6 Best Uses of Predictive Analytics for Cross-Sells and Upsells
Miller adds: “Use pattern analysis on all your digital data—from clickstream to tweets to email response data to score customers, campaigns and channels. This will help you improve your segments and personas. It will also help you identify your best customers, prospects and upsell opportunities, as well as the best offers to send to each group.”
2. Go ahead and get started. Miller says: “The end goal is to automate the offer placement based on analysis and predictive models for your particular customer base. However, every marketer can get started by using pattern analysis in your existing response data to identify the factors that lead to purchase behavior. Use that data (even through manual integrations at first) to improve your segmentations and send more relevant offers.”
3. Segment to aid model performance. Roberts says modeling can predict response rate, but can’t explain why that’s happening. So models shouldn’t be considered customer profiles.
So leverage predictive modeling and segmentation tools simultaneously, Roberts advises. “When combined, you’ll find variances in performance by decile per segment,” he says. “This informs model depth selection on any [key performance indicator] basis: response, cost per response, conversion, cost per acquisition, cumulative values, etc.”
Dogan says: “Start with segmentation that captures the unique needs, product and channel preferences of distinct audience groups in the marketable customer universe. Use predictive modeling to determine the best targets for various products and services that the company offers. Develop a next-best product optimization process that takes cross-sell/upsell propensity, as well as expected profits, into account and optimizes the contact cadence.”
Hassemer says because of the variety of data now available—from sentiment analysis to neural responses—marketers can even travel beyond segmentation to “real-time micro-segmentation” that is often known by another name-personalization.
Go one step further than optimizing the models for real-time interactions with customers—know which prospects may eventually be interested in cross-sells and upsells, McConville says. He says: “Companies need to use predictive analytics to make better decisions at the moment of interaction with the prospect in order to optimize the engagement. Doing this allows the company to customize interactions regardless of channel, route prospects to their best agents in call centers, assign their best team to interested leads, etc.”