How AI is Changing Marketing and Marketers

Data science, machine learning, and AI have changed the nature of marketing, and marketers need to understand this shift so they won’t be left in the dust by well-executed AI marketing projects from competitors.

In the face of a complex and fast-evolving MarTech ecosystem, marketing departments experience difficulty implementing and connecting all these technologies. The increasingly AI-driven transformation in marketing and business also means a fair bit of change not only at the tactical and organizational levels, but also at an individual upskill level, with the continuing education marketers will need to stay current.

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Data Challenges Marketers Face Today

Today’s marketing teams have no shortage of business questions they want to solve, yet they run into all kinds of challenges when trying to make AI a reality, including:

  • Lack of data or incomplete data.
  • Difficulty integrating data from multiple marketing platforms.
  • Data projects that rely on limited statistical models (instead of more sophisticated machine learning models).
  • Difficulty deploying and automating models due to complex links with frontend systems.
  • Data silos and lack of tools or easy access that allow them to dig into data themselves.

Avoiding AI in marketing could lead to big consequences down the road, such as a loss of market share and the creation of a deep technical debt compared to companies that have made AI an integral part of their market strategy.

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What are the next steps that marketing professionals – and wider organizations –  can take now to adapt to the data-driven times?

  • Education. Modern marketers don’t need to become data scientists overnight, but in order to successfully collaborate with data teams and implement AI solutions into their marketing operations, they need to be able to speak the language of machine learning, deep learning, and AI. These notions have gone from the realm of a relatively small number of data scientists to the marketing and business analytics mainstream. In order to stay competitive and deliver real value from their data, marketers need to understand the basics of marketing AI.
  • Collaboration. Invest in technology (like a data science, machine learning, or AI platform) that can be used not only by data experts, but new data champions, for everything from managing data projects to connecting to data themselves. Rather than the unrealistic expectation that marketers should be experts in data science (or the dream that each marketing team has its own data scientist or other data expert on staff), it’s critical to centralize data (including projects, processes, and knowledge surrounding it) in a shared environment, allowing team members to pick up the work of their colleagues, keeping tabs on progress and allowing marketing analysts to collaborate efficiently on data projects.
  • Exploration. Start driving change by choosing at least two or three simple data projects that would help provide more marketing insight or efficiency, and partner with data experts to get started. Beginning with a few low-hanging fruits will up the chances that at least one is a success.

7 Steps to Completing a Data Project

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To compete and stay relevant in this rapidly changing world, businesses and marketers need to embrace the idea of using data and AI for their marketing activities. It’s crucial for marketing teams to leverage AI; If you don’t, you risk falling out of sync with customers and losing the competitive edge as consumers turn to AI solutions as well.

Dataiku for marketers

For marketing departments in a data-fueled organization, this means centralizing data (including projects, processes, and 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.

Dataiku is for:

  • Chief marketing officers who view data as fundamental to marketing, making better decisions, generating actionable insights from marketing AI activities, and helping the organization move forward in their Enterprise AI journey.
  • Marketing analysts: Dataiku’s visual platform and automated ML features cover the whole data pipeline, making data science and machine learning a natural extension to marketing analysts’ current capabilities and skillsets.
  • Marketing analytics leaders, for harnessing a transparent yet structured environment in which they can not only track individual and team performance, but also become a vector for team collaboration, personal growth, and skill-set amplification.
  • Marketing data teams to connect to, explore, prepare, build, automate, and deploy machine learning-based models, applying them in different marketing AI use cases.
  • Other marketing and sales teams, who can use Dataiku’s point-and-click data preparation and modeling features to bootstrap their own data exploration or collaborate with data teams to deploy larger AI projects.
  • …and more – Dataiku is flexible yet robust, built for everyone in the data-powered organization.

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