Can we really know what outcomes are likely? It may not be as far-fetched as it sounds. Predictive analytics can give us a highly accurate “Crystal Ball,” allowing us to see into the future, leveraging insights gleaned from large data sets and advanced machine learning (ML) algorithms.
Predictive analytics is the use of data, algorithms, and ML techniques to assign ‘scores’ to various user segments based on historical data. Its goal is to assess a likelihood of future events — such as a purchase or customer churn — so that a specific action can be taken. Using predictive analytics, we can know with a high degree of certainty the outcomes for future customers and business activities.
Identification of Customers Likely to Churn
In modern growth marketing efforts, churn is a crucial statistic. The old axiom rings true, “It’s cheaper to keep an existing customer than to find a new one.” Predictive retention models can identify which customers are most likely to churn — and companies can respond by reaching out to them with education on product benefits or other promotions. Predictive scoring can also identify a set of behaviors in customers who are less likely to churn. Messaging likely churners and steering them to adopt behaviors of customers who are less likely to churn is a valuable outcome for any business.
Recommendations for eCommerce Cross-selling and Upselling
If you’re a retailer selling a variety of products, predictive scoring can help you tailor your ‘recommended for you’ product placements by analyzing historical customer data and applying customer profiles to offer look-alike targeting for optimal conversion. For example, someone who has purchased hiking boots might be shown advertising for other outdoor gear — while someone who has bought kitchenware might be shown ads for kitchenware.