What 3 Great Books Tell Us About Account Scoring

Read these books for account scoring

If you’re a fan of author Michael Lewis, you’ve probably read or at least heard of two of his books, “Moneyball” and “The Undoing Project”.  

“Moneyball” is about the statistics revolution in baseball, and how teams like the Oakland A’s were able to outperform teams with way higher budgets.  They did this by studying the stats that lead to wins instead of making personnel decisions based on gut.

“The Undoing Project” describes the work of two psychologists, Daniel Kahneman and Amos Tversky.  These guys did extensive research into why humans sometimes make bad decisions.  It turns out that we make bad decisions when we use our gut, and good decisions if we use data (even just a little bit of data).

In his book, “Thinking, Fast and Slow” Daniel Kahneman states

“Statistical algorithms greatly outdo humans in noisy environments for two reasons: they are more likely than human judges to detect weakly valid cues and much more likely to maintain a modest level of accuracy by using such cues consistently.”

Daniel Kahneman Thinking Fast and Slow

Kahneman has a Ph.D from Berkeley and won a Nobel Prize in Economic Sciences in 2002.  He’s a really smart guy.

Kahneman was describing how Israeli army trainers would try to predict which recruits would end up being successful and which wouldn’t.  What Kahneman found is that even the simplest mathematical model would outperform the best sergeant's guess as to who would succeed.

All this got me thinking about how marketing and sales teams go about their daily work of marketing and selling.  For the most part, there’s a lot of gut involved, especially when it comes to targeting. Almost all the sales teams I meet have their reps choosing which accounts to target, at least to some extent.  It makes me think -- is there a smarter way to do this?

If we look at what the smart Nobel Prize guy is saying, then the answer is… yeah, definitely.

What should we be doing for Account Scoring?

We should look at data and build even a very simple model to help prioritize accounts to sell to, rather than just choosing with gut.

What type of stuff should you score on? It doesn’t have to be anything complicated. Here are some ideas:

  • Accounts in your top industries should score higher.
  • Accounts in size bands that you are most effective with should score higher.
  • Accounts that have multiple leads created recently should score higher.
  • Accounts that are actively hitting your website should score higher.
  • Accounts that are filling out forms (not your unsubscribe form) should score higher.
  • Accounts where you have a contact in all the key roles you sell to should score higher.

We work with lots of companies that have started implementing scoring models to begin targeting accounts using data. It helps sales a lot.  It saves time and stress picking which accounts to sell to. It also drives better results because these accounts really do perform better.

Over time, as they collect data about which accounts are buying, our customers end up tweaking and improving their scoring model. Rekener makes it easy to do the analysis to figure out what the Account Scoring model should look like.

But the first step is to try it out. People get scared by the word “model”, but really all it means is combining some factors. It’s just a way to make your data tell you useful stuff. You have the data already, in your CRM, Marketing Automation system and other tools you use. Why not put it to work?

Things to do next:

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Gregory Keshian

Greg is COO and Co-Founder at Rekener. Greg’s career has been focused on using data to grow recurring revenue businesses. Before joining Rekener, he served as VP of Operations at ZeroTurnaround, where he built its strategy and operations practice, ran customer success and renewals, helped to grow and coach its high-velocity sales organization, and optimize its marketing efforts. Prior to that, he ran the BizOps and marketing functions for the AVOKE call center analytics business, a SaaS company within BBN Technologies. He got his start using data to improve sales and marketing efforts while at AppNeta. Greg is also a member of the Revenue Collective.

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