The next frontier of AI in marketing

As AI becomes more deeply embedded, it will also help more businesses identify the best channels for advertising and support radically enhanced accountability for marketing spend, enabling CMOs to measure the value of brand building and campaigns to a level of accuracy that historically has been incredibly difficult: distinguishing between various contact points to understand what truly influences customers’ purchasing decisions.

AI might also help overcome the erosion of trust by fake news and false product reviews, and it has the potential to enhance diversity, allowing marketers to take account of the views of all their potential customers—not just those who the CMO understands instinctively due to a similar background or shared culture.

Improved insight, accountability, and influence could help CMOs earn a seat at the top table when it comes to key business decisions—the absence of which has long been lamented by CMOs and marketing thinkers.

Unleashing the power of dynamic pricing

For CMOs, one of the most powerful potential applications of AI could be in dynamic pricing. Can marketers more accurately discern where they can or should charge more—and when they should charge less—in a way that takes account of the lifetime value of a customer, rather than pricing only for a single transaction?

Take the aviation sector, for example. Could an airline tell if somebody browsing for a last-minute ticket is flying to do a multi-million-dollar business deal—in which case they will probably happily pay substantially more than usual—or whether they are traveling for a family emergency—in which case, perhaps the company would prefer to charge less? The dynamic pricing that currently exists uses basic data such as the time of day of browsing, or the location. But what if machine learning has identified that the passenger is likely to have a high number of journeys on a given route over the next few years: should the airline charge less to secure that additional lifetime value? An improved picture of customer behavior might also point towards enhanced service for loyal customers.

This sort of capacity does, of course, raise ethical as well as practical questions. Would the airline want to adjust prices based on an AI determination that a passenger was likely to be traveling for a family emergency? What about privacy: does the company even want to infer such knowledge? But if those conversations can be resolved, AI and machine learning could enable marketers to develop truly dynamic pricing models that are both responsive and fair to each individual customer and help generate more value over a customer lifetime.

The next frontier: using AI strategically

How then can CMOs move to realize value from AI in a more strategic way? There are four key areas to consider.

1. Capture. Having the infrastructure to capture data, with processes to ensure it is of good quality, is now an absolute minimum requirement for CMOs. It is not a source of competitive advantage—it’s table stakes. Any business that doesn’t have the basics in place will rightly be left behind.
2. Prioritize. AI and machine learning enable organizations to do so much, so one of the most important challenges is prioritizing the problems to be solved. Does the organization have the skills, time, and data to solve the problem under consideration? CMOs need to understand how to prioritize projects that will show immediate returns, and at the same time are scalable and offer long-term impacts.
3. Scale. CMOs no longer have the luxury of pursuing interesting small-scale experiments in different parts of the world or the organization. Increasingly, there is an expectation that when something has been shown to work, it should be scaled quickly: from one market to 20, or from one product under one set of conditions to multiple products under multiple circumstances. This is where AI and machine learning move from being tactical to becoming strategic: but if they are kept as little islands of experimentation, organizations will never develop a clear strategic vision for the technology’s role in their business.
4. Orchestrate. The delivery of AI requires CMOs to work closely with colleagues in other parts of the business. I have seen AI initiatives fail simply because of a lack of awareness of what is happening in other areas of the business. CMOs might identify an important problem and the data to solve it, only to discover that IT doesn’t have the AI tool needed, or the data team doesn’t have the skill set required. Simply put, AI cannot be implemented effectively in a silo, so CMOs need to work hard on orchestrating and building alignment between their teams and technical specialists. Critically, those specialists need to be brought together with the end users who are going to be using the product. Indeed, the CMO may not be the right person to lead a project. They may sponsor an initiative, but it should be driven by the person who will use the end product.

Misfiring chatbots aside, it is clear that the use of AI in marketing is here to stay—but most marketers are only scratching the surface of its true potential. Learning to prioritize, scale, and orchestrate is essential if CMOs are to deliver on its true value.