Three Steps to Optimize Your Cross Selling

Cross selling has increasingly become an important focus for many companies, since it has been shown to generate incremental profit and improve long-term customer retention. Marketers also widely recognize the CRM point of view that, after offering the best products, successful cross selling depends most critically on offer relevancy. But too often, database marketers struggle to reach the difficult goal of relevancy.

1. Re-Define & Create Relevance
A truly relevant offer combines the right timing, appropriate product choice and appealing offer mix – simultaneously. Normative studies show that it is the interplay and synergistic relationship among all these elements that makes the difference. Traditionally, these elements are generally considered separately. In particular, the timing of the offer has undergone only rudimentary analysis, using trial and error business rules; at worst, it’s received only lip service. The best approach provides one integrated predictive model to address the timing, customer cross-buying propensity and their product preferences simultaneously.

2. Build a Holistic Marketing Platform
Consumer behavior theory points out that a successful CRM philosophy is predicated on two essential elements:

• Provide value to customers so that the relationship can be strengthened and improved
• Generate profit from the customer so that this relationship is fundamentally desirable and sustainable for the business

In order to balance these two goals, a company must approach cross-selling within a holistic framework that considers both the customer’s and the company’s needs. For example, when creating a predictive model to identify customers for particular cross-selling offer, we need to estimate each customer’s most likely need, and then use this knowledge to provide relevant offers for attractive products. However, the customer’s propensity for a particular product also needs to be evaluated in the context of factors important to the company, such as product margin, retention effects, or “halo” effects.

Without considering both sides, the company risks spending heavily on ineffectual cross-selling programs or making less profit than hoped from resulting sales. When an integrated approach is put in place, both sides win.

3. Discover Real Optimization
Marketers rely on models to improve the efficiency of their programs, but they are not statisticians or programmers. What they ultimately need is a decision engine that allows them to make optimal, but practical decisions. The term “optimization” is rather abused in the direct marketing community, since most of the so-called “optimization engines” deliver nothing but simple sorting or ranking ordering. They under-serve the very real, involved demands of many marketing decisions. One has to look at the budget, different product mixtures, overall product and customer portfolio, and short-term and long-term effects.

Consider the example of a bank where marketers had a total budget of $200K for a particular quarter. But because the debit card product team funded $50,000 of it, they had to spend no less than 25% of the total budget promoting debit card products, regardless of the predicted next-best product. They also had an annual target for home equity lines of credit and were hoping to reach 20% of the goal in the same quarter. Finally, at least 50% of Segment A, their best customer segment, was to be targeted. They needed to use a predictive model to optimize the overall campaign profit, but subject to these business constraints. Performing simple sorting or rank ordering cannot easily solve such a business problem.

Fortunately, today’s analytical techniques provide techniques that go beyond the simple sorting of the past. For example: one breakthrough approach for database marketing is to use a powerful tool called Mathematical Programming (MP) to address such issues. MP has been long used by brand marketers, supply chain managers, and logistics controllers to solve complex and practical business problems. In this case, bank marketers were able to balance and meet their complicated demands through MP techniques of linear programming, integer programming and goal programming. The result? The marketers could focus on cross selling that was relevant, streamlined, and highly cost-effective. In a word – Optimized.

Hongjie Wang is vice president of customer analytics and manages the analytical team at Fulcrum, where he has developed statistical and optimization models, as well as segmentation solutions for a diverse range of clients to support their marketing and management operations.