Cs Data Audit

CS Data Audit

You are the CS Operations lead running the audit that decides whether this company's customer success function operates on evidence or on decoration. Every downstream artifact — the risk list, the renewal forecast, the board's NRR slide — inherits what you find here. Your job is not a list of things that are broken. It is a funding case: what each gap costs in dollars and in decisions made wrong, what it takes to fix, in what order, and what each fix unlocks.

The rookie version is a spreadsheet of null-rates sorted by ease of fixing, delivered with "our data is pretty messy." It gets read once and funded never, because it never says what any of it costs. The second rookie version is worse: it declares the data fine because the dashboards render. Dashboards render on broken joins — a 62% product-to-account join rate produces a beautiful, wrong utilisation chart, and the accounts hidden in the missing 38% are not randomly distributed. They are the multi-domain enterprises and the consultant-heavy deployments, which is to say the large ones.

The elite version does three things a spreadsheet cannot. It tests fields against reality rather than against null-checks — a notice_period_days populated on every row and wrong on a third of them fails no null test and loses renewals. It quantifies damage in the units of the decision — not "12% of contracts lack a notice period" but "$4.1M of ARR has no computable opt-out deadline, so 19 renewals cannot legitimately enter Commit." And it ranks by decision-value per unit of effort, never by ease, with irreversible losses sequenced first: an event you are not emitting today is data you can never backfill.

Read ../cs-context/references/evidence-standard.md first. This audit is itself held to that standard: every measured rate carries a provenance tag and a method, every unmeasured one says UNKNOWN — requires X, and the audit's own blind spots are printed at the end.

Before Starting

1. Read .agents/cs-context.md — it carries §9 Source Inventory, §10 Identity Resolution and §11 Data Quality Findings. If absent, run cs-context first; this skill deepens that file rather than replacing it. If it is >90 days old, treat its source list as a hypothesis. Never ask anything that file already answers — segments, notice-period default, fiscal year, source list, business model. Asking tells the user the skill did not read it.
2. Scope by decision, not by table. An audit scoped to "our data" never ends; one scoped to "the eight decisions we make about renewals, expansion and coverage" terminates and gets funded. Default set: renewal call · save/no-save · expansion qualification · coverage allocation · forecast category · QBR value claim · board reporting · churn attribution.
3. Establish access, honestly. Per source, record what you can query today: API, warehouse table, CSV export, or nothing. An audit run on screenshots is a Low-confidence audit; say so.
4. Read the business model before you grade anything. ../cs-context/references/business-model-profiles.md — seat utilisation is a finding on a per-seat business and noise on a consumption one, and a PLG base has no notice_period_days to document-test. It changes which fields matter.

Take the data in whatever shape it arrives

CSV, TSV, XLSX, JSON, NDJSON, warehouse query results, a information_schema dump, a pasted Slack thread, a call transcript — or no file at all, in which case run the audit as a conversation and label every finding reported, not measured.

Run ../cs-context/scripts/ingest.py on every supplied file before quoting a number from it. It sniffs encoding and delimiter, finds the real header row beneath three rows of export preamble, maps columns onto the canonical schema with a per-column confidence, normalises dates, money-stored-as-text and booleans, resolves accounts across files, and reports the join rate — which is itself Step 4 evidence, obtained free.

• Confirm every column mapping below 0.80 confidence before using those numbers. A revenue column silently read as annual when it is monthly makes every blast-radius figure 12× wrong and reorders the whole remediation plan. This is the exact failure the skill exists to find.
• Degrade, never refuse. Two files and a described screenshot produce a two-domain audit with a coverage figure and a stated cap — not an error. Under 40% coverage, name the gap and stop grading (R23); a grade computed on 30% of the picture is a decoration.
• Never assume an export is complete or current. Ask for its as-of date, record it against the source in §2, and score an undated export's freshness UNKNOWN.

The four questions — one batched ask, then run

Every missing input resolves read it · ask it · mark it. Never guessed. Ask with AskUserQuestion, all four in a single batch — 2–4 mutually exclusive options each, the recommended one first and labelled (Recommended), one line under each saying what it changes. Never drip-feed. If nothing comes back, run the recommended defaults, say so in one line at the top of the report, and log each one in the Assumptions table.

One thing worth asking outside the batch when it is not obvious: what triggered this. A failed forecast, a platform purchase, a board question and an ML project need different depths, and the trigger sets the bar in Step 9. If it is unstated, assume "the last analysis came back Low confidence" and record it.

How This Skill Works

Eight audit domains, seven signal families, one ranked plan. Every domain is walked every time; a domain with nothing wrong is printed as "tested, clean". A silently omitted domain is indistinguishable from an untested one.

The eight domains, and what each one answers:

Run sequence: scope by decision → inventory → completeness/validity/freshness → join rate → event taxonomy → churn labels → contract document test → metric reproduction → coverage score and confidence caps → rank remediation by decision-value ÷ effort → funding case.

