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

Bob Hayes, PhD | Apr 25, 2017 207 views No Comments

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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.

The Need for Analytics Support

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.

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).

Figure 1.

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

Providing Your Customer Success Managers with Analytics Support

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.

A second approach companies can take is to hire data scientists to help support the CSMs. In fact, in our study of data professionals, we found that a team approach is an effective way of approaching your data projects. Solving problems using data (e.g., a data-driven approach) involves three major tasks: 1) identifying the right questions, 2) getting access to the right data and 3) analyzing the data to provide the answers. Each major task requires expertise in the different skills, often requiring a team approach. So, while your CSMs may not have the requisite skills to apply data science to their projects, you can always hire data professionals who possess the unique statistical skills to complement your CSMs’ skills.

While the team approach is one way of building up your data science capabilities, a third approach is to hire 3rd party companies to augment the skills of your CSMs. Technology companies are offering Data Science as a Service (DSaaS) in which they can provide the necessary technical and statistical skills needed for data-intensive projects. Companies have customer success managers who understand their solutions and their clients’ business needs. By adopting the DSaaS model, CSMs can leverage their vast reserves of customer data without the need for extensive training on technology or statistics.

At Appuri, we help businesses improve how their CSMs use existing data to help improve important customer outcomes, like conversions, retention and engagement. We help businesses integrate their data silos and then apply machine learning on the combined data set to help identify the drivers of those outcomes. The insights provide by our solution can be integrated back into the existing tools of your CSMs including your sales and marketing automation systems (e.g., Salesforce, Marketo, Eloqua and Hubspot). By applying data science to CSM’s workflow, our solution helps them improve their efficiency and effectiveness by:

  1. Identifying customers who are at risk of churning – good for using targeted outreach campaigns to mitigate churn
  2. Identifying customers who are ready for up/cross-selling – good for growing your Accounts
  3. Understanding the reasons customer are churning – good for fixing systemic processes that will improve customer loyalty for large customer segments

Summary

CSMs are becoming commonplace in SaaS and subscription-based businesses where recurring revenue is paramount for business growth. With the advent of cloud-based solutions, retaining customers is becoming more difficult because customers are now able to switch products and companies more easily.

The role of the CSM is to ensure that customers realize value from their solutions. The CSMs help their customers by understanding their needs and managing their expectations. Using their business and technical knowledge, CSMs are able to identify their customer’s needs, ultimately employing different company resources to ensure customers are successful in their use of the product. When the customer is successful, they are more likely to stay with you and expand their relationship with you, contributing to your business’ long-term success.

Data science is a method of extracting insights from data. CSMs can improve how they manage customer relationships by incorporating data science capabilities into their workflow. Because data science requires the application of three broad skill sets, CSMs can best augment their traditional skills by working with other employees who have complementary skills (e.g., statistics) or can work with 3rd party vendors who offer DSaaS.

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