After several years of development and heightened industry expectations, the MTA bubble is bursting before our eyes. With the sun-setting of Convertro by Oath, the internalization of Adometry by Google 360, and the recent purchase (and likely absorption) of Visual IQ by Nielsen, the once big three of MTA have now all but disappeared. Why?
On the surface, these large media networks saw a chance to bring in-house a technology to enhance and optimize ad-spend within their own programmatic platforms, but the truth is that MTA as a stand-alone service failed to deliver and failed to live up to the industry’s expectations.
That said, there is still an assumption today that MTA is the holy grail for measuring the impact and details of ads. For example, in Martin Kihn’s (Research VP, Gartner) recent analysis of Google’s decision to no longer make their DoubleClick IDs available in its ad server log files – a foundational element for any MTA solution – that AdExchanger recently published, he still asserts that “MTA is not the only way to measure the true impact of ads, but is theoretically the most accurate and provides by far the most detailed results.” However, we are far from convinced of MTA’s superiority as a measurement tool based upon experiences numerous companies have had with it during the past 10 years that MTA has been around.
Here are five inconvenient truths about MTA that also explain what is behind its missed opportunity.
The following points are not theoretical. They are results of first-hand observations and experiences developing and deploying MTA solutions. They are also based on numerous companies’ feedback, experiences, and their attempts to implement solutions from virtually all of the major vendors in the MTA space.
Particularly given recent issues around privacy we can only imagine this type of complete dataset is a very long way off. But, even if it were available, the MTA methodologies used are all inappropriate for such a high dimensional time-series data set.
Why does all this matter? It’s important to understand and agree on these facts as an industry. If the fast adoption of new approaches are to gain support, then it’s also important that we as an industry quickly and earnestly learn from our mistakes. In order to resolve and advance the quest for solid data and solid analytics that rigorously demonstrate and support marketing, it is imperative that we are all aligned on what is, and what is not, possible and why.