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 center 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.
Choose an approach by the question your sales and marketing teams need to answer. Compare prospect research with internal CRM analysis, then test the choice on a checked campaign export.
Oct 25, 20266 min readUse CRM and revenue exports to compare pipeline, ownership gaps and forecast changes. Build a weekly sales-operations review with spreadsheet formulas, a forecast bridge and clear exception handoffs.
Oct 24, 20265 min readCompetitor audience analysis compares supplied aggregate audience research with your own customer segment results. This workflow shows the spreadsheet calculations, denominator checks, and limits on the conclusions you can draw.
Oct 19, 20265 min readAn opportunities dashboard compares open sales deals by stage, owner, age, and expected close date. This worked example shows the source fields, the calculations, the checks, and the decisions each exception should trigger.
Oct 18, 20266 min readTechnographic segmentation groups business accounts by known technology attributes. Compare revenue and contribution using supplied CRM fields and ledger exports, then check that every account and dollar is counted once.
Oct 18, 20265 min readNeeds based segmentation groups customers by requirements they have actually expressed or confirmed. Build an evidence register, assign reproducible groups, then compare revenue, gross margin and service usage from your customer exports.
Oct 15, 20266 min read