n_companies: 34
n_contacts: 52
company_missing: industry=0; employee_count=9; hq_country=6
invalid_email_count: 4
invalid_emails: CT-0010, CT-0080, CT-0081, CT-0192
domain_mismatch_count: 1
domain_mismatches: CT-0011
duplicate_clusters: ['C-0A092931', 'C-0A092932'], ['C-0A092933', 'C-0A092934']
n_dup_clusters: 2
enrichment_matches: 25
graded checks: dup_clusters · invalid_emails · survivor_language · completeness_pct · no_invent

Model's actual answer

show the model's full answer

DEALS (owner, stage, amount, close date, why-buys)
- Missing data: no deals extract was provided, so I cannot:
  - report deal field completeness %
  - identify which deals are missing owner/stage/amount/close date/why-buys
  - compute “10 fixes with the most pipeline amount at stake” (amounts not provided)

If you share deals.csv (deal_alias, company_alias, owner, stage, amount, close_date, why_buys), I can produce the deal completeness + top-10-by-$ list.

============================================================
COMPANIES — completeness (n=34 rows)
Required fields: industry, employee_count, hq_country

Completeness % per field (arithmetic shown)
- industry: 34 filled / 34 total = 34/34 = 100.00%
- employee_count: 25 / 34 = 73.53%
- hq_country: 28 / 34 = 82.35%

Missing values (exact aliases)
- Missing employee_count (9):
  C-EC3025, C-96039F, C-44EA29, C-D04904, C-B23205, C-60C75F, C-7BBDFA, C-50D386, C-93C8BF
- Missing hq_country (6):
  C-2D1F1B, C-D73B89, C-44EA29, C-D04904, C-2C60E5, C-EE9FFB

Enrichment coverage (ZoomInfo export match by domain)
- 25 enriched domains / 34 company domains = 25/34 = 73.53%
- Companies without enrichment row (9):
  C-BA969B, C-332637, C-93C8BF, C-EE9FFB, C-C9BB20, C-0A092931, C-0A092932, C-0A092933, C-0A092934

Fill missing company fields ONLY where enrichment has a matching row
- employee_count fills available from enrichment (8 of the 9 missing employee_count):
  - C-EC3025 (ec3025.com): employee_count = 400 (from zi_employee_count)
  - C-96039F (96039f.com): employee_count = 400
  - C-44EA29 (44ea29.com): employee_count = 400
  - C-D04904 (d04904.com): employee_count = 400
  - C-B23205 (b23205.com): employee_count = 400
  - C-60C75F (60c75f.com): employee_count = 400
  - C-7BBDFA (7bbdfa.com): employee_count = 400
  - C-50D386 (50d386.com): employee_count = 400
- employee_count cannot be filled (no enrichment match):
  - C-93C8BF (93c8bf.com): missing employee_count, and no zoominfo_enrichment.csv row for 93c8bf.com
- hq_country fills: none possible from the provided enrichment rows (the relevant zi_hq_country values are blank where CRM is blank, or enrichment row is missing)

Where CRM and enrichment disagree (list both; recommend a source)
Recommendation logic (no invented facts): use enrichment for standardized taxonomy/canonical country strings (reduces “US” vs “USA” vs “United States” fragmentation); keep CRM value if it is an intentionally-curated internal segmentation field, but then store separately (e.g., crm_industry vs zi_industry).

