Wharton Customer Analytics
All Is Not Lost: Finding Value In Marketing Attribution Data
Bill Franks, Chief Analytics Officer for Teradata (also one of our great speakers from WCA’s Successful Applications of Customer Analytics conference), wrote a follow-up note to the self-termed downer” article he posted last month on how random attribution methods could produce the same aggregate results as more sophisticated ones that sought to tease out credit for the many individual touch points on the path to purchase. In his latest post, Franks notes that despite these findings, it’s still worth capturing the detailed information that feeds traditional and more sophisticated attribution processes, as it can help firms better segment customers and learn about them and the organization’s marketing efforts.
Another key point of Suresh’s talk, which I did not address last month, focused on how the patterns of touch points that preceded a purchase can provide useful insights in other ways. In other words, it is useful to know the fact that a customer first saw an email, then later performed a search, and then made a purchase. It ends up that there are certain mixes of touch points that outperform others when it comes to driving sales.
Suresh is not alone in this assertion. I also saw one of my colleagues from Teradata, Yasmeen Ahmad, discuss the exact same concept at an event that I also spoke at in London earlier this year. She discussed how segmenting customers based on the mix of touch points each customer utilized led to some very interesting insights.
As luck would have it, I also saw my friend Justin Cutroni, Google’s Analytics Evangelist, make the same point when we both spoke at an event at a Wharton Business School event a few weeks back. When I see three experts share the same suggestion within weeks of each other based on completely different work streams, it gets my attention.
THE PATH FORWARD
The important takeaway is that detailed data about how a customer works toward a purchase is, in fact, very useful. Rather than using it in the traditional way for attribution, use it to segment customers based on the combination of touch points that lead to purchase (or lack thereof). Once customers are segmented according to these interaction patterns, substantial insights can be achieved about how customers behave. It is also possible to apply very precise costs to each customer’s interactions since you’ll know what you had to pay for each touch point that the customer utilized.