How brands can predict their consumers’ next move

MoEngage's Kunal Badiani looks at how predictive marketing uses customer intelligence the right way

Companies are constantly looking for answers to questions like “who are my buyers, and what are they looking for at a particular time,” and “how are they interacting with my brand?” And equally important, “how are their needs changing, and what would they need next?”

Wouldn’t it be great if brands could read their customers’ minds and predict exactly what they are looking for, when they need it, and how they want to receive it? Absolutely a marketer’s dream come true and a customer’s total satisfaction with the brand. This mutual understanding between brand and customer does not come easy. It is the result of technology, intelligence, and psychology put together, leading to predictive analytics and harvesting information coming from this synthesis. World over, the demand for predictive marketing is on the rise, and so it is in the MEA region, thanks to the rapid growth of customer-centric sectors like banking, retail, travel, and hospitality.

To understand how companies can gain from this crucial customer intelligence, let’s understand what predictive marketing is and how predictive analytics solutions work for marketing.

Granular analysis and predictive marketing:

Predictive marketing is the smart way to leverage data coming from customer behavior. The behavior comes from customers’ product/brand search and purchase history, demographic data, and website behavior. The data works on outcomes that result in effective marketing strategies. Predictive marketing uses AI and machine learning to pre-empt customer behavior by creating marketing campaigns based on the data from customers’ previous behavior.

Businesses use predictive marketing to reach customers with personalized ads resulting in optimized conversion rates and enhanced ROI. Predictive analytics, in turn, deeply studies huge amounts of customer and market data and helps marketers understand why something has happened in the past and what can be done to improve those outcomes in the future. With predictive analytics, marketers can spot trends and assess how their marketing campaigns will most likely perform.

This information can be very beneficial to marketers because their efforts at reaching out to customers are not wasted with the wrong campaigns. Predictive analytics also allows for implementing changes and adjusting campaigns to increase the likelihood of reaching marketing goals.

Benefits of predictive marketing:

Predictive marketing can largely optimize marketing spends by delivering better customer experiences and bringing better ROIs for businesses. More specifically, it

  • Enhances marketing performance as it arrives at data-driven decisions about media planning and buying, which results in more efficient use of marketing resources. It identifies cohorts where certain campaigns have no impact and ceases those campaigns immediately.
  • Gives brands greater accuracy in predicting customer needs and identifies cohorts that are more likely to respond to certain campaigns. This, in turn, leads to improved customer retention rates. Even where there is an expected churn, brands are armed with data and can send them appealing discounts and offers with relevant messages, resulting in not only stalling the dropouts but also making use of cross-selling and upselling opportunities. For example, retail or e-commerce brands can retain customers who may be at risk of dropping out of the purchase funnel by sending them attractive retention offers. They can also cap the number of messages sent out to customers who are already on the conversion path.
  • Improves customer service by providing quick insights into customer needs. Predictive marketing makes every interaction a meaningful and personalized one leading to higher loyalty and longer LTV. Personalization does away with unnecessary communication and makes every interaction meaningful, resulting in customer acquisition and retention. For example, banks who are scrambling with humongous amounts of historical data can filter pertinent information and decide which credit card is suitable for which customer, who needs a loan or not disturb customers who have been prompt in paying their installments.
  • Enhances targeting capabilities by prioritizing leads and segmenting the leads based on the likeability of them making a purchase. Predictive marketing forecasts a customer’s likelihood to purchase products in the future and creates a campaign that will firmly drive the customers toward the purchase decision.

The new path to customer engagement

The predictive model is used to identify which customers are most likely to churn, buy a particular product, or respond to a specific campaign. With predictive marketing analytics, brands can harness huge amounts of data to understand their customers better and predict future trends. It can help marketers identify opportunities that may have slipped out of their sight.

Predictive analytics is the new point of difference between companies that resonate with their customers and companies that fail to do so. Predictions will take care of some of the biggest business challenges, like sending the right message to the right customers at the right time to create better experiences and engagement. It also predicts churn and speeds up responses to reverse it. Research shows that such a predictive marketing strategy can help brands increase their ROI by 5X. Surely something that all marketers and brands are looking for.


Kunal Badiani is the Regional Head of MoEngage, MEA