Understand which of these problems you are you are trying to solve. Second in a series.
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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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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