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Blog · Alternatives and comparisons · FMCG and CPG brands

How to choose analytics software for FMCG and CPG brands: questions, data and traps

How CPG brands 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 CPG brands 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 CPG brands 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
Explain where my revenue is coming from this quarter. Price, volume and mix bridge
Where are we losing market share? Rate of sale
Which promotions actually paid back? Promotional net lift
Where are we listed but not stocked, or stocked nowhere? Distribution voids
Are we shipping more than the retailer is selling? Sell-in against sell-out
What are chargebacks costing us, and why? Chargebacks by retailer and reason

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

The data you already hold

  • Store list
  • Sell-out data
  • Store attributes
  • Shipment ledger
  • Shipments
  • Opening stock
  • Daily sell-out by store
  • Promotions calendar

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 FMCG and CPG brands

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

  • Sell-in and sell-out: Opening retailer stock + sell-in − sell-out = closing stock
  • Net lift: Promo units = baseline + gross lift; net lift = gross lift − post-promo dip
  • Chargebacks: Deductions = valid + disputed open + recovered + written off
  • OTIF: Orders = OTIF + late only + short only + both

The traps

Net lift. Most promotion reviews stop at the uplift in the promotional weeks and never subtract the dip.

Sell-in against sell-out. The sales team is paid on sell-in and the data for sell-out sits in another team.

Chargebacks by reason. Treated as a finance line. Sorted by reason, most trace to a few fixable causes: labelling, appointment times, advance ship notices.

Measures to leave out

Sell-in revenue against last month. Driven by retailer ordering patterns and promotions, not by demand.

Number of stores ranged, without rate of sale. Distribution that does not sell gets delisted.

Gross promotional uplift. It always looks good.

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 FMCG and CPG brands compared, AI analytics for FMCG and CPG brands and Covirage for FMCG and CPG brands.

For the measures in full, with formulas and exports, read Sales KPIs for CPG and consumer brands.

Questions people ask

What should CPG brands 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 CPG brands?

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 CPG brands already hold?

Usually: store list, sell-out data, store attributes, shipment ledger, shipments, opening stock. Most analytics questions in this industry can be answered from those exports.

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