Mean 0.936 · 30/40 perfect tests · $0.42 total · each card: the question → the correct answer (gold) → this model's actual answer
Definitions: - DS2+ = deals currently in DS2, DS3, DS4, or DS5. - Why-buys non-compliant = blank, fewer than 15 characters, or a placeholder. `LONG(n)` is compliant. - Regression = any populated earlier-stage timestamp later than a populated later-stage timestamp. - DS1 missing = no `t_ds1` timestamp. Results: - DS2+ deals: 119 - DS2+ why-buys non-compliant: 97 - Overall non-compliance: 97 / 119 = 81.5% By owner: - Bryce Harmon: 15 / 18 = 83.3% - Alex Franklin: 45 / 57 = 78.9% - Dana Mercer: 17 / 21 = 81.0% - Cole Ingram: 19 / 22 = 86.4% - Farid Osman: 1 / 1 = 100.0% Other hygiene findings: - Deals with no DS1 timestamp: 0 - Deals with regressed stage timestamps: 0 Process fixes: 1. Make why-buys mandatory before moving a deal from DS1 to DS2; enforce a 15-character minimum and reject placeholders. 2. Add CRM validation preventing stage-entry timestamps from being edited into chronological regression. 3. Create a weekly hygiene report by owner showing DS2+ missing/invalid why-buys, missing DS1 timestamps, and timestamp regressions.
Deals resolved: 155 Unmatched owner IDs: None. Every owner ID in the deal extract matched an owner in owners.csv. Owner IDs mapped to archived/deactivated owners: None. - Archived owners in the owners table—1520255671 (Gavin Porter) and 77260721 (Hugo Lindqvist)—do not appear in the deal extract. Total pipeline amount per resolved owner: - Bryce Harmon (owner_id 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 + 30,275 + 17,400 + 12,600 + 18,000 + 37,440 + 18,828 + 2,880 + 36,000 + 20,880 + 10,920 + 25,200 + 10,800 = $1,054,144 - Alex Franklin (owner_id 84342457): 68 deals Sum of 68 deal amounts = $629,710 - Dana Mercer (owner_id 83155923): 22 deals 11,250 + 10,500 + 9,000 + 9,000 + 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 = $330,995 - Cole Ingram (owner_id 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 (owner_id 716654662): 7 deals 1,249 + 1 + 1 + 1 + 2,880 + 1 + 1 = $4,134 - Elena Sinclair (owner_id 701163055): 1 deal 2,100 = $2,100
(a) Orphan Gong rows: 30 Arithmetic: 67 Gong rows total − 37 rows whose deal IDs match deals_open = 30 orphans. Sample orphan aliases: - Deal-8FA85D - Deal-8FC3F9 - Deal-3B7945 - Deal-42B265 - Deal-9CCC42 (b) Duplicate conversation keys: 0 rows Arithmetic: For every Gong row, calls_90d = distinct_conversation_keys. Therefore, no row has calls_90d exceeding distinct_conversation_keys. (c) Open deals at DS3 or later with at least one logged call: 23 of 83 = 27.71% Arithmetic: 23 open DS3+ deals with a matching Gong row ÷ 83 open DS3+ deals = 0.2771 = 27.71%.
```sql -- Cannot produce a valid query using only the provided catalog. -- The catalog does not provide: -- 1. A customer-company identifier in PRODUCTION.PLG.COMPANY_COHORT_SUMMARY. -- 2. A giving/recognition event table for calculating unique givers. -- 3. A recognition-event table or documented recognition-count column. -- 4. A documented mapping between PRODUCTION.DEPRECATED_RECOGNITION.REDEMPTION_RECORDS_V2 -- and customer companies or calendar months. -- M1_USERS, M1_GIVES, and M1_REDEMPTIONS cannot be substituted because they do not -- document unique givers, recognition counts, or the requested calendar-month logic. ```
CRM completeness audit Scope: - Companies: 34 records - Contacts: 50 records - Deals: No deals file was provided, so deal-field completeness and pipeline exposure cannot be calculated. Completeness by field Companies | Field | Complete | Arithmetic | Completeness | |---|---:|---:|---:| | industry | 34/34 | 34 ÷ 34 | 100.00% | | employee_count | 23/34 | 23 ÷ 34 | 67.65% | | hq_country | 28/34 | 28 ÷ 34 | 82.35% | Missing company fields: - employee_count: 11 records - hq_country: 6 records - industry: 0 records Contacts | Field | Complete | Arithmetic | Completeness | |---|---:|---:|---:| | email populated | 50/50 | 50 ÷ 50 | 100.00% | | email valid format | 46/50 | 46 ÷ 50 | 92.00% | | title | 41/50 | 41 ÷ 50 | 82.00% | | persona | 36/50 | 36 ÷ 50 | 72.00% | Deals No `deals.csv` data was provided. Completeness for owner, stage, amount, close date, and why-buys is therefore not computable. Duplicate company clusters 1. Shared domain: `acme-corp.com` - C-0A092931 - C-0A092932 - Recommended survivor: C-0A092931 - Recommended action: merge C-0A092932 into C-0A092931 after resolving `employee_count` (500 vs 510), `industry` (`Technology` vs `tech`), and `hq_country` (`US` vs `USA`). 2. Shared domain: `globex.io` - C-0A092933 - C-0A092934 - Recommended survivor: C-0A092933 - Recommended action: merge C-0A092934 into C-0A092933 after resolving `industry` (`SaaS` vs `Technology`). Employee count and HQ country agree. Invalid emails Malformed: - CT-0010: `user0@` - CT-0080: `user0@` - CT-0081: `user1@` - CT-0192: `user2@` Domain mismatch: - CT-0011: `user1@other-domain.com` - Contact/company domain field: `66d1fc.com` - Email domain: `other-domain.com` No other email-domain mismatches were identified. Company enrichment fixes The enrichment export has matching rows for the following missing CRM `employee_count` values: | Company alias | CRM value | Enrichment value | Recommendation | |---|---|---:|---| | C-EC3025 | missing | 400 | Fill from enrichment | | C-96039F | missing | 400 | Fill from enrichment | | C-44EA29 | missing | 400 | Fill from enrichment | | C-D04904 | missing | 400 | Fill from enrichment | | C-B23205 | missing | 400 | Fill from enrichment | | C-60C75F | missing | 400 | Fill from enrichment | | C-2C60E5 | missing | 340 | Fill from enrichment | | C-7BBDFA | missing | 400 | Fill from enrichment | | C-50D386 | missing | 400 | Fill from enrichment | No missing CRM `hq_country` values can be filled from enrichment because the matching enrichment rows are also blank or unavailable: - C-2D1F1B: CRM blank; enrichment blank - C-D73B89: CRM blank; enrichment blank - C-2C60E5: CRM blank; enrichment blank - C-EE9FFB: CRM blank; no matching enrichment row Industry disagreements | Company alias | CRM value | Enrichment value | Recommendation | |---|---|---|---| | C-66D1FC | tech | Computer Software | Prefer enrichment as the standardized value | | C-EC3025 | Technology | Computer Software | Prefer enrichment as the standardized value | | C-44EA29 | tech | Computer Software | Prefer enrichment as the standardized value | | C-92D97D | Technology | Computer Software | Prefer enrichment as the standardized value | | C-D04904 | Technology | Computer Software | Prefer enrichment as the standardized value | | C-77A95A | Technology | Computer Software | Prefer enrichment as the standardized value | | C-AA8DDA | Technology | Computer Software | Prefer enrichment as the standardized value | | C-B25F40 | Technology | Computer Software | Prefer enrichment as the standardized value | | C-60C75F | tech | Computer Software | Prefer enrichment as the standardized value | | C-425E2A | Tech | Computer Software | Prefer enrichment as the standardized value | The recommendation is to use the enrichment value for normalization, while retaining the CRM value in history or an audit field. Employee-count disagreements The matching enrichment rows disagree with CRM because CRM is blank for the nine companies listed in the enrichment-fix table above. No populated CRM employee counts conflict with populated enrichment counts. HQ-country representation disagreements These appear to be country-label normalization differences rather than different countries: | Company alias | CRM value | Enrichment value | Recommendation | |---|---|---|---| | C-66D1FC | US | United States | Standardize to `United States` | | C-C6FE92 | United States | United States | No substantive disagreement | | C-950043 | US | United States | Standardize to `United States` | | C-EC3025 | USA | United States | Standardize to `United States` | | C-96039F | USA | United States | Standardize to `United States` | | C-77A95A | US | United States | Standardize to `United States` | | C-E51FB7 | USA | United States | Standardize to `United States` | | C-D0662E | US | United States | Standardize to `United States` | | C-425E2A | USA | United States | Standardize to `United States` | Contact fixes - Correct malformed emails for CT-0010, CT-0080, CT-0081, and CT-0192. - Resolve the domain mismatch for CT-0011. - Populate missing titles for 9 contacts. - Populate missing personas for 14 contacts. - Validate whether contact `domain` should be derived from the email domain or remain linked to the company domain. Top 10 fixes by pipeline amount at stake Cannot be calculated. No deal records, deal amounts, owners, stages, close dates, or why-buys were provided.
Classification of all 89 lost deals | Deal alias | Primary category | Side | |---|---|---| | Deal-DB0AAC | timing | buyer | | Deal-F7F635 | competitor | buyer | | Deal-AC944F | other | unknown | | Deal-214060 | other | unknown | | Deal-91A056 | timing | buyer | | Deal-29326C | timing | buyer | | Deal-5DB9B0 | other | buyer | | Deal-831B7B | timing | buyer | | Deal-F97C37 | competitor | buyer | | Deal-13E9CF | timing | buyer | | Deal-39E25C | timing | buyer | | Deal-7ED004 | pricing | buyer | | Deal-21B045 | other | unknown | | Deal-B3ABED | timing | buyer | | Deal-422BA6 | competitor | buyer | | Deal-ED9AE7 | timing | buyer | | Deal-988493 | other | unknown | | Deal-381C8C | other | unknown | | Deal-F308CA | other | unknown | | Deal-F1E8A6 | other | unknown | | Deal-B6AC09 | timing | buyer | | Deal-70F704 | no decision | buyer | | Deal-E6E80A | timing | buyer | | Deal-B038F0 | timing | buyer | | Deal-4664E1 | other | unknown | | Deal-175756 | timing | buyer | | Deal-E74A73 | no decision | buyer | | Deal-DDAB52 | competitor | buyer | | Deal-ACE061 | competitor | buyer | | Deal-BB78F3 | timing | buyer | | Deal-D48E0B | other | unknown | | Deal-15DA99 | timing | buyer | | Deal-F4AF5D | timing | buyer | | Deal-79B7A1 | timing | buyer | | Deal-583ADB | other | unknown | | Deal-8E27DA | product gap | Bonusly | | Deal-2D2F8D | competitor | buyer | | Deal-E0441F | other | unknown | | Deal-7CB44D | other | unknown | | Deal-0F96AA | competitor | buyer | | Deal-1BCA50 | competitor | buyer | | Deal-7CC678 | other | unknown | | Deal-FAC17C | no decision | buyer | | Deal-242273 | competitor | buyer | | Deal-50E5D8 | no decision | buyer | | Deal-A2C349 | competitor | buyer | | Deal-9F176A | timing | buyer | | Deal-7B2236 | pricing | buyer | | Deal-AFA56C | other | unknown | | 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-2A292B | no decision | buyer | | Deal-D1AABF | other | unknown | | Deal-FEDBCB | timing | buyer | | Deal-1E7DA9 | competitor | buyer | | Deal-2BBA21 | other | unknown | | Deal-286F9C | competitor | buyer | | Deal-7FBAC6 | no decision | buyer | | Deal-369281 | competitor | buyer | | Deal-386F6E | other | unknown | | Deal-9FCD0D | competitor | buyer | | Deal-55867E | other | unknown | | Deal-DAFB82 | pricing | buyer | | Deal-2FEDDB | no decision | buyer | | Deal-64B19A | competitor | buyer | | Deal-3F86A0 | other | unknown | | Deal-096750 | other | unknown | | Deal-F325A5 | no decision | buyer | | Deal-ABD14C | no decision | buyer | | Deal-79E61A | other | unknown | | Deal-8A119B | pricing | buyer | | Deal-AE7C4E | other | unknown | | Deal-DAB4F1 | other | unknown | | Deal-B4B50F | other | unknown | | Deal-981AD4 | product gap | Bonusly | | Deal-DC77FE | product gap | Bonusly | | Deal-5885B9 | other | unknown | Category counts - Other: 26 - Timing: 22 - Competitor: 20 - No decision: 10 - Pricing: 6 - Product gap: 5 - Champion left: 0 Arithmetic: 26 + 22 + 20 + 10 + 6 + 5 + 0 = 89 deals. Side split - Buyer: 59 - Unknown: 25 - Bonusly: 5 Arithmetic: 59 + 25 + 5 = 89 deals. Structured-tag disagreements 15 deals have a structured `closed_lost_tag` that clearly disagrees with the primary category indicated by the free-text reason. Two patterns most worth acting on 1. Timing and no-decision losses are substantial: - Timing: 22 - No decision: 10 - Combined: 22 + 10 = 32 deals - The text frequently cites pauses, deprioritization, budget cycles, future-year follow-up, or lack of approval. 2. Competitor losses are concentrated and often have specific product or commercial drivers: - Competitor: 20 deals - The cited drivers include broader offerings, integrations or preferred partnerships, customization, local/Canadian providers, existing platforms, and budget-related considerations. Five deals were classified as product gaps, including cases where the structured tag was Competitor or MIA.
