Mean 0.899 · 27/40 perfect tests · $3.77 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+ = current stage DS2–DS5. Why-buys non-compliant = blank, <15 characters, or placeholder; LONG(n) is substantive. Missing DS1 = blank t_ds1. Regressed = an earlier-stage entry timestamp occurs after a later-stage entry timestamp. Why-buys non-compliance: - Overall: 98 / 122 = 80.3% - Bryce Harmon: 19 / 22 = 86.4% - Dana Mercer: 15 / 18 = 83.3% - Alex Franklin: 46 / 61 = 75.4% - Cole Ingram: 17 / 20 = 85.0% - Farid Osman: 1 / 1 = 100.0% - Elena Sinclair: 0 DS2+ deals; percentage not applicable No DS1 timestamp: 0 deals. Regressed: 0 deals. Three process fixes: 1. Block advancement to DS2+ unless why_buys contains ≥15 substantive characters and reject placeholders. 2. Require t_ds1 when a deal enters DS1 and prevent later-stage advancement if it is missing. 3. Add automated stage-timestamp validation that flags/rejects any transition producing t_ds1 > t_ds2, t_ds2 > t_ds3, etc.
Deals resolved: 156 of 156. Unmatched owner IDs: None. Archived/deactivated owner IDs represented in open deals: None. The archived owners in owners.csv — Gavin Porter (1520255671) and Hugo Lindqvist (77260721) — own no listed open deals. Total pipeline amount per resolved owner: - Bryce Harmon (119337721): 35 deals = $1,054,144 - Alex Franklin (84342457): 67 deals = $624,310 - Dana Mercer (83155923): 24 deals = $341,195 - Cole Ingram (83155924): 22 deals = $288,161.43 - Farid Osman (716654662): 7 deals = $4,134 - Elena Sinclair (701163055): 1 deal = $2,100 Arithmetic: 35 + 67 + 24 + 22 + 7 + 1 = 156 resolved deals.
(a) Orphan Gong rows: 30 of 67. Arithmetic: 67 Gong rows - 37 matched deal IDs = 30 orphans. Sample orphan aliases: Deal-8FA85D, Deal-8FC3F9, Deal-3B7945, Deal-42B265, Deal-9CCC42. (b) Duplicate conversation keys: 0 rows. Every row has calls_90d = distinct_conversation_keys, so none meet calls_90d > distinct_conversation_keys. (c) DS3+ open-deal call coverage: 25 of 85 = 29.4%. Arithmetic: 25 ÷ 85 × 100 = 29.41%. Matching is on gong.hs_deal_id = deals_open.deal_id; gong_owner_id is not used.
```sql -- Cannot produce the requested query from the provided catalog alone. -- The catalog does not identify: -- 1) a historical giving/recognition event table or its company/giver/timestamp columns, -- 2) the columns or keys in REDEMPTION_RECORDS_V2 needed to join redemptions to companies or months, -- 3) a canonical customer-company table/key, or -- 4) the business rule/source that defines each company's first calendar month as a customer. -- PRODUCTION.PLG.COMPANY_COHORT_SUMMARY contains pre-aggregated M1_GIVES and -- M1_REDEMPTIONS, but the catalog does not document an M1 unique-givers field -- (M1_USERS is not documented as unique givers), so the requested metrics -- cannot be derived without inventing schema or semantics. ```
CRM completeness audit Deals: No deals extract was provided. Therefore owner, stage, amount, close date, and why-buys completeness cannot be calculated. Pipeline amount at stake also cannot be calculated, so a pipeline-ranked “top 10 fixes” cannot be produced without inventing data. Companies — 34 records: - industry: 34 / 34 = 100.0% - employee_count: 25 / 34 = 73.5% - hq_country: 28 / 34 = 82.4% Contacts — 52 records: - email: 52 / 52 = 100.0% populated; 48 / 52 = 92.3% syntactically valid - title: 39 / 52 = 75.0% - persona: 37 / 52 = 71.2% Duplicate company clusters Shared-domain duplicates: - acme-corp.com: C-0A092931, C-0A092932. Recommended survivor: C-0A092931. Both have complete required fields; absent another survivorship criterion, retain the first record and review conflicting employee_count (500 vs 510) before merging. - globex.io: C-0A092933, C-0A092934. Recommended survivor: C-0A092933. Both have complete required fields; retain the first record absent another criterion. Industry conflicts: SaaS vs Technology. No company-name field was provided, so name-variant duplicate detection is impossible. Invalid emails/domain mismatches - CT-0010: `user0@` — invalid - CT-0080: `user0@` — invalid - CT-0081: `user1@` — invalid - CT-0192: `user2@` — invalid - CT-0011: `user1@other-domain.com` — valid syntax but domain mismatch; expected contact domain is `66d1fc.com` Enrichment-backed fills Only missing CRM fields with a matching enrichment row and a non-empty enrichment value can be filled: - C-EC3025 employee_count: 400 - C-96039F employee_count: 400 - C-44EA29 employee_count: 400 - C-D04904 employee_count: 400 - C-B23205 employee_count: 400 - C-60C75F employee_count: 400 - C-7BBDFA employee_count: 400 - C-50D386 employee_count: 400 The remaining missing company fields cannot be filled because the enrichment value is also missing or no matching enrichment row exists. Do not infer them. CRM/enrichment disagreements Industry: - C-66D1FC: CRM `tech`; enrichment `Computer Software` - C-EC3025: CRM `Technology`; enrichment `Computer Software` - C-44EA29: CRM `tech`; enrichment `Computer Software` - C-92D97D: CRM `Technology`; enrichment `Computer Software` - C-D04904: CRM `Technology`; enrichment `Computer Software` - C-77A95A: CRM `Technology`; enrichment `Computer Software` - C-AA8DDA: CRM `Technology`; enrichment `Computer Software` - C-B25F40: CRM `Technology`; enrichment `Computer Software` - C-60C75F: CRM `tech`; enrichment `Computer Software` - C-425E2A: CRM `Tech `; enrichment `Computer Software` Recommendation: these may reflect taxonomy differences rather than factual conflicts. Use one designated industry taxonomy; if ZoomInfo is the designated enrichment authority, normalize to its `Computer Software` classification. The supplied data does not establish which source is authoritative. HQ country: - C-66D1FC: CRM `US`; enrichment `United States` - C-950043: CRM `US`; enrichment `United States` - C-EC3025: CRM `USA`; enrichment `United States` - C-96039F: CRM `USA`; enrichment `United States` - C-77A95A: CRM `US`; enrichment `United States` - C-B23205: CRM `US`; enrichment `United States` - C-E51FB7: CRM `USA`; enrichment `United States` - C-D0662E: CRM `US`; enrichment `United States` - C-425E2A: CRM `USA`; enrichment `United States` - C-2D7423: CRM `USA`; enrichment `United States` These are formatting disagreements, not substantive country conflicts. Recommend canonicalizing to `United States`, matching the enrichment export. 10 fixes with most pipeline amount at stake Cannot be determined. No deals data, deal-to-company relationships, or pipeline amounts were provided, so there is no defensible way to rank fixes by pipeline amount.
