Current QTD, SQMs and active pipeline are ahead of prorated pace; SQOs, DS2s, and same-quarter closes are behind, while the closed-lost MIA rate is 10 percentage points above its target.
Q3 forecast: $115,976.75 = $44,729 COMMIT + 35% × $203,565 BEST_CASE ($71,247.75); PIPELINE = $0. 32 deals totaling $227,575 close after Sep 30, including COMMIT Deal-D348E1 ($13,770, Oct 15); several October DS2/DS3 dates remain within four weeks of quarter-end. Treat the forecast as unverified: owners are blank on 85/86 deals, and why-buys are missing on 71/86, including all 7 in-quarter COMMIT deals, leaving no rep/quota rollup or documented rationale for the $44,729 COMMIT.
Subject: Following up on 150-seat pricing Hi, I’m following up on the recap I emailed on August 5 with pricing for 150 seats after our July 28 demo. Would you be open to reviewing the pricing together? Best, Alex
Marketing: The team delivered 46 SQMs against a weekly target of 52, a gap of 6 (52 − 46 = 6). The webinar channel contributed 18 SQMs, while paid search held at $150 per SQM. Sales: The team converted 14 SQOs, set 9 DS2 meetings, created $310,000 in new pipeline, and recorded 3 same-quarter closes this week. CS: The team saved 2 renewals, and NPS moved to 61. There are 3 open red-flag accounts heading into next week. PLG: PLG added 412 signups, with activation at 31%. 38 companies reached the aha moment of 10 recognition gives.
Apex Rewards Co — Active. Two sourced DS1 deals: Deal-DDAAF2 ($180,000) + Deal-2CF33E ($95,000) = $275,000. Co-webinar locked for 09-15. HRCloud Partners — Active. One sourced deal, Deal-F1CDA5 ($140,000), moved to DS2 after the security review closed. CultureBridge — Active. Two sourced early-stage deals: Deal-096E1D ($60,000) + Deal-067213 ($75,000) = $135,000. WorkWell Group — Active. No sourced deals this period (0 deals, $0). Q4 joint-playbook restart discussed; planning call booked for 09-09. Recogniq — Quiet; no activity reported. KudosWave — Quiet; no activity reported. PeopleFirst Advisors — Quiet; no activity reported. TotalPerk — Quiet; no activity reported. Partner-sourced pipeline: 5 deals; $550,000 total ($275,000 + $140,000 + $135,000).
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. Prospects can obtain it under NDA. [P2] Q4. Customer data is retained for 90 days after the contract ends, 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; they do not state whether Bonusly holds ISO 27001 certification. The ISO 27001 certificate or certification statement would answer this. [P1–P8] Q9. Unanswerable from the provided excerpts; they do not specify a contractual uptime SLA percentage. The applicable uptime SLA or contract would answer this. [P1–P8] Q10. Unanswerable from the provided excerpts; they do not state whether Bonusly will sign a HIPAA Business Associate Agreement. Bonusly’s HIPAA/BAA policy or contract terms would answer this. [P1–P8]
1. ALWAYS-trigger overlap / duplication - WARNING · MERGE — `comms-drafter` and `email-drafter`: both trigger on writing, drafting, reviewing, rewriting, and replying to customer-facing communications. Their descriptions overlap on phrases including “write me an email,” “draft a follow-up,” and “what should I say.” Proposal: merge the email-specific workflow into one authoritative communication skill. - WARNING · REVIEW — `pipeline-intelligence-report` and `weekly-pipeline-report`: both trigger on pipeline reports/updates and “what does pipeline look like” requests. Proposal: narrow their trigger boundaries so a full scored pipeline report and a weekly performance update route distinctly. - WARNING · REVIEW — `analysis-validator`, `signalforge-claim-compressor`, and `signalforge-feedback`: all claim mandatory coverage of SignalForge analysis/report outputs. Their stated execution order separates the steps, but their broad ALWAYS triggers overlap. Proposal: define the shared scope and explicit handoff boundaries. 2. Circular delegation - WARNING · REVIEW — `deal-strategy-coach` ↔ `email-drafter`: `deal-strategy-coach` directs manager-to-prospect emails to `email-drafter`; `email-drafter` routes strategy and coaching requests back to `deal-strategy-coach`. Proposal: define a one-way handoff rule for mixed strategy-and-drafting requests. 3. Dangling delegation targets Relative to the supplied files and manifest, these referenced skill targets have no matching file/manifest row: - WARNING · REVIEW — `bonusly-brand`, `prospect-research-multithreading`, `signalforge-reports`, `skill-orchestrator`. - WARNING · REVIEW — `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`. Proposal: confirm these targets exist outside the supplied set or update the references. 4. Version conflict - WARNING · UPDATE_BODY — `analysis-validator` declares v3.6, but its validation-trail template identifies the validator as v3.2. Proposal: make the trail version consistent with v3.6; v3.6 is the version to retain. 