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How AI Will Change Marketing (and Marketers)

Use Cases & Projects, Scaling AI Lynn Heidmann

It's no secret that introducing data science, machine learning, automation, and (eventually) AI into the world of marketing will be a critical factor to success. Yet it also means a fair bit of change not only at the tactical and organizational levels, but also at a personal level with the skills marketers will need to have to execute. But it doesn't mean all marketers need to become data scientists overnight — instead, the answer is collaboration.

two colleagues collaborating

Today's Challenges

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
  • 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
  • Lack of tools or easy access that allow them to dig into data themselves

Plus, even when projects do end up being a success, their ongoing maintenance can present continual challenges (in case you haven't heard, machine learning models aren't like software — they do need quite a bit of upkeep).  

Of course, the other side of the coin is data or AI projects that don't end up being a success, which can be challenging not only because of questions that arise surrounding return on investment (ROI), but because any resources devoted to the project might be taken away following a failed project. So what can be done?

The Evolving Role of Marketing 

In the face of a more and more complex and fast-evolving martech ecosystem, marketing departments experience difficulty in adopting and connecting all these technologies. However, adopting a lot of different technological solutions does not seem to be the right answer — in fact, research shows that the technology itself is developing more rapidly than marketers can keep up with it. Indeed, more technology can ultimately hamper productivity instead of help.

So instead of training marketers on the detailed techniques of predictive analytics and machine learning or AI, successful brands made sure to provide marketers with a basic understanding and then foster collaboration between marketers and data or technology experts. Marketers don't need to be experts themselves, but the do need to learn to efficiently leverage the advanced capabilities developed by these experts." 

For example, 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.

worker pointing to a computer screen in DataikuMarketers don't need to be data experts, but they should have an easy way to work with them.

Bain & Company, in collaboration with Google, recently surveyed more than 600 marketing executives in the U.S., U.K., and Canada, and the report they created with the results echos this sentiment:

“Exciting new measurement technologies hold the promise of lifting marketing effectiveness to new heights but, in the real world, no marketer can thrive on technology alone. Marketers constantly battle rising expectations from the C-suite, new regulations around privacy, and fast-changing rules of engagement,” said John Grudnowski, digital marketing expert at Bain & Company. “The savviest marketers realize that making progress on the measurement journey involves mobilizing both their teams and their technologies, which leads to better business outcomes, whether that’s revenue, profitability or customer churn.”

How to Act Now

OK, so marketing is changing. What are the next steps that marketing professionals — and wider organizations — can take now to adapt to the data-driven times?

  1. Education: Be able to speak the basics about machine learning, deep learning, and AI. A few suggested resources are Machine Learning Basics — an Illustrated Guide for Non-Technical Readers and Introduction to Deep Learning.
  2. Foster collaboration: Invest in technology (like a data science, machine learning, or AI platform) that can be used not only by data experts, but data beginners, for everything from managing data projects to connecting to data themselves. Check out the white paper Why Enterprises Need Data Science, Machine Learning, and AI Platforms to learn more about what they can provide.
  3. Dive in: 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. Why not just one project? Well, data science isn't really an exact science, so it's possible that for one project, the right data doesn't exist. Or the project gets executed, and the results aren't helpful. Getting started with a few low-hanging fruits will up the chances that at least one is a success. Get more tips for running a data science POC.

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