44 questions to ask any pricing vendor
If you’re leading a pricing software evaluation, you’re likely running it on top of your day job. Somewhere between deadlines you’re sitting through vendor demos that start to blur together.
Every pricing vendor says it optimizes price and promotion, and on sample data, most of them look good. The differences tend to show up after go-live, when a forecast misses, when a category manager can’t see why the system recommended a price, or when IT finds more of the integration on its plate than anyone planned for.
Through all of it, you’re the one who’ll stand behind the choice. Finance wants proof the payback is real, merchandising wants recommendations its category managers will trust, and IT wants to know what it’s signing up for.
What should you ask a pricing software vendor? These 44 questions cover the 10 areas that matter most when grocery and convenience retailers choose a pricing platform. Each one is paired with what a strong answer shows and the committee seat best placed to score it, so the differences surface before you sign.
Want to use these in an upcoming RFP and score the answers? Get the free pricing software RFP template, which includes a built-in scorecard.
How to use these questions
Pick the weights that match your pricing maturity stage, or set your own. When the committee agrees on weights up front, every vendor gets judged the same way.
Use “What a strong answer shows” as your guide. Each seat on the committee scores the questions it owns.
When totals land close, reference calls settle it.
How to score an answer
Showed it on our data or in a live demo, with evidence
Showed it in a demo on sample data
Described it clearly, with documentation
Described it vaguely, or it's on the roadmap
Couldn't answer, or it isn't available
Score what the vendor shows you. A roadmap promise scores a 2 until it's live.
1. Can they prove it worked?
Every vendor has a success story. These questions test whether the result was measured, and whether it would hold at a business like yours.
Walk us through one customer result and how you separated your impact from everything else that changed that year.
A named method, such as a backcast against a no-change baseline or holdout stores, with the math shown.
Which goals do your customers optimize for (traffic, units, revenue, margin, or price image), and how did results differ by goal?
Results tied to each customer's chosen goal, with the trade-offs named.
Tell us about an implementation that struggled. What went wrong, and what changed afterward?
A specific cause and a specific fix. Candor here predicts how problems get handled later.
2. What happens when the model is wrong?
Every forecast misses sometimes. A good system learns from each miss and tells you how often it happens.
Will you back-test the model on our sales history before go-live, and what result counts as a pass?
Yes, with the pass threshold agreed before the pilot starts.
When a price change underperforms the forecast, what does the system do with that result?
Actual results feed back into the model on a stated cadence, with an example of an estimate that changed.
How do you keep thin or messy data from producing extreme prices on new items, slow movers, out-of-stocks, and heavily promoted items?
Confidence scores, fallbacks to similar items or category-level estimates, and guardrails that cap the move.
How did your models hold up through the last two years of cost inflation and tariff swings?
The retraining cadence and a recent example of the model adjusting to a cost or demand shock.
3. Can your team see why?
Category managers act on recommendations they understand. Explainability decides whether the tool still gets used after the first month.
Pick one of our items. Show us why the system recommends this price: the drivers, the rules that limited it, and the forecasted result.
A live demo on a real item that a category manager can read without help.
What changed since last week, and what triggered this recommendation: a cost change, a competitor move, a rule, or an opportunity?
A reason tag on every recommendation.
Can we see several price options for an item, each with its projected result?
Side-by-side options or a trade-off curve between goals.
Walk us through a typical week for one of our pricing analysts using your system.
What surfaces first, how work gets prioritized, and how long the weekly cycle takes.
4. Do price and promotion work from the same model?
When base price and promotion are planned in separate systems, one can undercut the other. These questions find out whether they work as one.
Do base price, promotion, and competitive data feed one demand model, or do separate modules pass data between them?
A demo where a base price change updates the forecast for an upcoming promotion.
How do you separate a promotion's lift from the item's everyday price sensitivity?
A clear treatment of promoted weeks when estimating base elasticity.
How do you measure whether a promotion paid back after cannibalization, halo, pantry loading, and vendor funding?
Incremental units and profit net of those effects, with vendor funds included.
Can the system show us which promotions to stop running?
A ranked view of events by incremental return and an example of a customer redeploying that spend.
5. Does it respect your price architecture?
Size parity, private label gaps, and price ladders took years to build. The system should protect them on every recommendation.
Which rules do you enforce out of the box: size parity, line pricing, private label to national brand gaps, good-better-best ladders, price endings, MAP, and state minimum markup laws?
