Would you like to find profitable customers and influence their behaviors?
A new model I have formulated through my research can help you develop customer-centric marketing strategies that will lead to better, measurable financial results.
This model will help you, like a treasure hunter, see in the data some clues to the map of how customers behave. These clues can help you answer several questions about your target customers:
- What do they buy?
- What are their attitudes?
- What might they buy in the future?
Through this model, you can develop a picture of customer behavior and motivation, and project what the customer profit would be, given what is known about how customers react and what their preferences are.
Seeing Customers Across All Dimensions
Marketing used to be more art than science. But with the advent of point-of-sale scanners and the use of loyalty cards and credit cards, data started pouring in. Firms began using Customer Relationship Management (CRM) tools and technologies to capture what customers bought and when.
The data provides us with greater insights into how our customers are reached — by phone, e-mail, direct mail, television ads or print ads. We look at everything a marketer does to link to a customer. My model answers two critical questions:
- Which customers should be targeted?
- How should they be targeted?
A three-year study my colleagues and I conducted with a big pharmaceutical company ― pertaining to sales calls directed toward physicians ― showed that firms obtain better predictions of customers’ future profit potential if their marketing strategies include:
- Information on customer attitudes
- Information about past customer behaviors
Conceptually, I have built a framework through which one can do this. I use historical data on customer interactions to spot trends and to project the customer value — the profits.
To determine your target market, you must measure the value of a customer. If the customer will spend $100, you shouldn’t spend more than that to attract him or her. To determine how to target, you must measure customer preferences.
This is how you can build your company strategy.
Current marketing strategy recommends that firms should look at customer behavior only — not attitude — because it works on the belief that behavior includes attitude.
People behave certain ways based on preferences they have in a given moment.
We found that these preferences are forward-looking measures; people’s attitudes give us insight into what happens tomorrow. If I find what someone’s preference is today, I can do something about it tomorrow.
Think of it as an early warning indicator.
The attitudes of the “top” customers — on whom companies traditionally focus — and the attitudes of the “bottom,” or less profitable customers, are pretty self-evident. The question is which of the customers in the middle will become top customers and which will become bottom ones.
Finding out attitude lets you know what those middle customers are going to do because behavior is noisy. It’s up, it’s down. That’s why they’re in the middle. If you merge attitude and behavior, you become better at knowing who to target and how to target. It’s a better approach than one or the other.
For example, Jane and Sara both buy Apollo Greek yogurt once every two months. Just looking at this information would suggest that Jane and Sara are equally valuable for Apollo Greek yogurt. But it turns out that one of them is more valuable.
Upon closer inspection, we find that Jane really loves Apollo Greek yogurt. However, she just started eating yogurt and is slowly building her consumption rate. Sometimes a discount may motivate Jane to buy the Apollo. On the other hand, Sara is indifferent among the different Greek yogurt brands and only buys Apollo Greek yogurt when it is on sale.
It is more likely that Jane will continue to buy Apollo Greek yogurt longer than Sara.
This scenario shows how firms like Apollo can use knowledge of consumer attitudes to predict the most profitable consumers.
Until now, all the visualization-software companies were, in a sense, reporting tools that tell you what happened, not what could happen. My model allows firms to target those mid-tier and lower-tier customers who have the potential to grow in the future. Knowledge of customer attitudes makes this action possible.
This methodology improves firms’ performance. In fact, managers can actually increase revenue while contacting customers less because, as we’ve discovered, too many sales calls or e-mails can backfire.
Each customer has his or her threshold.
The bottom line: Pay attention to customer attitude as well as behavior.
Raj Venkatesan co-wrote the paper “Measuring and Managing Returns From Retailer-Customized Coupon Campaigns,” published in the Journal of Marketing in 2012. With Darden Professors Paul Farris and Ron Wilcox, he is co-author of the forthcoming book “Cutting-Edge Marketing Analytics: Real-World Cases and Datasets for Hands-on Learning,” which will illustrate the marketing model to help managers predict customer behavior.