Data preparation and analytical design
Prepared the analytical foundation and shaped the measures needed to evaluate pricing and promotional decisions.
BI and Advanced Analytics case study
PRISM: Machine-Learning Pricing and Promotions Optimisation
NexAura delivered PRISM for a major retailer to improve visibility into whether pricing and promotion decisions were creating the intended commercial effect. The completed solution combined machine-learning analysis, scenario modelling and a custom dashboard for evidence-led commercial review.
Project summary
A completed decision-support solution for pricing and promotion effectiveness.
The retailer needed stronger visibility into how pricing and promotion decisions affected commercial outcomes, especially when price elasticity, uplift, fatigue and product substitution could interact in the same decision.
PRISM was delivered to bring analytical modelling, Python-based analysis, scenario modelling and dashboard visibility into one decision-support tool for commercial, pricing, merchandising and data stakeholders.
- Client description
- A major retailer
- Status
- Completed
- Service area
- BI and Advanced Analytics
- Delivery focus
- Machine-learning analysis, scenario modelling and a custom dashboard.
The commercial decision challenge
Pricing and promotions are difficult to evaluate when effects overlap.
A change in price can affect demand directly, but it can also change how customers respond to promotions, how products compete with one another and whether repeated offers begin to lose impact.
Decision-makers needed a clearer way to assess these interactions before reviewing alternative pricing or promotional scenarios. The challenge was not only to analyse individual measures, but to bring them together in a way that supported structured commercial judgement.
- Price elasticity needed to be assessed alongside volume response.
- Promotional uplift had to be considered with potential fatigue over time.
- Product cannibalisation could affect whether apparent gains were commercially useful.
- Scenario comparison required a consistent view of likely commercial effects.
What NexAura delivered
Machine-learning analysis and decision-support tooling for commercial teams.
NexAura's work covered the full analytical path from data preparation and model design to scenario modelling, dashboard visibility and delivery of the completed solution.
Machine-learning models
Developed machine-learning models for pricing and promotional analysis while keeping implementation details private.
Price-elasticity modelling
Modelled how demand could respond to pricing changes across relevant commercial contexts.
Volume-uplift analysis
Analysed how promotional activity could influence volume response for commercial review.
Promotion-fatigue analysis
Assessed how repeated promotional exposure could affect response and decision quality.
Cannibalisation analysis
Considered where movement between products could affect the interpretation of promotional performance.
What-if scenario modelling
Created scenario modelling so teams could compare alternative pricing or promotional choices before review.
Custom dashboard delivery
Delivered a custom dashboard that brought multiple analytical measures into one commercial decision-support tool.
Pricing and promotion analysis visual
How the analytical measures relate to commercial impact.
PRISM connected pricing and promotion measures so decision-makers could understand directional relationships without relying on private screenshots, fabricated charts or public model outputs.
Price changes
Proposed changes are assessed as part of a wider commercial context.
Elasticity
Models estimate how demand may respond to pricing movement.
Volume uplift
Promotional activity is reviewed for likely volume effects.
Promotion fatigue
Promotion response is considered where repeated offers may weaken impact.
Cannibalisation
Product-level movement is considered before interpreting commercial value.
PRISM brings the measures together for scenario comparison and commercial review.
Scenario-modelling workflow
From historical data to commercial scenario review.
The workflow keeps the decision-support logic visible: historical evidence informs analytical models, scenario inputs are compared and the dashboard gives teams a structured basis for review.
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Historical data
Commercial history is prepared for pricing and promotion analysis.
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Analytical models
Machine-learning models assess pricing and promotional relationships.
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Scenario inputs
Alternative pricing or promotional choices are entered for comparison.
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Predicted effects
Likely commercial effects are surfaced without exposing private outputs.
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Dashboard comparison
Scenarios are reviewed in one custom decision-support tool.
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Commercial review
Teams compare evidence before making pricing or promotion decisions.
Historical data → analytical models → scenario inputs → predicted commercial effects → dashboard comparison → commercial review
Outcome and commercial value
Improved decision visibility for pricing and promotion review.
PRISM improved visibility into the commercial effects of pricing and promotions by bringing multiple analytical measures into one decision-support tool.
For this completed project, the approved commercial outcome is 2–4% gross-margin uplift. The solution supported pricing and promotions decisions through price-elasticity analysis, volume-uplift analysis, promotion-fatigue analysis, cannibalisation analysis and what-if scenario modelling.
- 2–4% Gross-margin uplift.
- Improved visibility into pricing and promotion effects.
- Enabled comparison of alternative pricing and promotional scenarios.
- Brought elasticity, uplift, fatigue and cannibalisation measures into one tool.
- Supported more structured commercial review before decisions were made.