Ad agencies and consultancies alike continue to focus their acquisition and consolidation strategies on “data” firms as they build-out their future service offerings.
One only has to consider Publicis Groupe’s recent advances toward Epsilon or note Interpublic’s 2018 acquisition of Acxiom and Dentsu Aegis’ acquisition of Merkle in 2016.
No one questions the importance of data analytics and its role in key aspects of the marketing process from target audience segmentation and enhanced digital media performance to optimizing lifetime customer value. However, adopting a data-centric mindset that focuses on lower funnel conversion tactics to satisfy advertisers’ near-term revenue generation needs should not be mistaken for a customer-centric approach to addressing the problems facing advertisers today.
To the extent that data immersion yields intelligence and insights that help position brands in a relevant and compelling manner to make it easier for consumers to associate themselves with those brands that is good. But if the focus is to forgo brand building in the hope of driving results through the creation of on-demand experiences with the goal of driving conversion, the risk is that marketers may simply annoy consumers and not endear their brands to their target audience.
A fundamental question to be addressed is: “Why is it that in the age of “big data” customers are becoming less brand loyal?”
Perhaps the focus should be data analytics ability to generate insights that inform brand strategy, boost a brand’s emotional appeal, build its value proposition and build an emotional connection with the consumer.
To this end, it was with great interest that I noted one of the key findings from PwC’s recent Retail Survey; “Consumers want benefits, not surveillance”
Artificial intelligence (AI) is already reshaping how advertising is developed, planned and placed. The marketing applications being envisioned and adopted by agencies, consultancies, publishers and advertisers are nothing short of remarkable.
It has been decades since the concept of Marketing Mix Modeling (MMM), the forerunner to Attribution Modeling, was introduced. The concept was relatively straightforward, marketers would apply statistical analysis to sales and marketing data to quantify the impact that each element of the marketing mix had in driving brand sales and profit. Once the causal relationship had been modeled, marketers would then be able to accurately forecast outcomes and inform resource allocation decisions.
The evolution of media channels, ad targeting and the role of ad tech have significantly reshaped the media marketplace, allowing advertisers to select inventory and direct their messaging with an incredible level of precision. These developments have long been hoped for and yet, now that they are here, there is much that advertisers don’t understand about one important bi-product of the ad tech revolution… the disposition of the data gleaned from their investment at all levels of the media investment cycle.
Marketing Math 