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.
