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Blog · Alternatives and comparisons · Sales teams

How to choose analytics software for sales teams: questions, data and traps

How sales teams should choose analytics software: start from the questions, check the data you hold, ask vendors ten questions, avoid the traps.

By CoviragePublished 24 September 20264 min read
The short answerStart from the questions sales teams ask every month, not from features. List the exports you already hold, ask every vendor what it needs before the first answer and whether its AI calculates figures, and check that every total reconciles. Then compare the first-year cost, all in.

Most buying decisions for analytics start from a feature list. For sales teams the better start is the questions that come back every month, the files already on hand, and the traps that make a tool look right in a demonstration and wrong in the first board meeting.

Start from the questions

The question The measure behind it
Are we covering the accounts that matter? Value coverage at cadence
Which accounts have gone quiet, and how much were they worth? Dormant accounts, by prior value
Where do similar customers buy more from us than this one does? Share of wallet against similar customers
What is our real win rate? Win rate from a stated stage
Is there enough pipeline? Pipeline coverage against required
Who is overloaded, and who has room? Rep load against capacity

Any tool you consider should answer these from your data, not from a sample. Ask to see it.

The data you already hold

  • CRM activity
  • Assignment file
  • Ledger
  • Account master
  • CRM opportunities
  • Targets
  • Weekly forecast snapshots
  • Closed revenue

If a vendor needs a warehouse built before it can read these, count that in the cost and the time.

Ten questions to ask any vendor

  1. What does it need in place before the first answer? A warehouse, a data model, a modelling language, a partner? Ask for the list and the typical weeks.
  2. Who does the setup, and who maintains it? Your team, the vendor, or a partner, and what that costs after year one.
  3. Does the AI calculate figures, or choose from computed ones? A language model that writes queries or code can produce a plausible wrong number. Ask what it is allowed to do.
  4. Does every total reconcile to a control figure? Ask to see a bridge that does not sum and what the product does about it.
  5. Can every figure be opened to its rows? An answer nobody can check becomes a debate in the meeting.
  6. What does it cost in the first year, all in? Licences, consumption, implementation, modelling and training, not only the seat price.
  7. How does data arrive, and who holds credentials? A file your systems already export, a scheduled drop, or a live connection with the vendor holding keys.
  8. What happens to the data, and where is it stored? Residency, retention, deletion, and whether names can be replaced with identifiers.
  9. Can we see it on our own data before we sign? A demonstration on a sample dataset tells you little about your own.
  10. What does the tool do when it cannot answer? It should say so. A confident guess does more harm than no answer.

Checks specific to sales teams

Ask whether the tool enforces these, and what it does when they fail:

  • Coverage: Assigned accounts = covered + overdue + never touched; one owner each
  • Pipeline: Deals = won + lost + open; open = in period + out of period
  • Forecast: Every actual is matched to a snapshot at the stated horizon
  • Load: Touches owed sum across tiers; every account has a tier

The traps

Coverage by value. Dashboards count calls. Whether the top fifty accounts were spoken to is not on them.

Forecast bias per rep. The team lands within 3 percent because two reps cancel each other out.

Rep load. Accounts are added to books for years and nobody checks the arithmetic of covering them.

Measures to leave out

Calls, emails and meetings as counts. Coverage at cadence, by value.

Total pipeline value. In-period, aged removed, against required.

Leaderboard by revenue. It ranks the books reps were given.

A scorecard

Criterion Weight Tool A Tool B Covirage
Answers our six questions on our own data High
Time to the first answer High
Needs a warehouse or data team Medium
AI calculates figures, or only explains computed ones High
Every total reconciles; figures open to rows High
First-year cost, all in Medium

Where Covirage fits

Covirage reads the exports above, answers the questions with figures our tools compute and check, and is set up for you within a week. See analytics software for sales teams compared, AI analytics for sales teams and Covirage for Sales teams.

For the measures in full, with formulas and exports, read Sales KPIs for B2B sales teams.

Questions people ask

What should sales teams look for in analytics software?

The answer to their own questions, from the data they already hold, with every figure reconciled. Features matter less than what the tool needs before the first answer and who maintains it.

Is a BI suite enough for sales teams?

It can be, with a warehouse and someone to build and maintain the model. Without them, the dashboard shows what changed and the explanation is still an analyst's job.

What data do sales teams already hold?

Usually: crm activity, assignment file, ledger, account master, crm opportunities, targets. Most analytics questions in this industry can be answered from those exports.

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