Key takeaways:
- Most personalisation is actually based on group patters, rather than individual insight, as customers don’t interact enough to build reliable profiles (especially in lower frequency categories).
- Grouping customers who behave similarly – microsegmentation – creates stronger signals, making marketing more relevant and effective, even if it isn’t perfectly tailored.
- In low purchase frequency retail categories, rich individual data may never come. Using the data you do have via smart segmentation delivers value sooner and more reliably.
Personalisation is a dominant narrative in retail.
Personalisation is proven to drive loyalty and value for customers and businesses alike. It increases relevance, improves conversion, and strengthens long-term relationships. That’s why it’s become such a dominant narrative in retail.
But the way personalisation is talked about often glosses over how it actually works in practice. Even the most ‘personalised’ systems are rarely built on deep individual understanding. They’re powered by patterns learned from groups of customers who behave similarly.
When you dive into a personalisation project, it’s easy to see how quickly these projects stall. Data limitations, and operational challenges can delay projects, and that gap between ambition and reality is especially clear in low purchase frequency retail categories, where it’s harder to gather a profile for an individual customer. There are many industries where this is applicable, e.g. furniture, travel, automotive and luxury retail.
Why does individual-level personalisation break down?
True customer-level personalisation needs two things: reliable item level data and frequent customer activity. To have any confidence in understanding someone’s activity, you need them to be shopping frequently to identify habits. Without that, it all becomes guesswork.
Many businesses have a solid data infrastructure but still face a structural challenge. In low touch point industries, customers simply don’t shop often enough. A handful of transactions per year doesn’t tell a stable story, and one purchase can dominate a customer profile for months. And how do you know it isn’t a random one-off shop, and the customer will never buy into that category again?
Microsegmentation is the alternative.
When individual-level personalisation isn’t viable, what’s the alternative? Well, instead of focusing on the individual, we personalise at a (micro) segment level.
Micro segments group customers with similar behaviour. These segments can be as broad or narrow as needed, depending on data depth and customer volume. There’s no universal ‘right’ size, only what works for your business. That being said, each segment needs enough customers to avoid noise and false signals, but smaller unstable segments can be worse than no segmentation at all. It may end up being an iterative process to identify the best size for you!
By grouping customers who behave alike, we dramatically increase usable behavioural history. We move from analysing one customer’s limited past to analysing a shared pattern. That gives us more signal and more confidence.
Segments can be built using simple rules or more advanced machine learning (ML) approaches. Rules-based methods are often easier to explain and activate, but ML clustering can uncover patterns humans wouldn’t spot. Neither approach is automatically better. Again, the right choice depends on which approach is most operationally viable. A perfect model that can’t be activated delivers no value.
Why this matters in low purchase frequency retail categories.
In retail categories with longer purchase cycles, the biggest challenge is often driving the next visit. A segment-level personalised offer can be enough to tip behaviour. It doesn’t need to be perfectly tailored to be effective – it just needs to be relevant.
This is where micro segmentation shines. It reduces the risk of irrelevance. In many cases, reducing downside risk matters more than chasing the absolute perfect offer.
One of our favourite examples of how micro segmentation works well is the travel industry. A customer booking one holiday a year won’t tell you much. But if they fall into a segment of ‘early-booking family travellers’ or ‘last minute city break shoppers’, you can tailor messaging to that group.
It’s still not one-to-one personalisation, but it’s relevant enough to drive action, and give that personalised feel.
How to offset the limitations.
Though we wish it was, microsegmentation isn’t magic. Limited data can still make segmentation difficult. Even when grouping customers, if a customer has only bought once the challenge moves from what offer to give this one customer, to which segment shall we put them in?
However, there’s no reason you can’t supplement transaction data with other information. Browsing behaviour, engagement data, location, life stage, and other demographic signals can all help build a more complete profile. For example, in furniture retail, a customer who’s bought a single bedside table looks unpredictable in isolation. But if they’ve also been browsing wardrobes, chests of drawers, and bed frames over several weeks, it’s far more likely they’re furnishing a bedroom rather than making a one-off purchase.
Despite this, we must accept customers within the same segment can still diverge. With this approach we are increasing the likelihood we’re giving a relevant offer, but we can’t guarantee a specific outcome.
Probably the most common restraint, is operational complexity. More segments mean more offers, more creative, and more decision logic. This is why it’s important to define scope at the beginning of the project to make sure everything is aligned, so no one gets hit with a nasty surprise further down the line.
Why waiting is the real risk.
It’s easy to put off personalisation and wait for ‘enough data’. But in some categories, that data may never arrive. The opportunity cost is often invisible, but it shouldn’t be ignored.
Micro segmentation works with your data, rather than pretending it’s richer than it actually is. Personalisation doesn’t need to be one-to-one to be effective, and in low purchase frequency industries, smart grouping is often the only approach that works. And in many cases, it works surprisingly well.
Curious how microsegmentation could work in your retail business? Let’s chat about building a segmentation approach that actually works with your data.