{"tier_counts":{"LOCK":8,"ACTION":47,"BUILD":28,"REVIVE":35,"WATCH":32,"RISKY":5},"tier_examples":{"LOCK":["Deal-25F752","Deal-C26D20","Deal-403845"],"ACTION":["Deal-A5E80A","Deal-63436734854","Deal-63925115724"],"BUILD":["Deal-66D1FC","Deal-D73B89","Deal-523604"],"REVIVE":["Deal-61129636004","Deal-7BBDFA","Deal-950043"],"WATCH":["Deal-E53952","Deal-5408B0","Deal-C6FE92"],"RISKY":["Deal-547B2B","Deal-B7EBD1","Deal-A2B47C"]},"risky_deals":["Deal-547B2B","Deal-B7EBD1","Deal-A2B47C","Deal-2465CE","Deal-584EE5"],"lock_violations":0,"pipeline_shape":"The pipeline is heavily concentrated in early-stage PIPELINE deals, with limited meeting activity relative to total coverage. A small advanced-stage group has current engagement and supports LOCK or ACTION treatment, while several COMMIT deals lack meetings_30d and are RISKY because forecast category conflicts with engagement evidence; older last-contact dates create a meaningful REVIVE segment."}
[
{
"transcript_id": "TX-001",
"deal_alias": "Deal-CFE7F4",
"why_buys": [
"Automating anniversary and birthday awards would be a big win.",
"The HR team of three cannot keep up with the process manually."
],
"pain_points": [
"Awards are tracked manually in a spreadsheet.",
"People slip through the cracks."
],
"stakeholders": [
"Prospect (VP People)",
"Prospect (HR Admin)"
],
"budget_signal": "$40k earmarked for engagement tools this fiscal year.",
"timeline_signal": "Ideally live before open enrollment in November.",
"competitor_mentioned": "Achievers",
"next_step": "Security review on September 12.",
"objections": [
"Needs SSO and audit logs for IT sign-off.",
"Achievers was too heavy for a team their size."
],
"confidence": "high"
},
{
"transcript_id": "TX-002",
"deal_alias": "Deal-70BB30",
"why_buys": [
"Tie recognition to retention for the hourly workforce."
],
"pain_points": [
"Regretted turnover among the hourly workforce is over 30%."
],
"stakeholders": [
"Prospect (Head of Total Rewards)",
"Prospect (CFO)"
],
"budget_signal": "$25k pilot budget approved for this quarter.",
"timeline_signal": "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 has to be rock solid."
],
"confidence": "high"
},
{
"transcript_id": "TX-003",
"deal_alias": "Deal-530B50",
"why_buys": [
"Make recognition visible across 12 retail locations."
],
"pain_points": [
"Store managers have zero budget autonomy for on-the-spot recognition.",
"The CEO must be sold first because she decides anything people-related."
],
"stakeholders": [
"Prospect (People Ops Manager)"
],
"budget_signal": null,
"timeline_signal": "No rush until Q1.",
"competitor_mentioned": "Bucketlist",
"next_step": "Schedule a call with the CEO; the prospect will send two times.",
"objections": [
"The CEO has to be sold first."
],
"confidence": "high"
},
{
"transcript_id": "TX-004",
"deal_alias": "Deal-180D02",
"why_buys": [
"Consolidate three separate recognition tools into one."
],
"pain_points": [
"They are paying for three tools.",
"The tools do not integrate with their HRIS."
],
"stakeholders": [
"Prospect (VP People)",
"Prospect (IT Security Lead)"
],
"budget_signal": "If under $15k annually, the VP People can approve it without going to the board.",
"timeline_signal": "Procurement cycle runs six to eight weeks minimum.",
"competitor_mentioned": null,
"next_step": null,
"objections": [
"The security review took three months for the last vendor.",
"The prospect needs to check the CFO's calendar and made no promise about a follow-up."
],
"confidence": "high"
},
{
"transcript_id": "TX-005",
"deal_alias": "Deal-F8767A",
"why_buys": [
"Automate service milestones.",
"Get analytics on recognition equity across departments."
],
"pain_points": [
"Night-shift teams feel invisible.",
"Night-shift engagement scores run 20 points lower.",
"The executive team is skeptical after a failed rollout two years ago."
],
"stakeholders": [
"Prospect (HR Director)",
"Prospect (People Ops Coordinator)"
],
"budget_signal": "$12k approved under the engagement line.",
"timeline_signal": "Need it running before the January all-hands.",
"competitor_mentioned": "Nectar",
"next_step": "Present to the executive team on October 2.",
"objections": [
"They are mid-pilot with Nectar, so the experience must be better.",
"The executive team is skeptical after a failed rollout two years ago."
],
"confidence": "high"
},
{
"transcript_id": "TX-006",
"deal_alias": "Deal-EE195F",
"why_buys": [
"Cut the administrative time spent on service awards."
],
"pain_points": [
"The HR Manager spends five hours a month ordering and shipping plaques.",
"The COO usually prefers building things in-house."
],
"stakeholders": [
"Prospect (HR Manager)"
],
"budget_signal": "Budget is not the issue.",
"timeline_signal": "A Q1 start is realistic because the fiscal year resets in February.",
"competitor_mentioned": "Doing it internally / building things in-house",
"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"
}
]
1. Deal-547B2B — $11,200 Why close: Redlines came back clean, the signing page is out, and the VP People said they are signing tomorrow. Slack explicitly calls it “signature-imminent.” What’s left: Customer signature. 2. Deal-403845 — $9,000 Why close: Slack says it is “moving fine,” and the order form is with the customer’s finance team. It is DS5/COMMIT with a 2026-09-11 close date. What’s left: Finance-team review and order-form signature. 3. Deal-A2B47C — $6,360 Why close: Slack says it is “still warm” and progressing at a normal legal-review pace. It is DS5/COMMIT with a 2026-09-11 close date. What’s left: Completion of legal review and signature. Deal-D348E1 is similarly described as “still warm” and at a normal legal-review pace, but its CRM close date is later (2026-10-15 versus Deal-A2B47C’s 2026-09-11), so Deal-A2B47C ranks third. Amount arithmetic: $11,200 + $9,000 + $6,360 = $26,560.
Candidate classification | Deal | Amount | Prospect quote | Classification | Basis | |---|---:|---|---|---| | Deal-EC3025 | Not provided | “We need SCIM user provisioning — our security team will not approve any tool that requires manual account management.” | Real gap | Product docs state SCIM user provisioning is not currently listed as supported. | | Deal-D0D6B5 | Not provided | “Our payroll runs on ADP Workforce Now and I don't see ADP anywhere in your integration list — that's a dealbreaker for us.” | Real gap | Product docs state ADP Workforce Now integration is not currently listed as supported. | | Deal-CFE7F4 | Not provided | “I tried to build a custom report combining recognition frequency with turnover and couldn't — is that possible on our current plan?” | Plan gate | Product docs place the custom report builder on Enterprise only. | | Deal-84DBA6 | Not provided | “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.” | Rollout/enablement issue | Slack integration is supported on all plans; the stated issue is lack of manager training. | | Deal-36C33F | Not provided | “Good to know. The web version should be fine for our office staff for now.” | Not a product gap raised by the prospect | The mobile-app limitation was stated by the rep, not the prospect, and the prospect accepted the web version. | Summary — real product gaps only - Deal-EC3025 — Amount: not provided “We need SCIM user provisioning — our security team will not approve any tool that requires manual account management.” Product gap: SCIM user provisioning is not listed as supported. - Deal-D0D6B5 — Amount: not provided “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 gap: ADP Workforce Now integration is not listed as supported. Arithmetic: No deal amounts were provided, so no amount calculation is possible.
