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It’s tempting to believe that hyper-personalisation "the ultra-tailored targeting of customers" is simply a numbers game. Marketers often assume that if they collect more customer data and use AI, they’ll master personalisation. If you're familiar with the concept, you'll probably relate to being overwhelmed by GA4 reports, ad metrics and attribution data, yet still searching for conversion uplift. The myth is that mastering hyper-personalised marketing is all about amassing data points and crunching numbers. But the truth is more human: the most effective personalisation comes from a genuine understanding of your customers, not just algorithms.
In an earlier article 'More data ≠ better decisions' we saw how information overload can backfire: 86% of people in a study said that more data actually made them less confident in their decisions. The same applies here: drowning in data won’t automatically create a great customer experience if you’re not tuned in to the why behind the numbers.
Nearly a decade ago, retail giant Target learned this the hard way. Its predictive model correctly identified that a teenager was pregnant before her family knew, and promptly sent her coupons for maternity products. The result? Public outrage and a very uncomfortable customer. While Target's data was accurate, its approach lacked human sensitivity. This cautionary tale highlights the fine line between clever and creepy. Personalisation without empathy can betray trust. As one marketing expert put it, people appreciate personalised offers, but only when they feel that the main purpose is to improve their experience. If customers sense that personalisation is being used merely to boost the company’s sales or that their data is being misused, they start to feel uncomfortable.
In short, hyper-personalisation driven purely by data can easily go too far, causing campaign fatigue or even a backlash when it ignores the customer’s perspective.
Focusing solely on raw data often leads to what we call the identification fallacy: using a first name in an email subject line; automatically inserting a product recommendation based on clicks; and segmenting users into endless micro-groups. While these tactics tick the 'personalisation' box, they rarely spark a real connection.
But 'why?'
Because traditional personalisation often focuses on identification rather than understanding. It treats people as entries in a database, rather than as individuals with context, emotions and nuances. The result is marketing that, while technically “personalised”, still feels impersonal. Generic retargeting messages and one-size-fits-all coupon blasts may reach the right audience at the right time, but without relevance or emotion, they become lost in the background noise. It's no wonder that many shoppers tune out, or worse; become sceptical, when every interaction screams “algorithm” rather than empathy.
While numbers can certainly highlight what is happening in your funnel (e.g. a high mobile drop-off rate or a 70% cart abandonment rate), but they can’t tell you why. Take shopping cart abandonment, for example, a top concern for e-commerce teams. Analytics might flag that 7 out of 10 shoppers leave without buying, but you need a human lens to diagnose the causes. Often the reasons are surprisingly human: according to Baymard Institute, 39% of U.S. online shoppers abandon carts due to extra costs like shipping, 18% quit because the checkout was too long or complex, and 19% abort the purchase simply because they didn’t trust the website with their credit card information.
Think about that. Nearly one in five shoppers bolted not due to a price or product issue, but a trust issue. This insight doesn't come from the raw abandonment rate alone, but from an understanding of the customer psyche (fear of fraud and privacy concerns) and how your checkout experience makes customers feel. Likewise, if your email campaign open rates are dropping due to fatigue, data might show the decline, but improving it requires asking, 'Are we saying something meaningful, or just spamming personalised "{FirstName}" lines?' The lesson is that data exposes symptoms, but human insight finds the cure.
In truth, hyper-personalisation is fueled by human understanding – the kind of insight that comes from empathy, psychology, and yes, qualitative data. It’s telling that marketers are increasingly talking about “humanisation” as the next step beyond personalisation. After years of reducing audiences to click streams and purchase histories, there is a push to reintroduce authentic human connection to marketing. “As we enter 2025, empathy-led marketing is emerging as the antidote to the impersonal nature of hyper-personalisation,” one industry piece explains, urging brands to stop viewing customers merely as a digital data trail. Instead, we must treat customers as individuals, prioritising emotional intelligence, cultural context and ethical responsibility alongside data. In other words, numbers alone cannot delight a customer; it is understanding their needs, anxieties and aspirations that makes personalisation feel personal.
Even the tech giants that set the standard for personalisation owe their success to an in-depth understanding of their customers. Amazon’s renowned recommendation engine works well not just because of big data, but because it consistently frames suggestions as helpful and gives users control, for example through the use of “Not interested” buttons, thus respecting the customer’s comfort zone.
When personalisation genuinely improves the user experience, it builds trust and loyalty. One marketing veteran summed it up perfectly: people enjoy personalisation “only when the main purpose is to improve their experience”. That means our personalisation efforts should always answer the question: “How is this helping the customer right now?” If we can’t find a good answer, no amount of data wizardry will save the campaign.
So, how can we incorporate a human touch into hyper-personalisation? The key is to use data as a tool, not a crutch. Data-driven algorithms excel at pattern recognition and real-time adaptation, they can analyse who clicked what at 2 pm on a Tuesday, for example. However, it is up to us (and our teams) to interpret those patterns in the context of customer psychology. As one insightful article noted, the goal of hyper-personalisation is to make each customer feel 'seen and understood', and achieving this 'doesn't always require the latest gadgetry'. In practice, this means blending quantitative insight with qualitative understanding.
Listen to your customers directly.
Don't just observe their behaviour online; engage with them. Surveys, user interviews, customer service logs and social media comments can reveal pain points or motivations that numbers alone cannot. For example, a spike in mobile exits might be revealed by feedback to indicate that your site was perceived as untrustworthy or difficult to navigate on a phone screen.
Focus on context and timing.
Personalisation is most powerful when it recognises context. Rather than sending more frequent offers to a lapsed customer by default, a human-centric strategy would ask why they lapsed. Perhaps they received too many similar emails (campaign fatigue) or lost trust due to a delayed shipment. Sending a friendly check-in message or one that addresses their concern can re-engage them more effectively than offering a generic discount. It’s about meeting the customer where their head is at.
Blend human creativity with AI smarts.
The magic happens when you strike a balance between machine precision and human empathy. A great example of this is Stitch Fix, an online retailer that uses AI to curate clothing recommendations, but relies on 1,700 human stylists to finalise selections and build relationships. “AI has been at the centre of our business from day one,” says Noah Zamansky, Stitch Fix's VP of Client Experience, in an interview with Retail TouchPoints “but it's always been about this balance... AI-driven recommendations together with the creativity and human empathy of our stylists. We believe the magic happens when we marry human creativity with the science”.
Their stylists often form long-term bonds with clients, remembering important life events such as starting a new job or having a baby, and can adjust the AI suggestions accordingly. This human touch transforms one-off transactions into ongoing relationships. It's a powerful reminder that personalisation is ultimately about people, not pixels.
Hyper-personalisation is not powered by having the most data; it is powered by the insights and trust that you build from the data. Ultimately, a customer is much more than just a set of metrics. To personalise effectively on a large scale, we must treat customers as people first and use data to support that approach, rather than the other way around. Brands that get it right approach personalisation as an exercise in empathy and problem solving. They ask human questions such as 'What does this person need or fear?' and 'What would truly help them?', and then use the data to inform the answers, rather than letting the data dictate them. Fuel your personalisation strategy with human understanding to ensure those fancy numbers translate into meaningful experiences. It is meaningful customer experiences, not sheer data volume, that drive conversions and loyalty in the long run.
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