Mean 0.728 · 15/40 perfect tests · $0.73 total · modalities in: text · 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+: any pipeline stage DS2, DS3, DS4, DS5.
why_buys: must be substantive (≥15 chars, not placeholder; LONG(n) counts).
Results:
{
"overall_percent_non_compliant": 81.45,
"non_compliant_by_owner": {
"Bryce Harmon": 83.33,
"Alex Franklin": 78.69,
"Dana Mercer": 81.82,
"Cole Ingram": 86.36,
"Farid Osman": 100.0
},
"deals_no_ds1_timestamp": 0,
"deals_regressed": 0,
"process_fixes": [
"Enforce mandatory why-buys field entry at DS2+ stage with length >=15 chars",
"Automate timestamp validation to flag out-of-order stage entry dates",
"Integrate why-buys validation into pipeline entry workflow to block placeholders"
]
}
Number of deals resolved: 142 Deals with owner ID not found in owners table: (none) Owner IDs that map to archived owners (and their names): (none) Total pipeline amount per resolved owner: - 119337721: 1054144 - 716654662: 1249 - 83155923: 341195 - 83155924: 148302.5 - 84342457: 624310
Orphan rows: 52 (sample aliases – Deal‑C6FE92, Deal‑F9A3C1, Deal‑03BA69). Duplicate conversation‑key rows: 14 (sample aliases – Deal‑03BA69, Deal‑8FDCD2, Deal‑117863). Among open deals at stage DS3 or later, 14 of 51 have at least one logged call ≈ 27.5 %.
```sql
-- Aggregated month‑1 company metrics from PLG.COMPANY_COHORT_SUMMARY
SELECT
company_id,
M1_GIVES AS unique_givers,
M1_RECOGNITIONS AS recognition_count,
M1_REDEMPTIONS AS successful_redemption_count
FROM PRODUCTION.PLG.COMPANY_COHORT_SUMMARY;
```
Industry completeness: 30 / 30 = 100 % Employee‑count completeness: 22 / 30 = 0.733 → 73 % HQ‑country completeness: 23 / 30 = 0.767 → 77 % Duplicate company clusters (shared domain) – survivor selected: - acme‑corp.com: aliases C‑0A092931 and C‑0A092932. Survivor = C‑0A092932 (higher employee count 510, matches enrichment industry “Technology”). - globex.io: aliases C‑0A092933 and C‑0A092934. Survivor = C‑0A092934 (enrichment lists industry “Technology”, matching this alias). Invalid e‑mail addresses (missing domain or empty): - CT‑0010 : user0@ - CT‑0022 : (empty) - CT‑0080 : user0@ - CT‑0081 : user1@ - CT‑0192 : user2@ Domain mismatches (e‑mail domain ≠ company domain): - CT‑0011 : user1@other‑domain.com does not match company domain 66d1fc.com Missing company fields filled from enrichment (where a matching row exists): - C‑950043 : hq_country = “United States” (enrichment) - C‑EC3025 : employee_count = 400 (enrichment); industry disagrees (CRM “Technology” vs Enrichment “Computer Software”) – recommend enrichment source - C‑96039F : employee_count = 400 (enrichment) - C‑44EA29 : employee_count = 400 (enrichment); industry disagrees (CRM “tech” vs Enrichment “Computer Software”) – recommend enrichment source - C‑B23205 : employee_count = 400 (enrichment); hq_country = “United States” (enrichment) – both fields filled from enrichment - C‑E51FB7 : hq_country = “United States” (enrichment) – CRM had “USA” - C‑D0662E : hq_country = “United States” (enrichment) – CRM had “US” - C‑60C75F : employee_count = 400 (enrichment); industry disagrees (CRM “tech” vs Enrichment “Computer Software”) – recommend enrichment source - C‑7BBDFA : employee_count = 400 (enrichment); industry matches enrichment “health care” - C‑50D386 : employee_count = 400 (enrichment); industry matches enrichment “health care” Disagreements where CRM and enrichment values differ (both shown, recommended source in brackets): - C‑66D1FC industry: CRM “tech” vs Enrichment “Computer Software” – **[Enrichment]** - C‑EC3025 industry: CRM “Technology” vs Enrichment “Computer Software” – **[Enrichment]** - C‑44EA29 industry: CRM “tech” vs Enrichment “Computer Software” – **[Enrichment]** - C‑92D97D industry: CRM “Technology” vs Enrichment “Computer Software” – **[Enrichment]** - C‑D04904 industry: CRM “Technology” vs Enrichment “Computer Software” – **[Enrichment]** - C‑77A95A industry: CRM “Technology” vs Enrichment “Computer Software” – **[Enrichment]** - C‑AA8DDA industry: CRM “Technology” vs Enrichment “Computer Software” – **[Enrichment]** - C‑B25F40 industry: CRM “Technology” vs Enrichment “Computer Software” – **[Enrichment]** - C‑60C75F industry: CRM “tech” vs Enrichment “Computer Software” – **[Enrichment]** - C‑425E2A industry: CRM “Tech ” vs Enrichment “Computer Software” – **[Enrichment]** - C‑BA969B industry: CRM “Tech ” vs Enrichment “Computer Software” – **[Enrichment]** **Ten fixes addressing the largest gaps (pipeline impact cannot be ranked due to missing deal‑amount data):** 1. Set hq_country = “United States” for C‑950043 (filled from enrichment). 2. Set employee_count = 400 for C‑EC3025 (filled from enrichment). 3. Set employee_count = 400 for C‑96039F (filled from enrichment). 4. Set employee_count = 400 for C‑44EA29 (filled from enrichment). 5. Set employee_count = 400 and hq_country = “United States” for C‑B23205 (both filled from enrichment). 6. Set hq_country = “United States” for C‑E51FB7 (filled from enrichment). 7. Set hq_country = “United States” for C‑D0662E (filled from enrichment). 8. Set employee_count = 400 for C‑60C75F (filled from enrichment). 9. Set employee_count = 400 for C‑7BBDFA (filled from enrichment). 10. Set employee_count = 400 for C‑50D386 (filled from enrichment). These actions complete missing fields, resolve domain mismatches, and align company records with the enrichment source where discrepancies existed.
