Move Over Nostradamus: Prescriptive Analytics Takes Control of Customer Engagement

“What next?” is a question most marketers ask themselves every day. Some base their answers on experience while others rely on the gut. However today we see a new breed of marketing professionals that are basing their decisions on evidence, thanks to new technologies and prescriptive analytics companies that help make sense of the seemingly random data.

In the increasingly competitive fashion retail industry, where little can be left to chance, while planning is critical, the course of action for every shopper must be decided in a split second to capitalize on the micro-moments.

While predictive analytics tools gives you a view of what’s coming, prescriptive analytics models tells you exactly what should be done to make the most of the situation.

Machine learning has taken decision-making in marketing departments to the next level. Predictions and recommendations are being made by matching customer profile with product attributes. Easy to do with a handful of products, but virtually impossible to manage when customer segments and product categories go through the roof.

In fashion, for example, a purchase goes far beyond the basic attributes of color, size, fabric, design, and price. Sticking to basic attributes such as ‘t-shirt, blue, large, polo’ isn’t going to make the sale; there are hundreds of product subtleties that need to be factored in. Placement of logo, sleeve length, the texture of buttons – these features can make or break the sale for today’s discerning customers. While these are obvious to a consumer the moment they see the item, it’s extremely tricky for the marketing analyst to get right.

Given the ever-multiplying number of attributes, decoding data into actionable insights is now best left to AI driven analytics. Large retailers can predict what customers are likely to buy, and then prescribe the most efficient route to close the sale.

Last year, UK based retailer ASOS announced a significant improvement in predicting Customer Lifetime Value (CLTV) in marketing through the use of AI. The retailer built a model that classifies a given customer as valuable, and potentially how valuable based on signals such as customer’s demographics, purchase history, returns history, and web and app session logs.

The biggest reason for using AI is its inherent ability to perceive a deeper understanding of context, customer preferences and how they make purchase decisions. AI-driven analytics enables retailers to dive deeper into consumer data, automating recommendations for customers, providing them with information that is relevant and meaningful. Gartner’s Hype Cycle for Retail Technologies, 2018 mentions cognitive expert advisors (CEAs) as a technology with high benefit, with the potential to improve customer engagement by making recommendations and aiding decisions.

Crucially, this ace up the sleeve of retailers can bring about customer loyalty by helping them find what they want quickly through a comprehensive understanding of their preferences.

By accurately predicting the product-likely-to-be-bought-next, and the kind of promotion that will appeal to the given customer, retailers can immensely impact customer loyalty and drive sales.

Effective ‘next best offer’ systems rely heavily on AI and advanced analytics and can drive personalization at scale.

To learn more about how you can improve customer engagement using prescriptive analytics models, Download our guide to driving next-best-actions in Retail.

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