Snapshot date: 2026-09-05. Stale means the latest of last_email, last_call, and last_meeting was before 2026-08-29, or all three fields were missing. Bryce Harmon | Deal alias | Stage | Amount | Days since last contact | |---|---:|---:|---:| | Deal-2D1F1B | DS1 | $240,000.00 | 81 | | Deal-66D1FC | DS1 | $99,000.00 | 16 | | Deal-950043 | DS1 | $70,000.00 | 19 | | Deal-B23205 | DS1 | $45,000.00 | 16 | | Deal-7BBDFA | DS3 | $37,440.00 | 46 | | Deal-332637 | DS2 | $36,000.00 | 9 | | Deal-1BEEBF | DS1 | $31,500.00 | 19 | | Deal-C5658B | DS1 | $23,400.00 | 16 | | Deal-40522D | DS3 | $21,000.00 | 19 | | Deal-F0EBBB | DS3 | $11,400.00 | 24 | | Deal-E25A09 | DS1 | $6,000.00 | 9 | | Deal-C9C286 | DS2 | $5,502.00 | 9 | | Deal-012CB1 | DS1 | $1.00 | 23 | Arithmetic: $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 = $626,243.00 Stale deals: 13 Total stale amount: $626,243.00 Dana Mercer | Deal alias | Stage | Amount | Days since last contact | |---|---:|---:|---:| | Deal-44EA29 | DS2 | $60,000.00 | 10 | | Deal-E51FB7 | DS2 | $43,875.00 | 12 | | Deal-B42F46 | DS1 | $27,000.00 | 19 | | Deal-BA3DDC | DS3 | $23,400.00 | 15 | | Deal-9DDE86 | DS2 | $20,000.00 | 15 | | Deal-215CCA | DS3 | $18,900.00 | 17 | | Deal-B7EBD1 | DS5 | $9,000.00 | 16 | | Deal-3974EB | DS4 | $9,000.00 | 8 | | Deal-F40F04 | DS2 | $8,100.00 | 15 | | Deal-87DDD1 | DS1 | $5,000.00 | 19 | | Deal-F336B6 | DS3 | $4,200.00 | 15 | | Deal-0660B4 | DS4 | $1,920.00 | 16 | | Deal-FD9F4E | DS5 | $1,330.00 | 10 | Arithmetic: $60,000 + $43,875 + $27,000 + $23,400 + $20,000 + $18,900 + $9,000 + $9,000 + $8,100 + $5,000 + $4,200 + $1,920 + $1,330 = $231,725.00 Stale deals: 13 Total stale amount: $231,725.00 Alex Franklin | Deal alias | Stage | Amount | Days since last contact | |---|---:|---:|---:| | Deal-CC08D1 | DS1 | $24,000.00 | 16 | | Deal-E73427 | DS3 | $18,000.00 | 10 | | Deal-885F45 | DS2 | $9,300.00 | 12 | | Deal-C2FF3C | DS1 | $8,316.00 | 10 | | Deal-0D2F7A | DS3 | $5,100.00 | 12 | | Deal-6C60D4 | DS3 | $4,800.00 | 12 | | Deal-13FEBD | DS2 | $4,680.00 | 12 | | Deal-9D0060 | DS3 | $3,840.00 | 12 | | Deal-690476 | DS2 | $3,600.00 | 18 | | Deal-C6D97A | DS4 | $3,240.00 | 8 | | Deal-EE195F | DS3 | $3,120.00 | 8 | | Deal-278DEC | DS3 | $2,700.00 | 8 | | Deal-635B8E | DS3 | $2,600.00 | 18 | | Deal-6883F3 | DS1 | $2,400.00 | 16 | | Deal-4A13AD | DS3 | $2,160.00 | 26 | | Deal-F67D31 | DS2 | $1,800.00 | 8 | | Deal-5FDCE4 | DS3 | $1,600.00 | 12 | | Deal-7FA0C3 | DS2 | $1,400.00 | 8 | | Deal-BA571A | DS4 | $1,080.00 | 18 | Arithmetic: $24,000 + $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 + $1,400 + $1,080 = $103,736.00 Stale deals: 19 Total stale amount: $103,736.00 Cole Ingram | Deal alias | Stage | Amount | Days since last contact | |---|---:|---:|---:| | Deal-D04904 | DS2 | $58,529.25 | 11 | | Deal-B25F40 | DS3 | $40,000.00 | 8 | | Deal-813836 | DS2 | $32,175.00 | 11 | | Deal-1BA595 | DS2 | $31,750.00 | 11 | | Deal-CFE1E8 | DS3 | $18,000.00 | 11 | | Deal-898FC5 | DS3 | $12,600.00 | 11 | | Deal-CD47A6 | DS2 | $12,168.00 | 11 | | Deal-627646 | DS3 | $11,193.00 | 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 | $5,616.00 | 10 | | Deal-175395 | DS3 | $4,779.88 | 11 | | Deal-481E24 | DS3 | $4,140.00 | 10 | | Deal-2F3A66 | DS3 | $3,334.80 | 11 | | Deal-342E96 | DS2 | $2,700.00 | 24 | | Deal-E568D5 | DS3 | $1,875.00 | 11 | Arithmetic: $58,529.25 + $40,000 + $32,175 + $31,750 + $18,000 + $12,600 + $12,168 + $11,193 + $7,781.20 + $7,225.40 + $6,947.50 + $5,616 + $4,779.88 + $4,140 + $3,334.80 + $2,700 + $1,875 = $260,815.03 Stale deals: 17 Total stale amount: $260,815.03 Elena Sinclair | Deal alias | Stage | Amount | Days since last contact | |---|---:|---:|---:| | Deal-57FF13 | DS1 | $2,100.00 | Not computable — no last_email, last_call, or last_meeting value provided | Arithmetic: $2,100 = $2,100.00 Stale deals: 1 Total stale amount: $2,100.00
30-day window: 2026-08-06 through 2026-09-05, inclusive.
Efficiency ranking: lowest activities per DS2 entry first.
1. Alex Franklin
- Emails: 307
- Calls: 36
- Meetings: 41
- Total activities: 307 + 36 + 41 = 384
- Activity mix:
- Emails: 307 / 384 = 79.9%
- Calls: 36 / 384 = 9.4%
- Meetings: 41 / 384 = 10.7%
- DS2 entries: 18
- Activities per DS2 entry: 384 / 18 = 21.3
2. Bryce Harmon
- Emails: 162
- Calls: 0
- Meetings: 43
- Total activities: 162 + 0 + 43 = 205
- Activity mix:
- Emails: 162 / 205 = 79.0%
- Calls: 0 / 205 = 0.0%
- Meetings: 43 / 205 = 21.0%
- DS2 entries: 4
- Activities per DS2 entry: 205 / 4 = 51.3
3. Cole Ingram
- Emails: 96
- Calls: 14
- Meetings: 1
- Total activities: 96 + 14 + 1 = 111
- Activity mix:
- Emails: 96 / 111 = 86.5%
- Calls: 14 / 111 = 12.6%
- Meetings: 1 / 111 = 0.9%
- DS2 entries: 2
- Activities per DS2 entry: 111 / 2 = 55.5
4. Farid Osman
- Emails: 38
- Calls: 0
- Meetings: 34
- Total activities: 38 + 0 + 34 = 72
- Activity mix:
- Emails: 38 / 72 = 52.8%
- Calls: 0 / 72 = 0.0%
- Meetings: 34 / 72 = 47.2%
- DS2 entries: 1
- Activities per DS2 entry: 72 / 1 = 72.0
5. Dana Mercer
- Emails: 84
- Calls: 18
- Meetings: 11
- Total activities: 84 + 18 + 11 = 113
- Activity mix:
- Emails: 84 / 113 = 74.3%
- Calls: 18 / 113 = 15.9%
- Meetings: 11 / 113 = 9.7%
- DS2 entries: 1
- Activities per DS2 entry: 113 / 1 = 113.0
Excluded from efficiency ranking:
- Elena Sinclair: 0 emails, 0 calls, 0 meetings; 0 DS2 entries. Ratio is undefined because 0 / 0.
- Gavin Porter: archived; no deals or engagement rows provided.
- Hugo Lindqvist: archived; no deals or engagement rows provided.
Most efficient rep: Alex Franklin, at 21.3 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 - Quota: $200,000 - QTD closed-won bookings: $150,000 - $40,000 + $20,000 + $35,000 + $21,000 + $12,000 + $11,000 + $6,500 + $4,500 = $150,000 - Attainment: $150,000 / $200,000 = 75.0% - Remaining to quota: $200,000 − $150,000 = $50,000 New vs. expansion - New: $113,500 - $40,000 + $35,000 + $21,000 + $11,000 + $6,500 = $113,500 - Expansion: $36,500 - $20,000 + $12,000 + $4,500 = $36,500 - Split: 75.7% new / 24.3% expansion Active pipeline by stage - DS1: 20 deals, $284,621 - DS2: 28 deals, $353,760 - DS3: 54 deals, $445,063 - DS4: 5 deals, $23,574 - DS5: 5 deals, $45,730 - Total active pipeline: 112 deals, $1,152,748 Rolling 90-day DS2-to-won rate Using deals that entered DS2 from 2026-06-07 through 2026-09-05 and reached a closed outcome: - Won: 8 - Lost: 27 - Rate: 8 / (8 + 27) = 8 / 35 = 22.9% Win and loss counts - QTD wins: 8 - QTD losses: 27 - Total closed deals: 8 + 27 = 35 - Win rate: 8 / 35 = 22.9% - Top loss reason: Lost- Timing (1 year or more), 13 losses - 13 / 27 = 48.1% Activity volume in the last 30 days The provided engagement data is deal-level. Summing activity for the 35 QTD closed deals: - Emails: 208 - 109 on lost deals + 99 on won deals = 208 - Calls: 58 - 25 + 33 = 58 - Meetings: 38 - 13 + 25 = 38 - Notes: 49 - 25 + 24 = 49 Coaching observations 1. Alex is at 75.0% attainment with $50,000 remaining, so closing at least the largest near-term opportunities is necessary to reach quota. 2. Timing is the dominant loss driver: 13 of 27 losses, or 48.1%. Qualification should more aggressively test budget, urgency, and a credible purchase timeline before opportunities advance. 3. The DS2-to-won rate is 22.9%, while active DS2 pipeline totals $353,760. Improving DS2 conversion is the clearest leverage point; at the observed rate, that stage alone would imply approximately $81,000 of expected bookings, calculated as $353,760 × 22.9%.
Open status, amount, and stage are not provided, so amount and stage are unavailable and stage-based persona prioritization cannot be determined. Using 2026-09-05 as the reference date, active means engaged on or after 2026-07-07 and not former. Deal-EC3025 - Amount: not provided - Stage: not provided - Active contact count: 1 — 2 total listed contacts − 1 former = 1 - Personas present: champion - Personas missing: economic buyer, HR admin, IT security, finance - Most valuable persona to add given the stage: cannot determine; stage not provided - Matching unengaged contact on file: CT-6827DB, Chief People Officer, economic buyer Deal-92D97D - Amount: not provided - Stage: not provided - Active contact count: 1 — CT-01F5B4 is active; CT-A902AE last engaged 2026-06-01, outside the 60-day window - Personas present: HR admin - Personas missing: economic buyer, champion, IT security, finance - Most valuable persona to add given the stage: cannot determine; stage not provided - Matching unengaged contact on file: none on file Deal-50D386 - Amount: not provided - Stage: not provided - Active contact count: 2 - Personas present: champion, HR admin - Personas missing: economic buyer, IT security, finance - Most valuable persona to add given the stage: cannot determine; stage not provided - Matching unengaged contact on file: CT-A1C4B3, Chief People Officer, economic buyer Deal-D0D6B5 - Amount: not provided - Stage: not provided - Active contact count: 3 - Personas present: champion only — 3 active contacts, all champion - Personas missing: economic buyer, HR admin, IT security, finance - Most valuable persona to add given the stage: cannot determine; stage not provided - Matching unengaged contact on file: CT-1FA4DB, Chief People Officer, economic buyer Deal-5BFE3B - Amount: not provided - Stage: not provided - Active contact count: 2 - Personas present: champion only - Personas missing: economic buyer, HR admin, IT security, finance - Most valuable persona to add given the stage: cannot determine; stage not provided - Matching unengaged contact on file: none on file Deal-36C33F - Amount: not provided - Stage: not provided - Active contact count: 1 — 2 former contacts excluded; 1 IT security contact remains active - Personas present: IT security - Personas missing: economic buyer, champion, HR admin, finance - Most valuable persona to add given the stage: cannot determine; stage not provided - Matching unengaged contact on file: CT-1DB73E, Chief People Officer, economic buyer Deal-885F45 - Amount: not provided - Stage: not provided - Active contact count: 2 - Personas present: economic buyer, champion - Personas missing: HR admin, IT security, finance - Most valuable persona to add given the stage: cannot determine; stage not provided - Matching unengaged contact on file: CT-B3F25D, IT Security Lead, IT security Deal-FCBE5B - Amount: not provided - Stage: not provided - Active contact count: 1 - Personas present: champion - Personas missing: economic buyer, HR admin, IT security, finance - Most valuable persona to add given the stage: cannot determine; stage not provided - Matching unengaged contact on file: none on file Deal-5408B0 - Amount: not provided - Stage: not provided - Active contact count: 2 - Personas present: champion, HR admin - Personas missing: economic buyer, IT security, finance - Most valuable persona to add given the stage: cannot determine; stage not provided - Matching unengaged contact on file: CT-07FA76, Chief People Officer, economic buyer Deal-C6D97A - Amount: not provided - Stage: not provided - Active contact count: 3 - Personas present: champion only — 3 active contacts, all champion - Personas missing: economic buyer, HR admin, IT security, finance - Most valuable persona to add given the stage: cannot determine; stage not provided - Matching unengaged contact on file: none on file Deal-F9A08A - Amount: not provided - Stage: not provided - Active contact count: 1 — CT-913581 last engaged 2026-06-20, outside the 60-day window - Personas present: champion - Personas missing: economic buyer, HR admin, IT security, finance - Most valuable persona to add given the stage: cannot determine; stage not provided - Matching unengaged contact on file: CT-697541, Chief People Officer, economic buyer
- First five minutes: They lead with a customer proof point about a 400-person retailer reducing regretted turnover through automated milestone awards in 8 of 10 calls (80%). The other two starts were an agenda/security-and-pricing opener (TT-004) and a pricing-first opener (TT-009). Quote: “Quick one to kick off: a 400-person retailer we work with cut regretted turnover 18% in two quarters after automating milestone awards, and I wanted to walk you through exactly how they did it.” - Objection 1 — budget locked: 3 of 10 calls. They reframe the purchase as funded by turnover savings and use avoided-backfill savings as financial proof. Quote: “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.” - Objection 2 — revisit next quarter/timing: 3 of 10 calls. They propose a limited 90-day, one-department pilot to generate internal data before 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?” - Objection 3 — current spreadsheet and gift-card process is sufficient: 3 of 10 calls. They position automation and recognition analytics as the reason to change. 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.” - Concrete next step agreed: 7 of 10 calls = 70%. Agreements occurred in TT-001, TT-002, TT-003, TT-005, TT-006, TT-008, and TT-009. In each, the prospect agreed to a Thursday 2pm working session and to bring the HRIS manager. - Competitors raised by prospects: Awardco (TT-003) and Kudos (TT-007). Workhuman was mentioned by Alex Franklin, not raised by a prospect. Coaching notes: 1. Preserve the strong, repeatable financial and pilot-based objection handling, but tailor the opening more often to the prospect’s stated context instead of using the same retailer proof point. 2. When a prospect signals no urgency, committee dependency, or a desire to wait, replace “I'll leave it with you” or “Fair enough” with a specific low-commitment follow-up or decision date.
