We all love the term relevance, but is it really relevance we are striving for? What most people don't realize is that in email marketing, segmentation is not about defining all your customers and how to treat them; instead, it's about helping you make decisions at a very granular level. Being too high-level with your approach is useless in day-to-day email marketing. If you are too granular in your definitions, you'll struggle to get programs out the door. While you may have a very clear view of your highest valued customers, the question is: Should you focus on these highest-valued customers, or should you work harder at gaining a more granular picture of the less-valued, less-defined segments?
I've seen great uses of various types of segmentation. I've seen demographic segments work really well for businesses that need baseline qualifiers to assess the degree of promotion, incentive and interaction with the brand. Yet household income, age, gender, presence of kids, education and geo-targeting are only part of the way to this elusive stage of "relevance" we seek. Using approaches like Personicx and Prizm Clusters will help you in targeting, but they are less effective at helping you understand motivations -- those beliefs that drive behaviors (frequency of visit, spend, and potential cross-product interests). This approach can be amazingly expensive to keep updated, but again, there are economies of scale if you set up an effective approach to data collection in the first place.
Transactional-based approaches are much more empirical in terms of identifying past purchase histories, along with variables that influence increased purchase, purchase value and category base spend and predictors. Certain categories of business are no-brainers for this model, yet it becomes a really daunting task to manage your email programs with this approach without a very refined approach to modeling that continually updates itself.
You can spend years trying to do predictive modeling, but if your products turn over quickly, or you have thousands of SKUs, it can be impossible to manage iteratively outside of simple targeting or high/low transactional periods. While this is used in email most frequently for personalization and promotional strategies, still, unlike other marketing forms, email marketers still have to deal with the entire database. So the challenge is to have enough resources to consistently execute against the database.
What about taking it a step further with behavioral data? This is sometimes considered the same as RFM-type approaches. A segment is generated based on some behavior: a purchase, an Interaction or event. This is what the online space has been struggling with for years as the customer journey is so much more fragmented between the web, store, call center and outbound channels. Behavioral segmentation is an operations persons' nightmare -- mainly due to lack of agreement on how targeting decisions are made in companies, with a lack of understanding of the customer experience making it even harder to execute at the channel level (who drove the sale?).
Attitudinal segmentation is the coolest form, in my opinion, but also the hardest to operationalize. Now you know who your customers are and you know how to target them, and you may even have some idea of the types of motivations that drive types of promotions, categories of interest and emotional drivers to purchase.
The challenge with this approach is twofold. One, it's expensive to commit to. I've seen many companies do so -- and it just sits there for the annual meeting. I've seen others try to operationalize this without the commitment, funding, resources or technology to extend this past the Business Intelligence groups. This makes decisions that much harder; if you don't have a clear understanding of the values your business or service delivers, along with competitive considerations, it can be a fruitless cause done in a vacuum.