Blog · Alternatives and comparisons · SaaS

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

How SaaS companies 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 SaaS companies 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 SaaS companies 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
What is net revenue retention by cohort, not blended?	Net revenue retention by cohort
Which renewals have low seat utilisation?	Seat utilisation before renewal
Where is the whitespace, and does it add up?	Whitespace reconciled to ARR
Which accounts have had no touch?	Untouched accounts, by ARR
How long does a new account take to reach value?	Time to value
Is every renewal in the next two quarters owned?	Renewal calendar coverage

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

The data you already hold

Subscription or billing system
Subscription system
Usage export
Contract file
Account data
CRM
Whitespace table
Onboarding records

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

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.
Who does the setup, and who maintains it? Your team, the vendor, or a partner, and what that costs after year one.
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.
Does every total reconcile to a control figure? Ask to see a bridge that does not sum and what the product does about it.
Can every figure be opened to its rows? An answer nobody can check becomes a debate in the meeting.
What does it cost in the first year, all in? Licences, consumption, implementation, modelling and training, not only the seat price.
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.
What happens to the data, and where is it stored? Residency, retention, deletion, and whether names can be replaced with identifiers.
Can we see it on our own data before we sign? A demonstration on a sample dataset tells you little about your own.
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 SaaS

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

ARR movement: Opening ARR + new + expansion − contraction − churn = closing ARR
Cohorts: Cohort ARR sums to total ARR
Seats: Contracted = active + inactive assigned + unassigned
Whitespace: Account ARR + whitespace = stated potential; account ARR sums to company ARR

The traps

Retention by cohort. The blended figure goes in the board pack and the recent cohort goes unexamined.

Utilisation against the curve. One threshold for every account flags new customers and misses old ones.

Pipeline against whitespace. Expansion pipeline is reviewed as a total, never against where the room is.

Measures to leave out

Logo count. ARR-weighted measures.

Blended net revenue retention alone. By cohort, with gross beside it.

Logins as usage. A qualifying action within a window.

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 SaaS compared, AI analytics for SaaS and Covirage for SaaS.

For the measures in full, with formulas and exports, read Customer base KPIs for SaaS sales teams.

Questions people ask

What should SaaS companies 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 SaaS companies?

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 SaaS companies already hold?

Usually: subscription or billing system, subscription system, usage export, contract file, account data, crm. Most analytics questions in this industry can be answered from those exports.

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