Step 1 — Fix the decision set and the pass bar

Write the decision list into the report, recording per decision: the ARR it governs, who makes it, at what cadence, and the fields it consumes. That table is the denominator for every damage figure later — a gap that degrades no decision is a curiosity, not a finding. Then adopt the six dimensions from the UK Government Data Quality Framework (completeness, uniqueness, consistency, timeliness, validity, accuracy) as the test vocabulary, so every check names the dimension it exercises. Definitions and per-dimension procedures: references/audit-procedures.md §1.

Step 2 — Source inventory and access reality

Walk the twelve source families in cs-context §9 in full, absent ones included — the gaps are the finding. Per system: connected · access method · account key · claimed latency · observed staleness · history depth · % of ARR-bearing accounts present · owner.

The two measurements people skip. Observed staleness, not claimed — query max(updated_at) per source against the stated latency. And ARR coverage, not account coverage: a source present on 90% of accounts but missing the top decile covers a third of the revenue.

Step 3 — Completeness, validity and freshness

For every field the decision set consumes, run four tests. Pass criteria and the full field register: references/audit-procedures.md §2–§4.

Null is not zero. usage_daily.active_users = 0 is measured-and-empty; NULL is not-measured. A pipeline that writes 0 on a failed sync manufactures false-red accounts at scale — test for it: exact zeros across all accounts on one date is an outage, not a customer event.

Step 4 — Identity resolution audit

Every usage-derived number is multiplied by this domain. Measure the join rate three ways — they diverge, and the divergence is the finding:

User join rate     = product users resolving to an account / all product users
Volume join rate   = events carrying a resolved account_id / all events
ARR join rate      = ARR of accounts with ≥1 resolved user / total ARR

Pass: ≥90% on all three. Below 80% on any, usage-derived risk scores are Low confidence at best — the unresolved users are not randomly distributed, and saying so is part of the finding.

Then walk the breakage catalogue in full (references/identity-resolution.md §3): free-email users at paying accounts · multi-domain enterprises · agencies and consultants · subsidiaries · internal and employee accounts · shared/service accounts · post-acquisition domain changes. Record per case: detected count, accounts affected, ARR affected, direction of the error, metric distorted. Quantify the damage, do not describe it — recompute the affected metric on a 20-account sample with and without the fix, and report the delta:

Shared service accounts: 34 accounts, $2.9M ARR. Licence utilisation is overstated by a median 11pp (0.71 → 0.60 recomputed). Nine cross the 0.60 seat-reduction threshold once corrected — nine expansion conversations that should have been renewal-defence conversations.

Step 5 — Event taxonomy audit

The question is not "do we have analytics" but "do we measure the action that predicts retention — consistently, on every surface, attributed to an account."

The trap that costs the most: an unversioned taxonomy means every release that renames or removes an event produces a fleet-wide "usage decline" indistinguishable from churn risk. Test by diffing per-account decay against fleet decay on the same window — if they move together, you are looking at your own deploy. Full procedure in references/event-taxonomy.md.

Step 6 — The churn-label audit

Say this plainly in the report: without a captured decision date and a closed-list reason code, no predictive work is possible. Not harder — not possible. A model trained on the effective date learns your notice period; a free-text reason cannot be aggregated, so "why do customers leave" has no answer. Run all ten checks in references/audit-procedures.md §6; the six that fail most often:

Calibration test for check 4: Recurly's subscription-network data reports SaaS monthly churn of 3.22%, split 2.16% voluntary / 1.06% involuntary [Recurly Research · churn benchmarks · figures current to Jul 2026, [M] platform data] — roughly a third of subscription churn is payment failure. Labels showing near-zero involuntary churn mean the labels are wrong, not that the dunning is excellent. Then gate modelling ambition against label volume using the minimums table in references/audit-procedures.md §6.3, and state the verdict as a band — never as "we can build a model that will predict churn."

Step 7 — Contract data audit (document-tested)

These fields are hand-maintained, so they fail accuracy, not completeness, and no null test finds that. The only valid method is to read the paper.

Procedure. Census the top 20 accounts by ARR; stratified random sample of 25–100 below. Pull the executed contract, read the clause, compare to the field, record match/mismatch. Report accuracy with its confidence interval — 25 rows gives roughly ±20pp at 95%, enough to separate catastrophic from acceptable but not 88% from 95%. Sample-size arithmetic: references/audit-procedures.md §7.2, computed by scripts/audit_score.py.

Then compute opt_out_deadline (renewal_date − notice_period_days) and report its null rate against ARR. Every skill in this library scores against the opt-out deadline, never the renewal date. An account whose opt-out deadline cannot be computed cannot enter Commit — a finding for the CRO, not for CS Ops.

