Documenting Customer Cancellations to Drive Actions

Amber Monroe

Hi Everyone, 

I wanted to reach out to see if I could gather some insight from other organizations on how you are capturing customer cancellation data and to better understand if you are using that data to drive actions in your organization. 

At this time the CS department is notified of a customer cancellation, we log a cancellation ticket in our HubSpot CRM system and then a member of the CS department will use a high-touch engagement approach to reach out to the customer to better understand why they are cancelling and ensure that the cancellation terms & conditions are outlined and complete an Exit Interview and at times we have been able to save accounts. We have been capturing data since the beginning of the year and we better understand the volume of cancellations over the last three quarters and primary reasons why those customers are cancelling with us. 

We want to now take this data and what we have learned and begin to start driving actions that will have an impact on preventing some future cancellations. 

I am curious as to how others are managing churn data to drive actions. If this isn't an approach that you focus on, I'd also like to understand what approaches you have used that have proven to be beneficial in your organization. 

Appreciate the help!

Ed Powers

Hi @Amber Monroe--

Kudos for taking the first step, understanding why customers leave and why they stay. You're ready to start making breakthroughs! Here's how:

Let me know if I can help.

Mark Flanagan

Hi Amber. Shouldn't CS know what the circumstances are with the account before they cancel, giving your company an opportunity to remedy the situation?

matmoody

Hi Amber,

Have been working on this very thing for about 6 years. How you optimize this depends a lot on the kinds of data that you're capturing. The nice thing about capturing the data at the time of the cancel is that you get the full "Customer Lifecycle" (they can always be reactivated but that's another topic). You can see when they converted, how much they spent, the duration of the lifecycle, etc. Then if you merge in firmographics and behavior metrics across all of the customers who have cancelled you can start to identify patterns.

Which customers are likely to cancel in the next 90 days, why are they likely to cancel, which actions/"treatments" impact the probability of them renewing... 

This gives you the ability to optimize your touchpoints -- based on probability instead of opinions. If it's of interest I'm happy to put together a few slides and a jupyter notebook showing a few ways to do this.

Will Stevenson

I love this question. So many thoughts pop into my head. Here they are (in no particular order):

• Make sure the churn reasons are standardized and they cover everything from misaligned expectations, to poor onboarding, etc. Don't include a "general" churn reason, as most people will default to that. 
• A lot of times the customer will shy away from saying there was a problem with service, especially if the person that serviced them is calling them. In the past I've used $10 Amazon gift cards as an incentive to respond to a survey and I saw great success with this. 
• I'm a big fan of "see a problem, correct it". Meaning sending these surveys directly to management and taking action right away. If someone feels oversold, dive into that specific customer use case and understand why. From there have a conversation with the salesperson/sales manager and provide feedback. The same can be done with the Onboarding team and the CS team. Sometimes this isn't scalable and that's fine. I'm also a believer that the unscalable work is what really pushes a company ahead. 
• My biggest feedback is try to get ahead of the churn before it's a problem. I've done this through surveys (so for all the survey talk lol). One immediately following the kickoff call to ensure expectations were aligned and one after onboarding is complete, then periodic NPS surveys. Here is a blog I have on that: Survey Blog. Also, product data feeds help with this. If you see customers not hitting KPIs, that's a good early sign of struggle. 

Hope this helps and would love to continue the convo if you're interested. 

Thanks,

Guy Galon

Hi Amber,
I think being "ahead of the curve" is what CS team should do to learn about their customer intentions before they make a decision to churn. Selecting the metrics to measure is indeed a key point  but it may not be straightforward.   
Basics steps that use to work for me:
1. Getting to know the usage pattern of customers who adopt  the product and obtain significant value (via NPS, surveys and during QBRs/meetings)
2. Monitor and the metrics that these customers produce and identify the ones that are indicative of the value to the customers.
3. Look for customers with low usage of these metrics and start the discussion with your customers as early as possible.

When customer churn (and it happens unfortunately)   we verify their usage patterns in attempt to fine tune our internal CS playbooks and metrics monitoring (as product evolves with new features and usage pattern can change accordingly).

Good luck

Joe Spellman

I love this question. Try as we might - sometimes you lose a client and there are always reasons why. I think regardless of the reason - there needs to be an internal willingness to listen to the 'real' reasons. Unless the executive team is receptive to hearing that a client was oversold or the product was deficient or the service was poor - it sort of becomes information stuffed on a slide that gets buried in a deck and gets dragged out during QBRs. 

Assuming the willingness is there I'd suggest an active deep dive into each churn event with the same team that you might gather to support a new sales effort. All the orgs I have ever worked with can darken the sky with resources to land the next new logo but having that same team gather to understand why a customer leaves would be super impactful. The CS team can and should lead these conversations and the outcome should be actionable data on how to do better.  

Good luck with this part of the CS function!

Amber Monroe

This is great @Ed Powers, thanks so much for sharing!

Amber Monroe

@Mark Flanagan absolutely, right!! CS maturity is early at this organization, new CS department, new CS team, new CS processes, but a Churn problem nonetheless. A lot has been put in place to build/maintain relationships early, increase value for the customers and improve their experiences that will most definitely shape the impact on the churn for this organization undoubtedly in the future.

Amber Monroe

@Matt Moody you are speaking my language, this is amazing and it is so great to hear that others are working on this too. The goal here for us is a level of predictability about future churn, right?!? Identify and prevent it from occurring by finding central and common themes to ensure that we are creating greater value during those pivotal parts of the customer journey. 

I would love a few slides on this topic, thanks so much for the offer. I'll never turn down knowledge sharing :-).

matmoody

Yep, exactly. If you can capture the data on why customers are cancelling and what does/doesn't work to retain them during that experience, you can use this data to model out which customers have an increased probability of hitting your cancel flow in the future. I'll put together some materials...