Custom Model Predictive Audiences Dynamically with Speed and Precision
Facebook recently announced that it will no longer support 3rd Party Data Partner Categories or enable advertisers to create campaigns against custom audiences in their platform due to privacy regulations. As a result, there is going to be a big shift back to advertisers and their supporting marketing database / agency or onboarding partners for audience creation. This shift is particularly relevant for larger scale, more sophisticated, advertisers who already are looking to advance their audience segmentation capabilities for greater digital campaign precision and efficiency.
As a result of Facebook’s announcement, there is going to be a big shift back to advertisers and their supporting marketing database, agency, or onboarding partners for custom modeled audience creation.
As a result, custom modeled audiences, using an advertisers’ first, second and thirty party data, are now the “new normal” for creating ad campaigns that reach a desired audience. Enter Dynamic Predictive Audiences based on explainable AI machine learning technology that reveals predictive audience data, in weighted order of predictive value, and clusters each individual audience prediction that share common characteristics into dynamically generated segments for campaign execution.
GDPR’s Impact on Marketing
More than ever, advertisers need the ability to create audiences themselves, and to do so faster and more efficiently than status quo methods have allowed. Furthermore, advertisers need to reach the right audiences effectively, who are actually predicted to buy their products and services, and be relevant to the audience they are reaching! Somewhere along the line, these goals were a sacrifice on the alter of a digital ecosystem that was based on sustaining an inefficient mass ad impression model that tolerated massive waste and weak, unmeasurable audience targeting and campaign performance.
Now enter a post GDPR world where large scale advertisers have already validated that they can pull massive dollars out of this ecosystem and not sacrifice sales. The principles of direct marketing are back for digital advertising; analytic and data drive audience targeting for efficient campaigns that produce measurable results. And these market driven forces are perfectly timed with the availability of explainable AI technology that can increase ad campaign precision by 30% or more, reduce cycle time for creating custom modeled audiences to minutes, hours and days vs. weeks and months, and scale across an enormous volume of data fields to create predictive campaign segments with their weighted predictive factors enabling greater relevancy of marketing communications.
Efficiency and speed of explainable AI in providing Dynamic Predictive Audiences is one side of the equation, but where the real heart of value is for advertisers is in XAI’s predictive value and relevancy…
Efficiency and speed of explainable AI in providing Dynamic Predictive Audiences is one side of the equation, but where the real heart of value is for advertisers is in XAI’s predictive value and relevancy in order to achieve what is always the goal – to increase sales conversion rates, revenue volume and ROMI. At a time when addressable TV is adding in more ways to reach your audience, it’s time to put the customer back in the center of the equation vs. overweighting the media – and start to get really scientific about how to maximize return through relevance regardless of customer touchpoint.
Explainable AI and Customer Segmentation
Imagine being able to generate predicted buyer segments for each campaign dynamically, based on up to the moment data (available through ethical data sources of course), match relevant offers, messages and creative based on the predicted drivers of buyer behavior – and do this is hours and days vs. weeks? This is possible today and the ROMI is two-fold: 1) predictive lift in audience selection and 2) increased conversion rates from more relevant offers.
Impact #1: Predictive Lift in Audience Selection
Traditional customer segmentation for digital ad campaigns often leverages descriptive segmentation to select prospects that look like recent buyers. Predictive machine learning algorithms leverage predictive characteristics of recent buyers for creating algorithms that fine tune and rank order predicted buyers vs. descriptive look alikes. According to Forrester’s, Capture the Customer Moment with Dynamic Predictive Segmentation, January 2018, marketers already recognize this value.
Decision makers responsible for program execution or campaign planning fear that not implementing dynamic predictive segmentation will translate to: executing campaigns sloppily; inaccurately predicting what customers want during interactions; or imprecisely forecasting the best fit, next offer, or recommendation.
The result is lost revenue and customers lost to competitors.1
Impact #2: Increased Conversion Rates from more Contextually Relevant Offers
The second part of the equation is leveraging the predictive factors themselves that are dynamically weighted for each individual prediction. Explainable AI reveals these factors to enable advertisers to match offer, message and creative treatment to the real drivers of the predicted behavior. This could be preference for certain product features, purchase timing and frequency, promotional or channel specific characteristics or other correlated data that stands independent of descriptive customer attributes like age and income.
Today’s most sophisticated agencies and demand-side platforms are using classic machine learning techniques to identify prospects with a high likelihood to purchase. While this technique identifies the right customer to target, it does nothing to reveal the best message for that customer. For example, one customer may be particularly enamored by a luxury car’s exterior while another may be more interested in the engine under the hood.1
Dynamic Predictive Audiences helps level the playing field with consumers who expect advertisers to understand what matters to them most. And when it comes to the agencies, digital onboarders and DMP’s whose job it is to instrument more relevant ads efficiently to predicted buyers, speed and efficiency give time back to campaign planners to hone and evolve increasingly impactful ad campaigns to achieve increasing performance lift. Explainable AI makes this a reality. It’s time to start testing this new technology and what it can do for premium accounts. You won’t believe the results that can be achieved!
The need for speed has never been as evident and significant
as it is today. And marketers understand that the consequences
of not investing in dynamic predictive segmentation will negatively impact their ability to act quickly…1