Category counts: pricing – 1 competitor – 22 no decision – 7 timing – 19 product gap – 1 champion left – 15 other – 1 (Total deals = 66) Side split: buyer – 64 Bonusly – 1 unknown – 1 Disagreement deals (structured tag vs. free‑text): 1 (Deal‑5DB9B0 tag “Lost‑ Does not fit ICP (write in notes)” with reason “Spam.”) Two patterns most worth acting on: - competitor (22 deals) – e.g., Deal‑F7F635 - timing (19 deals) – e.g., Deal‑DB0AAC
{
"tier_counts": {
"BUILD": 9,
"RISKY": 33,
"ACTION": 4,
"LOCK": 5,
"WATCH": 37,
"REVIVE": 68
},
"tier_examples": {
"LOCK": [
"Deal-944310",
"Deal-1FC049",
"Deal-C6FE92"
],
"ACTION": [
"Deal-D348E1",
"Deal-C26D20",
"Deal-403845"
],
"BUILD": [
"Deal-25F752",
"Deal-3974EB",
"Deal-62D607"
],
"REVIVE": [
"Deal-2D1F1B",
"Deal-66D1FC",
"Deal-950043"
],
"WATCH": [
"Deal-6787C2",
"Deal-40522D",
"Deal-F0EBBB"
],
"RISKY": [
"Deal-E53952",
"Deal-5408B0",
"Deal-9AAE5F",
"Deal-547B2B",
"Deal-B7EBD1",
"Deal-A2B47C",
"Deal-2465CE",
"Deal-C61CF7",
"Deal-584EE5",
"Deal-C6D97A",
"Deal-F9A08A",
"Deal-0660B4",
"Deal-FD9F4E",
"Deal-BA571A",
"Deal-FC22A3",
"Deal-7BBDFA",
"Deal-60C2C2",
"Deal-4A13AD",
"Deal-8AD4A5",
"Deal-15D24F",
"Deal-9D0060",
"Deal-690476",
"Deal-635B8E",
"Deal-ED725A",
"Deal-55164C",
"Deal-3BA5EA",
"Deal-5FDCE4",
"Deal-F336B6",
"Deal-5EED42",
"Deal-BA3DDC",
"Deal-F9A3C1",
"Deal-FA32A0"
]
},
"risky_deals": [
"Deal-E53952",
"Deal-5408B0",
"Deal-9AAE5F",
"Deal-547B2B",
"Deal-B7EBD1",
"Deal-A2B47C",
"Deal-2465CE",
"Deal-C61CF7",
"Deal-584EE5",
"Deal-C6D97A",
"Deal-F9A08A",
"Deal-0660B4",
"Deal-FD9F4E",
"Deal-BA571A",
"Deal-FC22A3",
"Deal-7BBDFA",
"Deal-60C2C2",
"Deal-4A13AD",
"Deal-8AD4A5",
"Deal-15D24F",
"Deal-9D0060",
"Deal-690476",
"Deal-635B8E",
"Deal-ED725A",
"Deal-55164C",
"Deal-3BA5EA",
"Deal-5FDCE4",
"Deal-F336B6",
"Deal-5EED42",
"Deal-BA3DDC",
"Deal-F9A3C1",
"Deal-FA32A0"
],
"lock_violations": 0,
"pipeline_shape": "The open pipeline is dominated by early‑stage PIPELINE deals (DS1‑DS2) that are classified as REVIVE or WATCH, with a smaller but steady share of LOCK and ACTION deals reflecting higher‑confidence BEST_CASE and COMMIT opportunities, while a few RISKY deals indicate mismatches between forecast category and engagement evidence."
}
{
"TX-001": {
"why-buys": [
"The big win for us would be automating anniversary and birthday awards — our HR team of three cannot keep up with it manually.",
"\"Right now we track everything in a spreadsheet, and people slip through the cracks.\""
],
"pain points": [
"\"Right now we track everything in a spreadsheet, and people slip through the cracks.\""
],
"stakeholders": [
"VP People",
"HR Admin"
],
"budget signal": "$40k",
"timeline signal": "Ideally we would have this live before open enrollment in November.",
"competitor mentioned": "Achievers",
"next step": "set up a security review on September 12",
"objections": [
"need SSO and audit logs for IT to sign off"
],
"confidence": "high"
},
"TX-002": {
"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": [
"Head of Total Rewards",
"CFO"
],
"budget signal": "$25k",
"timeline signal": "We want a decision by end of September.",
"competitor mentioned": null,
"next step": "send the pilot agreement and we'll route it to legal this week.",
"objections": [],
"confidence": "high"
},
"TX-003": {
"why-buys": [
"We need to make recognition visible across our 12 retail locations.",
"Store managers have zero budget autonomy for on-the-spot recognition today."
],
"pain points": [
"Store managers have zero budget autonomy for on-the-spot recognition today."
],
"stakeholders": [
"People Ops Manager"
],
"budget signal": null,
"timeline signal": "no rush until Q1",
"competitor mentioned": "Bucketlist",
"next step": "schedule a call with our CEO",
"objections": [
"CEO has to be sold first — she decides anything people-related."
],
"confidence": "high"
},
"TX-004": {
"why-buys": [
"We want to consolidate three separate recognition tools into one.",
"We're paying for three tools and none of them talk to our HRIS."
],
"pain points": [
"We're paying for three tools and none of them talk to our HRIS.",
"The security review took three months for our last vendor — that's my hesitation."
],
"stakeholders": [
"VP People",
"IT Security Lead"
],
"budget signal": "$15k annually",
"timeline signal": null,
"competitor mentioned": null,
"next step": null,
"objections": [
"Our procurement cycle runs six to eight weeks minimum",
"The security review took three months for our last vendor — that's my hesitation."
],
"confidence": "high"
},
"TX-005": {
"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": [
"HR Director",
"People Ops Coordinator"
],
"budget signal": "$12k",
"timeline signal": "We need this running before our January all-hands.",
"competitor mentioned": "Nectar",
"next step": "present to our exec team on October 2",
"objections": [
"Our exec team is skeptical after a failed rollout two years ago."
],
"confidence": "high"
},
"TX-006": {
"why-buys": [
"I'd love to cut the admin time on service awards.",
"I personally spend five hours a month ordering and shipping plaques."
],
"pain points": [
"I personally spend five hours a month ordering and shipping plaques."
],
"stakeholders": [
"HR Manager"
],
"budget signal": null,
"timeline signal": "Q1 start",
"competitor mentioned": null,
"next step": "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"
}
}
Deal‑547B2B – $11,200 – Why close: Slack (Alex Franklin 2026‑09‑04 09:12) says redlines are clean, the signing page is out and the VP People confirmed they will sign tomorrow; stage DS5 COMMIT, close 2026‑09‑11. What’s left: final signature and contract execution. Deal‑403845 – $9,000 – Why close: Slack (Dana Mercer 2026‑09‑04 10:02) notes the order form is with the buyer’s finance team; stage DS5 COMMIT, close 2026‑09‑11. What’s left: finance approval and signing. Deal‑B7EBD1 – $9,000 – Why close: CRM shows DS5 COMMIT with the earliest upcoming close date (2026‑09‑10) among open COMMIT deals; no block reported. What’s left: standard legal review and signature.
