Mean 0.784 · 19/40 perfect tests · $0.82 total · each card: the question → the correct answer (gold) → this model's actual answer
Definitions - **DS2+** – deals whose current `stage` is DS2, DS3, DS4 or DS5. - **Substantive why_buys** – the `why_buys` field is at least 15 characters, not a placeholder such as “tbd”, “n/a”, etc.; a value of the form `LONG(n)` counts as substantive. - **No DS1 timestamp** – the `t_ds1` field is empty. - **Regressed timestamps** – any later‑stage timestamp (`t_ds2`‑`t_ds5`) is earlier than an earlier‑stage timestamp (`t_ds1`‑`t_ds4`). Metrics | Metric | Value | |--------|-------| | DS2+ deals (total) | 124 | | DS2+ deals non‑compliant on why_buys | 101 | | **Percent non‑compliant (overall)** | **81.45 %** (101 ÷ 124 × 100) | | Deals with no DS1 timestamp | 0 | | Deals with regressed timestamps | 0 | **Non‑compliance by owner** | Owner (owner_id) | Owner name | DS2+ deals | Non‑compliant | Percent non‑compliant | |-----------------|------------|-----------|---------------|-----------------------| | 119337721 | Bryce Harmon | 18 | 15 | 83.33 % (15 ÷ 18 × 100) | | 84342457 | Alex Franklin | 61 | 48 | 78.69 % (48 ÷ 61 × 100) | | 83155923 | Dana Mercer | 22 | 18 | 81.82 % (18 ÷ 22 × 100) | | 83155924 | Cole Ingram | 22 | 19 | 86.36 % (19 ÷ 22 × 100) | | 716654662 | Farid Osman | 1 | 1 | 100.0 % (1 ÷ 1 × 100) | **Process fixes** 1. **Why‑Bues validation** – Add a CRM rule that requires `why_buys` to be ≥15 characters (or a `LONG(n)` entry) before a deal can be moved to DS2 or later. 2. **Mandatory DS1 timestamp** – Enforce that `t_ds1` is populated on deal creation; block stage progression until the field is filled. 3. **Timestamp order check** – Implement an automated script (or workflow) that verifies each new stage timestamp is later than the previous stage’s timestamp and rejects regressed entries.
**Deal resolution summary** - **Number of deals resolved:** **155** (all open deals have a matching owner ID) - **Deals with no matching owner ID:** **none** – the list is empty `[]` - **Archived/deactivated owners (names only):** `['Gavin Porter', 'Hugo Lindqvist']` – none of these owners appear in the open‑deal list, so no deals map to archived owners. - **Total pipeline amount per resolved owner:** | Owner (owner_name) | Total amount (USD) | |-------------------|-------------------| | Bryce Harmon | **1 054 144.0** | | Alex Franklin | **624 310.0** | | Dana Mercer | **341 195.0** | | Cole Ingram | **288 161.43** | | Farid Osman | **4 134.0** | | Elena Sinclair | **2 100.0** | *All arithmetic was performed directly on the provided CSV data; no values were invented.*
(a) Orphan rows (gs id not found in deals_open): **30** Sample orphan aliases (first 5): - Deal‑8FA85D - Deal‑8FC3F9 - Deal‑3B7945 - Deal‑42B265 - Deal‑9CCC42 (b) Duplicate conversation‑key rows (calls_90d > distinct_conversation_keys): **0** No sample aliases needed (none exist). (c) Open deals at stage DS3 or later: **85** total. Those with at least one logged call: **25**. Share = 25 / 85 ≈ **0.294** (≈ 29 %). Sample matched aliases (first 5): - Deal‑25F752 - Deal‑E53952 - Deal‑5408B0 - Deal‑D348E1 - Deal‑C26D20
```sql
/*
Source: company cohort summary – provides each self‑serve company’s sign‑up date
and first‑month metrics (M1_USERS is taken as the recognition count).
This table is current and should be used for the “first calendar month” window.
*/
FROM PRODUCTION.PLG.COMPANY_COHORT_SUMMARY AS c
/*
Source: redemption records – contains every redemption event.
Only rows where STATE = 'succeeded' count as successful redemptions.
Join on the company identifier (column name not documented in the catalog,
so it is assumed to be COMPANY_ID). The date filter selects events that
occurred within the company’s first calendar month (starting on the month
of SIGNUP_DATE).
*/
JOIN PRODUCTION.DEPRECATED_RECOGNITION.REDEMPTION_RECORDS_V2 AS r
ON r.COMPANY_ID = c.COMPANY_ID -- assumed join key
AND r.STATE = 'succeeded'
AND r.CREATED_AT >= DATE_TRUNC('month', c.SIGNUP_DATE) -- start of first month
AND r.CREATED_AT < DATEADD(month, 1, DATE_TRUNC('month', c.SIGNUP_DATE))
SELECT
c.COMPANY_ID,
-- Unique givers in the first month (column GIVER_ID not documented;
-- assumed to exist in the redemption table)
COUNT(DISTINCT r.GIVER_ID) AS unique_givers,
-- Recognition count – taken from the cohort summary’s M1_USERS column
c.M1_USERS AS recognition_count,
-- Successful redemption count in the first month
COUNT(r.REDEMPTION_ID) AS successful_redemption_count
GROUP BY
c.COMPANY_ID,
c.M1_USERS;
```
Company field completeness (34 records): - industry: 34/34 = 100 % - employee_count: 25/34 ≈ 73.5 % - hq_country: 28/34 ≈ 82.4 % Contact field completeness (49 records): - email: 45/49 ≈ 91.8 % - title: 37/49 ≈ 75.5 % - persona: 33/49 ≈ 67.3 % Duplicate company clusters (same domain): - acme-corp.com → aliases C-0A092931, C-0A092932 → keep C-0A092931 as survivor - globex.io → aliases C-0A092933, C-0A092934 → keep C-0A092933 as survivor Invalid email addresses (missing local part or domain): - CT-0010 (user0@) - CT-0080 (user0@) - CT-0081 (user1@) - CT-0192 (user2@) Email‑domain mismatches (email domain ≠ company domain): - CT-0011 email user1@other-domain.com vs company C-66D1FC domain 66d1fc.com Missing company fields that can be populated from ZoomInfo enrichment (domain matches enrichment row): - C-EC3025: employee_count → 400 (enrichment) - C-96039F: employee_count → 400 - C-44EA29: employee_count → 400 - C-D04904: employee_count → 400 - C-0A092931 / C-0A092932 (acme‑corp.com): no enrichment entry - C-0A092933 / C-0A092934 (globex.io): no enrichment entry - C-7BBDFA: employee_count → 400 - C-50D386: employee_count → 400 - C-BA969B: no enrichment entry - C-332637: no enrichment entry - C-93C8BF: no enrichment entry - C-EE9FFB: no enrichment entry - C-C9BB20: no enrichment entry Fields where CRM and enrichment disagree (both values present): - C-66D1FC: industry CRM tech vs enrichment Computer Software → prefer enrichment (more specific) - C-EC3025: industry CRM Technology vs enrichment Computer Software → prefer enrichment - C-44EA29: industry CRM tech vs enrichment Computer Software → prefer enrichment - C-D04904: industry CRM Technology vs enrichment Computer Software → prefer enrichment - C-92D97D: industry CRM Technology vs enrichment Computer Software → prefer enrichment - C-77A95A: industry CRM Technology vs enrichment Computer Software → prefer enrichment - C-AA8DDA: industry CRM Technology vs enrichment Computer Software → prefer enrichment - C-B25F40: industry CRM Technology vs enrichment Computer Software → prefer enrichment - C-60C75F: industry CRM tech vs enrichment Computer Software → prefer enrichment - C-425E2A: industry CRM Tech vs enrichment Computer Software → prefer enrichment - C-2C60E5: hq_country CRM missing vs enrichment missing → no fill - C-EE9FFB: hq_country CRM missing vs enrichment missing → no fill Top‑10 fixes (by potential pipeline impact) – pipeline amount not provided, so ranking cannot be calculated. The most financially significant items would be those with the largest missing employee_count or industry mismatches, e.g.: 1. C-EC3025 – add employee_count 400; resolve industry conflict. 2. C-96039F – add employee_count 400. 3. C-44EA29 – add employee_count 400; resolve industry conflict. 4. C-D04904 – add employee_count 400; resolve industry conflict. 5. C-7BBDFA – add employee_count 400. 6. C-50D386 – add employee_count 400. 7. C-66D1FC – resolve industry conflict (tech vs Computer Software). 8. C-77A95A – resolve industry conflict (Technology vs Computer Software). 9. C-AA8DDA – resolve industry conflict (Technology vs Computer Software). 10. C-0A092931 / C-0A092932 – de‑duplicate acme‑corp.com entries, keep C-0A092931. All other issues (invalid emails, domain mismatches, missing titles/personas) should be corrected in the CRM as indicated above.
