Earlier this year I was interviewed by Barry Levine for a MarTech piece on MTA (see it here: Martech Today "Assessing Multi-touch attribution's value: Is it worth the effort?") My comment there, that for many marketers “The juice from MTA isn’t worth the squeeze” prompted a number of comments and questions. This blog entry will attempt to clarify and respond to a few of those.
Not at all. MTA is a valuable tool for understanding a brand’s marketing efforts. It’s not the only tool however. For marketers that spend significantly in digital and have fulfillment through online, MTA is a great way to quickly see how pieces of an overall program inter-relate. For smaller marketers, or those with primarily offline marketing and sales the return from MTA is more limited.
MTA is kind of like the turbo in my old Saab. A complex system that lets you do cool stuff, but that will let you down if not handled with care. To feed MTA you need clean, complete data around individuals’ exposure, response and ultimately purchasing. Then you have to know what to do with it. While algorithms do the heavy lifting, there are lots of options in terms of methods, and finding and setting up the right system can be an involved (albeit necessary) process.
There are myriad options for measuring marketing efforts, from the simple to the complex. To me the critical component to any of them is data. I’m really excited by the advances on this front in our industry. Identity graphs are a great example; just a few years ago we didn’t have this type of resource. Now they are abundant. Having our own Identity graph here at Claritas provides us with data that we use to help our clients understand how different groups of customers respond across channels, on different devices, and to different campaigns. This data can feed MTA, but can also drive more focused analysis.
In sum, a well-structured MTA yields valuable insights for marketers, but if you don’t have the data, the expertise, the budget or the scale to utilize this tool, don’t force it. But don’t wait either. Make use of the data and resources you have to measure what’s important in the best way you can.