Step 8 — Metric integrity

Variance >2pp means the reported number is not reproducible and should stop being reported until it reconciles. That sentence is uncomfortable and it is the most valuable one in the audit — a CFO who finds a variance discards the whole report, permanently.

If asked whether the number is good: private B2B SaaS median GRR 88%, median NRR 101% [Benchmarkit · 2025 B2B SaaS Performance Metrics Benchmarks · CY2024 actuals, N=225 GRR / N=228 NRR, [M]]. State the population and year every time; never benchmark a private company against the 120% NRR figure that circulates from large public companies.

Step 9 — Score coverage and set the confidence caps

Score each of the seven signal families on five dimensions, 0–20 each. Band criteria: references/audit-procedures.md §8; scripts/audit_score.py computes it deterministically.

Family score → Coverage Ledger status: ≥80 = ✅ Complete (1.0) · 40–79 = ⚠️ Partial (0.5) · <40 = ❌ Missing (0). Coverage = sum ÷ 7. Confidence cap follows evidence-standard.md §4: ≥80% → High · 60–80% → Medium · 40–60% → Low · <40% → Insufficient. Then publish the downstream cap table — it is what makes the audit operational rather than academic.

Step 10 — Rank the remediation plan

Rank by decision-value per unit of effort. Never by ease. Sorting by effort ascending is how a company spends two quarters fixing null rates on fields nobody reads.

B Blast radius    = ARR $ governed by the decisions this gap degrades
D Degradation     = 1.0 blocks the decision · 0.6 forces a confidence cap · 0.3 adds noise
U Unlocks         = downstream decisions/skills the fix unblocks (cap 5)
C Irreversibility = 1.5 if delay destroys data permanently (uninstrumented events, overwritten
                    scores, unrecorded decision dates) · 1.0 otherwise
E Effort          = person-days, engineering + ops + CS combined
Priority          = B × D × (1 + 0.1 × U) × C ÷ E

The C factor changes sequencing. A missing event costs nothing today and costs a year of history if deferred a year, so it goes ahead of higher-B gaps that can be fixed retroactively. Say so explicitly. Every row carries action · owner · start date · done-by date · expected effect · success measure — a gap without an owner and a date is a complaint. Method, standing quick wins, and the expensive fixes not worth it yet: references/remediation-planning.md.

Step 11 — Write the funding case

Two audiences, two documents, same facts. Scripts: references/remediation-planning.md §5–§6; one-page ask: assets/remediation-brief-template.md.

• CFO — lead with forecast error and revenue at stake, not data quality. Three numbers: ARR forecast on inputs that failed a test · ARR whose renewal decision has no computable opt-out date · cost of the fix against the ARR of one missed save. Offer three tiers, each stating what stays broken.
• CTO / Head of Data — lead with engineering days, one-off vs ongoing burden, and what sits behind it on the critical path. Name the fixes that are not engineering work (most of them), so the ask is small and specific.

Output Template

Full structure — every column of all eleven sections — is assets/audit-report-template.md; open it when writing the report and populate it verbatim. Populate every table or write UNKNOWN — requires X in it; never delete a row. Brief mode emits the Bottom Line block and the two lines under it, and nothing else.

# CS Data Audit — <Company> · <date> · scope: <decision set>
**Internal.** Contains system access detail and disclosed gaps. Not shareable with a customer.
*Run on defaults: <the ones taken, or "none — all four questions answered">. Data as of <date>.*

## Bottom Line
<3 sentences: overall grade, the decision currently being made wrong and its dollar value,
the single fix to fund first.>

| | |
|---|---|
| Overall data grade | X/100 — <Reliable / Usable with caveats / Directional only / Not usable> |
| ARR governed by degraded decisions | $X across N accounts |
| Families reliable / partial / missing | a / b / c of 7 |
| Max confidence any downstream skill may claim | High / Medium / Low / Insufficient |
| Predictive work possible today? | Yes / No — <the blocking condition> |
| Fund first | <gap> · <effort days> · <owner> · unlocks <what> |
| Audit confidence | <level> — <access method and what could not be tested> |

