Optimizing sales and connecting with customers in the age of big data and machine learning

Written by Ryan Fuller, Sanaz Namdar

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.

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. 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 reorganization can 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 started a program to teach 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 sales force to rebuild their networks from scratch, which could be costly and suboptimal for our customers. To mitigate this cost, we rolled out programs to emphasize manager coaching and invested in enabling rapid network growth for those newly hired.

2. We engage very differently with high-growth accounts

Another key aspect of the reorganiztion was to ensure continued growth 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 spent twice the time collaborating with them compared to lower-growth accounts.

To make sure this wasn’t just an anomaly we also confirmed that this pattern was consistent month over month. The notion of correlation versus causation is always problematic with an initial finding like this: do the accounts’ higher growth reflect the fact that we spend more time with them? Or do we spend more time with that account because they show 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.

3. Relationship investments correlate with customer satisfaction

It was important that the new sales model also drives happier customers and partners. Therefore, our next step was to look for patterns associated with customer satisfaction. We found that it is directly correlated with customer collaboration time (via email and meetings) across all Microsoft roles and teams engaging with customers, including our product engineering and marketing teams.

In the enterprise segment specifically, the satisfied customers are the ones we spend the most time with and the least satisfied are the ones we barely keep in touch with. This and other findings encouraged our sales leaders to revamp internal business processes such as business reviews and forecasting meetings to make them more efficient. We also reduced the number of enterprise accounts per seller to allow for more customer interaction. This enabled our sales teams to spend more time building and maintaining relationships across their entire account portfolios. We also observed behavioral differences in different countries.

4. Customer satisfaction (and churn) can be predicted

As part of our ongoing organizational efforts to better understand our customers, one of our teams built a machine learning model that uses more than 100 features to predict customer satisfaction. We worked closely with this team to add the behavioral data about collaboration we gathered into the model. After our analysis, we discovered that collaboration became the top feature in predicting customer satisfaction, and helped increase the accuracy of the model from 78 percent to 93 percent.

Being able to predict the satisfaction of each of our customers at any given time with this level of accuracy was a groundbreaking discovery for us. Further, having a deeper understanding of how our team’s interactions influence customer satisfaction by segment has huge upsides: it enables us to intervene in time to change high-risk customers into low-risk ones, and to offer new opportunities to highly satisfied customers. Our ability to predict customer satisfaction with this level of accuracy will help us check the pulse of our transformation and intervene in a timely manner to ensure customer satisfaction at all times.

What’s next? Our goal is to arm each seller with these four insights on an ongoing basis, setting them up to be as successful as possible in creating value for our customers. We are currently testing a prototype in which a customized and automated email is sent monthly to each seller to help guide them toward behaviors that drive higher outcomes. Importantly, the data sent to each seller is set up for their eyes only; to protect everyone’s privacy and retain trust in the system, no one else, not even upper management, can see anyone else’s data.

We have a long way to go, but so far, our transformation is working. Pushing the envelope on behavioral analytics has been a key ingredient in our success, and hopefully our insights can help your salespeople, too.


This article was published on

HARVARD BUSINESS REVIEW
Ryan Fuller
Ryan Fuller

Ryan Fuller was the CEO and co-founder of VoloMetrix, a leading people analytics company acquired by Microsoft in 2015. Within Microsoft, Ryan leads a business unit focused on making organizational analytics capabilities broadly available. Previously he was a management consultant at Bain & Company.

Sanaz Namdar
Sanaz Namdar

Sanaz Namdar leads Seller Behavioral Analytics in Microsoft’s Commercial Business organization. Her team’s charter is exploring, experimenting, and harnessing behavioral data in a predictive fashion to better fuel strategic sales decisions and growth of the business. Prior to Microsoft, Sanaz was a leader in Accenture’s Sales Talent and Transformation practice.

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