AI sales intelligence
Give salespeople the ability to understand their customers, discover opportunities and question their own accounts without combining spreadsheets, cleaning datasets or waiting for somebody else to produce a report. Finance, CRM, activity and product data, with the norm for similar customers, unified and answerable in plain English.
A traditional dashboard answers the questions somebody anticipated. Sales intelligence lets the salesperson ask the next one, and gets an answer with the rows behind it, on the same figures the dashboard shows.
Revenue, product mix, activity and the norm for similar customers, for every account a salesperson owns, rolled up to the team and reconciled.
Products a customer buys elsewhere, penetration against similar customers, and the value of the gap, ranked.
Customers whose activity or revenue has fallen against their own history, flagged before the quarter ends.
Three steps, in this order.
Finance, CRM and activity files, on a schedule or by upload. Client IDs only.
Company equals regions equals teams equals salespeople equals accounts, asserted on every file.
Every salesperson, their own accounts, any question, with the tool that produced the answer named.
Short answers. The Help centre has the long ones.
This is the whole view: accounts, opportunities, at-risk and the assistant together. The industry pages are the same engine set up in one industry's vocabulary.
No. Deterministic tools aggregate, rank and calculate penetration. The model handles intent, tool selection and explanation.
Revenue by customer and product, CRM activity, and optionally what customers have told you about their total spend. Client IDs only.
Written for this job: the measures, the data you already hold, and the arithmetic.
Account penetration rate defined, accounts buying over accounts in a stated universe, the four denominators it is computed against and what each answers, the target list, the estate, the segment universe and the installed base, a worked example per rep against a target list, the roll-up, the identity to the universe file, and why a penetration rate with no stated universe is a percentage of nothing.
16 Sept 20262 min readHow to segment a B2B customer base from data the company holds: the two or three fields that make a segment useful for norms, size, sector and channel, why behavioural fields like order frequency and product mix belong in the measures rather than the segment definition, the minimum segment size that makes a norm mean something, the test that a segmentation is good, and why personas do not survive contact with a ledger.
16 Sept 20263 min readThe difference between cadence, the expected interval between events for an account, from its tier for touches or from its own history for orders, and recency, the days since the last event actually happened, why every list on this site is recency against cadence rather than either alone, the two cadences, tier cadence for effort and own cadence for behaviour, and the four readings that come from putting the two columns beside each other.
16 Sept 20262 min readHow an asset manager's distribution team measures net flows per strategy by channel from the transfer agent and platform data, sets each strategy's expected channel mix from its own peers in the range, and finds the strategy that is under-distributed in one channel while the range as a whole looks fine, without ever naming an end client.
16 Sept 20263 min readWhy pipeline, forecast and win rate all rest on whether anyone contacted the account, and how any B2B sales team measures coverage from CRM activity: untouched accounts, rep load, territory trend against plan, and the reconciliation to invoiced revenue.
16 Sept 20263 min readThe complete coverage calculation worked on a book of ten accounts small enough to check by hand: the tiers and cadences, the activity log, which touches qualify, which accounts are covered at cadence, the coverage ratio by count and by value, the untouched list ranked by revenue, the roll-up identity, and the data quality score, so that a reader can reproduce every number and then run the same arithmetic on their own export.
16 Sept 20263 min read