In Praise of the Media Planner: Why Attribution Tools Should Be Built for the People Who Use Them
in the beginning
When marketing attribution hit the mainstream five years ago, it seemed like an answer to marketers’ prayers. Finally: a data-driven way to go beyond last-click models and indeed give credit where credit is due across the cross-channel landscape. Attribution complements MMM (marketing mix modeling) with the power of big data and real-time analysis, providing insight into each touch of the conversion funnel in seconds instead of weeks. For direct response marketers, this is a slam dunk: in the post-800 number days, understanding purchase path to conversion is critical.
So, marketers decided attribution was valuable: let’s get us one of those. Analytics experts then evaluated the data science, inputs, and outputs of each solution. CMOs signed checks. And media practitioners got handed complex SaaS platforms and were told to go forth and prosper.
Attribution modeling is a set of rules for assigning credit to the various touch points in the conversion path it helps marketers understand trends in how prospects move through the path to purchase.
Early Attribution Platforms
Predictably, it wasn’t that easy. Early attribution platforms (and some today) took up to a year to get up and to run. The built-in processes were rigid, requiring each organization to adapt to the platform instead of the other way around. Once deployed, they churned out massive amounts of data, but it was difficult to sift through the noise to find meaningful performance insights – especially given that the end users, usually media planners, were not data and analytics experts. The depth and volume of the potential reports seemed endless, but their interpretation proved challenging.
As a result, many attribution solutions became shelfware, leaving many media planners with less information, but higher expectations.
This scenario is neither new nor inevitable. How have many software solutions with the best technology failed due to poor user experience? There is no excuse for ignoring the end user. But somehow, despite dedicated UX/UI teams, complex, QA, and UAT, and tremendous competitive pressure, tech vendors continue to turn out software built by engineers, for engineers (or in the case of attribution, by data scientists, for data scientists.)
Time for change
It’s time for a different approach to attribution. To paraphrase, let us not bury the media practitioner, but praise them. Attribution platforms should not require users to choose between so much information that it’s overwhelming – all the data, all the time – and so little data that while you can understand it, you can’t do much with it.
There is both a middle ground and a best-of-both-worlds approach.
Modern attribution liberates the data from the system, making it entirely accessible both within the tool and for export when in-depth analysis is required. At the same time, it provides readily digested reports that map to the most critical business KPIs.
The interface is designed and built with media practitioners and customer insights professionals in mind, serving up the data that is most meaningful to them. It operates under the assumption that its end users add value and serves up information accordingly; users should be able to get in and out of the system, with the information they need, in under five minutes. In short, attribution empowers media professionals instead of automating them out of a job.
What does this mean for the direct response type performance marketer? That attribution is very valuable when done right. A holistic understanding of your target customer’s purchase path to conversion offers nearly countless opportunities for optimization, delivering direct impact to your bottom line. But to achieve these benefits, and ROI on your attribution platform, it must provide the right data, in the right way. Far from replacing media professionals, attribution should empower the media buyer to offer the unique – human – insight that makes all the difference in today’s competitive landscape.
Learn More About Attribution Modelling
About the Author
Alison Latimer Lohse
Phone: 310.997.0901 x102