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Microsoft analyzes relationships among successful sales people and business customers

Microsoft now has benchmarks for their salespeople on how big their network of connections should be and how much time they should be collaborating with their customers. And their salespeople are relying on the LinkedIn Sales Navigator product to build their networks. We don't know these numbers, which probably would vary across companies and industries, but we know what to watch!

Using their own Workplace Analytics software, Microsoft analyzed their own "complex sales organization of 20,000-plus salespeople covering large enterprises to small business customer segments, and spanning 100 countries."

Harvard Business Review Blog: A Look into Microsoft's Data-Driven Approach to Improving Sales, 2018-Dec-17 by Sanaz Namdar and Ryan Fuller

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.... 

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.... 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.... 

We found that customer satisfaction is directly correlated with customer collaboration time (email and meetings) across all Microsoft roles and teams engaging with customers, including product engineering and marketing teams.... 

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% to 93%.