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Future of Retail — Customer Engagement


issue 05


With the robust


predictive analytics tools that are now available, retailers can set prices that allow their products to sell better and predict what prices will be most effective at what times.


The team was able to get its pricing


optimisation model up and running in a total of six months, and it is working with other parts of the data team to optimise other parts of the business (like supply chain and inventory management) using predictive analytics.


IMPACT: REDUCE EXCESS INVENTORY, BOOST SALES With the robust predictive pricing solution developed using Dataiku, the retailer was able to reduce excess inventory by setting prices that allow products to sell better by correctly predicting what price will be effective at what time.


In addition, the retailer:


• Increased the productivity of brick-and- mortar store managers, who now use the pricing dashboard to automatically inform data-driven pricing changes


• Reduced the number of discrepancies between online and in-store pricing (using customer service complaints about those discrepancies as a proxy) by 65%


• Boosted sales by 10% by using the model to determine optimal pricing at the right time instead of conducting huge temporary markdowns at the end of a season.


In conclusion, it’s imperative that retailers


take a comprehensive, data-driven approach to pricing if they wish to keep their current customers and attract new ones. With retailers expecting to need 29% more digital and analytical talent in their businesses by 2020, many have begun hiring data scientists to fulfil these roles. However, as many as 43% of organisations surveyed in a report by AT Kearney claimed that at least 10% of their company’s digital and analytical positions are currently unfilled. With that in mind, there is certainly a


significant shift taking place in what is required to make a retail company successful in today’s digital world. Fortunately, with the help of predictive analytics, retailers can make good use of all of the data they are already collecting on the customers’ buying behaviours to not only adjust their pricing strategies to what customers will find most appealing but also to adjust it at the right time.


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