Three different approaches to segmenting your online audience. First in a series.
One of the things I like about my job at a customer experience consultancy is that I'm surrounded by people with a very different outlook on life than Web analysts. Our user experience consultants, who tend to have backgrounds in behavioral psychology, are great at using qualitative research techniques such as lab testing, eye tracking, and ethnographic studies to get into the mind of users and to understand what makes for a good or bad experience. That's obviously a different set of skills and tools from our quantitative, analytical approach to solving problems using vast quantities of data. Each approach complements the other: quantitative data is good for asking the "what" and "when" type questions, and qualitative techniques are good at helping to understand the "why."
Every now and then we get into one of those interesting conversations about which approach is best for solving a particular type of problem. Last week, one of these conversations turned to the topic of segmentation and which types are best for addressing particular issues. Segmentation, one of those popular words used a lot these days in the digital marketing world, usually means different things to different people.
Segmentation is the process of creating groups of individuals (customers, Web site visitors, prospects, etc.) that have something in common. Importantly, what one group has in common is then different to the other groups. Segmentation's purpose is to make you, your marketing communications, your Web site experience, your product offering, and so on more relevant to these different groups. But how are these groups defined? There are three main ways:
Segments can be defined by demographics, i.e., based on who someone is. Typically, classical marketing approaches use demographics as the basis for segmentation and then targeting. Demographic segmentation in online can also be useful. For example, "gender" can be a useful segmentation split because people can behave very differently online depending on whether they are male or female. So, to be able to segment your audience by gender, age, income, and more can be useful.
Another approach to segmentation is behavioral segmentation. This is not classifying people according to who they are, but on the basis of what they do. This segmentation approach is very popular in digital marketing because it's quite easy to understand how people behave thanks to the loads of available behavioral data. Again, it can be a very powerful technique to group people according to different behavioral criteria and to use that knowledge to improve the effectiveness of campaigns or to present different Web site experiences. For example, the way that people behave when they first visit a Web site is often very different from the way they behave on a subsequent visit. What's more, their needs are also often different on follow up visits. So, why not present that visitor with a different experience? Behavioral segmentation lies at the heart of personalization.
Finally, attitudinal segmentation is about classifying people not according to who they are, or what they do, but about what they think. Attitudinal segmentation is about getting into the minds of customers and understanding what makes them tick. People of different genders and ages may have similar needs when it comes to interacting with products and services; they may be trying to pursue the same goal or trying to achieve the same outcome. Often, attitudinal segmentation is used for the development of "personas," which are tools to help designers get closer to the people they are designing for.
So, which type of segmentation is best? Well, of course, the answer is that "it depends." What problem are you trying to solve? What will you do with the segments when you've got them? The other questions then are: "What data do I need?" and "Where do I get the data from?" I'll be looking at the answers to these questions next time. Till then...
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Neil Mason is SVP, Customer Engagement at iJento. He is responsible for providing iJento clients with the most valuable customer insights and business benefits from iJento's digital and multichannel customer intelligence solutions.
Neil has been at the forefront of marketing analytics for over 25 years. Prior to joining iJento, Neil was Consultancy Director at Foviance, the UK's leading user experience and analytics consultancy, heading up the user experience design, research, and digital analytics practices. For the last 12 years Neil has worked predominantly in digital channels both as a marketer and as a consultant, combining a strong blend of commercial and technical understanding in the application of consumer insight to help major brands improve digital marketing performance. During this time he also served as a Director of the Web Analytics Association (DAA) for two years and currently serves as a Director Emeritus of the DAA. Neil is also a frequent speaker at conferences and events.
Neil's expertise ranges from advanced analytical techniques such as segmentation, predictive analytics, and modelling through to quantitative and qualitative customer research. Neil has a BA in Engineering from Cambridge University and an MBA and a postgraduate diploma in business and economic forecasting.
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