Take the Guesswork out of Customer Retention

The importance of improving customer retention

There are two ways you can grow revenue. You can find new customers, and you can bring existing customers back to buy more. Companies that master the latter have an advantage. Not only do they increase the lifetime value of their customers, which boosts revenue, but they’re also building amazing relationships with those customers. 

To improve customer retention, you need to earn trust by delivering a great user experience—by ensuring your product or service works consistently, the support you provide is effective, etc. To do this, you must understand the drivers that increase customer retention. This includes removing obstacles (such as friction in the checkout process), improving usability (like adding links to related products), and staying in touch with relevant information (such as sending emails about new or on-sale products that match customer preferences). 

Understanding the customer journey

The first step is to understand the differences between the customer journey that loyal customers experience compared to that of those who churn early on. What are the pain points that are preventing customers from coming back? What are the behaviors and attributes of repeat buyers? Only then can you modify the customer experience accordingly, track and monitor those changes to confirm they’re improving results, and continue to adapt as needed over time. 

Easy, right? Except, it turns out that it isn’t. 

The road to retention isn’t a straight line

To measure retention, you need to be able to understand the rates of behaviors or actions over different time periods. For example, how many users interacted with a certain feature, or performed a particular action, in a specific timeframe? And did that affect whether they performed other desired actions in a subsequent time period? For example, are customers who use a help feature within their first week more likely to make an additional purchase in the future? 

The problem is that most analytics tools can’t answer this type of question. And it’s exactly this type of question you need to be able to answer to uncover the real obstacles to customer retention. If you just have individual data points, without understanding the sequences of events, you’re left to stitch it all together yourself with assumptions and best guesses. 

Ditch the guesswork with Scuba Analytics

Scuba Analytics was designed to allow users to easily understand the customer journey over time, so you can improve retention. Scuba enables you to:

• Define customer segments on the fly. Users can easily define different segments in the UI on the fly and immediately apply them to any ad hoc query. Scuba allows this without having to pre-define them in your data model or deploy a segment and wait for customers to populate.
  
  
• Associate customer segments over user-defined time periods. No need to roll up definitions about segments, customer usage, or behavior to the product’s schema or data dictionary. This gives users the freedom to create their own definitions around customer feature usage and sessions, and instantly analyze and iterate without having to wait or rely on other teams to write the queries.
  
  
• Associate retention rates with user behavior. For example, users can understand how the retention rate differs for users who perform action A compared to those who perform action B.  Learn how using certain features in a customer's first week will drive long-term retention compared to users who did not use that feature in their first week.
  
  
• Ask questions about the frequency of particular behaviors. For example, users can look at customers who performed a particular action and understand how retention rates differ based on the number of times those customers performed that action over a given timeframe. 
  
  
• Do it all with zero SQL or other technical skills required. Generating queries of this nature typically requires sophisticated skills in languages like SQL, Python, or Java, and often involves multiple BI or data science teams. With Scuba, users can get answers within minutes, while still exploring the full depth of their dataset, without restrictions and without requiring specialized technical training.