Does Your Customer Success Manager Need Data Science Skills? Part 2

Does Your Customer Success Manager Need Data Science Skills? Part 2

Does Your Customer Success Manager Need Data Science Skills? Part 2
In the last post, I discussed the general idea that Customer Success Managers (CSMs) need to be knowledgeable in business and technology and possess good interpersonal (customer-facing) skills. We now live in a world of Big Data where everything is quantified. This statement holds true for customer success managers who have access to many different data sources that contain metrics that are used to measure the health of the customer relationship. Furthermore, customer success managers often rely on Excel as their main technology to analyze their data.

Despite the increased role that data play in customer success management, only half of CS teams have a data analyst. Without the proper analytics support for your customer success managers, they are leaving a lot of potential insight trapped inside their data, insights that could help them more efficiently and effectively manage their customers.

Analytics skills are not only useful to people who analyze data (e.g., data scientists, data analysts), they are also useful to people who consume, interpret and make decisions based the analysis of those data. Think of CSM systems, customer surveys, social media posts and review sites and how information from those sources are used to create dashboards that help front-line employees make better decisions to improve the success of their customers.

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We studied hundreds of data professionals to understand the data skills that contributed to the effectiveness of their work. One of the most interesting results of our study was that statistical analysis skills were the most important skills in determining project success for Business Managers. CSMs, as they are defined above, appear to be closely matched with Business Managers. They both show strong proficiency in business-related skills like business development, project management, product design and budgeting.

When we examined the impact of different data science skills on project outcomes for Business Managers, the top drivers of project outcomes were not related to their business acumen. Rather, the top drivers of project outcomes were related to their knowledge of statistics (e.g., data mining and visualization tools, statistics and statistical modeling, science/scientific method). Yes, Business Managers who were more proficient in quantitative skills reported better outcomes of their projects compared to Business Managers who were not proficient in quantitative skills (see Figure 1).

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Figure 1. The Impact of Different Data Science Skills on Project Outcomes.

There are three ways that companies can provide analytics support to their CSMs: 1) training, 2) hire a data scientist and 3) utilize 3rd party vendors that provide data science as a service (DaaS).

One approach is to provide CSMs with analytics training to help them analyze and interpret the data themselves. While this approach will broaden your CSMs’ skill set, I consider this approach more of a long-term approach. Becoming proficient in statistics and analytics could take away a significant amount of time and resources from customer-facing activities.

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