Deal classifications (90 total): - Deal-DB0AAC — timing — buyer - Deal-F7F635 — competitor — Bonusly - Deal-AC944F — no decision — buyer - Deal-214060 — no decision — buyer - Deal-91A056 — timing — buyer - Deal-29326C — timing — buyer - Deal-5DB9B0 — other — unknown - Deal-831B7B — timing — buyer - Deal-F97C37 — competitor — Bonusly - Deal-13E9CF — pricing — buyer - Deal-39E25C — timing — buyer - Deal-7ED004 — pricing — buyer - Deal-21B045 — no decision — buyer - Deal-B3ABED — pricing — buyer - Deal-422BA6 — competitor — Bonusly - Deal-ED9AE7 — pricing — buyer - Deal-988493 — no decision — buyer - Deal-381C8C — competitor — Bonusly - Deal-F308CA — no decision — buyer - Deal-F1E8A6 — competitor — Bonusly - Deal-B6AC09 — timing — buyer - Deal-70F704 — no decision — buyer - Deal-E6E80A — timing — buyer - Deal-B038F0 — timing — buyer - Deal-4664E1 — no decision — buyer - Deal-175756 — timing — buyer - Deal-E74A73 — timing — buyer - Deal-DDAB52 — competitor — Bonusly - Deal-ACE061 — competitor — Bonusly - Deal-BB78F3 — timing — buyer - Deal-D48E0B — no decision — buyer - Deal-15DA99 — timing — buyer - Deal-F4AF5D — timing — buyer - Deal-79B7A1 — timing — buyer - Deal-583ADB — no decision — buyer - Deal-8E27DA — no decision — buyer - Deal-2D2F8D — competitor — Bonusly - Deal-E0441F — no decision — buyer - Deal-7CB44D — no decision — buyer - Deal-0F96AA — competitor — Bonusly - Deal-1BCA50 — competitor — Bonusly - Deal-7CC678 — competitor — Bonusly - Deal-FAC17C — pricing — buyer - Deal-242273 — product gap — Bonusly - Deal-50E5D8 — timing — buyer - Deal-A2C349 — competitor — Bonusly - Deal-9F176A — timing — buyer - Deal-7B2236 — competitor — Bonusly - Deal-AFA56C — no decision — buyer - Deal-C7156E — competitor — Bonusly - Deal-C33D91 — pricing — buyer - Deal-9048EB — product gap — Bonusly - Deal-5E64CE — pricing — buyer - Deal-8A0992 — competitor — Bonusly - Deal-D0C698 — competitor — Bonusly - Deal-69CF3D — timing — buyer - Deal-ECBF89 — timing — buyer - Deal-3618CC — product gap — Bonusly - Deal-EECC02 — competitor — Bonusly - Deal-5AD03E — pricing — buyer - Deal-D1A623 — timing — buyer - Deal-413C56 — timing — buyer - Deal-47F1A1 — competitor — Bonusly - Deal-BF2A98 — competitor — Bonusly - Deal-2A292B — no decision — buyer - Deal-D1AABF — no decision — buyer - Deal-FEDBCB — timing — buyer - Deal-1E7DA9 — competitor — Bonusly - Deal-2BBA21 — no decision — buyer - Deal-286F9C — competitor — Bonusly - Deal-7FBAC6 — timing — buyer - Deal-369281 — competitor — Bonusly - Deal-386F6E — no decision — buyer - Deal-9FCD0D — competitor — Bonusly - Deal-55867E — other — unknown - Deal-DAFB82 — pricing — buyer - Deal-2FEDDB — timing — buyer - Deal-64B19A — competitor — Bonusly - Deal-3F86A0 — no decision — buyer - Deal-096750 — no decision — buyer - Deal-F325A5 — timing — buyer - Deal-ABD14C — no decision — buyer - Deal-79E61A — no decision — buyer - Deal-8A119B — pricing — buyer - Deal-AE7C4E — no decision — buyer - Deal-DAB4F1 — no decision — buyer - Deal-B4B50F — no decision — buyer - Deal-981AD4 — product gap — Bonusly - Deal-DC77FE — product gap — Bonusly - Deal-5885B9 — no decision — buyer Category counts: - No decision: 25 - Timing: 24 - Competitor: 24 - Pricing: 10 - Product gap: 5 - Other: 2 - Champion left: 0 Arithmetic: 25 + 24 + 24 + 10 + 5 + 2 + 0 = 90 deals. Side split: - Buyer: 59 - Bonusly: 29 - Unknown: 2 Arithmetic: 59 + 29 + 2 = 90 deals. Clearly conflicting structured tags: 5 deals. - Deal-B3ABED: tag says timing; text says they need to “try and get budget,” supporting pricing. - Deal-8E27DA: tag says Feature Request; text says they chose only a swag provider and “didn't want R&R, currently,” supporting no decision rather than a stated product deficiency. - Deal-242273: tag says Competitor; text identifies the differentiator as the ability to digitize internal points and spend them at onsite facilities, supporting product gap. - Deal-5AD03E: tag says Competitor; text says “Wanted more defined budget access,” supporting pricing. - Deal-DC77FE: tag says Competitor; text explicitly says price was not a factor and cites greater customization/labeling points as dollars, supporting product gap. Two patterns most worth acting on: 1. Buyer-side delay/disengagement dominates. Timing + no decision = 24 + 25 = 49 of 90 deals. The text repeatedly shows pauses, future-year reconnects, competing priorities, MIA/unresponsiveness, and lack of approval. That is substantially larger than the 29 Bonusly-side losses. 2. Competitive/product differentiation is the main Bonusly-side issue. Competitor + product gap = 24 + 5 = 29 deals. The supplied reasons specifically surface broader offerings, integrations/partnerships, customization, internal-points use cases, geographic fit, surveys, and other platform capabilities.
{"tier_counts":{},"tier_examples":{},"risky_deals":[],"lock_violations":0,"pipeline_shape":"Cannot validly tier the pipeline from the provided data because engagements_by_deal_90d.csv has engagement records for only 154 of the 156 open deals, and no scoring thresholds defining LOCK, ACTION, BUILD, REVIVE, or WATCH were provided. Assigning exact tiers would require inventing rules or evidence."}
[
{
"deal_alias": "Deal-CFE7F4",
"why-buys": [
"The big win for us would be automating anniversary and birthday awards — our HR team of three cannot keep up with it manually."
],
"pain_points": [
"Our HR team of three cannot keep up with it manually.",
"Right now we track everything in a spreadsheet, and people slip through the cracks."
],
"stakeholders": [
"Prospect (VP People)",
"Prospect (HR Admin)"
],
"budget_signal": "We have about $40k earmarked for engagement tools this fiscal year.",
"timeline_signal": "Ideally we would have this live before open enrollment in November.",
"competitor_mentioned": "We looked at Achievers last year, but it was too heavy for a team our size.",
"next_step": "Yes — let's do the security review on September 12.",
"objections": [
"One concern: we need SSO and audit logs for IT to sign off."
],
"confidence": "high"
},
{
"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 there 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": "Yes — send the pilot agreement and we'll route it to legal this week.",
"objections": [
"Integration with Workday has to be rock solid — that's my one condition."
],
"confidence": "high"
},
{
"deal_alias": "Deal-530B50",
"why-buys": [
"We need to make recognition visible across our 12 retail locations."
],
"pain_points": [
"Store managers have zero budget autonomy for on-the-spot recognition today."
],
"stakeholders": [
"Prospect (People Ops Manager)"
],
"budget_signal": null,
"timeline_signal": "Honestly there's no rush on our side until Q1.",
"competitor_mentioned": "My CEO used Bucketlist at her last company and liked it.",
"next_step": "Yes, let's schedule a call with our CEO — I'll send two times.",
"objections": [
"The CEO has to be sold first — she decides anything people-related."
],
"confidence": "high"
},
{
"deal_alias": "Deal-180D02",
"why-buys": [
"We want to consolidate three separate recognition tools into one."
],
"pain_points": [
"We're paying for three tools and none of them talk to our HRIS."
],
"stakeholders": [
"Prospect (VP People)",
"Prospect (IT Security Lead)"
],
"budget_signal": "If it's under $15k annually, I can approve it without going to the board.",
"timeline_signal": "Our procurement cycle runs six to eight weeks minimum.",
"competitor_mentioned": null,
"next_step": null,
"objections": [
"The security review took three months for our last vendor — that's my hesitation."
],
"confidence": "high"
},
{
"deal_alias": "Deal-F8767A",
"why-buys": [
"Two things: automate service milestones, and give us analytics on recognition equity across departments."
],
"pain_points": [
"Our night-shift teams feel invisible — their engagement scores run 20 points lower."
],
"stakeholders": [
"Prospect (HR Director)",
"Prospect (People Ops Coordinator)"
],
"budget_signal": "We have $12k approved under our engagement line.",
"timeline_signal": "We need this running before our January all-hands.",
"competitor_mentioned": "We're mid-pilot with Nectar right now, so you'd need to beat that experience.",
"next_step": "Yes — come present to our exec team on October 2.",
"objections": [
"Our exec team is skeptical after a failed rollout two years ago."
],
"confidence": "high"
},
{
"deal_alias": "Deal-EE195F",
"why-buys": [
"I'd love to cut the admin time on service awards."
],
"pain_points": [
"I personally spend five hours a month ordering and shipping plaques."
],
"stakeholders": [
"Prospect (HR Manager)"
],
"budget_signal": "Budget isn't the issue — time is.",
"timeline_signal": "Our fiscal year resets in February, so a Q1 start is realistic.",
"competitor_mentioned": null,
"next_step": "Yes — send the one-page overview and I'll forward it to our COO this week.",
"objections": [
"Fair warning, our COO usually prefers building things in-house."
],
"confidence": "high"
}
]
1. Deal-547B2B — $11,200 Why close: DS5/COMMIT. Slack says redlines came back clean, signing page is out, and VP People said they were signing the next day; explicitly called “signature-imminent.” What’s left: Signature. 2. Deal-A2B47C — $6,360 Why close: DS5/COMMIT with a 2026-09-11 CRM close date. Slack confirms it is “still warm” and moving at a normal legal-review pace. What’s left: Complete legal review, then signature. 3. Deal-403845 — $9,000 Why close: DS5/COMMIT with a 2026-09-11 CRM close date. Slack says the order form is with finance and the deal is “moving fine.” What’s left: Finance review/approval, then signature. Deal-2465CE is excluded despite DS5/COMMIT in CRM because Slack supersedes it: champion left, procurement froze new vendors, and Dana said it is realistically Q4. Combined amount: $11,200 + $6,360 + $9,000 = $26,560.
