How to Use Machine Learning to Further Retail Analytic Capacity

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If you’re only using big data to target consumers based on demographics or purchase history, you’re making a mistake that will cost your company a significant amount of revenue.

Big data can provide retailers with a wealth of highly personalized information about their customers, informing meaningful customer interactions. But most retailers are only using the data to make very simple assumptions about their customers. As a result, shoppers are given cookie-cutter personas that do not evolve along with their shopping behaviors.

Targeting a consumer based on her age or purchase history certainly makes sense in theory. However, consumers are much more complicated, and make purchase decisions based on a complex set of factors that are constantly changing and evolving. Retailers are missing out on a significant opportunity to drive revenue and loyalty by failing to truly understand their customers with more advanced data analytics.

So what’s the answer? Advanced machine learning platforms. These tools have the ability to grow and change a retailer’s understanding of customers over time based on evolving data.

Read the full article on InsideBigData.

This post was originally published on Rubikloud's blog.

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