Analytics Basics: Segmentation

  |  December 9, 2008   |  Comments

Thanks to segmentation tools, there's no reason for Web sites to treat visitors with one-size-fits-all marketing.

Over the past few weeks, I've tried out Google Analytics' new Advanced Segments feature. Although it's a beta version, the tool allows Google Analytics users to take a deeper dive into their data and to discover underlying patterns in visitor behavior.

Since I first started writing about segmentation in this column a few years ago, it's become easier for digital marketers to look at different groups of site users. With technology developments, there's almost no excuse for not segmenting your site visitors and understanding how different types of visitors behave on your site. Still, many organizations only look at top-line numbers and treat all visitors with one-size-fits-all marketing.

What is segmentation and how can it be used? In marketing terms, segmentation is the process of identifying groups of individuals who have something in common. Those individuals then belong to the same segment. What's important is how individuals from a segment differ from those in other segments. A simple example is to segment users by the number of times they've visited the Web site. You could classify them into "buckets," such as new users (the first time they've visited), light users (e.g., have visited two to three times), and heavy users (e.g., have visited four times or more). The number of buckets in this instance is determined by looking at the distribution of site visits per visitor and making an appropriate decision.

Under this scenario, the segments are set up to help understand if these different groups behave differently -- and help marketers determine if they should develop a differentiated marketing message or user experience for each segment and improve the expected return on investment. Let's imagine that you have just run an e-mail marketing campaign and experienced a 10 percent uplift in sales. If the campaign was a generic one that you sent to all your users on your database, then some user segments likely responded to your campaign while others didn't. The reality is you might have experienced a 20 percent sales uplift in some segments and none in others. With segmentation, you'd design a different campaign relevant to each segment -- a move that should enable you to achieve a 20 percent increase in sales across all segments.

With most Web analytics tools, it's possible to carry out basic data segmentation. It's up to the analyst or user to determine what segments are useful to look at. In Google Analytics, some segments that cover obvious behaviors that might be of interest are already set up, such as new versus returning visitors, paid versus nonpaid traffic. But analysts will want to explore and create their own segments based on their Web site and company's business issues. For instance, a travel site could try to analyze why people look at a quote for a hotel room but don't book it. In that instance, the analyst may set up three different visitors segments:

  • Browsers (people who visited the Web site but didn't reach the quote process)

  • Quoters (people who got a quote but didn't book a room)

  • Bookers (people who booked online)

Once the segments are created, the analyst can look for differences between these segments in terms of how they get to the Web site and what they do when they get there. Are there differences in the type of channel they come from or the keywords they use? Do they look at different types of content? Do they use different tools and applications on the site, such as the onsite search? If so, what keywords do they use in the onsite search engine?

Once the ability to create segments becomes a reality, the analysis possibilities become endless. Different tools offer different capabilities, but the principles remain the same for the curious analyst. The challenge with segmentation isn't so much in doing the analysis but in taking action as a result. The benefits are from taking action based on insight and building and deploying more targeted marketing programs as a result.

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ABOUT THE AUTHOR

Neil Mason

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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