Setsail Blog

Dealing with a tire kicker? Data can help spot them

Jen Hsin
Head of Data Science, SetSail

“I have been meeting with a contact several times, but why does the deal feel stuck?” 

“These back-and-forth emails are taking me the better half of the week, but are things really moving along?”  

“Why are some deals still not closed when my reps have been so busy working on them?”  

These are questions that salespeople ask themselves constantly.  Sales managers, as well, wonder if their teams are properly allocating their efforts.  It’s a real dilemma and the questions are logical. Not doing enough can mean opportunities to close deals are missed.  Doing too much means wasted effort that would be better spent on other contacts, deals, and prospects. 

At SetSail, we measure how rep actions impact the likelihood of winning a deal.  We automate the capture of sales activities and run them through a machine learning engine to turn your sales data into better sales team behavior.  In this process, we also discover insights on best sales practices specific to your organization. One particular insight can help identify when a deal is stuck doing busy work without really moving forward.  

“S-Curve of Sales Activity ROI”: Turning Points in a Sales Motion 

The Data Science Team at SetSail thinks about the question of effort vs reward constantly. Specifically, we think about it in the context of sales activities (effort) vs deal outcome (reward).  We can visualize the “effort vs reward” relationship by plotting the numbers (one of my favorite activities!). For example, here is an “effort vs reward” plot for the number of meetings held for a deal, compared with how much the deal win rate is increased by each meeting.  

Looking at this plot, you see a distinct pattern: there is a low reward for the first couple of meetings, followed by a steeply increasing deal win rate at two to four meetings, and finally a flatter and even negligible incremental improvement at five and above.  This “S-shape” pattern is in fact quite common. Be it meetings, emails, phone calls, or any other type of sales activity, this general trend of activity ROI follows (more plots and math at the bottom of the post for the math enthusiasts among you).  

A plot like this highlights the turning points specific to your sales workflow.  For example, having the first or second meetings might not correspond to a very high win rate lift (they are probably introductory or discovery meetings).  Adding a third meeting, though, can increase the likelihood of a win by triple-digit percentages! Beyond meeting number 5, however, the potential ROI is no longer impressive.  Armed with this data, you can now quickly flag deals that are somehow demanding those extra meetings, and see if there’s a good reason, or if you might be stuck with a tire kicker and it’s time to invest your effort elsewhere. 

Your Mileage May Vary: Different Curves for Different Organizations

While the “S-shape” pattern is common, the exact moments when turning points happen depends heavily on your sales motion.  For example, the plot above corresponds to a sales process with typical deal sizes of about $30K, closing after about two months.  For SDRs versus AEs, and for longer, more complex sales cycles versus shorter sales cycles, we saw different rates of return.  

To illustrate, let’s look at two very different sales motions.  Below are two S-shape curves overlaid on top of each other, both representing the number of emails vs win rate increase, but for two distinct sales motions.  For the sales motion with the blue curve, you probably want to hit the brakes after twenty or so emails. But for the sales motion with the green curve (which might be a longer, more complex motion), things are just getting started!

What’s more, the rates of return can change over time.  That’s why it’s crucial to frequently revisit your rates-of-return analysis, and ensure that sales behaviors are optimized for maximum productivity.

The Takeaway

Optimizing sales behaviors and determining what your “effort vs. reward” curve looks like is non-trivial and requires dealing with a lot of moving parts.  Broadly, these curves conform to an S-shape with a steep increase in activity ROI and then a plateau where additional activities do little to increase the likelihood of a win.  In other words, the same amount of effort can mean drastically different rewards.  The potential value of a meeting could be extremely high if it’s happening in the middle of a turning point.  But that n-th meeting with a tire kicker? Not so much.  

Your sales team’s data tells a story. Let it speak to you and guide you with what has worked.  SetSail automates the analytical process to extract the best sales practices learned from your data and tailor-designs incentives to optimize your team’s effort versus reward. 

Bonus: Extra Material for Math Enthusiasts

Here we provide more details and plots on the analysis.  We performed this analysis on various sales activities, for example, emails and phone calls.  Just like meetings, these two types of activities also follow the general S-shaped trend.   

The S-shaped curve is used as a fitting function overlaid on the actual data shown as bars.  The curve is probably immediately recognizable by readers venturing into a section called “Extra Materials for Math Enthusiasts”.  It is well-known as the sigmoid function.  

This simple looking curve has an amazingly wide range of applications.  An ML specialist might have used it in logistic regression modeling or applied it as an activation function for neurons in a neural network.  It’s also used in chemistry to model catalytic and saturation processes, in biology for population growth, in physics for particle states and energy distribution.  

Here, we use sigmoid function to characterize the relationship between effort and reward.  It’s inviting to make a mechanistic interpretation when fitting a sigmoid function to sales activities versus win rate, a function so widely applied to capture natural phenomenons.  Indeed, a huge element of winning deals is relationship building. It’s perhaps not too daring to model relationship building like a catalytic process.

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