All You Need to Know About Customer Churn Prediction

It’s common knowledge that human beings are unpredictable creatures. The good news is that they often transmit subtle hints or start venting their feelings when they start getting frustrated. Customer success teams simply can’t afford to ignore these human signals anymore because the result is obvious – customer churn. Let’s learn about the ins and outs of churn prediction. 

What is churn prediction?

As the name suggests, churn prediction is a methodology that involves the data-driven pinpointing of customer accounts that are at high risk of downgrading their engagement, canceling their subscription, or disengaging with your company. 

Customer churn prediction is based on multiple parameters:

• Relationship changes – The dynamics within each and every customer account are different. Some may be advocates that have been laid off or transferred. Many points-of-contact may be on maternity leave. Sometimes, companies are acquired and undergo massive restructuring. These changes can happen quite fast and render your CS playbook irrelevant.
  
• Communications and sentiment fluctuations – The devil lies in between the lines. Tracking emails and chats with customers is a good start, but the true sentiment lies in the fine details. Has the communication frequency changed drastically? What words are being repeated constantly? Is there a common theme in all customer accounts? These questions need to be answered.
  
• Product usage trends – Last but not the least, churn prediction also involves the use of product analytics. The way your customers use your application or service can say a lot about their current status. For example, if a customer is no longer using certain features or isn’t logging in as frequently as before, you have serious red flags that need your urgent attention.

Churn prediction is a multi-step approach. Understanding the risk factors and uncovering blind spots is just the beginning. You’ll then need to coordinate your efforts with other departments like support, marketing, and other post-sales personas to really eliminate the issues you have detected. Needless to say, the accounts you have saved should also be tracked and monitored closely. 

Related: The Ins and Outs of Churn Analysis

Main benefits of churn prediction

Implementing an accurate and reliable churn prediction model can help your B2B business achieve sustainable growth. More and more post-sales executives are now turning to this methodology to fine tune their CS playbooks and strategies.

Here are a few benefits of using churn prediction:

• Detect at-risk accounts – By pinpointing accounts that are about to churn in real time, you have time to save them with support and marketing efforts. You also don’t have to wait for subscription renewal periods in a reactive manner.
  
• Improved user experience – The insights you gain from your churn prediction models can also be used to enlighten your product teams. The onboarding experience can be finetuned and optimized with these key insights.
  
• Identify pain points – Having a capable customer churn prediction system in place will also help you understand where the friction points lie. For example, you may discover that certain accounts are avoiding features altogether.
  
• Create growth opportunities – When you have more active long-term customers, they automatically become prime candidates for advocacy and can also be targeted with upselling or cross-selling campaigns. 
  
• Better CS playbooks – A recent Staircase AI survey showed that 62% of post sales executives find their playbook tasks to be partially or fully irrelevant. Churn analysis can help optimize CS playbooks based on current trends. 

Churn prediction: The challenges

While the benefits of having a capable customer churn prediction model in place are quite clear, implementing one is a whole new story. Traditional methods and techniques are becoming more and more outdated as online businesses evolve.

Let’s take a closer look:

1. Stakeholder changes – As mentioned earlier, relationship changes are happening all the time, often without prior notice. Once a champion leaves his or her position, it directly impacts your account. This is a real account risk (and a new opportunity with new personas) that can literally crop up overnight and can be extremely hard to track manually in companies that are scaling up.
   
2. Sentiment trends – Human beings are unpredictable by nature and the same applies to your customers. It’s extremely tricky to quantify and measure sentiment because customers are constantly changing their minds and developing new feelings about your product or service. This is something most businesses are still struggling with in 2023.
   
   The biggest reason for this struggle is that most traditional methods (NPS and CSAT surveys, CSM pulse, etc.) are reactive in nature and also don’t give you the complete picture since you’ll never have 100% of the responses. Furthermore, breaking down the results of these activities and creating actionable insights manually is simply an unrealistic approach.
   
3. Scaling up – Businesses often scale up fast and not in linear fashion. This means that CS executives need to ignore the noise and detect trends that apply to multiple accounts. What people are talking about at scale? What topics are generating friction across the board with a certain group of customers? Gaining these insights manually is extremely challenging.
   

Building a modern churn prediction model

The ideal churn prediction model can vary based upon the industry you belong to and other factors like geolocations, company size, target audience profiles, and more. But the foundation of the modern churn prediction model is the same.

You’ll need to integrate the following components into your model.

• Start off with granular customer segmentation

Customer segmentation is a mandatory prerequisite because you have more and more use cases today. Every segment has different needs and product usage differs from case to case, not to mention the service requirements. Some customers are high-touch and like to be approached daily, while others may prefer the solo route and like to work with online documentation or knowledgebases.

Key parameters you need to consider for this step include – demographics (age, gender, etc.), behavioral data (for example, how many tickets are opened on average?), revenue information, and subscription contract terms.

• Constantly track and monitor product usage patterns

The traditional method was waiting for customer usage to dip to dangerous levels before getting involved, but this approach is no longer useful. You need to set KPI thresholds for product usage and use the right solutions to make sure they are not crossed. Furthermore, your teams should be looking out for trends. For example, if a new feature has not been received well by multiple customers, you’re in trouble.

• Real-time sentiment score tracking

Customer sentiment is a fusion of perception, opinions, and also the overall user experience. This can be related to the onboarding process, unresolved issues, in-app friction points, the performance of the support team, and more. CS executives need to have a system in place to feel the pulse and detect sudden drops in customer sentiment. Failing to do so can result in unexpected churn and brand damage that’s hard to fix. 

• Stakeholder mapping and ongoing monitoring

Stakeholders are eventually at the core of what CS teams do, especially brand advocates and champions. Any change in their position or status has a direct impact on accounts, something that can affect churn rates out-of-the-blue. You need to conduct a comprehensive stakeholder analysis and mapping, after which they must be nurtured and contacted to convert new engagement and growth opportunities.

AI: Taking churn prediction to the next level

As it must be clearly evident by now, predicting churn comes down to continuously feeling the customers’ pulse. But unlike medical tests where the doctor just needs to touch your wrist for 30 seconds, CS executives need to touch multiple points – the product, the service being provided, and support levels, all of which add up to the overall value that your business is creating in the eyes of the customer.

Churn prediction can be done pretty efficiently using traditional methods when businesses are just getting started, but things get complicated at scale. This is when companies have to increase the number of accounts per CSM and cut corners with their support operations. Add in the multiplying use cases with tough customer requirements and you have a situation that can go downhill pretty quickly.

AI is helping B2B businesses keep their service levels high to unlock more NRR. 

AI scans all vendor-customer communications automatically to track relationships and sentiment fluctuations to highlight risks and point at new growth opportunities. Not only are these activities more accurate and data-driven, CS teams have more time on their hands. They can proactively act upon these unbiased insights and put more effort into growing and nurturing accounts in real-time.

You can now scale up your business even with limited post-sales resources without overwhelming your CSMs with manual playbook-related tasks and with no impact on the quality of your service or support. AI is a true gamechanger to customer-facing teams.