How predictive analytics powers proactive interventions before customers churn

What if the most valuable customer interactions are the ones that never happen?

The future of customer experience isn’t reactive — it’s predictive. While most customer service teams still rely on customers to reach out for support and to subsequently complete post-interaction surveys, a growing number of brands use predictive analytics to anticipate customer needs, identify churn risks and enable proactive care. Forrester identifies predictive analytics for proactive CX interventions as a key experimental area in their Budget Planning Guide 2026: Customer Experience.

The challenge is that building predictive capabilities requires integrated data, cross-functional alignment and analytics expertise — operational requirements that many organizations struggle to build out in-house. A survey from TELUS Digital, in collaboration with Statista, found that 36% of enterprise leaders saw data compliance as their biggest internal challenge, whereas 31% cited combatting silos, 29% cited unstructured or incomplete customer data and 23% cited lack of internal expertise.

This article explores why predictive analytics in customer experience matters now, how churn prevention works in practice and what’s required to run an effective program.

Forrester: Budget Planning Guide 2026: Customer Experience

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Why satisfied customers leave (and what predictive analytics can reveal)

High customer satisfaction scores don’t necessarily lead to loyalty. Customers benchmark brands against the best experiences they’ve had — a phenomenon referred to as “liquid expectations” — and even satisfied customers will move on if brands don’t make their lives easier. Predictive analytics addresses this by monitoring behavioral signals across channels to identify when customer effort is increasing, flagging friction points before customers vocalize frustration or decide to leave.

Brian Breslin, vice president, fintech and SaaS at TELUS Digital, speaking on the TELUS Digital podcast Questions for now, emphasized that customer effort is the largest predictor of disloyalty. “How hard does a customer have to work to get their problem solved or to make a purchase or to find information?” Breslin asked. “The brands that relentlessly focus on reducing this effort tend to win.”

Digital-native customers have raised the bar higher in expecting omnichannel experiences that allow them to interact with brands in the channel of their choice. These customers look for immediate solutions and seamless service at every interaction. Meeting these expectations and fostering lasting loyalty requires more than reactive problem-solving; it requires anticipating needs before customers articulate them.

Risk of churn is particularly high during the early stages of the customer relationship. Keynote speaker and author Joey Coleman, explained on the same podcast episode: “To a brand new customer, they have no idea what it’s like to do business with you. They have no idea about your cadence of communication. They have no idea when to expect deliveries. They have no idea how to do set-up. They have no idea how to get up and running. They have no idea who to call if something goes wrong.” And while you might expect a new customer to be invested in your offering (they did choose you, after all), these customers have not yet formed a strong emotional connection with your brand. As a result, buyer’s remorse can set in quickly, creating a high risk of churn.

This is precisely what predictive analytics reveals: the behavioral patterns during onboarding that precede churn. By analyzing usage data, support interactions and engagement levels during those critical first 30-60-90 days, predictive models can identify which customers are at risk and trigger proactive interventions.

The business opportunity is significant for those who minimize effort and anticipate customer needs. Research from Motista reveals that customers who form emotional connections with brands demonstrate a 306% higher lifetime value over satisfied customers who hold no emotional connection. Meanwhile, McKinsey reports that AI-powered predictive customer experiences can enhance customer satisfaction by 15-20%, increase revenue by 5-8% and reduce the cost to serve by 20-30%. Breslin noted that the brands that are succeeding in retention are making customers’ lives “most seamlessly automated.”

How predictive analytics enable proactive CX interventions

In customer experience today, predictive analytics use artificial intelligence to monitor behavioral signals, contextual patterns and interaction history to enable brands to make proactive interventions. While traditional CX analytics looks backward, measuring what already happened through post-interaction surveys and historical performance data, predictive analytics looks forward.

The technology works by integrating data from multiple sources to create a comprehensive view of customer behavior. Predictive analytics platforms pull from disparate data sources to reveal different signals that, when combined, can predict churn risk and identify intervention opportunities. Typically, predictive systems capture data from:

• Omnichannel feedback including social media mentions, website behavior, email engagement, chatbot conversations and support tickets to detect sentiment shifts.
• Operational data including service disruptions, product problems and delivery delays that correlate with churn.
• Financial records including payment pattern changes, subscription downgrades and billing anomalies that often precede cancellation decisions.
• Behavioral signals including usage patterns, engagement drops and feature adoption rates to spot customers who are disengaging from the product or service.

When these data sources are meshed together, patterns emerge that would be invisible in isolation — revealing not just what customers are doing, but what they’re likely to do next.

Examples of predictive models in action

This isn’t just hypothetical. Leading brands are using predictive analytics to make real-world interventions that prevent churn and build loyalty. Consider the following examples:

• Amazon has pioneered the concept of “anticipatory shipping,” which leverages AI to identify what products are likely to be purchased in the near future, in which locations and by whom. The company then uses these insights to proactively move relevant products to targeted warehouses as a way to reduce the time it may take for your next shipment to be fulfilled.
• Delta Air Lines demonstrates how predictive models transform frustration into trust. Instead of waiting for travelers to discover flight delays, Delta’s systems identify disruptions early, proactively reroute baggage and notify customers with solutions — often before travelers realize there’s a problem.
• A SaaS company supported by TELUS Digital detects when customers struggle with certain product or platform features and automatically sends short tutorial videos showing exactly how to complete the task, plus a link to book a brief call with an expert. Breslin, describing this on the TELUS Digital podcast referenced earlier, notes that this is a way to turn a “moment of frustration into a moment of delight and empowerment.”
• Etsy and Shopify have partnered with OpenAI to use agentic AI to fundamentally change how purchases happen. Together, they have launched a feature called “Instant Checkout” right within the ChatGPT interface used by 800 million weekly active users, per TechCrunch reporting. The feature analyzes usage patterns and conversation history to make personalized product recommendations. Users can then buy agent-recommended products without leaving their chat, reducing clicks and friction.

