Creating buyer personas with advanced predictive analytics
Maybe you remember being told by your mother, when you were much younger, that you were “special.” And you can imagine that all the other children in your class were told the same thing by their mothers. This same mentality—that every individual is unique, classed in a “group of one”—is the prevailing mindset among customers today. Customers expect the brands and companies with which they interact to understand the context in which they make purchases and to cater to their individual needs.
The challenge that faces companies is to manage the vast amounts of data associated with each customer—to scale. Doing so begins with integration of the countless touchpoints available to the customer. Customers can follow you or interact with you using Instagram, Twitter, LinkedIn and any of many other social media sites. Customers may explore your website, join an online mailing list, call your customer service line, visit your store and—ideally—purchase what you have to sell them. And when customers do these things using a mobile device, every interaction adds new, rich data to the customer profile. But how can you manage these data points on an individual scale for all your company’s customers?
The solution is to operate using personas created by advanced analytic platforms. Doing so is certainly not a new concept—no doubt you’ve been doing it all your life in your individual capacity. For example, recently, after I moved to a new city, I started looking for new dining places to explore. As I conducted my research, I mentally added “notes” to each new restaurant I discovered: good place to work over coffee, ideal for a nice date, exciting place to bring family—and so forth. By noting the characteristics of each place, along with the observable data from my observations, I was creating categories that suggested future action. Not, necessarily, that each restaurant in the “date” category was identical—but rather that, based on observable characteristics and behavior, they prompted similar action.
When a company is able to adopt this same core practice, using observable interactions to create classifications based on best future action, and apply it to customers, the company is using buyer personas. In the same way that I categorized restaurants into groups, your company may create buyer personas such as new customer; likes exclusive offers and use that information to prompt your next interaction with a customer. At its core, this is the idea behind buyer personas. An organization observes the many individuals interacting with it and creates categories of customers that suggest preferred future actions to optimize return on operations.
Using advanced predictive analytics, you can use various sources of customer data to define your company’s buyer personas. When you understand the context of a customer’s actions, as well as how that customer might interact with your company, you can employ predictive algorithms to calculate what actions you can take to produce your desired result: cross-sell, up-sell, prevention of churn, in-store visit. Such insight, in turn, can be used to make personalized offers. Watch predictive analytics being used to create specialized customer offers.
When your offers and communications are reinforced with deeper insight into customers and how they act as individuals, your messaging will be more relevant. So long as you are careful not to be too relevant, the result will be more active, more engaged and more satisfied customers.
That’s because each of your customers expects you to understand him or her intimately, as an individual. And remember—the customer is always right. To see the capabilities of an advanced predictive analytics solution in action, watch “Providing the total customer experience with IBM Predictive Customer Intelligence.”