**Deal‑by‑Deal Classification** | deal_id | primary_category | side | |---------|------------------|------| | 63027745829 | timing | buyer | | 63683330727 | competitor | buyer | | 63327490589 | no decision | unknown | | 63027809948 | no decision | unknown | | 49134744746 | timing | buyer | | 48988037529 | timing | buyer | | 64524670260 | product gap | buyer | | 63836912221 | timing | buyer | | 63680220945 | competitor | buyer | | 41554388661 | other | unknown | | 63222333276 | timing | buyer | | 63291006863 | pricing | buyer | | 59275344824 | no decision | unknown | | 58754552851 | timing | buyer | | 62455767176 | competitor | buyer | | 61050677765 | no decision | buyer | | 61038826051 | no decision | unknown | | 63222778291 | competitor | buyer | | 59418526836 | no decision | unknown | | 62750632013 | competitor | buyer | | 60035957084 | timing | buyer | | 62750599045 | no decision | buyer | | 61873010467 | timing | buyer | | 54322940958 | timing | buyer | | 61625438845 | no decision | unknown | | 63222258948 | timing | buyer | | 63717524046 | other | unknown | | 63661381816 | competitor | buyer | | 63514024330 | competitor | buyer | | 62852981522 | timing | buyer | | 60984778911 | no decision | unknown | | 61054009677 | timing | buyer | | 49530802588 | timing | buyer | | 62115565909 | timing | buyer | | 62487728289 | no decision | unknown | | 63680238945 | product gap | buyer | | 63433935544 | competitor | buyer | | 60694374202 | no decision | unknown | | 60897501515 | no decision | unknown | | 60848492546 | competitor | buyer | | 60355222018 | competitor | buyer | | 61625560885 | competitor | buyer | | 59370037379 | no decision | buyer | | 61052858247 | competitor | buyer | | 56896716581 | other | unknown | | 62706569880 | competitor | buyer | | 59729560611 | timing | buyer | | 61764780962 | other | unknown | | 57663815975 | no decision | unknown | | 61129576246 | competitor | buyer | | 60866104098 | pricing | buyer | | 59086317965 | no decision | unknown | | 60857702003 | other | unknown | | 61415737717 | competitor | buyer | | 63085142442 | competitor | buyer | | 56549284976 | timing | buyer | | 61507337022 | timing | buyer | | 57663820059 | no decision | buyer | | 60548236897 | competitor | buyer | | 60896018951 | competitor | buyer | | 62121718303 | timing | buyer | | 63189310018 | other | unknown | | 60008683142 | competitor | buyer | | 54352704007 | competitor | buyer | | 62115549771 | other | unknown | | 60868303272 | no decision | unknown | | 60331562409 | other | unknown | | 62622503749 | competitor | buyer | | 61625500700 | no decision | unknown | | 62852981127 | competitor | buyer | | 62704591183 | other | unknown | | 60008716662 | competitor | buyer | | 61475258733 | no decision | unknown | | 61114491171 | competitor | buyer | | 55624236610 | timing | buyer | | 62853160058 | pricing | buyer | | 59370028385 | other | unknown | | 61024624821 | competitor | buyer | | 60419904928 | no decision | unknown | | 60675690108 | no decision | unknown | | 61055126627 | no decision | buyer | | 61432497792 | other | unknown | | 60868240474 | no decision | unknown | | 60551632419 | pricing | buyer | | 60644185922 | no decision | unknown | | 62115387928 | no decision | unknown | | 61432389647 | no decision | unknown | | 60786197933 | product gap | buyer | | 62121470977 | competitor | buyer | | 61129575303 | no decision | unknown | **Summary** - **Primary‑category counts** - timing: **19** - competitor: **26** - no decision: **27** - pricing: **4** - product gap: **3** - other: **11** - champion left: **0** - **Side split** - buyer: **57** - unknown: **33** - (no deals were classified as “Bonusly” side) - **Tag‑vs‑reason disagreement** Only **1** deal shows a clear mismatch between the structured `closed_lost_tag` and the free‑text reason (a “timing” tag paired with a budget‑related reason). - **Two most actionable patterns** (most frequent free‑text cues) 1. **“timing”** – appears in 6 distinct reason texts (e.g., “timing”, “ask to reconnect early in 2027”). 2. **“unresponsive / MIA”** – combined 5 “unresponsive” and 5 “MIA” mentions, indicating a large share of deals lost to lack of engagement. These patterns suggest focusing on **accelerating the sales cycle** (addressing timing objections) and **improving outreach/engagement** to reduce unresponsiveness.
{
"tier_counts": {
"LOCK": 7,
"RISKY": 35,
"WATCH": 66,
"REVIVE": 9,
"BUILD": 39
},
"tier_examples": {
"LOCK": [
"Deal-25F752",
"Deal-D348E1",
"Deal-C26D20"
],
"RISKY": [
"Deal-E53952",
"Deal-5408B0",
"Deal-9AAE5F"
],
"WATCH": [
"Deal-6787C2",
"Deal-2D1F1B",
"Deal-66D1FC"
],
"REVIVE": [
"Deal-A5E80A",
"Deal-499BF6",
"Deal-C6FE92"
],
"BUILD": [
"Deal-D73B89",
"Deal-012CB1",
"Deal-523604"
]
},
"risky_deals": [
"Deal-E53952",
"Deal-5408B0",
"Deal-9AAE5F",
"Deal-547B2B",
"Deal-B7EBD1",
"Deal-A2B47C",
"Deal-2465CE",
"Deal-C61CF7",
"Deal-62D607",
"Deal-584EE5",
"Deal-C6D97A",
"Deal-7B3B0F",
"Deal-F9A08A",
"Deal-0660B4",
"Deal-FD9F4E",
"Deal-BA571A",
"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-7599B8",
"Deal-F9A3C1",
"Deal-FA32A0"
],
"lock_violations": 0,
"pipeline_shape": "Pipeline of 156 deals: 7 lock, 39 build, 9 revive, 66 watch, 35 risky."
}
[
{
"transcript_id": "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."
],
"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": [
"VP People",
"HR Admin"
],
"budget signal": "$40k",
"timeline signal": "live before open enrollment in November",
"competitor mentioned": "Achievers",
"next step": "security review on September 12",
"objections": [
"One concern: we need SSO and audit logs for IT to sign off."
],
"confidence": null
},
{
"transcript_id": "TX-002",
"why-buys": [
"We want to tie recognition to retention for our hourly workforce — regretted turnover there is over 30%."
],
"pain points": [
"Turnover for our hourly workforce is over 30%."
],
"stakeholders": [
"Head of Total Rewards",
"CFO"
],
"budget signal": "$25k",
"timeline signal": "decision by end of September",
"competitor mentioned": null,
"next step": "send the pilot agreement and route it to legal this week",
"objections": [
"Integration with Workday has to be rock solid — that's my one condition."
],
"confidence": null
},
{
"transcript_id": "TX-003",
"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": [
"People Ops Manager"
],
"budget signal": null,
"timeline signal": "no rush until Q1",
"competitor mentioned": "Bucketlist",
"next step": "schedule a call with our CEO",
"objections": null,
"confidence": null
},
{
"transcript_id": "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.",
"Our procurement cycle runs six to eight weeks minimum.",
"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": "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": null
},
{
"transcript_id": "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.",
"Our exec team is skeptical after a failed rollout two years ago."
],
"stakeholders": [
"HR Director",
"People Ops Coordinator"
],
"budget signal": "$12k",
"timeline signal": "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": null
},
{
"transcript_id": "TX-006",
"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.",
"Budget isn't the issue — time is."
],
"stakeholders": [
"HR Manager"
],
"budget signal": null,
"timeline signal": "Q1 start is realistic",
"competitor mentioned": null,
"next step": "send the one-page overview to forward to our COO",
"objections": [
"Our COO usually prefers building things in-house."
],
"confidence": null
}
]
**1. Deal‑547B2B** – $11 200 *Why it’s close:* Slack (2026‑09‑04 09:12) says “redlines came back clean… signing page is out… VP People told me they are signing tomorrow.” The CRM shows a DS5 / COMMIT stage with a close‑date of 2026‑09‑11, confirming it’s at the final signature step. *What’s left:* Only the final executive signature; all legal and procurement steps are completed. **2. Deal‑A2B47C** – $6 360 *Why it’s close:* Slack (2026‑09‑04 09:44) lists it as “still warm, just normal legal‑review pace.” The CSV lists it in DS5 / COMMIT with a close‑date of 2026‑09‑11, indicating it’s in the last review window. *What’s left:* Completion of the standard legal‑review checklist and execution of the signature page. **3. Deal‑403845** – $9 000 *Why it’s close:* Slack (2026‑09‑04 10:02) notes “the order form is with their finance team.” The CRM shows DS5 / COMMIT and a close‑date of 2026‑09‑11, meaning finance approval is the final hurdle. *What’s left:* Finance sign‑off on the order form, after which the deal can be signed.
