What to do first

These changes move the grade, in order of effect. The check each one relies on is listed under “Each check” below.

1. Assign an owner to $12.9M of ARR. 20 active accounts have no rep. The top rep holds 29.5% of ARR. The least-covered tag field is parent. Owner coverage drives both the Multi-Dimensional Insights score and the rep concentration result. Moves Multi-Dimensional Insights toward B.
2. Remove 6 row-level defects. 6 rows are exact duplicates, and the same 6 rows are flagged as defects. Removing them moves Anomalies and Audit-Grade Accuracy up together.
3. Restate the KPI table from the bridge. The KPI table reports churn below 1% for both 2024 and 2025. The bridge implies 2.2% for 2024 and 3.45% for 2025. Align the table with the bridge, and add one line of definition per metric. Moves Audit-Grade Accuracy toward B.
4. Add a definitions tab. Write one line each for ARR, NRR, GRR, churn, new, expansion, reactivation and cohort. Written definitions make the KPI table reproducible. This lifts the definitions check (cited as CP-07) and unlocks the diligence lens.
5. Normalize 2 tag fields. 2 fields carry case or whitespace variants of the same value. One pass of normalization removes them. Moves Multi-Dimensional Insights up one band.

Where the grade comes from

Inputs: 2 file(s), 8 sheet(s), 6,517 rows. Hazards handled: none. Checks run: 39 of 41. The 2 that could not be checked are listed below.

• Unified, Finance-Approved Foundation: 87.3 (B+)
• Audit-Grade Accuracy: 85.7 (B)
• Cohort-Centric Design: 100 (A+)
• Multi-Dimensional Insights: 77.5 (B-)
• Anomalies: 95.4 (A)

Each check

Each item below is one piece of the rubric. The heading says what the check looks for. The short code is the citation.

Unified, Finance-Approved Foundation

Bookings with no product or no owner

Unified, Finance-Approved Foundation. Cited as CP-05, owner.

This check measures how much of the latest book has no product and how much has no owner.

In this file, 20 accounts carry 22.5% of the book with no owner. 0% of the book is missing a product. Check score 30.

A buyer would ask: who manages these 20 accounts today, and who will manage them after a transaction?

Next step. Assign a product and an owner to the unassigned book before the next export.

The words in the definitions were written down

Unified, Finance-Approved Foundation. Cited as CP-07.

This check looks for the standard terms in the definitions text. It does not test whether those words match the numbers.

In this file, 5 of 9 terms are defined. MRR, GRR, new and reactivation have no written definition. Check score 56.

A buyer would ask: how is gross retention calculated, and what counts as a new customer?

Next step. Write a short definition for each headline term the summary uses, in the same words the rubric expects.

Came through clean: Enough history to follow a customer (CP-01); Months where the whole book went quiet (CP-02); A customer goes quiet without a churn mark (CP-03); Revenue with no customer behind it (CP-04); Bookings with no product, on the product side (CP-05); The product file covers the same months as billing (CP-06); Billed customers appear on the customer list (CP-08).

Audit-Grade Accuracy

The bridge and the KPI table agree

Audit-Grade Accuracy. Cited as CR-08.

This check derives churn and net retention from the bridge and compares them with the KPI table.

In this file, 2 of 6 comparisons contradict each other. Both concern churn rate, in 2024 and 2025. Check score 40.

A buyer would ask: which churn figure is correct, and which one appeared in materials already shared?

Next step. Align the KPI table with what the bridge implies, or show the definition that explains the gap.

Duplicate dollars that break the tie

Audit-Grade Accuracy. Cited as CR-10.

This check measures duplicate dollars whose removal would make the detail match the summary.

In this file, 6 defect rows carry $5.9M, which is 0.4% of the book. Check score 63.

A buyer would ask: if these rows were duplicated, what process allowed it, and could it happen again?

Next step. Delete or net the duplicate rows that are holding the tie open, then re-export.

Product and segment totals add up

Audit-Grade Accuracy. Cited as CR-09.

This check adds the product or segment summary rows and compares them with the summary total and with ending ARR.

In this file, 1 period fails: 2024. In that year the subtotal rows do not add to the printed total. Check score 75.

A buyer would ask: which 2024 total is the one to rely on?

Next step. Make the product and segment subtotals add to the summary, and to ending ARR.