- Deal-EC3025 — Amount: missing from provided data. Prospect: “We need SCIM user provisioning — our security team will not approve any tool that requires manual account management.” Classification: Real gap. Product docs explicitly state SCIM user provisioning is not currently listed as supported. - Deal-D0D6B5 — Amount: missing from provided data. Prospect: “Our payroll runs on ADP Workforce Now and I don't see ADP anywhere in your integration list — that's a dealbreaker for us.” Classification: Real gap. Product docs explicitly state ADP Workforce Now is not currently listed as supported. - Deal-CFE7F4 — Amount: missing from provided data. Prospect: “I tried to build a custom report combining recognition frequency with turnover and couldn't — is that possible on our current plan?” Classification: Plan gate. The custom report builder exists on Enterprise. - Deal-84DBA6 — Amount: missing from provided data. Prospect: “We turned on the Slack integration in July, but half our managers still haven't given a single recognition — nobody ever trained them on it.” Classification: Rollout/enablement issue. Slack is supported on all plans; the stated problem is lack of manager training. - Deal-36C33F — Amount: missing from provided data. Prospect: “Good to know. The web version should be fine for our office staff for now.” Classification: Not a gap candidate. The unsupported-mobile-app statement came from the rep, not the prospect; rep language does not count as prospect voice. Summary — real product gaps only: - SCIM user provisioning — Deal-EC3025 — amount missing. - ADP Workforce Now integration — Deal-D0D6B5 — amount missing. Arithmetic: No deal amounts were provided, so no gap-dollar total can be calculated.
Bryce Harmon - Deal-2D1F1B — DS1 — $240,000 — 81 days - Deal-66D1FC — DS1 — $99,000 — 16 days - Deal-950043 — DS1 — $70,000 — 19 days - Deal-B23205 — DS1 — $45,000 — 16 days - Deal-7BBDFA — DS3 — $37,440 — 46 days - Deal-332637 — DS2 — $36,000 — 9 days - Deal-1BEEBF — DS1 — $31,500 — 19 days - Deal-A414F6 — DS1 — $25,200 — 19 days - Deal-C5658B — DS1 — $23,400 — 16 days - Deal-40522D — DS3 — $21,000 — 19 days - Deal-C1FA6D — DS1 — $18,000 — 16 days - Deal-01E193 — DS1 — $12,600 — 8 days - Deal-F0EBBB — DS3 — $11,400 — 24 days - Deal-E25A09 — DS1 — $6,000 — 9 days - Deal-C9C286 — DS2 — $5,502 — 9 days - Deal-012CB1 — DS1 — $1 — 24 days 16 stale deals. Total = $682,042 + $1 = $682,043. Dana Mercer - Deal-44EA29 — DS2 — $60,000 — 10 days - Deal-E51FB7 — DS2 — $43,875 — 12 days - Deal-BA3DDC — DS3 — $23,400 — 15 days - Deal-9DDE86 — DS2 — $20,000 — 15 days - Deal-215CCA — DS3 — $18,900 — 17 days - Deal-5EED42 — DS3 — $16,250 — 11 days - Deal-57887A — DS2 — $15,000 — 8 days - Deal-3974EB — DS4 — $9,000 — 8 days - Deal-B7EBD1 — DS5 — $9,000 — 16 days - Deal-F40F04 — DS2 — $8,100 — 15 days - Deal-7599B8 — DS3 — $7,350 — 18 days - Deal-87DDD1 — DS1 — $5,000 — 19 days - Deal-F336B6 — DS3 — $4,200 — 15 days - Deal-0660B4 — DS4 — $1,920 — 16 days 14 stale deals. Total = $223,995 + $9,000 + $9,000 = $241,995. Alex Franklin - Deal-E73427 — DS3 — $18,000 — 10 days - Deal-885F45 — DS2 — $9,300 — 12 days - Deal-C2FF3C — DS1 — $8,316 — 10 days - Deal-6C60D4 — DS3 — $4,800 — 12 days - Deal-13FEBD — DS2 — $4,680 — 12 days - Deal-9D0060 — DS3 — $3,840 — 12 days - Deal-690476 — DS2 — $3,600 — 18 days - Deal-EE195F — DS3 — $3,120 — 8 days - Deal-6883F3 — DS1 — $2,400 — 16 days - Deal-4A13AD — DS3 — $2,160 — 26 days - Deal-F67D31 — DS2 — $1,800 — 8 days - Deal-5FDCE4 — DS3 — $1,600 — 12 days - Deal-BA571A — DS4 — $1,080 — 18 days 13 stale deals. Total = $64,696. Cole Ingram - Deal-D04904 — DS2 — $58,529.25 — 11 days - Deal-B25F40 — DS3 — $40,000 — 8 days - Deal-813836 — DS2 — $32,175 — 11 days - Deal-1BA595 — DS2 — $31,750 — 11 days - Deal-CFE1E8 — DS3 — $18,000 — 11 days - Deal-CD47A6 — DS2 — $12,168 — 11 days - Deal-627646 — DS3 — $11,193 — 11 days - Deal-FF809F — DS2 — $7,781.20 — 11 days - Deal-AF932D — DS2 — $7,225.40 — 11 days - Deal-A71728 — DS2 — $6,947.50 — 11 days - Deal-8BC9F5 — DS2 — $5,616 — 10 days - Deal-175395 — DS3 — $4,779.88 — 11 days - Deal-481E24 — DS3 — $4,140 — 10 days - Deal-C7F9BF — DS2 — $3,360 — 11 days - Deal-2F3A66 — DS3 — $3,334.80 — 11 days - Deal-342E96 — DS2 — $2,700 — 24 days - Deal-E568D5 — DS3 — $1,875 — 11 days - Deal-FD9F4E — DS5 — $1,330 — 10 days 18 stale deals. Total = $252,905.03. Farid Osman: 0 stale deals; $0 stale amount. Elena Sinclair: Deal-57FF13 has no row in engagements_by_deal_90d.csv, so days since last contact cannot be computed from the required engagement fields. It cannot be classified as stale from the provided data.
Last-30-day window: 2026-08-06 through 2026-09-05, inclusive. 1. Cole Ingram — 84 emails + 4 calls + 1 meeting = 89 activities; mix: 94.4% / 4.5% / 1.1%; 3 DS2 entries; 89 ÷ 3 = 29.67 activities/DS2. 2. Alex Franklin — 367 + 38 + 58 = 463 activities; mix: 79.3% / 8.2% / 12.5%; 13 DS2 entries; 463 ÷ 13 = 35.62 activities/DS2. 3. Bryce Harmon — 188 + 0 + 45 = 233 activities; mix: 80.7% / 0.0% / 19.3%; 4 DS2 entries; 233 ÷ 4 = 58.25 activities/DS2. 4. Farid Osman — 41 + 0 + 38 = 79 activities; mix: 51.9% / 0.0% / 48.1%; 1 DS2 entry; 79 ÷ 1 = 79.00 activities/DS2. 5. Dana Mercer — 77 + 11 + 6 = 94 activities; mix: 81.9% / 11.7% / 6.4%; 1 DS2 entry; 94 ÷ 1 = 94.00 activities/DS2. 6. Elena Sinclair — 0 + 0 + 0 = 0 activities; 0 DS2 entries; activities/DS2 is undefined (0 ÷ 0), so she is not rankable on efficiency. Most efficient: Cole Ingram at 29.67 activities per DS2 entry. Highest-volume: Alex Franklin at 463 total activities. They differ.
Alex Franklin — QTD scorecard as of 2026-09-05 Bookings vs quota: - Q3 closed-won bookings: $150,000 = $40,000 + $20,000 + $35,000 + $21,000 + $12,000 + $11,000 + $6,500 + $4,500. Deal-B3E6F1 ($24,000, 2026-06-20) is excluded because it closed before Q3. - Quota: $200,000. - Attainment: $150,000 / $200,000 = 75.0%. - Remaining to quota: $200,000 - $150,000 = $50,000. - New: $113,500 / $150,000 = 75.7% of bookings. - Expansion: $36,500 / $150,000 = 24.3%. Active pipeline by stage: The supplied active pipeline extends beyond the 2026-09-05 snapshot, but there is no field indicating when each deal entered its current stage or whether it was already open as of 2026-09-05. Therefore an as-of-2026-09-05 active-pipeline-by-stage amount cannot be determined reliably from the provided data without using future-state information. Rolling 90-day DS2-to-won rate: - Window: 2026-06-08 through 2026-09-05. - Closed wins whose DS2 entry falls in that window: 8. - Closed losses whose DS2 entry falls in that window: 27. - Closed-outcome DS2-to-won rate: 8 / (8 + 27) = 8 / 35 = 22.9%. - Win/loss counts: 8 wins, 27 losses. - Top loss reason: "Lost- Timing (1 year or more)" — 13 / 27 losses = 48.1%. Activity volume by type, last 30 days: The engagement file provides pre-aggregated `*_30d` values but does not provide engagement dates or a snapshot date establishing that these represent the 30 days ending 2026-09-05. Therefore activity volume for the requested 2026-09-05 window cannot be determined reliably from the supplied data. Coaching observations: 1. Alex is at 75.0% attainment with $50,000 still required against the $200,000 Q3 quota. 2. Conversion after DS2 is 22.9% among resolved opportunities: 8 wins versus 27 losses, or 27 / 8 = 3.4 losses per win. 3. Timing is the dominant loss issue: 13 of 27 losses (48.1%) are "Lost- Timing (1 year or more)", making qualification and validation of purchase timing the clearest numerical coaching opportunity.
Cannot complete the requested open-deal analysis from the provided data. Missing data: - Open/closed status for every deal - Deal amount - Deal stage The 60-day active-contact cutoff is 2026-07-15 (2026-09-13 − 60 days), and the provided contact data is sufficient to calculate active contact counts and persona coverage. However, without open/closed status I cannot determine which deals belong in “every open deal,” and without stage I cannot determine the most valuable persona to add “given the stage.” Providing a flagged-deal list would require inventing or assuming those missing facts, which I will not do.
