The Value of Predictive Analytics in Retail


The Value of Predictive Analytics in Retail

The last few years have proven that any industry can change, even one as traditionally structured as retail.

Where we used to rely primarily on in-person exchanges to view, compare, and purchase products, there’s now a wealth of online and offline channels that serve each stage of the customer journey.

This shift has also coincided with growing customer expectations — which include tailored experiences and lightning-quick responses — and retailers are having to meet them.

One of the tools currently being employed by modern retailers is predictive analytics. In this article, we’re taking a look at what that is and how it helps brands build more meaningful relationships with their customers.

What Is Predictive Analysis?

Using a wealth of historical and real-time data gathered across customer touchpoints, predictive analytics helps brands identify potential customer trends and behaviour, and respond proactively. For instance, if a customer has purchased a raincoat, the next time they’re on the site, the brand could use predictive analytics to automatically prompt them to buy rain boots, as they are two items commonly purchased together.

Predictive Analysis

While this may still sound like science fiction to some retailers, most brands are already in a position to set up this capability. The pandemic forced most retailers to go online and embrace ecommerce solutions. With these tools in place, they now have access to a wealth of customer data that spans browsing behaviour, average shopping cart values, inventory, and purchase data. By integrating and harnessing these various data points — and pulling them into a centralized dashboard powered by Microsoft Dynamics, for instance — brands can access the right insights to reach their customers with the right product suggestions at the right time.

The added bonus? Predictive analytics can also connect the dots between stores and back-office operations. For instance, with a powerful tool that reviews your purchase and inventory data, you can get automated flags that prompt your procurement team or manufacturers to secure more inventory.

Providing an Engaging Omnichannel Experience

As we’ve implied, the concept of predictive analytics doesn’t just exist in the digital realm. Savvy retailers that are making the most of predictive analytics are connecting the dots between their online and in-person experiences so that they can provide truly tailored experiences for their customers.

Take Sephora as an example. Customers can engage with the retailer across multiple channels, including stores, their website, and their mobile application. Beyond browsing products, customers can use the digital services to book appointments with consultants. Then, when they reach the store for their appointment, any information that they’ve chosen to share on their customer profile (e.g. skin type and product preference) is readily available to the consultant.

Engaging Omnichannel Experience

This omnichannel approach empowers brands to gather as much data as they can to then provide optimal and tailored experiences to their customers — and keep them coming back. The result? Higher retention and more revenue from repeat customers.

Making the Most of What You Have

The biggest piece of advice we can give to retailers that are looking to adopt predictive analytics is to first set your goals. What are you trying to achieve with these insights? Are you looking to build stronger relationships with your customers? Or are you hoping to refine your inventory management? Or both?

Once you’ve established your goals, that will put you in a better position to choose the right context and approach for how you build and use your predictive analytics. Plus, you’ll also be able to look back on how you’re tracking against those goals and adjust as needed.

Retailers have so much customer data at their fingertips — they just have to harness it. To learn about how Intwo can help you do just that, get in touch.


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