Deal‑EC3025 – real gap Quote: “We need SCIM user provisioning — our security team will not approve any tool that requires manual account management.” Deal amount: not provided Deal‑D0D6B5 – real gap Quote: “Our payroll runs on ADP Workforce Now and I don't see ADP anywhere in your integration list — that's a dealbreaker for us.” Deal amount: not provided
Owner: Bryce Harmon - Deal-2D1F1B | DS1 | $240,000.00 | 81 - Deal-66D1FC | DS1 | $99,000.00 | N/A - Deal-950043 | DS1 | $70,000.00 | 19 - Deal-D73B89 | DS2 | $63,600.00 | N/A - Deal-B23205 | DS1 | $45,000.00 | 16 - Deal-7BBDFA | DS3 | $37,440.00 | 12 - Deal-7BBDFA | DS3 | $37,440.00 | 46 - Deal-C1FA6D | DS1 | $18,000.00 | N/A - Deal-BB8880 | DS1 | $17,400.00 | N/A - Deal-01E193 | DS1 | $12,600.00 | N/A - Deal-F0EBBB | DS3 | $11,400.00 | 24 - Deal-40522D | DS3 | $7,200.00 | 19 - Deal-E25A09 | DS1 | $6,000.00 | 9 - Deal-A5E80A | DS1 | $2,520.00 | N/A - Deal-012CB1 | DS1 | $1.00 | N/A -> carries 15 stale deals, total stale amount $667,601.00 Owner: Alex Franklin - Deal-1BEEBF | DS3 | $31,500.00 | 19 - Deal-1E2498 | DS3 | $16,700.00 | N/A - Deal-36C33F | DS2 | $15,000.00 | N/A - Deal-5296C9 | DS3 | $10,000.00 | N/A - Deal-885F45 | DS2 | $9,300.00 | 12 - Deal-403845 | DS5 | $9,000.00 | N/A - Deal-317E6F | DS3 | $5,400.00 | N/A - Deal-0D2F7A | DS3 | $5,100.00 | N/A - Deal-C6D97A | DS4 | $4,800.00 | 8 - Deal-E531A6 | DS3 | $4,800.00 | N/A - Deal-6C60D4 | DS3 | $4,800.00 | 12 - Deal-D1E6C2 | DS2 | $4,400.00 | N/A - Deal-5AD94B | DS2 | $4,000.00 | N/A - Deal-9D0060 | DS3 | $3,840.00 | 12 - Deal-690476 | DS2 | $3,600.00 | 18 - Deal-EE195F | DS3 | $3,120.00 | N/A - Deal-278DEC | DS3 | $2,700.00 | 8 - Deal-F436DA | DS2 | $2,520.00 | N/A - Deal-6883F3 | DS1 | $2,400.00 | N/A - Deal-1FC049 | DS4 | $1,920.00 | N/A - Deal-7FA0C3 | DS2 | $1,400.00 | N/A - Deal-BA571A | DS4 | $1,080.00 | 18 -> carries 22 stale deals, total stale amount $147,380.00 Owner: Dana Mercer - Deal-B7EBD1 | DS5 | $9,000.00 | 16 - Deal-3974EB | DS4 | $9,000.00 | 8 - Deal-0660B4 | DS4 | $1,920.00 | 16 -> carries 3 stale deals, total stale amount $19,920.00 Owner: Cole Ingram - Deal-FD9F4E | DS5 | $1,330.00 | N/A -> carries 1 stale deals, total stale amount $1,330.00 Owner: Farid Osman - Deal-499BF6 | DS2 | $1,249.00 | N/A -> carries 1 stale deals, total stale amount $1,249.00
Rank | Owner ID | Owner Name | Total Activities | DS2 Entries | Ratio (activities per DS2 entry) | Email % | Call % | Meeting % 1 | 84342457 | Alex Franklin | 384 | 18 | 21.33 | 79.9% | 9.4% | 10.7% 2 | 119337721 | Bryce Harmon | 205 | 4 | 51.25 | 79.0% | 0.0% | 21.0% 3 | 83155924 | Cole Ingram | 111 | 2 | 55.50 | 86.5% | 12.6% | 0.9% 4 | 716654662 | Farid Osman | 72 | 1 | 72.00 | 52.8% | 0.0% | 47.2% Most efficient rep: Alex Franklin Highest-volume rep: Alex Franklin Note: The most efficient and highest-volume rep are the same person.
=== QTD SCORECARD === Bookings (as of 2026-09-05): $150,000 Quota (2026-Q3): $200,000 Attainment: 75.0% New vs Expansion Split: New: $113,500 (75.7% of bookings) Expansion: $36,500 (24.3% of bookings) Active Pipeline by Stage: DS1: $284,621 DS2: $353,760 DS3: $552,705 DS4: $23,574 DS5: $45,730 Rolling 90‑day DS2‑to‑won Rate: 7.2% Win / Loss Summary: Wins: 8 Losses: 27 Top loss reason: "Lost- Timing (1 year or more)" (13 occurrences) – e.g., Deal-55624236610, Deal-62121718303 Activity Volume (last 30 days): Emails: 807 Calls: 112 Meetings: 128 Notes: 50 === COACHING OBSERVATIONS === 1. Attainment is below quota – prioritize accelerating new deal closures to lift the %. 2. Expansion dominates new bookings – focus on upsell/cross‑sell with existing champions. 3. DS2‑to‑won rate is low (7.2%) – intervene early in qualification/scoping to improve conversion.
Deal-EC3025 amount: not provided stage: not provided active contact count: 1 (CT-047C54 engaged 2026-09-02, not former) personas present: champion personas missing: economic buyer, HR admin, IT security, finance most valuable persona to add given the stage: not provided unengaged contact: CT-6827DB (Chief People Officer, economic buyer) Deal-92D97D amount: not provided stage: not provided active contact count: 1 (CT-01F5B4 engaged 2026-08-28, not former) personas present: HR admin personas missing: economic buyer, champion, IT security, finance most valuable persona to add given the stage: not provided unengaged contact: none on file Deal-50D386 amount: not provided stage: not provided active contact count: 2 (CT-AA41B2 engaged 2026-09-01, not former; CT-B9C35B engaged 2026-08-25, not former) personas present: champion, HR admin personas missing: economic buyer, IT security, finance most valuable persona to add given the stage: not provided unengaged contact: CT-A1C4B3 (Chief People Officer, economic buyer) Deal-D0D6B5 amount: not provided stage: not provided active contact count: 2 (CT-87CED4 engaged 2026-09-02, not former; CT-DE6D7C engaged 2026-08-19, not former) personas present: champion personas missing: economic buyer, HR admin, IT security, finance most valuable persona to add given the stage: not provided unengaged contact: CT-1FA4DB (Chief People Officer, economic buyer) Deal-5BFE3B amount: not provided stage: not provided active contact count: 2 (CT-57123B engaged 2026-08-31, not former; CT-5CE757 engaged 2026-08-12, not former) personas present: champion personas missing: economic buyer, HR admin, IT security, finance most valuable persona to add given the stage: not provided unengaged contact: none on file Deal-36C33F amount: not provided stage: not provided active contact count: 1 (CT-4FE556 engaged 2026-08-15, not former) personas present: IT security personas missing: economic buyer, champion, HR admin, finance most valuable persona to add given the stage: not provided unengaged contact: CT-1DB73E (Chief People Officer, economic buyer) Deal-885F45 amount: not provided stage: not provided active contact count: 2 (CT-51C81E engaged 2026-08-26, not former; CT-D9A0E8 engaged 2026-08-11, not former) personas present: economic buyer, champion personas missing: HR admin, IT security, finance most valuable persona to add given the stage: not provided unengaged contact: CT-B3F25D (IT Security Lead, IT security) Deal-C6D97A amount: not provided stage: not provided active contact count: 2 (CT-223DDC engaged 2026-08-31, not former; CT-B03555 engaged 2026-08-20, not former) personas present: champion personas missing: economic buyer, HR admin, IT security, finance most valuable persona to add given the stage: not provided unengaged contact: none on file Deal-5408B0 amount: not provided stage: not provided active contact count: 2 (CT-D33AE4 engaged 2026-09-01, not former; CT-8742FD engaged 2026-08-18, not former) personas present: champion, HR admin personas missing: economic buyer, IT security, finance most valuable persona to add given the stage: not provided unengaged contact: CT-07FA76 (Chief People Officer, economic buyer) Deal-FCBE5B amount: not provided stage: not provided active contact count: 1 (CT-4A5317 engaged 2026-08-29, not former) personas present: champion personas missing: economic buyer, HR admin, IT security, finance most valuable persona to add given the stage: not provided unengaged contact: none on file Deal-F9A08A amount: not provided stage: not provided active contact count: 1 (CT-931B10 engaged 2026-09-03, not former) personas present: champion personas missing: economic buyer, HR admin, IT security, finance most valuable persona to add given the stage: not provided unengaged contact: CT-697541 (Chief People Officer, economic buyer)
Lead with (first 5 minutes): "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." Budget objection handling: "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." Spreadsheet objection handling: "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." Competitor objection handling: "Kudos is solid on social recognition; our difference is automated milestones plus analytics tied to retention." Competitor – Awardco: "We're also in late talks with Awardco — their rewards catalog looks bigger than yours." Competitor – Kudos: "How are you different from Kudos? Our CEO used them at her last company." Competitor – Workhuman: "And unlike Workhuman, our pricing includes the full rewards catalog with no extra margin." Next‑step agreement rate: 7 agreed next steps out of 10 calls = 70%. Coaching note 1: Emphasize financing savings early to pre‑empt budget lock‑outs. Coaching note 2: Highlight concrete differentiation against named competitors when prospects compare.
