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4 Vital Steps To Building A Robust Churn Prediction Model

For recurring revenue enterprises, maximizing customer retention is one of the most critical and challenging business imperatives. While some customer churn is an inevitable reality of doing business in the subscription economy, there are some high-impact actions you can take to ensure your customers are happy and healthy. To succeed in the hyper-competitive technology industry, you need an airtight strategy to proactively get ahead of and prevent customer churn.

In this article, Tony D’Auria, a Valuize Customer Success strategy & operations expert, shares 4 integral steps from Valuize’s ValueXperience Framework that enable the construction of an effective churn prediction model that will empower your organization to accurately predict at-risk customers, proactively prevent churn and maximize Net Dollar Retention.

1. Identify & Gather The Right Customer Data

In order to create an effective churn prediction model, you need to fuel it with the right data. While your ecosystem is chock full of customer data, not all of it is relevant or applicable when it comes to predicting churn. In order to select the most accurate data points for your churn model, start by identifying customers from your base that have already churned, almost churned, renewed and expanded. Within these cohorts, gather your customers’ justification for churn, retention or expansion and collect usage and adoption data for churned, renewed and expanded customers for 6 months prior to the event.

While pulling Customer Success (CS) data is a good starting point, don’t make the costly mistake of looking at this data in isolation. Churn is a multi-facetted problem. Remember, there isn’t a single reason or single point in time that a churned customer decides to leave; most often, there are a multitude of motivations and events that causes the customer to cancel their subscription.

To build a truly predictive model, you need to combine your CS data with customer data from across your organizational ecosystem. Collaborate with your Data Science, Product, Services, Support and other cross-functional teams to develop a holistic view of your customers. Tap into niche data sources from across your organization, such as:

  • Customer health trending data

  • Customer engagement data across the customer lifecycle, including Support, Marketing and interaction with other customer-facing teams

  • Customer sentiment over time

  • Feature requests from customers

  • Attained value milestones or other identifiers that may indicate that your product or service is or is not meeting customer expectations/needs

  • CRM, analytics and tracking systems data

  • Customer feedback from social media and other online sources

Each of your cross-functional teams has unique insight into your customers. By developing a 360-degree view of your customers that integrates all of these unique perspectives, you will build a comprehensive Customer Intelligence model that successfully identifies relevant customer behaviors and clearly indicates the possibility of customer churn, retention or expansion.

Take your churn prediction model one step further by putting your collected customer data into more context. Are there external factors, such as market and financial events or movement within the customer industry, that you should take into account? Factoring in both internal and external data will help you gather the most comprehensive perspective for your churn prediction model.

2. Use Predictive Indicators In Your Churn Model

Churn in and of itself is a lagging indicator - the loss has already happened and the damage has been done. However, this historical data is critical to predicting the behavior of your future customers. Based on the data you’ve gathered from your ecosystem, analyze the relationship between churn and the different variables you believe impact churn. Uncover any common trends or patterns among your customer cohorts by asking the following questions:

  • Are there any consistent behaviors across customers that have churned, renewed and/or expanded?

  • Were there consistent signals leading up to the event?

  • At what stage of the customer journey did these behaviors and signals occur?

  • How did our team respond to these actions and customers? Was their response effective?

Determine whether your selected variables have a positive relationship with churn (i.e. a higher probability that a customer will churn) or a negative relationship (i.e. a higher probability that a customer is less likely to churn). While there are multiple methods you can apply to your prediction model, such as binary classification, a regression analysis or decision trees, this analysis will form the basis of your churn prediction model.

With this foundation in place, you can begin to forecast customer needs, detect impending customer behavior and ensure that you’re engaging with the right customers in the right way at the right time. Your teams will be able to spot behavior patterns of potential churners, segment these at-risk customers, and take appropriate actions to improve their experience and get them back on the path to retention. At the same time, your teams will be able to better allocate the appropriate resources, services and incentives required to get at-risk customers back on track.

The benefits and application of your churn prediction model extend beyond your Customer Success function; your cross-functional teams can also use this data to optimize their customer engagements. For example, if your model indicates that a particular customer is at risk of churning, it may not be the right time for your Sales team to reach out with information about additional services the customer might be interested in. Instead, your Customer Success Managers (CSMs) could reach out to the customer and help them realize value from the product or service that they’ve currently invested in.

3. Test And Iterate On Your Data Model

Your data model should be urgent, actionable and iterative, but don’t make the mistake of waiting for your churn prediction model to be perfect. Instead, focus on directional data and a strategy that allows you to evolve your ecosystem over time.

Once you have a data model that you believe helps you predict churn, run an acid test against customers that have already churned to see if the model actually tells you what you think it should. Based on the results of this test, you may need to revisit your data model and iterate on your approach. While this may take several iterations, testing and monitoring your model’s performance and adjusting its features will greatly improve your model’s accuracy and your ability to precisely predict churn.

4. Operationalize Your Model Through Technology

Bring your churn prediction model to life through technology. Operationalize your model in a system that clearly identifies data patterns and supplies proactive and actionable guidance to your customer-facing teams. Give your organization access to these valuable insights through dashboards and notifications so that they can ensure they are speaking to the right customers with the right messaging and actions at the right time. Ultimately, you want to optimize and automate your teams’ ability to to take action by allowing them to modify scoring models, journey maps, automated campaigns, triggers and playbooks based on the insights and actions generated from your churn prediction model.

Take your model’s actionable insights to the next level by integrating this knowledge with other platforms in your organization’s tech stack, such as your CRM, to create a unified approach to customer engagement.

Leverage The Power Of Predictive Insights

Building an accurate and effective churn prediction model is the answer to fixing the leaky bucket problem faced by all B2B technology companies. By precisely identifying the patterns and behaviors of at-risk customers, you can properly empower your organization with the tools and insights they need to counteract churn and guide your customers on the fast-track to retention and expansion.



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