Blog · AI and self-service analytics
Three software categories with overlapping names. Sales analytics measures what happened in your own sales data. Sales intelligence, as the market uses the term, mostly means third-party data about companies and contacts to prospect into. Revenue intelligence mostly means capturing and analysing calls, emails and CRM activity to inspect deals and forecast. This page sets out what each actually does, the data each runs on, the question each answers, where coverage intelligence sits among them, and how to tell which one a problem needs.
Three category names, used loosely, for three different jobs. The difference is in the data each one runs on.
| Sales analytics | Sales intelligence | Revenue intelligence | |
|---|---|---|---|
| Runs on | Your ledger, CRM, orders, contracts | A vendor's database of companies, contacts, signals | Your calls, emails, meetings, CRM activity |
| Answers | What happened, where, by whom? | Who should we approach, and how do we reach them? | What is going on in this deal, and will it close? |
| Typical user | Sales operations, finance, sales leaders | SDRs, marketing, new-business reps | Frontline managers, enablement, forecasting |
| Output | Reports, dashboards, ranked lists | Prospect lists, contact details, alerts | Deal inspections, call summaries, forecast roll-ups |
| Best for | Any business with history | New-logo acquisition | Long, conversation-heavy deal cycles |
| Weak for | Businesses with no clean history | Growing existing accounts | Transactional and account-managed revenue |
Measurement of what your own systems recorded. Revenue by customer and product, pipeline by stage, activity by rep, period against period. It is the oldest of the three and the one most businesses believe they already have, usually as a BI tool nobody opens or a spreadsheet one person maintains. Its limits are the limits of the data: duplicate customers, unmapped territories, totals that do not match finance. The KPI definitions page covers the measures.
As a product category, this means data you buy: company size, industry, technology used, contact names and emails, funding events, hiring signals, intent data from web behaviour. It tells you about accounts you do not have. It tells you almost nothing about the ones you do, because the vendor cannot see your ledger. The phrase is also used in its plain-English sense, intelligence about your sales, which is where the confusion starts; the sales intelligence from the data you already hold page uses it that way deliberately.
Recording and analysing the interactions: calls transcribed, emails logged, meetings captured, all attached to opportunities. Managers see which deals have had no contact with a decision-maker, which competitor is being mentioned, how much the rep talked. Forecasting modules roll this up. It depends on deals being conversations. A distributor with forty thousand orders a month and no opportunities in its CRM has nothing for it to read.
Closest to sales analytics, with a narrower question: of the accounts we already have, which are not getting the attention their value warrants, and what is the gap worth? It joins the ledger, which knows what each account buys, to the activity record, which knows who has been spoken to, and to a norm built from the business's own similar customers. Its five measures are coverage, share of wallet, dormancy, concentration and whitespace; the coverage intelligence definition sets them out. The output is a list with owners rather than a dashboard.
| Question | Category |
|---|---|
| How did we do last quarter, by region? | Sales analytics |
| Which companies in this sector have over 500 staff and use a competitor? | Sales intelligence |
| Has anyone senior joined the calls on this deal? | Revenue intelligence |
| Which of our top 200 accounts has nobody spoken to in 60 days? | Coverage intelligence |
| Which customers buy half of what similar customers buy from us? | Coverage intelligence |
| Whose forecast is always 12 percent low? | Sales analytics |
Most revenue from new logos, short cycles. Sales intelligence data first; analytics to measure conversion by source.
Large deals, long cycles, many stakeholders. Revenue intelligence on the conversations; analytics for win rate and coverage.
Most revenue from existing accounts. Analytics and coverage intelligence on your own data. This is distribution, manufacturing, banking, asset management, broking, professional services, logistics, foodservice: most of the economy, and the part the other two categories serve least.
Do not know where revenue comes from. Analytics first. Everything else assumes you can answer that.
A prospecting database bought to fix retention. It cannot see your customers.
Call recording bought for an order-taking business. There are no deals to inspect.
A BI tool bought as the answer. It is a canvas; someone still has to define the measures, fix the customer list and build the lists.
Intelligence taken to mean AI. In two of the three category names it predates AI and just means data.
Analytics looks at your numbers, sales intelligence looks at other companies, revenue intelligence looks at your conversations. If most of your revenue is in accounts you already have, the first is where the money is, and coverage intelligence is the part of it that turns into a Monday list. Covirage is that: it reads the exports a business already has, files only, and produces the computed tables and the lists, with the arithmetic done in code and checked.
Rarely at once. A team selling to new logos with a short cycle gets most from sales intelligence data. A team with long enterprise deals gets most from revenue intelligence on its calls. A business where most revenue comes from existing accounts, distribution, banking, broking, professional services, manufacturing, gets most from analytics and coverage intelligence on its own data, and usually has the least of it.
No, though the words suggest it. Business intelligence is general-purpose reporting and dashboards over any data. Sales intelligence became a vendor category name for contact and company data. If someone asks for sales intelligence, ask whether they mean a prospecting database or insight into their own numbers; about half the time it is the second.
In all three, differently. In sales intelligence it ranks and enriches prospects. In revenue intelligence it transcribes and summarises calls. In analytics and coverage intelligence it reads a business's own exports, maps them, and explains computed tables in plain language; the arithmetic itself should be done by code, not by the model, so the figures can be checked.