What's your risk process for lower ARR / tech touch / digital led customers?
Expert 
Looking for some feedback on risk process for high volume, low ARR customers!
Currently, our risk process for all customers is:
- Create risk CTA in Gainsight (or any platform that provides your team visibility that this account is at risk)
- Provide weekly update on the risk account:
- Background
- What we've done so far
- Next steps
I'd love to scale this effectively when you have a high volume of customers.
Any thoughts or advice on how to manage the risk process for a lot of customers?
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- Simplify the process. Come up with a V1 and grow from there, if necessary.
- The primary objective of spotting risk customers is probably because they're your closest customer to churning. Reducing churn is probably way up there on your CSM team's OKRs.
- Identify how customers get put into the "At Risk" phase.
- I think it's important CSMs have the ability to manually move their customers. This may not be as scalable but CSMs may often identify non-quantifiable reasons that can't be picked up with data or product usage.
- Automating the move based on health score or even better, % negative change in health score. This, of course, depends how well you've built out your health scores.
- Classify/group the risk customers into categories - these should be directly related to your churn reasons (product fit, competitor, product needs etc.)
- Along with the classification of risk, we then have an "At Risk Matrix" and of course looking at customer LTV is important.
- Matrix (again linked to churn)
-
- Expected & unavoidable
- Unexpected and unavoidable
- Expected & avoidable
-
- I think classification and matrix need to be manually completed by the CSM who owns the account. It isn't difficult and doesn't take long.
- Create risk CTA in Gainsight (or any platform that provides your team visibility that this account is at risk)
- Provide weekly update on the risk account:
- Background
- What we've done so far
- Next steps
Expert
This is spot on, @Jarren Pinchuck!
I would reenforce the notion that tracking this (even if manual at first) is key to understanding why someone is a risk in the first place. This data coupled with the Matrix will help guide where resources and energy should be put to first deal with the at-risk customers in the first place, then create proactive strategies to minimize risk.
I would reenforce the notion that tracking this (even if manual at first) is key to understanding why someone is a risk in the first place. This data coupled with the Matrix will help guide where resources and energy should be put to first deal with the at-risk customers in the first place, then create proactive strategies to minimize risk.
Expert
Hi @Alex Tran,
Risk and dealing with risk at scale is a challenging question and I could probably write a couple of pages on strategy but here are a few thoughts and action items I've used that have worked well at a decent scale.
Example: Anything sitting in "expected and unavoidable" is probably not worth spending time on. And customers of lower value can also fall down the list.
By doing this you can at least remove 25%+ of at-risk cases that aren't worth the time. Then the rest need to be dealt with on a case-by-case basis. Again, this doesn't sound scalable but if you're serious about preventing churn, accounts probably need some personal focus.
My team would then sit together in bi-weekly risk huddles and each CSM would pick their top 5 at-risk customers and explain the when, why, what etc. to the team. This would help with a strategy on how to "fix" any issues and save the customer.
The TL (team leaders) would then compile a monthly "Risk Report" for me to review which is more strategic and helped identify key issues that may keep arising. I could then discuss with product, sales, marketing etc.
That was way longer than it seemed in my head, I hope it made sense. Using our CS platform made it much simpler though once all the processes were set up.
------------------------------
Jarren Pinchuck
Global Head of CS and Operations
------------------------------
-------------------------------------------
Original Message:
Sent: 07-21-2020 03:08
From: Alex Tran
Subject: What's your risk process for lower ARR / tech touch / digital led customers?
Risk and dealing with risk at scale is a challenging question and I could probably write a couple of pages on strategy but here are a few thoughts and action items I've used that have worked well at a decent scale.
Example: Anything sitting in "expected and unavoidable" is probably not worth spending time on. And customers of lower value can also fall down the list.
By doing this you can at least remove 25%+ of at-risk cases that aren't worth the time. Then the rest need to be dealt with on a case-by-case basis. Again, this doesn't sound scalable but if you're serious about preventing churn, accounts probably need some personal focus.
My team would then sit together in bi-weekly risk huddles and each CSM would pick their top 5 at-risk customers and explain the when, why, what etc. to the team. This would help with a strategy on how to "fix" any issues and save the customer.
The TL (team leaders) would then compile a monthly "Risk Report" for me to review which is more strategic and helped identify key issues that may keep arising. I could then discuss with product, sales, marketing etc.
That was way longer than it seemed in my head, I hope it made sense. Using our CS platform made it much simpler though once all the processes were set up.
------------------------------
Jarren Pinchuck
Global Head of CS and Operations
------------------------------
-------------------------------------------
Original Message:
Sent: 07-21-2020 03:08
From: Alex Tran
Subject: What's your risk process for lower ARR / tech touch / digital led customers?
Looking for some feedback on risk process for high volume, low ARR customers!
Currently, our risk process for all customers is:
I'd love to scale this effectively when you have a high volume of customers.
Any thoughts or advice on how to manage the risk process for a lot of customers?
Expert
Following also. @Alex Tran and anyone else who has something in place - what creates that initial risk CTA? If this is a high-volume segment, I assume CSMs aren't proactively updating and analyzing data points that would raise a flag. Is this all based on inbound cancel requests? Or is there data in your CRM that updates by itself that Gainsight flags if it meets risk threshholds?
Contributor 
Hi Alex,
For this specific topic, would you ask the question how to identify these customers are at risk ?
or would it be how to communicate and take action more effectively?
For this specific topic, would you ask the question how to identify these customers are at risk ?
or would it be how to communicate and take action more effectively?
Thought Leader 
Following.... Doing this with a lean team is what I'll need to do as well.
In my experience, with a high volume(10K customers) and low arr to go with them, the bottom line is this, you can't build a risk process.
Risk and measuring risk is a reactive activity. If you are constantly scanning a large customer base trying to build a process to target high risk customers is not fathomable.
My recommendation:
- Understand your attrition rate. What is acceptable to lose? How much are you looking to improve.
- You'll want to get really intimate with your churn data.
- When do customers leave
- Why are they leaving
- What are do your separation survey's tell you?
- When understand your weak points in the customer journey
- begin targeting those points to improve
- set up triggers with data to deliver training, education, or best practice based on what the data is telling you
- Data is your only friend - When dealing with volume and low customer interactions
- map out all of your data points
- determine what it is telling you about your customers
- and build your risk mitigation playbook
- Build your playbook
- with the mindset that you have no one working on the accounts
- remove all human touchpoints and convert to a digital touchpoints
- Measure, iterate, measure, iterate
- there is no quick fix to solving low touch churn at scale
- you have to keep testing, measuring and deploying new
There is no perfect answer to this problem, but we have seen success with this general approach over the last 3 years and continue to push the boundaries on what is possible.