Do you Measure Churn Rate?

You love your customers. They enable your business to exist. Do you want to keep them and worry which ones might look over your shoulder thinking maybe your competitors are better? Churn can happen suddenly due to any number of potential factors, and customers are unlikely to write you a "Respect ..." letter explaining why it is not about you, but about them.

There are many methods for calculating the probability of churn per customer - perhaps too many. When it comes to just measuring customer retention rates, there are dozens of different metrics that companies use to analyze. What's more, a simple Google search might tell you that the only way to prevent churn is to create your own end-to-end solution.

However, there are ways to predict churn without having to be a high-level programmer. Being able to make data work for you and successfully prevent churn can be the difference between retaining profitable customers and losing them.

What is a customer churn forecasting system?

The churn forecast system uses big data to identify customers who may stop using your service or buy your product regularly. Such systems are used by most large SAAS companies to identify the most at risk of customer churn. If the system is done well, it translates into great business benefits, no matter the size.

The basics of churn forecasting

The customer database is combined with algorithmic machine learning methods that estimate the churn rate of a given user. Various algorithms are compatible with churn forecasting. The machine learning model most associated with this practice is the Decision Tree (ie, “Random Forest”) model, which involves preprocessing data followed by training and evaluation.Companies with their own data science team can create their own solution. Some people prefer this approach given the lack of industry consensus on the best way to predict churn.

Outflow is killing companies; prevention keeps them healthy.

This is one of the most widely spoken truths about doing business based on selling to loyal customers, but it bears repeating: even seemingly low churn rates can stunt a company or kill it altogether. Even a small number, churn 1.0%, churn 2.5%, churn 5.0% are still potentially fatal.

It's safe to say that churn prevention should be a priority.

Prevention starts with forecasting

Stop customers before they leave. When used correctly, churn forecasting can be an important benefit to better understanding your customers' experience with your service (product).While the range of potential drivers of churn can be large, stopping churn is often associated with a personalized approach to improving customer service. A churn forecasting system gives you the opportunity to improve customer experiences before they are gone forever.

Get insight into churn trends

Let's take a situation in which a fairly large group of customers left within a certain period with no apparent explanation: some were new users; some have used your product for a long time. Why did this happen? Some potential causes may include:

- Unsuccessful product change;

- Exponential growth coupled with high churn during the adaptation phase;

- Launching a new competitor that serves the same product at lower prices;

The fact is that all of these factors (some or none of them) could cause churn.Users could leave for different individual reasons or for one interrelated reason. Without the right churn forecasting solution, you won't be able to connect all the dots and determine which churn trends are affecting your business the most.

How it works

There are four basic steps to predicting churn

1. Export and segmentation of the customer database

Churn forecasting is entirely based on using your company's database of your customer. You will need an analysis of your customers to accurately predict how customer churn is affecting your business.Start by exporting all data that could potentially affect the likelihood of a customer leaving. They can include:

- Demographic and behavioral data

- Is this user an individual or is using your product on behalf of their company?

- What is the degree of use of the service (product) in general and what functions are available to them?

- How often does this user submit support tickets?

- Sometimes data on the gender and age of the user is known, they can also play a role

- Income information

- Subscription date - is this user a long-term subscriber or is he new?

- Regular monthly income from that customer is an individual search responsibility, when it comes to applying your churn forecast, you need to target high income and exit risk customers first

- Subscription status

- What is the plan of this customer?

- How much time is left for their plan before the deadline? This is an especially important piece of data that can be used as late payment

Once you have the historical data you need, divide your customers into churn forecasting segments, for example:

- Clients with numerous updates / clients of daily use (low risk of churn)

- Customers who keep in touch regularly (support tickets / calls / update requests (low risk of churn)

- Customers whose volumes of use of the service (product) have decreased over the last period (high risk of churn)

- Customers who signed up but didn't complete their registration (high risk of churn)

- Customers who have never submitted a support ticket / submitted many similar support tickets (high risk of churn)

2. Continue manually or use the forecasting service.

Once you have all the data points, depending on the resources you have in your company, the predictive model you use to predict churn can be either your own (custom solution) or from one of several available forecasting services.As we noted above, custom solutions can be tailored to the amount and type of data you want to analyze, as well as the preferred choice of your Data Scientists when it comes to forecasting. If you need to define complex relationships in your data, then machine learning solutions are for you. If you are looking for answers to other questions, such as predicting churn over a specific period of time, you need to use a survival or risk analysis.

3. Use your databases to see which customers are leaving

Once you've segmented and analyzed your data, you can see which customers are at risk of churn. You can find the following correlations:

- Large numbers of customers leave after registration due to poorly set price levels, resulting in customers choosing the wrong plan for their needs;

- Low communication, leading late credit card payments as these veteran customers move away from the product;

- Frequent spikes in churn after product updates due to poor visibility / instructional resources;

4. Save the customer!

Once you are confident that you are managing the most churn-prone segments of your customers, you can begin to analyze which aspect of their relationship with your product is at risk of churn. Let's repeat the examples above and see how we could improve the situation:

- A large number of customers leaving after registration

Make sure your pricing page is very clear about what's on offer and what types of customers can benefit the most. Stay tuned to customers during check-in to make sure any urgent issues are resolved.

- Long-term customer churn

Don't take the safety of veteran customers for granted. Consider incentives specifically designed for them (discounts, special upgrade deals, loyalty bonuses) to make sure their eyes don't wander over your competitors. Maintain a stable level of communication, especially on the eve of the expiration of the subscription.

- Frequent spikes in churn after a product update

Ensure that product updates are visible to customers in advance and that changes in functionality are obvious. Include resources to help users manage new features or improve old ones.

Churn prevention doesn't have to be difficult

Predicting churn can be a battle in itself, so you probably don't want to hear that this is really only half the battle. Once you've accurately predicted your churn rate, the next step is to do something that positively affects those customers who might leave.

Keep in touch

There are a number of reasons why a customer might leave. One very simple, very common reason: they are not being helped to benefit from your service (product). In these cases, communicating with the client directly may be the best way to transform them from an at-risk care into a satisfied one.

Stimulate

Promotions, discounts, loyalty programs: they all help customers feel valuable and desirable. This will likely help convince them to stay, especially if your market competition is strong and you risk being undermined financially or while working on feature updates.

Analyze outflow

Even the best strategy is not reliable. Customers will continue to leave (although hopefully in significantly smaller numbers) regardless of the success of your forecast. Don't bury your head in the sand, look at these numbers and find out what went wrong with these clients.You can even reach out to customers who have left a review. They may not be willing to provide it, but any kind of feedback of this kind you may get is extremely valuable.

Conclusion

Companies often use a passive approach when it comes to churn - setting prices as low as possible, targeting the highest possible customer growth per month - all but a careful assessment of the factors affecting your churn and making sure that data works for itself. Ultra-aggressive selling methods can be effective in the short term, but you are treating the symptoms, not the disease.

A smart approach to predicting churn through integration of your own solution can enable you to understand and respond to the reasons why a user might leave. The user's success with your service (product) may depend on the most insignificant conditions. Using a predictive solution to handle your churn will give you clarity on each of them, and ultimately help you overcome churn.