Whether you’re new to database marketing or a seasoned pro looking for some new idea to get your creative juices flowing, one of the most useful, and impactful, activities you can embark upon is to create what is called a “Bucket List.”
No, I’m not talking about a building a list of activities that you and a middle-aged companion wish to complete before you shed this mortal coil. I’m talking about taking a long, hard, and close look at your customers or prospects and getting to know them—really getting to know them—well enough to create broad classifications about who they are, what they do, what they like, and what affinities they share.
Remember, at the end of the day, database marketing is about sending out the right message to the right people at the right time—and, hopefully, achieving the desired response from the customer or prospect as a result. And without proper customer segmentation, this task simply cannot be done cost effectively, if at all.
Now, of course, there are many great customer segmentation models out there you can use. In a great article titled “Selecting a Customer Segmentation Approach” by Andrew Banasiewicz, Director of Analytic Services at Epsilon, four groups are identified: Predictive, Descriptive, Behavioral and Attitudinal.
Out of these four, the Predictive and Attitudinal models are arguably the most popular and widely used. Predictive is a model that uses value segments driven by customer purchase behaviors, extrapolating past behavior into future actions. An Attitudinal model, on the other hand, identifies affinity segments based on respondents’ expressed attitudes toward a company’s brand or products.
Now of course this list isn’t exhaustive and there are other models you can use. One popular alternative is Psychographic Profiling, which is used widely in the B-to-C space. In this model, consumers are assigned into groups according to their lifestyle, personality, attitude, interests and values.
Many B-to-B marketers, on the other hand, may prefer to use a segmentation model based on Firmographic variables, such as industry, number of locations, annual sales, job function and so on. Many software companies, not surprisingly, trend toward usage-based profiling, which includes variables, such as type of device used (desktop, tablet, mobile device), Operating System and so on.
One important fact that’s routinely overlooked is that successful customer segmentation requires taking a holistic approach. This includes aligning a firm’s segmentation goals to its marketing objectives and data acquisition investments. In other words, the data you have will determine not only which model you use, but also what marketing campaigns you’re able to run.
Now of course both data inputs and needs are in flux throughout the firm’s lifecycle. As Banasiewicz points out, for a firm in high-growth customer acquisition mode an Attitudinal model might work effectively for demand generation initiatives among qualified and segmented pools of prospects. Marketing campaigns in this scenario, we can assume, would speak to customer desires and affinities, with purpose of lead generation/nurture.
On the other hand, once the firm has acquired a large pool of customers, it’s not unrealistic to think that transitioning to a Behavioral model using inputs from past purchases will be more effective for running what are now CRM campaigns, focusing on driving lifetime value and repeat purchases.
Different groups not only have different attributes and attitudes, but consume different types of media. As such, they will respond to different types of offers, communicated in different ways and in different places. Where should a firm spend its marketing budget? Online display, email, direct mail, social media, print? … The choices are dizzying in today’s multichannel environment. Having a robust customer segmentation model can definitely help in the decision-making process.
Another important feature of customer segmentation is the realization that different customer groups can not only have wildly different demographic and psychographic identities, but very often will have strikingly varying lifetime values. To the surprise of some, a customer segment with a with younger average age will very often have a higher lifetime value than a group far senior to them, despite having far less disposable income to spend today. This may be based solely on the fact that the younger customers have, simply by being younger, many more years of being a loyal customer ahead of them. Taking this into account, many brands’ obsession with successfully penetrating the youth market should come as no surprise.
Now of course it’s easy to miss the forest for the trees, as customer segmentation is simply a means to an end, not an end in itself. Once you have broken your customers or prospects down into segments, the trickier (and for those who are not data geeks) more fun part of the equation involves devising incentive and reward strategies for each segment, and creating compelling marketing messages and collateral that can be used to get the message out across the various marketing channels. Knowing your customers, this part is a lot easier, which brings me full circle back to my point from the top: Create a bucket list.