While a whomping ninety-six percent of customer churn is theoretically avoidable1, we know that companies’ churn rates are much higher than four percent because preventing churn is really complicated. Knowing your customers at an intimate level, selling the products they most want at the right price, providing consistently excellent service, and tailoring every customer interaction according to individuals’ preferences are the best tactics to avoid churn. While these strategies are obvious and seemingly simple, we all know it’s next to impossible to do so with 100% consistency at scale.
Therefore, companies accept that a certain level of customer churn is inevitable; however, understanding that churn impacts profits, most companies do everything they can prevent it. While some consider preventing churn to be a whole company responsibility, too many view it as a marketing responsibility. In either event, leveraging internal customer data to help predict and prevent churn is a mainstream capability. For example, companies measure recency of purchases, frequency of purchases, and spend amounts to identify any undesirable trends (i.e., RFM Models) so they can act to reverse them.
Most often, when companies detect that customers are at risk of defecting, they proactively market to them. This might come through an email proclaiming how important the customers are or providing a compelling incentive to make a new purchase. However, these tactical, reactive efforts often don’t address the ultimate source of why a customer’s purchase behaviors changed (e.g., poor shopping experience at a store, turned-off by an incompetent customer service associate, bought a cheaper competitor product, etc.).
Getting to the root cause of the real issues is key to slowing the revolving door of customer churn that is holding back a profitable growth agenda.
Unlocking root source issues behind churn is the real key.
While targeted marketing efforts may generate incremental, one-off purchases with dissatisfied customers, winning back their hearts and minds for the long-term is unlikely – unless root issues can be addressed in the product itself or in the service they experience. One-off marketing victories of a click or a conversion are a shallow false positive that generate a low-margin sale and muddy the data. These reactive efforts are generally too little and too late.
To move beyond the reactive to attempt to counter this, many companies are applying advanced analytics to predict when customers may be at risk using a broad internal data set. In addition to historical purchase data, web and mobile traffic, marketing and customer service data can be leveraged to create a 360-degree view of the customer. These efforts primarily focus on personalizing marketing offers, with customers at risk of attrition being one of the campaign types.
A broader view of the customer and events that may be chipping away at loyalty enables companies to be more proactive and authentic by triggering outreach or enabling real-time service prompts with highly-tailored messages based on where the customer is on the continuum of satisfaction. For example, if a customer who is part of the ‘best customer’ segment experiences a 10-minute hold time when calling the contact center, had an order canceled due to an over-promise issue, and received a concession due to a defective product, a large language model and predictive analytics can quantify the risk of attrition and recommend the next best action to prevent further service or product failures – increasing the likelihood of retaining the customer.
Even companies that have this level of data and AI sophistication tend to address the situation in silos—more specifically, this data is most commonly used by sales and marketing to fuel a customer retention campaign. But that’s not always sufficient with today’s highly-sophisticated consumer. A marketing campaign or personalized outreach by a salesperson isn’t going to solve the underlying root problem in supply chain, contact center staffing, product assortment, product pricing, or product quality.
Detecting churn signals early will drive customer value creation.
Cutting-edge emerging technologies, especially gen AI and large language models, have the potential to strengthen the signal of customer churn and detect it earlier. The future of customer retention will use internal and external data to understand market dynamics, competitor shifts, and customer sentiments.
Gen AI offers capabilities beyond traditional predictive models by analyzing vast amounts of unstructured data, such as customer reviews, social media posts, and support interactions, to identify early signs of dissatisfaction that conventional models might miss. While traditional models might flag a drop in purchase frequency, gen AI can detect nuanced sentiment changes in customer feedback, revealing early dissatisfaction even if purchasing patterns remain stable. Finally, gen AI can simulate various intervention scenarios to predict the most effective retention strategies, providing tailored recommendations and addressing churn risks proactively with more effectiveness.
Using a broader data set with applied customer churn modelling can enable companies to identify with conviction which customers need what type of attention and when. It can enable one to take action before customers start looking elsewhere to solve their needs.
An added benefit is that the data foundation that is created to solve customer churn is multi-purpose. In addition to predicting and preventing churn, organizations will be able to identify what actions to take to drive growth with the existing customer base, what the profile is for the ideal future customer, and where to reach them.
3 strategies to apply generative AI to drastically elevate customer retention.
Traditional efforts to use data to solve customer churn are limited by their reliance on history and predefined patterns. There are often too many problems to fix with too little budget, not to mention organizational barriers that prevent key insights from reaching the right stakeholders. Gen AI, however, brings several advancements to churn analysis that have the potential to be game-changing.
- Contextual Understanding – Incorporating a broader range of internal and external, structured and unstructured contextual data can enhance churn predictions. This multi-faceted approach provides a richer understanding of the factors influencing customer behavior. For example, a company might discover that certain weather conditions correlate with higher demand for a product and, with it, stronger customer dissatisfaction if the product is out of stock, suggesting proactive overstocking is warranted in these conditions, not only to avoid lost sales, but also to preserve customer loyalty.
- Personalized Retention Strategies – Generating insights into the specific reasons behind customer churn can enable the development of personalized retention strategies that address individual customer needs and concerns. For instance, a company could use gen AI to analyze customer reviews and feedback to identify common pain points and then tailor retention offers, recommend specialized training topics for customer service associates, or suggest improvement actions related to product design, pricing, or operations.
- Enhanced Predictive Accuracy – By integrating complex data sources and learning from them in a more sophisticated manner, predictive accuracy will be enhanced. Combining a broad range of data such as transaction logs, browsing behavior, social media activity, and customer service interactions, will lead to more accurate churn predictions. For example, a company could accumulate both internal and external indicators of churn risk to create a strong signal of predicted churn earlier and with more precision, enabling them to design more targeted interventions.
For more foundational information about how to unlock the value of AI, check out our article: “A pragmatic 3-step approach to drive AI value creation.”
Now is the moment to level-up your customer retention capabilities.
While the data and surrounding technology won’t overcome the organizational hurdles, there is some soft benefit in riding the tide of the AI buzz to get the attention of executives and lead the organization to make meaningful changes. Retaining customers is the ideal AI use case since it’s ubiquitously understood that the customer is at the heart of a company’s success or failure. The time is now to up-level your customer retention capabilities.
For more insights on how to leverage data, analytics, and AI in your business, contact a Cuesta Partners’ expert today.
Source: 1-Customer Thermometer
