Leveraging the two metrics (match rate and view-through rate) and the two shifts in methodology (multi-channel attribution and cohort analysis) enables marketers to achieve a higher level of insight into the specific contributions of each channel.
By now, you know that consumers don't interact with just one marketing channel on their path to conversion. Chances are, your company understands it can't develop and deliver marketing on only one channel if it wants to remain competitive. But are you still using old-school, one-channel metrics (CTR, impressions) to measure success?
A cross-channel marketing approach requires cross-channel metrics. Read on to learn about two specific metrics and two measurement approaches that will give you a better perspective on your cross-channel marketing effectiveness.
The Match Rate (or Identification Rate)
If you're going to engage in retargeting using display, Facebook or other channels, a key measure of success is how many people you'll be able to reliably identify on those channels.
More generally, if you're leveraging multiple channels, you need to understand exactly how many people you can identify for targeted content.
One company recently developed a technology to identify three times as many people on its site and across various digital channels. This one change tripled their identification rate, increasing the audience of people that can be nurtured.
Of course, you still need to be good at marketing to those people that you cannot identify, and you'll never be able to identify everyone. But with identity comes information, which allows a marketer to be more relevant and therefore more likely to drive action.
The View-Through Rate
The view-through rate tells us how many people who've seen or engaged with an ad go on to participate in an important interaction, such as viewing a video or visiting a site. Many of the channels in a cross-channel campaign won't lend themselves to a direct click-through, so the view-through rate allows a marketer to better understand the contribution of these channels, helping to justify spend in channels that aren't seen as generating ROI as directly as other channels, such as email.
The view-through rate has it's own complications. It's not always clear what the value of a view-through is, and it's not always clear that the ad unit itself was seen. Still, tracking this metric will move a marketer beyond rigid focus on the last interaction to build a better picture of other channels' contributions.
Adopting a Multi-channel Attribution Approach
A cross-channel customer experience should necessitate multichannel attribution of success to your different campaigns. Why should the last click on an email get all the credit for a $60 sale when it was a PPC ad that caused the subscription, site interactions that built the interest and retargeted display ads that drove the purchaser to the point just before he bought? The goal of a multichannel attribution model is to spread attribution intelligently over all of the channels that contribute to a conversion.
Thankfully, most of the top analytics platforms offer some multichannel attribution reports for you to leverage. Before you begin implementing multichannel attribution, however, you should be aware of the technical and strategic challenges this entails (Avinash Kaushik's post does a great job of explaining some of these difficulties and how to approach them). But don't let the difficulty deter you! The multichannel attribution approach allows for a much more accurate understanding of how the entire marketing mix contributes to results.
Using Cohort Analysis
One-channel metrics provide precise insight into the performance of single interaction points, such as a click on a link or time spent on a page. The problem with these metrics is the lack of visibility before and after the target interaction. What else did the person see before they clicked the link? After purchasing, does the person purchase again, and how long does it take him to do so?
Cohort analysis, in contrast, allows a long-term comparison between groups, thus negating the focus on single interactions. In cohort analysis, an objective (ie.: an increased retention rate for a subscription product) is defined and then measured for different groups of people over time.
To do this, a group with a similarity is identified (ie.: joined the list on the week of November 17th) and considered against other groups (ie.: joined the list on the week of November 24th). After a certain period of time (ie.: 2 months), the groups' performances are compared. Cohorts can also be formed around other criteria, such as previous purchase history or engagement data.
In the image below, a service with a weekly subscription model is comparing retention rates of members who joined during different weeks. Why is there such a large drop in retention for the second cohort who joined on the week of 11/24/13 when compared to the other two cohorts? Such a report allows the marketer to review the marketing mixes of that cohort specifically to see if anything obvious stands out.
Retention rate of user cohorts based on join date.
Cohort analysis provides a broader view of how differences in experiences create different results. You lose the level of detail that one-channel metrics provide, but you gain that broader view that is essential to understanding cross-channel campaign contribution.
Leveraging the two metrics (match rate and view-through rate) and the two shifts in methodology (multi-channel attribution and cohort analysis) will enable marketers to achieve a higher level of insight into the specific contributions of each channel.
Title image courtesy of Shutterstock
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As one of StrongView's in-house marketing strategists, Justin Williams helps email marketers develop and implement strategic lifecycle marketing campaigns that are continually optimized to increase engagement and revenue. For the past five years, Justin has applied his expertise in email marketing, social media, web design, and other interactive marketing disciplines across a variety of industries, including retail, finance, media, and technology. In addition to founding his own consulting company, Justin has built go-to-market strategies for early-stage startups and worked with brands like Cisco, Qualcomm, and Geeknet. Justin holds a BA in cognitive science from the University of California at San Diego.
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