“The future is already here – it’s just not evenly distributed.”
Having worked at major advertising networks delivering strategic and tactical marketing programs with Fortune 500 companies, it was apparent that traditional Marketing Mix Models (“MMM”) were broken.
For the past 3 years, we at Data POEM have looked at this problem with fresh eyes and applied new advanced AI based Causal learning technologies that address the many challenges of a traditional MMM approach.
It almost feels like we are in the early stages of electric vs gas cars debate.
Let’s understand why we feel the same way in ROI measurement space.
Business leaders, Marketing leaders and Analytics teams want speed to insight, velocity of MROI data and actionable insights. What do marketers have today? Static models with outdated data at an aggregate level with no actionability. In a new omnichannel world,
The fundamental limitation of traditional MMM’s approach is its inability to learn the interconnected impact of multiple variables impacting the ROI . This is compounded by the interconnected media behavior and shopping behavior of the new age consumer.
The other biggest limitation is the lack of actionability. Traditional approach works on a very aggregate level of data and hence is not able to provide the insights at a granular level of audience level or a platform level which limits actionability.
In addition to the above, the operational problems of old, outdated data, negligible speed to insight is a frustrating experience.
The New Beginnings
In the last two to three years, advanced learning techniques have revolutionized the way organizations and industry sectors use Causal Learning Models to enable their decision making.
We are seeing the results of these new techniques being applied in Marketing ROI measurement across our clients and are proving to be the game-changers for their businesses.
Why is this a big deal?
The modern Causal learning techniques recognize & solve for the Interconnected Impact of marketing elements on Sales in line with consumer behavior.
Causal Learning Models also solve the biggest problem of Omnichannel marketing through a Unified Measurement best suited for an Omnichannel world. We can measure the ROI of both the Measured and Non-measured inputs in a single view.
The Granularity of Insights we get through this methodology is mind-blowing. We help brands discover the ROI insights of close to 150+ features in one single model. These systems understand the interconnected effect of every element of marketing and understand the impact of various features like audience-level investments for digital channels, day part lev? message, size? of the ad, duration of the ad, genres, channels and even the on-ground elements like sponsorships.
AI models learn in a bottom-up approach which helps them to learn at individual market level and not at one aggregate market level.
A revolutionary feature of this model is that you can have monthly incrementally trained models to review marketing performance so you can make decisions dynamically. No more waiting for 12–18-month-old post-mortem data to see how your marketing spend has performed!
Causal Learning Models are going to create a new paradigm of ROI planning, optimization and measurement. This methodology is faster, smarter, comprehensive and agile. It will help marketers make data-driven decisions in an agile manner.
At Data POEM, we built the world’s first Plug & Play AI platform for Marketing ROI measurement and Agile planning powered by Causal learning.
We believe that this is the future, and it already feels like a new normal for us – a new way of life. We can’t wait for the world of marketing, media, analytics and business to adopt and make ROI measurement Challenges a thing of the past.