A demo of the rule setup, with hard and soft constraints shown.
When two rules conflict, how does the system decide, and how does it show us?
User-set rule priority and a broken-rules view.
How do you handle our price zones, and can you recommend a better zone structure?
Zones that can vary by category, a stated zoning method, and clarity on whether zone work costs extra.
How does the system handle weekly cost changes, DSD costs that vary by distributor, temporary allowances, and manufacturer buydowns?
A cost-change workflow that shows the effect on related items, plus buydown compliance tracking where it applies.
How do you respect store operations: limits on changes per store per week, retag labor, and electronic shelf labels versus paper tags?
Change caps that keep the highest-value changes first.
The free pricing software RFP template puts all 44 questions in one file for vendors to answer, and its built-in scorecard calculates weights and totals for you.
Get the RFP template6. How good is the competitive data?
Every competitive rule depends on the item match behind it. A bad match produces a confident, wrong price.
Where does competitor price data come from, how often does it refresh, and how do you match items, including fresh and private label?
Named sources, refresh cadence, match method, and reported match accuracy.
How do you compare prices when sizes and pack counts differ?
Unit normalization and equivalent-item matching shown on real examples.
How do you identify our Key Value Items (KVIs), and can the list differ by zone or format?
A transaction-based method, refreshed on a schedule, with room for the merchant's judgment.
Can we hold an index target, such as staying within a set percentage of a competitor on KVIs, across several competitors and zones at once?
Weighted indices by competitor and zone, enforced during optimization.
When a competitor moves, how fast do we know, and what does the system recommend?
Configurable alerts paired with a recommended response.
7. Who approved this price?
Pricing is a strategic priority, so leadership needs to see who changed what, when, and why.
Walk us through the path from recommendation to approval to publish.
Configurable approval steps, such as a second approval for any KVI change.
For any price on the shelf today, can we see who recommended it, who approved it, and when?
A full audit trail inside the application.
How do approved prices reach POS, electronic shelf labels, and ecommerce with the right effective dates?
The export method, the timing, and how publish errors get caught.
How do you report adoption: accepted, overridden, and ignored recommendations by user and category?
Adoption reporting that leadership can pull without a services request.
8. What will IT need to do?
Integration is usually the longest part of an implementation. Know the IT workload before you sign.
What data do you need on day one, what can wait, and how much sales history do you require?
A data specification with required and optional fields.
How does data move between our systems and yours, how often, and who builds and maintains the connections?
Supported methods, cadence, and a clear split of responsibilities.
How many hours will our IT team spend during implementation and after go-live?
A resource estimate by phase, drawn from a customer of similar size.
How do you keep our data separate from other customers' data, including our competitors'?
Tenant isolation explained in plain terms and a current SOC 2 Type 2 report.
Can we export our data and model outputs to our own warehouse or BI tools, and who owns those outputs?
Export options, with data ownership written into the contract.
9. How fast will we see value?
Finance wants a date. These questions turn a vendor's timeline into one you can hold them to.
When will the first optimized prices reach our shelves, and what has to be true for that date to hold?
A phased plan with dependencies named, most of them data.
How would you structure a pilot: categories, stores, duration, and success measure?
A test-and-control design with a success threshold agreed before launch.
Who's on our team after go-live, and how much grocery or convenience pricing experience do they bring?
Named roles and relevant experience.
What are your support hours and response commitments, and how have you performed against them this year?
The commitments and the measured adherence.
How do you train category managers and analysts, and what does training cost after launch?
Role-based training and clear terms for new hires and refreshers.
10. Will it grow with us?
Your pricing will mature. Confirm the platform can grow with it, and what leaving would cost.
Which stages of pricing maturity does your platform cover, and can we start with one capability and add more without re-implementing?
A modular path, such as competitive data and rules first, optimization next.
What AI runs in your product today, and what's still on the roadmap?
A clear line between live and planned, with the live pieces shown in the demo.
What shipped in the last year because a customer asked for it?
Specific examples and the process for submitting requests.
How is pricing structured (stores, SKUs, users, or modules), and what costs sit outside the license?
A three-year total cost that includes implementation, services, and competitive data.
If we leave, what happens to our data and configurations?
Exit terms and export formats in writing.
Build the business case first
Choosing a vendor comes after leadership agrees to invest. The free pricing business case toolkit walks you through it, from finding your pricing maturity stage to the one-page business case your leadership signs.
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