CMOs are now turning to new technologies to sharpen their marketing focus and optimize their spend. Predictive analytics is one of these technologies. It’s not new, but a growing number of CMOs and CIOs see predictive analytics as a way of delivering the tailored experience consumers and younger generations demand. So what is predictive analytics? Predictive analytics is a blend of mathematics and technology learning from experience (that is, the data companies are already collecting) to predict a future behavior or outcome within an acceptable level of reliability.

Companies are creating and collecting data at an accelerated rate, and more and more they are connecting data sources together. Data that could only be integrated by an IT-sanctioned team a few years ago can now be accessed and combined in minutes using self-service tools. With a complete digital picture of customers and by using predictive analytics, CMOs are in a position to redefine several key marketing activities and put their companies in the lead. Let’s explore three of the most popular cases where predictive analytics can play a substantial role in redefining marketing activities.

INCREASE CROSS-SELL AND UP-SELL

Predictive analytics is used in campaign optimization to identify the best potential customers. Instead of sending an offer to everyone, you let predictive models analyze the customer data you’ve collected in order to determine the best target customers. This data could include demographics (age, gender, and zip code) and behaviors (purchases, Web site clicks, spending for each of the past 12 months). In the case of cross-selling and up-selling, predictive analytics allows you to focus on a small group of customers that represent the large majority of your potential buyers for a specific offer.

Consider the example chart below. You can focus your campaign on a target of 200,000 prospects or customers, which includes the large majority (40,000) of your buyers for a specific offer. You can also optimize your spend by spending a fraction of the budget to reach a smaller group (in the example, only 20%) instead of saturating your entire customer base. Predictive analytics allows you to avoid overreaching. Digital marketers know that overreaching and flooding customers with offers may lead to the customers rescinding the consent to be reached or worse – customers seeking alternatives from competitors.

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REDUCE CUSTOMER CHURN

Many business leaders and marketers live and die by the churn rate. Churn rate is a measure of the number of individuals leaving your service or product. In any industry where you have customers subscribing to a digital service (like LinkedIn Premium) or a traditional product packaged as a service (like Dollar Shave Club), churn rate is a key performance metric. From an annual churn rate, you can derive the customer lifetime value (CLV). For example, if the churn rate is 20%, it implies that customers, on average, will be with you for five years. A customer consuming US$1,000 of your services per year would be worth roughly US$5,000.

Predictive analytics is used to create models that anticipate if a specific customer is at risk of churning and counter quickly with the appropriate retention programs and offers. Predictive models take into consideration a variety of data – demographics, purchase history, products and services consumed, and customer service interactions – and score each customer’s likelihood of leaving or canceling service. This allows you to understand why customers leave – what the triggers are and their relative importance. You can also anticipate customers who are likely to leave and act quickly to keep them during interactions. Plus, you can focus retention programs on customers more likely to respond positively to retention activity.

GENERATE TAILORED PRODUCT RECOMMENDATION

Any business-to-consumer company needs to understand product associations for better campaigns, ad placements, and inventory planning. Product recommendation can be a significant sales driver. According to Forrester Research, product recommendations are responsible for an average of 10%–30% of online retailers’ sales. Predictive analytics is used to analyze customer behaviors like transactions, page views, and clicks by comparing them against the same data from other customers. This results in a product recommendation or a set of recommendations for each customer to provide personalized offers or campaigns.

Predictive analytics allows you to increase basket size with real-time, personalized product recommendations that have a higher probability of generating sales. It also simplifies the customer experience, especially when customers search for categories or products. Plus you can understand shopping trends and the importance of specific products and brands in the customers’ purchasing decisions.

INNOVATION THROUGH COLLABORATION

Predictive analytics provides an excellent opportunity for CMOs and CIOs to share a common IT vision. Technologies like predictive analytics can be used to extract high value from existing data assets and ultimately shape the tailored experience customers are now demanding. Whether applied to cross-selling, reducing churn, recommending products, or other marketing initiatives, predictive analytics has the power to get your company ahead of the game.

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