Four Data Science Imperatives for Customer Success Executives

Business growth depends on ensuring customers recommend, stay and expand their relationship with you. Businesses are implementing customer success management (CSM) programs to help improve their relationship with customers to improve their chances of success. In our Big Data world, Customer Success Management (CSM) programs are now able to leverage large amounts of customer data to help them better understand their customers’ needs to decrease customer churn and increase up/cross-selling opportunities. In today’s post, I will discuss the intersection of Big Data and CSM and illustrate how the adoption of data science practices can improve how CS personnel can improve the health of the customer relationship.

Big Data and Data Science

We live in a world of Big Data where everything is quantified, where technological advances makes it easy to collect vast amounts of data. For businesses, these data include customer satisfaction ratings of product quality, real product usage metrics as well as customers’ interactions with the company. But those data are not valuable by themselves.

Big Data is less about the data itself and more about what you do with the data. Gil Press offers a great summary of the field of data science. He traces the literary history of the term (term first appears in use in 1974) and settles on the idea that data science is a way of extracting insights from data using the powers of computer science and statistics applied to data from a specific field of study.

In a study of data scientists, we found that data science skills fall into three broad areas (see Figure 1):

• Domain Expertise (i.e., Business)
• Computer Science (e.g., Technology / Programming)
• Statistics / Mathematics

A data project is more successful when you can bring these three skills to bear on the problem.

Customer Success Management

Customer Success Management (CSM) is the process of ensuring customers and users receive value from your solutions. CSM is integral to the success of SaaS companies, where customers are not locked into long-term contracts and can leave at any time. Increasing customers’ perceived value of your solutions will lead to lower churn rates and improve your chances of expanding the adoption of your solutions within Accounts.

Last year, I attended the Customer SuccessCon in Seattle to learn more about the field of customer success management. The conference was led by Mikael Blaisdell, Executive Director of The Customer Success Association. Mikael presented preliminary results of an upcoming report on the state of Customer Success Management (he’ll be publishing this report soon). He mentioned that:

• The field of Customer Success (CS) started in 2006.
• Data is an important element in the practice of CSM.
• Only half of CS programs have a data analyst.
• The key technology in CS is Excel.
• Roughly half of CS programs do not use or track a metric.
• About ¼ to ⅓ of executives and customer-facing employees are being compensated on customer retention metrics.

So, while data plays a significant role in customer success management, CS programs are not adequately leveraging either the technology or data science practices to get the most value from their data. Here are five ways businesses can improve the effectiveness of their CSM program.

1. Integrate Your Customer Data

Customer success managers need to make sense of customer information. Unfortunately, that information is housed in different systems, requiring the CS professionals to collect and aggregate it, taking time away from CS activities that improve the customer relationship. For example, businesses use Google Analytics to understand customers’ search behavior. They leverage Mixpanel to learn how customers use their applications. They rely on Marketo to track the effectiveness of different forms of communication. They rely on Salesforce to track customer interactions across throughout the lifecycle. The use of these separate tools results in data silos, each one housing a particular piece of the customer puzzle.

Analyzing each data source separately is limited by the variables in each data set. To get the complete picture of your customers, you need to connect the dots across the data silos. By integrating all your data, you will be able to analyze all your data to extract deeper insights into the causes of customer churn.

2. Adopt Machine Learning Techniques

To decrease customer churn, you can use predictive modeling to identify the variables that are predictive of customer churn. While you can find drivers of churn manually when the data set is small, you will need to rely on the power of machine learning when you integrate all your data sources. Because integrated data sets can contain many variables, data analysts/scientists are simply unable to quickly sift through the sheer volume of data manually. Instead, to create predictive models of customer churn, businesses can now rely on the power of machine learning.

Machine learning is a set of techniques that allow computers to make dynamic, data-driven decisions without explicit human input. In the context of CSM, machine learning helps computers “learn” the differences between users who stay and those who leave. These results help CS managers better manage their time by focusing on helping customers who are at-risk of churning.

Additionally, machine learning algorithms continually learn from data. The more data they ingest, the better they get. Based on math, statistics and probability, algorithms find connections among variables that help you identify the causes of customer churn. Coupled with the processing capability of today, these algorithms are continually updated to optimize your ability to identify healthy and unhealthy Accounts.

