How AI Is Transforming the Role of the CMO

 

All successful B2C companies have one thing in common: their success is dependent on how well they understand, serve and predict the behaviors and needs of their customers. This has always been the CMO’s top challenge, which requires them to constantly look for and leverage disruptive technology innovations. Today, more than ever before!

The historical role of the CMO

The well-known TV series Mad Men, for example, depicts the golden age of print and TV in the 60s where CMOs (and of course the agencies they worked with) focused on content, brand positioning, and strategy. At that time the available channels were pretty much static and the audience segments using these channels were very loyal and therefore predictable. This was a time when content was king: companies were competing on the production of the best content to get the highest share of viewership.

As two major revolutions took place – first, the internet and second, smartphones the number of ways in which people could access and consume content exploded. Consumers became more sophisticated, more demanding and less loyal than they used to be. To create engagement and improve customer acquisition, it became obvious that CMOs needed to embrace consumer data, the collection, and analysis of which would allow personalizing messages across channels. Software vendors in adtech understood this very quickly which led to a plethora of technical solutions CMOs invested in, namely: DMPs, web analytics tools, email marketing solutions, AB testing solutions, etc…

More options to communicate with consumers also meant that the business models involving marketers, consumers, and technology vendors changed considerably. For example, CPM-based business models, which were traditionally used for TV and print, were adopted for digital advertising too. Of course, this was before programmatic ad-buying (and CPC-based models) became the new norm. While ‘more relevant’ ads (paid on a per-click basis) brought an easy way to measure ROI for CMOs, personalization in advertising was (and still is) perceived as intrusive and sometimes scary by consumers (11% of the global internet population is using adblockers, growing 30% YoY.

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Personalization at scale

The learning here is: personalization focused on short-term transactions is not enough. Today’s modern marketers are looking at personalization in a much broader way: how to build long-lasting personal relationships with each individual consumer. B2C companies have evolved from having one static website as a storefront for their business to using a complex combination of email, multiple websites, mobile apps, push notifications, SMS, in-store personalization, etc. in order to bring the most relevant experience to each consumer at every moment. Laser sharp focus on this particular consumer (and all the data which is available about him) during this moment is necessary to offer him the best possible interaction: today’s consumer wants to speak to one brand throughout the entire journey and does not want to have to navigate through the brand’s organizational silos to get what he wants. From informational communications to pre-sales, to purchase, to service and support, today’s entitled consumers expect a relevant, personal and timely experience at every touch point and agree to share their personal data with brands in order to get it.

Tackling this challenge 10 years ago with the technologies available at the time would have been impossible as it would have required massive efforts on segmentation, offer creation and multichannel campaign orchestration to essentially build audience segments of single individuals. However today, with the help of Machine Learning (ML) CMOs can make a massive step towards truly understanding every single consumer.

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From analytics to predictions

With or without AI, base marketing objectives still hold true: today’s CMOs still work on identifying personas, building a lead generation engine, optimizing the conversion funnel, and maximizing the customer lifetime value (LTV). What changed with the advent of AI was that it became possible to adapt marketing strategies automatically and in real-time for every consumer individually.

Machine Learning is all about learning from experience and using analysis of historical data to make predictions on future events. Using Machine Learning, it became possible to answer questions such as: what is this person most likely to buy today; what is this person’s overall predicted spend; how likely is it that this specific person churns within a year; what is the best channel to communicate on with this person to maximize the probability of her buying again in the next month, etc.. Without AI, these questions would have been impossible to answer as they require a careful analysis of large quantities of data of various types (interaction data on emails/webpages/push, PII data coming from forms, transaction data coming from POS systems, geolocation data if available…) and the correlation of all that data to accurately predict an answer… not something a human could easily do for millions of individuals!

Being able to do that on a per-person basis and in real-time is a true revolution and it has a strong impact on the CMO’s role and responsibility. Any kind of project always starts with the question of which problem needs to be solved as well as who are the right people to work on it. This is especially true for AI-related undertakings. It is the role of the CMO to answer both questions.

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Collaborating and innovating

Running an internal AI development project is by nature more complex than any other R&D project as it needs extra steps for consolidating the data necessary for training, the development of the model itself as well as fine-tuning of parameters which often needs to be done manually. The definition of the goal (usually maximizing a specific metric) is especially important as all the steps above will need to be aligned accordingly to that goal. While the actual implementation of the AI system will likely be done by the IT/data team, the definition of the goal, the consolidation of the consumer data and the monitoring of the success of the project is often done by the CMO. Close collaboration between the teams is, therefore, all the more necessary.

But building an internal data science team is complex and costly. Many companies take another path and work with vendors and/or agencies who can help them make use of Machine Learning to optimize their marketing campaigns without having to build these features internally. Finding and curating the right technology stack is also the role of the CMO, and a very important one. The CMO plays the role of the innovator in the company, growing the most important KPIs the company relies on (from lead generation to customer acquisition and retention, as well as churn prevention). It is not a coincidence that most adtech/martech firms out there focus their efforts on convincing CMOs across major industries (retail, banking, entertainment, media & publishing, hospitality…) of the performance of their tech solution.

AI-features which deliver personalization at scale can have rather simple goals: from automatically adjusting the cadence of communications to reduce customer fatigue, to increasing the efficiency of campaigns by automatically promoting products to people who are likely to be interested in them, or automatically creating target audiences who have a high propensity of engaging over a specific period of time: these are all features which run on top of, or within, marketing cloud software. AI is a key focus for most of these software vendors and is used for automating specific decisions inside complex multichannel campaigns. This approach allows the CMO and his team to spend less time on low-level campaign management and execution to focus more effort on consumer data consolidation and data-driven strategy, which in turn transforms into goals to train AI algorithms on.

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From predicting to generating: the next big thing

By embracing and leveraging AI-driven optimization and predictive features CMOs can positively impact the performance of their marketing today, and they can do so by working with companies that have already built tools that use Machine-Learning to optimize marketing campaigns. However, this is just the beginning of AI as an enabling technology for marketers.

Today, one of the keys focuses of the AI community in academia is generative AIs: systems that go beyond analyzing and predicting based on data, and on to generating artificial content which has never existed previously. These systems, which are often referred to as GANs (Generative Adversarial Networks), can be used to generate any kind of content: from images (taking a picture and applying Van Gogh’s touch to it) to video (“deep fake” Obama videos saying things the real Obama would never say) to text (financial magazines generating earning call summaries), or even music. These systems have many interesting properties – for example, they do not require a lot of data to train on – but the most important thing about them is that it opens a whole new range of possibilities for CMOs to create content.

Just like creating one campaign for a one-person segment is impossible, content creation is yet another challenge any CMO faces today: it is expensive and time-consuming, it requires skills (internally or via agencies) that are difficult to find, and because of that, creating personalized content is currently not possible at scale… Imagine having Mad Men-like projects for each individual consumer! Using generative AI-based systems to create content using only a few parameters inputted by a marketer would bring the cost of content generation down and allow marketers to be even more creative by testing different options in a very short amount of time. These key innovations in the world of AI are already being productized in the form of features that can be a powerful side-kick for CMOs to run their personalized marketing at scale.

AI is an enabler. Already today, modern CMOs use AI-driven features to increase the KPIs of their business. Tomorrow, more AI-based systems will be on the market for various needs. It is the role of the CMO to wear the innovator’s hat and keep an open eye on what is taking place in this fast-evolving space.

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