Most growth plans still start by looking at estate-level sales, distribution, and a few headline comparisons.
That approach can summarise performance.
It doesn’t give a buyer enough clarity to approve a change.
Estate averages hide the most important factor for commercial value: variation. They mask where demand is actually strong, where performance relies on promotions or temporary distribution, and where low sales are due to execution problems instead of weak shopper demand.
This leads to a common problem. Teams find themselves defending numbers they can’t repeat, asking for more space without knowing what will be replaced, or facing range reviews where they can’t clearly answer the key question: why this SKU, in these stores, right now?
To get buyer-ready, start by separating real demand from the noise around it. This makes decisions safer, even if execution isn’t perfect.
The “Estate Average” trap
Estate averages hide where demand really exists.
Baseline demand means the sales you’d expect without promotions, distribution changes, seasonal effects, or execution issues.
This difference lets you test and improve a plan, instead of having to rebuild it from scratch.
In practice, you need to look at sales along with the factors that affect them. Performance outside of deal weeks is more important than the peaks during them. Separate distribution changes from real demand. Treat availability gaps, substitutions, and depot issues as distortions, not insights. Also, consider waste and oversupply, since low RoS often points to stock problems, not weak shopper interest.
Advanced teams take it further by clearly separating baseline demand from the extra sales created by specific actions. This gives a stable starting point before anything changes, so teams can test scenarios, model likely outcomes, and set realistic expectations. Afterward, it’s easier to see what actually made a difference and replace estimated uplift with a clear view of true ROI.
Proof of concept: The 7.8% granularity uplift
In CPG range reviews, scenario-led recommendations grounded in incremental demand have been shown to materially change outcomes. Kantar reports that one manufacturer delivered +7.8% incremental brand sales [1] by modelling range decisions around baseline demand rather than estate averages — while also driving category growth for the retailer.
When store-level demand changes the outcome
At Vibrant Foods, the Tesco account team moved away from blended estate views and used SKUtrak to analyse daily store-level sell-out, allowing them to see where demand was genuinely coming from.
By separating baseline demand from promotions and execution effects, they could identify which products sold steadily where they were listed — and where performance was being held back by distribution gaps rather than weak shopper demand.
Ahead of a range review, this approach revealed a SKU with strong, consistent sales in the stores where it was ranged, but limited visibility across the wider estate. On an estate average, it appeared marginal. At store level, it was clearly earning its space.
Without that signal, the SKU faced a real threat of being delisted for the wrong reason in the conversation with the buyer.
With it, the conversation shifted from defending averages to demonstrating proven demand in specific store groups — reframing the issue as a distribution opportunity rather than a product failure.
Often, the goal isn’t to ask for more space everywhere. It’s to show where a product already deserves more space.
Read the case study.
A buyer-readiness check for commercial teams
Before taking a growth plan into a buyer meeting, sanity-check it against the questions the buyer will ask:
- Can we show exactly which stores, formats, or regions this works in?
- What does performance look like if nothing changes?
- How much of the volume is genuine demand versus shifted or promotional?
- What space, range, or operational trade-offs are required?
- Does volume hold outside deal weeks and one-off effects?
- Where does the risk sit if availability or compliance isn’t clean?
- What improves for the buyer — margin, space productivity, or simplicity?
If a plan can’t answer these clearly, it risks stalling — not because the idea is wrong, but because the decision isn’t defensible.
A decision-ready planning framework
Buyer-ready planning follows a clear process.
1. Start with what actually changed
Identify what changed in performance, then figure out if it was caused by price or promotion, distribution shifts, availability or waste issues, or real demand. If you can’t explain the cause, you can’t expect a buyer to support the fix.
2. Isolate where demand is proven
Go beyond estate averages and look for consistency. The real question isn’t average RoS, but where sales stay strong when conditions change, by format, region, or cluster. High RoS with low distribution points to a distribution issue, not a product problem.
3. Overlay range reality
Compare proven demand with the current range. This shows which under-listed products are already performing well, and which over-ranged SKUs add complexity without benefit. Plans then focus on efficiency as much as growth.
4. Establish the do-nothing baseline
Remove temporary effects and show what happens if nothing changes. This stops the upside from being overstated by one-off conditions and gives buyers a clear reference point for their decision.
5. Pressure-test the uplift
Only then should you test the upside under real-world constraints. Make space, margin, operational load, availability risk, and waste clear, not hidden in assumptions.
6. Land a decision-ready ask
The result is a proposal that shows where it works, what changes, what happens if nothing changes, and where the risks are. The buyer isn’t being asked to just believe. They’re being asked to make a decision.
The cost of “good enough” data
When plans rely on estate averages and reported performance, buyers struggle to see where change would hold up. The default outcome is often delay, smaller tests, or requests for more evidence — rather than a clear decision.
Planning based on baseline demand changes the tone and approach to the conversation. It shows where demand is already proven, what value is being missed, and what improves both commercially and operationally if action is taken.
Teams using Crisp SKUtrak are already automating this shift—turning store-level sell-out data into decision-ready proposals in minutes, not days.
If these challenges sound familiar from your own range reviews or growth planning, it’s worth looking at how others are using this approach in real situations.
Sources
[1] Bold SKU swap decision delivers unexpected growth, Kantar