No customer wants to feel they’re just a number to a company. That’s where customer segmentation comes in – allowing companies to target different customer profiles with communications, offers and support designed just for them.
Amazon set a high bar when it started using AI to make personalised recommendations to online shoppers (‘People who bought this, also bought this’, or ‘Jane, based on your recent browsing and purchasing history, we think you’d love these’).
Entertainment streaming services like Netflix also use these machine learning techniques to propose other content users might like. These techniques can be adopted by B2B organisations including wholesale distributors too, to help customers hone their searches across extensive product ranges.
Over the course of a relationship with a customer, companies can keep refining their targeting to deliver some really impressive experiences. That could be greeting a hotel loyalty programme member by name each time they check in and offering them their favourite type of room, or seat at the bar. Or picking out shoes in the customer’s known size when offering personalised recommendations.
But what about new customers, who come without buying history? How can companies appeal to these individuals in more personal and targeted ways, when they don’t know nearly as much – if anything – about them?
‘Smart’ assumptions can help. Segmenting target customers into groups or clusters that share certain characteristics makes it easier to appeal to them in more specific ways.
Beyond obvious, broad demographic categories like age band, sex or nationality, marketing departments can slice-and-dice their target markets by income bracket, profession, hobbies, dietary preferences, favoured travel destinations and so on.
Although there could be big variances within each category (not all vegans like processed meat substitutes; not all cyclists wear Lycra), the narrower the definitions, the easier it is to craft relevant and enticing messaging and offers.
Clever algorithms and rich data about customer behaviour, beliefs and preferences can make targeting easier.
‘Social listening’ – using AI algorithms to scan and analyse public forums for common interests, preferences and behaviours – offers a powerful way to understand more about target customer segments. It can also generate important new insights for product teams, to refine existing offerings or create new ones to fulfil needs that aren’t currently being met.
This can help companies drill beneath stereotypes and discover common traits, values or passions shared by their target customers. Cyclists may be more environmentally or health conscious. Cash-strapped students may be more open to location-based marketing via mobile apps. Vegans may be more inclined to donate to animal shelters.
Segmenting customers according to their actions or past experiences can be valuable, too. For instance, you could convert disgruntled customers to loyal ones with a targeted offer to compensate for a failed web transaction, missed delivery or faulty product.
Tuning in to the evolving needs of target customer groups is more important than ever. It’s something future-ready businesses are putting a lot of energy and investment into. Compared to their peers, these forward-thinking companies actively go after new data insights to help them enhance customer experiences.
Companies still have a long way to go, though. A 2020 study into personalisation maturity found that just 6 per cent of companies have what it takes to combine data and create a single view of existing customers, to enable personalised experiences across channels. Just 6 per cent are able to use data to find personalisation opportunities through machine learning technologies.
That’s a huge missed opportunity, considering that 80 per cent of consumers say they’re more likely to buy something from brands that offer personalised experiences.
To start capturing and analysing existing and new customer data, look to your contact centre and other feedback mechanisms to see customer queries and reviews. Who are these customers and what do they want more of?
Another tip is to prompt customers and prospects to agree to data capture with the promise of better services or more relevant offers in return.
Giving customers a chance to be remembered carries the added benefit of making them feel important and listened to.
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