AI is moving from helping marketers execute to helping them decide what should happen next, with human judgment still at the center.
At this quarter’s Beyond the Message, Attentive’s Eric Miao, Chief Strategy Officer, opened with a reality marketers know well: even after years of investment across the industry, true 1:1 personalization is still hard.
Many marketing programs still rely on manual oversight, hard-coded rules, and templates built to serve broad audiences rather than individual shoppers.
Eric outlined four capabilities marketers increasingly need to move beyond those limitations: recognize more visitors, predict who to message—and who not to—create more individualized experiences, and continuously learn from customer data.
The challenge is doing all of that at the scale modern marketing requires.
Attentive is building toward that with a flexible data platform and agentic tools designed to work both with marketers and for them—surfacing insights, recommending what to do next, and taking on more of the work behind a decision while marketers remain responsible for the strategy and judgment behind it.
AI is moving beyond helping marketers create and execute campaigns. It is starting to help decide what should happen next.
The decisions themselves are not new. What is changing is the ability to make more of them, with more context, at greater scale.
Bringing channels together changes more than reporting
Eric was joined by Rosanna Davies, Group Digital Marketing and CRM Manager at The Range, who shared what changed after her team brought brands and channels that had been operating across separate systems onto one platform.
Even relatively simple reporting and day-to-day tasks had previously happened in multiple places. Consolidating those programs gave the team what Rosanna described as a single source of truth.
More importantly, email, SMS, and push could start working together.
Exit rules could remove someone from subsequent messaging after a purchase or another meaningful action, rather than allowing separate journeys to continue operating in isolation. The team could also become more deliberate about the role of each channel.
Push has become especially useful for urgent moments like price drops and back-in-stock alerts. SMS also plays an important role when immediacy matters, while email often works as the softer follow-up.
Rosanna noted that the strategy is still evolving as the team continues to learn from the data.
Better orchestration is not about deciding once what every channel should do. It is about having enough context to keep adjusting as customer behavior changes.
List growth works better when timing can adapt
List growth has traditionally relied on a lot of educated guessing: choose a sign-up experience, decide when it should appear, then test whether the timing was right.
Matt Schorr, Product Manager at Attentive, described how that model is becoming more adaptive. AI can use signals such as browsing behavior, traffic source, and activity happening in real time to help determine when a sign-up unit should appear.
That is the idea behind AI Grow.
Attentive reported a 20% increase in list growth and a 35% increase in welcome revenue.
Rosanna’s team was an early adopter. Their previous approach followed a blanket timing rule that treated visitors essentially the same, sometimes prompting complaints from shoppers who had barely arrived before seeing a sign-up unit.
With AI Grow, timing can adapt based on individual behavior. Rosanna said the improvements her team saw in revenue and conversion made the value clear.
The right time to ask someone to subscribe is not necessarily the same for every shopper.
The session also covered the expansion of two-tap™ to RCS for Business, extending Attentive’s subscriber-initiated sign-up experience to richer RCS messaging.
Taken together, the shift is toward a more adaptive approach to acquisition: less dependence on one universal rule and more ability to respond to what the visitor is actually doing.
AI is making it easier to get from a question to an answer
Adam Hayim, CMO at Spiraledge, shared how his team is using Attentive MCP across four broad areas: reporting, auditing, audience building, and creative strategy.
He described MCP simply as a bridge between the AI tool a marketer is already using and the marketing platform they already work in.
One example stood out. Spiraledge has more than 60 active journeys, and Adam said a thorough review had previously taken anywhere from one to three weeks, depending on the level of depth.
Using MCP, he completed a review in one afternoon.
But the time savings were only part of the story. Adam was deliberate about where he wanted AI to help and where he still wanted human judgment.
He sees AI as especially useful for collecting and analyzing data and taking on repetitive work. He remains more cautious about handing over decisions involving brand positioning, promotions, storytelling, tone, and emotion.
More capable AI does not eliminate the need for marketing judgment. It changes where that judgment can be applied.
Better personalization starts with better messaging decisions
During the session, Candice Sparks, Senior Director of Product Marketing at Attentive, shared that 46% of consumers who unsubscribe do so because they received the same promotion again, while another 17% unsubscribe because the message felt too generic or impersonal.
Research cited during the webinar also found that personalization can increase revenue by 5% to 15%.
That is a useful reminder that relevance is about more than personalizing copy. It also means asking better questions:
- Should we message this customer at all?
- Which channel makes the most sense?
- Is this moment urgent?
- What has the customer already done?
When channels and customer behavior are considered together, marketers can make more thoughtful choices about what should happen next—and avoid messaging that feels repetitive or disconnected from the customer’s experience.
Personalization is not only about what you say. It is also about knowing when, where, and whether to say it.
Customer context can shape your BFCM strategy
Having more customer data only matters if marketers can turn it into a decision.
Kristen Fang, Product Manager at Attentive, cited research showing that marketers can lose 20 to 30 hours a week to manual reporting and data pulls, and that 59% of marketers say their segmentation lacks enough behavioral data to act on.
Attentive’s Reporting Agent is designed to help marketers investigate performance using plain-English questions rather than spending as much time moving between reports. It can help compare performance, explore possible reasons behind a change, and surface recommendations for what to investigate or do next.
The session also introduced capabilities including RFM segmentation, Customer LTV, and Product Affinity to help marketers make more informed audience decisions.
Kristen brought those ideas to life through a Black Friday and Cyber Monday planning example built around three questions:
- Who needs my attention?
- Who is worth more investment?
- What products or categories is each shopper interested in?
Those answers can shape very different approaches to the same promotional period.
Loyal customers might receive early access instead of another aggressive incentive. Black Friday can become a re-engagement opportunity for customers at risk of lapsing. Promising customers can be approached with a second purchase in mind. And product interest can shape which categories a shopper sees instead of relying only on a generic site-wide sale message.
The same thinking can carry through the BFCM calendar: reward loyalty before the event, use high-intent moments to reach relevant audiences during the sale, then compare how different customer groups performed afterward.
For marketers, the useful takeaway is not simply that more segmentation is possible.
The goal is not more segments. It is making different decisions because you know more about the customer.
The bigger takeaway
The most interesting part of this quarter’s Beyond the Message was not any one product announcement. It was seeing how marketing changes when AI can participate in more of the decisions behind execution.
More of the analysis, searching, sorting, and repetitive work can happen before a marketer has to manually find the answer. That is where the idea of an AI teammate becomes useful—not as a system making every decision on its own or as a replacement for strategy, but as technology that can interpret more signals, investigate more possibilities, and support more decisions.
Marketers still own the context, creativity, priorities, and judgment that determine whether those decisions are good ones.
The decisions are not new. The ability to make more of them, with more context and less manual work, is.