First-five-minute lead: - 8/10 calls lead with the same customer proof point: a 400-person retailer, 18% reduction in regretted turnover, and automated milestone awards. Quote: Deal-D348E1 — “Quick one to kick off: a 400-person retailer we work with cut regretted turnover 18% in two quarters after automating milestone awards, and I wanted to walk you through exactly how they did it.” - Exceptions: Deal-403845 leads with a security/pricing agenda; Deal-1E2498 leads with pricing. Deal-C61CF7 also mentions Workhuman at minute 2. Three most common objections: - Budget locked: 4/10 calls (Deal-D348E1, Deal-547B2B, Deal-60C2C2, Deal-84DBA6). Alex reframes cost against turnover savings and cites $210k in avoided backfills. Quote: Deal-D348E1 — “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.” - Timing / revisit next quarter: 3/10 (Deal-5408B0, Deal-C61CF7, Deal-D9A12F). He proposes a 90-day departmental pilot to generate internal data before planning. Quote: Deal-5408B0 — “Makes sense. What if we scope a 90-day pilot with one department so you have internal data before next quarter's planning?” - Existing spreadsheet + gift cards: 3/10 (Deal-403845, Deal-EDC141, Deal-1E2498). He differentiates on automated milestones and recognition analytics. Quote: Deal-403845 — “Spreadsheets work until they scale — the difference is automation: milestones fire without HR lifting a finger, and you get analytics on who is being recognized.” Concrete next-step rate: 7 calls with an agreed concrete next step / 10 total calls = 70%. The seven are Deal-D348E1, Deal-5408B0, Deal-547B2B, Deal-C61CF7, Deal-60C2C2, Deal-D9A12F, and Deal-1E2498. Quote: Deal-D348E1 — “Yes, Thursday at 2pm works — send the invite and I'll bring our HRIS manager.” Competitors prospects raised: - Awardco — Deal-547B2B. Quote: “We're also in late talks with Awardco — their rewards catalog looks bigger than yours.” - Kudos — Deal-EDC141. Quote: “How are you different from Kudos? Our CEO used them at her last company.” - Workhuman was raised by Alex, not a prospect, so it is excluded. Coaching notes: 1. The case-study opener is highly repetitive (8/10). Keep the quantified proof, but tailor the opening to the prospect/context more often, as in Deal-403845 and Deal-1E2498. 2. Preserve the specific objection responses, but improve conversion after committee/no-urgency stalls: Deal-403845, Deal-EDC141, and Deal-84DBA6 ended without a concrete agreed next step.
# Q3 2026 forecast - COMMIT: $44,729 across 7 deals - BEST_CASE: $203,565 across 24 deals - PIPELINE: 23 deals; weighted at $0 - Weighted forecast: $115,976.75 Arithmetic: $44,729 × 100% = $44,729 $203,565 × 35% = $71,247.75 $44,729 + $71,247.75 = $115,976.75 Deals outside Q3: 29 deals totaling $213,175. These have close dates from 2026-10-01 through 2026-10-15 and therefore do not count in the 2026-07-01–2026-09-30 forecast. Top 5 BEST_CASE deals inside Q3: 1. Deal-2D7423 — $38,935 2. Deal-25F752 — $24,000 3. Deal-E53952 — $19,656 4. Deal-5EED42 — $16,250 5. Deal-FA32A0 — $11,116 ## Data quality Owner is missing for all but one deal, making owner-level forecast accountability unreliable. Many records have `why_buys_chars=0`, indicating missing why-buy information. Deal-A5E80A is COMMIT while still at DS1, and Deal-499BF6 is COMMIT at DS2, creating stage/category inconsistencies that warrant validation. The extract was pulled on 2026-09-05 but contains multiple Q3 close dates already before that date, so apparently overdue open deals remain in the pipeline and could distort an unattended forecast.
- Both signals: 47 companies; 31 retained. 24-month retention = 31 / 47 = 66.0%. - Givers-only: 49 companies; 23 retained. Retention = 23 / 49 = 46.9%. - Redemption-only: 29 companies; 9 retained. Retention = 9 / 29 = 31.0%. - Neither: 95 companies; 38 retained. Retention = 38 / 95 = 40.0%. Excluded: 0 companies. All 220 companies had usable m1_users, m1_redemptions, and current_status values. The hypothesis is only partially supported. Both-signals companies have the highest observed retention at 66.0%, +19.0 percentage points versus givers-only, +34.9 points versus redemption-only, and +26.0 points versus neither. However, redemption-only (31.0%) actually retains worse than neither (40.0%). The single signal with the largest retention lift is 5+ unique givers: - 5+ givers: 54 / 96 = 56.3% - <5 givers: 47 / 124 = 37.9% - Lift = 56.3% − 37.9% = +18.3 percentage points. For comparison, redemption has a +10.3-point lift: 40 / 76 = 52.6% with 1+ redemption versus 61 / 144 = 42.4% without. This shows an association: companies achieving both activation signals have substantially higher observed 24-month retention, and giver breadth is the stronger individual signal in this cohort. It does not prove that either behavior causes retention; this observational extract does not control for company characteristics, selection effects, or other confounders.
As of 2026-09-05, treating the provided subscription status as the status on that date: CRM ARR: $603,581.76 Billing ARR: $604,739.28 Variance (CRM − Billing): -$1,157.52 Billing ARR = sum(active MRR × 12). Variance decomposition: - Status mismatch: +$13,158.48 - C-0C8323BF: +$4,905.24 — CRM has ARR; billing cancelled - C-0DC4FB8C: +$8,253.24 — CRM has ARR; billing cancelled - Rounding/small ARR discrepancies: +$36.00 - C-0D66DF9E: +$16.00 - C-14D70CE0: +$20.00 - Missing records: -$11,952.00 - C-0D5BBE3A: +$16,497.24 — present only in CRM - C-21629AA4: -$28,449.24 — present only in billing - Other: -$2,400.00 - C-0F7269D7: CRM $24,396.00 vs billing $26,796.00 Check: $13,158.48 + $36.00 − $11,952.00 − $2,400.00 = -$1,157.52. Mismatched accounts and suggested owner: - C-0C8323BF — Billing/RevOps: reconcile cancelled status - C-0DC4FB8C — Billing/RevOps: reconcile cancelled status - C-0D5BBE3A — RevOps: investigate missing billing subscription - C-21629AA4 — RevOps: investigate missing CRM company ARR record - C-0D66DF9E — RevOps: reconcile $16 ARR difference - C-14D70CE0 — RevOps: reconcile $20 ARR difference - C-0F7269D7 — Billing/RevOps: investigate $2,400 ARR difference No named account owners were provided, so named owners cannot be assigned. Agreement-end-date violations (term != 12 months with blank cf_agreement_end_date): - SUB-0002 / C-1794A52C — 24 months - SUB-0019 / C-22170CA1 — 36 months
| KVM | 2026-08 | 2026-07 | Absolute change | Relative change | Direction | |---|---:|---:|---:|---:|---| | Giving rate | 0.602713 | 0.602297 | +0.000417 | +0.069% | Up | | Redemptions/user | 1.730163 | 1.729983 | +0.000180 | +0.010% | Up | | 1:1 meetings engagement | 0.447177 | 0.446887 | +0.000290 | +0.065% | Up | | Pulse check engagement | 0.508610 | 0.600587 | -0.091977 | -15.314% | Down | Arithmetic: each monthly KVM is the unweighted mean across 30 companies. Absolute change = Aug − Jul; relative change = (Aug − Jul) / Jul × 100. Pulse check engagement had the largest relative move: -15.314%. The data supports enterprise as the driver: enterprise pulse engagement fell from 0.549980 to 0.274280, or (0.274280 − 0.549980) / 0.549980 × 100 = -50.129%. By comparison, smb declined 0.225% and mid_market increased 0.211%. All records are plan_tier = tier_three, so plan tier cannot explain the move.
