The next frontier of AI in marketing

Chief marketing officers (CMOs) need to learn how to use AI to drive wholesale change in their marketing strategies, writes IMD's Amit Joshi

This article is republished with permission from I by IMD, the knowledge platform of IMD Business School. You may access the original article here.

Is AI ready to take over your marketing outreach to potential customers? Facebook’s owner, Meta, recently launched its BlenderBot 3 AI chat tool, yet so far the suggestion is that AI is still some way from completely taking the reins from your marketing team. Journalists quickly found the tool expressing some dubious views – not least about its parent company – telling the Wall Street Journal that Facebook has “a lot of fake news on it these days,” while asserting that Mark Zuckerberg’s business practices are “not always ethical”.

And yet the march of AI in marketing continues apace. Salesforce’s latest State of Marketing report found that 64 per cent of marketers use AI. The demand for AI in marketing has been estimated at $12 billion worldwide, and could rise to $108 billion by 2028. A McKinsey analysis has identified marketing as one of the business areas that could benefit the most from AI. They estimate it could generate up to $2.6tn added value to marketing and sales. The challenge for CMOs is how they can use AI to secure their organisation a slice of that value.

Firms can build a better picture of their customers with more data, like Starbucks does through its rewards program and mobile app.jpg
Firms can build a better picture of their customers with more data, like Starbucks does through its rewards program and mobile app. Photo: Shutterstock

Some firms already use AI extensively. Common applications include automating processes, including targeting emails or online advertising, and creating ad content. For example, JPMorgan Chase has used the AI tool Persado to improve the wording used in its direct response emails and online display ads, honing in on word choices that deliver better customer responses.

Many organisations also use AI to sift customer data, seeking improved insights, a more detailed understanding of customer lifetime value (the revenue associated with a customer over the course of their entire relationship with the company), and more nuanced segmentation. As firms build a better picture of their customers, they can offer personalised recommendations and new products, much like Starbucks does with data gathered through its rewards program and mobile app. Also interesting is the use of AI and machine learning to support sales teams, for example in B2B companies, to identify next best actions: when to contact your best customers, the mode of contact, and what type of messages might be most effective.

These applications are delivering real value for businesses. However, for most companies, the current use of AI in marketing is generally ad hoc and piecemeal. I have yet to see AI and machine learning deployed in marketing in a truly well-thought-out and strategic fashion. These are game-changing technologies, but they are presently being used to improve execution of familiar marketing strategies. Using them to their full potential would mean ripping up and replacing old strategies – and that potential remains under-explored.

Read more: How leaders should weigh up the risks and rewards of AI

Where to play and how to win

In practice, the bigger opportunity for CMOs lies at the point where marketing and sales meet strategy. If strategy is about where to play and how to win, the real value for marketers lies in determining where a business should compete – and, equally importantly, where it should not.

CMOs should consider how they can use AI and machine learning to discover new markets or new product segments, or to pinpoint markets they would be better served by avoiding. For example, AI could lead a company to right-size its product or service portfolio, driving simplification if machine learning revealed that a streamlined offering was optimal. Another example might be to completely change the types of customers the organisation targets. This is beginning to happen, but marketers are so far only scratching the surface.

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.

As AI becomes more embedded, it will help more businesses identify the best channels for advertising and support marketing spend accountability.jpg
As AI becomes more embedded, it will help more businesses identify the best channels for advertising and also support marketing spend accountability. Photo: Shutterstock

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 behaviour 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's lifetime.

Read more: Marketing analytics: are you a data-savvy marketer?

The next frontier: using AI strategically

How then can CMOs move to realise 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. Prioritise. AI and machine learning enable organisations to do so much, so one of the most important challenges is prioritising the problems to be solved. Does the organisation have the skills, time, and data to solve the problem under consideration? CMOs need to understand how to prioritise 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 organisation. 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, organisations 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.

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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 prioritise, scale, and orchestrate is essential if CMOs are to deliver on its true value.

Amit Joshi is a Professor of AI, Analytics, and Marketing Strategy at IMD. This research builds on his 2020 paper Hollywood studio filmmaking in the age of Netflix: a tale of two institutional logics, which was co-authored by Allègre L. Hadida, Joseph Lampel, W. David Walls) and published in Journal of Cultural Economics.


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