Understand your customer data and use it to reduce churn and keep your customers.
Churn is the feat of SaaS businesses. Churn (or attrition) rate is the number of people who discontinued your services during a given period. It tells you how many customers you lost. In the SaaS world, it is essential to maintain a high Net Revenue Retention. However, if the customers cancel their subscription, the NRR will decrease. By predicting and controlling customer churn, you can increase, or at the very least maintain, a healthy NRR.
In order to reduce the churn rate, you have to analyze customer data, then strategize based on the insights. Data can help understanding which customers churned, why, and how to improve processes.
However, a surprising amount of business don’t have the privilege to use data if they don't have the proper data infrastructure like warehousing or piping to leverage for insights. Many teams are relying on guesses, hearsay and gut - which are all prone to human bias and error. These non-data methods force the customer success managers to opt for trial and error methods, while letting many valuable customers slip through the cracks. In such case, no definitive patterns or values are indicating the cause of the churn. Thus, the processes are subjective, unreliable and inaccurate.
Without proper analysis and metrics, teams are not enabled with the proper information to best serve and retain their customers, let alone creating effective Customer Success plans and playbooks. Thought-leading customer teams are moving away from subjective and unmeasurable methods to a data-driven approach with the option of a human-in-the-loop.
By using data in the right way, your Customer Success Manager can map the customer journey for your product. Customer retention metrics such as product usage rate, engagement, sentiment, and net promoter score are useful in the process. Each metric help you understand the problem behind churn and then find the appropriate solution. Automated analytics will allow you to understand changes and patterns to go deeper into customer behaviour. It will help you find the root of the problem, the reason behind unhappy customers and churn.
After studying the customer behaviour of the users who churn from your service or product, it’s time to find similarities. You can use data to find the reasons and correlation of churned customers. It is rare (thought not impossible) that a customer will leave without any churn indicators, and that is a chance to spot the patterns to win-back those customers and avoid churn for similar customers in the future.
Along with the pattern in the customer journey, you can find correlations in your different customer types with customer behaviour. Using the power of machine learning you can to find the segment of your customer base that have the highest tendency to cancel to predict future churn. Analytics can help you gather the demographic, psychographic and behavioural details about the churning customers. This in turn can also help identify your ideal customer profile and segments for targeting and expansion.
Set the goals linked to your customer KPIs and metrics to improve e.g. reduce churn rate by x percentage. Find a single problem that is linked to churn from your data to start with. Find potential solutions, turn them into action plans, measure what resonates and works for your customers (and what doesn't), record your findings into your playbook, and keep your customers (happy).
Once you start using data to understand your customers, you can start making data-driven decisions to deliver value and success to your accounts, reduce churn, and optimize expansion to increase your Net Revenue Retention rate. You are an expert in your business and running it which is more reason why you don't have time (or it isn't feasible to) monitor and analyze every data point from every customer. Adaptive Pulse enables you to run your business with a data-driven and proactive approach.
If you want relevant updates occasionally, sign up for the private newsletter. Your email is never shared.