5. Descriptions over 1,024 characters - INFO · REVIEW — 0. Arithmetic: 14 manifest descriptions checked; 0 have `description_chars > 1,024`. 6. Hardcoded page IDs, dates, or person names - WARNING · UPDATE_BODY — `analysis-validator`: dates include April 26, 2026 and May 9, 2026. Named people include Amani Phipps, Manish, Alaina Loori, Shealagh Coughlin, Bryce Harmon, Hugo Lindqvist, Dana Mercer, Alex Franklin, Cole Ingram, Gavin Porter, Colleen Perry, Ellie Barton, Ashley Reyer, Megan Franz, Elena Sinclair, Youssef Elkhateeb, Amanda Czenkus, Ben Castelli, John Thomas, and Yasmin Wahid. Proposal: make roster and date-dependent content live or clearly mark it as historical. - WARNING · UPDATE_BODY — `closed-lost-analysis`: hardcoded dates include May 2026, May 4–12, and 4/13. Proposal: mark dated examples and sample findings as historical rather than current guidance. - WARNING · UPDATE_BODY — `deal-strategy-coach`: Confluence page ID `2257879045`; person names `Perseus` and `Farid`; pricing is labeled 2026. Proposal: verify the fixed reference data and page target remain current. - WARNING · UPDATE_BODY — `model-selection`: hardcoded registry check date `2026-05-19`. Proposal: keep the registry check date current. - WARNING · UPDATE_BODY — `partner-digest`: page/folder IDs `2286616609`, `2286321666`, `2265382925`, `2236940297`, `2237825028`, `2239365136`, and `2238283777`; dates include May 16, 2026, May 19, 2026, and June 2, 2026; person names include Amani Phipps, Kelli, Jen Lee, Hani, Bryce, and Sara. Proposal: validate IDs and refresh dated examples and named contact references. - WARNING · UPDATE_BODY — `pipeline-intelligence-report`: hardcoded dates include May 2026 and March 28, 2023; named people include Bryce Harmon, Dana Mercer, Cole Ingram, Alex Franklin, and Gavin Porter. Proposal: verify the roster and date-sensitive system claims at runtime. - WARNING · UPDATE_BODY — `sales-forecast`: parent page ID `2232582148`; hardcoded dates include April 27, 2026 and July 9, 2026; person names include Alaina and Elena. Proposal: verify the page target and keep examples/person references current. - WARNING · UPDATE_BODY — `signalforge-feedback`: page IDs `2295136266`, `2234417154`, and `2247295002`. Proposal: verify these Confluence targets remain valid. - WARNING · UPDATE_BODY — `stale-pipeline-report`: dates include June 10, 2026, 5/15, 5/7, and 5/19. Proposal: label dated examples as examples or use runtime dates. - WARNING · UPDATE_BODY — `weekly-pipeline-report`: hardcoded periods include Q2 2026 and Q1 2026; named person: Ben Lavin. Proposal: make reporting periods and owner references runtime-driven where appropriate. 7. Manifest drift - INFO · REVIEW — No drift in either direction. Arithmetic: 14 supplied skill files − 14 manifest rows = 0 files without rows; 14 manifest rows − 14 supplied files = 0 rows without files.
## Reward-worker queue incident runbook
1. **Acknowledge alert and take incident command** — **Bryce Harmon** (M01)
**Action:** Acknowledged the PagerDuty alert for queue depth over 10k and took IC.
**Success verification:** The thread records the acknowledgment and IC handoff; no separate verification is reported.
**Rollback:** Not applicable; no system state change is documented.
2. **Check queue depth** — **Farid Osman** (M02)
**Command:** `bundle exec rake sidekiq:queue_depth`
**Result:** 48,213 pending jobs; normal is under 500.
**Rollback:** Not applicable; diagnostic command only.
3. **Inspect dead set** — **Farid Osman** (M03)
**Action:** Reported 112 dead-set jobs, all `Redis::TimeoutError` from around 13:58. The inspection command is not recorded.
**Success verification:** The reported finding is the only verification given.
**Rollback:** Not applicable; no state change is documented.
4. **Pause enqueue** — **Farid Osman** (M04)
**Command:** `bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'`
**Success verification:** No direct flag-state verification is recorded; needs confirmation.
**Rollback:** `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'` (explicitly supplied in the thread).
5. **Clear the dead set** — **Elena Sinclair** (M05)
**Action:** Reported clearing the dead set in the console. Exact command/action details are not recorded; needs confirmation.
**Success verification:** Not recorded; needs confirmation.
**Rollback:** Not recorded; needs confirmation.
6. **Scale workers up** — **Bryce Harmon** (M06)
**Command:** `kubectl scale deployment/reward-worker --replicas=6` (reported as changing from 3).
**Success verification:** No direct replica-count verification is recorded; needs confirmation. Later queue improvements are reported in M07–M08 but are not direct verification of the replica count.