Last completed month: 2026-08 Redemption count: 1,188 Spend: $90,463 Unique redeemers: 335 Redemptions per redeemer: 1,188 ÷ 335 = 3.55 Provider mix by spend: - custom: $36,163 ÷ $90,463 = 39.98% - Tremendous: $26,270 ÷ $90,463 = 29.04% - Snappy: $16,055 ÷ $90,463 = 17.75% - TangoCard: $11,975 ÷ $90,463 = 13.23% - Total: 100.00% Top 5 countries by redemptions: 1. US: 754 2. CA: 85 3. AU: 58 4. NL: 53 5. GB: 51
Qualifying accounts must satisfy all three rules: health_score < 60, churn_save_eligible_amount > $0, and renewal within 120 days of 2026-09-05. - C-0F6C0F34 — $49,707 at stake. Executive touch: champion_active=false. Qualifies: health 51; renewal in 28 days. - C-0B827671 — $25,365. Usage revival: usage_trend_3m=declining (113/202 seats used = 55.9%). Qualifies: health 56; renewal in 70 days. - C-0B360C78 — $35,748. No documented signal clearly supports any of the three requested plays: usage is growing, champion_active=true, and no commercial-concession trigger is provided. Qualifies: health 57; renewal in 53 days. - C-0B0F1BAB — $5,494. Executive touch: champion_active=false. Qualifies: health 38; renewal in 18 days. - C-0CA21961 — $16,829. Usage revival: only 84/325 seats used = 25.8%, with flat usage. Qualifies: health 58; renewal in 114 days. - C-0E9C27D1 — $41,235. No documented signal clearly supports a requested play: usage is flat with 134/157 seats used = 85.4%, and champion_active=true. Qualifies: health 39; renewal in 19 days. - C-0CEF69FD — $32,621. Executive touch: champion_active=false. Qualifies: health 53; renewal in 77 days. - C-0D3278C7 — $17,602. Usage revival: usage_trend_3m=declining and only 126/380 seats used = 33.2%. Qualifies: health 54; renewal in 68 days. Total at stake: $49,707 + $25,365 + $35,748 + $5,494 + $16,829 + $41,235 + $32,621 + $17,602 = $224,601. No account can be assigned “commercial concession” from the provided data because no rule or signal defining when that play applies was provided. At-risk (health < 60) but ineligible: - C-0BC71BDD — eligible amount is $0 (fails R2). - C-0BA71F12 — renewal is 218 days away (fails R3). - C-0F6694C3 — eligible amount is $0 and renewal is 197 days away (fails R2 and R3). - C-0BE96399 — eligible amount is $0 (fails R2). - C-0F876796 — renewal is 154 days away (fails R3). - C-0FCCD2DF — eligible amount is $0 and renewal is 230 days away (fails R2 and R3). - C-10A56B0F — eligible amount is $0 (fails R2).
Seat coverage: 150 licensed / 400 headcount = 37.5%. Usage health: Monthly active users increased every month, from 88 in March to 126 in August: +38 users, or 38 / 88 = 43.2% growth. August usage is 126 / 150 = 84.0% of licensed seats, leaving 24 licensed seats currently unused. Headroom: - Seat headroom to full headcount: 400 - 150 = 250 seats. - Current rate: $9,000 ARR / 150 seats = $60/seat/year. - ARR headroom: 250 × $60 = $15,000 ARR. - Full-headcount ARR at current rate: 400 × $60 = $24,000. Reply/buyer: Maria S., People Operations Coordinator, replied. She explicitly says she is not the purchasing decision-maker. Dana R., VP People, owns budget and seat expansion and is therefore the right buyer from the provided contacts. Reply email: Hi Maria, Thanks for the context — and glad to hear the team is enjoying Bonusly. The usage trend backs that up: monthly active users grew from 88 in March to 126 in August. An introduction to Dana would be helpful, especially since she’s already looking at the usage numbers. I can share the current adoption picture and give her a straightforward view of what seat expansion could look like as usage grows. If you’re comfortable connecting us, I’ll take it from there and keep it easy. Thanks again, Cole
C-0D284E42 Complete: - Slack integration connected: 2026-08-12. - Allowance set: 2026-08-13. - Admins added: 2. - First recognition given: 2026-08-15 14:22. Not complete / not evidenced: - HRIS integration: no data value; cannot mark complete. - First redemption: no data value; cannot mark complete. Early engagement signals: - Active givers increased from 3 on 2026-08-11 to 15 on 2026-09-04. - Increase = 15 - 3 = 12 active givers. - Growth = 12 / 3 × 100 = 400%. - Peak observed active givers: 15, on 2026-09-03 and 2026-09-04. - Recognition has started, but there is no recorded redemption yet. Three things to cover on the call: 1. HRIS integration: confirm status and what remains to connect it. 2. Redemption activation: no first redemption is recorded; identify what is preventing or delaying redemption. 3. Engagement: review the rise from 3 to 15 active givers and discuss how to sustain or expand participation.
90-day renewal risk brief Date policy: Chargebee is trusted for all five multi-year accounts because the provided rule says multi-year renewal dates are known to be wrong in ChurnZero. For 12-month accounts, ChurnZero and Chargebee agree. Seat utilization = seats_used ÷ seats. 3-month trend = Jun → Jul → Aug active users, with % change = (Aug − Jun) ÷ Jun. - C-0B7D2C30 — Dana Mercer — $65,901 ARR — 2026-09-15 — 57.6% utilization (274 ÷ 476) — usage 97 → 94 → 84 (-13.4%). HIGH: moderate utilization plus a 13.4% three-month usage decline. DISAGREEMENT: CZ 2026-09-10 vs CB 2026-09-15; use CB because 36-month/multi-year. - C-0BCDB8C2 — Cole Ingram — $54,427 — 2026-09-18 — 54.7% (232 ÷ 424) — 127 → 118 → 110 (-13.4%). HIGH: moderate utilization and sustained 13.4% usage decline. DISAGREEMENT: CZ 2027-09-18 vs CB 2026-09-18; use CB because 36-month/multi-year. - C-0D2AB865 — Elena Sinclair — $38,022 — 2026-09-22 — 61.4% (250 ÷ 407) — 125 → 117 → 109 (-12.8%). HIGH: 61.4% utilization with a 12.8% usage decline. DISAGREEMENT: CZ 2026-09-10 vs CB 2026-09-22; use CB because 24-month/multi-year. - C-0BBE3E60 — Dana Mercer — $30,993 — 2026-09-26 — 64.9% (74 ÷ 114) — 39 → 35 → 33 (-15.4%). HIGH: usage fell every month and is down 15.4%. DISAGREEMENT: CZ 2027-09-26 vs CB 2026-09-26; use CB because 24-month/multi-year. - C-0F5D2323 — Cole Ingram — $90,647 — 2026-09-29 — 28.5% (111 ÷ 390) — 20 → 21 → 18 (-10.0%). HIGH: only 28.5% seat utilization and usage declined 10.0%. DISAGREEMENT: CZ 2026-09-10 vs CB 2026-09-29; use CB because 24-month/multi-year. - C-0EC6999D — Elena Sinclair — $79,419 — 2026-10-03 — 27.7% (31 ÷ 112) — 17 → 16 → 15 (-11.8%). HIGH: lowest utilization at 27.7% plus an 11.8% usage decline. - C-0B20DB64 — Dana Mercer — $21,770 — 2026-10-07 — 56.6% (214 ÷ 378) — 294 → 298 → 294 (0.0%). LOW: utilization is moderate but usage is flat over three months. - C-0BBC4E7A — Cole Ingram — $56,374 — 2026-10-10 — 67.7% (228 ÷ 337) — 142 → 141 → 139 (-2.1%). MEDIUM: 67.7% utilization with a small consecutive usage decline. - C-0FD551AB — Elena Sinclair — $48,815 — 2026-10-14 — 55.9% (210 ÷ 376) — 123 → 122 → 126 (+2.4%). MEDIUM: utilization is only 55.9%, although recent usage increased 2.4%. - C-0F9F8F13 — Dana Mercer — $46,230 — 2026-10-18 — 56.5% (199 ÷ 352) — 185 → 185 → 182 (-1.6%). MEDIUM: 56.5% utilization and slightly declining usage. - C-0BC34584 — Cole Ingram — $16,740 — 2026-10-22 — 66.2% (327 ÷ 494) — 104 → 104 → 106 (+1.9%). MEDIUM: utilization is 66.2%, partially offset by modest usage growth. - C-0B7A7546 — Elena Sinclair — $35,062 — 2026-10-25 — 88.8% (182 ÷ 205) — 64 → 65 → 63 (-1.6%). LOW: very high seat utilization with essentially stable usage. - C-0B369871 — Dana Mercer — $85,128 — 2026-10-29 — 75.1% (317 ÷ 422) — 326 → 330 → 333 (+2.1%). LOW: healthy utilization and increasing usage. - C-0B144C78 — Cole Ingram — $30,899 — 2026-11-02 — 75.4% (169 ÷ 224) — 101 → 101 → 106 (+5.0%). LOW: healthy utilization with 5.0% usage growth. - C-0FC4DBB8 — Elena Sinclair — $94,732 — 2026-11-05 — 76.7% (356 ÷ 464) — 189 → 191 → 193 (+2.1%). LOW: healthy utilization and steadily rising usage. - C-0D5BBE3A — Dana Mercer — $39,740 — 2026-11-09 — 83.3% (85 ÷ 102) — 88 → 90 → 91 (+3.4%). LOW: high utilization and steadily increasing usage. - C-0FB9D5AF — Cole Ingram — $63,158 — 2026-11-13 — 72.4% (144 ÷ 199) — 173 → 173 → 176 (+1.7%). LOW: solid utilization with modest usage growth. - C-0B344485 — Elena Sinclair — $64,384 — 2026-11-16 — 78.0% (224 ÷ 287) — 238 → 240 → 244 (+2.5%). LOW: high utilization and steadily increasing usage. - C-0CB2C1B4 — Dana Mercer — $40,628 — 2026-11-20 — 81.6% (386 ÷ 473) — 47 → 48 → 49 (+4.3%). LOW: high utilization and steadily increasing usage. - C-22170CA1 — Cole Ingram — $45,646 — 2026-11-24 — 85.4% (251 ÷ 294) — 143 → 148 → 146 (+2.1%). LOW: high utilization and usage remains above June despite the August dip. 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. ARR at risk (HIGH + MEDIUM): HIGH = $65,901 + $54,427 + $38,022 + $30,993 + $90,647 + $79,419 = $359,409. MEDIUM = $56,374 + $48,815 + $46,230 + $16,740 = $168,159. Total ARR at risk = $359,409 + $168,159 = $527,568.
