OPINION: The basics of developing a psychographic segmentation model

Sara Boltman

Sara Boltman, Founding Director at data services company Butterfly Data, explores psychographic segmentation and how it can lead to better understanding of customer attitudes to concepts such as debt.


I first came across the concept of psychographic segmentation whilst working for a mobile phone operator in 2006 – a now distant, pre-social media age when Facebook hadn’t quite made it to UK users and Twitter was just about to launch.

They had profiled their users and divided them up into animal characters: for example Canaries were the ones who made lots of calls and talked a lot. There was another category for international roamers or people who mostly texted, while mystifyingly those who used their phones for work were Raccoons rather than another famously “busy” animal, which was presumably vetoed by management!

Mobile phone usage can be very specific to the individual, their network of family and friends and their level of connectedness – if you see someone every day you may not need to call or text them at all. But the company had just acquired an Internet Service Provider, so all that household level data needed to be incorporated into the model. Families are made up of individuals, but the category system no longer worked. We needed new psychographic segmentation for combinations of individuals – Tolstoy said Happy families are all alike; every unhappy family is unhappy in its own way.

Family internet and mobile phone usage is something that can be very telling of a certain stage of life, helping with psychographic segmentation. For example, in a household with young children the internet usage may go up slightly, streaming kids TV and searching mumsnet in the early hours of the morning during sleepless nights, but the kids won’t have their own mobile phones for a few years. In the teen years the evening internet usage may be markedly higher.

To be able to identify these households is extremely valuable, as firstly the company would be able to offer some kind of family deal on mobile phone contracts, but also those teenagers will be opening their first bank accounts, establishing brand loyalty that could last years or even decades.

An area in financial services where individual psychographic analysis is important to look at is attitudes to debt. Butterfly Data assisted a tier one bank throughout an FCA market study into problem credit card debt. They extracted and profiled credit card usage over a 5 year period for millions of customers. A surprisingly large chunk of credit card customers are not profitable for the bank – they pay off balances in full every month and don’t incur any interest charges, late fees or overlimit fees. Amongst the older generations, a credit card is something you keep in a drawer for an emergency – rarely used but there if you need it. Again, the bank can learn relatively little about these customers from their spending habits if they never spend. But these were not the customers the FCA was worried about – they identified four types of problem debt, each with its own pattern in the data, like a signature for a particular psychographic segment.

Persistent debt is the kind that never gets paid off – the customer may balance transfer to a different card or consolidate to a loan but sooner or later they have run up the debt again. This is not necessarily a problem for the bank: if the customer owns a house which is increasing in value, they may remortgage to clear all their debts, or move house and take on a larger mortgage on the new house, tidying up a few credit card balances along the way.

Problem debt is when the customer has missed a few payments and tarnished their credit score, every card is maxed out, there is no eligibility for balance transfer because no other company would take on this customer so they end up debt-trapped. One step along the way to this can be the third group, the direct debit minimum payment. Whilst at first sight this can seem quite innocuous, a simple habit that ensures no accidental penalties for missed payments, it’s easy for customers to think they are too busy to open those pesky statements every month. Firstly, this means they are less likely to spot any fraudulent use of their card if it’s been skimmed or their details have been included in one of the (many) data breaches. Secondly, they may be unaware of the steadily climbing balance and how much interest they may end up paying over the long term for some of those impulse purchases.

Finally, the category the FCA was most concerned in identifying – the vulnerable customers. This is best considered as a transient population – people go through phases of life which put them in vulnerable situations, it’s not a permanent flag on a banking database forever. Mental health issues may mean that escalating debt is causing anxiety and the customer feels overwhelmed whenever they try to tackle their finances. It’s important to be able to identify this as it’s not appropriate to try to ‘help’ by offering an unsolicited credit limit increase or consolidation loan.

At the time we did this project, many of the people falling into this psychographic segment would have been older people, unfamiliar with using the internet to search out the best deals for them and ensure they are not paying too much interest.

Today we find the younger generation are likely to feel anxious about their finances too. In a way, all those price-comparison apps can mean there is too much choice, too much information, leading to analysis paralysis, inaction and the same result – unopened credit card statements and a large interest bill.

Using these techniques to understand the context of a customer’s whole life – their family situation, their attitudes to debt and their level of connectedness – can give marketers the insight needed to really help the customer meet their needs. Delivering that message in the right way at exactly the right time is much easier when you have that context.


Register here for our upcoming event on psychographic segmentation, featuring a case study from wealth management firm Charles Stanley.

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