## Q3 2026 forecast Quarter: 2026-07-01 through 2026-09-30, inclusive. | Category | Deal count | Total amount | |---|---:|---:| | COMMIT | 7 | 44,729 | | BEST_CASE | 24 | 203,565 | | PIPELINE | 23 | 201,637.40 | Weighted forecast rule: - COMMIT: 100% × 44,729 = 44,729 - BEST_CASE: 35% × 203,565 = 71,247.75 - PIPELINE: 0% × 201,637.40 = 0 Weighted forecast = 44,729 + 71,247.75 + 0 = 115,976.75 ## Excluded outside-quarter deals 32 deals were excluded because their close dates were after 2026-09-30. Total excluded amount: 43,875 + 18,000 + 17,000 + 13,770 + 10,800 + 9,000 + 9,000 + 7,920 + 7,690 + 7,500 + 7,200 + 5,700 + 5,400 + 5,400 + 5,400 + 5,400 + 5,160 + 4,800 + 4,400 + 4,300 + 4,000 + 3,600 + 3,600 + 3,600 + 3,300 + 2,400 + 1,800 + 1,800 + 1,680 + 1,600 + 1,400 + 1,080 = 227,575 Excluded deals: - Deal-E51FB7 - Deal-B936FE - Deal-D9A12F - Deal-D348E1 - Deal-4062CF - Deal-293AF3 - Deal-034D49 - Deal-E0ADD8 - Deal-9F2E43 - Deal-FCBE5B - Deal-712010 - Deal-6691E0 - Deal-C61CF7 - Deal-600CD9 - Deal-A92065 - Deal-1D532E - Deal-48B656 - Deal-E531A6 - Deal-D1E6C2 - Deal-D9E112 - Deal-5AD94B - Deal-901332 - Deal-47AE31 - Deal-15D24F - Deal-766C74 - Deal-ED725A - Deal-8AD4A5 - Deal-D7E999 - Deal-ED13B0 - Deal-5FDCE4 - Deal-7FA0C3 - Deal-F5A622 ## 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 Top 5 total = 38,935 + 24,000 + 19,656 + 16,250 + 11,116 = 109,957 ## Data quality 85 of 86 deals have a blank owner, so ownership and accountability cannot be reliably analyzed. The `why_buys_chars` field is zero for most deals, indicating largely missing or unpopulated qualification data. No currency field is provided, so the forecast amount’s currency is unspecified. The extract contains no explicit completeness or validation indicators for forecast category, stage, amount, or date values.
| First-month signal cohort | Cohort size | Retained at 24 months (`active`) | 24-month retention rate | |---|---:|---:|---:| | Both signals: `m1_users >= 5` and `m1_redemptions >= 1` | 47 | 31 | `31 / 47 = 66.0%` | | Givers-only: `m1_users >= 5` and `m1_redemptions = 0` | 49 | 23 | `23 / 49 = 46.9%` | | Redemption-only: `m1_users < 5` and `m1_redemptions >= 1` | 29 | 9 | `9 / 29 = 31.0%` | | Neither: `m1_users < 5` and `m1_redemptions = 0` | 95 | 38 | `38 / 95 = 40.0%` | Total denominator: `47 + 49 + 29 + 95 = 220 companies` Excluded from denominator: 0 companies. No required signal or status fields are missing, and all companies are stated to be at least 25 months old. Only `current_status = 'active'` is counted as retained; `cancelled` and `non_renewing` are not retained. Hypothesis result: descriptively supported. The both-signals cohort retained at `66.0%`, compared with `40.0%` for neither: `66.0% - 40.0% = +26.0 percentage points` Single signal with the largest retention lift: 5+ unique givers (`m1_users >= 5`). - Giver signal: `(31 + 23) / (47 + 49) = 54 / 96 = 56.2%` - No giver signal: `(9 + 38) / (29 + 95) = 47 / 124 = 37.9%` - Giver-signal lift: `56.2% - 37.9% = +18.3 percentage points` For comparison: - Redemption signal: `(31 + 9) / (47 + 29) = 40 / 76 = 52.6%` - No redemption signal: `(23 + 38) / (49 + 95) = 61 / 144 = 42.4%` - Redemption-signal lift: `52.6% - 42.4% = +10.3 percentage points` This proves an association in this extract: both first-month signals coincide with the highest observed 24-month retention. It does not prove that either signal causes retention, that the thresholds are optimal, that the differences are statistically significant, or that the result generalizes beyond these 220 companies.
ARR reconciliation as of 2026-09-05 Basis: Billing ARR includes active subscriptions only. Billing ARR = MRR × 12. Totals - CRM company ARR: $603,581.76 - Billing active-subscription ARR: $604,739.28 - Variance: $1,157.52 favorable to billing Arithmetic: $604,739.28 − $603,581.76 = $1,157.52 Variance decomposition | Bucket | Arithmetic | Variance | |---|---:|---:| | Status mismatch | −$4,905.24 − $8,253.24 | −$13,158.48 | | Rounding | −$16.00 − $20.00 | −$36.00 | | Missing records | $28,449.24 − $16,497.24 | $11,952.00 | | Other | $2,400.00 | $2,400.00 | | Total | −$13,158.48 − $36.00 + $11,952.00 + $2,400.00 | $1,157.52 | Mismatched accounts | Company alias | Subscription | Issue | ARR impact | Suggested owner | |---|---|---|---:|---| | C-0C8323BF | SUB-000E | Billing subscription is cancelled; CRM ARR is still present | −$4,905.24 | RevOps / CRM Ops | | C-0DC4FB8C | SUB-000F | Billing subscription is cancelled; CRM ARR is still present | −$8,253.24 | RevOps / CRM Ops | | C-21629AA4 | SUB-0004 | Billing record exists; CRM company ARR record is missing | +$28,449.24 | CRM Ops | | C-0D5BBE3A | None | CRM company ARR record exists; billing subscription is missing | −$16,497.24 | Billing Ops | | C-0F7269D7 | SUB-0006 | Billing ARR exceeds CRM ARR: $26,796.00 − $24,396.00 | +$2,400.00 | RevOps / Finance | | C-0D66DF9E | SUB-0005 | Billing ARR is below CRM ARR: $23,184.00 − $23,200.00 | −$16.00 | RevOps / Finance | | C-14D70CE0 | SUB-0008 | Billing ARR is below CRM ARR: $18,180.00 − $18,200.00 | −$20.00 | RevOps / Finance | Term-date violations Any subscription with a term other than 12 months must have cf_agreement_end_date populated. | Subscription | Company alias | Term | cf_agreement_end_date | |---|---|---:|---| | SUB-0002 | C-1794A52C | 24 months | Missing | | SUB-0019 | C-22170CA1 | 36 months | Missing |
Unweighted averages across the 30 company_alias records. Relative change = (2026-08 − 2026-07) / 2026-07. | KVM | 2026-08 value | 2026-07 value | Absolute change | Relative change | Direction | |---|---:|---:|---:|---:|---| | Giving rate | 0.602713 | 0.602297 | 0.602713 − 0.602297 = +0.000417 | +0.000417 / 0.602297 = +0.0692% | Up | | Redemptions per user | 1.730163 | 1.729983 | 1.730163 − 1.729983 = +0.000180 | +0.000180 / 1.729983 = +0.0104% | Up | | 1:1 meetings engagement | 0.447177 | 0.446887 | 0.447177 − 0.446887 = +0.000290 | +0.000290 / 0.446887 = +0.0649% | Up | | Pulse check engagement | 0.508610 | 0.600587 | 0.508610 − 0.600587 = −0.091977 | −0.091977 / 0.600587 = −15.3145% | Down | Largest relative move: pulse check engagement, down 15.3145%. The enterprise size_band drives this: its average changed by −0.2757, versus −0.00148 for smb and +0.00125 for mid_market.