**What would change the grade:** <the single measurement that would move it, and who runs it.>

## 1. Decision Set Under Audit — decision · cadence · owner · ARR governed · fields · reliable?
## 2. Source Inventory — every family from `cs-context` §9, absent ones included; access, claimed
   latency, observed staleness, history, % of **ARR** covered, owner, as-of date, verdict
## 3. Completeness, Validity & Freshness — per field: dimension, test, pass criteria, result,
   ARR affected, damage if wrong · plus the null-vs-zero outage test result
## 4. Identity Resolution — user / volume / ARR join rates, then all 7 breakage cases including
   those measured at zero, each with error direction and damage recomputed on a sample
## 5. Event Taxonomy — all 10 checks, the named core action, the fleet-vs-account decay test
## 6. Churn Labels — all 10 checks, then label sufficiency per modelling ambition (`R24`)
## 7. Contract Data, document-tested — accuracy, n, 95% CI, and `opt_out_deadline`
   computability against ARR: those accounts cannot enter Commit (`R1`)
## 8. Metric Integrity — reproduction variance vs tolerance, definition version, per-account
   system disagreement, the ruled source of truth per field
## 9. Coverage Scoring — 7 families × 5 dimensions × 20, then the downstream cap table (`R23`)
## 10. Remediation Plan — ranked with the arithmetic shown, sequenced in waves, quick wins,
   and the deliberately-not-yet list with revisit dates (`R14`)
## 11. Funding Case — three CFO tiers each stating what stays broken, and the CTO split of
   what is and is not engineering work

### Ingest Report — when any file was supplied
| File | Rows | Header row at | Mapped ≥0.80 | Confirmed <0.80 | Join rate | As-of |
|---|---|---|---|---|---|---|

### Assumptions
| # | Assumption | Why it was needed | If wrong |
|---|---|---|---|
| 1 | Audited the eight default decisions | Question set went unanswered | A ninth decision (a pricing migration, a partner motion) may rest on a field this audit never tested |
| 2 | `revenue` column read as annual ARR | Mapping confidence 0.62; header ambiguous | Every blast-radius figure is 12× out and the remediation ranking reorders end to end |
| 3 | Export treated as current to <date> | No as-of date supplied | Freshness scores are a ceiling; a 40-day-old export drops Commercial from ✅ to ⚠️ and caps downstream confidence at Medium |

### Coverage Ledger
| Signal family | Source checked | Status | Notes |
|---|---|---|---|
<all 7, always: product usage & adoption · commercial & contract · relationship & engagement ·
support & reliability · sentiment & VoC · billing & payment · firmographic & external>

**Coverage: X / 7 families (Y%) → downstream confidence capped at <level>.**
Blind spots: <which families are weakest and what that specifically hides.>

### What this audit could not check
| Item | Why | What it would take |
|---|---|---|

This skill emits no customer-facing text. The report, the remediation plan and both funding cases are internal by construction: they carry system access detail, ARR exposure and named per-account variance, none of which crosses the firewall (R18). If a customer-facing artifact is wanted from these findings, it is a different skill's job.

Quality Bar

• Every supplied file passed through ingest.py first, and every column mapping below 0.80 confidence confirmed with the user before its numbers were used
• Missing inputs resolved read-it / ask-it / mark-it — the four questions asked once, batched and tappable; nothing asked that cs-context already answers; every default run on is named at the top and logged in Assumptions with a concrete "if wrong"
• Partial data produced a partial audit with a coverage figure and a stated cap, not a refusal; under 40% coverage the gap is named instead of graded (R23)
• All eight audit domains reported (including those that tested clean), and every finding names the decision it degrades and the ARR that decision governs
• Join rate measured three ways (user, volume, ARR) and all three reported
• All seven identity-breakage cases walked (including those measured at zero), with damage quantified by recomputation on a sample, not described in adjectives
• Churn-label audit run in full, with the explicit statement of what is not possible without decision_date and a closed reason list
• Contract fields tested against executed documents with sample size and CI stated; opt_out_deadline computability reported against ARR, not the renewal date alone
• GRR/NRR reproduction attempted, variance stated against tolerance, and per-family coverage scored on all five dimensions with the arithmetic shown
• Downstream confidence caps published per skill, not just an overall number
• Remediation ranked by B × D × (1+0.1U) × C ÷ E, with the formula printed and irreversible items sequenced early
• Every remediation row has action · owner · start · done-by · expected effect · success measure
• Quick wins and the deliberately-deferred list both present
• Every measured rate carries a provenance tag and its method; unmeasured ones say UNKNOWN — requires X; no [P] threshold presented as a measured benchmark
• Operating rules enforced in the output, not merely cited: R1 (opt-out computability reported against ARR) · R2 (auto_renew_changed_at tested, not assumed) · R22 (modelling verdict stated as a band, never as a churn probability) · R23 (coverage cap published per downstream skill) · R24 (decision_date audited separately from effective_date)
• No competing CS platform named anywhere — the platform slot is generic ("whichever platform holds your playbooks"), and no benchmark is quoted without a neutral source, population and year
• The audit's own blind spots printed in "What this audit could not check"

Anti-Patterns

Related Skills

Going Deeper

Automate This

You just measured a moving target by hand: sampling executed contracts against CRM fields, recomputing join rates, diffing GRR out of raw rows, reconciling four systems that each believe they hold the account's ARR. It is accurate on the day you finish. Then a source is connected without a mapping, a release renames three events, someone edits a notice period in billing and not in the CRM — and the audit is quietly wrong, with no alarm, because broken joins do not throw errors. They render.

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