Most FMCG brands are leaving margin on the table not because their products are wrong, but because their pricing strategy is. Getting the framework right before you allocate a single dollar of trade spend is the difference between a plan that builds the business and one that just moves volume at cost.
Price responsiveness — or elasticity — sits at the centre of every effective trade plan. Understanding how elastic each product in your portfolio is gives you the foundation to make smarter decisions about where to invest, where to hold price, and where to pull back support entirely.
Why Elasticity Is the Starting Point, Not an Afterthought
Elasticity tells you how much a product’s sales volume responds to a change in price. A highly elastic product will see significant volume movement when you discount it. A low-elasticity product will barely flinch.
The mistake many brand managers make is treating all products in a portfolio the same way — applying blanket promotional mechanics without understanding which items actually respond to them. That approach burns trade spend on products that don’t need it and under-invests in the ones that do.
Econometric modelling gives you two distinct elasticity readings: base elasticity, which reflects how shoppers respond to changes in everyday shelf price, and promotional elasticity, which captures the response to temporary price reductions. Both matter, and they don’t always move in the same direction for the same product.
The Four Pricing Strategies and Where Each One Fits
Once you have reliable elasticity data, you can classify every SKU in your portfolio into one of four strategic buckets. Each bucket calls for a different approach to pricing and trade investment.
| Strategy Type | Base Elasticity | Promotional Elasticity | Recommended Action |
|---|---|---|---|
| Margin Builders | Low | Low | Take price increases; reduce trade support |
| Promotional Drivers | Low | High | Invest in promotional activity; hold base price |
| Everyday Value | High | Low | Prioritise competitive shelf pricing |
| Volume Maximisers | High | High | Balance base and promotional investment carefully |
Margin Builders are the most straightforward opportunity in any portfolio. These products have both low base and low promotional price responses, meaning shoppers will buy them regardless of whether they’re on promotion or sitting at full shelf price. For these SKUs, the right move is to take a price increase where the category and retailer relationship allows it, and to pull back trade support. The sales risk is low, and the margin upside is real.
Promotional Drivers behave differently. Their base elasticity is low, so shoppers aren’t particularly sensitive to the everyday shelf price — but they respond strongly when a promotion hits. These products are built for feature and display activity. The strategy here is to hold base price firm and concentrate trade investment into well-timed promotional windows that drive genuine volume uplift.
Everyday Value products are the inverse. Shoppers are sensitive to the shelf price but don’t respond strongly to promotional mechanics. For these items, competitive everyday pricing is the lever. Over-investing in promotions for an Everyday Value SKU is a common and costly mistake — the volume response simply isn’t there to justify the spend.
Volume Maximisers are the most complex to manage. High sensitivity on both dimensions means both base price and promotional activity drive meaningful volume movement. These products require the most careful calibration — a price increase can erode base volume, while poorly structured promotions can cannibalise margin without building long-term penetration.
What the Data Can and Cannot Tell You
Sales data is essential for measuring the impact of any pricing strategy, but it has real limits. A product’s performance at any given point is shaped by a wide range of factors — competitor activity, ranging changes, seasonal demand shifts, supply disruptions, and broader economic conditions affecting shopper behaviour.
Isolating the true effect of your own price and promotional decisions from that noise is genuinely difficult. Econometric modelling helps by controlling for those external variables, but it requires clean, consistent data inputs and enough historical depth to produce reliable outputs. Brands that rely on raw scan data alone, without modelling, are often drawing the wrong conclusions about what’s actually driving their numbers.
It’s also worth being clear about what this framework doesn’t resolve. Elasticity classification tells you the optimal strategy for each product in isolation — it doesn’t automatically account for portfolio cannibalisation, retailer ranging constraints, or the competitive dynamics of your specific category. Those factors require a layer of commercial judgement on top of the modelling output.
Reallocating Trade Spend Where It Actually Works
The practical payoff from this kind of analysis is reallocation. Most FMCG portfolios have Margin Builder SKUs that are being over-supported with trade spend that would generate a better return elsewhere. Identifying those products and redirecting that investment toward high-elasticity items — or simply banking the margin — is one of the clearest efficiency gains available to a brand manager without changing a single product formulation or pack format.
Circana’s econometric modelling capability is designed to help brand teams do exactly this: build a high-level price strategy and classify each product in the portfolio by the pricing approach that best fits its elasticity profile. For teams managing large or complex portfolios across multiple retail channels, that classification work is the foundation everything else is built on.
If your current trade plan was built on category convention rather than your own elasticity data, now is the time to run the numbers — because your competitors almost certainly already are.