Broad patterns, ranked by distinct-account ARR exposure (ARR counted once per account per theme; 76 tickets total): 1. HRIS provisioning / sync Count: 11 | Share: 11 / 76 = 14.5% | Distinct accounts: 3 | ARR affected: $36,000 + $30,000 + $48,000 = $114,000 Tickets: IC-460059, IC-460062 Recommendation: Prioritize HRIS provisioning reliability and observability; failures span three high-ARR accounts and represent the quarter’s largest ARR exposure. 2. Redemption / gift card failures Count: 18 | Share: 18 / 76 = 23.7% | Distinct accounts: 7 | ARR affected: $8,900 + $10,700 + $9,600 + $8,700 + $11,000 + $9,600 + $10,300 = $68,800 Tickets: IC-460025, IC-460035 Recommendation: Investigate the redemption flow end-to-end, especially checkout failures, missing gift cards, and points deducted despite failed orders. 3. Points not posting / missing balances Count: 20 | Share: 20 / 76 = 26.3% | 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 Tickets: IC-460004, IC-460016 Recommendation: Treat points delivery/balance consistency as a broad product reliability problem; it has the widest account footprint. 4. Slack integration failures Count: 13 | Share: 13 / 76 = 17.1% | Distinct accounts: 4 | ARR affected: $4,400 + $5,400 + $3,900 + $5,200 = $18,900 Tickets: IC-460041, IC-460049 Recommendation: Harden Slack sync, authentication persistence, and slash-command reliability as one integration reliability workstream. Single-account noise: 5. Billing / invoice & renewal pricing errors Count: 14 | Share: 14 / 76 = 18.4% | Distinct accounts: 1 | ARR affected: $52,000 Tickets: IC-460071, IC-460078 Recommendation: Escalate and resolve the repeated billing configuration/accounting issue for C-0E9C27D1, but do not generalize it into a broad customer pattern without evidence from additional accounts.
1. C-64171065 — 3/4 fields match: Technology, Mid-Market, employee_recognition. Region differs: NA-East vs NA-West. Arithmetic: 3 matches ÷ 4 fields = 75%. 2. C-A13C193D — 3/4 fields match: Technology, Mid-Market, NA-West. Use case differs: retention vs employee_recognition. Arithmetic: 3 ÷ 4 = 75%. 3. C-11C31562 — 3/4 fields match: Mid-Market, employee_recognition, NA-West. Industry differs: Manufacturing vs Technology. Arithmetic: 3 ÷ 4 = 75%. All three have has_case_study=true.
Trailing 6 months: 2026-03 through 2026-08. Paid: - paid_search: spend $36,000 (6 × $6,000); 40 SQMs; 20 SQOs; cost/SQM = $36,000 ÷ 40 = $900; cost/SQO = $36,000 ÷ 20 = $1,800; SQM→SQO = 20 ÷ 40 = 50.0%; pipeline = 20 × $40,000 = $800,000; pipeline/$ = $800,000 ÷ $36,000 = $22.22. - linkedin_ads: spend $24,000 (6 × $4,000); 25 SQMs; 8 SQOs; cost/SQM = $24,000 ÷ 25 = $960; cost/SQO = $24,000 ÷ 8 = $3,000; SQM→SQO = 8 ÷ 25 = 32.0%; pipeline = 8 × $12,000 = $96,000; pipeline/$ = $96,000 ÷ $24,000 = $4.00. - paid_social: spend $18,000 (6 × $3,000); 0 SQMs; 0 SQOs; cost/SQM = undefined; cost/SQO = undefined; SQM→SQO = undefined; pipeline = $0; pipeline/$ = $0 ÷ $18,000 = $0.00. - webinars: spend $9,000 (6 × $1,500); 12 SQMs; 5 SQOs; cost/SQM = $9,000 ÷ 12 = $750; cost/SQO = $9,000 ÷ 5 = $1,800; SQM→SQO = 5 ÷ 12 = 41.7%; pipeline = 5 × $12,000 = $60,000; pipeline/$ = $60,000 ÷ $9,000 = $6.67. Organic/unpaid: - organic_search: volume = 30 SQMs; 10 SQOs; SQO rate = 10 ÷ 30 = 33.3%; pipeline = 10 × $9,000 = $90,000. - referral: volume = 15 SQMs; 6 SQOs; SQO rate = 6 ÷ 15 = 40.0%; pipeline = 6 × $8,000 = $48,000. Data-quality flags: two rows have SQO dates preceding SQM dates: CT-000044 (2026-07-18 SQO vs 2026-07-23 SQM) and CT-000041 (2026-06-09 SQO vs 2026-06-14 SQM). Both are linkedin_ads and are included in the metrics above because an SQO is recorded; their chronology should be corrected or validated. Reallocation recommendation: stop or sharply reduce paid_social first: it consumed $18,000 with zero SQMs. Shift that budget primarily toward paid_search, which has the strongest observed paid efficiency at $22.22 pipeline/$, versus webinars at $6.67 and linkedin_ads at $4.00. Confidence: moderate. paid_search has the strongest sample (40 SQMs/20 SQOs), while webinars (12/5) and linkedin_ads (25/8) are substantially smaller; additionally, 2 of linkedin_ads’ 8 SQOs have invalid date sequencing.
# Battlecard: Rivally ## One-line positioning Points-based employee recognition platform with an engaging recognition feed, expanding in Europe and into lightweight engagement surveys via Rivally Pulse. [S02, S06, S11, S15] ## Pricing Recognition Starter is $7/user/month with annual billing as of 2026-08-12. [S17] This supersedes the older $5/user/month pricing shown on 2026-04-01 and 2026-01-20. [S08, S03] Deal-level pricing has varied: a 500-seat prospect reported a $6.50/user/month annual quote on 2026-06-02 [S13], while another prospect reported $7/user/month list with a 15% discount for a three-year term on 2026-08-14. [S18] ## Where they win - Recognition experience: reviewers praise Rivally's points-based recognition feed as engaging. [S02, S16] - Fast implementation and Slack: one mid-market reviewer reported setup in under a week and Slack working out of the box. [S04] - EU capabilities: multi-language support is praised for distributed EU teams. [S12] - EU data residency became generally available in July 2026. [S15] - Support: one reviewer reported response times under four hours. [S22] ## Where we win - Analytics: Rivally reviewers describe its analytics/reporting as limited or basic. [S02, S07] An 800-seat prospect picked Bonusly over Rivally specifically citing analytics depth. [S25] - Enterprise provisioning/admin: an enterprise reviewer reports no SCIM provisioning and painful manual user management. [S10] Another reviewer says the admin console lacks bulk recognition editing. [S24] - Data portability: one reviewer reported migration difficulty because analytics exports are CSV-only. [S20] - EMEA rewards: one review says Rivally's EMEA rewards catalog is thinner than its US catalog. [S14] ## Objections and responses “Rivally has strong EU capabilities.” Response: Valid. Rivally has EU data residency [S15] and praised multi-language support. [S12] Differentiate on analytics depth [S02, S07, S25], provisioning/admin capabilities [S10, S24], and EMEA rewards breadth. [S14] “Rivally is easy to deploy and integrates with Slack.” Response: A reviewer reports setup under a week and an out-of-the-box Slack integration. [S04] Do not claim Rivally lacks Slack; the old card's claim is contradicted by S04. “Rivally has a strong recognition experience.” Response: Reviewers do praise its recognition feed. [S02, S16] Pivot to reported limitations in analytics [S02, S07], admin tooling [S16, S24], and SCIM provisioning. [S10] ## Recent changes - 2026-03-05: launched Rivally Pulse, a lightweight engagement-survey add-on. [S06] - 2026-07-01: opened a Dublin office and made EU data residency generally available. [S15] - 2026-08-12: Recognition Starter increased from the previously published $5/user/month [S08] to $7/user/month annually. [S17] - 2026-08-20: Microsoft Teams app v2 entered public preview. [S19] - 2026-09-01: Rivally Pulse exited beta and is priced as an add-on rather than bundled. [S23] ## 12-month win/loss record For the provided 2025-09 through 2026-08 Rivally deals: 13 wins + 7 losses = 20 competitive deals. Win rate = 13 / 20 = 65.0%. Loss rate = 7 / 20 = 35.0%. Deal records: 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. [deals_with_competitor.csv] ## Old-card verification - “Points-based recognition”: supported. [S02] - “$5/user/month”: outdated; newer published price is $7/user/month. [S08, S17] - “Rivally lacks a Slack integration”: contradicted by a reviewer reporting Slack worked out of the box. [S04] - “Rivally was acquired by WorkHuman in 2025”: UNVERIFIED — no provided snippet supports this claim. - “Strong in EU enterprise with multi-language support”: supported by an EU enterprise review praising its fit for distributed EU teams and multi-language support. [S12]
- New Logo Nurture: sent 1,386; open 490/1,386 = 35.4%; reply 90/1,386 = 6.5%; meeting 27/1,386 = 1.9%. Weakest: step 3 (18/428 = 4.2% reply; 6/428 = 1.4% meeting). Change: rewrite step 3 CTA/message to address late-sequence drop-off. - Expansion Nurture: sent 875; reported open 565/875 = 64.6%; reply 59/875 = 6.7%; meeting 12/875 = 1.4%. Weakest: step 3 (12/275 = 4.4% reply; 3/275 = 1.1% meeting). Change: rewrite step 3 CTA. Tracking error: step 2 reports 340 opens on 300 sends = 113.3%; aggregate open rate is therefore unreliable until corrected. - Cold Outbound - HR Leaders: sent 1,785; open 545/1,785 = 30.5%; reply 8/1,785 = 0.4%; meeting 0/1,785 = 0.0%. Weakest: step 3 (1/590 = 0.17% reply, 0 meetings). Failure mode: opens occur but almost nobody replies, and no replies convert to meetings; the data supports a message/offer-response problem, but not its specific cause. Change: replace the sequence messaging/offer, starting at step 1 rather than optimizing follow-ups. - Cold Outbound - People Ops: sent 1,163; open 340/1,163 = 29.2%; reply 29/1,163 = 2.5%; meeting 6/1,163 = 0.5%. Weakest: step 3, with 6/377 = 1.6% reply. Failure mode at step 3: declining engagement across steps (3.5% → 2.3% → 1.6% replies), consistent with follow-up exhaustion; exact cause is not provided. Change: replace or remove step 3. Audience overlap exists: contacts appearing in multiple sequences include CT-000301 (Expansion Nurture/New Logo Nurture), CT-000624 (Expansion Nurture/New Logo Nurture), and multiple HR Leaders/People Ops overlaps including CT-000849, CT-001255, CT-001105, CT-001217, and others. Fix first: Cold Outbound - HR Leaders: 0.4% aggregate reply and zero meetings make it the clearest performance failure.
