Cross selling has been shown to generate incremental profit and improve long-term customer retention. Many marketers also 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.
Here are three ways to improve your cross-selling:
1. Redefine and 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 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
- Generating profit from the customer so that this relationship is fundamentally desirable and sustainable for the business
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 a 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. But 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 you use these in an integrated approach, 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 underserve 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 in which the marketers had a total budget of $200,000 for a particular quarter. But because the debit card product team funded $50,000 of it, the marketing team had to spend no less than 25% of the total budget promoting debit card products, regardless of the predicted next-best product. The bank also had an annual target for home equity lines of credit and was hoping to reach 20% of the goal in the same quarter. Finally, at least 50% of Segment A, the bank’s 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 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, the bank’s marketers were able to balance and meet its 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.