Redemption section — through 2026-08 Last completed month: 2026-08 - Redemption count: 378 - Spend: $27,846.00 - Unique redeemers: 235 - Redemptions per redeemer: 378 ÷ 235 = 1.6085 ≈ 1.61 Provider mix by spend: - custom: $10,873.00 ÷ $27,846.00 = 39.05% - Tremendous: $8,505.00 ÷ $27,846.00 = 30.54% - Snappy: $5,238.00 ÷ $27,846.00 = 18.81% - TangoCard: $3,230.00 ÷ $27,846.00 = 11.60% - Total: 39.05% + 30.54% + 18.81% + 11.60% = 100.00% Top 5 countries by redemptions: 1. US: 244 2. CA: 24 3. AU: 21 4. GB: 17 5. NL: 17
Snapshot: 2026-09-05 R3 window: 2026-09-05 through 2027-01-03. Eligibility requires all three: - Health score < 60 - Churn-save eligible amount > $0 - Renewal within the 120-day window The provided rules determine eligibility, but do not formally assign plays. The plays below are best-fit recommendations based only on the available signals. Qualifying at-risk accounts 1. C-0F6C0F34 — $49,707 at stake - Arithmetic: health 51 < 60; eligible amount $49,707 > $0; renewal 2026-10-03 is within the window. - Play: Executive touch - Signal: champion_active=false, while usage_trend_3m=growing and seats used are 308/395. The engagement risk is stronger than the usage risk. 2. C-0B827671 — $25,365 at stake - Arithmetic: health 56 < 60; $25,365 > $0; renewal 2026-11-14 is within the window. - Play: Usage revival - Signal: usage_trend_3m=declining and 113/202 seats are used. 3. C-0B360C78 — $35,748 at stake - Arithmetic: health 57 < 60; $35,748 > $0; renewal 2026-10-28 is within the window. - Play: Commercial concession - Signal: usage_trend_3m=growing, champion_active=true, and 246/327 seats are used. No clear usage or champion-engagement problem is present, leaving the commercial lever as the best fit among the listed plays. 4. C-0B0F1BAB — $5,494 at stake - Arithmetic: health 38 < 60; $5,494 > $0; renewal 2026-09-23 is within the window. - Play: Executive touch - Signal: champion_active=false. Usage is flat, not declining. 5. C-0CA21961 — $16,829 at stake - Arithmetic: health 58 < 60; $16,829 > $0; renewal 2026-12-28 is within the window. - Play: Usage revival - Signal: only 84/325 seats are used, with usage_trend_3m=flat. 6. C-0E9C27D1 — $41,235 at stake - Arithmetic: health 39 < 60; $41,235 > $0; renewal 2026-09-24 is within the window. - Play: Commercial concession - Signal: usage_trend_3m=flat, champion_active=true, and 134/157 seats are used. The data does not show a usage or champion-engagement gap; the eligible commercial amount is the clearest available lever. 7. C-0CEF69FD — $32,621 at stake - Arithmetic: health 53 < 60; $32,621 > $0; renewal 2026-11-21 is within the window. - Play: Executive touch - Signal: champion_active=false. Usage is growing, with 97/136 seats used. 8. C-0D3278C7 — $17,602 at stake - Arithmetic: health 54 < 60; $17,602 > $0; renewal 2026-11-12 is within the window. - Play: Usage revival - Signal: usage_trend_3m=declining and only 126/380 seats are used. Total amount at stake $49,707 + $25,365 + $35,748 + $5,494 + $16,829 + $41,235 + $32,621 + $17,602 = $224,601 At-risk accounts that do not qualify These accounts have health scores below 60 but fail at least one other eligibility rule: - C-0BC71BDD - Health: 55, so R1 passes. - Renewal: 2026-10-27, within 120 days, so R3 passes. - Churn-save eligible amount: $0.00, so R2 fails. - Reason: no eligible amount. - C-0BA71F12 - Health: 52, so R1 passes. - Churn-save eligible amount: $6,824, so R2 passes. - Renewal: 2027-04-11, outside the 2026-09-05 to 2027-01-03 window, so R3 fails. - Reason: renewal is outside the eligibility window. - C-0F6694C3 - Health: 43, so R1 passes. - Churn-save eligible amount: $0.00, so R2 fails. - Renewal: 2027-03-21, outside the window, so R3 fails. - Reason: no eligible amount and renewal is outside the window. - C-0BE96399 - Health: 54, so R1 passes. - Renewal: 2026-10-29, within the window, so R3 passes. - Churn-save eligible amount: $0.00, so R2 fails. - Reason: no eligible amount. - C-0F876796 - Health: 47, so R1 passes. - Churn-save eligible amount: $19,958, so R2 passes. - Renewal: 2027-02-06, outside the window, so R3 fails. - Reason: renewal is outside the eligibility window. - C-0FCCD2DF - Health: 43, so R1 passes. - Churn-save eligible amount: $0.00, so R2 fails. - Renewal: 2027-04-23, outside the window, so R3 fails. - Reason: no eligible amount and renewal is outside the window. - C-10A56B0F - Health: 54, so R1 passes. - Renewal: 2026-12-12, within the window, so R3 passes. - Churn-save eligible amount: $0.00, so R2 fails. - Reason: no eligible amount.
Account: C-0DDFC9A7 Seat coverage - Licensed seats / headcount: 150 / 400 = 37.5% - Unlicensed headcount headroom: 400 − 150 = 250 seats Usage health - Monthly active users increased from 88 in 2026-03 to 126 in 2026-08: 126 − 88 = 38 users, or 43.2%. - 2026-08 active users represent 126 / 150 = 84.0% of licensed seats, indicating limited current usage headroom. ARR headroom - Current per-seat ARR: $9,000 / 150 = $60 per seat - ARR headroom at current rate: 250 × $60 = $15,000 Reply and buying authority - Maria S., People Operations Coordinator, replied. - Maria S. cannot make the purchasing decision; she stated that budget and seat expansion sit with Dana R. - Right buyer: Dana R., VP People. Reply email Subject: Re: Growing your team's recognition program Hi Maria, Thanks for the note—and glad to hear the team genuinely loves Bonusly. Usage has grown from 88 monthly active users in March to 126 in August. I appreciate the context that Dana R. owns budget and seat expansion. Since she’s been asking about usage numbers, would you be open to introducing us? I can send a concise usage snapshot and let her decide whether a conversation makes sense. Thanks again, Cole
C-0D284E42 — mid-onboarding prep Complete - Slack integration: complete — `integration_slack=2026-08-12` - Allowance: set — `allowance_set=2026-08-13` - Admins: added — `admins_added=2` - First recognition: given — `first_recognition_at=2026-08-15 14:22` Not complete / missing - HRIS integration: not complete — `integration_hris` is blank - First redemption: not complete — `first_redemption_at` is blank Early engagement signals - Active givers increased from 3 on 2026-08-11 to 15 on 2026-09-04. - Arithmetic: `15 - 3 = 12` additional active givers; `15 / 3 = 5x` the starting level. - Active givers averaged `214 / 25 = 8.56` per day. - The first 7 days averaged `30 / 7 = 4.29` active givers; the last 7 days averaged `91 / 7 = 13`. - Engagement is increasing, but no redemption has been recorded. Three things to cover 1. Complete the HRIS integration and identify what is blocking it. 2. Understand why no redemption has occurred and agree on the next step to generate the first redemption. 3. Build on the rising giver activity by confirming how the two admins will sustain recognition activity.
90-Day Renewal Risk Brief Date-source rule: - Trust Chargebee for multi-year contracts because multi-year dates in ChurnZero are known to be wrong. - For non-multi-year contracts, Chargebee and ChurnZero agree; use the Chargebee date. - 4 date disagreements are flagged below. | Company | CSM | ARR | Date used | Date decision / disagreement | Seat utilization | 3-month usage trend | Risk | Evidence | |---|---|---:|---|---|---|---|---|---| | C-0B7D2C30 | Dana Mercer | $65,901.00 | 2026-09-15 | Chargebee; disagreement: ChurnZero 2026-09-10 vs. Chargebee 2026-09-15; multi-year | 274 / 476 = 57.6% | 97 → 84 users, -13 (-13.4%) | High | Utilization is 57.6% and usage declined 13.4% from June to August. | | C-0BCDB8C2 | Cole Ingram | $54,427.00 | 2026-09-18 | Chargebee; disagreement: ChurnZero 2027-09-18 vs. Chargebee 2026-09-18; multi-year | 232 / 424 = 54.7% | 127 → 110 users, -17 (-13.4%) | High | Utilization is 54.7% and usage declined 13.4% from June to August. | | C-0D2AB865 | Elena Sinclair | $38,022.00 | 2026-09-22 | Chargebee; disagreement: ChurnZero 2026-09-10 vs. Chargebee 2026-09-22; multi-year | 250 / 407 = 61.4% | 125 → 109 users, -16 (-12.8%) | Medium | Utilization is 61.4% and usage declined 12.8% from June to August. | | C-0BBE3E60 | Dana Mercer | $30,993.00 | 2026-09-26 | Chargebee; no disagreement: both systems show 2026-09-26 | 74 / 114 = 64.9% | 39 → 33 users, -6 (-15.4%) | Medium | Usage declined 15.4% from June to August despite 64.9% seat utilization. | | C-0F5D2323 | Cole Ingram | $90,647.00 | 2026-09-29 | Chargebee; disagreement: ChurnZero 2026-09-10 vs. Chargebee 2026-09-29; multi-year | 111 / 390 = 28.5% | 20 → 18 users, -2 (-10.0%) | High | Utilization is only 28.5% and usage declined 10.0% from June to August. | | C-0EC6999D | Elena Sinclair | $79,419.00 | 2026-10-03 | Chargebee; no disagreement: both systems show 2026-10-03 | 31 / 112 = 27.7% | 17 → 15 users, -2 (-11.8%) | High | Utilization is only 27.7% and usage declined 11.8% from June to August. | | C-0B20DB64 | Dana Mercer | $21,770.00 | 2026-10-07 | Chargebee; no disagreement: both systems show 2026-10-07 | 214 / 378 = 56.6% | 294 → 294 users, 0 (0.0%) | Medium | Usage is flat, but seat utilization is only 56.6%. | | C-0BBC4E7A | Cole Ingram | $56,374.00 | 2026-10-10 | Chargebee; no disagreement: both systems show 2026-10-10 | 228 / 337 = 67.7% | 142 → 139 users, -3 (-2.1%) | Medium | Utilization is 67.7% and usage declined 2.1% from June to August. | | C-0FD551AB | Elena Sinclair | $48,815.00 | 2026-10-14 | Chargebee; no disagreement: both systems show 2026-10-14 | 210 / 376 = 55.9% | 123 → 126 users, +3 (+2.4%) | Medium | Usage increased 2.4%, but seat utilization remains 55.9%. | | C-0F9F8F13 | Dana Mercer | $46,230.00 | 2026-10-18 | Chargebee; no disagreement: both systems show 2026-10-18 | 199 / 352 = 56.5% | 185 → 182 users, -3 (-1.6%) | Medium | Utilization is 56.5% and usage declined slightly by 1.6%. | | C-0BC34584 | Cole Ingram | $16,740.00 | 2026-10-22 | Chargebee; no disagreement: both systems show 2026-10-22 | 327 / 494 = 66.2% | 104 → 106 users, +2 (+1.9%) | Low | Usage increased 1.9% and utilization is 66.2%. | | C-0B7A7546 | Elena Sinclair | $35,062.00 | 2026-10-25 | Chargebee; no disagreement: both systems show 2026-10-25 | 182 / 205 = 88.8% | 64 → 63 users, -1 (-1.6%) | Medium | Utilization is strong at 88.8%, but usage declined 1.6%. | | C-0B369871 | Dana Mercer | $85,128.00 | 2026-10-29 | Chargebee; no disagreement: both systems show 2026-10-29 | 317 / 422 = 75.1% | 326 → 333 users, +7 (+2.1%) | Low | Utilization is 75.1% and usage increased 2.1%. | | C-0B144C78 | Cole Ingram | $30,899.00 | 2026-11-02 | Chargebee; no disagreement: both systems show 2026-11-02 | 169 / 224 = 75.4% | 101 → 106 users, +5 (+5.0%) | Low | Utilization is 75.4% and usage increased 5.0%. | | C-0FC4DBB8 | Elena Sinclair | $94,732.00 | 2026-11-05 | Chargebee; no disagreement: both systems show 2026-11-05 | 356 / 464 = 76.7% | 189 → 193 users, +4 (+2.1%) | Low | Utilization is 76.7% and usage increased 2.1%. | | C-0D5BBE3A | Dana Mercer | $39,740.00 | 2026-11-09 | Chargebee; no disagreement: both systems show 2026-11-09 | 85 / 102 = 83.3% | 88 → 91 users, +3 (+3.4%) | Low | Utilization is 83.3% and usage increased 3.4%. | | C-0FB9D5AF | Cole Ingram | $63,158.00 | 2026-11-13 | Chargebee; no disagreement: both systems show 2026-11-13 | 144 / 199 = 72.4% | 173 → 176 users, +3 (+1.7%) | Low | Utilization is 72.4% and usage increased 1.7%. | | C-0B344485 | Elena Sinclair | $64,384.00 | 2026-11-16 | Chargebee; no disagreement: both systems show 2026-11-16 | 224 / 287 = 78.0% | 238 → 244 users, +6 (+2.5%) | Low | Utilization is 78.0% and usage increased 2.5%. | | C-0CB2C1B4 | Dana Mercer | $40,628.00 | 2026-11-20 | Chargebee; no disagreement: both systems show 2026-11-20 | 386 / 473 = 81.6% | 47 → 49 users, +2 (+4.3%) | Low | Utilization is 81.6% and usage increased 4.3%. | | C-22170CA1 | Cole Ingram | $45,646.00 | 2026-11-24 | Chargebee; no disagreement: both systems show 2026-11-24 | 251 / 294 = 85.4% | 143 → 146 users, +3 (+2.1%) | Low | Utilization is 85.4% and usage increased 2.1%. | Summary - Total ARR renewing: $65,901 + $54,427 + $38,022 + $30,993 + $90,647 + $79,419 + $21,770 + $56,374 + $48,815 + $46,230 + $16,740 + $35,062 + $85,128 + $30,899 + $94,732 + $39,740 + $63,158 + $64,384 + $40,628 + $45,646 = $1,048,715.00 - ARR at risk, High + Medium: - High: $65,901 + $54,427 + $90,647 + $79,419 = $290,394.00 - Medium: $38,022 + $30,993 + $21,770 + $56,374 + $48,815 + $46,230 + $35,062 = $277,266.00 - Total ARR at risk: $290,394 + $277,266 = $567,660.00
Quarter total: 80 tickets. Ranked by ARR exposure. Theme classification is based on ticket text, not existing tags. 1. HRIS provisioning failures — broad pattern - Count: 12 - Share: 12 / 80 = 15.0% - Distinct accounts: 3 - ARR affected: $36,000 + $30,000 + $48,000 = $114,000 - Ticket IDs: IC-460059, IC-460062 - Recommendation: Prioritize investigation of HRIS sync and provisioning failures, including skipped hires and missing provisioning errors. 2. Redemption and gift-card failures — broad pattern - Count: 18 - Share: 18 / 80 = 22.5% - Distinct accounts: 7 - ARR affected: $8,900 + $10,700 + $9,600 + $8,700 + $11,000 + $9,600 + $10,300 = $68,800 - Ticket IDs: IC-460025, IC-460024 - Recommendation: Stabilize checkout and gift-card fulfillment, including cases where points are deducted but the order or delivery fails. 3. Billing and invoice discrepancies — single-account noise - Count: 16 - Share: 16 / 80 = 20.0% - Distinct accounts: 1 - ARR affected: $52,000 = $52,000 - Ticket IDs: IC-460071, IC-460078 - Recommendation: Treat this as a concentrated account issue for C-0E9C27D1 and resolve the recurring seat-count and renewal-tier discrepancies directly. 4. Recognition points not posting — broad pattern - Count: 20 - Share: 20 / 80 = 25.0% - Distinct accounts: 9 - ARR affected: $3,500 + $4,500 + $4,500 + $2,700 + $3,400 + $4,200 + $2,900 + $2,500 + $2,900 = $31,100 - Ticket IDs: IC-460004, IC-460016 - Recommendation: Investigate the points-posting pipeline for both individual recognitions and team-wide posting delays. 5. Slack integration and command failures — broad pattern - Count: 14 - Share: 14 / 80 = 17.5% - Distinct accounts: 4 - ARR affected: $4,400 + $5,400 + $3,900 + $5,200 = $18,900 - Ticket IDs: IC-460041, IC-460047 - Recommendation: Fix Slack authentication persistence, recognition syncing, and slash-command errors across affected accounts. Total ARR exposure calculated from distinct accounts within each theme: $114,000 + $68,800 + $52,000 + $31,100 + $18,900 = $284,800.