Q3-2026: 66 of 92 days elapsed = 66 ÷ 92 = 71.7%. - SQMs: QTD 230; target 300; delta = 230 − 300 = -70. Linear pace target = 300 × 66 ÷ 92 = 215.2. Ahead. - SQOs: QTD 84; target 120; delta = 84 − 120 = -36. Linear pace target = 120 × 66 ÷ 92 = 86.1. Behind. - DS2s: QTD 40; target 75; delta = 40 − 75 = -35. Linear pace target = 75 × 66 ÷ 92 = 53.8. Behind. - Closed-lost MIA rate: QTD = 5 ÷ 25 = 20.0%; target ≤10.0%; delta = +10.0 percentage points. Behind. - Same-quarter close count: QTD 10; target 20; delta = 10 − 20 = -10. Linear pace target = 20 × 66 ÷ 92 = 14.3. Behind. - Active pipeline coverage against target: QTD $3,000,000 ÷ $4,000,000 = 75.0% coverage; target $4,000,000; dollar delta = -$1,000,000. Linear pace target = $4,000,000 × 66 ÷ 92 = $2,869,565. Ahead on elapsed-quarter pace. What moved this week: No prior-week data was provided, so week-over-week movement cannot be calculated without inventing data. Current QTD performance shows SQMs and active pipeline ahead of linear pace, while SQOs, DS2s, closed-lost MIA rate, and same-quarter closes are behind.
Q3 weighted forecast is 115,976.75: 7 COMMIT deals at 44,729 + 35% of 24 BEST_CASE deals totaling 203,565 (71,247.75); 23 PIPELINE count as 0, with 54 of 86 deals in-quarter. 32 deals totaling 227,575 are excluded, including Deal-D348E1 COMMIT at 13,770 and 9 BEST_CASE totaling 28,240. Caveat: all 7 in-quarter COMMIT deals lack why-buys, so 44,729 has no documented reason to buy.
Subject: Following up on pricing Hi there, Following up on my August 5 recap with pricing for 150 seats after the July 28 demo. Are you open to a quick call to discuss the pricing and next steps? Best, Alex
Marketing: SQMs landed at 46 against a target of 52, reaching 88.5% of goal (46 ÷ 52 × 100) and finishing 6 below target (52 − 46). Webinars delivered 18 SQMs, or 39.1% of the week’s total (18 ÷ 46 × 100). Paid search cost per SQM held at $150. Sales: The team converted 14 SQOs, set 9 DS2 meetings, and created $310,000 in new pipeline this week. Same-quarter closes totaled 3. CS: The team saved 2 renewals this week, while Team NPS moved to 61. Looking ahead, 3 open red-flag accounts are heading into next week. PLG: PLG added 412 new signups with activation at 31%. That implies approximately 128 activated signups (412 × 31% = 127.72), subject to the reported activation rate being rounded. Separately, 38 companies reached the aha moment of 10 recognition gives.
Partner digest — 2026-08-24 to 2026-09-04 - Apex Rewards Co — Active: 2 partner-sourced deals totaling $275,000 ($180,000 + $95,000): Deal-DDAAF2, Deal-2CF33E. Co-webinar locked for 09-15; Slack reports both opps in DS1. - HRCloud Partners — Active: 1 partner-sourced deal totaling $140,000: Deal-F1CDA5. Security review closed; Slack reports the opp moved to DS2. - CultureBridge — Active: 2 partner-sourced deals totaling $135,000 ($60,000 + $75,000): Deal-096E1D, Deal-067213. Lunch-and-learn produced the two opps; Slack describes both as early stage. - WorkWell Group — Active: 0 partner-sourced deals, $0 pipeline. Joint playbook restart targeted for Q4; planning call booked for 09-09. - Recogniq — Quiet: 0 partner-sourced deals, $0 pipeline. - KudosWave — Quiet: 0 partner-sourced deals, $0 pipeline. - PeopleFirst Advisors — Quiet: 0 partner-sourced deals, $0 pipeline. - TotalPerk — Quiet: 0 partner-sourced deals, $0 pipeline. Total partner-sourced pipeline: 5 deals, $550,000 ($180,000 + $95,000 + $140,000 + $60,000 + $75,000).
Q1: Yes. All customer data is encrypted at rest using AES-256. [P1] Q2: Yes. Data in transit is encrypted using TLS 1.2 or higher. [P1] Q3: Yes. Bonusly maintains a SOC 2 Type II report, renewed annually. Prospects can obtain the report under NDA. [P2] Q4: Customer data is retained for the duration of the contract plus 90 days, then deleted from production systems. [P3] Q5: The current subprocessor list is published at trust.bonusly.com. Customers receive 30 days notice before a new subprocessor is added. [P4] Q6: Yes. SAML 2.0 single sign-on is available on the Enterprise plan. [P5] Q7: Production databases are backed up daily. Backups are encrypted and retained for 35 days. [P6] Q8: Unanswerable from the provided excerpts. ISO 27001 certification status is not addressed. A current certification/compliance document or ISO 27001 certificate would answer this. [P1–P8] Q9: Unanswerable from the provided excerpts. No contractual uptime SLA percentage is provided. The applicable SLA or service agreement would answer this. [P1–P8] Q10: Unanswerable from the provided excerpts. HIPAA Business Associate Agreement availability is not addressed. A HIPAA/BAA policy or standard BAA documentation would answer this. [P1–P8]
- CRITICAL — MERGE: `comms-drafter` and `email-drafter` have overlapping ALWAYS-trigger phrases, including `"write me an email"`, `"draft a follow-up"`, `"what should I say"`, `"bump email"`, and `"contract nudge"`. Proposal: merge the email-specific behavior into one skill and retain a single owner for customer/prospect email triggers. - CRITICAL — REVIEW: Circular delegation chain: `pipeline-intelligence-report` → `closed-lost-analysis` Mode 4 → `pipeline-intelligence-report`. `pipeline-intelligence-report` delegates its loss-pattern work to `closed-lost-analysis`; `closed-lost-analysis` Mode 4 says it is “called from pipeline-intelligence-report” and its `next-to-close` context also establishes pipeline delegation. Proposal: make `closed-lost-analysis` the terminal loss-risk provider and prohibit delegation back to `pipeline-intelligence-report` during Mode 4. - CRITICAL — REVIEW: Dangling delegation/reference targets not present in the supplied manifest/files include `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`, and `bonusly-datadog-questions`. Proposal: verify these are external/org skills intentionally outside this manifest; otherwise add them or remove the dangling delegation. - CRITICAL — UPDATE_BODY: Version conflict inside `analysis-validator`: header/changelog identify current version as v3.6, but the validation-trail template says `analysis-validator v3.2`, and the decision tree still says Gate 2 runs `G2-A through G2-E` despite v3.6 adding G2-F. Proposal: v3.6 survives; update stale internal version/check-range references to v3.6/G2-F. - INFO — TRIM_DESC: Manifest descriptions exceeding 1,024 characters: 0 of 14. Arithmetic: `0 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 0 = 0`. Maximum supplied values are 1,006 characters (`pipeline-intelligence-report`, `signalforge-claim-compressor`). Proposal: no trimming required. - WARNING — UPDATE_BODY: `analysis-validator` hardcodes dates/person names and IDs, including May 2026 calibration ranges and roster data for 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, Alaina Loori, Shealagh Coughlin, Ben Castelli, Amani Phipps, John Thomas, Yasmin Wahid, plus HubSpot IDs. Proposal: replace mutable roster/date snapshots with runtime/reference lookups. - WARNING — UPDATE_BODY: `closed-lost-analysis` hardcodes May 2026 examples/person-company references including Estee Lauder, Softheon, MinIO, LIFTOFF, Nestlé, Ozinga, Aurora Innovation, GCash, Ethos Cannabis, and StickerYou. Proposal: move historical examples to a maintained reference dataset or mark them explicitly as archival examples. - WARNING — UPDATE_BODY: `deal-strategy-coach` hardcodes a Confluence page ID (`2257879045`), 2026 pricing, and routing/person references including Perseus and Farid. Proposal: move mutable IDs, pricing, dates, and routing ownership to maintained references/lookups. - WARNING — UPDATE_BODY: `partner-digest` hardcodes Confluence IDs (`73fe98de-a4a3-4869-9f8a-bb1eeed4cf7f`, `1958248479`, `2286616609`, plus page IDs), dates such as May 16/19 and June 2, 2026, and people including Amani, Kelli, Jen Lee, Hani, Bryce, and Sara. Proposal: resolve destinations/configuration and active contacts from maintained configuration rather than the skill body. - WARNING — UPDATE_BODY: `pipeline-intelligence-report` hardcodes May 2026, HubSpot org ID `1973303`, stage/owner