Behavioural Segmentation: 3 Ways Retailers Can Use This Powerful Strategy
20 December 2021
At a time when brands are keen to attract more first-party data to fuel advanced personalisation, behavioural segmentation is increasingly popular.
Yet according to a Forrester report, only 33% of companies using this type of segmentation today say they find it significantly impactful.
This guide will explore the benefits of this strategy, before diving into the tactics retailers can use today.
What is behavioural segmentation?
Behavioural segmentation refers to the process of grouping customers together based on their interactions with a brand.
Rather than focusing on broad demographics, behavioural segmentation understands customers based on their actions. For example, demographic analysis might reveal that a brand’s consumers are typically aged 35-44 and 60% are female. Behavioural segmentation, on the other hand, uses data from customer actions to infer their interests.
For instance, the customer may be shopping for an occasion or they may seek specific types of benefits from a product. This is a customer-centric approach and highlights much more about the customer’s intentions than a pure demographic analysis.
Behavioural segmentation is not a new approach to audience analysis. Advertisers have sliced consumer groups in this way for decades.
It is perhaps for this reason that just 33% of brands say their segmentation strategy is impactful, per Forrester’s research.
In the digital age, retailers have access to huge amounts of data but too many are stuck in an analogue approach.
The below are examples of behavioural segmentation retailers can apply today.
Henry Assael defined four core buying behaviour stages:
There are clear distinctions between these behavioural patterns. A customer that displays habitual buying behaviour knows what they are looking for and has purchased the item before. The retailer should help guide them to the correct item(s) with as little friction as possible.
Conversely, a consumer that exhibits complex buying behaviour is unfamiliar with the product category. They may not even know precisely what their buying criteria are. In these moments, the consumer requires guidance from the retailer.
If we take a furniture retailer as our example, it becomes clear how purchasing behaviour data could feed a segmentation strategy. The retailer can pick up on cues from the consumer’s on-site interactions to discern their intentions. Customers who have purchased an item before and then return to purchase a replacement after a set period of time can be segmented as “habitual buyers”.
Those that arrive at the site and make a broad search such as “living room sofa” qualify as “complex buying behaviour”. This is a simplified version of the data taken into account, of course, but it serves to illustrate the differences in the customer journeys brands can create today.
2. Benefits-driven segmentation
Often, customers who look identical based on a demographic segmentation are in fact looking for different benefits from a product or service.
To stick with our furniture example, customers may be looking for a sense of comfort, style, or practicality from their purchase. There are clues in how a customer researches on a website, if the retailer uses the data sensitively. Beauty brands have taken the lead on this front; they ask their audience to share their preferences up front, so that they can receive a benefit-driven experience. This example from Olay shows how this can be a collaborative effort between brand and consumer:
This also creates zero-party data that the brand can use for email and search remarketing campaigns.
The key with benefits-driven segmentation is to take a truly customer-centric perspective. Go beyond the surface of customer data to figure out what benefits they are hoping to find. When retailers get this right, it gets them much closer to the customer’s real intentions.
3. Buying stage
Customers move through a range of stages as they progress towards a purchase, and then onto loyalty. There are patterns hidden within the historical and current data that customers create when they visit websites and apps.
If brands segment their audience using the stages of awareness, interest, desire, and action, they can communicate with these segments much more effectively.
Genesys offers this visualisation of how a weighted algorithm can cluster customers by buying stage:
How Cadeera helps retailers segment customers
Cadeera’s multimodal search adapts to each retailer’s unique customer data. Our AI preference engine then uses this data to create bespoke journeys for each customer segment, based on their buying stage, interests, and benefits sought.
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