¿Cómo analizar los datos de retención?

How to analyze customer retention data?

Analyzing retention data is critical for any company that seeks not only to keep its customers, but also to increase its revenue in a sustainable way. Retention becomes a beacon that guides us to better understand our customers' behavior over time. At times, retention may even exceed 100%. How is that possible? Well, it happens when measured not only in terms of number of customers, but also in terms of monthly recurring revenue (MRR). If the same customers start spending more through upsells, this can give the impression of retention exceeding 100%, which is an excellent indicator for investors and for the growth of the company.

How important is onboarding in short-term retention?

A good onboarding process is crucial to help new customers understand and adopt your products or services. Effective onboarding can prevent early churn, which often occurs in the first month. It is in these early stages that many companies lose customers who paid once but did not return.

Some common reasons for early churn may include:

• Marketing quality: attracting poorly qualified customers who only want to try the service once.
• Poor onboarding: Not offering a proper onboarding process can lead to the customer not understanding the value of the product or service.

A good example of an onboarding process is the case of CRMs such as HubSpot or Active Campaign, which offer detailed sessions to integrate the user into their platform, ensuring that the tools offered are used effectively.

What are the challenges of medium and long-term retention?

Medium and long-term retention requires a detailed analysis of service issues, product quality and competitive benchmarking.

Medium-term

Here, customers who stay more than one month face other reasons for leaving:

• Service or product problems: Poor performance or lack of upgrades may cause customers to look for other options.
• Competition: The existence of competitors offering better conditions or prices can be disadvantageous.

Long-term

Long-term customers may leave due to:

• Decrease in added value: If the service no longer offers the same value as at the beginning, they may look for alternatives.
• Changing market trends: Technological developments or industry changes can impact the attractiveness of a service or product.

How to segment data to improve retention?

It is essential to split retention data by customer type, product type, or even ticket size. Companies that sell to large customers, such as in the Enterprise sector, will most likely experience less bounce, while smaller ticket customers, such as freelancers, may show high churn.

For example, PorterMetric identified that their highest churn rate was in their lowest $15/month plans, which led them to adjust their retention and onboarding optimization strategies specifically for these customers.

Analyzing retention in this way not only helps to understand the reasons for churn, but also to identify potential opportunities for improvement in product and service offerings.

With all this data and analysis, concrete strategies can be developed to optimize retention in the future. The next step is precisely to use this information to improve your tactics to become a benchmark in customer retention. It is always a great time to learn more about how to optimize and ensure the sustainable growth of your company.