AI for insights and analytics
Blended net revenue retention is 103 percent, and the newest cohort, 38 percent of ARR, is at 89 because accounts that took more than sixty days to reach their first report retain at 71. AI analytics reads the ARR, usage and CRM files, and answers the head of customer success: where is retention failing, and which renewals are unprepared.
These are the questions SaaS companies ask. Each one maps to a measure our tools compute from your files. The AI model chooses the measure and explains the result; the arithmetic is done by our code, and every total is checked.
| The question | The measure behind it | What comes back |
|---|---|---|
| What is net revenue retention by cohort, not blended? | Net revenue retention by cohort | NRR by purchase cohort with ARR weight, and gross retention beside it. |
| Which renewals have low seat utilisation? | Seat utilisation before renewal | Active seats over licensed seats for accounts renewing in the window, by ARR. |
| Where is the whitespace, and does it add up? | Whitespace reconciled to ARR | Potential by account against current ARR, reconciled so the sum matches the plan. |
| Which accounts have had no touch? | Untouched accounts, by ARR | Accounts with no logged activity in the period, by ARR and renewal date. |
| How long does a new account take to reach value? | Time to value | Days from contract to the first value milestone by cohort, with retention beside it. |
| Is every renewal in the next two quarters owned? | Renewal calendar coverage | Renewals due with an owner and a plan over renewals due, by month. |
Every figure below was computed by a tool from the rows and checked before it was shown. The lines sum; the percentages match; each line opens to the rows that make it.
Where is net revenue retention failing?
ARR lost to churn and contraction in the last four quarters is $3.5m across 3 cohorts, against expansion of 3.1 million. 2024 cohort accounts for $1.9m, 54% of the loss.
| 2024 cohort | $1.9m | 54% of the total |
| 2023 cohort | $1.1m | 31% of the total |
| 2022 cohort | $500k | 14% of the total |
| The 3 lines sum to | $3.5m | 0 unexplained |
What is different about the 2024 cohort?
3 groups within it account for $1.9m of the $1.9m lost in the 2024 cohort, 100% of it. Accounts that took longer than sixty days to reach their first report retain at 71 percent; those under thirty days retain at 108.
| Slow to first report, over 60 days | $1.2m | retention 71%; 41 accounts |
| Seat utilisation under 40% at renewal | $500k | 18 accounts |
| No touch in the 90 days before renewal | $200k | 9 accounts |
| These 3 are | $1.9m | 100% of 2024 cohort |
What does customer success change?
Onboarding, for the newest customers first: a thirty-day first-report target, an owner per account, and a renewal calendar that flags low utilisation ninety days out. NRR by cohort goes into the board pack beside the blended figure.
| Customer success | Put every 2025 account on a 30-day first-report plan | This quarter |
| Account managers | Contact the 18 low-utilisation renewals now | This month |
| Head of customer success | Report NRR by cohort to the board | Quarterly |
Each measure has one formula, one source and one meaning. They are computed per rep and cohort and in total, and every one carries an identity that must hold before it is shown.
| Measure | Formula | From | What it tells you |
|---|---|---|---|
| Net revenue retention by cohort | ARR now from customers active a year ago ÷ their ARR then, by start-year cohort | Subscription or billing system | Which cohorts grow and which shrink |
| Gross revenue retention | As above, capped at each customer's prior ARR | Subscription system | The floor under the expansion |
| Seat utilisation before renewal | Active seats ÷ contracted seats, against the curve for months since start; accounts under the curve within 6 months of renewal | Usage export; contract file | Downsells and cancellations, ahead of time |
| Whitespace reconciled to ARR | Potential seats and products − held, valued at the account's prices; ARR + whitespace = stated potential | Subscription system; account data | Room to grow, by account |
| Expansion pipeline against whitespace | Open expansion pipeline at accounts ÷ whitespace at those accounts; whitespace with no pipeline | CRM; whitespace table | Whether reps work the gaps the data found |
| Untouched accounts, by ARR | ARR of accounts with no two-way contact within cadence ÷ total ARR | CRM; subscription system | Revenue nobody is talking to |
| Time to value | Days from contract to the usage milestone that predicts renewal | Usage export; onboarding records | First renewals at risk from the start |
| Renewal calendar coverage | ARR renewing in 180 days with an owner, a plan and a recent contact ÷ ARR renewing | Contract file; CRM | Renewals drifting to the date |
| Expansion by source | Expansion ARR from seats, products, usage and price, separately | Billing with line detail | Whether growth is customers buying more or being charged more |
| ARR concentration | Top ten customers' share of ARR; largest customer | Subscription system | Dependence |
Each measure is worked through, with the export it comes from and what to drop, in Customer base KPIs for SaaS sales teams.
From your question and the measures declared for SaaS, the model picks the one that answers it, and the period and comparison the question implies.
Deterministic code reads the rows, computes the measure, and checks the identities below. The same question on the same data gives the same answer, every time.
The AI model writes the sentence around the result, naming the rep or account behind it. It states no figure that is not in the result, and every figure links to its rows.
| ARR movement | Opening ARR + new + expansion − contraction − churn = closing ARR |
| Cohorts | Cohort ARR sums to total ARR |
| Seats | Contracted = active + inactive assigned + unassigned |
| Whitespace | Account ARR + whitespace = stated potential; account ARR sums to company ARR |
The exports SaaS companies already produce. Column names are mapped once and the mapping is reused. A file is the way in; scheduled delivery and connections to your systems come with the plan, and every source is listed here.
An answer is a list with an owner and a cadence, or it is a chart nobody works.
| Measures | Owner | Cadence |
|---|---|---|
| Untouched accounts; renewal calendar coverage | Account managers; head of customer success | Weekly |
| Utilisation before renewal; time to value | Customer success | Weekly to monthly |
| Expansion pipeline against whitespace; expansion by source | Chief revenue officer | Monthly |
| Retention by cohort; concentration | Chief revenue officer; finance | Quarterly |
Because a blended 103 percent can be 120 in an old cohort and 89 in the newest, which is 38 percent of ARR. The blended figure hides where the problem is and when it started. NRR by cohort is computed from the ARR file by purchase month, and the ARR weight is shown so nobody mistakes a small cohort for a trend.
Potential per account is estimated from the sizing you provide, and the tool checks that whitespace plus current ARR equals the potential and that the total matches the plan. A whitespace figure that does not reconcile is reported as such, not quietly rounded.
Yes: the ARR file by account and month, the usage export with active seats, the CRM activity log and the renewal dates. Account names can be replaced with identifiers before upload. The tool joins them by the account key you map once.
No. It shows the facts that precede it: utilisation, time to value, touches and cohort retention, per account, with the renewal date. The customer success lead decides which accounts to act on; no model produces a score.
Net revenue retention · Seat utilisation · Whitespace · Untouched account · Time to value · Renewal calendar
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Bring a few thousand rows. The data map opens next, every column mapped once, and the first question is answered in minutes. Free, in your browser, no account.