Optimizing channel allocation has always been a major challenge for marketers. But to make the most out of the available data, they now need to employ some new technologies to be able to predict outcomes and improve performance.
The average marketer’s data set is bigger, more fragmented, and more confusing than ever before. This presents a real challenge and can feel pretty overwhelming at times, especially considering the number of different platforms that marketers have to go to in order to get their data into reports.
On top of the increasingly complex data landscape, marketers also face the added challenge of deciding what technology you need to do your job more effectively. “You may or may not know that each marketing platform that you're using today is already using a plethora of different AI technologies. But regardless of this invasion of technology, it is still hard for many marketing teams to get things right,” emphasized Joseph Caston, Director of Solutions Consulting at Adverity, at a webinar we organized recently.
The key struggle is deciding how much to spend on each marketing channel, what's the right mix, and at what point in the customer journey do you need to invest in marketing. And this is an area where the implementation of AI can help a lot.
Marketers have seen the rise and fall of many technologies and platforms over the years, and the one which has been highlighted lately is marketing attribution. Many models have been developed over the years to try and solve the attribution puzzle, and multi-touch attribution is just one of them.
“MTA uses a bottom-up method to data science, which generates absolutely tons of recommendations,” explains Caston, and adds: “In fact, there were so many recommendations provided at such a granular level that it was impossible even to act on half of them.”
Recent developments in the measurement landscape, like Apple’s iOS 14 restrictions and the demise of third-party cookies, were the final nail in the coffin for MTA. “MTA led us to believe that advertisers can do better than just sit back and let all of these technologies run, and kind of hope for the best. But that doesn't mean that we should just give up on the ambitions of MTA, right?” concludes Caston.
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There are new generations of solutions out there — instead of bottom-up, a top-down approach to data science is used for finding the right mix across channels. This top-down approach to data science seeks to identify the big picture and all of its components, and in order to make this approach work, marketers absolutely need to have their data cleaned and integrated into a single source of truth.
Once you have this in place, you can start applying intelligent, top-down approaches to your data, and start moving up on the analytical maturity scale to reach advanced stages, such as proactive and predictive analytics. “With this in mind, we've built a new application that can be added on top of your unified data sets, and that is able to really make sense of all the data using modeling and forecasting,” says Claudie Poirier, Product Marketing Manager at Adverity.
Its name is Reveal, and it uses advanced statistical methods to continuously assess and interpret data, so it can then give marketers actionable recommendations on how to optimize campaigns and maximize ROI, all without the use of third-party cookies.
Reveal includes several features, such as Proactive Analytics, a newsfeed that displays trends, anomalies, and opportunities for campaign optimizations, ROI Advisor, a continuous marketing modeling mix solution that gives insights into your channel performance, and Forecasting, which uses historical data and current information to provide a future-looking insight on your marketing budget spend.
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