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3 Critical Questions Customer Success Executives Need to Answer

Bob Hayes, PhD | Feb 7, 2017 171 views No Comments

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In today’s subscription-based economy, customers are no longer trapped in long-term contracts and are able to jump to competitors easily when they become dissatisfied with their current vendor. Consequently, many subscription-based and SaaS companies are turning to the practice of Customer Success to keep their customers. Customer Success is the function in a company that manages the relationship it has with its customers to ensure the customers receive value from the product or solution. Customer Success is about making customers as profitable and productive as possible.

The three questions on which this blog post is based were inspired by Mikael Blaisdell of the Customer Success Association. These questions were designed to help you think about important elements of your Customer Success (CS) program. I believe that answers to these three questions will help clarify and operationalize your Customer Success (CS) program. I answered the questions based on my professional experience as well as decades of scientific research on and practical experience in customer-centric programs.

1. What is the primary purpose of the Customer Success team?

I see two reasons why customer success teams exist.

  1. Improve the value that customers receive from products / solutions by making the customers productive
  2. Improve customer loyalty (build advocacy, decrease churn, expand relationships) to optimize profitability and growth for the company

These two questions are necessarily related to each other. Improving the value that your customers receive from your products will ultimately lead to them engaging in more loyalty behaviors. When your customers succeed, so does your company. The figure below (Figure 1) shows a model that represents how company performance is dependent on customer loyalty, which, in turn, is dependent customers’ perceived value.

Links_CS_CX_Loyalty.pngFigure 1. Business model illustrates the path from the company’s business strategy to company performance. KPIs will include perceived value, customer satisfaction and customer loyalty (e.g., retention, advocacy, expansion)

2. What do you see as the greatest challenges confronting Customer Success executives (team/group leaders)?

The challenges that CS executives face will dictate the course of action they take to reach their ultimate goal of building an effective CS program. To stay ahead of the competition, they need to build a program that improves how they manage customer relationships.

We live in a world of Big Data where everything is quantified. As such, customers are leaving behind a digital trail of information about their behaviors, attitudes and interactions. This treasure trove of data can be analyzed to tell you many different things about the health of the customer relationship. It can tell you the current status of their health (descriptive). It can tell you what their health will be next quarter (predictive). It can also tell you what course of action you need to take to improve their health (prescriptive).


Engage with customers in real-time across every channel, no matter the medium. Use visitor tracking and email analytics to know what your customers are seeing.

Customer Success, like many disciplines today, is morphing into an exercise of using data to make better business decisions. Despite the increased role that data play in customer success management, only half of CS teams have a data analyst. So, while data plays a significant role in customer success management, CS programs are not adequately leveraging either the technology or analytics practices to get the most value from their data.

I see three primary challenges for CS executives:

  1. Getting value/insights from the vast amounts of available data. The key points to remember when analyzing data are that you are able to get two types of insights from your data: 1) identify which customers are going to leave and 2) identify the reasons why customers churn.
  2. Understanding how best to introduce the power of analytics into the organization. In addition to having strong executive support around the use of data, there are a few ways to build a strong, data-driven approach to your customer success initiatives. These include: 1) Hiring a full-time data scientist, 2) Training existing staff on statistics and research, 3) Employing 3rd party provider (e.g., Data Science as a Service – DSaaS) and 4) Implementing a CS data/analytics platform.
  3. Ensuring the CS team members are successful. First, successful CS initiatives rely on a clear definition of success. By clearly articulating the meaning of “success,” you are helping your CS team understand what they need to do for their customers. Second, you need to provide the CS team the tools and resources they need to be successful. Finally, you need to measure the effectiveness of help your team; using the business model above, some common measures of success include: churn rates, growth (up/cross-selling) and advocacy.

3. How do you know a company is serious about Customer Success?

Talk is cheap. Instead, you need to look for specific practices that companies adopt in their customer success programs to determine if the company is serious about ensuring the success of their customers. In an earlier study, we found that loyalty leading companies adopted specific business practices at a higher rate than their loyalty lagging counterparts. As is reflected in their actions, loyalty leaders show that they are serious about their customers by how they use customers’ data across all levels of the organization.

Loyalty leading companies tend to adopt these practices:

  1. Integrate their data silos. Integrating disparate data silos not only allows you to see a 360⁰ view of each customer, it also allows your analytics efforts to uncover deeper insights that are simply not possible when you look at each data silo separately. We like to say that the sum of your data is more valuable than some of your data.
  2. Employ predictive and prescriptive analytics. While descriptive analytics provide insight about the past, predictive and prescriptive analytics help you peer into the future and take action to mitigate the risk of customer churn or to take advantage of opportunities to grow existing relationships.
  3. Utilize machine learning (e.g., intelligent system) to automate the process of extracting customer insights from data. Data scientists, despite what you might hear, are mere mortals. They have limited capacity to make sense of data. Rather than relying on data scientists alone, companies are using the power of machine learning to quickly surface insights in their vast amounts of data to proactively manage customer relationships. camm.pngFigure 2. The Customer Analytics Maturity Matrix: A Model for Evaluating Your Customer Analytics Effort. To see where you rank, take the free Customer Analytics Best Practices Assessment.

Because data are only as valuable as what you do with them, CS professionals need to apply the right analytics to their data to help them make the right decisions about their customers. We recommend using the scientific method to finding these insights. Using the scientific approach requires asking the right questions, stating testable hypotheses, gathering the necessary data, analyzing those data and communicating the results / taking action.

Summary and Conclusions

In today’s highly digitized world, Customer Success, like many disciplines, is necessarily an analytics endeavor. Customer Success executives can take the following actions to modernize their CS program:

  1. Clearly define and articulate the goals of your CS program to your team: to increase customer perceived value of your solution and to improve customer loyalty.
  2. Track the right outcome metrics. These metrics include measures of perceived value and loyalty (i.e., retention, advocacy and growth/expansion). Depending on your business needs, some measures will be more important than others. If you have a churn problem, you might focus on retention metrics.
  3. Integrate your data silos to get a comprehensive picture of each customer.
  4. Unleash the power of machine learning on your aggregated data set to build better predictive models of customer behavior.

How well does your CS program optimize the use of data and analytics? To find out, take our free Customer Analytics Best Practices Assessment. To complete the 10-minute assessment, click the link below.

To read more about the Customer Analytics Best Practices Assessment, click here.

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Republished with author's permission from original post.


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