Pricing intelligence that goes deeper
Every price point carries a hidden cost: set too high and volume leaks to competitors; set too low and margin is left on the table. Accuris gives FMCG commercial teams the econometric evidence to defend and optimise every pricing decision — at SKU level, by retailer, every quarter.
01
Base Price Elasticity
Understand how shoppers respond to permanent shelf-price changes. We map the full elasticity curve across the observed price range — revealing the optimal pricing corridor where increases can be taken profitably before triggering accelerated volume loss.
02
Promotional Price Elasticity
Measure the true incremental return of every pound invested in temporary price reductions and multibuy mechanics. Our models pinpoint the optimal discount depth — deep enough to drive meaningful uplift, not so deep that margin is destroyed for diminishing returns.
03
Cross-Price & Competitive Intelligence
No SKU exists in isolation. We identify the competitive set for every pack, quantify aggregate competitive exposure, and decompose it into pairwise cross-elasticities — separating cannibalisation risk from competitive switching and retailer switching.
04
Pricing Simulation Dashboard
An interactive scenario-planning tool that lets commercial teams model the impact of pricing and promotional changes before they go to market. Adjust price or discount depth, compare scenarios side by side, and see the volume, revenue, and margin impact instantly.
Built on revealed shopper behaviour, not surveys
Unlike stated-preference approaches such as conjoint analysis, our elasticity models are grounded entirely in what consumers actually do at the till. The methodology rests on three pillars:
Accurate Baselines
A proprietary Bayesian time-series decomposition separates observed sales into structural baseline, seasonal and event effects, promotional uplift, and residual noise. Bayesian shrinkage priors ensure robust estimates even for low-frequency SKUs — and avoid the common pitfall of conflating promotional volume with seasonal peaks.
Non-Linear Price Response
Consumer response to price changes is non-linear. A 20% discount generates a disproportionately larger uplift than a 10% discount. We normalise all price movements to a per-1% elasticity and map the full response curve, so you can see exactly where volume erosion accelerates.
Competitor-Aware Modelling
Every elasticity estimate explicitly includes competitor price variables. Your elasticity of −1.8 is not estimated in isolation — it accounts for the simultaneous price positioning of rival brands in the same retailer.
Quarterly deliverables that drive decisions
Quarterly Elasticities
A living reference document, updated every quarter, containing the full coefficient library and elasticity curves for every SKU-retailer combination. This is the analytical backbone from which all strategic outputs are derived.
Value-at-Risk Report
Translates raw elasticity coefficients into strategic pricing intelligence. Every SKU is classified as over-priced (share risk) or under-priced (margin opportunity), quantifying the commercial impact of the current price position.
Promo Efficiency Quadrant
Categorises every promoted pack into one of four strategic roles:
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Efficiency Drivers - High volume lift AND high margin ROI. Protect and optimise.
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Volume Drivers - High lift, low ROI. Use strategically; cap frequency.
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Brand Builders - Low lift, high ROI. Deploy for margin enhancement.
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Subsidisers - Low lift AND low ROI. Redirect spend to higher-performing packs.
Pricing Simulation Dashboard
Interactive Power BI environment for scenario planning. Build and compare pricing scenarios, visualise profit waterfall decomposition, identify cannibalisation effects, and project volume and revenue impact over time.