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Maximize Marketing Investments with Machine Learning

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The retail industry exists in a constant state of change, with new customer demands appearing as quickly as the technologies developed to help deliver on them. Personalization is the name of the game for retailers interested in staying competitive, and the next generation of truly one-to-one pricing and communications simply cannot be reached without taking full advantage of one the most important technologies to rise to prominence in the recent past — machine learning. 

Savvy brands are already putting the practice to work in a number of marketing and merchandising areas, ranging from product pricing and product assortment to promotions scheduling and marketing planning. Because of the almost infinite variables that go into optimizing marketing tactics for true customer-centricity, machine learning can be particularly useful for any retailer hoping to maximize the ROI on their communications spend.

Brands need to go far beyond marketing to broad customer segments or creating monthly or weekly campaigns, and hyper personalization can be accomplished only through learning dynamically, one customer at a time. But how do retailers determine which customers to target and which offers to extend?

Personalized marketing optimization requires determining which offer will incent behavior, based on the shopper’s history, profile and a host of other factors, while also giving the ability to test and learn to get to that perfect offer. It’s an incredibly complex undertaking, one that can be accomplished far more quickly and efficiently when leveraging a thoughtful machine learning program.  

In the U.S., Precima partnered with a major retailer to apply machine learning to drive customer personalization, delivering highly relevant communications that maximized key customer interactions, drove profitable growth and simplified sales activation. To accomplish this, they built a machine learning system that analyzed an incredible number of combinations of incentives and product/customer combinations. The system could accurately predict customer response on specific incentives, in turn optimizing the allocation of marketing investments and personalized incentives that drove customers into action with timely offers on relevant products.

The program uncovered deep customer insights about why customers buy, clarified helpful details about customer needs, purchase behaviors, life stages and potential value and drove marketing strategies to boost sales and profits. This sort of definitive view of each customer lays the groundwork for smarter customer objectives and measurable performance metrics, as well as relevant product offers, incentives and customer treatments (cross-selling, upselling, etc.) that improve the overall customer experience.

A machine learning system focused on marketing optimization can efficiently sift through trillions of scenarios and automatically generate a set of offers that maximize the return on marketing investments — a feat that is not humanly possible. If retailers are interested in besting the competition with a customer-centric marketing approach, that’s a powerful tool they can’t afford to pass up.


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