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Blog · Alternatives and comparisons · Customer service

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

How service 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 service 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 service 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
Which accounts are raising far more tickets than usual? Contact volume against own baseline
Are we meeting each account's own SLA? SLA attainment, account's own SLA
Which accounts have gone silent? Silence list
Where does effort exceed revenue? Effort per account against revenue
What keeps coming back? Recurring causes
Which tickets could have been deflected? Deflectable tickets

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

The data you already hold

  • Ticket export
  • Account list
  • Contract terms
  • Time records or handling time
  • 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 customer service

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

  • Tickets: Tickets = first-contact resolved + repeat + open
  • SLA: Tickets = met + breached + not yet due; every account has its SLA terms recorded
  • Accounts: Every ticket belongs to one account; unmatched tickets are counted, not dropped
  • Backlog: Opening + raised − closed = closing

The traps

The per-account view. Tools report by agent, queue and team. The account is a field few reports group by.

Silence. Nobody is alerted by a ticket that was not raised.

SLA against each account's own contract. One SLA target applied to everyone, when enterprise accounts signed for something tighter.

Measures to leave out

Tickets closed per agent. Rewards closing, not resolving; drives repeat contact.

Average handling time as a target. Shorter calls, more of them.

A single blended SLA percentage. By account, against the contract.

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 customer service compared, AI analytics for customer service and Covirage for Customer service.

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

Questions people ask

What should service 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 service 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 service teams already hold?

Usually: ticket export, account list, contract terms, time records or handling time, revenue. Most analytics questions in this industry can be answered from those exports.

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