What to do first

These five changes move the grade, in priority order. Each one points to a check in the next section.

1. Assign an owner to $3.1M of ARR. 164 active accounts have no rep, and the top rep holds 27.6% of ARR. Parent is the least-covered tag field. Owner coverage affects two checks: whether the tags actually cover the book, and whether one owner holds the book. This moves Multi-Dimensional Insights toward B.
2. Remove 6 row-level defects. The same 6 rows are exact duplicates. Deleting them lifts Anomalies and Audit-Grade Accuracy together.
3. Restate the KPI table from the bridge. The table reports churn of <1% for 2024 and 2025, while the bridge implies 8.33% and 16.28%. Either restate the figures or add a one-line definition per metric that explains the difference. This moves Audit-Grade Accuracy toward B.
4. Add a definitions tab. Write one line each for the headline terms, including the missing ones: MRR, GRR, new, and reactivation. Written definitions let a buyer reproduce the KPI table, and this raises the score on the definitions check.
5. Normalize 2 tag fields. Segment and status carry case or spacing variants of the same value. One normalization pass removes them and moves Multi-Dimensional Insights up one band.

Where the grade comes from

The grade rests on 2 files, 8 sheets, and 57,329 rows. No input hazards needed handling. 39 of 41 checks ran. The 2 that could not run are listed below.

• Unified, Finance-Approved Foundation: 87.3 (B+)
• Audit-Grade Accuracy: 88.6 (B+)
• Cohort-Centric Design: 100 (A+)
• Multi-Dimensional Insights: 77.5 (B-)
• Anomalies: 95.4 (A)

Each check

Each item below is one part of the rubric, grouped by pillar. The heading is the check. The short code is a citation only. Items that need attention come first, and the checks that came through clean are listed at the end of each pillar.

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. Every account has a product, with 0% unassigned. Ownership is the gap: 164 accounts, or 23% of the book, have no owner. A buyer will ask who is accountable for renewing that revenue. Check score 30.

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 terms match the numbers. The definitions cover 5 of the 9 terms. MRR, GRR, new, and reactivation are not written down, so a buyer would have to guess how they are calculated. Check score 56.

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); the product half of Bookings with no product or no owner (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. Of 6 comparisons, 2 contradict each other, and both are churn: the table reports <1% for 2024 and 2025, while the bridge implies 8.33% and 16.28%. A buyer will ask which number the company stands behind. Check score 40.

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

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 1 period, 2024, the subtotals do not add to the printed total. A buyer will ask which figure is the real ending ARR for that year. Check score 75.

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); Duplicate dollars that break the tie (CR-10). The duplicate-dollars check passed, but it involves the same 6 duplicate rows noted under Anomalies.

Cohort-Centric Design

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. The book is spread across 3 reps. The top rep holds $3.8M, or 27.6%. A buyer will ask what happens to those accounts if that rep leaves, and who covers the unassigned book. Check score 30.

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. Across 6 tags, average coverage is 79.5%. Parent is the least-covered field, and owner is also incomplete. A buyer cutting the book by those fields will see blanks. Check score 55.

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. In 2 tag fields, segment and status, the same category is spelled more than one way. A buyer filtering on those fields would split one group into several. Check score 80.

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 customer-list rows. The file has 6 duplicate rows carrying $133K. 5 sit in the ARR detail, and none sit on the customer list. A buyer will ask whether that $133K is counted twice anywhere it is summarized. Check score 88.

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. Of 4 sentences tested, 1 failed: the churn rate claim. This is the same churn question raised above, now appearing in the narrative as well as the KPI table. Check score 75.

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 single month moved the whole book, so there was nothing 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 seen.

How this was graded

The pillar weights are Unified, Finance-Approved Foundation 25%, Audit-Grade Accuracy 30%, Cohort-Centric Design 15%, Multi-Dimensional Insights 15%, and Anomalies 15%. The bands are A ≥ 90, B ≥ 75, C ≥ 60, and D ≥ 45.

The bridge convention detected was monthly_sum. Rules version caeb8e512330.

Readiness lenses: decision-ready is not yet and diligence-ready is no, both because of the two ownership findings above. AI-ready is yes.

Next step

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

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