**Rollback:** `kubectl scale deployment/reward-worker --replicas=3` (explicitly supplied in the thread).
7. **Check queue progress** — **Farid Osman** (M07)
**Action:** Reported queue depth at 9,400 and falling approximately 1,200/min. Measurement command is not recorded.
**Rollback:** Not applicable; no state change is documented.
8. **Verify queue and error rate** — **Cole Ingram** (M08)
**Command:** `bundle exec rake sidekiq:queue_depth`
**Success verification:** Command returned 0; Datadog error rate was reported back to baseline.
**Rollback:** Not applicable; diagnostic verification only.
9. **Re-enable enqueue** — **Bryce Harmon** (M09)
**Command:** `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'`
**Success verification:** 40 new jobs processed cleanly in the next 3 minutes.
**Rollback:** Not documented; needs confirmation.
10. **Scale workers back down and resolve** — **Bryce Harmon** (M10)
**Command:** `kubectl scale deployment/reward-worker --replicas=3`
**Success verification:** Queue reported stable at 0; incident marked resolved.
**Rollback:** Not documented; needs confirmation.
First error — 2026-09-03 14:01:12 UTC: `reward-service` logged `Redis::TimeoutError: Connection to Redis at redis-primary:6379 timed out after 5s`. Cascade, in timestamp order: - 14:01:20, 14:01:30, 14:01:40 — `reward-service` reports retries exhausted for `RewardGiveJob`. At 14:01:40, `sidekiq` logs that `RewardGiveJob` failed with the Redis timeout and will retry in 60 seconds. - 14:01:46–14:01:57 — `sidekiq_jobs.csv` records six `RewardGiveJob` failures: J-00005 (14:01:46), J-00001 (14:01:51), J-00003 (14:01:54), J-00006 (14:01:55), J-00002 (14:01:56), J-00004 (14:01:57). - 14:02:28 — `sidekiq` logs another `RewardGiveJob` retry. At 14:02:30 it warns that reward queue depth is above 10,000. - 14:02:36–14:05:50 — job records show additional Redis-timeout failures: `RecognitionDigestJob` J-00013 (14:02:36), `RewardGiveJob` J-00007 and J-00011 (14:02:51), J-00008 (14:02:56), J-00010 (14:02:57), J-00009 (14:02:58), then `RecognitionDigestJob` J-00014 (14:03:15), J-00015 (14:04:55), and J-00016 (14:05:50). Sidekiq logs further `RewardGiveJob` retries at 14:03:31, 14:04:22, 14:05:26, and 14:06:47. - 14:03:05 onward — `api-gateway` logs 502 upstream timeouts calling `reward-service`; `web-app` then logs failed Give form submissions, first at 14:03:30. Both services log further errors through 14:06:52. - 14:22:10 — `reward-service` logs that Redis connectivity is restored and job processing resumes. At 14:24:45, `sidekiq` reports reward queue depth below 500. Service and jobs involved: the first error is in `reward-service`; the worker errors are logged by `sidekiq`. The named jobs are `RewardGiveJob` and `RecognitionDigestJob`. The later request failures involve `api-gateway` and `web-app`. Datadog query to confirm the first error: `service:reward-service "Redis::TimeoutError" "redis-primary:6379"` Set the time range to 2026-09-03 14:01:00–14:01:30 UTC. If `level` is indexed as a log attribute, add `@level:ERROR`. What the logs do not show: why Redis timed out, Redis-side health or network evidence, the exact number of affected user requests or jobs beyond the listed records, whether any reward operations were lost or duplicated, or what caused recovery. The sequence is consistent with the timeout preceding the retries, queue growth, and request errors, but the slice alone does not establish the underlying Redis cause.
| Flag | State | What it controls per code | Targeting / company count | |---|---|---|---| | `recognition_streaks_v2` | On | Records a streak when a recognition is given. | `segment:beta_companies` — 42 | | `points_budget_guardrails` | On | Enforces the company’s points budget when points are given. | `all_companies` — 220 | | `slack_dm_nudges` | On | Sends a Slack DM nudge to a user. | `segment:region_na` — 87 | | `redeem_flow_redesign` | Off | Selects the redeem experience: enabled renders `RedeemV2Component`; otherwise it renders `RedeemV1Component`. | `targeted_list` — 12 | | `analytics_dashboard_v3` | On | Uses the V3 analytics dashboard. | `segment:tier_three` — 65 | | `ms_teams_app_v2` | Off | Installs `TeamsAppV2`. | `targeted_list` — 9 | | `legacy_give_modal` | Off | No code reference in the excerpt. | `segment:legacy_plan` — 14 | | `survey_boosters_q3` | On | No code reference in the excerpt. | `segment:legacy_plan` — 7 | | `paused_offboard_cleanup` | Off | No code reference in the excerpt. | Targeting rule is blank; company count is 0, so it has no targets in this export. | The export gives targeting rules and counts, but not company names or identities.
- NDA-1 — GREEN: Standard mutual confidentiality; “Term: two (2) years from the Effective Date; obligations survive for three (3) years after termination,” with stated carve-outs and Delaware governing law. - NDA-2 — YELLOW: Counsel review for the embedded non-solicit: “During the term of this Agreement and for eighteen (18) months thereafter, neither party shall solicit for employment or hire any employee of the other party with whom it came into contact under this Agreement.” - NDA-3 — RED: Full legal review for 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”—plus one-way confidentiality, no stated carve-outs, and exclusive Irish jurisdiction.