How AI Can Be Used to Target Micro-segments

How AI Can Be Used to Target Micro-segments

Micro-segmentation (as the name might suggest) takes your regular marketing segmentation to the microscopic level. It’s not just about segmenting audiences into tinier, more granular groups. It’s also about using the data you hold on them to form more sophisticated segments that combine their various attributes.

Demographics are combined with behavioural data; geographic location with device attributes and everything’s broken down into subcategories to really target people very specifically. It adds up to a far more insightful view of the customer and a far better way to engage them.

Of course, it’s all ultimately in pursuit of sales; specifically, eCommerce sales. AI combined with digital marketing seems to offer a lot of potential for this ‘extreme personalisation’ trend. Already there are tools on the market offering what’s being called micro-segmentation marketing.

They promise a far more accurate and insightful view of purchase intent and the possibility of customising product recommendations to a highly advanced degree. There’s also the promise of website performance tracking at the most microscopic level to really gain clues as to what triggers behaviours online by different types of visitor.

Beer and nappies

There are two sides to AI-enabled segmentation. AI not only helps you segment out a group of consumers on a microscopic scale, but it also uses insights about their behaviour to make your offering to these consumers more impactful. A properly managed AI segmentation program will identify what audience members in each micro-segment want and target them with the appropriate offering.

In fact, that’s the beauty of an AI-driven system; the insights and associated decisions are made based purely on data, not on human decision-making. This may lead to some unusual but hopefully effective targeting, such as the so-called ‘impossible correlation‘ between sales of nappies and beer. Whilst the connection between sales of these products to the same customers is now seen as apocryphal, the original correlation was thought to have been based on data science that apparently once spotted the odd trend.

It’s all about making better, faster decisions about how best to serve customers and then executing that for each individual audience member. It’s a pretty revolutionary concept and one which has enormous potential to disrupt the marketing industry.

RELATED: Why Understanding Consumer Intent is Key to Understanding Your Customers

AI-driven micro-segmentation marketing could radically alter roles such as marketing manager, copywriter, acquisition managers, pricing strategists and ad execs. It’s also going to change the customer’s online retail experience – hopefully for the better. Whilst customers can perhaps hope to receive offers more relevant to them, they may also experience pricing strategies that don’t always benefit them.

Close up view of male hand holding credit card buying doing internet shopping

AI-driven micro-segmentation has the potential to benefit consumers with better online offers, however, it’s vital to ensure over sophisticated AI-driven pricing doesn’t end up alienating them too.

This type of AI-driven micro-segmentation could be particularly good for retailers that have a very large catalogue. Rather than obliging customers to explore huge ranges of items to get what they want, AI can help personalise their choice to help customers seem less overwhelmed.

This could happen either by taking the bold decision to restrict what a customer even sees onsite, or more gently by using chatbots to provide a personal shopper-type experience and help them refine their choices.

But there are also pitfalls. Having a human in the mix can help soften messages and avoid causing offence. AI might be a little too blunt with people in terms of the choices it offers them. For example, although 90% of what a customer buys from an online supermarket maybe beer and crisps, the customer might not like to be reminded of this in targeted messaging as it holds an unflattering mirror up to their lifestyle.

Challenges of using micro-segments

One of the challenges of AI is harnessing the data in a way that makes it available to be used by your AI software. For most businesses, this means having a suitable CMS in place. And for most businesses, data management is a significant existing challenge and usually one involving many legacy systems.

AI works best based on huge and complex datasets which will encompass metrics including sales point data, social media activity and website traffic. It’s tough to get all this data into one repository. Investing in expensive new systems and tools to implement AI and micro-segmentation marketing across all business functions is likely to constitute a major challenge for most businesses.

AI targeting is about creating long-term value. Cultivating your marketing audience using AI will enable you to identify what’s working and refine your offering. For this reason, it should be seen as an activity to commit to rather than a one-off.

rear view of two people looking at analytics.

Diving into your analytics data will enable you to take advantage of creating the long-term value needed when implementing AI-driven targeting.

It’s likely that attempts to implement this kind of program will deliver some shocks to the business and to the customer base alike. Both sides will need to adjust to new ways of doing things.

It’ll be interesting to see whether the new technology embraces language targeting. We’re already well aware that communicating with audiences using their own language is the most effective way to engage them. But of course, language has its own potential micro-segments. There are lots of opportunities to engage by dialect, for example, or perhaps local slang.

The potential for targeting goes way beyond the mere formal written language – there’s room to engage with people in their very personal take on that language.

Peoples’ language use is arguably the most reliable indicator we have of their behaviour online, so AI can glean a lot of information about how best to communicate with them from how they, and others they are linked to, communicate. This could lead to a whole new era of language personalisation of content that could really change the type of content we’re exposed to online.

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