The Problem with Rules of Thumb

In the course of sales process optimization projects, we invariably come across assumptions, arbitrary rules and hypotheses that we will characterize here as “Rules of Thumb”.  Every sales organization has them and since they are ingrained in the sales culture, they are accepted as fact: “That’s how we do things here.”  

When we at Acuity come along, we ask “Why?  Why was that rule established?  What data was used to validate the assumption and what is the process used to make those decisions?”  The answers reveal reasons for underachievement vs. goals and opportunities for productivity improvement. 

Example #1: Prospecting Cadences

Companies adopt sales engagement cadences for their SDR/BDR teams based on “best practices” ranging from 5 – 20 call, email and social media activities over 1 – 3 weeks.  

Why?  

At best, if actually followed, this “one size fits all” cadence approach treats every targeted account the same, based on an unproven hypothesis, or generalizations from other companies that are not relevant to your business.  And often the defined process is not actually followed due to overwhelming complexity.  Reps tend to get stuck because static, linear cadences break down, which makes it nearly impossible to measure compliance and therefore process efficiency… which is needed to compare one process to another.   

And how does the company analyze and experiment with behavioral patterns to improve Rep productivity?   A method for figuring out a better, more efficient approach is needed.

Example #2: Last Touch Date Coverage Methodology

We see many teams use Last Touch Date in Salesforce or other CRMs as the mechanism for deciding when to re-engage a prospect.  It’s a simple rule of thumb.  If followed, this approach could spread Rep selling effort across a large prospect population.

However, it’s hard to implement this approach, manually, without getting lost.  And it ignores the fact that not all prospects are created equal: some are more or less likely to become customers. 

So why try to engage all prospects the same amount if some are more likely to buy? 

Using predictive models to rank prospects would help to focus Rep time on more productive activities.  And then effort could be applied in a tiered fashion so higher probability prospects receive more attention.

Example #3: Opportunity Disqualification Cliffs

A Sales VP at a software company has a rule that if there has been no activity or updates on an opportunity for 30 days, it should be removed from the current pipeline and forecast.  

Some may applaud this method for cleaning “stuck” deals out of the sales funnel, but in actuality it is not substantiated by data and could have harmful results.  Why 30 days?  Why not 67 or 26?  (Our data analysis showed that the win probability for deals in the pipeline increased for the first 187 days, and day 30 had the same probability as day 389.) 

If a distribution of actual deal cycle times were created, and/or if a predictive model were built to show the correlation of days-in-stage vs. eventual win/loss, and it showed that “30 days” summarized actual outcomes, great.   That would be a useful rule of thumb. 

But 99% of the time, neither has been done, and there are so many differences between companies, deals, reps and business situations, that a rule like this is too broad to be useful or accurate.  It can’t be right!  There is no “average” deal.   This practice could limit sales growth by accidentally killing viable deals.

Modern Sales Management 

The prevalence of “rules of thumb” underlines a greater problem: The lack of a cohesive, data-driven method for managing and understanding the behavioral process of selling.  

• What does your team do to create more pipeline?  Why?
• How are you engaging customers to drive retention and up-sell?
• Is your team implementing your go-to-market strategy?  
• How are you using data-driven insights to improve processes, continuously?

Companies have sophisticated, long-running approaches for managing mechanical and physical processes, such as manufacturing and distribution, but don’t have the same capabilities for the selling process.  Unfortunately, behavioral processes often rely on arbitrary rules and individual salesperson guesses, rather than incorporate data that can be used to make informed decisions that improve productivity.  That’s the bad news.

The good news is that this situation creates an amazing opportunity.  

Capitalizing on these improvement and growth opportunities is the essence of what the Acuity 3D platform does.  It  creates robust prospect and customer engagement processes, which are consistent and measurable.  

This capability creates useful behavioral pattern data that can then be used to eliminate guesswork and ensure that sales processes effectively implement the go-to-market strategy.  

Rules of thumb can be a great, simple way to ensure that your sales team has the right focus, week-to-week, to deliver the strongest and most predictable results.  

How much better would performance be if your rules were based on robust analytics?