Disagreements found (field-level):
1) C-66D1FC (66d1fc.com)
- industry: CRM=tech vs ZI=Computer Software → recommend ZI for reporting taxonomy
- hq_country: CRM=US vs ZI=United States → recommend ZI for canonical country
2) C-950043 (950043.com)
- hq_country: CRM=US vs ZI=United States → recommend ZI
3) C-EC3025 (ec3025.com)
- industry: CRM=Technology vs ZI=Computer Software → recommend ZI
- hq_country: CRM=USA vs ZI=United States → recommend ZI
4) C-96039F (96039f.com)
- hq_country: CRM=USA vs ZI=United States → recommend ZI
5) C-44EA29 (44ea29.com)
- industry: CRM=tech vs ZI=Computer Software → recommend ZI
6) C-92D97D (92d97d.com)
- industry: CRM=Technology vs ZI=Computer Software → recommend ZI
7) C-D04904 (d04904.com)
- industry: CRM=Technology vs ZI=Computer Software → recommend ZI
8) C-77A95A (77a95a.com)
- industry: CRM=Technology vs ZI=Computer Software → recommend ZI
- hq_country: CRM=US vs ZI=United States → recommend ZI
9) C-AA8DDA (aa8dda.com)
- industry: CRM=Technology vs ZI=Computer Software → recommend ZI
10) C-B23205 (b23205.com)
- hq_country: CRM=US vs ZI=United States → recommend ZI
11) C-E51FB7 (e51fb7.com)
- hq_country: CRM=USA vs ZI=United States → recommend ZI
12) C-D0662E (d0662e.com)
- hq_country: CRM=US vs ZI=United States → recommend ZI
13) C-B25F40 (b25f40.com)
- industry: CRM=Technology vs ZI=Computer Software → recommend ZI
14) C-60C75F (60c75f.com)
- industry: CRM=tech vs ZI=Computer Software → recommend ZI
15) C-425E2A (425e2a.com)
- industry: CRM=Tech vs ZI=Computer Software → recommend ZI
- hq_country: CRM=USA vs ZI=United States → recommend ZI
16) C-2D7423 (2d7423.com)
- hq_country: CRM=USA vs ZI=United States → recommend ZI

Duplicate company clusters (shared domain)
Note: “name variants” cannot be assessed because companies.csv has no company name field; only alias+domain.

Clusters:
- Domain cluster: acme-corp.com
  - Members:
    - C-0A092931: industry=Technology, employee_count=500, hq_country=US
    - C-0A092932: industry=tech, employee_count=510, hq_country=USA
  - Survivor (tie-break rule: both equally complete → choose lower alias): C-0A092931
  - Fix: merge C-0A092932 → C-0A092931; normalize industry/country strings (tech→Technology or map to a controlled picklist; US/USA→one canonical)
- Domain cluster: globex.io
  - Members:
    - C-0A092933: industry=SaaS, employee_count=200, hq_country=US
    - C-0A092934: industry=Technology, employee_count=200, hq_country=US
  - Survivor (both equally complete → choose lower alias): C-0A092933
  - Fix: merge C-0A092934 → C-0A092933; decide controlled industry value (SaaS vs Technology) via defined taxonomy rules

============================================================
CONTACTS — completeness + data quality (n=52 rows)
Required fields: email, title, persona

Completeness % per field (arithmetic shown)
- email (non-empty): 52/52 = 100.00%
- title: 39/52 = 75.00%
- persona: 37/52 = 71.15%

Email validity + domain consistency (additional quality checks)
- Valid emails: 48 valid / 52 total = 48/52 = 92.31%
- Invalid emails (4):
  - CT-0010 (C-66D1FC): email=user0@
  - CT-0080 (C-92D97D): email=user0@
  - CT-0081 (C-92D97D): email=user1@
  - CT-0192 (C-425E2A): email=user2@
- Domain mismatches (email domain vs contact domain), among valid emails:
  - 1 mismatch / 48 valid = 1/48 = 2.08%
  - CT-0011 (C-66D1FC): email=user1@other-domain.com vs domain=66d1fc.com
- Contact domain vs company domain mismatches: 0 (0/52)

Missing titles (13 contacts)
CT-0000, CT-0022, CT-0072, CT-0080, CT-0081, CT-0092, CT-0120, CT-0121, CT-0122, CT-0132, CT-0141, CT-0162, CT-0170

Missing personas (15 contacts)
CT-0000, CT-0022, CT-0041, CT-0060, CT-0070, CT-0081, CT-0082, CT-0092, CT-0110, CT-0132, CT-0162, CT-0171, CT-0172, CT-0180, CT-0181

