Which type of segmentation is best – Part 2

This article, written by Neil Mason, was originally published on Clickz.com on 01/03/10 and is republished here with permission.

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, behavioural segmentation and attitudinal segmentation. But which one is best? The answer is that it really does depend on what problem you’re trying to solve.

Demographic segmentation strategies have traditionally been used by marketers for targeting purposes. 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 very powerful and result in much higher returns on marketing investment.

Demographic segmentation could be a useful approach for digital marketers but it does rely 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 a particular demographic segment or segments. The data for developing the segmentation might come from existing customer databases or if you don’t have a customer database then it might need to be collected using other data sources such as online surveys. Integrating survey data with web analytics data could help you to understand for example conversion rates amongst 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 the data can be a problem. However, we are generally not short of data on how people behave online and so behavioural segmentation approaches can be not only powerful but easier to adopt. Behavioural segmentation lies at the heart of most personalisation and behavioural targeting techniques whether they are based on relatively simple “rules based” approaches or more complex models and algorithms. The data for behavioural segmentation is readily available in your web analytics system and these days most web analytics tools give you the ability to cut the data a number of different ways. So there really isn’t much excuse nowadays not to start to look at segmenting your audience or customers based on how they behave on your website or how they interact with you over a period of time.

Some simple behavioural segmentation strategies can be very powerful. Optimising landing pages based on source of acquisition is a simple but effective behavioural segmentation approach. Creating different experiences base on the number of times that someone has visited the website is another. One of the classic behavioural segmentation strategies is Recency Frequency Monetary (RFM) analysis. Developed originally by catalogue retailers, in RFM customers are categorised 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 are your most valuable customers, (for example an airline’s Gold Card customers) and the way that you would market to them would be different to 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 behavioural segmentation approaches is to manage data across different systems either doing that manually or by having more integrated solutions. Once gain this is becoming easier for digital marketers as many of the web analytics providers have interfaces to other marketing systems (such as email tools) to enable these types of behavioural segmentation strategies to be implemented.

One of the limitations of behavioural segmentation is that whilst 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 upon 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.
Whereas we have data on behaviours in abundance in our digital marketing world, we rarely have abundant data on our customers or visitors. As with demographic data we need to go out and collect that data from sources such as surveys or to get really deep insight other techniques such as depth interviews or focus groups. As a result the data that feeds into attitudinal segmentations may be rather sparser than that for behavioural segmentation approaches but 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 really 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 behavioural techniques such as RFM can be powerful. Whichever approach you use though, there really isn’t any excuse these days for carrying on with “one size fits all” digital marketing strategies.