Objectively, more than 50% of Product PoCs (or simply sales-led trials) fail to achieve their goals.

In fact, this ratio is far worse for startups.

As per a study by Sapphire ventures, more than 80% of the CIOs believed that just 30% of the sales-led trials for startup products result in successful implementation.

However, most (if not all) sales leaders (specifically sales engineering leaders) would put this number between 60% to 80% for their organizations. To a certain extent, their reasons are a bit understandable, if not desirable.

The eternal cycle of hope and doom

If you are a product manager or a B2B sales leader and have been sitting in weekly sales trial (PoCs) review calls with some frequency, you would recognize this scenario.

Your sales engineering team starts the quarter with 10 (or take any number of your choice) of PoCs. Week after week, your sales engineering team would estimate to identify most PoCs state of play in very broad terms.

“It is going good or it is going bad, or I did not have the chance to sync with my prospect, so I don’t know and will go with the last known status for this account.”

Some seasoned sales engineers will bring in the learned heuristics of a seasoned operator by observing the tone or lack/frequency of prospect communication to assign a gut estimate. More often than not, their estimates will play in a small band of cautiously hopeful to cautiously pessimistic for most PoCs until the last 2-3 weeks of the quarter.

In the last 2-3 weeks, the dominos start to fall. Suddenly, cautious optimism would turn into a pall of gloom for many trials.

“As a product manager, I have lost count of pulling my hair on last-minute reasons for PoC losses by lamenting, only if I had known earlier in the quarter about this, I could have done something about it.”

The problem with the “Intuition” of Sales Engineers

Expert intuition is the ability to recognize familiar clues and provide skilled responses to issues faced over time. Firefighters or Clinical Nurses are great examples of professionals with expert intuitions built over being in similar, repeated situations for long periods.

However, Expert Intuition takes a long time to develop. As per Nobel Prize winner Author Daniel Kahneman, developing skilled (expert) intuition requires two conditions to be satisfied.

1. An environment that is sufficiently regular to be predictable

2. An opportunity to learn these regularities with prolonged practice

Unfortunately, for most sales engineers, none of these conditions are satisfied. As per research, more than 40% of sales engineers spend no more than 12-24 months in a job. More than 60% spend no more than 36-48 months in a job.

So much of the estimation skills for most sales engineers, unfortunately, reflect their System 1 thinking biases, where they perform the easy substitution of the question that is actually asked (what is the state of a sales trial?) with an easy response that reflects their biases (e.g., I get a warm or cold fuzzy feeling from this account).

In fact, As Professor Daniel Kahneman describes in his excellent book, a lack of rigorous analysis is substituted by most folks by what their System 1 mind is telling them.

Anecdotally, sales teams are passionate and, for obvious reasons, emotionally invested in many of their deals. As a sales leader, you want to encourage and celebrate optimism and passion as it drives people to achieve sometimes impossible results. However, as we have established, unbridled, intuitive optimism is not ideal for performing dispassionate analysis and prediction about sales trials.

And then there were product managers!

While a bad sales forecast and flawed trial prioritization are fundamental sales problems, the lack of objective assessment creates a major long-term product strategy problem.

Among other sources, B2B Enterprise Product managers rely on sales engineers to develop a sense of how key features drive technical wins during a sales trial. They also use win/loss reports as a critical factor in driving the product roadmap.

A lack of objective data coupled with an overtly optimistic view of trials driven by sales teams leads to biased and misleading assessments of what is happening in the marketplace.

Why is data a better predictor of sales trials?

In simple terms, formulas or data driven assessment do not suffer from "extraneous knowledge" usually brought in by human beings (reflecting their biases) that, in most cases, may not be relevant to predicting the outcome of sales trials. Formulae or data driven assessment are rather repeatable and predictable.

There is also a significant practical utility of harnessing your sales engineer's optimism to drive them to conquer, especially the complex deals, while using a cold formulaic assessment method for predicting the outcome of sales trials.

Data-based assessments should help sales engineers make critical decisions - Fix or Kill a trial if the signals are negative or indifferent. Double down on the trial if the signals are positive. In either case, data can lead to early prioritization and better prediction of sales outcomes.

It is a travesty of sorts that not enough tools are developed for sales engineers, although they play a crucial role in proving the value of most enterprise products to the end customers.

We at FunnelStory aim to solve this problem and empower sales engineers to become superheroes of sales trial conversions.