3. Adhere to the Scientific Method

The scientific method is a body of techniques for objectively investigating phenomena, acquiring new knowledge, or correcting and integrating previous knowledge. The scientific method is an effective way to systematically interrogate data. Scientists may differ with respect to the variables they use and the problems they study (e.g., medicine, education and business), but they all use the scientific method to advance bodies of knowledge.

The scientific method includes the collection of empirical evidence, subject to specific principles of reasoning. The scientific method follows these general steps (see Figure 2):

1. Formulate a question or problem statement
2. Generate a hypothesis that is testable
3. Gather/Generate data to understand the phenomenon in question. Data can be generated through experimentation; when we can’t conduct true experiments, data are obtained through observations and measurements.
4. Analyze data to test the hypotheses / Draw conclusions
5. Communicate results to interested parties or take action (e.g., change processes) based on the conclusions. Additionally, the outcome of the scientific method can help us refine our hypotheses for further testing.

When I map the three data science skills against the five steps of the scientific method (see Figure 3), it’s clear why data science skills are so important in extracting insight from data. Proficiency in each of the three data science skills is required to successfully implement the scientific method as a way to get insights from data. Business knowledge is necessary to help formulate the right questions, generate hypotheses, gather data and communicate results. Technology/Programming skills are needed to gather/generate data and analyze data/test hypotheses. Finally, Statistics/Math skills are necessary to gather data, analyze data/test hypotheses and communicate results.

Let’s apply the scientific method to a typical customer success management problem. Here are the five steps to using your data (i.e., scientific method) to uncover the reasons behind customer churn.

1. Formulate a question: How can we decrease customer churn?
2. Generate a hypothesis: What 5 to 10 factors are predictive of customer churn?
3. Gather/Generate data: Identify all customer data silos and integrate them into a single data warehouse.
4. Analyze data: Create/Calculate customer churn metrics. Apply machine learning capabilities to the aggregated data set to identify top correlates of customer churn.
5. Communicate results: Based on the analysis, communicate results to senior executives and CS leaders. Augment Account information in your current systems (e.g., Marketo, Salesforce) with the results of the algorithmic scoring to automate how you interact with at-risk as well as healthy Accounts.

4. Develop Your CSM Teams’ Statistics Skills

Statistics and statistical knowledge are not just for people who analyze data. They are also for people who consume, interpret and make decisions based the analysis of those data. Think of the data from wearable devices, home monitoring systems and health records and how they are turned into reports for fitness buffs, homeowners and patients. Think of CSM systems, customer surveys, social media posts and review sites and how dashboards are created to help front-line employees make better decisions to improve the success of their customers.

In our study of data science, we found that knowledge about statistics is the most important skill in determining project success, no matter what role (e.g., Business Managers, Developers, Researcher) you play in the data project. Specifically, 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, math).

I’m not saying that everyone needs an in-depth knowledge of statistics, but I do believe that everybody would benefit from knowing basic statistical concepts and principles. The better the grasp of statistics people have, the more insight/value/use they will get from the data. Statistics is the language of data. Like knowledge of your native language helps you maneuver in the world of words, statistics will help you maneuver in the world of data. As the world around us becomes more quantified, statistical skills will become more and more essential in our daily lives. If you want to make sense of our data-intensive world, you will need to understand statistics.

Summary

Companies are adopting data science practices to make use of their data. CS personnel use different types of customer data to help improve the health of the customer relationship, driving up retention and up/cross-selling opportunities. Because Customer Success Management is a data-intensive endeavor, businesses can improve the effectiveness of their CSM efforts by adopting a data science mindset.

First, we recommend that businesses integrate their data silos to help them connect the dots across more variables. The more you know about your customers, the better you are able to meet their needs and ensure they are receiving value from your solutions.

Second, adopt machine learning capabilities to uncover insights from your data. Because the size of your integrated data set is so large, data analytics/scientists are not capable of quickly getting insights from the plethora of variables available to them. The adoption of machine learning can improve time-to-insight to ensure your results are both accurate and timely.

Third, follow the scientific method when analyzing your data. The application of the scientific method helps us be honest with ourselves and minimizes the chances of us arriving at the wrong conclusion, helping us to understand how our business really works. Through trial and error, the scientific method helps us uncover the reasons why variables are related to each other and the underlying processes that drive the observed relationships.

Fourth, learn statistics. To get value from the data, you need to make sense of it, do something with it. How you do that is through statistics and applying statistical thinking to your data. Statistics is a way of helping people get value from their data. As the number of things that get quantified (e.g., data) continues to grow, so will the value of statistics.