Is There Value in AI for Marketing?

To many Chief Marketing Officers and their c-suite bosses, AI or Artificial Intelligence can seem to be somewhat of an oxymoron when it comes to marketing applications - like being clearly confused about a situation or asking for original copies of business documents.

What exactly is AI when it comes to marketing? Is it a bunch of hype? Is AI hard to plan and complicated to implement? Can it be really expensive? Is it worth the money? How do you measure its effectiveness or ROI?

Simply put AI is the simulation of human intelligence in machines that are programmed to think like people and then mimic their actions. AI can also involve more complex tasks such as problem-solving and learning. The good news is there are plenty of great AI applications for marketing that are driven by data, and produce tangible effectiveness metrics.

The strength of AI when it comes to marketing is leveraging its ability to analyze vast amounts of data, and then take specific actions informed by the data that have the best chance of achieving specific goals. These goals can include improving the customer experience (in some measurable way)?and perhaps even providing a sustainable competitive advantage. AI should be viewed as a means to these ends, and not an end in of itself.

An example of AI in practical use is Aiden? an AI-based electronic trading platform that helps improve trading results and insights for clients launched last year by RBC Capital Markets. At a recent Financial Communications Society online event - Jennifer Grazel Managing Director and CMO from RBC Capital Markets - spoke about their efforts to use AI to enhance the customer experience. The Aiden? Platform is able to use data analytics to navigate the challenges of fluid and dynamic market conditions in real time, without the need for continuous re-coding like traditional trading algorithms.

The Harvard Business Review recently observed “In order to realize AI’s giant potential, CMOs need to have a good grasp of the various kinds of applications available and how they may evolve.” The HBR went on to note that current AI applications for marketing should be categorized along two dimensions: Intelligence level and whether it stands along or is part of a broader platform.

The HBR recommend that simple stand-alone task-automation apps are a good place to start. They go on to state that advanced, integrated apps that incorporate machine learning have greatest potential to create value. However, all this may seem like a an academic ideal, the kind of thinking that the HBR loves to write about.

The unwillingness / inability to see AI as an increasingly important part of marketing, one that can be used as a true enterprise-level growth driver, can carry huge opportunity costs for companies both large and small.?

This situation reminds me of pitching online banking to a group of skeptical senior managers at Chase Manhattan Bank back in the early 1990s. Internet access was still a dial-up exercise for most people back then, and Citibank had already staked out the “techno-oriented” online banking marketplace for their initial online banking product.

Most of the Chase senior management team was highly skeptical that digital banking would ever catch on for personal or business banking. In fact, Chase had only gingerly dipped their toe into online banking by registering [email protected] for their first “Chase Direct” online banking platform, having yet to even secure the chase.com URL. They offered Microsoft Money v1.0 as the premium incentive.

When the nascent Chase digital banking team tried to lay out a future vision of a generally accepted and widely used online banking world, one senior executive quickly quipped “The only people I want to see online at Chase are going to be in our branches!” Seems that history has been on the side of the future-focused people at Chase.

As far back as 2017, McKinsey identified marketing was one of the four places in the value chain where AI would make significant contribution. AI can help improve core marketing activities including better understanding customer / prospect needs, developing and matching products and services to meet those needs, and finding the most effective ways to create awareness / consideration and generate trial. All these activities can and should be informed by concise data analytics versus relying simply on intuition which is often the case.

Last year the Deloitte’s State of AI in the Enterprise survey revealed that for early AI adopters, three of the top five AI objectives were marketing oriented, including enhancing existing products and services, creating new products and services, and finding ways to enhance relationships with existing customers.

It appears that there is a consensus among some smart people that AI holds great promise for marketing applications. The key is to create a plan of how, when, and where within an organization to get started with AI applications, and that will be the subject of the next blog post in this series.