Modeling as a service (MAAS) hasn’t yet hit the mainstream status in the adtech and media landscape, but with the natural move towards activating big data in a meaningful way to drive higher performance for brands, as well as the requirement to consolidate activation into fewer and fewer media outlets it’s only a matter of time before MAAS becomes common practice. Below I discuss what MAAS is,why should you care about look-alike and act-alike audiences, the workflow behind Audience Optimizer and why it’s set to grow and grow.
What is MAAS?
Modeling as a Service or MAAS describes a process or solution whereby advertisers or their agencies are able to utilize external big data and technology experts in order to produce highly effective predictive modeling solutions which capitalize on machine learning and adaptive algorithms in order to define new target audiences by looking at an existing data set.
In simple terms, modeling as a service produces an audience output, based on pattern recognition, scoring and cross referencing an existing data set with a much larger user base to find similar users who exhibit a higher propensity to complete a goal action.
Sounds Like Look-alike Modeling
Like look-alike modeling, the basic functionality of MAAS is to take 1st party data and to use this to model and scale out by capitalizing on a database of 3rd party profiles. However, contrary to many look-alike services, which are intrinsically linked to a buying platform for media activation, modeling as a service generates an agnostic audience output decoupled from activation, which can be activated universally across any given channel and through any buying platform.
Audience Optimizer adjusts campaigns purely focused on data, and not on bidding, creative, contextual placement, whitelists, blacklists etc. That means Lotame can focus on the data and the buyers can focus of the buying.
Although the technology and mathematics sitting behind Audience Optimizer is complex, the workflow for creating an optimization model is relatively simple:
Data to Insights
One of the biggest and best USP’s of Audience Optimizer is the transparency we can offer to clients with regards to the output of the model. We share audience profile reports showing audience overlaps, and the common patterns identified by the optimizer tool. These insights can be hugely valuable in determining audience insights and feeding into the planning and the strategy for future campaigns.
By Miles Pritchard – European Client Development Director at Lotame
Original can be found on LinkedIn here