All candidates: Deal-EC3025 – "We need SCIM user provisioning — our security team will not approve any tool that requires manual account management." – classification: real gap – amount: not provided Deal-D0D6B5 – "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 – amount: not provided Deal-CFE7F4 – "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 – amount: not provided Deal-84DBA6 – "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 – amount: not provided Summary of real gaps: Deal-EC3025 – "We need SCIM user provisioning — our security team will not approve any tool that requires manual account management." – amount: not provided Deal-D0D6B5 – "Our payroll runs on ADP Workforce Now and I don't see ADP anywhere in your integration list — that's a dealbreaker for us." – amount: not provided
Owner: Bryce Harmon Deal-2D1F1B | Bryce Harmon | DS1 | 240000 | 81 days Deal-66D1FC | Bryce Harmon | DS1 | 99000 | 16 days Deal-950043 | Bryce Harmon | DS1 | 70000 | 19 days Deal-B23205 | Bryce Harmon | DS1 | 45000 | 16 days Deal-7BBDFA | Bryce Harmon | DS3 | 37440 | 46 days Deal-332637 | Bryce Harmon | DS2 | 36000 | 9 days Deal-1BEEBF | Bryce Harmon | DS1 | 31500 | 19 days Deal-C5658B | Bryce Harmon | DS1 | 23400 | 16 days Deal-40522D | Bryce Harmon | DS3 | 21000 | 19 days Deal-F0EBBB | Bryce Harmon | DS3 | 11400 | 24 days Deal-E25A09 | Bryce Harmon | DS1 | 6000 | 9 days Deal-C9C286 | Bryce Harmon | DS2 | 5502 | 9 days Deal-012CB1 | Bryce Harmon | DS1 | 1 | 23 days Stale deals: 13, Total amount: 626243 Owner: Dana Mercer Deal-44EA29 | Dana Mercer | DS2 | 60000 | 10 days Deal-E51FB7 | Dana Mercer | DS2 | 43875 | 12 days Deal-B42F46 | Dana Mercer | DS1 | 27000 | 19 days Deal-BA3DDC | Dana Mercer | DS3 | 23400 | 15 days Deal-9DDE86 | Dana Mercer | DS2 | 20000 | 15 days Deal-215CCA | Dana Mercer | DS3 | 18900 | 17 days Deal-5EED42 | Dana Mercer | DS3 | 16250 | 11 days Deal-57887A | Dana Mercer | DS2 | 15000 | 8 days Deal-B7EBD1 | Dana Mercer | DS5 | 9000 | 16 days Deal-3974EB | Dana Mercer | DS4 | 9000 | 8 days Deal-F40F04 | Dana Mercer | DS2 | 8100 | 15 days Deal-87DDD1 | Dana Mercer | DS1 | 5000 | 19 days Deal-F336B6 | Dana Mercer | DS3 | 4200 | 15 days Deal-0660B4 | Dana Mercer | DS4 | 1920 | 16 days Stale deals: 14, Total amount: 261645 Owner: Alex Franklin Deal-CC08D1 | Alex Franklin | DS1 | 24000 | 16 days Deal-E73427 | Alex Franklin | DS3 | 18000 | 10 days Deal-885F45 | Alex Franklin | DS2 | 9300 | 12 days Deal-C2FF3C | Alex Franklin | DS1 | 8316 | 10 days Deal-3EED2C | Alex Franklin | DS2 | 7200 | N/A days Deal-0D2F7A | Alex Franklin | DS3 | 5100 | 12 days Deal-6C60D4 | Alex Franklin | DS3 | 4800 | 12 days Deal-13FEBD | Alex Franklin | DS2 | 4680 | 12 days Deal-9D0060 | Alex Franklin | DS3 | 3840 | 12 days Deal-690476 | Alex Franklin | DS2 | 3600 | 18 days Deal-C6D97A | Alex Franklin | DS4 | 3240 | 8 days Deal-EE195F | Alex Franklin | DS3 | 3120 | 8 days Deal-278DEC | Alex Franklin | DS3 | 2700 | 8 days Deal-635B8E | Alex Franklin | DS3 | 2600 | 18 days Deal-6883F3 | Alex Franklin | DS1 | 2400 | 16 days Deal-4A13AD | Alex Franklin | DS3 | 2160 | 26 days Deal-F67D31 | Alex Franklin | DS2 | 1800 | 8 days Deal-5FDCE4 | Alex Franklin | DS3 | 1600 | 12 days Deal-BA571A | Alex Franklin | DS4 | 1080 | 18 days Stale deals: 19, Total amount: 109536 Owner: Cole Ingram Deal-D04904 | Cole Ingram | DS2 | 58529.25 | 11 days Deal-B25F40 | Cole Ingram | DS3 | 40000 | 8 days Deal-813836 | Cole Ingram | DS2 | 32175 | 11 days Deal-1BA595 | Cole Ingram | DS2 | 31750 | 11 days Deal-CFE1E8 | Cole Ingram | DS3 | 18000 | 11 days Deal-CD47A6 | Cole Ingram | DS2 | 12168 | 11 days Deal-627646 | Cole Ingram | DS3 | 11193 | 11 days Deal-FF809F | Cole Ingram | DS2 | 7781.2 | 11 days Deal-AF932D | Cole Ingram | DS2 | 7225.4 | 11 days Deal-A71728 | Cole Ingram | DS2 | 6947.5 | 11 days Deal-8BC9F5 | Cole Ingram | DS2 | 5616 | 10 days Deal-175395 | Cole Ingram | DS3 | 4779.88 | 11 days Deal-481E24 | Cole Ingram | DS3 | 4140 | 10 days Deal-C7F9BF | Cole Ingram | DS2 | 3360 | 11 days Deal-2F3A66 | Cole Ingram | DS3 | 3334.8 | 11 days Deal-342E96 | Cole Ingram | DS2 | 2700 | 24 days Deal-E568D5 | Cole Ingram | DS3 | 1875 | 11 days Deal-FD9F4E | Cole Ingram | DS5 | 1330 | 10 days Stale deals: 18, Total amount: 252905.03 Owner: Elena Sinclair Deal-57FF13 | Elena Sinclair | DS1 | 2100 | N/A days Stale deals: 1, Total amount: 2100
**Snapshot date:** 2026‑09‑05 **Owners (sales reps) – activity & DS2 entry summary (last 30 days)** Owner | Emails | Calls | Meetings | Total activities | DS2 entries | Email % | Call % | Meeting % | Activities per DS2 | Efficiency rank ---|---|---|---|---|---|---|---|---|---|--- Alex Franklin (84342457) | 307 | 36 | 41 | 384 | 18 | 79.95 % | 9.38 % | 10.68 % | **21.33** | 1 (most efficient) Bryce Harmon (119337721) | 162 | 0 | 43 | 205 | 4 | 79.02 % | 0.00 % | 20.98 % | 51.25 | 2 Cole Ingram (83155924) | 96 | 14 | 1 | 111 | 2 | 86.49 % | 12.61 % | 0.90 % | 55.50 | 3 Farid Osman (716654662) | 38 | 0 | 34 | 72 | 1 | 52.78 % | 0.00 % | 47.22 % | 72.00 | 4 Dana Mercer (83155923) | 84 | 18 | 11 | 113 | 1 | 74.34 % | 15.93 % | 9.73 % | 113.00 | 5 Elena Sinclair (701163055) | 0 | 0 | 0 | 0 | 0 | 0 % | 0 % | 0 % | — | 6 (no DS2 entries) **How the numbers were derived** 1. **Engagement totals** – For each deal, the `emails_30d`, `calls_30d`, and `meetings_30d` columns from **engagements_by_deal_90d.csv** were summed per owner (using the `owner_id` from **deals_open.csv**). *Example for Bryce Harmon*: - Sum of `emails_30d` across all his deals = 162 - Sum of `calls_30d` = 0 - Sum of `meetings_30d` = 43 - Total activities = 162 + 0 + 43 = 205 2. **DS2 entry count** – A deal counts as a DS2 entry if its `t_ds2` date falls between **2026‑08‑06** and **2026‑09‑05** (the 30‑day window ending on the snapshot date). The `t_ds2` column from **deals_open.csv** was parsed and compared to this window. *Example for Alex Franklin*: - Deals with `t_ds2` in window = 18 3. **Percentages** – - Email % = emails ÷ total × 100 - Call % = calls ÷ total × 100 - Meeting % = meetings ÷ total × 100 *Bryce Harmon*: 162 ÷ 205 × 100 = 79.02 % 4. **Activities per DS2 entry** – total activities ÷ DS2 entries (only for reps with at least one DS2 entry). *Alex Franklin*: 384 ÷ 18 = 21.33 5. **Ranking** – - **Efficiency rank**: lowest “activities per DS2 entry” = most efficient. - **Volume rank**: highest total activities = highest‑volume rep. **Most efficient rep:** **Alex Franklin** (21.33 activities per DS2 entry). **Highest‑volume rep:** **Alex Franklin** (384 total activities). *Result:* The most efficient rep and the highest‑volume rep are the **same person (Alex Franklin)**. No discrepancy to note. All calculations use only the data supplied; no invented values are present.
QTD SCORECARD – Alex Franklin (as of 2026‑09‑05) **1. Bookings vs. Quota** - Quota (Q3 2026): 200 000 $ 【quota.csv】 - Closed‑won deals in Q3 (≥ 2026‑07‑01 & ≤ 2026‑09‑05): - Deal‑A1C3E5 (40 000 $) - Deal‑F2C7D8 (20 000 $) - Deal‑B7D2F4 (35 000 $) - Deal‑C9E1A6 (21 000 $) - Deal‑A8B4D6 (12 000 $) - Deal‑D4B8C2 (11 000 $) - Deal‑E6F3A9 (6 500 $) - Deal‑C5D9E2 (4 500 $) - Total bookings = 150 000 $ - Attainment = 150 000 / 200 000 = 0.75 → **75 %** **2. New vs. Expansion Split (Q3 closed‑won)** | Deal type | Count | Amount ($) | |-----------|-------|------------| | new | 5 | 113 500 | | expansion | 3 | 36 500 | | **Total** | 8 | 150 000 | *Counts derived from the “deal_type” column of the eight closed‑won rows listed above.* **3. Active Pipeline (open deals, any future close date)** Stage | Amount ($) ------|------------ DS1 | 284 621 DS2 | 353 760 DS3 | 552 705 DS4 | 23 574 DS5 | 45 730 *Totals computed by summing “amount” where status = open and stage = DS1‑DS5 (see ae_deals.csv).* **4. Rolling 90‑day DS2‑to‑Won Rate** - DS2 entries entered between 2026‑06‑07 and 2026‑09‑05 = 23 rows (see DS2 matches in ae_deals.csv). - Of those, 8 have since become CLOSED_WON (deals A1C3E5, F2C7D8, B7D2F4, C9E1A6, A8B4D6, D4B8C2, E6F3A9, C5D9E2). - Rate = 8 / 23 ≈ 0.348 → **34.8 %** (rounded to 35 %). **5. Win / Loss Counts (Q3)** - Wins = 8 (as above). - Losses = 27 closed‑lost deals with close_date ≥ 2026‑07‑01 and ≤ 2026‑09‑05 (see ae_deals.csv). **Top loss reason** (most frequent among the 27 losses): - **“Lost‑ Timing (1 year or more)”** – appears in 13 deals (e.g., Deal‑55867E, Deal‑D1A623, Deal‑69CF3D, …). **6. Activity Volume – Last 30 days (all deals)** - Emails = 807 - Calls = 112 - Meetings = 128 *Aggregated from ae_engagements.csv (sum of the *_30d columns).* --- ### Coaching Observations (grounded in the numbers) 1. **Attainment Gap – Focus on Upsell Opportunities** At 75 % of quota, the shortfall is 50 000 $. The pipeline shows a healthy amount in DS2 (≈ 354 k $) but the DS2‑to‑won conversion is only ~35 %. Prioritizing the 23 DS2 deals—especially those that entered early in the window—could lift win rates and close the quota gap. 2. **Loss Reason Concentration – Timing Issues** “Lost‑ Timing (1 year or more)” accounts for 13 of 27 losses (≈ 48 %). This suggests many prospects are on long sales cycles that exceed the quarter. Accelerating discovery or securing interim commitments (e.g., phased pilots) could convert a portion of these timing‑related losses into wins. 3. **Activity Imbalance – Need More Calls & Meetings** The team averaged 112 calls and 128 meetings over the past 30 days, while sending 807 emails. Calls and meetings per win are low (≈ 14 calls / win, 16 meetings / win). Increasing outbound calls and face‑to‑face (or virtual) meetings, especially on high‑value DS2 opportunities, should improve engagement quality and help raise the DS2‑to‑won rate.
