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Creating an Autonomous Marketing Future through Data and Integration

By Benjamin Bloom | July 27, 2018 | 1 Comment

MarketingData and Analyticsmultichannel marketing

Gartner’s latest Hype Cycle for Digital Marketing and Advertising is out now (Gartner clients may access it here) and one key takeaway is that hype surrounding Artificial Intelligence for Marketing has yet to reach its peak. It continues to intensify and it’s not letting up anytime soon.

For any marketer responsible for making decisions around buying and implementing technology, that shouldn’t be too much of a surprise. You’re inundated with grandiose claims that “AI” will deliver your team unprecedented efficiency and growth. You view these statements with a skeptical eye, as you should. But you also realize that the vision of autonomous, real-time, multichannel marketing programs being presented to you is quite appealing — less time building intricate yet imperfect campaign journeys, more time being creative and strategic.

We’ve seen the introduction of productized AI capabilities — Watson, Einstein, Sensei and Leonardo, among others — from existing marketing technology vendors over the past two years. But major R&D and M&A investments from many of these companies related to data and integration suggest there’s plenty of work to be done to make the autonomous marketing vision a reality. After all, data is necessary fuel for any AI-driven system. Consider the following:

  • Fresh off a substantial acquisition of integration platform MuleSoft, Salesforce announced its intent to acquire marketing dashboard provider Datorama. While both companies led with data integration as the top benefit of the impending tie-up, some industry chatter picked up on the combination of Salesforce’s Einstein and Datorama’s Genius AI capabilities. While MuleSoft helps connect Salesforce’s disparate clouds together and into the broader IT ecosystem, Datorama goes deep on bringing together siloed marketing data into a common model.
  • Adobe emphasized architectural improvements to Experience Cloud at its annual Summit event in March 2018, including more comprehensive customer profile unification. It also released the Experience Data Model (XDM), an open source effort in collaboration with Microsoft. The goal is to meet the needs of demanding marketers who wish to increase the scale and performance of customer experiences integrated with Adobe and its Sensei AI capabilities.
  • In a similar vein, SAP acquired Gigya, a customer identity and access management system, in September 2017. It has since rebranded the offering as SAP Customer Data Cloud.

Roadblocks in the connections between customer data, marketing performance and business data hamstring manual efforts at automating multichannel marketing.  Gartner’s 2017 Multichannel Marketing Effectiveness Survey found that marketing leaders overestimated their ability to advance real-time, data-driven marketing. The recently published Market Guide for Customer Data Platforms — another martech category radiating hype — shows that CDPs aim to produce unified customer profiles accessible to marketing systems via a plethora of prebuilt integrations, enabling a more data-driven marketing approach.

A surface justification for Salesforce acquiring Datorama is to produce greater near-term insight. Connected data sources and visualizations empower human decision-makers who use the Marketing Cloud. Lowering the barriers to insight, in order to to drive action that produces business impact will be the immediate task at hand. But on the horizon are automated systems that leverage artificial intelligence to act faster, in service of a marketers demands. With the alignment of Datorama and Salesforce’s resources and AI vision, marketing leaders should expect accelerated progress on the assistance that technology can offer to meet dynamic customer expectations.  

The great thing about the Hype Cycle is that after unmet expectations and a bout of discouragement, these innovations eventually reach maturity, eventually delivering productivity for the majority of users.

Many thanks to Bryan Yeager and Lizzy Foo Kune for the collaboration that led to this post.

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1 Comment

  • You said “Roadblocks in the connections between customer data, marketing performance and business data hamstring manual efforts at automating multichannel marketing.” — perhaps you could expand upon this insight in a follow-on editorial. Thanks in advance for your consideration.

    Personally, I’m not seeing any current MarTech platform deliver the type of actionable insight that would qualify as meaningful innovation. Much of what we do as marketers require research that is very labor intensive because the data is fragmented across best-of-breed tools that come from various sources.