Came through clean: The bridge foots on its own (CR-01); Customer detail matches the bridge ending (CR-02); Movements rebuild from the customer rows (CR-03); Product lines tie to billing (CR-04); The amount matches the status word (CR-05); Statuses tell a legal story over time (CR-06); Headline KPIs can be reproduced (CR-07).

Cohort-Centric Design

Every cohort check passed. Start dates, contract end dates and reactivation keys support cohort and retention analysis as filed.

Came through clean: Customers have a start date (CO-01); The start date matches the first revenue month (CO-02); Too many customers share one start month (CO-03); A returning customer kept the same key (CO-04); Contract end dates exist and fall after the start (CO-05); Retention looks earned, not just unexpired (CO-06); A trailing year has a year of history behind it (CO-07).

Multi-Dimensional Insights

One owner does not hold the book

Multi-Dimensional Insights. Cited as SG-06.

This check measures the top owner’s share of the latest book and the share with no owner.

In this file, the book is split across 3 reps. The top rep holds $16.9M, or 29.5% of the book. Check score 30.

A buyer would ask: what happens to these relationships if one rep leaves, and who covers the unowned accounts?

Next step. Reassign concentrated accounts and fill the unassigned ones so the book is not one rep’s.

Those tags actually cover the book

Multi-Dimensional Insights. Cited as SG-02.

For each tag, this check measures how much of the latest book sits on accounts with a real value.

In this file, 6 tags cover 79.6% of the book on average. Parent is the least-covered tag, and rep is also incomplete. Check score 55.

A buyer would ask: can the book be cut by parent company and by owner, or only by product and region?

Next step. Fill the blank tags on the accounts that carry the book, starting with product and owner.

The same idea is spelled one way

Multi-Dimensional Insights. Cited as SG-04.

This check groups tag values that match once case and spacing are removed, and scores how many spellings each idea has.

In this file, 2 tags need normalization: segment and status. Check score 80.

A buyer would ask: do segment totals change once the spellings are merged?

Next step. Pick one spelling for each category and apply it across the file.

Came through clean: The cuts a buyer expects are present (SG-01); A tag is a category, not free text (SG-03); Parent accounts are used (SG-05).

Anomalies

Duplicate rows

Anomalies. Cited as AN-06.

This check counts duplicate billing rows, duplicate product rows and duplicate master rows.

In this file, there are 6 duplicate rows carrying $5.9M. 5 of them sit in the ARR rows. Check score 88.

A buyer would ask: has any reported ARR figure counted these rows twice?

Next step. Remove the duplicate rows, keeping one record per account and month.

A sentence in the talking points does not match the file

Anomalies. Cited as AN-09.

This check tests quantified sentences in the talking points against figures the file can produce.

In this file, 1 of 4 tested sentences fails. The failing sentence is the churn rate claim. Check score 75.

A buyer would ask: which version of churn went into the talking points, and why does it differ?

Next step. Rewrite or remove the sentences the customer file cannot reproduce.

Came through clean: One customer spikes for a month and reverts (AN-01); A customer drops to zero and comes back unmarked (AN-02); One month moves the whole book (AN-03); Amounts do not match the file’s own format (AN-05); The book sits in a few customers (AN-07); Prices did not move where an escalator was promised (AN-08).

What we could not check

• A new product line copies a price the account already had (AN-04): no month moved the whole book, so there was no mass-attach month to test.
• Tags stay stable over the year (SG-07): the file stores one tag per account rather than one per month, so changes over time cannot be read.

How this was graded

Weights: Unified, Finance-Approved Foundation 25%, Audit-Grade Accuracy 30%, Cohort-Centric Design 15%, Multi-Dimensional Insights 15%, Anomalies 15%. Bands: A ≥ 90, B ≥ 75, C ≥ 60, D ≥ 45. Bridge convention detected: monthly_sum. Rules version caeb8e512330.

Lenses:

• Decision-ready: not yet. Two open high findings, on owner coverage and rep concentration, keep this lens closed.
• Diligence-ready: no, for the same two high findings.
• AI-ready: yes.

Next step

The Strategic Revenue Intelligence Assessment walks through these findings with a FinQore analyst. It also shows what a decision-ready and diligence-ready cube looks like for your business.

Your files are retained in FinQore infrastructure for the stated retention period and deleted on request.