AI-assisted insights and analytics
Every Monday, Covirage reads the week's data, finds what moved, explains why in figures that reconcile, and says what to do about it. The team opens the meeting already knowing.
An insight is a change big enough to matter, explained by the rows that caused it, with a value attached. Anything that fails that test does not make the digest.
Coverage, revenue, share and range, week on week and against plan.
The accounts, reps and territories behind each move, from the same tools the screens use.
A ranked action with the value at stake. Assigned to a person, tracked to the next week.
Three steps, in this order.
The CRM export, the revenue export, or the sheet you already keep.
Tell us what counts as material for your team. Defaults are sensible.
Monday, before the meeting. Reply with a question and get the rows.
Short answers. The Help centre has the long ones.
A change must pass a materiality threshold you set, be explained by rows, and have a value. Otherwise it is not shown.
Yes. Each person gets the signals for their scope, and managers get the roll-up.
It shortens it. The facts arrive first, the meeting is about the decisions.
Written for this job: the measures, the data you already hold, and the arithmetic.
An annotated example of a weekly sales digest built from computed tables: the header that states the period and the checks, ten movements each with the measure, the level, the change, the threshold and the citation, the three lists that follow, untouched, slipped and dormant, the line that says what did not move, and the footer with the definition version. What each element is for, and the version of the same digest that nobody reads.
16 Sept 20263 min readWhat is wrong with the red-amber-green account health field most CRMs carry, what a computed health score is made of, five inputs from the ledger and the CRM with stated weights, how the weights are set from the company's own churn history rather than opinion, why the score is shown with its inputs and never alone, the identity that every account has one score from the same rule, and how the score is validated against what happened.
16 Sept 20263 min readHow a company that sells on orders rather than subscriptions measures churn from its ledger: the definition of a lost customer from its own order cadence, churned revenue as the run rate before the silence, the churn rate by segment and by rep, the identity that ties churned plus retained plus new to the two periods' revenue, and why a fixed 12-month rule counts the wrong customers as lost.
16 Sept 20262 min readHow a SaaS revenue team checks whether its expansion pipeline covers the whitespace the account data shows: expansion opportunities per account against the account's valued whitespace, the accounts with large gaps and no open opportunity, the opportunities on accounts with no gap, the share of whitespace value under active pursuit per rep, and the identity that ties expansion pipeline to the whitespace grid.
16 Sept 20262 min readFive ratios that together describe whether a sales team is effective, each computed from exports the company holds: coverage at cadence, meeting-to-opportunity conversion, win rate by segment, revenue per touch, and share of wallet against the norm. Why activity counts and quota attainment are not among them, how the five are read together per rep, and the one-page view that replaces the activity dashboard.
16 Sept 20263 min readWhy the level of a measure, 52 percent concentration, 74 percent OTIF, 88 percent lock-up, is the least informative thing about it and the trend against the measure's own history is the finding, the four exceptions where a level is a hard threshold, the window a trend needs, the consecutive-period rule that separates a bad month from a deterioration, the trend of the reference itself, and the rule that every level on a report carries its trend beside it.
16 Sept 20263 min read