In each case, the brand moved from reactive to proactive — using data to anticipate needs and pain points to optimize customer experiences.

How predictive capabilities work in practice

Predictive CX transforms data into timely action through connected processes: analytical models identify patterns, detection systems flag risks and intervention technologies enable proactive responses.

• Churn models analyze patterns like decreased engagement, support ticket frequency or usage drops that historically precede cancellation, identifying customers at risk of leaving.
• Propensity models predict which actions customers are most likely to perform next, enabling brands to surface relevant sales offers or content at optimal moments.
• Forecasting models anticipate needs based on past purchases and behaviors, allowing proactive outreach before customers realize they need something.
• Anomaly detection systems flag unusual account activity, billing issues or service disruptions that require immediate intervention, catching problems before they escalate.
• Natural language processing and sentiment analysis identify customer mood and intent in real time, routing emotionally charged interactions to specialized agents for empathetic responses.
• Agent augmentation tools like AI-powered agent assist and speech enhancement technology surface key insights to customer-facing team members at critical moments, helping them find the right information quickly while ensuring clear communication.
• Autonomous agents handle routine service interactions, troubleshooting issues and detecting anomalies without human intervention, freeing human agents to focus on complex, high-value interactions that require empathy and nuanced judgment.

But having these capabilities is only part of the equation. Making them work requires something many organizations lack.

The operational challenge

Predictive CX requires data infrastructure to integrate information from across the organization, cross-functional alignment to act on insights and specialized analytics expertise to build and tune models. In the aforementioned Forrester report, the analyst firm notes, “While this shift eventually requires investments in data infrastructure and analytics capabilities, you can start by identifying obvious scenarios where CX impacts financial performance.” Many organizations lack the full set of these capabilities in-house — which is why strategic partnerships have become critical to accelerating results.

Start small, prove value, scale with strategic partnership

Forrester’s framework for getting started is clear: Identify obvious scenarios where CX impacts financial performance, connect operational drivers to frequent complaints and then determine how preventing those complaints changes customer behavior over time. As they state in the budget guide, “These connected insights can help you identify opportunities for immediate action and lay the foundation for predictive analytics.”

Begin with obvious patterns to build momentum

One way to get started is to identify scenarios where operational problems consistently precede customer churn.

For example:

• If late deliveries appear in complaints filed weeks before cancellations, create an early warning system that flags delays and triggers proactive communication with affected customers.
• If billing errors correlate with cancellations, implement anomaly detection that identifies discrepancies and rectifies them before invoices reach customers.
• If customers who don't adopt a specific feature within 30 days show higher churn rates, build automated interventions that guide them through how the feature works and why it's valuable.

Initial use cases like these can generate the baseline data needed to build more sophisticated predictive models over time.

Beyond these operational fixes, several other applications of predictive CX deliver results without requiring enterprise-wide data integration. Call summarization and sentiment analysis can be used to identify recurring issues and emotional patterns at scale. When a customer abandons a cart, struggles with a feature or submits multiple support tickets, automated systems can be used to trigger personalized outreach (like targeted emails, tutorial content or priority routing to specialized agents). Autonomous agents are increasingly deployed to manage service troubleshooting and account management while becoming more sophisticated at predicting which interventions will be most effective for specific scenarios.

The key is starting small with one high-impact use case, proving value through measurable churn reduction or Customer Effort Score improvement, and then scaling the approach across other parts of the customer journey.

Why brands seek out partners to act on predictive CX opportunities

Strategic partners can help companies accelerate this process by bringing specialized expertise that compresses the timeline from experimentation to results.

Our work with TELUS Communications, a global communication and technology leader, demonstrates this in practice. Together, our teams developed the Customer Network Experience Score (CNES), an AI-powered predictive framework that processes network data, customer feedback and usage patterns to identify service issues before customers experience them. The model generates hourly predictions for every wireless customer, enabling engineers to resolve problems proactively. CNES also proves highly effective at predicting churn — customers with low scores were 34% more likely to leave, facilitating targeted retention strategies that improve loyalty. This work illustrates one of TELUS Digital’s unique advantages: via our parent company, TELUS, we have the opportunity to battle-test customer experience innovations in a highly competitive, regulated environment before bringing those proven strategies to clients.

Partners, like TELUS Digital, who have implemented predictive CX programs across multiple brands and industries bring hard-won knowledge about what works, what doesn’t and how to avoid common pitfalls. This experience means organizations can skip the trial-and-error phase and move directly to approaches proven to deliver results.

Beyond expertise, the right partner provides access to existing technology infrastructure, technical capabilities and an ecosystem of technology providers without the need to evaluate, purchase and integrate systems independently. Organizations benefit from established relationships with best-in-class providers, technical teams who understand how to deploy predictive analytics at scale and operational experience turning customer data into interventions that actually prevent churn.

Enable predictive CX capabilities with TELUS Digital

Predictive analytics represents a fundamental shift in how brands approach customer experience. With CX leaders increasing investment and Forrester identifying predictive analytics as a key experimental area for 2026, the organizations that enable these capabilities now will separate themselves from competitors in the years to come. The question isn’t whether predictive CX will reshape customer retention — it’s whether your organization can move fast enough to capitalize on the opportunity before customers move on to brands that already have.

Contact us today to get in touch with relevant experts.