6 Best Uses of Predictive Analytics for Cross-Sells and Upsells

By Heather Fletcher

3 Goals Marketers Can Set When Developing Predictive Models for Upsells and Cross-sells

When developing predictive models for upsells and cross-sells, marketers should have short-, medium- and long-term goals for organizing their data and analytics tools, says Jesse Roberts, senior data strategist at Costa Mesa, Calif.-based marketing agency Rauxa.

First, of course, marketers should take stock of what they already have in terms of their current campaign performance and analytic toolbox. Roberts says: “Up-front discovery is quintessential in determining the best uses of predictive analytics to identify cross-sell and up-sell opportunities. Only then can you answer: Who should be solicited, through which channel, at what frequency, with what degree of relevance/personalization? Who should not be solicited, if the outreach yields shopping behavior/churn? (Never presume every record is a cross-sell or upsell opportunity.) What tools best identify each population?”

Moving on to the short-, medium- and long-term goals, Roberts suggests:
1. In the short-term, assess the data and analytic tools already available.

Determine which key performance indicator (KPI) will make the biggest impact.
Review which tools are being used (and which aren't) and record selection/suppression business rules;
Implement and adjust changes as needed for improved deliverability and desirability; and
Establish a test-and-learn environment for moving forward and implement random sampling and hold-out groups (perpetual and per campaign).

2. For the medium-term goal, solve for the gaps and other areas of improvement, including:

Hygiene (deliverability);
Enhancement (relevance);
Database convergence (accuracy); and
Toolkit development (campaign targeting).

3. Finally, for the longer-term goal, improve the value of the data for enhanced campaign targeting, greater relevance and cost-efficiency.

Broaden consumer touchpoints–identify channel usage and establish connectivity across channels;
Enhance the depth of knowledge per record with contact information, census and demography data, attitudinal and predictive behavioral indices, a segmentation schema, etc. This improves targeting, cost per lead/cost per acquisition and analytic prowess; and
This environment allows for the development of additional targeting tools, and if necessary, records most likely to respond, convert and broaden purchasing.

Predictive analytics can help marketers upsell and cross-sell products to customers. But the key word in that sentence is “customers.”

So says Stephen H. Yu, vice president of data strategies at Papillion, Neb.-based data provider Infogroup.

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.

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Heather Fletcher Author's page

Heather Fletcher is senior content editor with Target Marketing.