Many organisations possess the necessary data to comprehend customer attrition; however, they often lack the tools to sift through and interpret these insights for their customer success teams. Here are seven essential steps to utilise AI-driven data exploration to rapidly identify customer churn factors.
Select the churn indicator: Choose a representative value indicating customer churns, such as contract status or a defined time since the last purchase.
Drivers’ analysis: Analyse all data points to rank the impact of each attribute on churn outcomes.
Refined analysis: Remove fixed attributes and re-run the drivers’ analysis to identify deeper insights often missed through traditional analysis.
Unbiased understanding: An unbiased understanding of churn causes enables informed strategies to counter attrition.
Network graph analysis: Generate a network graph to reveal customer communities and relationships, visualising commonalities.
AI-generated customer profiles: Examine AI-generated profiles of identified communities to understand high-risk customer personas.
Proactive strategies: Proactively address at-risk clients with tailored strategies informed by AI-generated insights.
Key indicators for AI-driven churn prediction
Customer information is captured in several places, and the more you feed into your analysis, the more accurate a picture you can paint. You will want the basics, like order history, account rep, and account rep history (churn could result from a high turnover of account owners). But you can find information in your customer support channels, like open case history, time since the last case, or how long their cases were open and even customer comments (you can analyse text alongside other values if you process it first with NLP). It’s a good idea to sit down with your customer success team and ask them what customer data is out there. There may even be customer data in third-party platforms that you could access. Once you have all that, artificial intelligence can uncover hidden connections and relationships in your data that are often difficult to spot with traditional analysis.
Leveraging AI for accurate insights
This foundational AI-powered exploration tells us what data points matter for predicting churn. You could build a model predicting which customers will churn next based on your results. Or you could simply leverage these profiles, creating a regular report that looks for customers who match the churned customers’ profiles.
Seeing beyond hunches and limited analysis at the front end
Traditional methods for figuring out the causes or what to do about churn have made it hard to reach firm conclusions. For example, surveys completed by customers from a non-representative customer sample often limit ditch. At best, an organisation ends up with a best-guess hunch and starts down that path.
Additionally, traditional analysis of customer data with today’s BI tools is also challenging because they don’t enable analysts to assess many drivers at once. You are forced to look at attributes one by one. You may miss something when finding patterns across dozens of possible variables is needed.
Data scientists can code some of these exploration techniques but often have limited availability. If they do find something, it’s not visualised in a way that the business stakeholders can consume, understand, and provide domain expertise on.
Giving analyst access to out-of-the-box AI exploration techniques allows organisations to get a more in-depth understanding of their customers that’s grounded within the business, where domain experts can help put findings into context and make them actionable. There’s no need to wait on scarce data science resources or rely on surface-level BI analyses.
Effective intervention with AI-powered insights
When the business has a clear picture of the factors behind customer churn, it can craft targeted responses that are more likely to succeed. It’s the difference between trying something that might address the root cause and something that’s been specifically chosen because it addresses the root cause.
For example, suppose the results of your AI-powered exploration show that customers who fail to complete onboarding training within the first six months are most likely to churn. In that case, your customer success team can create strategies that encourage early training, follow up with customers who don’t complete it, and even look at ways to make training more appealing or accessible.
Using AI-driven data exploration to identify and reduce churn
Your strategies will depend on what you learn, and that’s the value of doing the exploration. You could guess at strategies to implement some generic ‘best practice’ customer retention methods, but it’s only by exploring what’s going on with your customers that you can zero in on the actual cause and devise a way to address it.
We use AI algorithms for data exploration to help us find which customer data attributes, or a combination of attributes, have predictive value. The Smart Mapping (drivers’) algorithms can process all of the attributes and create a ranking of their importance on the chosen behaviour so you can see which values correlate most strongly with churn. When the drivers are mapped on a 3D plot, it becomes obvious when a combination of factors drives churn.
The Network Extractor analysis provides another perspective on churn. This algorithm uses every data in the dataset to plot each customer—current or churned—based on how similar they are and groups them into a dozen communities. From there, the analyst can see which community has the highest churn and leverage the community definition.
Getting smarter about churn
When you can anticipate when a customer will churn and the reasons behind it, you can proactively intervene; whether changing your customer experience prevents anyone from getting to the point where they start to resemble a high-churn customer or flagging at-risk customers early for the customer success team to take action.
Getting a better understanding of the characteristics of a high-churn customer and the drivers behind churn means that you know where to direct your strategies. You can focus on the real problem, create real solutions, and intervene early with those solutions. Having a data baseline will help you measure the results of your efforts so you can see what works and what doesn’t.
To go back to our example of churn resulting from a lack of training, if you create a strategy to increase training, you can monitor enrollments to see if it’s working. If enrollment doesn’t go up, you know to try something else.