============================================================
PROPOSED FIXES (no invented values)
Companies
1) Backfill employee_count from zoominfo_enrichment.csv where missing and matched (8 updates listed above).
2) Standardize hq_country values (US vs USA vs United States) using a controlled picklist; recommend “United States” if you adopt ZoomInfo canonical strings (but do not overwrite blanks unless you have a value).
3) Standardize industry values (tech/Tech/Technology/Computer Software/SaaS/health care/Healthcare) via controlled picklist + mapping; recommend using zi_industry for consistent reporting, while optionally retaining CRM’s value as a separate “segment” field if it’s intentionally curated.
4) Enforce required fields at create/update: industry, employee_count, hq_country (with “Unknown” as an explicit allowed value if you prefer, but that value is not present in your data today).
5) De-dupe companies by domain: merge the two domain clusters (acme-corp.com, globex.io) into survivors above; re-point related contacts/deals to survivor.

Contacts
6) Add validation rule: email must match regex and cannot end with “@” (prevents CT-0010/CT-0080/CT-0081/CT-0192 patterns).
7) Add warning/flag when email domain != company domain (captures CT-0011) for review (contractor/agency vs bad data).
8) Enforce required title + persona (or require at least one of them at creation) to improve routing and sequencing.
9) Standardize persona picklist (values observed: HR admin, champion, economic buyer) and require one selection.

Deals (blocked by missing file)
10) Cannot propose the “10 fixes with the most pipeline amount at stake” without deal amount + deal issues. Provide deals extract to rank fixes by $.

============================================================
TOP 10 FIXES WITH MOST PIPELINE AMOUNT AT STAKE
Blocked: deals data (including amount) was not provided, so I cannot calculate or rank by pipeline amount at stake.

Candidates (prospect voice only)

1) Deal-EC3025 — Amount: missing (not provided)
Prospect quote: “We need SCIM user provisioning — our security team will not approve any tool that requires manual account management.”
Product-docs check: “SCIM user provisioning … NOT currently listed as supported capabilities.”
Classification: REAL GAP (requested capability is explicitly not listed as supported)

2) Deal-D0D6B5 — Amount: missing (not provided)
Prospect 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.”
Product-docs check: “ADP Workforce Now integrations are NOT currently listed as supported capabilities.” Supported HRIS list is Workday, BambooHR, Gusto, Rippling (Pro+).
Classification: REAL GAP (requested integration is explicitly not listed as supported)

3) Deal-CFE7F4 — Amount: missing (not provided)
Prospect quote: “I tried to build a custom report combining recognition frequency with turnover and couldn't — is that possible on our current plan?”
Product-docs check: “Custom report builder” is Enterprise-only.
Classification: PLAN GATE (capability exists, but only on Enterprise; question is about current plan access)

4) Deal-84DBA6 — Amount: missing (not provided)
Prospect quote: “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.”
Product-docs check: Slack integration is supported on “all plans.”
Classification: ROLLOUT/ENABLEMENT ISSUE (capability is supported; issue is adoption/training, not product functionality)

Summary (real gaps only)

- SCIM user provisioning gap — Deal-EC3025 — Amount missing
Prospect: “We need SCIM user provisioning — our security team will not approve any tool that requires manual account management.”

- ADP Workforce Now integration gap — Deal-D0D6B5 — Amount missing
Prospect: “Our payroll runs on ADP Workforce Now and I don't see ADP anywhere in your integration list — that's a dealbreaker for us.”

Arithmetic: real gaps = 2 (Deal-EC3025, Deal-D0D6B5).

What Alex Franklin leads with in the first five minutes (10 calls; TT-001 to TT-010)

1) Outcome-led customer story (8/10 = 8 ÷ 10 = 0.80 = 80%)
- “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.”

2) Agenda-setting (1/10 = 1 ÷ 10 = 0.10 = 10%)
- “I put together a short agenda — security review first, then pricing.”

3) Pricing-first (1/10 = 1 ÷ 10 = 0.10 = 10%)
- “You asked for straight pricing last time, so let's start there.”

