“The future is already here – it’s just not evenly distributed.”
Having lived it and practicing for the past three years, We believe that the future of ROI measurement, Planning and Optimization is already here and it is Causal learning based modelling. It is going to transform the existing broken paradigm of static, statistics-based marketing mix modeling. We are seeing definitive trends that this learning model heralds the beginning of the end of traditional MMM.
Let’s understand why.
Business leaders, marketing leaders and analytics teams want Speed to insight, Velocity of data and actionable insights. But they are stuck with a dinosaur of traditional linear market mix modelling for ROI measurement and optimization.
In a new omnichannel world, the litany of woes starts with MMM’s approach of treating every marketing channel and input as mutually-exclusive whereas the consumer clearly follows an interconnected behaviour.
The other limitations of MMM that gets the goat of marketers are lack of :
· Unified Measurement: Siloed models which don’t give a comprehensive ROI measurement
· Agility: Static models which are a post mortem but not learning dynamically
· Granularity: Traditional approach works on a very aggregate level of data and hence not able to provide the insights at a granular level of a particular channel, ad type, message
In addition to the above, the operational problems of outdated data, speed to insight is a frustrating experience.
All these limitations stem from the underlying statistical techniques used to do ROI measurement.
The New Beginnings
In the last two to three years, advanced learning techniques have revolutionized the way organizations and industry sectors use data to enable them their decision making.
We are seeing the results of these new techniques across our clients being applied in marketing ROI measurement are proving to be the game-changers for businesses.
The modern learning techniques solve the interconnected impact of marketing elements on Sales. This revolutionary approach solves for limitations of the siloed, linear regression-based statistical modelling and insights at a very aggregate level.
What’s the big deal?
The causal learning methodology is going to create a new paradigm of marketing planning, optimization and measurement. It is faster, smarter, comprehensive and agile. This will help marketers make data-driven decisions in an agile manner
Solves the biggest problem of Omnichannel marketing through a unified measurement best suited for an Omnichannel world. We are able to measure the ROI of both the measured and non-measured inputs in a single view.
The level of granularity of insights we get through these techniques is mind-blowing. These systems understand the interconnected effect of every element of marketing and also understands the impact of various features like the impact of the 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 one aggregate market level.
The most important feature that these models deliver for business is the agile learning models where the models learn continuously on the dynamic business data. This will help marketing teams create agile plans and optimise the ROI in a dynamic way. You can have monthly, quarterly and half-yearly models to review the performance and make the decisions in a dynamic way.
We at Data Poem strongly believe that this is the beginning of the end of the dinosaur era of measurement methodologies and built the world’s first Plug & Play AI platform marketing ROI measurement and Agile planning powered by deep learning.
We believe that this is the future and it already feels like a new normal for us. we can’t wait for the world of marketing to adopt and make data-driven ROI measurement challenge a thing of past