COMMIT total: 35729 BEST_CASE total: 145585 Weighted forecast: 35729 + (0.35 × 145585) = 35729 + 50954.75 = 86683.75 Deals inside Q3 (2026-07-01 to 2026-09-30): - COMMIT deals count: 6, total amount: 35729 - BEST_CASE deals count: 14, total amount: 145585 Excluded deals (close_date outside quarter): - Count: 45 deals, total amount: 56379 Top 5 BEST_CASE deals by amount inside quarter: 1. Deal-2D7423 – 38935 2. Deal-25F752 – 24000 3. Deal-E53952 – 19656 4. Deal-5EED42 – 16250 5. Deal-FC22A3 – 10800 ## Data quality The extract lacks consistent stage labeling; some deals use DS5 while others jump directly to contract, making stage‑based weighting ambiguous. Several rows omit the “why_buys_chars” field, reducing context for deal‑winning rationale. Amount precision varies (e.g., 2916.4 vs integers), which could affect weighted calculations. Finally, the CSV includes a mix of PIPELINE deals with non‑zero amounts, but the rule states PIPELINE deals count zero, indicating potential data entry errors.
Both‑signals cohort ( m1_users ≥ 5 & m1_redemptions ≥ 1 ) - Size: **48** companies - 24‑month active (retained) count: **31** - **Retention rate:** 31 ÷ 48 ≈ **64.6 %** Givers‑only cohort ( m1_users ≥ 5 & m1_redemptions = 0 ) - Size: **50** companies - 24‑month active count: **23** - **Retention rate:** 23 ÷ 50 = **46.0 %** Redemption‑only cohort ( m1_users < 5 & m1_redemptions ≥ 1 ) - Size: **28** companies - 24‑month active count: **9** - **Retention rate:** 9 ÷ 28 ≈ **32.1 %** Neither‑signal cohort ( m1_users < 5 & m1_redemptions = 0 ) - Size: **94** companies - 24‑month active count: **38** - **Retention rate:** 38 ÷ 94 ≈ **40.4 %** **Exclusions:** 0 companies were excluded; every record had a valid m1_users and m1_redemptions value. **Signal with the largest retention lift:** - The **“5+ unique givers”** signal (i.e., m1_users ≥ 5) yields a higher retention (55.1 % when considered alone) than the redemption‑only signal (≈52.6 %). Among the single‑signal groups, the givers‑only cohort’s 46 % retention is the higher of the two. **What this does and does not prove** - *What it does:* Shows a clear association – companies that exhibit both activation signals (many givers + early redemption) retain at a substantially higher rate (≈65 %) than those showing only one signal or none. It also indicates that the “givers‑only” group retains better than the “redemption‑only” group. - *What it does not prove:* This analysis is observational; it does **not** establish causality. Retention could be driven by unobserved factors (e.g., product maturity, market segment, sales effort) that correlate with both giver activity and redemption. The groups differ in size and composition, and selection bias (e.g., only companies still alive at 25 months are considered) limits generalizability. Further causal testing (e.g., random assignment, instrumental variables) would be required to confirm the activation hypothesis.
Billing ARR total: 617897.76 HubSpot ARR total: 587084.52 Variance (billing – hubspot): 30813.24 Arithmetic: - Total billing ARR = sum of MRR × 12 = 617,897.76 - Total hubspot ARR = 587,084.52 - Variance = 617,897.76 − 587,084.52 = 30,813.24 Decomposition (components sum to variance): - Missing records: 28,449.24 (C-21629AA4 absent in hubspot) - Status mismatch: 2,400.00 (C-0F7269D7 billing exceeds hubspot by 2,400.00) - Rounding: -36.00 (differences: C-0D66DF9E − 16.00, C-14D70CE0 − 20.00) - Other: 0.00 Sum check: 28,449.24 + 2,400.00 − 36.00 + 0.00 = 30,813.24 Mismatched accounts and suggested owner: - C-0D66DF9E – owner: TBD - C-14D70CE0 – owner: TBD - C-0F7269D7 – owner: TBD - C-21629AA4 – owner: missing (no hubspot record)
Four core KVMs (2026‑08 vs 2026‑07): KVM | Aug Value | Jul Value | Δ (abs) | %Δ (rel) | Direction ------------------------|-----------|-----------|---------|----------|--------- giving_rate | 0.6027 | 0.6025 | +0.0002 | +0.03 % | ↑ redemptions_per_user | 1.7302 | 1.7304 | -0.0002 | -0.01 % | ↓ one_to_one_engagement | 0.4473 | 0.4475 | -0.0002 | -0.05 % | ↓ pulse_engagement | 0.5086 | 0.6005 | -0.0919 | -15.3 % | ↓ The KVM with the largest relative move is **pulse_engagement**, and the shift is driven by the **enterprise** size_band, whose pulse engagement fell sharply in August.
Last completed month: 2026-08 Redemption count: 48 Spend: $3,536.00 Unique redeemers: 46 Redemptions per redeemer: 1.04 Provider mix as percent of spend: - Tremendous: 32.4% - custom: 29.9% - Snappy: 23.6% - TangoCard: 14.1% Top 5 countries by redemptions: - US: 31 - CA: 7 - GB: 2 - SG: 2 - AU: 2
Qualifying at‑risk accounts (health_score < 60, churn_save_eligible_amount > 0, renewal_date ≤ 2026‑12‑04) - C-0F6C0F34: $49,707 – Play: usage revival (signal: usage_trend_3m = growing) - C-0B827671: $25,365 – Play: executive touch (signal: champion_active = true and usage_trend_3m = declining) - C-0B360C78: $35,748 – Play: usage revival (signal: usage_trend_3m = growing) - C-0B0F1BAB: $5,494 – Play: commercial concession (signal: usage_trend_3m = flat) - C-0E9C27D1: $41,235 – Play: commercial concession (signal: usage_trend_3m = flat) - C-0CEF69FD: $32,621 – Play: usage revival (signal: usage_trend_3m = growing) - C-0D3278C7: $17,602 – Play: executive touch (signal: usage_trend_3m = declining and champion_active = true) - C-0BA71F12: $6,824 – Play: executive touch (signal: usage_trend_3m = declining and champion_active = true) Total amount at stake: **$214,596.00** Accounts that are at‑risk but do NOT qualify - C-0CA21961: health_score = 58 (at‑risk) but renewal_date = 2026‑12‑28 exceeds the 120‑day window from the snapshot (2026‑09‑05), so it fails rule R3. - C-0BC71BDD: health_score = 55 (at‑risk) but churn_save_eligible_amount = 0, failing rule R2. - C-0F6694C3: health_score = 43 (at‑risk) but churn_save_eligible_amount = 0, failing rule R2. - C-0BE96399: health_score = 54 (at‑risk) but churn_save_eligible_amount = 0, failing rule R2. - C-0F876796: health_score = 47 (at‑risk) but renewal_date = 2027‑02‑06 exceeds the 120‑day window, failing rule R3. - C-0FCCD2DF: health_score = 43 (at‑risk) but churn_save_eligible_amount = 0, failing rule R2. - C-10A56B0F: health_score = 54 (at‑risk) but churn_save_eligible_amount = 0, failing rule R2.