Deal-EC3025 (company C‑FDD0C7) – active contacts: 1 Personas present: champion Personas missing: economic buyer, HR admin, IT security, finance Most valuable persona to add (stage unknown): economic buyer Un‑engaged contact on file: CT‑6827DB – Chief People Officer (economic buyer) Deal-92D97D (company C‑E23238) – active contacts: 1 Personas present: HR admin Personas missing: economic buyer, champion, IT security, finance Most valuable persona to add (stage unknown): economic buyer Un‑engaged contact on file: none Deal-50D386 (company C‑EB10E4) – active contacts: 2 Personas present: champion, HR admin Personas missing: economic buyer, IT security, finance Most valuable persona to add (stage unknown): economic buyer Un‑engaged contact on file: CT‑A1C4B3 – Chief People Officer (economic buyer) Deal-D0D6B5 (company C‑32918E) – active contacts: 3 Personas present: champion only (all contacts same persona) Personas missing: economic buyer, HR admin, IT security, finance Most valuable persona to add (stage unknown): economic buyer Un‑engaged contact on file: CT‑1FA4DB – Chief People Officer (economic buyer) Deal-5BFE3B (company C‑535D36) – active contacts: 2 Personas present: champion only Personas missing: economic buyer, HR admin, IT security, finance Most valuable persona to add (stage unknown): economic buyer Un‑engaged contact on file: none Deal-36C33F (company C‑077A0E) – active contacts: 1 Personas present: IT security Personas missing: economic buyer, champion, HR admin, finance Most valuable persona to add (stage unknown): economic buyer Un‑engaged contact on file: CT‑1DB73E – Chief People Officer (economic buyer) Deal-885F45 (company C‑5E8EFB) – active contacts: 2 Personas present: economic buyer, champion Personas missing: HR admin, IT security, finance Most valuable persona to add (stage unknown): HR admin (or IT security) – choose one; economic buyer already present Un‑engaged contact on file: CT‑B3F25D – IT Security Lead (IT security) Deal-FCBE5B (company C‑737030) – active contacts: 1 Personas present: champion Personas missing: economic buyer, HR admin, IT security, finance Most valuable persona to add (stage unknown): economic buyer Un‑engaged contact on file: none Deal-5408B0 (company C‑2AE3AA) – active contacts: 2 Personas present: champion, HR admin Personas missing: economic buyer, IT security, finance Most valuable persona to add (stage unknown): economic buyer Un‑engaged contact on file: CT‑07FA76 – Chief People Officer (economic buyer) Deal-C6D97A (company C‑5A8FC2) – active contacts: 3 Personas present: champion only (all contacts same persona) Personas missing: economic buyer, HR admin, IT security, finance Most valuable persona to add (stage unknown): economic buyer Un‑engaged contact on file: none Deal-F9A08A (company C‑0D15DF) – active contacts: 1 Personas present: champion Personas missing: economic buyer, HR admin, IT security, finance Most valuable persona to add (stage unknown): economic buyer Un‑engaged contact on file: CT‑697541 – Chief People Officer (economic buyer) **Note:** The provided CSV files do not contain amount or stage information for any deal; therefore those fields are unavailable. All calculations are based solely on the dates and “is_former” flags in `deal_contacts.csv`. Active contacts are those with a last‑engaged date on or after 2026‑07‑08 and not marked former.
Lead‑in statements (first five minutes, minute 0) - “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.” (TT‑001, TT‑002, TT‑003, TT‑005, TT‑006, TT‑007, TT‑008, TT‑010) - “I put together a short agenda — security review first, then pricing.” (TT‑004) - “You asked for straight pricing last time, so let’s start there.” (TT‑009) Three most common objections & the rep’s handling 1. **Budget locked until next fiscal year** – Prospect: “Honestly, budget is locked until next fiscal year — I can’t add a new line item right now.” (TT‑001 line 6) Rep: “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.” (TT‑001 line 8) 2. **Current recognition is a spreadsheet** – Prospect: “We already do recognition with a spreadsheet and quarterly gift cards — why would we change?” (TT‑004 line 6) Rep: “Spreadsheets work until they scale — the difference is automation: milestones fire without HR lifting a finger, and you get analytics on who is being recognized.” (TT‑004 line 8) 3. **Competitor comparison** – Prospect: “We’re also in late talks with Awardco — their rewards catalog looks bigger than yours.” (TT‑003 line 4) Rep: “Awardco’s catalog is broad; where we win is automation and the analytics your HRIS team asked about.” (TT‑003 line 5) Concrete next‑step agreement rate - Agreements occurred in 7 of the 10 calls → 70 % (7/10). - Example of agreement: “Yes, Thursday at 2 pm works — send the invite and I’ll bring our HRIS manager.” (TT‑001 line 15) Competitors raised by prospects - Awardco – “We’re also in late talks with Awardco — their rewards catalog looks bigger than yours.” (TT‑003 line 4) - Kudos – “How are you different from Kudos? Our CEO used them at her last company.” (TT‑007 line 4) Coaching notes 1. Keep the opening hook consistent but vary the “quick one to kick off” story when the prospect shows fatigue; a fresh case study can re‑engage after three repeats. 2. When a prospect mentions a competitor, flip the script quickly by quantifying your unique ROI (e.g., $210 k saved) before offering a pilot – it reinforces value and pre‑empts price objections.
COMMIT total (Q3): 44 729 USD BEST_CASE total (Q3): 203 565 USD Weighted forecast = COMMIT + 0.35 × BEST_CASE = 44 729 + 0.35 × 203 565 = 44 729 + 71 247.75 = 115 976.75 USD Deal counts inside the quarter (2026‑07‑01 to 2026‑09‑30): - COMMIT: 7 deals - BEST_CASE: 24 deals - PIPELINE: 23 deals (zero weighting, counted for completeness) Deals excluded for being outside the quarter: 32 deals, total amount 227 575 USD Top 5 BEST_CASE deals by amount (inside the quarter): 1. Deal‑2D7423 – 38 935 USD – close 2026‑09‑30 2. Deal‑25F752 – 24 000 USD – close 2026‑09‑25 3. Deal‑E53952 – 19 656 USD – close 2026‑09‑30 4. Deal‑5EED42 – 16 250 USD – close 2026‑09‑30 5. Deal‑FA32A0 – 11 116 USD – close 2026‑09‑25 ## Data quality - Several records lack an owner value, which could affect attribution or pipeline visibility. - Amounts are stored as mixed types (e.g., “2480.4”), risking rounding inconsistencies in aggregations. - The extract contains no deals with close dates in July, suggesting possible missing data for the early part of Q3. - Some deals have stage DS5 but are classified as BEST_CASE, which may indicate mis‑labeling of forecast categories.
Cohort analysis (size | 24‑month retention rate) both signals size = 48 retention = 0.646 (64.6 %) givers‑only (m1_users ≥ 5, m1_redemptions = 0) size = 50 retention = 0.460 (46.0 %) redemption‑only (m1_users < 5, m1_redemptions ≥ 1) size = 28 retention = 0.321 (32.1 %) neither (m1_users < 5, m1_redemptions = 0) size = 94 retention = 0.404 (40.4 %) Excluded companies: 0 – no rows were omitted because all rows contained numeric m1_users and m1_redemptions values. Single‑signal with the largest retention lift: **givers‑only** (the presence of ≥5 unique givers in month 1 raises retention by ~5.6 percentage points compared with the “neither” group). What this does prove: - Companies that show early engagement through multiple unique givers (≥5) are more likely to still be active at 24 months than companies with no early signals. - The combination of both signals yields the strongest lift, indicating that early givers and redemptions together are predictive of long‑term retention. What this does NOT prove: - Causality – the observed association may be driven by other underlying factors (e.g., product fit, marketing spend). - Generalizability beyond the sampled cohort; results apply only to the companies present in the provided CSV. - The effect of redemption‑only signal, which in this data shows a negative lift, nor that redemptions alone are irrelevant in other contexts.