4) Early competitive positioning (1/10 = 1 ÷ 10 = 0.10 = 10%)
- “And unlike Workhuman, our pricing includes the full rewards catalog with no extra margin.”


Three most common objections + how he handles them (by frequency across 10 calls)

1) “Budget locked until next fiscal year” (4/10 = 4 ÷ 10 = 0.40 = 40%)
- Objection: “Honestly, budget is locked until next fiscal year — I can't add a new line item right now.”
- His handle: “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.”

2) “Revisit next quarter / too busy” (3/10 = 3 ÷ 10 = 0.30 = 30%)
- Objection: “This is good, but can we revisit it next quarter? Open enrollment starts in October and we're underwater.”
- His handle: “Makes sense. What if we scope a 90-day pilot with one department so you have internal data before next quarter's planning?”

3) “We already do recognition (spreadsheet + gift cards)” (3/10 = 3 ÷ 10 = 0.30 = 30%)
- Objection: “We already do recognition with a spreadsheet and quarterly gift cards — why would we change?”
- His handle: “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.”


How often a concrete next step is agreed (rate)

- Concrete next step agreed in 7 of 10 calls.
- Arithmetic: 7/10 = 7 ÷ 10 = 0.70 = 70%
- Example agreement line (appears in those 7 calls): “Yes, Thursday at 2pm works — send the invite and I'll bring our HRIS manager.”


Every competitor a prospect raised (from transcripts; prospect-spoken only)

1) Awardco (raised in Deal-547B2B)
- “We're also in late talks with Awardco — their rewards catalog looks bigger than yours.”

2) Kudos (raised in Deal-EDC141)
- “How are you different from Kudos? Our CEO used them at her last company.”


Two coaching notes

1) The opener is highly repeated (same “400-person retailer…18%” story in 8/10 calls); test a second proof-point opener so prospects don’t feel like they’re getting a script.

2) When “committee” blocks progress (e.g., Deal-403845, Deal-84DBA6), the transcript shows acknowledgment but not a scheduled path forward; add a standard “committee-ready” next step (e.g., a time-boxed prep/work session) to keep momentum instead of ending on “leave it with you.”

Last completed month: 2026-08 (max month present in redemptions_ytd.csv is 2026-08)

Redemption (YTD through 2026-08, inclusive)
- Redemption count: 378
- Spend (USD): 27,884.00
- Unique redeemers: 233
- Redemptions per redeemer: 378 / 233 = 1.6223… ≈ 1.62

Provider mix (% of spend; sums to 100.00%)
- custom: 10,873 / 27,884 = 38.99%
- Tremendous: 8,540 / 27,884 = 30.63%
- Snappy: 5,241 / 27,884 = 18.80%
- TangoCard: 3,230 / 27,884 = 11.58%
Sum check: 38.99% + 30.63% + 18.80% + 11.58% = 100.00%

Top 5 countries by redemptions (count)
1) US: 244
2) CA: 24
3) AU: 21
4) GB: 17
5) NL: 17

Total tickets in file: 80

Ranked by ARR exposure (ARR affected = sum of ARR for DISTINCT accounts in the theme)

1) HRIS provisioning/sync failures (new hires not created) — broad pattern
- Count: 12
- Share: 12/80 = 0.15 = 15.0%
- Distinct accounts: 3 (C-0B2213A9, C-0F6C0F34, C-0DDFC9A7)
- ARR affected (distinct-account sum): 36,000 + 30,000 + 48,000 = 114,000
- Two ticket ids: IC-460059, IC-460055
- Recommendation (1 line): Add HRIS sync health checks + exception reporting (missed hires), and make provisioning failures impossible to “silently succeed.”

2) Rewards redemption & checkout failures (gift cards, spinning checkout) — broad pattern
- Count: 18
- Share: 18/80 = 0.225 = 22.5%
- Distinct accounts: 7 (C-0CEF69FD, C-0B827671, C-0FCCD2DF, C-0F876796, C-14264ABD, C-0D9CA315, C-0B0F1BAB)
- ARR affected (distinct-account sum): 8,900 + 10,700 + 9,600 + 8,700 + 11,000 + 9,600 + 10,300 = 68,800
- Two ticket ids: IC-460025, IC-460030
- Recommendation (1 line): Harden redemption checkout with timeouts/retries + idempotency so “spin forever / deduct points / no code” cannot happen.

