David Dowhan has a successful track record of executive leadership at growing both private and public venture-backed companies and has been a pioneer in online marketing since 1997. Before joining TransUnion, Dowhan most recently lead TruSignal, Inc. in his role as CEO and founder before TruSignal was acquired by TransUnion in May 2019. Before TruSignal, Dowhan spearheaded strategic business and product development efforts at eBureau, managed marketing analytics at Aderactive, and held executive roles at Drugstore.com and NextCard, Inc.
On average, marketing spend on customer data analytics has nearly doubled over the past two years. Still, the majority of sales and marketing professionals say they face challenges across data organization, analysis, activation and more, according to a Forrester-commissioned study. That could be one reason why hundreds of martech and ad tech platforms enable some form of data management today.
Until recently, I’ve seen many of these platforms parse data management technology based on data type and use case, including customer data management (CDPs), new customer acquisition (DMPs), retargeting platforms and more. Today, there’s an increased need to manage and activate multiple datasets, which requires sophisticated technology capable of operating beyond singular applications.
As we exit the era of Big Data and enter a world of Bigger Data, data management will be forced to evolve. I expect automated data science, including tools like artificial intelligence, machine learning and neural nets, to name a few, will play a big role in this evolution. These tools can improve the speed and ease of mining, comparing, analyzing and activating the industry’s multiple, growing datasets to power insights and segmentation for various use cases. Here are four points of proof the industry is teeing up for an automated data management revolution.
Over recent years, brands, agencies and publishers alike have taken notable steps to prioritize first-party databases, increasing their collection and analysis capabilities.
First, there was the swift emergence of an entirely new martech product category: customer data platforms (CDPs). Estimated marketing spend on the data management, processing and integration category shot up 25% last year — partially in an effort to manage the valuable role this data plays in building a centralized, persistent view of customers for marketers.
Taking a cue from marketers’ piqued interest, agency holding companies, including, Dentsu Aegis Network, Interpublic Group and Publicis, have invested in acquiring their own marketing data assets to better serve marketers via insights and audiences.
Publishers have also shown their commitment to driving first-party data assets by increasing subscriber bases to drive revenue models and identity infrastructures, helping monetize their advertising inventory.
As these first-party databases grow across data owners, data categories, channels and devices, there will be a growing need for tools to manage, understand and activate against them. More importantly, there will be an increasing need to derive patterns and omnichannel insights across these first-party datasets, which will require technology that’s automated, scalable and easily integrated across multiple datasets or systems.
In the wake of unprecedented regulatory measures like GDPR and CCPA, the way advertisers activate against third-party data has changed. Perhaps most significantly, leading third-party audience data marketplaces have gone away and the industry continues its search for alternatives to third-party cookies.
Ultimately, third-party data remains critical to how advertisers, agencies and ad tech providers operate, and U.S. companies continue to invest in third-party audience data for advertising and marketing. With first-party data laying a critical foundation, these companies still require third-party data to build audiences that can compete in a digital world where scale still reigns supreme when it comes to securing inventory and fulfilling budgets.
Given the increased capabilities powered by automated data science, AI and machine learning can help bridge a critical gap between disparate datasets, including customer data, third-party data, online data and more.
Until now, the martech ecosystem has been a crowded landscape of CDPs, CRMs, DMPs, DSPs and more. Historically, each of these technologies have served a particular use case. However, a wave of consolidation via merger and acquisition activity indicates to me that many are interested in diversifying capabilities beyond single use cases.
CRM platform Salesforce acquired Tableau to improve data visualization and understanding of customer data. LinkedIn’s acquisition of Drawbridge was thought to be, in part, an effort to improve targeting via machine learning and personalization. Zeta Global snapped up Sizmek’s DSP and DMP assets to build on its cloud offering.
I believe data management and automation will continue to be a theme of future consolidation. This will empower next-generation platforms with more versatility to serve a broader group of data owners — extending beyond just marketers to a new class of data-driven agencies, publishers and more.
One reason marketers struggle to activate on data’s potential is because activation tools have been complex and difficult to implement. That's partially why data-driven technologies were siloed by use case — it was difficult to bring data together at scale with automated analysis.
With automation, marketers have the speed, ease and capacity to store, organize, manage and activate all of this data holistically.
As the martech and ad tech landscapes evolve in the coming years, I believe marketers and companies that embrace AI and machine learning will benefit from long-term gains. As the industry continues to consolidate around automated capabilities, it'll soon become impossible to function without them. The sooner companies can become integrated with these tactics of the future, the more they’ll be primed for performance and survival.
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