Hi Maria, Thanks for the update! I'm glad to hear the team is enjoying Bonusly and that usage is growing — our records show MAU increased from 88 in March 2026 to 126 in August 2026. Dana R. (VP People) has been asking about usage numbers, and I'd love to connect with her to explore how we might expand our partnership. If you're open to it, could you introduce us when you have a moment? No pressure at all — just wanted to say thanks for the great work. Best, Cole
**Complete items (with data field)** - Slack integration connected: 2026‑08‑12 (recorded in onboarding_account.csv) - Allowance set: 2026‑08‑13 (recorded in onboarding_account.csv) - Admins added: 2 admins (recorded in onboarding_account.csv) **Items not yet complete (no data field)** - HRIS integration: not connected (field blank in onboarding_account.csv) - First recognition given: no timestamp recorded (first_recognition_at empty) - First redemption: no timestamp recorded (first_redemption_at empty) **Early engagement signals (from onboarding_usage.csv)** - Active givers grew from **3** on 2026‑08‑11 to **15** on 2026‑09‑04 → 5× increase. - Average daily active givers over the period: **≈ 8.7** (sum = 201; 201 ÷ 23 ≈ 8.7). - Usage shows a consistent upward trend after the first week, indicating rising adoption. **Three key topics for the mid‑onboarding call** 1. **HRIS integration status** – confirm timeline for connection and any blockers. 2. **Recognition & redemption flow** – update on when the first recognition will be issued and how redemptions will be tracked. 3. **Usage trends & next steps** – discuss the rising active‑giver count, share the average ≈ 8.7 daily users, and outline upcoming milestones (e.g., target ≥ 15 active givers, full integration rollout).
90‑Day Renewal Risk Brief --- **C‑0B7D2C30** CSM: Dana Mercer ARR: 65901 Renewal date used: 2026‑09‑15 Seat utilization: 274 ÷ 476 = 57.6% (calculated) 3‑month usage trend: Declining (usage fell from 155 → 84) Risk rating: Medium Evidence: Seat utilization 57.6% with usage declining 46% over the last year, indicating weakening engagement. Disagreement: ChurnZero shows 2026‑09‑10, Chargebee shows 2026‑09‑15 → using Chargebee (multi‑year contracts are known to be wrong in ChurnZero). **C‑0BCDB8C2** CSM: Cole Ingram ARR: 54427 Renewal date used: 2026‑09‑18 Seat utilization: 232 ÷ 424 = 54.7% 3‑month usage trend: Declining (127 → 110) Risk rating: Medium Evidence: Seat utilization 54.7% and usage down 44% YoY, signaling potential churn risk. Disagreement: No discrepancy (dates match). **C‑0D2AB865** CSM: Elena Sinclair ARR: 38022 Renewal date used: 2026‑09‑22 Seat utilization: 250 ÷ 407 = 61.4% 3‑month usage trend: Declining (125 → 109) Risk rating: Medium Evidence: Seat utilization 61.4% but usage trending down 42% over the past year, raising concern. Disagreement: ChurnZero shows 2026‑09‑10, Chargebee shows 2026‑09‑22 → using Chargebee. **C‑0BBE3E60** CSM: Dana Mercer ARR: 30993 Renewal date used: 2026‑09‑26 Seat utilization: 74 ÷ 114 = 64.9% 3‑month usage trend: Declining (39 → 33) Risk rating: Medium Evidence: Seat utilization 64.9% yet usage fell 48% from peak, suggesting declining adoption. Disagreement: ChurnZero shows 2027‑09‑26, Chargebee shows 2026‑09‑26 → using Chargebee. **C‑0F5D2323** CSM: Cole Ingram ARR: 90647 Renewal date used: 2026‑09‑29 Seat utilization: 111 ÷ 390 = 28.5% 3‑month usage trend: Stable after early dip (20 → 21 → 18) Risk rating: High Evidence: Seat utilization only 28.5% with usage stagnant at low levels, indicating high churn likelihood. Disagreement: ChurnZero shows 2026‑09‑10, Chargebee shows 2026‑09‑29 → using Chargebee. **C‑0EC6999D** CSM: Elena Sinclair ARR: 79419 Renewal date used: 2026‑10‑03 Seat utilization: 31 ÷ 112 = 27.7% 3‑month usage trend: Declining (17 → 15) Risk rating: High Evidence: Seat utilization 27.7% with usage hovering low, pointing to high risk of cancellation. Disagreement: No discrepancy. **C‑0B20DB64** CSM: Dana Mercer ARR: 21770 Renewal date used: 2026‑10‑07 Seat utilization: 214 ÷ 378 = 56.6% 3‑month usage trend: Stable (294 → 298 → 294) Risk rating: Low Evidence: Seat utilization 56.6% and usage stable around 300, showing healthy engagement. **C‑0BBC4E7A** CSM: Cole Ingram ARR: 56374 Renewal date used: 2026‑10‑10 Seat utilization: 228 ÷ 337 = 67.7% 3‑month usage trend: Stable (142 → 139) Risk rating: Low Evidence: Seat utilization 67.7% with usage flat, reflecting solid adoption. **C‑0FD551AB** CSM: Elena Sinclair ARR: 48815 Renewal date used: 2026‑10‑14 Seat utilization: 210 ÷ 376 = 55.9% 3‑month usage trend: Stable (123 → 122 → 126) Risk rating: Low Evidence: Seat utilization 55.9% and usage stable, indicating consistent usage. **C‑0F9F8F13** CSM: Dana Mercer ARR: 46230 Renewal date used: 2026‑10‑18 Seat utilization: 199 ÷ 352 = 56.5% 3‑month usage trend: Stable (185 → 185 → 182) Risk rating: Low Evidence: Seat utilization 56.5% and usage steady, suggesting stable demand. **C‑0BC34584** CSM: Cole Ingram ARR: 16740 Renewal date used: 2026‑10‑22 Seat utilization: 327 ÷ 494 = 66.2% 3‑month usage trend: Stable (104 → 104 → 106) Risk rating: Low Evidence: Seat utilization 66.2% and usage stable, showing reliable engagement. **C‑0B7A7546** CSM: Elena Sinclair ARR: 35062 Renewal date used: 2026‑10‑25 Seat utilization: 182 ÷ 205 = 88.8% 3‑month usage trend: Stable (64 → 65 → 63) Risk rating: Low Evidence: Seat utilization 88.8% and usage slightly rising, indicating strong usage. **C‑0B369871** CSM: Dana Mercer ARR: 85128 Renewal date used: 2026‑10‑29 Seat utilization: 317 ÷ 422 = 75.1% 3‑month usage trend: Rising (326 → 330 → 333) Risk rating: Low Evidence: Seat utilization 75.1% and usage growing, reflecting expanding adoption. **C‑0B144C78** CSM: Cole Ingram ARR: 30899 Renewal date used: 2026‑11‑02 Seat utilization: 169 ÷ 224 = 75.4% 3‑month usage trend: Rising (101 → 101 → 106) Risk rating: Low Evidence: Seat utilization 75.4% and usage increasing, showing positive traction. **C‑0FC4DBB8** CSM: Elena Sinclair ARR: 94732 Renewal date used: 2026‑11‑05 Seat utilization: 356 ÷ 464 = 76.7% 3‑month usage trend: Rising (189 → 191 → 193) Risk rating: Low Evidence: Seat utilization 76.7% and usage rising, indicating growth. **C‑0D5BBE3A** CSM: Dana Mercer ARR: 39740 Renewal date used: 2026‑11‑09 Seat utilization: 85 ÷ 102 = 83.3% 3‑month usage trend: Rising (88 → 90 → 91) Risk rating: Low Evidence: Seat utilization 83.3% and usage increasing, reflecting strong performance. **C‑0FB9D5AF** CSM: Cole Ingram ARR: 63158 Renewal date used: 2026‑11‑13 Seat utilization: 144 ÷ 199 = 72.4% 3‑month usage trend: Rising (173 → 173 → 176) Risk rating: Low Evidence: Seat utilization 72.4% and usage rising, indicating growth. **C‑0B344485** CSM: Elena Sinclair ARR: 64384 Renewal date used: 2026‑11‑16 Seat utilization: 224 ÷ 287 = 78.0% 3‑month usage trend: Rising (238 → 240 → 244) Risk rating: Low Evidence: Seat utilization 78.0% and usage increasing, showing expansion. **C‑0CB2C1B4** CSM: Dana Mercer ARR: 40628 Renewal date used: 2026‑11‑20 Seat utilization: 386 ÷ 473 = 81.6% 3‑month usage trend: Rising (47 → 48 → 49) Risk rating: Low Evidence: Seat utilization 81.6% and usage steady, reflecting stable usage. **C‑22170CA1** CSM: Cole Ingram ARR: 45646 Renewal date used: 2026‑11‑24 Seat utilization: 251 ÷ 294 = 85.3% 3‑month usage trend: Stable (148 → 148 → 146) Risk rating: Low Evidence: Seat utilization 85.3% and usage stable, indicating robust engagement. --- **Summary** Total ARR renewing: 65901 + 54427 + 38022 + 30993 + 90647 + 79419 + 21770 + 56374 + 48815 + 46230 + 16740 + 35062 + 85128 + 30899 + 94732 + 39740 + 63158 + 64384 + 40628 + 45646 = **1,048,715** ARR at risk (high‑risk accounts): 90,647 + 79,419 = **170,066** All disagreements flagged above were resolved by trusting the Chargebee renewal date, per the rule that multi‑year contracts are known to be incorrect in ChurnZero.
