How AI, machine learning, and predictive analytics drive conversion

Today’s consumers are kind of a tough crowd. In order to keep up with ever-increasing demands and expectations, not only do brands need to show up wherever their customers want to do business, but they also need to show their customers that they understand who they are, recognize that they want to be treated as individuals, remember their previous interactions, and make them feel valued — these days 71% of consumers expect personalization and 76% of consumers get frustrated when they don’t receive tailored communications or relevant experiences.

The good news is that getting to know your customers at a deeper level and delivering the types of experiences that help drive conversion is easier than you think. Thanks to a combination of AI-powered algorithms and the advanced data crunching capabilities of CDPs, brands can create impactful, tailored experiences that have the potential to turn customers into brand advocates.

The (data) science bit

Behind every “recommended for you”, “surprise me”, or “we think you might like” digital experience is a predictive analytics process. Predictive analytics is a branch of advanced analytics that uses historical data, data mining, statistical modeling, plus AI/machine learning techniques to forecast future behavior, events, and trends. The technique can be used in several ways to help brands improve conversion and boost revenue.

Taking the guesswork out of product recommendations

Leveraging advanced machine learning algorithms, brands and retailers can create the types of relevant product recommendations that make customers feel like you really ‘get’ them and understand their taste in clothing, music, movies, groceries, homeware, content, travel destinations, or whatever goods or services they are browsing. And it certainly pays to put in this extra effort. According to statistics, 91% of consumers are more likely to purchase from a brand that remembers their preferences and delivers relevant recommendations.

Upselling and cross-selling

With the incremental cost of selling to existing customers generally much lower than the cost involved in generating new leads, it’s easy to see why upselling and cross-selling have become such popular use cases for predictive analytics. Using predictive analytics, retailers can forecast each customer’s propensity toward cross-sell or upsell offers. Based on data including products and/or services that are usually purchased together, marketing teams can create product bundles, tailored offers, or promotions that complement the customer’s existing purchases. Typically, cross sell recommendation messaging reads as: “customers who bought this…also bought”.

Upsell recommendations are generally presented to customers at the checkout stage and are based on a specific SKU (stock keeping unit). Every product is tied to a slightly higher-end version and is marketed to appeal to customers who, based on insights from predictive analytics, are likely to engage with upsell offers.

Reducing churn

The ability to identify customers who are likely to churn or unsubscribe from your services is one of the most valuable applications of machine learning and predictive analytics — especially if there is still time to prevent them from turning away from your brand. Using machine learning algorithms and models you can identify customer segments most at risk of churn and take the opportunity to win them back. In the long term, the results are far-reaching. Not only do you reduce revenue loss and cut down on acquisition costs, but you may also uncover pain points that help you work toward improving your overall CX.

AI, machine learning, predictive analytics, and the CDP

Considered the “beating heart of your martech stack”, the right CDP will seamlessly gather, unify, and segment all the data you need to successfully execute AI-driven campaigns. Advanced CDPs provide marketers with access to predictive analytics and machine learning technology, allowing them to apply predictive models to their data so that they can anticipate future behavior and trigger the most relevant, tailored experience for each individual customer.

Sitecore CDP

Sitecore CDP extracts value from your customer data and helps you drive exceptional experiences across every digital channel. The CDP’s advanced capabilities allow you to predict, test, and optimize every customer interaction across every product and experience. Predictive analytics and AI inform intelligent interactions while decisioning technology enables data driven decisions that create the type of personalized experiences that not only impress customers but also increase loyalty and trust.

A formula for success

Whether you utilize predictive analytics to upsell, cross-sell, reduce churn, or generate relevant product recommendations, you can feel confident that you have provided your customers with the best possible options and experiences to drive conversion and brand loyalty.

Want to learn more?

Sitecore CDP is helping some of the world’s best-known brands to deliver revenue-driving experiences. Find out how the CDP increases customer loyalty and trust or take the first steps toward getting buy-in from your organization by reading our guide to building a business case for a CDP.