Leveraging AI-Enabled Marketing Attribution

Marketers can improve holistic attribution metrics by leveraging AI to track consumers at every stage of their journey.

Marketing attribution is the process of measuring campaign effectiveness by quantifying the influence campaigns have on a desired outcome (e.g., starting a free trial, making a purchase, etc.). By understanding which channels or what content leads to a higher conversion rate to these desired outcomes, marketing teams can better optimize spend and messaging.

ML-Based Marketing Attribution

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Historically, marketing attribution has been a painstakingly manual process based on heuristic models that often turn out to be more difficult (and less effective) than necessary. And unfortunately, due to their perceived simplicity, many marketing teams turn to outdated techniques such as the last click heuristic, where all the conversion merits are attributed to the last media contact or channel the customer was exposed to. This does not adequately capture user engagement, and risks undervaluing campaigns and content that are critical to conversion.

According to a study by BCG on digital marketing maturity, 78% of the companies surveyed had difficulty attributing value to touchpoints along the customer journey.

The Dividends of Digital Marketing Maturity, BCG, February 2019

Fortunately, the significant advances in marketing AI and machine learning (ML) in recent years allow marketers today to solve perennial problems in new, more efficient ways.

Avoid the Pitfalls and Succeed with Marketing Attribution

Marketers who want to shift the paradigm and increase campaign effectiveness can follow these simple steps to avoid the pitfalls and maximize the value of their marketing attribution activities:

  • Scale and automate. With algorithms handling data from multiple sources and giving near real-time feedback on the most effective channels, marketing teams can scale their efforts to reach more people more effectively (ideally by spending less money). In addition, tight integration with customer relationship managers (CRM) or advertising platforms can reduce manual processes and introduce more automation.
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  • Get personal. Teams can build ideal customer journeys down to granular user segments or, in some cases, down to the individual level for hyper-personalization (which generally translates to more desired actions).
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  • Get creative. With all of the hard work being done by ML attribution models, marketing teams are free to get creative and experiment when it comes to channels and messaging (especially with real-time feedback and the ability to pivot if needed).

Power Marketing AI With Dataiku

To extract maximum value from AI initiatives, data must be at the disposal of everyone in the organization. For marketing departments in a data-fueled organization, this means centralizing data (including projects, processes, and the knowledge surrounding it) in a shared environment, allowing all team members to pick up the work of their colleagues and to collaborate efficiently on data projects.

Dataiku is the platform democratizing access to data and enabling companies to build their own path to Enterprise AI. With Dataiku, centralizing and cleaning web logs, customer data, CRM data, and more to create a holistic view of marketing performance is now available to all marketers, without relying on complex IT systems and data scattered in silos across other teams.

Dataiku’s visual platform and automated machine learning (AutoML) features cover the whole data pipeline, making data science and machine learning an extension rather than a limit to marketing analysts’ current capabilities and skillsets.

Bringing AI to Marketing

The evolution of AI, machine learning, and data science have been an increasingly integral part of the transformation of many industries, and marketing is no exception.

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