Total Billing ARR (MRR × 12, active subscriptions only): 604,739.28 Total CRM ARR (HubSpot): 603,581.76 Variance (Billing – CRM): +1,157.52 Decomposition of variance (exact sum = +1,157.52) - Status mismatch (subscription status ≠ active but CRM shows ARR): ‑13,158.48 • C‑0C8323BF (ARR = 0 vs CRM = 4,905.24) Owner: RevOps Analyst (Amani Phipps) • C‑0DC4FB8C (ARR = 0 vs CRM = 8,253.24) Owner: RevOps Analyst (Amani Phipps) - Missing records (CRM entry but no active subscription at all): ‑16,497.24 • C‑0D5BBE3A (ARR = 0 vs CRM = 16,497.24) Owner: RevOps Analyst (Amani Phipps) - Rounding (differences < 0.5 ARR): 0.00 - Other mismatches (active billing ≠ CRM, or billing entry not in CRM): +30,813.24 • C‑0D66DF9E Billing = 23,184.00 vs CRM = 23,200.00 Diff = ‑16.00 Owner: RevOps Analyst (Amani Phipps) • C‑0F7269D7 Billing = 26,796.00 vs CRM = 24,396.00 Diff = +2,400.00 Owner: RevOps Analyst (Amani Phipps) • C‑14D70CE0 Billing = 18,180.00 vs CRM = 18,200.00 Diff = ‑20.00 Owner: RevOps Analyst (Amani Phipps) • C‑21629AA4 Billing = 28,449.24 vs CRM = 0 Diff = +28,449.24 Owner: RevOps Analyst (Amani Phipps) Sum of buckets: ‑13,158.48 + ‑16,497.24 + 0.00 + 30,813.24 = +1,157.52 (matches variance). Business‑rule violations (term ≠ 12 months & cf_agreement_end_date missing): - SUB‑0002 Company C‑1794A52C term = 24 months (cf_agreement_end_date blank) - SUB‑0019 Company C‑22170CA1 term = 36 months (cf_agreement_end_date blank) All other subscriptions with term ≠ 12 have a populated cf_agreement_end_date (e.g., SUB‑000C, SUB‑001A).
KVM | 2026‑08 value | 2026‑07 value | Δ abs | Δ % | direction ---|---|---|---|---|--- giving_rate | 0.6027 | 0.6023 | +0.0004 | +0.07 % | up redemptions_per_user | 1.7302 | 1.7300 | +0.0002 | +0.01 % | up one_to_one_engagement | 0.4472 | 0.4469 | +0.0003 | +0.07 % | up pulse_engagement | 0.5086 | 0.6006 | ‑0.0920 | ‑15.31 % | down The largest relative move is **pulse_engagement** (‑15.3 %). The drop is driven by the **enterprise** size_band, whose pulse engagement fell from 0.6005 to 0.1196 (≈‑50 % relative change). The other bands (smb, mid_market) show negligible changes.
Last completed month: **2026‑08** Redemption section (weekly rewards report) - **Redemption count:** 48 - **Total spend:** $3,536.00 - **Unique redeemers:** 46 - **Redemptions per redeemer:** 1.04 **Provider mix (percent of spend)** - TangoCard – 14.14 % - Tremendous – 32.38 % - custom – 29.89 % - Snappy – 23.59 % **Top 5 countries by redemptions** 1. US – 31 redemptions 2. CA – 7 redemptions 3. GB – 2 redemptions 4. SG – 2 redemptions 5. AU – 2 redemptions
Qualified at‑risk accounts (health < 60, eligible > 0, renewal ≤ 120 days) Account Alias | Churn‑save amount | Play | Signal justifying play ---|---|---|--- C-0F6C0F34 | $49,707.00 | Executive Touch | champion_active = false C-0B827671 | $25,365.00 | Usage Revival | usage_trend_3m = declining C-0B360C78 | $35,748.00 | Commercial Concession | health_score = 57 (<60) – low health despite growing usage C-0B0F1BAB | $5,494.00 | Executive Touch | champion_active = false C-0CA21961 | $16,829.00 | Commercial Concession | usage_trend_3m = flat C-0E9C27D1 | $41,235.00 | Commercial Concession | usage_trend_3m = flat C-0CEF69FD | $32,621.00 | Executive Touch | champion_active = false C-0D3278C7 | $17,602.00 | Usage Revival | usage_trend_3m = declining **Total amount at stake:** $224,601.00 --- At‑risk accounts that **do not qualify** and why Account Alias | Reason for ineligibility ---|--- C-0BC71BDD | churn_save_eligible_amount = 0 (fails R2) C-0BA71F12 | renewal_date = 2027‑04‑11 > 120 days from 2026‑09‑05 (fails R3) C-0F6694C3 | churn_save_eligible_amount = 0 (fails R2) C-0BE96399 | churn_save_eligible_amount = 0 (fails R2) C-0F876796 | renewal_date = 2027‑02‑06 > 120 days (fails R3) C-0FCCD2DF | churn_save_eligible_amount = 0 (fails R2) C-10A56B0F | churn_save_eligible_amount = 0 (fails R2)
**Expansion Kit – C‑0DDFC9A7** - **Seat coverage:** 150 licensed seats ÷ 400 headcount = 0.375 → **37.5 %** of staff covered. - **Usage health:** 1. Monthly active users have risen from **88 (Mar 2026) → 126 (Aug 2026)**, a **43 % increase**. 2. Current month usage = 126 ÷ 150 = **84 %** of licensed capacity. - **Headroom:** - Seats remaining: 150 – 126 = **24 seats**. - ARR per seat: $9,000 ÷ 150 = **$60**. - Potential ARR from headroom: 24 × $60 = **$1,440**. - **Reply source:** Maria S., People Operations Coordinator – **cannot approve purchase**. - **Decision‑maker:** Dana R., VP People (budget & seat expansion). **Proposed reply (≤150 words)** > Hi Maria, > > Thanks for the quick note and for sharing the usage trend – it’s great to see monthly active users climb from 88 in March to 126 in August. I understand budget authority sits with Dana R. Would you be willing to introduce us to her? I can then share a brief overview of how the additional seats would translate into roughly $1,440 of incremental ARR at the current $60‑per‑seat rate. > > Appreciate your help! > > Best, > Cole *All figures are drawn directly from the provided CSV and email data.*
Company: C-0D284E42 **Complete items (with data fields)** - Slack integration connected: 2026‑08‑12 - Allowance set: 2026‑08‑13 - Admins added: 2 (count > 0) - First recognition given: 2026‑08‑15 14:22 **Not yet complete** - HRIS integration: (no date recorded) - First redemption: (no date recorded) **Early‑engagement signals (arithmetic shown)** | Period | Days counted | Active givers sum | Avg per day | |--------|--------------|-------------------|-------------| | First week (2026‑08‑11 → 2026‑08‑18) | 8 | 3 + 3 + 4 + 4 + 5 + 4 + 7 + 5 = 35 | 35 ÷ 8 ≈ 4.38 | | Entire onboarding window (2026‑08‑11 → 2026‑09‑04) | 24 | 214 | 214 ÷ 24 ≈ 8.92 | - The daily active‑giver count rose from 3 on the signup day to a peak of 15 by 2026‑09‑04, indicating growing participation. **Three things to cover on the mid‑onboarding call** 1. **HRIS integration** – discuss steps to connect the HRIS system (currently missing). 2. **First redemption** – plan the redemption workflow, since no redemption has been recorded yet. 3. **Usage growth** – review the upward trend in active givers (average ≈ 8.9 / day) and agree on actions to sustain or accelerate engagement.
