I seem to be getting further and further behind in topics I WANT to talk about! So I thought I’d try and knock out a couple of “short” mid-week posts!
Late last year I spent some time with the Webtrends team up in Portland discussing segmentation techniques in Digital Analytics and brainstorming on some industry-specific segmentation schemes. It was a great discussion and it produced some pretty interesting work on the different types of Digital Segmentation and how those types translated into specific industries.
Jennifer Wilson from Webtrends captured a significant chunk of that discussion and created a Whitepaper around it focused specifically on one of the verticals we discussed most – Financial Services. I have to confess that she did the bulk of the work on it, but the ideas and the final drafts were very collaborative, so I’m happy to share co-authorship credit.
The whitepaper describes the three main types of segmentation scheme – life-stage segments, targeting segments, and cohorts and the uses for each. Life-stage segments are designed to capture entire user-populations and classify every visitor by their current state. One of the most powerful aspects of life-stage segmentation is that you can track flows (and flow rates) between individual segments – often a critical indicator of both business success and individual marketing opportunity.
Targeting Segments are individual groups selected for a specific campaign or analysis. They often combine elements of Life-Stage segments but usually with additional characteristics tacked on.
Cohorts (also called Discrete Populations) are time-based. Once a group of visitors has been selected (often as a Targeting Segment), that visitor population can become a cohort. They are then tracked over time. You might, for example, create a Cohort of visitors who responded to your PPC campaign in August. That group of visitors is fixed and unchanging – it won’t include additional visitors who responded to your PPC campaign in September or October unless they were already August responders.
The whitepaper provides additional depth on these segmentation types and how/when to use them. It also describes several generally useful and representative Lifestage Segmentation schemes appropriate to Financial Services including a Sales-Cycle Segmentation and a Channel Preference Segmentation. These are simple but useful frameworks if you’re considering the creation of one or more Lifestage segmentations and they’ll give you a good idea of how to think about Lifestage segmentations.
Finally, the whitepaper provides some sample Targeting Segments. While Targeting Segments are almost always highly specific to campaigns, there are a few nearly universal segments. Being “nearly universal”, you’d think that everybody would already use them for targeting but that’s not quite the case. Though applicable to almost every Financial Services company they are, nevertheless, often ignored or under-utilized.
All in all, it’s an excellent short summary of segmentation in Digital Analytics for Financial Services. You can pick it up here from Webtrends.