Think of this analogy: vegetables don’t always taste great unless they are cooked. Furthermore, these vegetables won’t be loved unless they are cooked to reveal the right color and consistency thus making them appetizing. Likewise, for the best response to your marketing programs, interactions should be “cooked” according to your customers’ particular preference. It is a challenging task to understand your customers’ behavior and to forecast marketing performance appropriately based upon the potential outcomes of this behavior. Analysis plays a critical role in this process.
First, the structure of data decisively affects what kind of analysis can be brought to the table. Customer interaction analysis demands information about customer behavior, such as how they react to a certain product continuously, how they respond to a marketing campaign program in mail, email and phone, and so on. Also, demographic information is an important building block in this analysis. For example, I intended to initiate an analytical model for a client that has a combination of B-to-B and B-to-C business. As a healthcare company, its targeted customer includes non-profit and for-profit business entities and individuals. A behaviorally-driven customer analysis and marketing segments will drive the marketing campaign to be more efficient and cost-effective, as our client also recognizes. However, no real analysis can be carried out at this point, because little behavioral information is available from the current database. Now, we are using the campaign and business intelligence tools to create the behavioral-tracking system and accumulate information for future analytical modeling.
Secondly, selecting the right analytical tool can be equally tricky. Economic analysis assumes consumers are uniform and that they get satisfaction from increasing their monetary income. This classic interpretation of human nature encourages the marketing sector to make economic decisions at a strategic level. For example, game theory teaches marketing decision-makers how to compete against its rivals strategically. The popular author Gary Gagliardi wrote a series of strategy books, as did Sun Tzu in The Art of War, the world’s oldest philosophy of strategic decision-making. The in-depth marketing modeling technique, including econometrics, regression analysis, ANOVA, and multivariate analysis are all built upon basic economic assumptions. Statistical methods and econometrics are used widely in marketing analysis today.
On the other hand, behavioral scientists interpret human behavior from a different perspective and psychological measurement theory doesn’t agree with statistical methods. They emphasize individual differences and focus on measuring human’s un-monetary utility and performance. For example, meta-analysis is one of the most popular methods in measuring behavior and tails out measurement errors. In real business, we actually have more choices and can use two perspectives in our analytical work. For large customer bases, economic and statistical methods are more efficient and useful. For small customer bases with dynamic survey results available, we may also try to use the psychological measurement methods to gain other insights about customer behavior.
There is no rule of thumb for developing the best model among the variety of analytical tools. The only thing that is clear is that people understand themselves less than they think. What CEA gives us is not only the ability to impact customer interactions and improve marketing performance, but also to deepen our understanding of human nature and how decision-making is affected by a variety of environmental factors. Analysis is not only a science, but also an art, like cooking. It enables us to walk in our customers’ shoes and translate data into a cost-effective marketing plan that delivers a greater amount of strategic value to our customers.