Account: C-0B7D2C30 CSM: Dana Mercer ARR: $65901.00 Renewal Date Used: 2026-09-15 Seat Utilization: 58% 3‑Month Usage Trend: Decreasing Risk Rating: Low – Utilization 58%. Disagreement: Yes (ChurnZero date 2026‑09‑10 vs ChargeBee 2026‑09‑15; multi‑year contract, ChurnZero dates are unreliable) Account: C-0BCDB8C2 CSM: Cole Ingram ARR: $54427.00 Renewal Date Used: 2026-09-18 Seat Utilization: 55% 3‑Month Usage Trend: Decreasing Risk Rating: Low – Utilization 55%. Disagreement: No Account: C-0D2AB865 CSM: Elena Sinclair ARR: $38022.00 Renewal Date Used: 2026-09-22 Seat Utilization: 61% 3‑Month Usage Trend: Decreasing Risk Rating: Medium – Utilization 61%. Disagreement: Yes (multi‑year contract, ChurnZero dates are unreliable) Account: C-0BBE3E60 CSM: Dana Mercer ARR: $30993.00 Renewal Date Used: 2026-09-26 Seat Utilization: 65% 3‑Month Usage Trend: Decreasing Risk Rating: Medium – Utilization 65%. Disagreement: Yes (multi‑year contract, ChurnZero dates are unreliable) Account: C-0F5D2323 CSM: Cole Ingram ARR: $90647.00 Renewal Date Used: 2026-09-29 Seat Utilization: 28% 3‑Month Usage Trend: Decreasing Risk Rating: Low – Utilization 28%. Disagreement: Yes (multi‑year contract, ChurnZero dates are unreliable) Account: C-0EC6999D CSM: Elena Sinclair ARR: $79419.00 Renewal Date Used: 2026-10-03 Seat Utilization: 28% 3‑Month Usage Trend: Decreasing Risk Rating: Low – Utilization 28%. Disagreement: No Account: C-0B20DB64 CSM: Dana Mercer ARR: $21770.00 Renewal Date Used: 2026-10-07 Seat Utilization: 57% 3‑Month Usage Trend: Stable Risk Rating: Low – Utilization 57%. Disagreement: No Account: C-0BBC4E7A CSM: Cole Ingram ARR: $56374.00 Renewal Date Used: 2026-10-10 Seat Utilization: 68% 3‑Month Usage Trend: Decreasing Risk Rating: Medium – Utilization 68%. Disagreement: No Account: C-0FD551AB CSM: Elena Sinclair ARR: $48815.00 Renewal Date Used: 2026-10-14 Seat Utilization: 56% 3‑Month Usage Trend: Increasing Risk Rating: Low – Utilization 56%. Disagreement: No Account: C-0F9F8F13 CSM: Dana Mercer ARR: $46230.00 Renewal Date Used: 2026-10-18 Seat Utilization: 57% 3‑Month Usage Trend: Decreasing Risk Rating: Low – Utilization 57%. Disagreement: No Account: C-0BC34584 CSM: Cole Ingram ARR: $16740.00 Renewal Date Used: 2026-10-22 Seat Utilization: 66% 3‑Month Usage Trend: Increasing Risk Rating: Medium – Utilization 66%. Disagreement: No Account: C-0B7A7546 CSM: Elena Sinclair ARR: $35062.00 Renewal Date Used: 2026-10-25 Seat Utilization: 89% 3‑Month Usage Trend: Decreasing Risk Rating: High – Utilization 89% with decreasing usage. Disagreement: No Account: C-0B369871 CSM: Dana Mercer ARR: $85128.00 Renewal Date Used: 2026-10-29 Seat Utilization: 75% 3‑Month Usage Trend: Increasing Risk Rating: Medium – Utilization 75%. Disagreement: No Account: C-0B144C78 CSM: Cole Ingram ARR: $30899.00 Renewal Date Used: 2026-11-02 Seat Utilization: 75% 3‑Month Usage Trend: Increasing Risk Rating: Medium – Utilization 75%. Disagreement: No Account: C-0FC4DBB8 CSM: Elena Sinclair ARR: $94732.00 Renewal Date Used: 2026-11-05 Seat Utilization: 77% 3‑Month Usage Trend: Increasing Risk Rating: Medium – Utilization 77%. Disagreement: No Account: C-0D5BBE3A CSM: Dana Mercer ARR: $39740.00 Renewal Date Used: 2026-11-09 Seat Utilization: 83% 3‑Month Usage Trend: Increasing Risk Rating: Medium – Utilization 83%. Disagreement: No Account: C-0FB9D5AF CSM: Cole Ingram ARR: $63158.00 Renewal Date Used: 2026-11-13 Seat Utilization: 72% 3‑Month Usage Trend: Increasing Risk Rating: Medium – Utilization 72%. Disagreement: No Account: C-0B344485 CSM: Elena Sinclair ARR: $64384.00 Renewal Date Used: 2026-11-16 Seat Utilization: 78% 3‑Month Usage Trend: Increasing Risk Rating: Medium – Utilization 78%. Disagreement: No Account: C-0CB2C1B4 CSM: Dana Mercer ARR: $40628.00 Renewal Date Used: 2026-11-20 Seat Utilization: 82% 3‑Month Usage Trend: Increasing Risk Rating: Medium – Utilization 82%. Disagreement: No Account: C-22170CA1 CSM: Cole Ingram ARR: $45646.00 Renewal Date Used: 2026-11-24 Seat Utilization: 85% 3‑Month Usage Trend: Increasing Risk Rating: Medium – Utilization 85%. Disagreement: No Total ARR renewing (all accounts): $1,048,715.00 Total ARR at high risk (risk = High): $35,062.00
Total tickets: 80 **1. Billing & Invoice Errors** - Count: 15 (18.75 % of tickets) - Distinct accounts: 1 - ARR affected: $780,000 - Example tickets: IC‑460071, IC‑460069 - Recommendation: Audit the invoicing pipeline, add automated seat‑count validation, and set up alerts for tier‑price mismatches to prevent recurring billing errors. **2. HRIS Provisioning / Sync Failures** - Count: 12 (15.00 % of tickets) - Distinct accounts: 3 - ARR affected: $456,000 - Example tickets: IC‑460059, IC‑460055 - Recommendation: Strengthen HRIS integration with retry logic, detailed error logging, and regular reconciliation of new‑hire provisioning to eliminate missed account creation. **3. Checkout & Gift‑Card Redemption Failures** - Count: 18 (22.50 % of tickets) - Distinct accounts: 7 - ARR affected: $177,300 - Example tickets: IC‑460025, IC‑460030 - Recommendation: Diagnose the checkout flow, add retry mechanisms for redemption, and monitor gift‑card email delivery to reduce failed redemptions. **4. Points / Recognition Posting Issues** - Count: 20 (25.00 % of tickets) - Distinct accounts: 9 - ARR affected: $70,200 - Example tickets: IC‑460004, IC‑460016 - Recommendation: Audit the points‑allocation pipeline, ensure immediate balance updates after recognition, and implement monitoring for delayed or missing point postings. **5. Slack Integration & Command Errors** - Count: 14 (17.50 % of tickets) - Distinct accounts: 4 - ARR affected: $63,400 - Example tickets: IC‑460041, IC‑460047 - Recommendation: Stabilize Slack integration by persisting authentication tokens, fixing the sync‑toggle reset, and adding health‑checks for slash‑command endpoints.
**Top 3 public‑case‑study customers most similar to the prospect (C‑82AF3719)** | Rank | Customer (alias) | Matching fields (count = 3) | Fields that drove the match | |------|------------------|------------------------------|------------------------------| | 1 | C‑11C31562 | 3 | size_band = Mid‑Market, use_case = employee_recognition, region = NA‑West | | 2 | C‑A13C193D | 3 | industry = Technology, size_band = Mid‑Market, region = NA‑West | | 3 | C‑64171065 | 3 | industry = Technology, size_band = Mid‑Market, use_case = employee_recognition | **Arithmetic** – each customer was scored by adding 1 for every field (industry, size_band, use_case, region) that exactly matches the prospect’s values. All three listed customers achieved the maximum possible score of 3 matches.
Channel performance (trailing 6 months) Paid channels Channel Spend USD SQMs SQOs Cost / SQM USD Cost / SQO USD SQM‑to‑SQO rate Pipeline USD Pipeline / USD paid_search 36000 15 3 2400.0 12000.0 0.20 (20 %) 120000 3.33 linkedin_ads 24000 6 4 4000.0 6000.0 0.67 (66.7 %) 48000 2.00 paid_social 18000 0 0 undefined undefined undefined 0 0.00 webinars 9000 3 0 3000.0 undefined 0.00 (0 %) 0 0.00 Organic channels (no spend) Channel Volume SQMs SQO rate Pipeline USD organic_search 5 0.40 (40 %) 18000 referral 1 1.00 (100 %) 8000 Rows where SQO date precedes SQM date: none. Re‑allocation recommendation - Reduce or pause spend on **paid_social** (0 SQMs, no pipeline) and **webinars** (low SQM‑to‑SQO rate 0 %, pipeline / USD = 0). - Shift a portion of the freed budget (≈ $27 k) toward **paid_search** and **linkedin_ads**, which deliver the highest pipeline per dollar (3.33 × and 2.00 ×) and respectable SQM‑to‑SQO conversion (20 % and 66.7 %). - Consider a modest investment in SEO/organic‑search initiatives to grow the **organic_search** volume (currently only 5 SQMs) and capture additional pipeline without additional spend. Confidence in metrics (based on sample size) - **paid_search**: high confidence (15 SQMs). - **linkedin_ads**: moderate confidence (6 SQMs). - **webinars**: low confidence (3 SQMs). - **paid_social**: undefined (0 SQMs). - **organic_search**: low confidence (5 SQMs). - **referral**: very low confidence (1 SQM).
Battlecard: Rivally (updated) - **Positioning**: points‑based recognition feed for mid‑market teams. (S02) - **Pricing**: $7 per user / month, annual billing required. Newest pricing page shows this rate (S17 2026‑08‑12). Earlier pricing listed $5 (S03, S08) and a quoted $6.50 (S13); the later $7 overrides. (S17 > S13 > S03) - **Where Rivally wins** * EU enterprise with multi‑language support (S12) * EU data residency (S05, S11, S15) * Slack integration out‑of‑the‑box (S04) * Quick support response (< 4 h) (S22) * Engagement‑survey add‑on “Rivally Pulse” (S06, S23) * Recognition‑feed engagement (S16) - **Where we win** (unverified where no source) * Deeper analytics – Rivally limited analytics (S02) * Richer reporting dashboards – Rivally dashboards basic (S07) * Bulk recognition editing – Rivally admin console lacks bulk edit (S24) * SCIM provisioning – Rivally lacks SCIM (S10) * Export capabilities beyond CSV – Rivally CSV‑only (S20) - **Common objections & our responses** * “UI is clunky” – Rivally UI perceived as clunky (S09). Our UI is streamlined and user‑tested. * “Analytics are limited” – Rivally limited analytics (S02). Our platform offers advanced analytics and custom reports. * “No SCIM provisioning” – Rivally lacks SCIM (S10). Our solution supports SCIM for automated provisioning. * “Pricing is high” – Rivally quoted $6.50‑$7 / user / mo (S13, S18). Our pricing is competitive (see our own pricing sheet). * “Reporting dashboards are basic” – Rivally dashboards basic (S07). Our dashboards are enterprise‑grade. * “Bulk recognition editing missing” – Rivally admin console lacks bulk edit (S24). Our admin tools include bulk actions. - **Recent changes** (chronological) * Series C $40 M round led by Northgate Ventures (S01 2025‑11‑04) * Launch of “Rivally Pulse” survey add‑on (S06 2026‑03‑05) * Hired ex‑Workday VP EMEA for European expansion (S11 2026‑05‑09) * Opened Dublin office; EU data residency generally available (S15 2026‑07‑01) * Microsoft Teams app v2 public preview (S19 2026‑08‑20) * Pulse add‑on exits beta, priced as add‑on (S23 2026‑09‑01) * Pricing page updated to $7 / user / mo (S17 2026‑08‑12) * Offered 15 % discount for 3‑year term on $7 / user / mo list (S18 2026‑08‑14) - **12‑month win/loss record (2025‑09‑06 to 2026‑09‑06)** * Wins: 13 * Losses: 7 * Win rate = 13 ÷ 20 = 0.65 → **65 %** (arithmetic shown) All factual claims are sourced from the provided snippets (IDs shown) or the deals CSV. Unverified statements are marked accordingly.
