Top 3 Predictive Analytics Use Cases in Retail

By: Retail CIO Outlook | Friday, August 14, 2020

To remain competitive in the fast-growing marketplace, it is becoming necessary for retailers to look for proactive methods of leveraging extensive data sources in unique ways.

FREMONT, CA: The retail industry today has more access to customer data than ever before. Today’s retailers are more hard-pressed to translate user customer information into actionable insights that give them an edge over competitors in attracting customers. The following are the use cases that are currently in use in leading retails companies to have better extract the value of predictive analytics.

• Behaviour Analytics

Improving customer conversion rates, personalizing marketing campaigns to increase revenue, predicting and avoiding customer churn, and lowering customer acquisition costs are major roadblocks in the retail industry. These can be effectively tackled with data-driven insights on the customer. When all this data is collated and analyzed, it can provide insights into recognizing high-value customers, their motives behind the purchase, their buying patterns, and the best channels to market to them. Having these detailed insights increases the probability and perhaps drives more customer loyalty towards brands.

• Personalizing In-Store Experience

With the significant increase in online sales, a new shopping pattern has emerged where the consumer physically research the desired products in-store and then go ahead and purchase it online. To optimize merchandising tricks, a predictive analytics platform can be of great help to retailers. They can personalize the in-store experience to drive loyalty by giving offers to consumers and make more purchases, thereby achieving increased sales across all channels. Data insights can also help increase promotional effectiveness, drive cross-selling, and many more.

• Customer Journey Analytics

Today’s customers can access any kind of information using channels like mobile, social media and e-commerce, which makes the decision of buying and purchases convenient for them. In this context, marketers need to continuously adapt to understanding and connecting with their customers. This is possible when retailers have data-driven insights that can help brands understand each customer’s profile and history across all channels.

In conclusion, predictive analytics can help retailers achieve a deeper understanding of their customers and offer actionable insights that will transform them into market leaders.