HomeBlog I'm a Mac ... I'm a PC: Using Predictive Analytics to Differentiate Between Your Buyers and Their Buyers

I'm a Mac ... I'm a PC: Using Predictive Analytics to Differentiate Between Your Buyers and Their Buyers

January 14, 2016 | By Kerry Cunningham

  • Most b-to-b personas today do not go beyond describing typical buyers, perhaps because we have not known enough about buyers – until now
  • If your organization leverages predictive lead scoring or lookalike modeling, you may be able to identify “your buyer” vs “their buyer” personas
  • If you don’t have predictive analytic experience, consider your unique competitive differentiators that attract a specific type of buyer

Here’s a thought experiment based on the iconic Apple ads from the early 2000s, in which a young hip guy calling himself a Mac exchanges quips with, and verbally thrashes (in a calm, hip way) a less young, less hip guy calling himself a PC. In this series of commercials, Apple parlayed the stereotypical differences between hip Apple customers and stodgier Windows PC customers. 

When you think about it, it’s easy to imagine these caricatures of Mac and Windows users coming straight off the PowerPoint (okay, Keynote) slides of a buyer persona exercise by the clever marketers at Apple. And because we are so familiar with it, we might imagine that such distinctions would be the inevitable result of any such exercise – clear and clever differences between our buyers and our competitors’ buyers.

But here is the premise of this thought experiment: Imagine that group of Apple marketers sitting in a room asking themselves, “Who are our buyers? How would we describe our buyers?” and coming up with descriptions that apply equally well to PC and Mac users. It is likely that any description of typical PC users includes many items that describe Mac users just as well.

Certainly, somewhere in the persona descriptions of a Mac user lie many such descriptive items – things that are true of the typical Mac user but also true of the typical PC user. But Apple went a step further, digging deep to find key characteristics and behaviors that ultimately allowed Apple to create consistent, compelling content that doesn’t simply attract potential buyers but speaks to and attracts the right kinds of buyers – those who are willing to pay a premium price for what they perceive to be premium products.

Today, most b-to-b personas come up well short of the “I’m a Mac/I’m a PC” standard. They only reach so far as to describe typical buyers; they don’t differentiate buyers who are likely to prefer buying from us rather than the competitor. To some extent, this is because we have not known enough about buyers, their habits and their behaviors to distinguish between the types most likely to buy from us and those who are likely to prefer our competitors. But with the proliferation of data in b-to-b, the time has come to ask the question: Is there data available that can help parse those likely to favor us from those who favor our competitors? 

One way to answer this question is to ask if your organization is able to use predictive lead scoring and look-alike modeling to effectively prioritize and source new leads. If the answer is yes, then you may be able to identify “your buyer” personas from “their buyer” personas. If you have less experience with predictive analytics, you’ll have to work harder to answer the question, but it is still worth exploring. Absent predictive experience, consider the following questions:

  • Considering your unique competitive differentiators, what specific conditions within a prospect organization make your differentiators particularly attractive? It doesn’t help to blindly claim superiority over your competitors. Realistically, what conditions make your solution look most attractive?
  • For whom are your unique differentiators particularly attractive? You can think of this question this way: What characteristics make a prospect more likely to like your approach to solving a business problem vs. how your competitor does it? Is your interface more stylish? Do you have a simpler pricing model that might appeal to prospects of a particular bent? Again, it doesn’t help to rest on the assumption that anyone who really thinks hard about the subject is bound to choose you over the competition. A good answer requires some soul searching. What could you say about a buyer who is likely to prefer the experience of using your solution over what your competitor provides? Hint: If you can’t think of any reasonable buyers who might prefer your competitor for legitimate reasons, you’re probably not being honest. If you really, really can’t think of any, congratulations and get the money printer warmed up!
  • Given what you’ve just determined about unique conditions and people who will prefer your solution, what information (data), if you could have it for all your prospects, would help you distinguish the prospects who will be attracted to your particular solution vs. your competitors’? You’ll have to be creative here. It may not be apparent that you can get this information, but don’t worry about that. Brainstorm about the data, then take it to the subject matter experts in b-to-b data science and pose the question.

If you are a SiriusDecisions customer, check out our recent brief “Optimizing Personas With Predictive Analytics: From Cluster to Buyer” for an in-depth discussion of how leading organizations are using predictive analytics to derive deep insights that drive powerful persona development. To download an overview of the SiriusDecisions Persona Framework, click here

Kerry Cunningham

Kerry Cunningham is a Senior Research Director of Demand Creation Strategies at SiriusDecisions. Kerry has more than 20 years of experience in b-to-b demand creation and management, spanning a broad array of industries and markets. Follow Kerry on Twitter @KerrySirius.