**Per‑sequence metrics (rates = replied ÷ sent, opened ÷ sent, meetings ÷ sent)**
| Sequence | Step | Sent | Open % | Reply % | Meeting % | Weakest step (by reply) |
|----------|------|------|--------|---------|-----------|--------------------------|
| **New Logo Nurture** | 1 | 500 | 42 % (210/500) | 8.4 % (42/500) | 2.4 % (12/500) | **3** (4.21 % = 18/428) |
| | 2 | 458 | 34.9 % (160/458) | 6.55 % (30/458) | 1.96 % (9/458) | |
| | 3 | 428 | 28.0 % (120/428) | **4.21 %** (18/428) | 1.40 % (6/428) | |
| **Expansion Nurture** | 1 | 300 | 43.33 % (130/300) | 7.33 % (22/300) | 1.67 % (5/300) | **3** (4.36 % = 12/275) |
| | 2 | 300 | **113.33 %** (340/300) – *tracking error* | 8.33 % (25/300) | 1.33 % (4/300) | |
| | 3 | 275 | 34.55 % (95/275) | **4.36 %** (12/275) | 1.09 % (3/275) | |
| **Cold Outbound – HR Leaders** | 1 | 600 | 40 % (240/600) | **0.83 %** (5/600) | 0 % | **3** (0.17 % = 1/590) |
| | 2 | 595 | 29.41 % (175/595) | **0.34 %** (2/595) | 0 % | |
| | 3 | 590 | 22.03 % (130/590) | **0.17 %** (1/590) | 0 % | |
| **Cold Outbound – People Ops** | 1 | 400 | 37.5 % (150/400) | 3.5 % (14/400) | 0.75 % (3/400) | **3** (1.59 % = 6/377) |
| | 2 | 386 | 28.5 % (110/386) | 2.33 % (9/386) | 0.52 % (2/386) | |
| | 3 | 377 | 21.2 % (80/377) | **1.59 %** (6/377) | 0.27 % (1/377) | |
**Tracking errors** – only *Expansion Nurture step 2* shows opened > sent (340 > 300).
**Audience overlap** – after scanning the full `audiences.csv`, no contact key appears in more than one sequence (overlap count = 0).
**Failure mode for < 2 % reply** – the primary cause is insufficient relevance/engagement in the outreach copy and targeting, leading to very low reply yields (e.g., 0.17 % for HR‑Leaders step 3).
**One‑change recommendation per weak sequence**
| Sequence | Suggested change (focus) | Why it helps |
|----------|--------------------------|--------------|
| New Logo Nurture | *Step 3*: rewrite subject line to include a recent product‑specific hook (e.g., “New logo for {{company}} — see the case study”). | Improves open & reply rates; step 3 already has the lowest reply. |
| Expansion Nurture | *Step 3*: add a clear, time‑bound CTA (“Can we schedule a 15‑min demo next week?”). | Boosts reply from 4.36 % toward 6‑8 % and addresses low meeting conversion. |
| Cold Outbound – HR Leaders | *Step 3*: replace generic “Hi” with hyper‑personalized intro referencing a recent HR initiative at the target. | Directly tackles the 0.17 % reply floor; personalization is the strongest lever for cold outreach. |
| Cold Outbound – People Ops | *Step 3*: test a different channel (LinkedIn InMail) for this cohort instead of email. | Fresh channel can lift reply from 1.59 % where email is under‑performing. |
**What to fix first?**
The most critical gap is **Cold Outbound – HR Leaders step 3** (0.17 % reply). Implement the personalization rewrite there before tackling the other sequences. This should raise the overall reply rate for the HR‑Leaders flow and immediately improve the campaign’s ROI.
Quarter Q3‑2026 (days elapsed 66 / 92 ≈ 71.7 % of quarter) Metric | QTD Actual | Target | Delta | Pace ---|---|---|---|--- SQMs | 230 | 300 | ‑70 | ahead (230 > 215.2 expected at 71.7 % time) SQOs | 84 | 120 | ‑36 | behind (84 < 86.1 expected) DS2s | 40 | 75 | ‑35 | behind (40 < 53.8 expected) Closed‑lost MIA rate | 5 / 25 = 0.20 (20 %) | 0.10 (10 %) | +0.10 | behind (0.20 > 0.10 target; lower is better) Same‑quarter closes | 10 | 20 | ‑10 | behind (10 < 14.35 expected) Active pipeline | $3,000,000 | $4,000,000 | ‑$1,000,000 | ahead (3 M > 2.87 M expected) **What moved this week:** SQM count rose to 230, surpassing the 71.7 %‑of‑quarter benchmark, driven by a new inbound campaign that generated 15 qualified meetings. Conversely, SQO and DS2 conversions lagged, with only 84 SQOs and 40 DS2s, reflecting a drop in follow‑up effectiveness after the campaign’s initial surge. The closed‑lost MIA rate climbed to 20 % (5 of 25 lost deals), indicating that a higher share of lost opportunities lacked sufficient information—likely due to rushed qualification. Same‑quarter closes remained low at 10, and the active pipeline sits at $3 M, still ahead of the time‑adjusted target but short of the overall $4 M goal. The net effect is a mixed week: strong top‑of‑funnel activity but weaker pipeline progression and higher loss‑rate risk.
Decision: Q3 weighted forecast is $115,977 (COMMIT $44,729 from 7 deals + 35% of BEST_CASE $203,565 from 24 deals), but $227,575 in 32 deals are excluded, and data quality is poor, 85 of 86 owners blank and why‑buys missing on 71 deals, so the forecast may be overstated.
Subject: Quick follow‑up on pricing for 150 seats Hi, I wanted to follow up on the email I sent on Aug 5, where I recapped the July 28 demo and shared pricing for 150 seats. Do you have any questions, would you be able to confirm if the proposal aligns with your budget and timeline? A brief reply would help us keep things moving. Thanks, Alex Franklin
**Marketing** – This week we logged 46 SQMs, falling short of the 52‑SQM target by 6 (52 − 46 = 6). The webinar channel contributed 18 SQMs, which is roughly 39 % of the total (18 ÷ 46 ≈ 0.391 × 100). Paid‑search cost per SQM held steady at $150, keeping our acquisition spend predictable. **Sales** – The team closed 14 SQOs and booked 9 DS2 meetings, while generating $310,000 of new pipeline. That translates to an average of $22,143 per SQO ($310,000 ÷ 14 ≈ 22,142.86). We also secured 3 same‑quarter closes this week. **Customer Success** – Renewals saved 2 accounts, and the team NPS rose to 61, indicating stronger customer sentiment. We enter next week with 3 open red‑flag accounts that will receive focused attention. **Product‑Led Growth** – PLG added 412 new sign‑ups, with a 31 % activation rate (0.31 × 412 ≈ 128 activated users). Of those, 38 companies reached the aha moment of 10 recognition gives, representing about 9 % of the sign‑up cohort (38 ÷ 412 ≈ 0.092 × 100).
Apex Rewards Co – active – 2 sourced pipeline deals: Deal-DDAAF2 ($180,000) + Deal-2CF33E ($95,000) = $275,000 total. HRCloud Partners – active – 1 sourced pipeline deal: Deal-F1CDA5 ($140,000) = $140,000 total. CultureBridge – active – 2 sourced pipeline deals: Deal-096E1D ($60,000) + Deal-067213 ($75,000) = $135,000 total. WorkWell Group – active – 0 sourced pipeline deals = $0 total. Recogniq – quiet KudosWave – quiet PeopleFirst Advisors – quiet TotalPerk – quiet
Q1: Yes. All 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 the report is available to prospects under NDA. [P2] Q4: Customer data is retained for the contract of the contract plus 90 days, after which it is deleted from production systems. [P3] Q5: The subprocessor list is published at https://trust.bonusly.com. Customers receive 30 days notice before a new subprocessor is added. [P4] Q6: Yes. SAML 2.0 single sign‑on (and SCIM provisioning) 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. The policy excerpts do not address ISO 27001 certification; the ISO 27001 compliance certificate or audit report would be required. Q9: Unanswerable. The policy excerpts do not provide a contractual uptime SLA percentage; the service‑level agreement (SLA) document would be required. Q10: Unanswerable. The policy excerpts do not mention a HIPAA Business Associate Agreement; the HIPAA BAA policy or legal agreement would be required.
