One belief we hold pretty deeply at Variance is that data produced by customers is more valuable than data produced about them by internal teams. While on first reading that doesn’t sound too controversial, when you start to think about the set of technologies that exist around the go-to-market process, it becomes clear how misaligned they are to the data that matters most. 

We have three fundamental beliefs about how software sales work that sits at the center of how we build our product:

  1. Customer Data >= Seller Data: Not only do customers produce many times more data than an individual manually entering it, but it is also more accurate and doesn’t suck the life out of the seller to generate. 
  2. Post-Signup >= Pre-Signup: In a world where everything is software, what happens after a prospect signs up is at least as important to the revenue team as what happens before. We also believe this data becomes more and more important in a world where the customer is asking (demanding?) to try your product before they purchase.
  3. Quant Stages >= Qual Stages: This one is touchy but it shouldn’t be. We know that human judgment is better than anything a machine can judge in a critical, high-stakes sales stage. Much like a human can make a left turn better than a self-driving car when it really counts. But most of the time you aren’t in a critical turn and you need something watching out for you and the stages of your sales.  A milestone or quantitative approach to sales is fundamental to being a data-driven sales org. Milestones represent the actual behavior of prospects and customers and complement the insights of hard-charging revenue teams.

Types of Data along the Customer Journey

When we think about the Customer Journey it’s not enough to just say you’re collecting data along the way. The type of data, the systems you use to process it, and the ways your teams engage with those systems are equally, if not more, important than just filing away piles of journey data with no real plan on to use it. 

To that end, we find it helpful to think about the categories of data based on its source. One note: These are our own definitions and don’t necessarily fit how the industry defines things. For instance, second party data is defined by some as first party data that is shared with another company. We don’t find this overly useful.

Zero Party: Data that comes from your company internally. eg. your sales or customer success representatives inputting data into GTM tools. 

First Party: The data generated from a self-identified prospect or customer of your website, content, product, or services. 

Second Party: Data that comes from unknown visitors to your website that can be enriched to provide you a directional insight on who they might be. (The idea behind calling it second party is that it’s third party data combined with some first party data.) 

Third Party: Data that comes from other sources that you can purchased. This is also sometimes referred to as intent or dark data. 

I’ve tried to create a way to visualize this data along the customer journey to help think through what type of data you are getting at each point in the customer journey. The more you can understand this, the better you can think about how to capture that data and how it can interact with your organization. 

A few things might stick out to you on this graph: 

  • The Seller generated data stays fairly consistent through the pre- and post-sales process. This is most likely because of the methodology (MEDDPICC, Sandler, BANT, etc) the company uses and the way they’ve set up workflows in their CRM to capture the output of that methodology throughout the buying journey.
  • Deanonymized and dark web data is almost completely gone by the time the customer is won. This should be fairly obvious, but it’s also important to realize that unlike customer/seller generated data, 2nd- and 3rd-party is most valuable in the pre-sales motion. 
  • Freemium and trial is like a data accelerator. Whatever you knew about a customer before is dwarfed by what you know about them after a trial and the gap only widens post-sale. 

Once you have started to think through the journey, the next step would be to think about what system to collect this data in and how to let your revenue team engage with it. This process would involve traditional systems of records (eg. CRM, Warehouse, etc) along with systems of engagement (eg. marketing automation, Slack, etc). In our experience, this leaves a hole for the revenue team who need something more akin to a system of intelligence to help manage this data and make it as actionable as possible. 

What do you think? Our hope is to write a follow-up piece that starts to catalog what data recording, intelligence, and engagement looks like for different types of companies. 

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