Future of Retail — Customer Engagement

issue 05

effective, they are not optimal. For example, price optimisation based on averages does not take into account subtle nuances in different geographies or in specific stores. And considering markdowns only when there’s inventory to get rid of is a reactive solution instead of a proactive pricing strategy. Ultimately, neither of these strategies takes account of the specific data behind what drives sales of a particular individual product and how that specific product can be best priced to maximise revenue. Using predictive analytics, retailers can

set prices that take all possible factors into account in real-time - something that would be impossible without data science and machine learning. Predictive analytics also allows retailers to factor in competitors’ pricing, real- time sales data, and even information such as weather predictions to optimise pricing for a particular season. 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. Here’s a further look at how retailers can optimise their pricing in these ways using predictive analytics.

1. UNDERSTAND WHAT DRIVES CUSTOMER PURCHASING DECISIONS Effective pricing requires extensive analysis of both pricing trends and the consumers’ potential actions. With predictive analytics, retailers can monitor consumer buyer habits and offer predictions on what actions consumers may take at a certain price point. Predictive analytics can also anticipate what the responses might be to a specific set of bundled offerings or discounts. This is especially useful for retailers who

often find it necessary to conduct huge temporary markdowns at the end of a season to move inventory and meet falling consumer demand. With predictive analytics, business owners can determine the optimal price for their merchandise at the right time (i.e. when customers are most likely to buy) and avoid relying on significant, one-time markdowns

to get customers through the door. Instead, prices can be reduced gradually at the optimal times, delivering maximum profits.

2. UNCOVER COMPETITOR PRICING IN REAL-TIME Many predictive analytics tools offer pricing optimisation, but it’s important that retailers prevent themselves from relying too heavily on deep discounts and markdowns or entering into a rat race to the bottom with competitors. Predictive analytics can be used in more

valuable ways than just offering the lowest price, such as determining whether or not the business can support aggressive price-based competition for a particular product in the first place. The software can be used to adjust pricing in production automatically based on a specific set of predefined features. Using real- time monitoring in production can help retailers ensure their pricing model performance isn’t drifting and that price changes in production are well-documented over time.

3. UPDATE PRICING CONSISTENTLY ACROSS STORES AND ONLINE One of the most essential uses of predictive analytics for retailers is for reducing discrepancies between their online and in- store pricing. Within a predictive analytics dashboard, bricks-and-mortar store owners can receive alerts when online pricing changes occur as well as recommendations for their own in-store changes. For retailers with multiple physical locations

and store managers that are allowed to set their own pricing, this can be extremely beneficial. Each location can use a predictive analytics dashboard to automatically alert them to pricing changes in other locations, as well as online price changes, in order to make better and more data-driven decisions of their own.

USE-CASE: HOW ONE INTERNATIONAL RETAILER BOOSTED SALES BY 10% A leading retailer in Europe with more than 3,500 stores as well as an ecommerce component that offers home delivery services, has applied these predictive pricing strategies

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