This article appeared in Forbes. Read it in full here.
Bryan Gernert is CEO at Resonate, an AI-driven predictive consumer intelligence and data company.
Personalized marketing has rapidly evolved within the last decade, from the early days of basic segmentation to more modern practices like targeted campaigns that often depended on predefined rules and workflows or poor implementations of retargeting that seem more invasive than personalized. In the beginning, personalization was either not personal enough or overly persistent to the point of being unsettling. Remember those ads that seemed to follow you around for days after one happenstance look at a product?
Despite numerous attempts to get it right, personalization has failed to make the impact we’ve all desired. A few obstacles are to blame, including past technology shortcomings where early solutions relied on traditional, static methods and fragmented, incomplete customer datasets—which provided only a high-level understanding, even with substantial investment. Even with the right technology and data, personalization can still fail due to simple human shortcomings, like marketers relying on assumptions based on broad segmentations or hesitating to deploy new technologies like AI.
Today, we’ve entered into the hyperpersonalized marketing era fueled by AI and real-time, deep data to gain dynamic, actionable insights that marketers can use to make personalization truly personal (and far less creepy). Think about being served an ad that goes beyond the shoes you looked at and instead connects to you based on your values as a consumer. In addition, the interaction occurs naturally where you spend time online versus one that assumes your typical behavior based on stereotypes attributed to your age and household income.
In this new era of AI and advanced datasets, the opportunity exists for marketers to achieve the holy grail of marketing: personalization that’s actually personal. Let’s explore what it takes to get there.
Shifting The Mindset
Marketers face a pivotal transition: moving beyond assumption-based targeting to embrace AI-driven consumer intelligence. For some, this shift requires abandoning long-held ideas about their audiences, while others simply need to implement AI into their workflow.
Either way, for those on the fence about using AI-powered data intelligence, it’s helpful to understand why it has become essential in today’s era of hyperpersonalization, significantly enhancing marketers’ business impact.
Unlike traditional static segmentation, continuously updated AI models adapt to changing consumer behaviors, ensuring marketing efforts remain relevant, cost-effective and results-driven. Historically, the only scaled dataset available has been demographics; however, demographic-based segmentation only offers a broad overview, whereas purchasing behavior points marketers to true consumer motivations.
AI-driven consumer data provides a depth of understanding never before available. For instance, what’s important in the consumer’s decision and what influences are driving the consumer’s action to buy? Armed with these kinds of insights, marketers who embrace this new approach can accurately predict when customers are likely to make their next purchase, what products they’re likely to select and whether they’re inclined to purchase from a brand again.
When they choose to adopt these advanced AI models, marketing teams can foresee potential customer churn and optimize engagement across the entire customer life cycle. By integrating first-party data with AI-driven observations beyond the company’s first-party data, brands can deliver timely, relevant messaging that engages consumers throughout an entirely personalized journey.
Laying The Foundation For Success With AI
Effective, AI-driven personalization requires a solid foundation of high-quality data. Not all data carries equal value—it can vary significantly in its accuracy, relevancy and utility for customer engagements.
For example, last year’s summer travel trends data doesn’t reflect current consumer behaviors. The landscape has changed dramatically since then, with economic uncertainty, political shifts and social concerns that are drastically reshaping how people approach summer travel this year.
Similarly, purchasing patterns and motivations from a year ago don’t align with today’s consumer realities. From rising costs to tariffs, year-old insights fail to capture the financial concerns many consumers are currently navigating, like being able to afford basic household necessities.
AI-powered models that analyze fresh, real-time insights allow marketers to move beyond static trends and match the ebbs and flows of consumer motivations. By leveraging dynamic datasets, brands can ensure their personalization strategies remain relevant in addressing current events and factors that play into consumer decisions today.
Barriers To Mainstream Adoption
Predictive analytics is widely used in marketing; a 2022 Wakefield Research study found that 95% of respondents use it in some form. However, AI-powered consumer data models (in particular, enriched custom models) aren’t yet mainstream. In the past, without AI, building and maintaining custom models demanded advanced data science expertise. Today, some marketers are wary of AI for fear of complexity, transparency or organizational friction in transitioning tools, slowing or stalling adoption.
Another reason for underutilization is that many predictive efforts don’t deliver the expected return on investment (ROI). This often stems from overreliance on first-party data—information limited to known users and owned channels. This dataset is useful but narrow. What about the thousands of qualified prospects outside of a company’s database? Most of the buying journey takes place outside of a brand’s four walls.
Some modeling techniques are also too tightly tuned to past (outdated) data and cannot be applied to new audiences, or they’re too generic to understand subtle customer intent signals (e.g., purchase readiness).
Marketing teams should embrace a broader, enriched data strategy and evaluate modeling techniques for robustness and adaptability before experimenting. This approach can help demystify AI, build trust and unlock stronger performance from predictive investments.
The Path Forward For Personalization In Marketing
AI has emerged as potentially providing the holy grail in marketing, allowing teams to cut through the noise with a comprehensive, real-time view of individual customers to create hyperpersonalized marketing experiences. By enhancing fundamental data with broader psychographic insights that reveal underlying consumer motivations and deliver a real-time analysis, brands gain an authentic understanding of purchasing patterns and customer loyalty drivers—allowing them to create truly personalized experiences.
