Predictive analytics can help marketers upsell and cross-sell products to customers. But the key word in that sentence is “customers.”
Different divisions handling different channels or various departments in charge of various products can cause problems for customers if companies decide to organize data based on those guidelines alone when creating predictive models. So instead, Yu suggests, “Companies should go through a paradigm shift towards ‘customer-centric’ marketing. Even the best-designed databases and models will turn out to be ineffective if marketers use such tools with ‘division-centric’ minds. That is how one customer ends up getting confusing offers in [a] short timeframe from the same company.”
This is just one bit of advice about how marketers can best use predictive analytics to identify cross-sell and upsell opportunities. More suggestions come from Yu and:
- Ozgur Dogan, vice president and general manager of the Data Solutions Group at Columbia, Md.-based marketing agency Merkle;
- Jeff Hassemer, vice president of product strategy at New York-based marketing services provider Experian Marketing Services;
- Paul McConville, senior vice president of sales and marketing at Vienna, Va.-based data provider TARGUSinfo;
- Stephanie Miller, vice president of email and digital services at Indianapolis-based marketing software provider Aprimo, a Teradata company;
- Barbara Nelson, a product manager of analytic and segmentation products for Little Rock, Ark.-based data solutions firm Acxiom Corporation;
- Wilson Raj, global product marketing principal for customer intelligence at Cary, N.C.-based business analytics software and services provider SAS; and
- Jesse Roberts, senior data strategist at Costa Mesa, Calif.-based marketing agency Rauxa.
1. Rethink the data to include. Miller’s comment that “CRM is the new black” is only slightly tongue-in-cheek. Customer relationship management efforts in channels such as social media are yielding many insights that she says should be included in data-gathering efforts for predictive models.
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.