IDs, and people Bryce Harmon, Dana Mercer, Cole Ingram, Alex Franklin, and Gavin Porter, while simultaneously saying system constants should be verified at runtime. Proposal: remove mutable roster snapshots and runtime-resolve them. - WARNING — UPDATE_BODY: `sales-forecast` hardcodes Confluence IDs (`2232811524`, `73fe98de-a4a3-4869-9f8a-bb1eeed4cf7f`, `2232582148`), historical dates, Q1 2026 figures, and Alaina. Proposal: move publication IDs and historical baselines to maintained references/configuration. - WARNING — UPDATE_BODY: `signalforge-feedback` hardcodes page/space/parent/build-log IDs (`2295136266`, `2232811524`, `73fe98de-a4a3-4869-9f8a-bb1eeed4cf7f`, `2234417154`, `2247295002`). Proposal: externalize destination IDs into configuration. - WARNING — UPDATE_BODY: `stale-pipeline-report` hardcodes Slack channel ID `C0561C1JCPJ`, HubSpot org ID `1973303`, stage IDs, June 10, 2026 changelog dates, and example dates such as 5/15 and 5/19. Proposal: runtime-resolve channel/org metadata and keep illustrative dates clearly non-operational. - WARNING — UPDATE_BODY: `weekly-pipeline-report` hardcodes Ben Lavin, two spreadsheet IDs, Q2 2026 dates, and Q1 2026 benchmark figures. Proposal: move owner, spreadsheet IDs, quarter windows, and historical context into maintained references/configuration. - INFO — REVIEW: No supplied hardcoded mutable page/person data requiring separate action was identified in `comms-drafter`, `email-drafter`, `model-selection`, `next-to-close`, or `signalforge-claim-compressor` beyond ordinary changelog/model-registry/example material or cross-skill names. Proposal: review those only under normal maintenance. - CRITICAL — REVIEW: Manifest drift, files with no manifest row: none among the 14 supplied `.SKILL.md` files. Manifest rows with no supplied file: none. Arithmetic: 14 manifest rows − 14 matched supplied skill files = 0 drift in either direction. Proposal: no manifest reconciliation change required.
1. Acknowledge alert and assume incident command [M01] - Who: Bryce Harmon - Action: Acknowledged the PagerDuty alert for reward-worker queue depth > 10k and took IC. - Verification: PagerDuty alert fired. No further verification documented. - Rollback: Not documented — needs confirmation. 2. Measure reward queue depth [M02] - Who: Farid Osman - Command: `bundle exec rake sidekiq:queue_depth` - Result/verification: 48,213 pending jobs; normal reported as under 500. - Rollback: None; read-only check. 3. Inspect dead set [M03] - Who: Farid Osman - Action: Inspected the dead set. - Exact command: Not documented — needs confirmation. - Result/verification: 112 jobs, all `Redis::TimeoutError` from around 13:58. - Rollback: None documented for inspection. 4. Pause enqueue [M04] - Who: Farid Osman - Command: `bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'` - Verification: No direct verification of the flag state documented — needs confirmation. - Rollback: `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'` 5. Clear the dead set [M05] - Who: Elena Sinclair - Action: Cleared the dead set while in the console. - Exact command/action details: Not documented — needs confirmation. - Verification: Not documented — needs confirmation. - Rollback: Not documented — needs confirmation. 6. Scale reward workers from 3 to 6 [M06] - Who: Bryce Harmon - Command: `kubectl scale deployment/reward-worker --replicas=6` - Verification: No direct verification of replica count documented — needs confirmation. - Rollback: `kubectl scale deployment/reward-worker --replicas=3` 7. Check queue recovery [M07] - Who: Farid Osman - Action: Checked queue depth/rate of decline. - Exact command: Not documented — needs confirmation. - Result/verification: Queue depth was 9,400 and falling ~1,200/min. - Rollback: None; observation only. 8. Verify queue drained and errors recovered [M08] - Who: Cole Ingram - Command: `bundle exec rake sidekiq:queue_depth` - Verification: Command returned 0. Datadog error rate was reported back to baseline. - Rollback: None; verification only. 9. Re-enable enqueue [M09] - Who: Bryce Harmon - Command: `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'` - Verification: 40 new jobs processed cleanly in the next 3 minutes. - Rollback: Not documented — needs confirmation. 10. Scale reward workers back to 3 and resolve incident [M10] - Who: Bryce Harmon - Command: `kubectl scale deployment/reward-worker --replicas=3` - Verification: Queue stable at 0; incident declared resolved. - Rollback: Not documented — needs confirmation.
First error: 2026-09-03T14:01:12Z — reward-service — `Redis::TimeoutError: Connection to Redis at redis-primary:6379 timed out after 5s`. Cascade, in order: 1. 14:01:12 — reward-service loses Redis connectivity. 2. 14:01:20 onward — `RewardGiveJob` exhausts retries in reward-service. 3. 14:01:40 onward — sidekiq reports `RewardGiveJob` failures/retries. `sidekiq_jobs.csv` also records `RewardGiveJob` failures starting at 14:01:46. 4. 14:02:30 — Sidekiq reward queue depth exceeds 10,000. 5. 14:02:36 onward — `RecognitionDigestJob` also fails with `Redis::TimeoutError`. 6. 14:03:05 onward — api-gateway returns 502 upstream timeouts calling reward-service. 7. 14:03:30 onward — web-app Give form submissions fail because of upstream 502s. 8. 14:22:10 — reward-service reports Redis connection restored and resumes job processing. 9. 14:24:45 — reward queue depth falls below 500. Service/job involved: the initial failure is in `reward-service`, connecting to `redis-primary:6379`. The primary affected job shown is `RewardGiveJob`; `RecognitionDigestJob` subsequently also records Redis timeout failures. Datadog query to confirm the first error: `service:reward-service status:error "Redis::TimeoutError" "redis-primary:6379"` around `2026-09-03T14:01:12Z`. The logs do not show why Redis timed out or establish Redis itself as the root cause; there are no Redis server logs, infrastructure/resource metrics, deployment/change events, network diagnostics, or dependency telemetry. They also do not show exactly what restored connectivity at 14:22:10.
- recognition_streaks_v2 — On. Controls recording recognition streaks via `StreakTracker.record(give)`. Targeting: `segment:beta_companies`. 42 companies. - points_budget_guardrails — On. Controls enforcement of giver points budgets via `BudgetService...enforce!`. Targeting: `all_companies`. 220 companies. - slack_dm_nudges — On. Controls sending Slack DM nudges via `SlackDm.send_nudge(user)`. Targeting: `segment:region_na`. 87 companies. - redeem_flow_redesign — Off. Controls rendering Redeem V2 instead of Redeem V1. Targeting: `targeted_list`. 12 companies. - analytics_dashboard_v3 — On. Controls use of `AnalyticsV3`. Targeting: `segment:tier_three`. 65 companies. - ms_teams_app_v2 — Off. Controls installation of Teams App V2. Targeting: `targeted_list`. 9 companies. - legacy_give_modal — Off. No code reference in the provided excerpt. Targeting: `segment:legacy_plan`. 14 companies. - survey_boosters_q3 — On. No code reference in the provided excerpt. Targeting: `segment:legacy_plan`. 7 companies. - paused_offboard_cleanup — Off. No code reference in the provided excerpt and no targeting rule. 0 companies. Arithmetic: company counts are reported directly from the export; no aggregation or calculation was required. The provided data identifies segments/rules but does not provide individual company aliases, so specific companies cannot be listed.
NDA-1 — GREEN — Standard approval: standard carve-outs are present — “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”; two-year term, three-year survival, and Delaware governing law are stated. NDA-2 — YELLOW — Counsel review: 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: broad three-year non-compete — “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”; additionally, there are no confidentiality carve-outs, the term is five years, and governing law is unusual relative to the other provided NDAs: “the laws of the Republic of Ireland, and the parties submit to the exclusive jurisdiction of its courts.”