Article

Beyond Targeting: Listening for Signals of Customer Intent

Move beyond predictive targeting to create more relevant customer experiences with signal-driven marketing.

Mark Swenson
Mark Swenson
October 6, 2025 2 min read

Direct marketers know they can improve their response rates significantly by carefully selecting their targeted audience. But with limited insight into customer behavior, they struggle to deliver relevant messages that encourage customers to engage with their campaigns.

Now, thanks to advances in AI, marketers no longer have to guess which customers will be interested. Rather, they can listen for signals, or indicators of customer intent, in vast sets of customer data, then target the specific customers who require contact from the brand immediately.

The limitations of traditional targeting

Traditional marketing campaigns often use simplistic targeting, broad segmentation models, or basic propensity scores. But these strategies provide only a limited view of the customer. A customer with a strong propensity score for a product might not need the product now. A customer who, by some metrics, looks like other customers who bought the product may have vastly different needs.

Without real-time insight into the actions of specific customers, marketing efforts miss their mark, and company goals aren’t met.

How signal-driven marketing works

Using AI and language models, marketers can detect signals of customer intent in vast datasets that include structured, semi-structured, and unstructured data. Signals can be found in transactional data, derived attributes, and analytic scores, but the strongest signals actually come from voice-of-the-customer data, such as call center recordings, customer chats, and emails. These data sources offer the best insights because these channels are where customers express their needs in their own words.

The customer may be expressing frustration with hitting friction in the buying experience or getting a service issue resolved. With AI and language models, these customer behaviors can be isolated in real time from other noise in the data and scored to determine urgency and relevance.

By listening to these signals in customer data, marketers know which customers have a need that demands outreach from the brand. They can focus their customer contact efforts when these signals are at their peak and use them to proactively communicate with customers and address the opportunity or task represented by the signal.

Signals improve response rates

When brands focus their efforts on responding to customer signals in real time, customers don’t have to initiate a call to the company to open a complaint or service ticket. This results in lower costs, improved customer retention, and enhanced customer experience.

Also, the more often customers experience relevant signal-driven messages from a brand, the more they trust that future communications will be meaningful. This leads to greater engagement with the brand overall.

Download Mark’s latest whitepaper, The Signal-Driven Marketer, for a deeper look into the signals marketers are using today to gauge customer intent.

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About Mark Swenson

Mark Swenson helps enterprise clients activate their customer data, artificial intelligence (AI), and customer data platforms (CDPs) to improve customer experiences. His 25 years at Teradata have covered digital and loyalty marketing, segmentation, offer optimization, customer analytics, and AI, with clients in all industries around the globe. View all posts by Mark Swenson
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