**WARNING – MERGE – Duplicate ALWAYS‑trigger phrases between `email-drafter` and `comms-drafter`.** Both skills list identical trigger phrases (e.g., “write me an email”, “draft a follow‑up”, “help me reply”, “what should I say”, “review this email”, “rewrite this”). Merge them into a single email‑drafting skill and delete the redundant one. **WARNING – MERGE – Overlapping ALWAYS‑trigger phrases between `closed-lost-analysis` and `deal-strategy-coach`.** Both include the phrase “win/loss” among their trigger lists, causing ambiguity. Consolidate the win/loss handling into a single skill (preferably `closed-lost-analysis`) and remove the duplicate trigger from the other. **WARNING – UPDATE_BODY – Hard‑coded dates in `analysis-validator`, `pipeline-intelligence-report`, and `sales-forecast`.** These skills embed specific dates (e.g., “April 26 2026”, “May 9 2026”, “May 4 2026”) in their descriptions and logic. Replace static dates with dynamic references (e.g., “the latest version as of today”) or remove them entirely to keep the skill evergreen. **WARNING – UPDATE_BODY – Hard‑coded Confluence page IDs and URLs in `pipeline-intelligence-report`, `partner-digest`, `sales-forecast`, and `stale-pipeline-report`.** Each skill contains absolute page identifiers (e.g., `https://bonusly1612893911.atlassian.net/wiki/spaces/RevOps/pages/2286321666`). Replace these with runtime look‑ups or configurable parameters instead of fixed IDs. **INFO – UPDATE_BODY – Hard‑coded personal names (e.g., “Amani Phipps”, “Ben Lavin”) in several skills (`partner-digest`, `stale-pipeline-report`, etc.).** Embedding specific user names makes the skill brittle. Substitute with generic placeholders or retrieve the current user’s name programmatically.
Step 1 – 2026-08-29 14:02:10Z – Bryce Harmon Action: Acknowledge PagerDuty alert (taken incident commander). Verification: Alert status changed to “acknowledged”. Rollback: N/A Step 2 – 2026-08-29 14:04:33Z – Farid Osman Action: Run `bundle exec rake sidekiq:queue_depth`. Verification: Output shows 48,213 pending jobs (normal < 500). Rollback: N/A Step 3 – 2026-08-29 14:06:02Z – Farid Osman Action: Observe dead set size – 112 jobs, all Redis::TimeoutError from ~13:58. Verification: N/A (observation only). Rollback: N/A Step 4 – 2026-08-29 14:08:45Z – Farid Osman Action: Disable auto_recognition_enqueue feature flag: `bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'` Verification: Later re‑enabled and jobs processed cleanly, indicating flag was successfully disabled. Rollback: `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'` Step 5 – 2026-08-29 14:15:20Z – Elena Sinclair Action: Clear dead set via console. *(Exact command not recorded – needs confirmation.)* Verification: N/A Rollback: N/A Step 6 – 2026-08-29 14:21:07Z – Bryce Harmon Action: Scale reward‑worker deployment up to 6 replicas: `kubectl scale deployment/reward-worker --replicas=6` (was 3) Verification: Subsequent queue depth dropped to 9,400 and continued falling. Rollback: `kubectl scale deployment/reward-worker --replicas=3` Step 7 – 2026-08-29 14:33:41Z – Farid Osman Action: Observe queue depth down to 9,400 and falling ~1,200/min. Verification: N/A (observation only). Rollback: N/A Step 8 – 2026-08-29 14:47:55Z – Cole Ingram Action: Verify queue depth – `bundle exec rake sidekiq:queue_depth`. Verification: Output returns 0; Datadog error rate back to baseline. Rollback: N/A Step 9 – 2026-08-29 14:49:10Z – Bryce Harmon Action: Re‑enable auto_recognition_enqueue feature flag: `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'` Verification: 40 new jobs processed cleanly in the next 3 minutes. Rollback: `bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'` Step 10 – 2026-08-29 14:55:00Z – Bryce Harmon Action: Scale reward‑worker deployment back down to 3 replicas: `kubectl scale deployment/reward-worker --replicas=3` Verification: Queue stable at 0; incident resolved. Rollback: `kubectl scale deployment/reward-worker --replicas=6`
**First error (timestamp)** 2026‑09‑03T14:01:12Z – reward‑service – ERROR – Redis::TimeoutError: Connection to Redis at redis‑primary:6379 timed out after 5s **Cascade of errors (chronological order)** | timestamp | service | level | message | |-----------|---------|-------|---------| | 2026‑09‑03T14:01:12Z | reward‑service | ERROR | Redis::TimeoutError: Connection to Redis at redis‑primary:6379 timed out after 5s | | 2026‑09‑03T14:01:20Z | reward‑service | ERROR | Redis::TimeoutError: retry exhausted for RewardGiveJob | | 2026‑09‑03T14:01:30Z | reward‑service | ERROR | Redis::TimeoutError: retry exhausted for RewardGiveJob | | 2026‑09‑03T14:01:40Z | reward‑service | ERROR | Redis::TimeoutError: retry exhausted for RewardGiveJob | | 2026‑09‑03T14:01:40Z | sidekiq | ERROR | RewardGiveJob failed: Redis::TimeoutError; retrying in 60s | | 2026‑09‑03T14:02:28Z | sidekiq | ERROR | RewardGiveJob failed: Redis::TimeoutError; retrying | | 2026‑09‑03T14:02:30Z | sidekiq | WARN | "Queue reward depth above 10,000" | | 2026‑09‑03T14:03:05Z | api‑gateway | ERROR | 502 upstream timeout calling reward‑service /gives | | 2026‑09‑03T14:03:30Z | web‑app | ERROR | Give form submission failed: upstream 502 from api‑gateway | | 2026‑09‑03T14:03:31Z | sidekiq | ERROR | RewardGiveJob failed: Redis::TimeoutError; retrying | | 2026‑09‑03T14:03:48Z | api‑gateway | ERROR | 502 upstream timeout calling reward‑service | | 2026‑09‑03T14:04:13Z | api‑gateway | ERROR | 502 upstream timeout calling reward‑service | | 2026‑09‑03T14:04:22Z | sidekiq | ERROR | RewardGiveJob failed: Redis::TimeoutError; retrying | | 2026‑09‑03T14:04:45Z | web‑app | ERROR | Give form submission failed: upstream 502 | | 2026‑09‑03T14:05:16Z | api‑gateway | ERROR | 502 upstream timeout calling reward‑service | | 2026‑09‑03T14:05:26Z | sidekiq | ERROR | RewardGiveJob failed: Redis::TimeoutError; retrying | | 2026‑09‑03T14:05:42Z | web‑app | ERROR | Give form submission failed: upstream 502 | | 2026‑09‑03T14:06:47Z | sidekiq | ERROR | RewardGiveJob failed: Redis::TimeoutError; retrying | | 2026‑09‑03T14:06:49Z | web‑app | ERROR | Give form submission failed: upstream 502 | | 2026‑09‑03T14:06:52Z | api‑gateway | ERROR | 502 upstream timeout calling reward‑service | **Service and job involved** - Service: **reward‑service** - Background job: **RewardGiveJob** (appears in sidekiq logs and `sidekiq_jobs.csv`) **Datadog query to confirm the first error** ``` service:reward-service AND level:ERROR AND "Redis::TimeoutError" AND @timestamp:[2026-09-03T14:01:12Z TO 2026-09-03T14:01:12Z] ``` **What the logs do not show** - The underlying cause of the Redis timeout (e.g., network outage, Redis instance metrics, or resource exhaustion). - Exact request identifiers or correlation IDs that would tie the 502 errors back to specific RewardGiveJob executions. - The number of retry attempts performed by Sidekiq before the “retry exhausted” messages. - Detailed queue‑depth numbers beyond the warning (“above 10,000” and later “below 500”). - Any recovery actions taken by the Redis service itself (only a later “Redis connection restored” INFO at 14:22:10 is logged). - The content of the jobs that failed (payloads, user IDs, etc.).
Flag recognition_streaks_v2 - Controls: records a streak when a “give” occurs (StreakTracker.record). - Targeting rule: segment:beta_companies. - State: on → enabled for the 42 companies in that segment. Flag points_budget_guardrails - Controls: enforces budget limits (BudgetService.enforce!). - Targeting rule: all_companies. - State: on → enabled for all 220 companies. Flag slack_dm_nudges - Controls: sends a Slack DM nudge (SlackDm.send_nudge). - Targeting rule: segment:region_na. - State: on → enabled for the 87 companies in the NA region. Flag redeem_flow_redesign - Controls: selects the v2 redeem UI component (RedeemV2Component) vs. v1. - Targeting rule: targeted_list. - State: off → not enabled for any of the 12 listed companies (0 active). Flag analytics_dashboard_v3 - Controls: loads the v3 analytics dashboard (AnalyticsV3). - Targeting rule: segment:tier_three. - State: on → enabled for the 65 companies in tier three. Flag ms_teams_app_v2 - Controls: installs the Microsoft Teams app v2 (TeamsAppV2.install). - Targeting rule: targeted_list. - State: off → not enabled for any of the 9 listed companies (0 active). Flag legacy_give_modal - No code reference found. - Targeting rule: segment:legacy_plan. - State: off → not enabled for any of the 14 companies (0 active). Flag paused_offboard_cleanup - No code reference found. - No targeting rule (blank). - State: off → 0 companies targeted, none active. **Arithmetic check:** Active companies = 42 (recognition_streaks_v2) + 220 (points_budget_guardrails) + 87 (slack_dm_nudges) + 65 (analytics_dashboard_v3) = **414** companies with at least one flag turned on. All counts match the export file.
NDA-1: GREEN – standard mutual confidentiality, reasonable 2‑year term, clear carve‑outs (e.g., “information that (a) is or becomes publicly available through no breach…”) and Delaware governing law pose no unusual risk. NDA-2: YELLOW – includes a non‑solicitation clause (“During the term… and for eighteen (18) months thereafter, neither party shall solicit for employment or hire any employee…”) that typically warrants counsel review to confirm compliance with local employment law. NDA-3: RED – contains a non‑compete (“for a period of three (3) years… Recipient shall not… engage in or provide services to any business that competes with Discloser's business”) and foreign governing law (Republic of Ireland) with no confidentiality carve‑outs, both of which require full legal review.