Retail · Predictive analytics & automation
ShyftLabs built an intelligent forecasting solution that helps a national retailer predict product-level demand, measure incremental sales, and make more informed promotional decisions
- Demand forecasts for individual products
- Promotional impact separated from baseline sales
- Model retraining to keep predictions current
At a glance
- Client
- National retailer
- Industry
- Retail
- Solution
- AI-powered promotional demand forecasting
- Capabilities
- Predictive modeling, promotional lift analysis, automated model retraining
- Users
- Merchandising and promotional planning teams
The challenge
Merchants planned campaigns using manual forecasts, historical reports, and individual judgment, with limited consistency in how past promotional performance was evaluated.
Without a reliable way to distinguish regular demand from promotion-driven sales, teams struggled to determine which campaigns generated meaningful incremental value. The impact on ROI, product cannibalization, and related product sales also remained difficult to assess.
As a result, promotional investments were often made without a clear understanding of their likely commercial return.
The data foundation
We developed a forecasting foundation using historical sales patterns to estimate baseline demand without promotions and measure promotional performance more accurately
Historical sales
Previous product performance provides context for demand patterns.
Baseline demand
Expected sales are estimated independently of promotional activity.
Promotional lift
Additional demand attributable to a campaign is modelled separately.
Product-level forecasts
Baseline demand and anticipated lift inform SKU-level projections.
What we built
A predictive forecasting system that combines demand modeling, explainable results, and automated model maintenance
- 01
Predict
Machine learning models estimate expected sales at the SKU level, separating baseline demand from the additional volume anticipated during a promotion.
- 02
Explain
Transparent forecasts show the contribution of promotional lift alongside expected sales. Merchants can examine the drivers behind predictions, challenge assumptions, and adjust their plans before committing.
- 03
Adapt
Automated retraining keeps forecasting models updated as new sales information becomes available, reducing manual maintenance and helping predictions remain relevant as demand patterns evolve.
The solution supports merchant judgment with evidence rather than replacing it with automated decisions.
The outcome
The retailer gained the ability to evaluate expected promotional performance before campaigns launch, shifting planning from manual estimates toward model-supported decisions.
Merchants can now distinguish anticipated incremental sales from demand that would likely occur without promotional activity. This gives them a clearer understanding of where campaigns are expected to generate additional value.
Explainable forecasts also make recommendations easier to review and challenge, allowing teams to apply commercial judgment rather than relying on predictions they cannot interpret.
Automated retraining keeps the forecasting capability current without repeatedly rebuilding models, reducing ongoing maintenance requirements.
The result is a more informed promotional planning process, where merchants can evaluate demand, understand potential lift, and refine their strategies before committing resources.
Discover how ShyftLabs combines data, predictive intelligence, and automation to help enterprises make better business decisions