1. C-64171065 — 3/4 matching fields - Industry: Technology = Technology - Size band: Mid-Market = Mid-Market - Use case: employee_recognition = employee_recognition - Region: NA-East ≠ NA-West - Case study: true 2. C-A13C193D — 3/4 matching fields - Industry: Technology = Technology - Size band: Mid-Market = Mid-Market - Use case: retention ≠ employee_recognition - Region: NA-West = NA-West - Case study: true 3. C-11C31562 — 3/4 matching fields - Industry: Manufacturing ≠ Technology - Size band: Mid-Market = Mid-Market - Use case: employee_recognition = employee_recognition - Region: NA-West = NA-West - Case study: true Arithmetic: Each listed customer matches 3 of 4 similarity fields: 3 ÷ 4 = 75%. These three are tied based on the provided data.
Trailing 6 months: 2026-03 through 2026-08 Paid channel performance | Channel | Spend | SQMs | SQOs | Cost/SQM | Cost/SQO | SQM-to-SQO | Pipeline | Pipeline / $ | |---|---:|---:|---:|---:|---:|---:|---:|---:| | paid_search | $36,000 | 34 | 18 | $36,000 ÷ 34 = $1,058.82 | $36,000 ÷ 18 = $2,000.00 | 18 ÷ 34 = 52.94% | $720,000 | $720,000 ÷ $36,000 = 20.00x | | linkedin_ads | $24,000 | 25 | 8 | $24,000 ÷ 25 = $960.00 | $24,000 ÷ 8 = $3,000.00 | 8 ÷ 25 = 32.00% | $96,000 | $96,000 ÷ $24,000 = 4.00x | | paid_social | $18,000 | 0 | 0 | Undefined: $18,000 ÷ 0 | Undefined: $18,000 ÷ 0 | Undefined: 0 ÷ 0 | $0 | $0 ÷ $18,000 = 0.00x | | webinars | $9,000 | 12 | 5 | $9,000 ÷ 12 = $750.00 | $9,000 ÷ 5 = $1,800.00 | 5 ÷ 12 = 41.67% | $60,000 | $60,000 ÷ $9,000 = 6.67x | Paid total - Spend: $36,000 + $24,000 + $18,000 + $9,000 = $87,000 - SQMs: 34 + 25 + 0 + 12 = 71 - SQOs: 18 + 8 + 0 + 5 = 31 - Cost per SQM: $87,000 ÷ 71 = $1,225.35 - Cost per SQO: $87,000 ÷ 31 = $2,806.45 - SQM-to-SQO rate: 31 ÷ 71 = 43.66% - Pipeline: $720,000 + $96,000 + $0 + $60,000 = $876,000 - Pipeline per dollar: $876,000 ÷ $87,000 = 10.07x Organic performance | Channel | Volume | SQOs | SQO rate | Pipeline | |---|---:|---:|---:|---:| | organic_search | 29 | 10 | 10 ÷ 29 = 34.48% | $90,000 | Organic pipeline arithmetic: - 10 SQOs × $9,000 = $90,000 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 Reallocate spend away from paid_social first because it produced 0 SQMs, 0 SQOs, $0 pipeline, and 0.00x pipeline per dollar. Reallocate linkedin_ads next, or reduce it while testing additional spend in paid_search and webinars: - paid_search had the strongest pipeline efficiency at 20.00x and the highest SQM-to-SQO rate at 52.94%. - webinars had the lowest cost per SQM at $750 and cost per SQO at $1,800, with 6.67x pipeline per dollar. - linkedin_ads generated 4.00x pipeline per dollar and includes two date-order anomalies. Confidence: Moderate for stopping or reducing paid_social because its sample includes 0 SQMs across $18,000 of spend. Low-to-moderate for broader reallocation because SQO samples are small: 18 for paid_search, 8 for linkedin_ads, and 5 for webinars.
# Battlecard: Rivally ## One-line positioning Rivally is a points-based recognition platform with an engaging recognition feed, focused on distributed and EU teams. [S02, S12, S16] ## Pricing - Current public price: $7 per user/month for Recognition Starter, with annual billing required, as of 2026-08-12. [S17] - Pricing history/conflict: the public price was previously listed at $5 per user/month on 2025-11-03 and 2026-04-01; the newer 2026-08-12 source supersedes those prices. [S03, S08, S17] - Deal-specific quote: Rivally quoted $6.50 per user/month to a 500-seat prospect for an annual term on 2026-06-02. [S13] - Deal-specific quote: a prospect reported a $7 per user/month list price with a 15% discount for a three-year term on 2026-08-14. [S18] - Rivally Pulse is priced as a separate add-on rather than bundled with the core product. [S23] ## Where they win - EU and distributed teams: EU reviewers praised Rivally’s strength with distributed EU teams and its multi-language support. [S12] - EU data residency: Rivally announced EU data residency as generally available and opened a Dublin office. [S15] - Fast implementation: a mid-market reviewer reported setup took under a week. [S04] - Slack deployment: a reviewer reported that the Slack integration worked out of the box. [S04] - User engagement: reviewers praised Rivally’s points-based recognition feed and described it as engaging. [S02, S16] - Support: a G2 review praised support response times of under four hours. [S22] ## Where we win - Analytics depth: an 800-seat prospect chose Bonusly over Rivally, citing analytics depth. [S25] - Rivally’s analytics and reporting are identified as weaknesses: reviewers described its analytics as limited, its dashboards as basic compared with enterprise tools, and its exports as CSV-only. [S02, S07, S20] ## Objections and responses - Objection: “Rivally supports EU teams better.” - Response: Confirm whether EU data residency and multilingual support are requirements, then position against those requirements. Rivally has EU data residency generally available and has received positive feedback for distributed EU teams and multilingual support. [S12, S15] - Objection: “Rivally is quick to deploy.” - Response: Acknowledge the reported under-one-week setup, then test whether the prospect also requires enterprise administration, provisioning, and analytics. [S04, S10] - Objection: “Rivally’s recognition experience is engaging.” - Response: Acknowledge the positive feedback on the recognition feed, then evaluate whether analytics depth and administrative controls carry greater weight. [S02, S16, S25] - Objection: “Rivally has strong integrations.” - Response: Rivally’s Slack integration was reported to work out of the box, while its Microsoft Teams app v2 is in public preview. Validate the prospect’s required collaboration platforms and deployment status. [S04, S19] - Objection: “Rivally is competitively priced.” - Response: Use the current public price of $7 per user/month as the baseline, confirm annual-term requirements, and distinguish public pricing from deal-specific quotes or discounts. [S13, S17, S18] ## Recent changes - Rivally launched Rivally Pulse, a lightweight engagement-survey add-on. [S06] - Rivally hired a former Workday VP for EMEA to lead European expansion. [S11] - Rivally opened a Dublin office and announced EU data residency as generally available. [S15] - Rivally updated Recognition Starter pricing from the previously listed $5 per user/month to $7 per user/month. The newer pricing-page source wins. [S08, S17] - Rivally announced Microsoft Teams app v2 in public preview. [S19] - Rivally Pulse exited beta and became a separately priced add-on rather than a bundled feature. [S23] ## 12-month win/loss record against Rivally Coverage: 2025-09 through 2026-08. [Deal-7767F5, Deal-A9FD43, Deal-7AA785, Deal-44C524, Deal-0D0CD6, Deal-D5B790, Deal-5C636E, Deal-5645A5, Deal-C6FFAA, Deal-67BE14, Deal-1B6969, Deal-F03E7B, Deal-072E31, Deal-F65C8F, Deal-D263E0, Deal-935746, Deal-E46EAB, Deal-1D2392, Deal-9066A6, Deal-72A02F] - Wins: 13 - Losses: 7 - Total: 20 - Arithmetic: 13 wins + 7 losses = 20 deals - Win rate: 13 / 20 = 65% Wins: Deal-A9FD43, Deal-7AA785, Deal-44C524, Deal-0D0CD6, Deal-D5B790, Deal-5C636E, Deal-67BE14, Deal-1B6969, Deal-F03E7B, Deal-072E31, Deal-F65C8F, Deal-E46EAB, Deal-1D2392 Losses: Deal-7767F5, Deal-5645A5, Deal-C6FFAA, Deal-D263E0, Deal-935746, Deal-9066A6, Deal-72A02F Deal records were provided without snippet IDs; the deal aliases above are the citations for those claims. ## Unverified items from the old card - “Rivally was acquired by WorkHuman in 2025.” Unverified; no provided snippet supports this claim. - “Rivally lacks a Slack integration.” Unverified and contradicted by a review reporting that the Slack integration worked out of the box. [S04] - “Points-based recognition for mid-market.” Partially supported: points-based recognition is supported, and a mid-market reviewer is present, but the provided data does not establish that Rivally’s overall positioning is specifically limited to mid-market. [S02, S04] - “Strong in EU enterprise with multi-language support.” “Strong for distributed EU teams” and positive multilingual-support feedback are supported; the broader “EU enterprise” characterization is not fully sourced. [S12]
New Logo Nurture - Sent: 1,386; opens: 490; replies: 90; meetings: 27. - Rates: open = 490/1,386 = 35.35%; reply = 90/1,386 = 6.49%; meeting = 27/1,386 = 1.95%. - Weakest step: Step 3 — 120/428 = 28.04% open, 18/428 = 4.21% reply, 6/428 = 1.40% meeting. - Change: Replace Step 3 with a shorter, direct meeting CTA. Expansion Nurture - Sent: 875; opens: 565; replies: 59; meetings: 12. - Rates: 565/875 = 64.57%; 59/875 = 6.74%; 12/875 = 1.37%. - Weakest step: Step 3 — 95/275 = 34.55% open, 12/275 = 4.36% reply, 3/275 = 1.09% meeting. - Change: Make Step 3 explicitly expansion-focused with a customer-outcome proof point. Cold Outbound - HR Leaders - Sent: 1,785; opens: 545; replies: 8; meetings: 0. - Rates: 545/1,785 = 30.53%; 8/1,785 = 0.45%; 0/1,785 = 0.00%. - Weakest step: Step 3 — 130/590 = 22.03% open, 1/590 = 0.17% reply. - Change: Rebuild targeting and messaging before sending another Step 3. - Failure mode: Every step is under 2% reply: 5/600 = 0.83%, 2/595 = 0.34%, 1/590 = 0.17%; zero meetings. Cold Outbound - People Ops - Sent: 1,163; opens: 340; replies: 29; meetings: 6. - Rates: 340/1,163 = 29.23%; 29/1,163 = 2.49%; 6/1,163 = 0.52%. - Weakest step: Step 3 — 80/377 = 21.22% open, 6/377 = 1.59% reply. - Change: Replace Step 3 with a new value proposition and CTA. - Failure mode: Step 3 is below 2% reply, indicating late-sequence message fatigue or weak relevance. Tracking error - Expansion Nurture Step 2: 340 opened / 300 sent = 113.33%; opens exceed sends. Audience overlap - New Logo Nurture / Expansion Nurture: CT-000301. - Cold Outbound - HR Leaders / Cold Outbound - People Ops: CT-000849, CT-000884, CT-000890, CT-000908, CT-001097, CT-001101, CT-001103, CT-001105, CT-001130, CT-001153, 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 all three steps are below 2% reply and produced 0 meetings.
