A large sales organization recently implemented an analytics strategy, with one of their critical metrics being lead conversion. The target number was set and reporting tools were put in place. Each sales team was scored on this metric, and the average team score was reported to the CEO. It became quickly apparent that there were vast differences in these sales teams, but opinions were split. Sales teams with low numbers blamed their products. Teams with high numbers gave themselves a pat on the back and criticized those bringing the score down by not taking appropriate action. The result: numbers were mistrusted and seemed irrelevant, teams felt disengaged from the business strategy, and there was limited conversation around improvement.
As many organizations rush to implement analytics solutions, this case of ‘data shaming’ is not all that uncommon. There is pressure to reduce noise and focus on only a handful of critical metrics that drive the business forward. Good decisions can only be made based on good information. The organization had the right technology in place, they selected what seemed like a reasonable target, and they were able to pull the numbers. So, what went wrong?
In the first iteration of this analytics strategy, the organization wasn’t even asking the right question. It turns out the sales cycle for each of the product groups, represented by the sales teams, had very different baseline conversion rates regardless of the salesperson or strategy involved. What this organization ultimately needed to focus on was improvements or declines in conversion rates.
At Pandata, we understand that data science is an iterative process with five key steps. We spend a lot of time helping our clients frame their analytics opportunity before jumping into a solution. Asking the right question is half the battle. The other half is making sure that solutions built consistently result in action that impacts the bottom line.
In the case of this sales organization, reporting conversion rates alone did not provide the context to make good decisions. We worked with the client to develop guidelines for interpreting the data and shifted the emphasis to reporting changes over time. Instead of pointing fingers, the sales organization is now having regular dialog and focused on strategies that affect growth.
Want to learn more about how putting a data strategy in place can help to align your sales teams, and their initiates, to ultimately drive revenue? I sure do!
Cal Al-Dhubaib is the Chief Data Scientist at Pandata.