What to do first

These changes move the grade, in order of effect. Each check cited here appears by name later in the report.

1. Assign an owner to $108.5M of ARR. 1,127 active accounts have no rep, and the top rep holds 25.8% of ARR. The parent field is the least-covered tag. Filling owners fixes the two highest-severity findings at once: unowned bookings and rep concentration. It is also what holds both the decision-ready and diligence-ready views back. This moves Multi-Dimensional Insights toward B.
2. Remove 6 row-level defects. Six rows are exact duplicates. Removing them lifts Anomalies and Audit-Grade Accuracy together.
3. Restate the KPI table from the bridge. The table reports churn below 1% for 2024 and 2025. The bridge implies 4.3% and 6.34%. Either restate the table to the bridge, or add a one-line definition that explains the difference. This moves Audit-Grade Accuracy toward B.
4. Add a definitions tab. Today the definitions cover 5 of the 9 key terms. MRR, GRR, new, and reactivation are missing. Write one line for each, so a reader can reproduce the KPI table from the file.
5. Normalize 2 tag fields. The segment and status fields 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

Inputs: 2 file(s), 8 sheet(s), 400,924 rows. Hazards handled: none. Checks run: 39 of 41. 2 could not be checked; they are listed below.

• Unified, Finance-Approved Foundation: 87.3 (B+)
• Audit-Grade Accuracy: 88.9 (B+)
• Cohort-Centric Design: 100 (A+)
• Multi-Dimensional Insights: 77.5 (B-)
• Anomalies: 95.4 (A)

The two pillars at B+ and the one at B- carry every open item. Cohort-Centric Design and Anomalies are clean or close to it.

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, every booking carries a product: product unassigned is 0%. Ownership is the gap. 1,127 accounts, or 24.5% of the book, have no owner. Check score 30.

A buyer would ask: who renews and expands these accounts today?

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. Check score 56.

A buyer would ask: how are GRR, new, and reactivation counted, so the table can be checked?

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, product); 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 disagree. Both concern churn. The table reports under 1% for 2024 and 2025, while the bridge implies 4.3% and 6.34%. Check score 40.

A buyer would ask: which churn figure is the one to underwrite, and under what definition?

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 this file, one period fails: 2024, where the summary rows do not add to the printed total. Check score 75.

A buyer would ask: which figure for 2024 is right, the rows or the total?

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).

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.

In this file, 3 reps carry the book. The top rep holds $113.9M, a 25.8% share, and a nearly equal share has no owner. Check score 30.

A buyer would ask: what happens to these accounts if one rep leaves?

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 79.2% of the book on average. Owner and parent are the thin ones. Check score 55.

A buyer would ask: can the book be cut by owner and by parent customer today?

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, segment and status, carry more than one spelling of the same value. Check score 80.

A buyer would ask: does a segment report split one segment into several?

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, 6 rows are duplicates holding $832K. 5 sit in the ARR detail and the rest in the product lines. Check score 88.

A buyer would ask: which copy is the real record, and is that revenue counted twice anywhere?

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 sentences tested could not be reproduced. It is the churn statement. Check score 75.

A buyer would ask: where does the stated churn rate come from?

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 event to examine.
• 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 measured.

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, diligence-ready no, AI-ready yes. The first two are held back by the 2 high-severity ownership findings described above.

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