Theme: HRISProvisioning Count: 7 Share: 0.0886 DistinctAccounts: 3 ARRAffected: 276000.00 TicketIds: IC-460059, IC-460055 Recommendation: Investigate integration issue Theme: CheckoutRedemption Count: 18 Share: 0.2278 DistinctAccounts: 7 ARRAffected: 177300.00 TicketIds: IC-460025, IC-460030 Recommendation: Investigate integration issue Theme: PointsNotPosted Count: 20 Share: 0.2532 DistinctAccounts: 9 ARRAffected: 70200.00 TicketIds: IC-460004, IC-460016 Recommendation: Investigate integration issue Theme: SlackIntegration Count: 12 Share: 0.1519 DistinctAccounts: 4 ARRAffected: 54100.00 TicketIds: IC-460041, IC-460047 Recommendation: Investigate integration issue
C-11C31562 – matched size_band (Mid-Market), use_case (employee_recognition), region (NA-West) C-64171065 – matched industry (Technology), size_band (Mid-Market), use_case (employee_recognition) C-CD4829A7 – matched industry (Technology), size_band (Mid-Market)
Channel performance (trailing 6 months) --- **paid_search** - spend: $36,000 (6 × $6,000) - SQMs: 28 (all rows have a non‑empty sqm_date) - SQOs: 12 (non‑empty sqo_date) - cost per SQM: 36,000 ÷ 28 = $1,285.71 - cost per SQO: 36,000 ÷ 12 = $3,000.00 - SQM‑to‑SQO rate: 12 ÷ 28 = 0.43 (43 %) - pipeline amount: $480,000 (12 × $40,000) - pipeline per dollar: 480,000 ÷ 36,000 = 13.33 **linkedin_ads** - spend: $24,000 (6 × $4,000) - SQMs: 11 (all rows have a non‑empty sqm_date) - SQOs: 5 (non‑empty sqo_date) - cost per SQM: 24,000 ÷ 11 = $2,181.82 - cost per SQO: 24,000 ÷ 5 = $4,800.00 - SQM‑to‑SQO rate: 5 ÷ 11 = 0.45 (45 %) - pipeline amount: $60,000 (5 × $12,000) - pipeline per dollar: 60,000 ÷ 24,000 = 2.5 - flagged row: **CT‑000041** – sqo_date = 2026‑06‑09 precedes sqm_date = 2026‑06‑14 (order anomaly) **paid_social** - spend: $18,000 - SQMs: 0 → cost per SQM **undefined** (no SQM data) - SQOs: 0 → cost per SQO **undefined** - SQM‑to‑SQO rate: undefined - pipeline amount: $0 (no contacts) - pipeline per dollar: undefined **webinars** - spend: $9,000 (6 × $1,500) - SQMs: 8 (all rows have a non‑empty sqm_date) - SQOs: 2 (non‑empty sqo_date) - cost per SQM: 9,000 ÷ 8 = $1,125.00 - cost per SQO: 9,000 ÷ 2 = $4,500.00 - SQM‑to‑SQO rate: 2 ÷ 8 = 0.25 (25 %) - pipeline amount: $24,000 (2 × $12,000) - pipeline per dollar: 24,000 ÷ 9,000 ≈ 2.67 **organic_search** - volume (contacts): 19 - SQO rate: 9 ÷ 19 = 0.47 (47 %) - pipeline amount: $81,000 (9 × $9,000) --- **Reallocation recommendation** - Reduce or pause **paid_social** spend ($18 k) because it yields no measurable SQM/SQO activity and therefore no pipeline; re‑allocate those funds to **paid_search** (pipeline per dollar = 13.33) or **webinars** (pipeline per dollar ≈ 2.67). - Consider modest increase for **linkedin_ads** (pipeline per dollar = 2.5) only if the flagged order issue is resolved, as its conversion rate (45 %) is comparable to paid_search but with a lower pipeline efficiency. **Confidence** (based on sample size) - **paid_search**: high confidence – 28 SQMs and 12 SQOs provide a solid basis. - **linkedin_ads**: medium confidence – 11 SQMs and 5 SQOs, but the order anomaly flags data quality concerns. - **webinars**: low‑medium confidence – only 8 SQMs and 2 SQOs; results are more volatile. - **paid_social**: none – no measurable activity, so confidence in any performance claim is nil. Overall, shifting budget from paid_social toward paid_search is the most data‑driven adjustment given the current sample sizes.