Weekly marketing goals update — Q3-2026 Pace calculation: 66 days elapsed ÷ 92 days = 71.7% of the quarter. Pace-to-date targets are calculated as full-quarter target × 71.7%. - SQMs: QTD actual 230; target 300; delta = 230 − 300 = −70; pace-to-date target = 300 × 66 ÷ 92 = 215.2; pace: ahead. - SQOs: QTD actual 84; target 120; delta = 84 − 120 = −36; pace-to-date target = 120 × 66 ÷ 92 = 86.1; pace: behind. - DS2s: QTD actual 40; target 75; delta = 40 − 75 = −35; pace-to-date target = 75 × 66 ÷ 92 = 53.8; pace: behind. - Closed-lost MIA rate: QTD actual = 5 ÷ 25 = 20.0%; target 10.0%; delta = 20.0% − 10.0% = +10.0 percentage points; pace: behind because lower is better. - Same-quarter closes: QTD actual 10; target 20; delta = 10 − 20 = −10; pace-to-date target = 20 × 66 ÷ 92 = 14.3; pace: behind. - Active pipeline coverage against target: QTD actual = $3,000,000; target = $4,000,000; delta = $3,000,000 − $4,000,000 = −$1,000,000; pace-to-date target = $4,000,000 × 66 ÷ 92 = $2,869,565; pace: ahead. What moved this week: The provided data contains only QTD totals and no prior-week totals, so week-over-week movement cannot be determined. Based on QTD pacing, SQMs and active pipeline are ahead, while SQOs, DS2s, closed-lost MIA rate, and same-quarter closes are behind.
Use $115,976.75 as the mechanical Q3 weighted forecast: $44,729 COMMIT + 35% of $203,565 BEST_CASE ($71,247.75), with PIPELINE at $0. Do not treat it as reliable: 85 of 86 deals lack owners and 71 of 86 lack Why-buys, including all 7 COMMIT deals, so it cannot be rolled up by rep or checked against quota. Re-qualify 32 post-quarter deals totaling $227,575, including COMMIT Deal-D348E1 at $13,770.
Hi, Following up on the August 5 recap with pricing for 150 seats. During the July 28 demo, your team reacted strongly to the automated milestone awards and points catalog. Could you reply with whether this is still under consideration? Best, Alex
Marketing delivered 46 SQMs against a weekly target of 52, a shortfall of 6 SQMs (52 − 46 = 6). The webinar channel contributed 18 SQMs, while paid search held at 150 dollars per SQM. Sales converted 14 SQOs and set 9 DS2 meetings, creating 310000 dollars in new pipeline. The team also recorded 3 same-quarter closes during the week. CS saved 2 renewals this week, with Team NPS moving to 61. Three open red-flag accounts are heading into next week. PLG added 412 new signups, with activation at 31 percent. A total of 38 companies reached the aha moment of 10 recognition gives.
Partner Digest — 2026-08-24 to 2026-09-04 - Apex Rewards Co — Active: 2 sourced opps totaling $275,000 ($180,000 + $95,000). Co-webinar locked for 09-15; both opps logged with UTM Source = Partner. - HRCloud Partners — Active: 1 sourced opp totaling $140,000. Integration referral moved to DS2 after completing security review. - CultureBridge — Active: 2 sourced opps totaling $135,000 ($60,000 + $75,000). Both are early stage. - WorkWell Group — Active: 0 sourced opps totaling $0. Joint playbook restart planned for Q4; planning call booked for 09-09. - Recogniq — Quiet. - KudosWave — Quiet. - PeopleFirst Advisors — Quiet. - TotalPerk — Quiet. Total partner-sourced pipeline: 5 opps totaling $550,000 ($180,000 + $95,000 + $140,000 + $60,000 + $75,000).
Q1: Yes. Customer data is encrypted at rest using AES-256. [P1] Q2: Yes. Data in transit is encrypted using TLS 1.2 or higher. [P1] Q3: Yes. We maintain a SOC 2 Type II report, renewed annually. The report is available to prospects under NDA. [P2] Q4: Customer data is retained for the duration of the contract plus 90 days. After that, it is 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 certification document or certification statement would be required. [P1–P8] Q9: Unanswerable from the provided excerpts. The contractual uptime SLA or service-level agreement would be required. [P1–P8] Q10: Unanswerable from the provided excerpts. A HIPAA compliance document or Business Associate Agreement policy would be required. [P1–P8]
Findings 1. WARNING — REVIEW — ALWAYS-trigger overlap: `comms-drafter` and `email-drafter` - Both trigger on writing, drafting, reviewing, or improving external/customer-facing communications. - Overlapping phrases include `"write me an email"`, `"draft a follow-up"`, `"help me reply"`, `"what should I say"`, `"review"`, and `"rewrite"`. - Proposal: designate `email-drafter` as the email-specific skill and `comms-drafter` as the non-email communications skill, or merge them. 2. WARNING — REVIEW — ALWAYS-trigger overlap: `pipeline-intelligence-report` and `weekly-pipeline-report` - Overlapping phrases include `"pipeline update"`, `"pipeline report"`, `"pipeline performance"`, and `"what's the pipeline look like"`. - `pipeline-intelligence-report` covers full scored/tiered pipeline intelligence; `weekly-pipeline-report` covers weekly pipeline performance reporting. - Proposal: narrow `weekly-pipeline-report` to weekly performance/MTD reporting and reserve scored/tiered pipeline requests for `pipeline-intelligence-report`. 3. WARNING — REVIEW — ALWAYS-trigger overlap: `analysis-validator` and `signalforge-claim-compressor` - Both trigger on SignalForge analysis/report outputs and quantitative or stakeholder-facing reports. - `analysis-validator` is the QA gate; `signalforge-claim-compressor` is the final style pass. - Proposal: retain both but make the sequencing boundary explicit: validation first, compression second. 4. WARNING — REVIEW — ALWAYS-trigger overlap: `signalforge-claim-compressor` and `signalforge-feedback` - Both trigger on SignalForge reports, analyses, findings, intelligence reports, and forecast outputs. - `signalforge-feedback` explicitly runs after `signalforge-claim-compressor`, so the overlap is intentional but broad. - Proposal: define `signalforge-feedback` as a terminal post-delivery step rather than a general output trigger. 5. CRITICAL — REVIEW — ALWAYS-trigger overlap: `model-selection` and every other `ALWAYS`-triggered skill - `model-selection` says `"ALWAYS run this skill at the start of every task"` and `"before any other skill"`. - Other skills also claim unconditional execution for their domains, including `analysis-validator`, `next-to-close`, `partner-digest`, `pipeline-intelligence-report`, `sales-forecast`, `signalforge-claim-compressor`, `signalforge-feedback`, `stale-pipeline-report`, and `weekly-pipeline-report`. - Proposal: define `model-selection` as a prerequisite/orchestrator rule, not a competing domain trigger. 6. CRITICAL — REVIEW — Circular delegation chain - `deal-strategy-coach` → `email-drafter` for manager-to-prospect emails. - `email-drafter` → `deal-strategy-coach` for strategic deal coaching. - `comms-drafter` also routes deep deal strategy to `deal-strategy-coach`. - Proposal: make `deal-strategy-coach` the strategy owner and `email-drafter` the execution owner; prohibit reverse delegation from `email-drafter` when only strategy is requested. 7. CRITICAL — UPDATE_BODY — Dangling delegation targets The following referenced skills/files do not have manifest rows in the supplied manifest: - `bonusly-brand` - `prospect-research-multithreading` - `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` - `CUSTOMER_DATA_REFERENCE` - `HUBSPOT_CONNECTOR_REFERENCE` - `SIGNALFORGE_PRODUCT_INSIGHT_SKILL` - `skill-orchestrator` - `signalforge-reports` Proposal: add each target to the manifest or replace each reference with an existing manifest skill. 8. WARNING — UPDATE_BODY — Version conflict in `analysis-validator` - Current version: `3.6`. - The validation trail still says: `analysis-validator v3.2`. - The footer in `pipeline-intelligence-report` also references `analysis-validator v3.6`. - Proposal: retain `analysis-validator` v3.6 and update the stale v3.2 reference. 9. INFO — TRIM_DESC — Manifest descriptions exceeding 1,024 characters Arithmetic: `0` descriptions exceed `1,024` characters. All 14 manifest descriptions are at or below the limit. Maximum listed length: `max(656, 897, 996, 792, 965, 676, 945, 1004, 1006, 962, 1006, 708, 762, 656) = 1006` 10. WARNING — UPDATE_BODY — Hardcoded dates and person names in `analysis-validator` - Dates include `April 26, 2026`, `May 4, 2026`, and `May 9, 2026`. - Person names include `Manish` and `Amani`. - Proposal: replace date- and roster-specific body content with live references or clearly labeled historical examples. 11. WARNING — UPDATE_BODY — Hardcoded dates and company aliases in `closed-lost-analysis` - Date references include `May 2026`. - Named aliases include `Softheon`, `Estee Lauder`, `LIFTOFF`, `Nestlé`, `Ozinga`, `MinIO`, `Aurora Innovation`, `GCash`, `Ethos Cannabis`, and `StickerYou`. - Proposal: label these as historical examples or move them to a dated reference source. 12. WARNING — UPDATE_BODY — Hardcoded page ID, dates, and person names in `deal-strategy-coach` - Page ID: `2257879045`. - Dates include `2026`, `April 2026`, and `April 27, 2026`. - Person name: `Alaina`. - Proposal: replace the page ID with a maintained reference and remove or explicitly label dated/person-specific content. 13. WARNING — UPDATE_BODY — Hardcoded model-registry date in `model-selection` - Date: `2026-05-19`. - Proposal: make `last_checked` runtime-managed or explicitly mark the registry as historical. 14. WARNING — UPDATE_BODY — Hardcoded dates, page IDs, and person names in `partner-digest` - Page IDs include `2286616609`, `2286321666`, `2265382925`, `2236940297`, `2237825028`, `2239365136`, and `2238283777`. - Dates include `May 16, 2026`, `May 19, 2026`, `June 2, 2026`, and `2026`. - Person names include `Amani Phipps`, `Kelli`, `Jen Lee`, `Hani`, `Bryce`, and `Sara`. - Proposal: move destination IDs and partner contacts to maintained configuration; make dates runtime-derived. 15. WARNING — UPDATE_BODY — Hardcoded dates and person names in `pipeline-intelligence-report` - Dates include `May 2026` and `May 4, 2026`. - Person names include `Bryce Harmon`, `Dana Mercer`, `Cole Ingram`, `Alex Franklin`, and `Gavin Porter`. - Proposal: resolve owners dynamically and replace dated roster content with runtime lookups. 16. WARNING — UPDATE_BODY — Hardcoded page IDs, dates, and person name in `sales-forecast` - Page IDs include `2232811524` and `2232582148`. - Dates include `April 1 – June 30, 2026`, `April 27, 2026`, and `2026`. - Person name: `Alaina`. - Proposal: derive quarter, owner, and Confluence destinations dynamically. 17. WARNING — UPDATE_BODY — Hardcoded page IDs and date placeholders in `signalforge-feedback` - Page IDs include `2295136266`, `2232811524`, `2234417154`, and `2247295002`. - Date placeholders include `[DATE]` and `[YYYY-MM-DD]`. - Proposal: maintain page destinations in configuration and generate dates at runtime. 18. WARNING — UPDATE_BODY — Hardcoded dates in `stale-pipeline-report` - Dates include `2026-06-10`. - Proposal: make changelog dates historical metadata only and derive all operational dates from runtime. 19. WARNING — UPDATE_BODY — Hardcoded date, person name, and historical quarter in `weekly-pipeline-report` - Date range: `April 1 – June 30, 2026`. - Date references include `2026`. - Person name: `Ben Lavin`. - Proposal: derive the reporting quarter and recipient dynamically, or clearly label this as a Ben-specific version. 20. INFO — REVIEW — Manifest drift - Files with no manifest row: none identifiable from the supplied file inventory. - Manifest rows with no corresponding supplied file: none. - Proposal: no change based on the supplied data; a directory-level check is required to detect files not included in the prompt.
