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

Most buying decisions for analytics start from a feature list. For pharma commercial 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

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

The data you already hold

• Call log
• Dated access file
• Prescription data
• Access file
• Versioned plan
• Sample log
• Target list
• Plan

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 pharma

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

• Access: Targets = open + restricted + blocked + unknown, on any date
• Calls: Calls = on plan + off plan; on accessible + on blocked + on unknown
• Plan: Every call is assessed against exactly one plan version
• Prescriptions: Account prescriptions sum to the territory total in the data source

The traps

Access as a dimension. The access file sits with market access, the call log with sales force effectiveness, and the two are rarely joined by date.

Pull-through. A formulary win is celebrated and never followed account by account.

Plan versions. Only the latest plan is kept, so last month's attainment cannot be recomputed.

Measures to leave out

Calls per day. Without access it counts doors, not opportunities.

Reach over all targets. Always beside the access-aware figure.

Samples distributed. What they moved is the measure.

A scorecard

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 pharma compared, AI analytics for pharma and Covirage for Pharma.

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