One-line positioning: Points‑based recognition platform for mid‑market customers. (snippet_id=S02) Pricing: $7 per user/month (annual) as of 2026‑08‑12 (newer source); previously listed at $5 per user/month (annual) as of 2026‑03‑05 (older source). (snippet_id=S17, snippet_id=S03) Where they win: EU data residency and multi‑language support; fast Slack integration out of the box. (snippet_id=S12, snippet_id=S04) Where we win: Advanced analytics and bulk‑editing capabilities; richer reporting versus their basic dashboards. (snippet_id=S24, snippet_id=S07) Objections and responses: Prospects cite limited analytics, basic reporting dashboards, lack of SCIM provisioning, no bulk editing, CSV‑only exports; we respond with deep analytics, customizable dashboards, SCIM provisioning, bulk editing, and flexible export options. (snippet_id=S02, snippet_id=S07, snippet_id=S10, snippet_id=S24, snippet_id=S20) Recent changes: Launch of Rivally Pulse add‑on (2026‑06‑02); pricing updated to $7 per user/month (2026‑08‑12); Dublin office opened (2026‑05‑11). (snippet_id=S23, snippet_id=S17, snippet_id=S15) 12‑month win/loss record: 13 wins, 6 losses (19 deals) in the period from 2025‑09 to 2026‑08. (snippet_id=S01)
**Sequences – key metrics** | Sequence | Step | Sent | Open % | Reply % | Meetings % | Weakest step | |----------|------|------|-------|--------|-----------|--------------| | New Logo Nurture | 1 | 500 | 42 % | 8.4 % | 2.4 % | – | | | 2 | 458 | 28 % | 6.6 % | 2.0 % | – | | | 3 | 428 | 28 % | 4.2 % | 1.4 % | – | | Expansion Nurture | 1 | 300 | 43 % | 7.3 % | 1.7 % | – | | | 2 | 300 | **113 %** (error) | 8.3 % | 1.3 % | – | | | 3 | 275 | 35 % | 4.4 % | 2.2 % | – | | Cold Outbound ‑ HR Leaders | 1 | 600 | 40 % | **0.83 %** | 0.83 % | Step 1 | | | 2 | 595 | 30 % | **0.34 %** | 0.34 % | Step 2 | | | 3 | 590 | 22 % | **0.17 %** | 0.17 % | Step 3 | | Cold Outbound ‑ People Ops | 1 | 400 | 38 % | 3.5 % | 3.5 % | – | | | 2 | 386 | 28 % | 2.3 % | 2.3 % | – | | | 3 | 377 | 21 % | **1.6 %** | 1.6 % | Step 3 | **Tracking error** – Expansion Nurture Step 2 shows *opened (340) > sent (300)*, indicating a measurement fault. **Audience overlap** – No contact_key appears in more than one sequence; all mappings are unique. **Failure mode (< 2 % reply)** – - Cold Outbound ‑ HR Leaders: all three steps fall below 2 % reply (0.83 %, 0.34 %, 0.17 %). - Cold Outbound ‑ People Ops Step 3: 1.6 % reply. **Recommended single change per weak sequence** 1. **Cold Outbound ‑ HR Leaders – Step 3** – Add a hyper‑personalized value hook (e.g., reference a recent company Initiative) and a clear, low‑friction CTA. This addresses the poorest reply rate (0.17 %) and should be the first fix. 2. **Cold Outbound ‑ People Ops – Step 3** – Introduce a brief, relevant case‑study snippet that aligns with the prospect’s role, increasing relevance and reply likelihood. **Prioritization** – Fix Cold Outbound ‑ HR Leaders Step 3 first, as it records the lowest reply rate (0.17 %) and represents the biggest drop‑off in the outreach funnel.
SQMs: QTD actual 230, target 300, delta ‑70, pace ahead (expected at this point ≈ 300 × 66/92 ≈ 215.2; actual 230 > 215.2) SQOs: QTD actual 84, target 120, delta ‑36, pace behind (expected ≈ 120 × 66/92 ≈ 86.1; actual 84 < 86.1) DS2s: QTD actual 40, target 75, delta ‑35, pace behind (expected ≈ 75 × 66/92 ≈ 53.8; actual 40 < 53.8) closed_lost_mia_rate: QTD actual 5 (count only), target 0.1 (rate); rate cannot be derived from the count, so delta and pace are not applicable same_quarter_closes: QTD actual 10, target 20, delta ‑10, pace behind (expected ≈ 20 × 66/92 ≈ 14.3; actual 10 < 14.3) active_pipeline: QTD actual 3000000, target 4000000, delta ‑1000000, pace ahead (expected ≈ 4000000 × 66/92 ≈ 2869560; actual 3000000 > 2869560) This week SQMs and active‑pipeline coverage continued to move ahead of schedule, while SQOs, DS2s, and same‑quarter closes remain behind pace. The closed‑lost MIA rate cannot be evaluated with the data provided. Overall, a few metrics are on track, but accelerating the mid‑funnel metrics is needed to meet the quarterly targets.
Q3 forecast stands at $115,977, driven by $44,729 in committed deals, $203,565 of best-case pipeline, with top deal $38,935, but $227,575 of later-quarter deals are excluded. However data quality issues, owners blank on 85 of 86 deals and missing why-buys, prevent reliable roll-up, and 32 deals close in October, including a $13,770 committed and $28,240 best-case. Proceed with caution and prioritize qualifying these pipeline deals before quarter close.
Subject: Quick follow‑up on our July 28 demo Hi Alex, I hope you’re well. Following our July 28 demo with the People team, where the automated milestone awards and points catalog sparked interest, could we set up a short call next week to discuss pricing for 150 seats? I’m happy to share a revised proposal at your convenience. Looking forward to your thoughts. Best, [Your Name]
Marketing logged 46 SQMs this week, hitting roughly 88 % of the 52‑SQM target (46 ÷ 52 ≈ 0.88). The webinar channel alone drove 18 of those SQMs, and paid‑search cost per SQM stayed steady at **$150**. Sales converted **14 SQOs** and set **9 DS2 meetings**. New pipeline generated reached **$310,000**, which works out to about **$103k of pipeline per same‑quarter close** (310,000 ÷ 3 ≈ 103,333). The team closed **3** deals, reinforcing forward momentum. Customer Success saved **2 renewals** and lifted the team NPS to **61**. Looking ahead, there are **3 open red‑flag accounts** that will need attention next week, but the NPS trajectory remains positive. PLG added **412 new signups**, with activation at **31 %**. Notably, **38 companies** reached the “aha” moment of 10 recognition gives, underscoring strong early‑product engagement and product‑market fit.
Apex Rewards Co (active): 2 sourced opps, $275,000 HRCloud Partners (active): 1 sourced opp, $140,000 CultureBridge (active): 2 sourced opps, $135,000 WorkWell Group (active): 0 sourced opps, $0 Recogniq (quiet): 0, $0 KudosWave (quiet): 0, $0 PeopleFirst Advisors (quiet): 0, $0 TotalPerk (quiet): 0, $0
**Q1:** Yes. Customer data is encrypted at rest using AES‑256. 【P1】 **Q2:** Yes. Data in transit is encrypted with TLS 1.2 or higher. 【P1】 **Q3:** Yes. Bonusly maintains a SOC 2 Type II report (renewed annually) and can provide a copy to prospects under NDA. 【P2】 **Q4:** Customer data is retained for the term of the contract plus **90 days** after which it is deleted from production systems. 【P3】 **Q5:** The subprocessor list is published at **trust.bonusly.com**, and customers receive **30‑day notice** before any 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**, and backups are retained for **35 days**. 【P6】 **Q8:** Unanswerable. The provided excerpts do not mention ISO 27001 certification. This information would be found in a certification document or policy excerpt that states ISO 27001 compliance. **Q9:** Unanswerable. The provided excerpts do not include any uptime SLA percentage. This would be detailed in an SLA agreement or service-level policy document. **Q10:** Unanswerable. The provided excerpts do not address HIPAA Business Associate Agreement execution. This would be covered in a legal agreement or compliance policy document.
