Customer success teams engage with thousands of customers every day. Each engagement and interaction are unique and the team learns something from it. However, handling this multitude of interactions individually can be tough to enhance the customer journey. In this situation, data science and being data driven helps. This blog will help you understand the benefits of a data science strategy and how it impacts customer success.

Why a Data Science Strategy is Important for Customer Success

Data is important in customer success. However, most customer success programs or strategies do not leverage the power of data science for best results. Some of the top benefits of how a data science strategy impacts customer success are the following.

Deeper Insights

With data science and data-driven customer success, you will get deeper insights into customer churn. You will also understand where customers are facing problems, what aspects of the product are they not able to use and more.

Customer Health Improvement

Data science will help you improve your customer health score. Customer loyalty also increases with the involvement of data science.

Predictive Models of Customer Churn

You can create predictive models of churn. This will help understand and prepare for what is coming.

Increased Retention

Data science also helps drive retention up and increases opportunities for upselling or cross selling.

The Ultimate Data Science Strategy for Customer Success

SaaS companies who have understood the importance of data are doing better than others. Data science has proven helpful in optimizing onboarding, reducing churn, and streamlining all business aspects. A data science strategy can help you gain understanding of why customers churn, who might churn, when to cross-sell, when to upsell and more.

Gather Customer Data

Gather the data you need to create valuable cross selling opportunities and insights. You can get data from CRM- how long have customers been with you? How much time they spend with the product, etc. You will also know customer usage data and how often they use the product, what the product features are and how concerns are answered.

Centralize the Data

You then need to centralize and integrate the data to make it as streamlined as possible. You must try to avoid any faulty data entries to reduce wrong results. When the business grows, it might be tough to get the right insights. This is where artificial intelligence and machine learning can help deliver accurate, timely results.

Learn from Data

You must adjust the data science strategy as per the company’s needs and size. Small businesses need to manually analyze data and find streaks that will set them apart from competitors. This may include processing information via machine learning and artificial intelligence to generate the right reports.

Implement and Integrate Your Learnings

The next step is to focus on loyalty and retention as this will help you in the long run. Once you have all customer data tactically compiled, you can include the results to draw relevant conclusions and enable improved customer success. You can be more efficient and prioritize leads to improve lead scoring, become informed, and deal with customers in a better fashion. This will help predict other purchasing decisions and willingness for cross selling and upselling. You also need to effectively scale and become proactive to churn situations.

Best Practices to Launch Data-Driven Customer Success

Establish Collaboration between Customer Facing Teams and data scientists

One needs to enable a strong collaboration between customer success teams and data science teams. This will help CS teams describe their issues better and allow for data teams to look at solutions that will address them. This effective collaboration will lead to a mutually beneficial relationship that will lead to improvements in both fields.

Switch to an Agile Approach

The adoption of agile methodologies can improve how the business works. It will reduce the risk of projects going wrong, improve impact and focus efforts on the right spots.

Predictive Insights Need to be Simplified

CS teams need to have a basic understanding of technology, and this is possible with simplification. Since CS teams might not be aware of the technology and tools, it has to be simple for them. Outcomes must be predicted and stimulated through data instead of just guesswork.

Fill in Data Gaps

To get started, you need to pick the data that exists. There might be some data and that will work. While utilizing machine learning tools might not be that easy, one must not wait. You need to work with data teams and move parallel to fill gaps.

Five Valuable Questions about Customer Churn that Data can Help With

Customer success leaders must answer important questions mostly regarding churn to the C-suite. These five aspects include-

• What is the current customer churn rate?
• What causes customer churn- is it how customers use the product, customer sentiment, and customer interactions
• Which customers might churn- which customers are at risk and why? Who is likely to stay? Customer success leaders can use these reports to save relationships.
• How customers should be segmented- based on demographic information, geographical location, gender, age or more. This will help create customer segments to deliver value accordingly. You can also apply data cluster analysis to identify high value customers and understand what makes them tick.
• Which marketing campaign has been successful than others at reducing churn? Experimenting actually via A/B testing can help understand the efficacy of marketing campaigns and attract or save new customers. This will also help understand customers who don’t get the message or service so that one can address that to decrease their chance of leaving.

Bottom Line

Data science for customer success is imperative for better customer retention, satisfaction, service, loyalty, and more. You need to experiment and find the right strategy to benefit your customer success strategy the most. You know important answers, can arrest churn, and generate key insights that are helpful for overall growth. You can also use data science to engage, retain and grow customer relationships.

---