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How retailers can use predictive analytics to optimise pricing


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to greatly increase productivity, reduce discrepancies, and increase sales.


CHALLENGE: SET PRICES CONSISTENTLY AND EFFICIENTLY This large retailer with physical stores, an online presence, and hundreds of thousands of employees and customers spanning multiple countries, was losing money due to being undercut by competitors on price. They also found that their customer base tended to wait until the end of seasons for huge markdowns and would only then buy products, which skewed their predictions for how to stock items in the future and perpetuated the pricing issue. In addition, they struggled to efficiently change prices and keep them consistent across stores and online - often, this resulted in inconsistent pricing between the store and online, especially when individual store managers made their own decisions on sales.


SOLUTION: INCORPORATE ROBUST PREDICTIVE ANALYTICS IN PRICING STRATEGY The retailer introduced a universal data science platform into their data team’s processes (specifically the smaller group responsible for advising on pricing) to incorporate predictive analytics at scale into their pricing strategy.


They worked with professional data scientists to produce a final data project that:


• Considers competitors’ pricing and uses it in a predictive pricing model to determine whether the overall business can support aggressive price-based competition


• Leverages a REST API to adjust pricing in production automatically based on a specific set of predefined features


• Uses real-time monitoring of models in production to ensure pricing model performance isn’t drifting and that pricing changes in production over time are well documented


• Includes a robust pricing dashboard based off the predictive pricing model that alerts and allows physical stores to react to recommended pricing changes or online pricing changes.


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