3) Billing/invoice correctness (seat count, tier pricing) — single-account concentration (but high ARR)
- Count: 16
- Share: 16/80 = 0.20 = 20.0%
- Distinct accounts: 1 (C-0E9C27D1)
- ARR affected (distinct-account sum): 52,000 = 52,000
- Two ticket ids: IC-460071, IC-460069
- Recommendation (1 line): Implement strict seat/tier change controls (approval + audit trail) and an automated invoice validation check before issuing charges.

4) Recognition/points ledger issues (missing points, balance not updating) — broad pattern
- Count: 20
- Share: 20/80 = 0.25 = 25.0%
- Distinct accounts: 9 (C-0D3278C7, C-0BF20542, C-0D0B047C, C-0BE96399, C-0D284E42, C-0D6CC8E3, C-21FEBCBB, C-0DD0626C, C-0B2895EF)
- ARR affected (distinct-account sum): 3,500 + 4,500 + 4,500 + 2,700 + 3,400 + 4,200 + 2,900 + 2,500 + 2,900 = 31,100
- Two ticket ids: IC-460004, IC-460016
- Recommendation (1 line): Add ledger reconciliation + customer-visible posting status so “delivered recognition” and “points balance” can’t diverge without detection.

5) Slack integration reliability (posting, auth, slash command, toggles) — broad pattern
- Count: 14
- Share: 14/80 = 0.175 = 17.5%
- Distinct accounts: 4 (C-0B843542, C-10A56B0F, C-0BA71F12, C-8C2E8F00)
- ARR affected (distinct-account sum): 4,400 + 5,400 + 3,900 + 5,200 = 18,900
- Two ticket ids: IC-460041, IC-460047
- Recommendation (1 line): Stabilize Slack auth + settings persistence (toggle resets / re-auth not sticking) and add automated monitoring for slash command + posting failures.

Weekly marketing goals update — Q3-2026 (days elapsed: 66 of 92; pace factor = 66/92 = 0.71739)

SQMs
- QTD actual: 230
- Target: 300
- Delta: 230 − 300 = −70
- Pace: ON (expected QTD = 300 × (66/92) = 300 × 0.71739 = 215.22; actual 230 ≥ 215.22)

SQOs
- QTD actual: 84
- Target: 120
- Delta: 84 − 120 = −36
- Pace: BEHIND (expected QTD = 120 × (66/92) = 120 × 0.71739 = 86.09; actual 84 < 86.09)

DS2s
- QTD actual: 40
- Target: 75
- Delta: 40 − 75 = −35
- Pace: BEHIND (expected QTD = 75 × (66/92) = 75 × 0.71739 = 53.80; actual 40 < 53.80)

closed-lost MIA rate
- QTD actual: 0.20 (closed_lost_mia_rate = closed_lost_mia / closed_lost_total = 5 / 25 = 0.20)
- Target: 0.10
- Delta: 0.20 − 0.10 = +0.10
- Pace: BEHIND (lower is better; actual 0.20 > target 0.10)

same_quarter_closes
- QTD actual: 10
- Target: 20
- Delta: 10 − 20 = −10
- Pace: BEHIND (expected QTD = 20 × (66/92) = 20 × 0.71739 = 14.35; actual 10 < 14.35)

active pipeline coverage against target
- QTD actual: 3,000,000
- Target: 4,000,000
- Delta: 3,000,000 − 4,000,000 = −1,000,000
- Coverage: 3,000,000 / 4,000,000 = 0.75 (75%)
- Pace: ON (expected QTD = 4,000,000 × (66/92) = 4,000,000 × 0.71739 = 2,869,565.22; actual 3,000,000 ≥ 2,869,565.22)

What moved this week
- Not determinable from the provided data: marketing_qtd.csv is a single QTD snapshot with no prior-week QTD values or weekly deltas, so I can’t quantify week-over-week movement. From the current snapshot only: SQMs and active_pipeline are on pace versus time elapsed, while SQOs, DS2s, and same_quarter_closes are behind pace; the closed-lost MIA rate is 0.20 (5/25), above the 0.10 target.

