For many companies the reality of inevitable churn is setting in as the economic downturn is hitting hard. While it’s too late to recession proof your company, as that horse has already left the barn, it is not too late to take action on churn mitigation.
At this stage every leader should be asking themselves:
“What can we as a company do to mitigate as much churn as possible in an economically responsible way?”
Take note of the last part of this statement: ‘an economically responsible way’. This is key. Churn risk mitigation in a down economy is not about ‘just try and save as many clients as possible’. It’s about being methodical and making some data driven, yet difficult decisions, on who you will focus on saving, and who you won’t.
There are four key steps you can take to do a churn analysis that will help to inform a churn mitigation strategy. These steps are:
- Understand the Drivers of Churn
- Conduct a Customer Segment Analysis
- Churn Prediction Analysis and
- Churn Triage
Let’s look at these in more detail.
Drivers of Churn
The first step is to identify the drivers of churn. By looking at historical and statistical data on why your clients have churned you can start to identify trends. This matters for churn mitigation as the historical trends will help to identify future potential churn as well.
Some common drivers of churn include:
- Lack of product market fit
- Customer service is not meeting client expectations
- Usability and Reliability issues with your product
- Ideal Customer Profile and target market is not well understood
- You have too many ‘bad fit customers (which is related to the above)
- Pricing is too high based on real or perceived value
- Seasonality with your customer base
- Missing product features
- Onboarding was never completed so clients did not adopt your product or service
- Competitors are taking your customers
Now some of you may be saying at this point ‘where is the lack of product usage in this list?’. Poor product usage and adoption certainly impacts churn, but it’s not the driver. Something drove the client to not adopt your product or service. The key is to identify that driver - the ‘why’ behind the low or no adoption. Adoption is also a leading indicator and not a lagging indicator of churn, which we will get into a little further into this post.
To put this into context let’s look at a couple of examples.
If you do not have a well defined target market you may have a broad range of customers across different industries, geographies and size. By looking at historical churn you can identify what types of customers churn at a higher rate and what customer types have a higher retention rate. Now you have a data point that starts to help you predict future churn and triage your customer base.
You may also identify that customers who churned had experienced product issues and that clients with a certain threshold of support tickets and those that experienced outages churned at a higher rate. With this data you can then look at what customers fall into a ‘bad fit’ category and what clients have product market fit and are a good fit from a target market perspective. These clients have the highest probability of not only being saved but of being successful in the long term. This data point now helps you to hone in your churn mitigation strategy.
Customer Segment Analysis
A key part of churn analysis and enabling your organization to predict churn is to understand your customer segments and cohorts. When you can group customers based on certain characteristics you can then identify churn and retention trends within these groups.
You may even have started to identify customer segmentations during your churn driver analysis. Common segmentations includes:
- Company size
- Use cases and Product used
- Customer tenure
First, analyze and identify historical trends of your customer segments. This will give you lagging indicators of not only churn but of expansion growth within each customer segment.
Next, examine the performance of each segment currently and identify trends that indicate poor health and could help predict churn.
Predicting Churn Risk
The next piece of putting together the churn mitigation puzzle is identifying leading indicators of churn that can help predict future customer churn. Common leading indicators of churn include:
- Low product adoption
- Client becoming unresponsive
- Champion leaving (and no other relationships)
- Detractors that are very influential or the decision maker
- Increase is customer support tickets and complaints
- Low customer satisfaction scores
- Stopped paying invoices
- Client communicated they may not renew and are unhappy
- Never completed onboarding
- Large layoffs occurring (which is really critical for license based products)
When these factors present themselves it’s an indication that the client is at risk of churning. If there is a combination of these factors then the churn risk increases.
Having the mechanisms in place to identify these factors early on allows teams time to determine how to approach the churn risk, gives time to save the client and allows the business to forecast potential lost revenue via customers churning or downgrading from their current usage.
Once we have this historical and predictive data it’s time to make decisions on resourcing and what action to take. Given the economic circumstances many companies find themselves in, it is also time to be ruthless in your decision making on where you are going to focus your resources on saving specific clients.
You want your churn triage to be more like a well trained border collie herding sheep as opposed to a cowboy trying to herd cats.
There are three fundamental factors to consider:
First, what clients have a negative economic impact on the business?
It’s important to understand the economic impact that certain types of customers have on your business. For example, bad fit clients can utilize a significant amount of support and customer success resources because they are constantly complaining that the product doesn’t function the way they need it to. As a result, the cost to serve this client exceeds the revenue earned. If there are bad fit clients that the product will never be designed and suitable for it’s best to not waste precious resources here.
Consider the Customer Lifetime Value. If their cost of customer acquisition and the cost to serve is higher than the CLV then the client will drive more losses than gains.
It’s also important to consider the reputational risk of working with bad fit clients and how their bad word of mouth will negatively impact new leads.
Second, and conversely to the above, determine what clients are a great fit and have a high probability of success and even potential growth if they were saved.
Your customer segmentation analysis should help with this as well. The customer segments that show positive retention trends could be indicators of the types of clients that should be prioritized when applying resources to prevent churn.
Stack rank your clients and allocate resources to save them accordingly.
Third, identify which clients are realistic to save and what clients are too far down the churn path that any efforts to save them would be fruitless. As hard of a pill as it is to swallow, it’s better to identify which clients you need to cut your losses on and then allocate resources to those who can truly be turned around and saved.
Following these steps will give you your best chance at not only mitigating as much churn as possible but mitigating the churn that will have the most detrimental impact on your business if lost.
Your organization will benefit from utilizing both leading and lagging indicators allowing you to create a more informed churn mitigation strategy. The insights from the past can be applied to existing churn risks. Leading indicators will highlight where your biggest churn risks are now.
This will enable you to make data driven decisions on how and where to apply your resources. Your churn risk mitigation will be methodically approached in a way that will have the most beneficial impact on your business.