The goal of every business is to have as many paying customers as possible. That’s not a secret to anyone. Many of these businesses are trying to focus on acquiring more and more new customers, which can be a good tactic, but there is something they must aim their attention at more. That is preventing churn.
This refers to any activity that is focused on avoiding circumstances that might lead customers to discontinue using the service and leave the business. And why is this important? Because in almost all cases, retaining customers costs far less than acquiring new ones. Hence, the company growth can continue at a steady and stable pace, which is always a very desirable outcome.
To put this into a more productive perspective, we’ll look into how you can decrease the likelihood of churn by using and analyzing different customer behavioral data types.
Why is Churn Prevention Important?
An article by Hüify explains why customer retention is such an important factor for businesses. Let’s look at some numbers:
- It costs five times more to acquire new customers than to retain existing ones.
- If you increase your customer retention rate by 5%, you can anticipate a 25-95% increase in your profits.
- The success rate of selling your services or products to new customers is 5-20% while selling these to long-time customers has a success rate of 60-70%.
These numbers are very convincing and prove the point that retention is one of the most important facets you need to focus on. And for that, you must predict and prevent customer churn.
Different Data Types to Analyse for Customer Churn Prevention
Behavioral analytics is one of the best ways to predict and prevent churn, as it provides insights into what customers are actually doing and might lead them to leave the service permanently.
When asked about the data they investigate in such cases, Antony Belonozhenko, Senior Data Analyst at Competera answered: “I usually work for B2B saas so it's simple:
- Client satisfaction rate (from core decision maker) on month a quarterly basis
- Amount of incidents
- Speed of change request closing
- User activity rate”
Here are some of the data types to look out for and analyze if you want to prevent a high churn rate:
- Customer engagement data: This can include such data as the frequency of usage, the time spent on the sites of the service, or the number of times when the customer interacted with the business through, for example, the support. If this engagement metric starts to suddenly decrease, it might be a good indicator that the customer is no longer interested in the service, or is dissatisfied with something.
- Customer journey data: If you look at customer behavioral data, you can gain great insight into a customer’s journey. With that, you can detect different patterns and trends that lead to churn throughout a journey. These can include such data as customer browser behavior, abandonment cart data, or purchase history. Once you identify what went wrong at these points, you can make adjustments to avoid them.
- Customer satisfaction rates: Measuring customer satisfaction is also a great way to predict and prevent churn in the future. Look at things like Net Promoter Score or customer feedback. If these indicate a significant decline or negative outcomes, it can lead to customers leaving your business eventually.
- Customer support interactions: You can track the type and frequency of customer support tickets, or see if customers are able to find the solutions to their problems with your self-help options or not. If there is an increased number of dissatisfied customers contacting the support team, you might want to consider making some changes to resolve the problems permanently or improving your self-help options.
- Usage patterns: You must take into consideration every usage pattern that you may find in your service if you want to prevent churn. If a customer or a group of customers suddenly stops using a service or feature, this might mean that the said feature doesn’t align with the initial expectations of the customers when starting to use the service.
Keep a close eye on these metrics, because they can tell a lot about your customers, about the ones that like what you offer and are consistently using it, and also about the ones that lost their interest in it for some reason. Your job is to make adjustments to keep these people and satisfy their needs for as long as you can.
How to Track Customer Behavioral Data
To track these customer behavioral data types, you’ll need comprehensive software that collects all the necessary information for you. These events must be tracked in real time to make sure you can react to them as soon as possible. They can include such data as demographic information, history of transactions, interactions with the business’s website, and customer support tickets.
At SAAS First, you can track events effectively to make sure you decrease your customer churn rate. You can add unlimited types of events based on your requirements and what you consider to be important to track. In addition, you can add an unlimited amount of data to each of these event types, so nothing goes unnoticed and you can ensure an extremely comprehensive report on your customers’ behavioral patterns.
Once you successfully collect data, you can make informed business decisions, conduct adjustments to improve, and retain customers more productively.
Conclusion
In summary, reducing customer churn is essential and can be effectively achieved by analyzing customer behavioral data. Key metrics such as engagement, journey data, satisfaction rates, support interactions, and usage patterns are crucial for identifying and addressing potential issues early on.
Using comprehensive software like SAAS First helps in tracking these metrics in real time, enabling businesses to quickly adapt and improve the customer experience. Ultimately, focusing on churn prevention not only retains customers but also builds a loyal base, driving long-term profitability and success.