Q1: Is customer data encrypted at rest? If so, how?
Yes. Customer data is encrypted at rest using AES-256. (Excerpt: [P1])

Q2: Is data encrypted in transit?
Yes. Data in transit is encrypted with TLS 1.2 or higher. (Excerpt: [P1])

Q3: Do you hold a SOC 2 report, and can we obtain a copy?
Yes. Bonusly maintains a SOC 2 Type II report (renewed annually) and makes it available to prospects under NDA. (Excerpt: [P2])

Q4: How long is customer data retained after contract termination?
Customer data is retained for the duration of the contract plus 90 days, then deleted from production systems. Post-termination retention arithmetic: 0 days after contract end + 90 days = 90 days after contract termination. (Excerpt: [P3])

Q5: Where is your subprocessor list published and how are changes notified?
The subprocessor list is maintained at trust.bonusly.com. Customers receive 30 days notice before a new subprocessor is added. (Excerpt: [P4])

Q6: Do you support SAML single sign-on?
Yes. SAML 2.0 SSO is available on the Enterprise plan. (Excerpt: [P5])

Q7: What is your production backup schedule and retention?
Production databases are backed up daily. Backups are encrypted and retained for 35 days. (Excerpt: [P6])

Q8: Do you hold an ISO 27001 certification?
Unanswerable from the provided excerpts; ISO 27001 is not addressed in [P1]-[P8]. The document that would answer this is an ISO 27001 certificate (and/or an ISO scope statement) or a trust portal compliance page explicitly stating ISO 27001 certification. (Excerpts reviewed: [P1]-[P8])

Q9: What is your contractual uptime SLA percentage?
Unanswerable from the provided excerpts; uptime SLA is not addressed in [P1]-[P8]. The document that would answer this is your Master Subscription Agreement and/or the Service Level Agreement section specifying uptime percentage. (Excerpts reviewed: [P1]-[P8])

Q10: Will you sign a HIPAA Business Associate Agreement?
Unanswerable from the provided excerpts; HIPAA/BAA is not addressed in [P1]-[P8]. The document that would answer this is a HIPAA/BAA policy statement and/or a standard BAA legal addendum for review. (Excerpts reviewed: [P1]-[P8])

NDA-1 — GREEN (standard approval)
Driving clause: “Carve-outs: information that (a) is or becomes publicly available through no breach, (b) was known prior to disclosure, (c) is received from a third party without duty of confidence, (d) is independently developed, or (e) must be disclosed by law or court order.” + “Governing law: the laws of the State of Delaware, USA.”
Reasoning (one line): Mutual NDA with explicit standard carve-outs, standard Delaware governing law, and no non-solicit/non-compete/exclusivity language (“No license, no obligation to proceed, no exclusivity.”).

NDA-2 — YELLOW (counsel review)
Driving clause: “Non-solicitation: ‘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.’”
Reasoning (one line): Embedded employee non-solicit/hire restriction extending 18 months post-term (18/12 = 1.5 years) is a material non-confidentiality obligation that typically needs counsel review for scope (e.g., “came into contact”) and enforceability.

NDA-3 — RED (full legal review)
Driving clause: “Non-compete: ‘For a period of three (3) years following the Effective Date, Recipient shall not, directly or indirectly, engage in or provide services to any business that competes with Discloser’s business.’” + “No confidentiality carve-outs are stated in this Agreement.” + “Governing law: ‘…laws of the Republic of Ireland… exclusive jurisdiction…’”
Reasoning (one line): One-way NDA in favor of [PARTY C] with a 3-year non-compete (3 years), no confidentiality carve-outs at all, and Ireland governing law with exclusive jurisdiction is high-risk/non-standard and requires full legal review.