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Which Type of Segmentation Is Best? By Neil Mason, ClickZ, Feb 16, 2010 Sponsored by Omniture Share 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: Demographic segmentation Behavioral segmentation Attitudinal segmentation 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... Which Type of Segmentation Is Best for You? ANALYZING CUSTOMER DATA By Neil Mason, ClickZ, Mar 2, 2010 Columns | Contact Neil In my last column, I took a look at the meaning of segmentation and the different types of segmentation strategies available to digital marketers. There are three main types of segmentation: demographic segmentation, behavioral segmentation, and attitudinal segmentation. But which one is best? It really depends on what problem you're trying to solve. Demographic segmentation strategies have traditionally been used by marketers for targeting. Customers or prospective customers are classified according to different demographic criteria and are then selected for different types of marketing activities or communications. Often predictive models can be used to predict which segments are most likely to respond to which types of campaigns based on their previous history. The ability to identify potentially lucrative segments and then target them can be powerful and result in much higher returns on marketing investment. Demographic segmentation can be useful for digital marketers, but it depends on the type of data on customers and prospects that can be collected. Strategically, it can be important to understand which type of people are likely to be interested in your product or service or to shape your product or service to appeal to particular demographic segments. Data for developing the segmentation might come from existing customer databases or, if you don't have a customer database, it might need to be collected using other sources such as online surveys. Integrating survey data with Web analytics data could help you to understand, for example, conversion rates among different demographic groups. Media planning tools can then be used to refine the acquisition strategy orientated around those demographic groups with the highest potential. Using demographic segmentation approaches can be as useful online as they are offline, but collecting data can be a problem. However, we are generally not short of data on how people behave online and so behavioral segmentation approaches can be powerful and easier to adopt. Behavioral segmentation lies at the heart of most personalization and behavioral targeting techniques, whether they are based on relatively simple rules-based approaches or more complex models and algorithms. The data for behavioral segmentation are readily available in your Web analytics system and these days most Web analytics tools give you the ability to cut data a number of different ways. So, there really is no excuse to not start segmenting your audience or customers based on how they behave on your Web site or how they interact with you over a period of time. Some simple behavioral segmentation strategies can be very powerful. Optimizing landing pages based on source of acquisition is a simple but effective behavioral segmentation approach. Creating different experiences based on the number of times that someone has visited the Web site is another. One of the classic behavioral segmentation strategies is recency, frequency, monetary (RFM) analysis. Developed originally by catalog retailers, RFM customers are categorized according to how recently they transacted with you, how frequently they have done that in the past, and the monetary value of those transactions. The high recency, high frequency, high monetary value group represents your most valuable customers, (for example an airline's Gold Card customers) and the way that you would market to them would be different than other groups. On the other hand, a new customer (high on the recency scale but low on the frequency scale) presents a different opportunity and the key thing is to get them to buy or transact again. The challenge of applying online behavioral segmentation approaches is to manage data across different systems either doing that manually or by having more integrated solutions. This is becoming easier for digital marketers as many of the Web analytics providers have interfaces to other marketing systems (such as e-mail tools) to enable these types of behavioral segmentation strategies to be implemented. One limitation of behavioral segmentation is that while you might know what works, there may not be a lot of insight into why it works and, consequently, how it might be improved. Attitudinal segmentation involves getting in the mindset of your customers and understanding what makes them tick. This allows you to potentially develop different strategies for different people based on their attitudes and opinions about your product or service rather than how they interact with you. This type of segmentation lends itself to applications such as design work, where you are trying to develop solutions that are appropriate for different groups of people based on their needs, goals, and ambitions. Although we have data on behaviors in abundance in our digital marketing world, we rarely have abundant data on our customers or visitors. As with demographic data, we need to collect that data from sources such as surveys (or to get really deep insight, other techniques such as in-depth interviews or focus groups). As a result, the data that feeds into attitudinal segmentations is more sparse than that for behavioral segmentation approaches, but it can be richer. So, which is best? As with most analytical techniques, it depends on what problem you are trying to solve. For developing acquisition strategies, demographic segmentation techniques can be useful. For improving design and conversion, attitudinal segmentation feeding into persona development can play a role. And for improving retention and customer lifetime value, classic behavioral techniques such as RFM can be powerful. Whichever approach you use though, there really isn't any excuse these days for carrying on with a one-size-fits-all digital marketing strategy.
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