AI Can Predict the Future—But Can It Save Your Customers from Churning?

The Gist

• Predictive power improves engagement. Top brands use AI in marketing to anticipate needs, prevent churn and personalize customer interactions.

• Deeper insights build trust. AI sentiment analysis helps brands craft relevant, timely messages that strengthen customer relationships.

• Humans and AI work better together. Leading companies blend AI tools with human oversight to create more consistent, scalable customer experiences.

Successfully using AI in marketing goes beyond just giving your teams access to a few platforms. If your marketing and CX teams use AI sporadically without a clear strategy, it’s no surprise the promised benefits still feel out of reach.

What do we know about how AI’s adoption has affected brands’ ability to perform? After all, chasing technical dominance in an area with little return is a risk even the most successful brands can’t afford in today’s constantly shifting environment.

For companies that made bigger and more strategic investments in AI adoption early on, the news is good. A recent survey of 2,300 global marketing executives shows that top-performing brands are more likely to be using several types of artificial intelligence, including predictive analytics and generative AI.

Let’s take a look at how leading brands are utilizing artificial intelligence to improve customer engagement, enhance trust and create experiences that fuel growth.

Table of Contents

How Anticipating Needs Drives Customer Engagement

Generative AI tools like ChatGPT or Claude appear to get most of the attention in terms of AI-hype these days. But another type of artificial intelligence, predictive analytics, is proving effective in building customer engagement and preventing avoidable churn.

According to the survey above, top performing companies are 30% more likely to employ predictive analytics to identify customers likely to churn. This gives brands the opportunity to make important course corrections by optimizing key touchpoints and intervening before it’s too late.

Churn signals can take different forms, such as reduced site visits, lower app usage, declining email open rates and reduced purchase frequency. Predictive AI models can help detect these patterns. For example, a subscription service may send a personalized, in-app incentive to users likely to abandon their subscription in the next 30 days. With the rich customer data available, this message can be tailored to resonate with the user and remind them of the benefits they’ve enjoyed.

Building Trust Through Deeper Customer Insights

Brands leading the way in AI are analyzing data and conversations to better understand sentiment and customer insights. According to the survey, nearly 40% of the top performers use AI tools for sentiment analysis and augment their human insights teams with AI tools to analyze data more deeply.

This deeper understanding helps marketers balance hyper-personalization with communication that builds brand trust through relevance, consistency and timeliness. Without a deeper understanding, the wrong signals can translate into well-intentioned but poorly timed or irrelevant communication, which will ultimately backfire and erode trust. After all, customers are willing to share information when they feel it will be used to create a better experience, but they can quickly change their mind if the messages they receive feel ill-conceived or creepy.

AI Needs Context: Why Human Oversight Still Matters in Marketing

Of course, the more customer information and behavioral data AI can access, the more accurate it can be. For example, let’s say a CRM team doesn’t have access to recent customer support interactions. They may be missing some key details, such as that the customer recently had an unhelpful series of interactions to attempt to solve an issue with their subscription. In this case, an automated message aimed at preventing churn may fall flat or worsen the relationship. 

While AI should do its work in identifying and responding to behavior patterns, it’s recommended to keep humans in the loop to understand what is happening and why. In other words, AI in marketing still isn’t at the “set it and forget it” phase.

Related Article: Privacy-First Personalization in Marketing Wins Customer Trust

Elevating Experiences with AI and Human Collaboration

Generative AI has a big part to play in enhancing customer experiences. In the survey, top performing brands were 15% more likely to use generative AI to create imagery. This helps them maintain brand consistency across websites, social media, email and other key channels, and it provides them the ability to scale up content production. This means that successful brands are offloading repetitive tasks like resizing imagery and maintaining brand compliance, all while letting human teams focus on more strategic work.

This efficiency is not relegated to just the beginning of the content creation process. Top performing brands were also 15% more likely to use AI for content quality assurance. Automating quality assurance of content across channels allows teams to maintain brand standards, detect typos or off-brand language or

To do all of this, brands need tools that do more than just fix typos. They need brand-aware AI tools that recognize unique product names or adhere to brand standards for color, font and image placement. Fortunately, these tools are increasingly becoming available, and major marketing suites and platforms are including these as part of their content workflow tools.

Related Article: Why the Future of Customer Service Depends on Human-AI Collaboration