In today’s era of data abundance, leaders have plenty of metrics to choose from to benchmark progress against their strategic initiatives. Yet even with all this data, far too many leaders focus on aggregate data, overlooking the metrics that matter most.
When you synthesize data at a high level, you risk creating metrics I call “watermelons:” numbers that are green at a glance, but under the surface are red. Watermelons hide underlying execution issues happening across your sales team – and if left on the vine for too long, can rot your business from the inside out.
Watermelons hide underlying execution issues – and if left on the vine for too long, they can rot your business from the inside out.
Leaders that rely on averages and aggregates are doing their business a disservice by neglecting to dig deep enough to understand the status of their business goals and areas for improvement.
For instance, in a recent earnings call, Cloudflare disclosed that they had “identified more than 100 people on our sales team who have consistently missed expectations. Simply put, a significant percentage of our sales force has been repeatedly underperforming based on measurable performance targets and critical KPIs.”
How did 100 people miss the mark for so long? Leaders weren’t digging deep enough into the data. It’s important to identify the root cause of these sellers’ misses and fix it from the ground up.
Here’s what it means to look for watermelons, how to identify them, and the framework that provides better insights and actions to improve your bottom line.
Finding watermelons using people-centric analysis
When looking at important performance metrics, many leaders take too limited a view of their data. Activity and outcome metrics are commonly sliced and diced by various dimensions such as industry, segment, geography, product line, customer cohort, and buying persona, so leaders can answer questions like, “What is the win rate for manufacturing in the mid-market in Europe?” This is great, but almost every company misses one of the most important dimensions: people.
Failing to look at your metrics by people obscures inconsistent performance across the team, which kills overall productivity. Say your average win rate is 34% – what may seem like a wonderfully healthy metric could be a complete watermelon. Frankly, for most companies, what’s probably happening is the win rate of the top-performing quartile is above high, while the majority of your team’s win rate is low.
You won’t discover this reality unless you look at the people dimension, examining the distribution of each person’s performance against the metric. This may look something like:
Looking at distributions can be a bit complicated, so here is a way to simplify the analysis. You use participation rate as a proxy for distribution, for every single cohort that you want.
Building off the previous example, you can look at your win rate for manufacturing in mid-market in Europe, and then analyze your reps’ performance in those deals to determine the Participation Rate against the win rate metric.