# Reward Queue Incident Runbook
1. Take incident ownership
- Message: M01
- Who: Bryce Harmon
- Action: Acknowledge the PagerDuty alert and take IC.
- Exact command/action: PagerDuty alert acknowledged; Bryce Harmon took IC.
- Success verification: Not stated.
- Rollback: Not applicable.
2. Check reward queue depth
- Message: M02
- Who: Farid Osman
- Exact command:
```bash
bundle exec rake sidekiq:queue_depth
```
- Result: Reward queue reported 48,213 pending jobs.
- Success verification: Command returned the queue depth.
- Rollback: Not applicable.
3. Inspect the dead set
- Message: M03
- Who: Farid Osman
- Action: Check the dead set.
- Result: 112 jobs were present, all `Redis::TimeoutError` from around 13:58.
- Success verification: Not stated.
- Rollback: Not applicable.
4. Pause reward enqueue
- Message: M04
- Who: Farid Osman
- Exact command:
```bash
bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'
```
- Action: Disable `:auto_recognition_enqueue` to stop the bleed.
- Success verification: Not stated; needs confirmation.
- Rollback:
```bash
bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'
```
5. Clear the dead set
- Message: M05
- Who: Elena Sinclair
- Action: Clear out the dead set while in the console.
- Exact command/action: The exact command or console action is not provided; needs confirmation.
- Success verification: Not stated; needs confirmation.
- Rollback: Not provided; needs confirmation.
6. Scale reward workers up
- Message: M06
- Who: Bryce Harmon
- Exact command:
```bash
kubectl scale deployment/reward-worker --replicas=6
```
- Action: Increase `reward-worker` from 3 to 6 replicas.
- Success verification: Not stated at the time of scaling; needs confirmation.
- Rollback:
```bash
kubectl scale deployment/reward-worker --replicas=3
```
7. Check queue recovery
- Message: M07
- Who: Farid Osman
- Action: Check queue depth and rate of decrease.
- Result: Queue depth was 9,400 and falling by approximately 1,200 per minute.
- Success verification: Queue was decreasing.
- Rollback: Not applicable.
8. Verify the queue is drained and errors have recovered
- Message: M08
- Who: Cole Ingram
- Exact command:
```bash
bundle exec rake sidekiq:queue_depth
```
- Verification:
- Command returned 0.
- Error rate in Datadog was back to baseline.
- Rollback: Not applicable.
9. Re-enable reward enqueue
- Message: M09
- Who: Bryce Harmon
- Exact command:
```bash
bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'
```
- Verification: 40 new jobs processed cleanly in the next 3 minutes.
- Rollback: The thread does not provide a rollback command; needs confirmation.
10. Scale reward workers back down
- Message: M10
- Who: Bryce Harmon
- Exact command:
```bash
kubectl scale deployment/reward-worker --replicas=3
```
- Verification: Queue was stable at 0.
- Rollback: The thread does not provide a rollback command from 3 replicas; needs confirmation.
First error - 2026-09-03T14:01:12Z - Service: `reward-service` - Error: `Redis::TimeoutError: Connection to Redis at redis-primary:6379 timed out after 5s` - This is the first error in the log slice. Failure cascade 1. Redis timeout affects `reward-service` at 14:01:12Z. 2. `reward-service` retries `RewardGiveJob`; retries are exhausted at 14:01:20Z, 14:01:30Z, and 14:01:40Z. 3. `sidekiq` reports `RewardGiveJob failed: Redis::TimeoutError; retrying in 60s` at 14:01:40Z. 4. The job data confirms `RewardGiveJob` failures, including: - `J-00005` at 14:01:46Z - `J-00001` at 14:01:51Z - `J-00003` at 14:01:54Z - `J-00006` at 14:01:55Z - `J-00002` at 14:01:56Z - `J-00004` at 14:01:57Z 5. `sidekiq` reports the reward queue above 10,000 at 14:02:30Z. 6. `api-gateway` returns `502 upstream timeout` calling `reward-service /gives` at 14:03:05Z. 7. `web-app` reports give-form submission failure from the upstream 502 at 14:03:30Z. 8. Additional `api-gateway` 502 errors and `web-app` submission failures continue. 9. `RecognitionDigestJob` also fails with `Redis::TimeoutError`, beginning with `J-00013` at 14:02:36Z and continuing through `J-00016` at 14:05:50Z. 10. `reward-service` reports Redis restored and resumes job processing at 14:22:10Z. 11. `sidekiq` reports the reward queue below 500 at 14:24:45Z. Timing arithmetic from the first error: - First Sidekiq retry: 14:01:40 − 14:01:12 = 28 seconds - Queue above 10,000: 14:02:30 − 14:01:12 = 78 seconds - First gateway 502: 14:03:05 − 14:01:12 = 113 seconds - First web-app failure: 14:03:30 − 14:01:12 = 138 seconds - Redis restored: 14:22:10 − 14:01:12 = 1,258 seconds, or 20 minutes 58 seconds - Queue below 500: 14:24:45 − 14:01:12 = 1,413 seconds, or 23 minutes 33 seconds Datadog query Use this query and sort the results by timestamp ascending: `service:reward-service level:ERROR "Redis::TimeoutError" @timestamp:[2026-09-03T14:00:00Z TO 2026-09-03T14:05:00Z]` The first matching event should be the 2026-09-03T14:01:12Z timeout. What the logs do not show - They do not identify why the Redis connection timed out. - They do not show whether `redis-primary` itself failed, became overloaded, or experienced a network issue. - They do not show the number of successful or permanently lost rewards. - They do not identify which users or reward transactions were affected. - They do not show whether queued jobs were eventually completed after recovery. - They do not establish that PostgreSQL caused or contributed to the failure; the PostgreSQL entries shown are only `checkpoint complete` messages.
- recognition_streaks_v2 — ON; controls recording recognition streaks via `StreakTracker.record(give)`. Target: `segment:beta_companies`. Export count: 42. Code reference: yes. - points_budget_guardrails — ON; controls enforcement of points budgets via `BudgetService.new(company).enforce!(giver, points)`. Target: `all_companies`. Export count: 220. Code reference: yes. - slack_dm_nudges — ON; controls Slack direct-message nudges via `SlackDm.send_nudge(user)`. Target: `segment:region_na`. Export count: 87. Code reference: yes. - redeem_flow_redesign — OFF; controls whether the company receives `RedeemV2Component` instead of `RedeemV1Component`. Target: `targeted_list`. Export count: 12. Code reference: yes. - analytics_dashboard_v3 — ON; controls use of `AnalyticsV3` for the dashboard. Target: `segment:tier_three`. Export count: 65. Code reference: yes. - ms_teams_app_v2 — OFF; controls installation of `TeamsAppV2`. Target: `targeted_list`. Export count: 9. Code reference: yes. - legacy_give_modal — OFF; targeting: `segment:legacy_plan`. Export count: 14. No code reference in the excerpt. - survey_boosters_q3 — ON; targeting: `segment:legacy_plan`. Export count: 7. No code reference in the excerpt. - paused_offboard_cleanup — OFF; no targeting rules; export count: 0. No code reference in the excerpt and no targets. Company-count arithmetic: 42 + 220 + 87 + 12 + 65 + 9 + 14 + 7 + 0 = 456 total exported companies. ON flags: 42 + 220 + 87 + 65 + 7 = 421. OFF flags: 12 + 9 + 14 + 0 = 35.
NDA-1 — GREEN: Standard mutual confidentiality with stated carve-outs, a two-year term, Delaware governing law, and no license, exclusivity, or obligation to proceed. Driving clauses: “Mutual confidentiality”; “Carve-outs: information that (a) is or becomes publicly available through no breach, (b) was known prior to disclosure, (c) is received from a third party without duty of confidence, (d) is independently developed, or (e) must be disclosed by law or court order”; “Governing law: the laws of the State of Delaware, USA.” NDA-2 — YELLOW: Counsel review is required because it contains an embedded 18-month employee non-solicitation and hiring restriction. Driving clause: “During the term of this Agreement and for eighteen (18) months thereafter, neither party shall solicit for employment or hire any employee of the other party with whom it came into contact under this Agreement.” NDA-3 — RED: Full legal review is required because it contains a broad three-year non-compete, one-way confidentiality favoring [PARTY C], no stated confidentiality carve-outs, and unusual Ireland governing law with exclusive court jurisdiction. Driving clauses: “For a period of three (3) years following the Effective Date, Recipient shall not, directly or indirectly, engage in or provide services to any business that competes with Discloser's business”; “One-way confidentiality in favor of [PARTY C]”; “No confidentiality carve-outs are stated in this Agreement”; “This Agreement shall be governed by and construed in accordance with the laws of the Republic of Ireland, and the parties submit to the exclusive jurisdiction of its courts.”