Buyer personas are a standard part of any sales process. Some sales teams use mocked-up business cards; others go so far as to build cardboard cut-outs surrounded by detail to humanize their customer segments and emphasize detail and accuracy.
But there’s a big problem with the buyer personas almost everyone is using: They’re based on speculation, CRM data, data sets drawn from website and social interactions that are out of date and narrow. If you’re lucky they’re good fiction, but fiction is what nearly all of them are. And it undermines their effectiveness big time.
Being buyer centric is a key business goal for sales teams. B2C teams have to deal with consumers used to being catered to; B2B teams must sell to buyers who have spent up to 60% of the buying cycle self-educating online.
But making a business buyer centric will take more than inaccurate buyer personas. How can we align our ideas about buyers with our actual customers?
Buyer Personas: A Flawed System
First, it’s important to realize that buyer personas as they stand are fundamentally flawed: this isn’t a problem that can be solved by doing the same thing better. Rather, a new approach is needed. Personas have the potential to be a useful tool but at the moment, their narrow database and reliance on old data means they can’t be predictive. Ideal customer fit is based on just a couple of variables, while detail is supplied from small-sample sources if it’s fact based at all.
We’re all telling each other to act like buyer personas are crisp, clear pictures of what’s going on when at best they’re murky snapshots of the past.
But if you think your buyer personas are pretty good, do you still need to keep reading? Oh, yeah. Because the chances are, you’re wrong. In 2013 Edelman Group asked 11,000 people in 8 countries whether they thought brands were doing a good job of communicating with them based on their needs, or asking about their needs.
How many said yes? 10%. Over half thought brands weren’t doing enough. And while you might believe your personas are accurate, your C-suite probably doesn’t: less than 6% of senior executives believe their brand knows enough about its customers to communicate effectively with them.
The way to make buyer personas the basis of an effective, customer-centric selling process is to build them on a larger body of real-time data.
Using Real Data to Refine Multidimensional Buyer Personas
Ardath Albee, who created the Up Close and Persona tool, says, ‘I see a lot of personas that are what I kind of call “Ouija Board” personas, because they are based on stuff that marketers would never know.’ Sticking your customer personas together out of bold ideas, hope and gut feelings isn’t the best way to wind up with something you can actually base sales success off of.
So how do you accumulate and analyze enough real data to make sense of your customers and deliver what they want?
Customer surveys can give you vital insights into your customers’ needs and motivations. While systems like Net Promotion Scores are useful for building an image of how popular your brand is, they’re not so fabulous at letting you know why. That kind of data is best revealed with questions like, ‘when did you first realize you needed a product like ours?’ or ‘what doubts did you feel before you decided to buy?’
You’re going to get a small, self-selecting group this way - but you’re also going to get real, useable information to build your personas around.
In-Person Or Phone Interviews
People will say things in conversation that they wouldn’t say on a survey form. Want to know what makes your customers tick? Ask them.
After doing this kind of surveying you should be in a position to sift the data you have and look or bases for authentic customer personas. Basing them on customers’ own statements means you have a depth of motivation and pain point knowledge that’s not going to come from even the best automated survey system. And the first, crucial layer of segmentation is into personas based on motivation - because that’s what really matters when you’re selling.
Build on your qualitative data with quantitative research. Stack buyers by average revenue, average transactions, whether they’re a new, repeat or frequent customer. You have the basis or a secondary layer of segmentation at this point. Pair the two for valuable insights: if customers who care most about price also spend least (which isn’t necessarily going to be true!), don’t compete on price alone.
Insight at this level will help you escape the kinds of mistakes made as big-box stores with very limited customer data attempted - and often failed - to transfer to a data-rich business model. Just ask JCPenney’s Ron Johnson, who cancelled JCP’s coupon system in a radical overhaul - that sent sales tanking into the gutter! Says Johnson, ‘I thought people were just tired of coupons and all this stuff… the reality is all of the couponing we did, there were a certain part of the customers that loved that.
So our core customer… was much more dependent and enjoyed coupons more than I understood.’
But it will also help escape the woeful influence of Ouija-Board personas that make dismal contributions to sales.
Using Predictive Analytics To Ensure Accurate Buyer Personas
So far we’ve mostly been talking about building buyer-centric personas using well-established methods. But if you’ve been paying attention, you’ll have noticed there’s an elephant in the room: big data.
Without a sales-oriented, buyer-centric approach to big data analytics, big data is useless. But you can utilize its capacity to tell you huge volumes of detail to sharpen your data-backed buyer personas.
Using big data, especially the data that comes from website interactions, you can build a clear picture of your customers’ drivers by matching behavior with offers, deals, exclusives and even times of year. In B2B, you can build more detailed impressions of multistage buyer journeys, segmenting buyers and researchers at every stage to ensure that the right approach is always being used on the right person.
Lead Score Based on Real Data
Leads are typically scored based on marketing automation data. Not only are sales and marketing often singing out of key when they’re not on whole different songs, so lead scoring can be inaccurate for sales purposes, but this kind of lead scoring is too data poor to give a clear idea of lead quality and warmth.
Datanyze can build you a personal predictive model that scores the leads you have, uncovering great sales leads that neither sales nor marketing have noticed. That’s possible because Datanyze uses a far wider base of data than almost any other system: thousands of data signals on each lead, including company information, web presence, current tech providers, and more. That means you can construct a rich customer profile on a lead, based on actual data, giving broad, deep background on leads from over 40m companies in our database.
But we’d never recommend scoring leads using a method you’re not allowed to understand. Too many lead scoring systems are ‘black boxes,’ where all the magic happens behind the curtain. With Datanyze it’s out in the open. You can see how it works, and we show you the data signals that have the highest and lowest impact on your conversion rates to help you optimise your use of the tool. When a lead gets a particular score, you’ll know why.
Psychological Profiling and Lead Matching
With clear, data-supported customer personas you can build psychological profiles of top-performing customers, allowing both a clearer understanding of motivation and pain points and a match with sales staff whose profiles synch with customers.
Building accurate buyer personas that can be used to do more than send targeted emails is obligatory for sales success. The figures speak for themselves: when this process was carried out by LeadMD.com, they saw a X2 increase in sales-accepted leads. That’s a dramatic leap - and one that’s based on clear, accurate and actionable customer personas.