From creating instant emails to summarizing customer accounts in seconds, AI is a dream tool for CSMs. Is it just as powerful for a customer success operations leader? As it turns out, using AI for customer success operations by leveraging its research and analytical capabilities can yield equally impressive results.
ChurnZero’s You Mon Tsang says that while AI’s speed and “wow” factor are impressive, its real value in SaaS comes from applying it to existing workflows to solve ambitious challenges. And, given that solving ambitious challenges is what CS ops is all about, I’ve already found generative, conversational AI to be an invaluable partner.
Industry research continues to reinforce this shift. For example, G2’s AI in churn reduction report highlights how SaaS teams are leveraging AI-driven insights to proactively identify churn risks, personalize engagement, and improve retention outcomes — underscoring why embedding AI into CS operations workflows is becoming mission-critical.
Using AI wisely for operations can enhance your customer experience team, supplement your available resources, and enable you to work smarter. We’re incorporating it into our operations in several ways at ChurnZero, and I’ll share some of them with you over the coming months.
You can apply the strategies and examples I’ll share to almost any operations puzzle, but let’s start with one of the most impactful ways to use AI in CS operations: strategic customer segmentation.
How to use AI as your assistant in designing a customer segmentation strategy.
Are you strategically segmenting your customers, and, if so, is your segmentation strategy working? Whether the concept of segmentation is entirely new, or your old system needs an overhaul, AI can be your best friend as you design this critical customer success strategy.
(If you’re behind the curve, check out this article on the importance of customer segmentation before we dive into how to make it happen.)
1: Use AI to help define your desired segmentation outcomes and scope.
ChurnZero recently went through a significant re-segmentation process. Our prior segmentation focused on company size, but we knew that incorporating other datapoints would help us better serve our customers while driving internal efficiencies.
We needed a segmentation strategy that looked at a blend of characteristics and remained simple, providing segmentation that is stable and allows us to do informed capacity planning for the CS Team. (For more on capacity planning and service models, check out this article on how to help CSMs succeed).
What goals would you focus on for your company and team? Common goals of segmentation include increasing retention, sourcing targeted upsells, improving customer survey responses or boosting specific feature adoption. Here, you can ideate your ideal outcomes with your favorite generative AI tool, talking with it as you would a helpful colleague.
Try these AI prompts as you define the goals of your customer segmentation strategy:
Suggest specific and measurable goals that can be achieved through strategic customer segmentation.
- I want to use customer segmentation at my SaaS company to help me find targeted expansion opportunities in my customer base. How can I measure the success of this initiative?
- I need to raise the NPS score of my SaaS company. What should I consider when building my customer segmentation strategy?
2: Use AI as a data analysis assistant.
Chances are that you already have an idea of what datapoints could be critical in your customer segmentation process. Think holistically about what you need to know to best serve your customer, how to allocate your internal resources, and your specific, desired outcomes.
Once you have defined the data you want to evaluate—which could include datapoints like company size, industry, product usage data, and growth stage—use AI to help you unpack it. Here are some prompts you can use:
- Help me figure out whether company size should be a factor in my customer segmentation strategy to increase retention.
- What data do I need to analyze to decide whether to incorporate product usage data into my segmentation strategy?
- Walk me through finding whether usage of X feature is correlated with renewal outcomes.
- Provide recommended ranges for customer segmentation based on ARR from this data set. (tip: include the data set).
Important note: When using AI for customer success operations challenges like data analysis, take care to protect your customers’ data. When using the final prompt above, for example, I provided only the ARR numbers, with no further data that could link them to customers in any way. For more on protecting customer information and trust, see the second item here.
3: Use AI to ideate on segmentation strategies and tactics.
With your needs defined and data analyzed, it is time to segment your customers and develop targeted strategies specific to their needs and your goals. Again, you can use specific AI prompts to help you in your approach. Try these sample prompts
- I am an operations leader at a SaaS company. Give me three high level strategies that I can use with our largest customers who have high product usage but low adoption of a key feature.
- Suggest two different tactics for each of these segments of customers to improve the response rate on a customer survey sent by a SaaS company.
- 1. Segment 1 – Low ARR + High Product Usage
- 2. Segment 2 – Low ARR + Low Product Usage
- 3. Segment 3 – High ARR + High Product Usage
- 4. Segment 3 – High ARR + Low Product Usage
- I know that customers that perform X behavior are more likely to buy Y feature. How do I encourage more of X behavior?
4: Monitor and adjust.
Establish a cadence of regularly reviewing the performance of your initiatives. AI can talk you through how to implement options for monitoring success. Find these by using prompts such as:
- How do I perform A/B testing with product adoption initiatives?
- How do I analyze qualitative feedback to determine the success of my survey initiatives?
- How do I decide whether X initiative is having an impact on GRR?
5: Apply your AI-enhanced framework to support other key CS operations initiatives.
Customer segmentation is just one example of the initiatives I’ve taken on at ChurnZero where AI has been my partner and consultant. I encourage you to use this framework for customer segmentation and apply it to other topics like:
- Customer feedback analysis
- Churn analysis and prevention
- Optimizing support functions
- Reducing time-to-value in onboarding
In all these examples, you want to start with a clear understanding of your needs and objectives, analyze data, act, and monitor and adjust based on the results you’re seeing. Keep the following best practices in mind as you are chatting with your new AI friend.
Four best practices for using AI in customer success operations.
1: Start broad and refine.
When your operations puzzle seems overwhelming or you are just unsure of where to begin, start broad and allow AI to help you narrow your focus. Focus on prompts that help you name goals and challenges. Refining your vision through collaboration with AI will allow you to craft more meaningful and effective prompts.
2: Be specific with your prompts.
You are always going to know more than your AI bot about the context in which you are acting. Input decides output! Use a prompt with as much information as possible to help AI help you.
I have found it very helpful to specify that I am asking the question in the context of a B2B SaaS business. A prompt like, “Write an email to customers encouraging them to buy more of Product X.” is going to yield a very different result than, “Write a five-sentence email to customers encouraging them to buy more of Product X from the viewpoint of a CSM at a B2B SaaS company.
3: Slow down.
This is especially true in the data analysis phase. We have been conditioned to expect instant results and this can translate into our work. Expect that improving retention, realizing meaningful improvement in customer onboarding, and other worthy initiatives will still take time. Time invested in approaching data analysis with an open-mind and truly understanding what the data is telling you will yield dividends. Test what AI is telling you. Make sure you can replicate and explain the results
4: Apply your own knowledge.
AI does not know the story the way you do. AI can’t contextualize in the same way you can. You are the expert, not AI. You know your business, your customers, the economy and the environment in which you’re operating. Before AI, most research likely would have started with a Google search, looking for relevant information and learning what you can. Treat AI as a tool in your toolbox when researching or learning something new.
Amanda Flurry is Customer Experience Operations Manager at ChurnZero. She specializes in optimizing customer experiences by leveraging AI and data-driven strategies to enhance satisfaction, retention, and operational efficiency. Amanda is passionate about strategic planning, process improvement, and accurate forecasting to drive business outcomes.