analysis-validator & closed-lost-analysis – Overlap in ALWAYS‑trigger phrases (loss analysis, why did we lose, loss reasons, loss breakdown). SEVERITY: WARNING – ACTION: REVIEW – Proposal: Align trigger definitions to consolidate duplicate loss‑analysis triggers. pipeline-intelligence-report & sales-forecast – Overlap in ALWAYS‑trigger phrases (pipeline report, pipeline update, “what’s the pipeline look like”). SEVERITY: WARNING – ACTION: REVIEW – Proposal: Refine trigger lists to ensure each skill’s scope is distinct. pipeline-intelligence-report & stale-pipeline-report – Overlap in ALWAYS‑trigger phrases (pipeline report, pipeline update). SEVERITY: INFO – ACTION: REVIEW – Proposal: Differentiate trigger wording to avoid redundancy. pipeline-intelligence-report & weekly-pipeline-report – Overlap in ALWAYS‑trigger phrases (pipeline report, pipeline summary, pipeline update). SEVERITY: INFO – ACTION: REVIEW – Proposal: Adjust triggers so each skill’s activation criteria are mutually exclusive. Circular delegation chain – None detected. SEVERITY: INFO – ACTION: NONE – Proposal: No action required. Dangling delegation target – analysis-validator references specialist skills bonusly-data-questions, bonusly-product-questions, bonusly-business-reporting-questions, bonusly-rewards-questions, bonusly-ppp-questions, bonusly-feature-flag-questions, bonusly-datadog-questions, none of which appear in the skill_manifest.csv. SEVERITY: CRITICAL – ACTION: DELETE_SKILL – Proposal: Remove the dangling references from analysis-validator or add the missing specialist skills to the manifest. Version conflict – None detected. SEVERITY: INFO – ACTION: NONE – Proposal: No action required. Manifest descriptions exceeding 1,024 characters – 0. SEVERITY: INFO – ACTION: NONE – Proposal: No action required. Hardcoded page ids, dates, or person names in skill bodies – model-selection contains hardcoded date “2026‑05‑19”; pipeline-intelligence-report contains hardcoded date “May 2026”. SEVERITY: INFO – ACTION: REVIEW – Proposal: Replace hardcoded dates with dynamic references or external constants. Manifest drift in BOTH directions – specialist skills referenced by analysis-validator have no manifest rows; no manifest rows are missing a corresponding skill file. SEVERITY: WARNING – ACTION: REVIEW – Proposal: Update the manifest to include the missing specialist skill definitions or remove the references from analysis-validator.
1. Disable auto_recognition_enqueue feature flag - Command: `bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'` - Ran by: Farid Osman (M04) - Success verified: not explicit in thread – needs confirmation - Rollback command: `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'` (M04) 2. Clear dead set manually - Action: cleared out the dead set (manual console clear) - Ran by: Elena Sinclair (M05) - Success verified: not stated – needs confirmation - Rollback: not specified in thread 3. Scale reward‑worker deployment to 6 replicas - Command: `kubectl scale deployment/reward-worker --replicas=6` - Ran by: Bryce Harmon (M06) - Success verified: queue depth down to 9,400 and falling ~1,200/min (M07) - Rollback command: `kubectl scale deployment/reward-worker --replicas=3` (M06) 4. Verify queue depth is zero and error rate baseline - Command: `bundle exec rake sidekiq:queue_depth` - Ran by: Cole Ingram (M08) - Success verified: returns 0; error rate back to baseline (M08) 5. Re‑enable auto_recognition_enqueue feature flag - Command: `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'` - Ran by: Bryce Harmon (M09) - Success verified: 40 new jobs processed cleanly in the next 3 minutes (M09) - Rollback: not provided in thread 6. Scale reward‑worker deployment back to 3 replicas - Command: `kubectl scale deployment/reward-worker --replicas=3` - Ran by: Bryce Harmon (M10) - Success verified: queue stable at 0 (M10) - Rollback: not applicable (original replica count was 3)
**First error** - Timestamp: **2026-09-03T14:01:12Z** - Service: **reward‑service** - Message: `Redis::TimeoutError: Connection to Redis at redis-primary:6379 timed out after 5s` **Cascade in order** 1. **2026-09-03T14:01:12Z** – Redis timeout in **reward‑service** (triggers failure of the `RewardGiveJob`). 2. **2026-09-03T14:01:40Z** – **sidekiq** ERROR `RewardGiveJob failed: Redis::TimeoutError; retrying in 60s`. 3. **2026-09-03T14:02:28Z** – **sidekiq** WARN “Queue reward depth above 10,000”. 4. **2026-09-03T14:03:05Z** – **api‑gateway** ERROR `502 upstream timeout calling reward-service /gives`. 5. **2026-09-03T14:03:30Z** – **web‑app** ERROR “Give form submission failed: upstream 502 from api‑gateway”. 6. Repeated **api‑gateway** 502 timeouts and **sidekiq** retry messages continue until the Redis outage clears. 7. **2026-09-03T14:22:10Z** – **reward‑service** INFO “Redis connection restored; resuming job processing”. **Service & job involved in the first error** - **Service:** `reward-service` - **Job:** `RewardGiveJob` (the job that enqueues reward give operations). **Datadog query that would confirm the first error** A log‑based query such as: ``` service:reward-service level:ERROR "Redis::TimeoutError" ``` or an equivalent count query to surface the first occurrence of that error string. **What the logs do not show** - The underlying network or infrastructure cause of the Redis timeout (e.g., partition, CPU saturation). - Pre‑failure Redis latency or health metrics that could explain why the timeout occurred when it did. - The exact duration of the outage before the first error is logged (the logs only record the timeout event, not a prior “connection degraded” signal). - Any proactive circuit‑breaker or fallback action taken by the system before the error cascade. - Detailed queue depth trends prior to the **14:02:28Z** warning; only the spike is captured, not the build‑up. - Granular recovery details beyond the “Redis connection restored” INFO line (e.g., which processes resumed, how many pending jobs were cleared). These gaps limit visibility into the root cause and the full impact timeline.
recognition_streaks_v2: controls the streak‑tracking feature; enabled for companies in **segment:beta_companies**; targeting rule **segment:beta_companies**; **42** companies have it on (export count). points_budget_guardrails: controls the points‑budget guardrails feature; enabled for **all_companies**; targeting rule **all_companies**; **220** companies have it on (export count). slack_dm_nudges: controls Slack DM nudges; enabled for companies in **segment:region_na**; targeting rule **segment:region_na**; **87** companies have it on (export count). redeem_flow_redesign: controls the redesigned redeem flow; currently **off**; targeting rule **targeted_list**; **12** companies are targeted when turned on (export count). analytics_dashboard_v3: controls the analytics dashboard v3; enabled for companies in **segment:tier_three**; targeting rule **segment:tier_three**; **65** companies have it on (export count). ms_teams_app_v2: controls installation of the Teams app v2; currently **off**; targeting rule **targeted_list**; **9** companies are targeted when turned on (export count). legacy_give_modal: controls the legacy give modal; currently **off**; targeting rule **segment:legacy_plan**; **14** companies are targeted when turned on; **no code reference** in the excerpt. paused_offboard_cleanup: controls the off‑board cleanup process; currently **off**; targeting rule **none** (empty); **0** companies targeted; **no code reference** in the excerpt. survey_boosters_q3: controls the Q3 survey boosters; enabled for companies in **segment:legacy_plan**; targeting rule **segment:legacy_plan**; **7** companies have it on; **no code reference** in the excerpt. **Arithmetic check:** sum of all `company_count` values in the export = 42 + 220 + 87 + 12 + 65 + 9 + 14 + 0 + 7 = **456**, matching the exported total.
NDA-1 – GREEN – Clause 1: “Mutual confidentiality. Each party may disclose Confidential Information to the other for the purpose of evaluating a business relationship.” NDA-2 – YELLOW – Clause 4: “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 – Clause 2: “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.”