A process called "appropriate attribution" offers insights into the roles that different channels play in leading to successful conversions.
Last month, at the eMetrics conference in DC, I picked up a white paper by Eric Peterson on marketing attribution. In it, Peterson outlines the challenges with marketers relying on the traditional last click attribution models built into most Web analytics systems, at least as a default setting. I've highlighted some of these issues before in this column, and it's good to see the subject getting more attention from other commentators in this space.
Peterson outlines a process he calls "appropriate attribution" to get some further insights into the way online marketing dynamics work and the roles that different channels play in the acquisition process. It's a good approach in the absence of analyzing the individual clickstream data, to understand the patterns in marketing touch points that lead to successful conversion outcomes.
Work we've done in the insurance market in the U.K. has shown how you can come to different conclusions about what marketing is working and what isn't. In this case, we looked at the behavior of individual visitors buying insurance policies (or not) over time and the various marketing channels used in the process. In one case, over half of the policies bought online required two or more visits to purchase a policy. This is understandable, as people will generally be doing a certain amount of research before buying a policy, making sure that the policy meets their needs, and looking at the price. In the U.K. market, price comparison sites are a major feature of the way that people buy certain types of financial services and travel products. When we looked at all those policies where the buyer had taken two or more visits to buy the policy, price comparison sites were the last channel used in a significant number of cases. So these sites come out very strongly when using a last click attribution approach to look at marketing effectiveness.
However, when we analyzed the channels people used to first come to the site, the picture was quite different. Only about half of those who completed the buying process via a price comparison site had started the process using that channel. Other channels, such as search and affiliates, were far more prominent when looking at sales attribution on the first click model than on the last click model. This evidence supports the notion that some channels are better for acquiring prospects, and others are used to close the deal. When we looked at the channels used between the first and last click, visits that came "direct to site" featured more strongly. In the case of insurance products in the U.K., a pattern of behavior was that people found the site through search or affiliates, bookmarked and returned to the site direct to do more research, and would then complete the purchase after checking the price on a price comparison site.
Uncovering these sorts of patterns isn't easy and we're limited by the constraints of the measurement technologies available. Peterson's concept of appropriate attribution is a good approach for working within those constraints and will work better with some of the mainstream Web analytics technologies than others. However, if an organization is spending significant sums of money on digital marketing, then it may be worth looking at the potential return from investing in getting a more detailed view of marketing response and extracting the relevant data from the Web analytics system -- then analyzing that data in something else. At the eMetrics Summit, Expedia explained how it is doing just that, in order to ensure that it fully understood how to attribute the effect of different digital marketing channels on sales. Getting data out of the system through a data feed has other benefits, as it allows you to create your own view of the world and to think differently about how to look at different channels, particularly search. More about that next time. Till then...
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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.
Hong Kong, May 5-6, 2015
Gartner Magic Quadrant for Digital Commerce
This Magic Quadrant examines leading digital commerce platforms that enable organizations to build digital commerce sites. These commerce platforms facilitate purchasing transactions over the Web, and support the creation and continuing development of an online relationship with a consumer.
Paid Search in the Mobile Era
Google reports that paid search ads are currently driving 40+ million calls per month. Cost per click is increasing, paid search budgets are growing, and mobile continues to dominate. It's time to revamp old search strategies, reimagine stale best practices, and add new layers data to your analytics.
May 6, 2015
12:00pm ET/9:00am PT