Companies are beginning to utilize their employees’ behavioral data — generally known as people analytics — to better understand and improve their sales operations, with strong results. Microsoft, where we work, is no exception, and B2B sales is one of the areas where we are seeing the most value.
Our findings, and the ways we came to them, can be useful to other sales organizations looking to make internal changes of this type or optimize how their salespeople relate to customers.
In mid-2017, we executed a major redesign of our sales organization in response to what our customers needed from us, and to better align our selling approach with cloud services sales model (in this model, customers pay based on usage versus a traditional fixed licensing deal).
We knew we needed a fast and effective transition to the new model without dropping the ball with our customers, but the undertaking was daunting and the stakes were high: With a complex sales organization of 20,000-plus salespeople covering large enterprises to small business customer segments, and spanning 100 countries, it was important to see how these changes impacted our customer collaboration and partnerships.
We needed to get answers to some of our biggest questions, including:
- Are we spending enough time with our most important customers?
- Are new hires ramping up and collaborating with customers as quickly as expected?
- Are they growing their internal and customer networks?
- Are salespeople collaborating with one another effectively?
- How is all this impacting our customers’ own business success?
Our hunt for answers started by using our own Workplace Analytics product to aggregate de-identified calendar and email metadata for thousands of enterprise salespeople. We then combined that with organizational and customer relationship management data to determine how the people selling via the cloud sales model were collaborating with their internal teams, customers, and partners.
The next step was to correlate sales outcomes with these behaviors to identify the patterns that correlated with better results. These analyses were done in part to help us through a massive transformation and in part to better align us in responding to our customers’ needs and expectations.
To date, the analyses revealed several actionable insights, which we came to with the help of our colleagues Ben Boatman, Chris Moss, Gabriel Zhou, Jared Baker, and Fabio Correa.
1. Networks are vital — and a reorg could destabilize them
One of the first things we learned is that salespeople with larger, more inclusive networks tended to have better outcomes. This is consistent with a number of other similar studies.
Based on this finding, we initiated a program to coach our sales teams to focus on efficiently building and growing their internal and external networks. By looking at network size relative to tenure within the company, we were further able to establish that it typically takes roughly 12 months for most people to build these networks.
This underpins the importance of stability in roles over that time period, and beyond. It also left us concerned that the reorganization was forcing the salesforce to rebuild their networks from scratch, which could be costly and sub-optimal for our customers.
To mitigate this cost, we rolled out programs to emphasize manager coaching and invested in facilitating rapid network growth for new hires.
2. We engage very differently with high-growth accounts
Another key aspect of the re-org was to ensure continued growth of our business and the right level of engagement with customers. Looking at the amount of time teams spent interacting with each of their accounts, as well as the number of individual contacts they were connecting with, allowed us to identify statistically significant differences in how teams engaged with the different account segments.
On average, teams engaged with twice the number of customer contacts in our higher growth accounts, and collaborated double the amount of time with these customers as compared to lower growth accounts.
To make sure this wasn’t just a one-time anomaly, we also confirmed that this pattern was consistent month over month. Correlation vs causation is always an open question with an initial finding like this: are the accounts higher growth because we spend more time with them? Or do we spend more time with them because they are higher growth?
Deeper analysis showed that investing more time and energy into partnering with some of these lower growth accounts could improve them. As a result, we adjusted our sales coverage models to enable more face time with these previously underserved customers.
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