Separating Buyers from Bystanders in Your CRM

June 24, 2013

 
 
In part 1 of this 2-part post, I explain how CRM analysis can be useful for buyer research, and how to find a list of qualified buyers from the thousands of people in your CRM. In part 2, I go deeper into how to analyze your list of buyers and build rough buyer personas based on this information.
So you have a B2B technology product, you’ve started to get traction in the market, and you want to learn a bit more about the people buying your product and why they buy it.

Where to Start?

The obvious starting point is just to start calling anyone and everyone you can think of who might possibly be a buyer, and see what sticks.
This isn’t the worst approach. You’ll probably get some people on the phone, and no call with a potential buyer is a waste of time. By the end of your first few interviews, you’ll certainly know more than you did before them. But it isn’t the optimal strategy either.
That’s because without a basic hypothesis of who you’re looking for and how they buy the product, you may waste a lot of time trying to speak to people who aren’t all that important in the process. Alternatively, you might reach the right person, but spend your whole interview asking the wrong questions.
Going into a Buyer Insights interview with a clear picture of what you think you know and what you definitely don’t will streamline the process and make each precious minute you spend with the buyer more valuable. Fortunately, if your company has a CRM, a lot of this information is already sitting at your fingertips. You just need to know how to make use of it.

Diving into CRM Analysis

This 2-part blog is intended as a quick manual to help you learn more about your buyers using only the data from your CRM. These findings, while only skin-deep, can serve as a jumping-off point for a deeper interview process by ensuring that you’re reaching the right people and aren’t wasting their (and your) time with relatively obvious questions.
Because OpenView and many of our portfolio companies use Salesforce as our CRM, the architecture and extraction examples will all frame it in terms of this system. However, insofar as CRMs are structured similarly and track similar information, your company’s specific configuration and information coverage should be the larger hurdle to exactly replicating our process. Still, by understanding the types of data we analyze and our methods for doing so, at least part of this guide should be replicable, regardless of your CRM.
In part 2 of this post, I’ll explain how to conduct this analysis, but in this post, I’ll just cover the first step: building a data-set that contains only buyers.

Separating Buyers from Bystanders

Before you can start to identify patterns in the people that buy your product, you’ll need to identify a bunch of buyers of your product. The best way to narrow your universe to just buyers will depend on your specific configuration and coverage of data, but some criteria we’ve used to identify buyers with a CRM are:

  1. Converted leads
  2. ‘Primary contacts’ associated with customer accounts or sales opportunities
  3. Anyone who was the object of a critical activity, such as a product demo or discovery call
  4. If all else fails, manually build a list of contacts who has been involved in multiple critical activities and appear from the call notes to be central the process. This is a last resort, because wading through call notes can be an imprecise and time consuming process

You may choose to use any combination of the above strategies, or perhaps something we haven’t even considered, to build a data set that contains only people of interest. It shouldn’t just be customers, but it should be a significantly more targeted universe than your overall CRM. Just because the lead lists you purchase to stock your CRM are full of CTOs doesn’t mean that’s the best title to focus on, and paring your data set down to only people who could potentially drive a sale before conducting your analysis is extremely important.
The data should be arranged as a list of contacts along with more detailed information such as their Title, Company, Industry, # of Employees, Revenues, Use Case, Competitors, Prior Solution, Lead/Account/Opportunity Status, and anything else that you’d want to make a generalization about. For instance, some of our portfolio companies keep a multi-select picklist for “objections”  or “lost reasons” associated with an opportunity. If your CRM contains a similar field, you’ll definitely want to include it in your data set, and if in doubt, include the field, as it can always be removed later.
Depending on how your CRM is set up, there may be an additional step of marrying this data to additional data, for instance, an Opportunities report. If “competitors” are associated with an opportunity, but your initial data set uses contacts, you may need to match your list of contacts to the first opportunity associated with their account.
The combined list of contacts will be your primary data set moving forward. Ideally, it should allow you to develop a set of characteristics of a buyer of your product. For bonus points, it might also include a generalization about someone who won’t end up buying your product. Feeding this information to your go-to-market organization can help them avoid wasting valuable resources on ‘tire kickers’ who never end up purchasing the product.
With this list in hand, you’re well on your way to building quick insights on your buyers. Part 2 of this series explains how to extract and communicate these insights to your stakeholders.

Behavioral Data Analyst

Nick is a Behavioral Data Analyst at <a href="https://www.betterment.com/">Betterment</a>. Previously he analyzed OpenView portfolio companies and their target markets to help them focus on opportunities for profitable growth.