How to integrate AI into your digital marketing strategy

30 second summary:

  • Marketers are faced with massive amounts of information. AI/ML tools are the key to unlocking insights from an avalanche of snowball data.
  • We interview Harvard Business School professor and AI marketing expert David Edelman to understand how marketers can harness the power of AI.
  • Edelman advises on topics such as navigating the growing landscape of AI tools, post-cookie attribution and personalization.

The amount of information you need to personalize, optimize and eventually automate marketing communications becomes too much. And yet, in this massiveness lies the granularity to understand the variability that enables personalization for each consumer. Hiring more data scientists who can fight the avalanche of snowballing data is an unsustainable arms race.

The key to unlocking it is AI.

You cannot browse this endless pool of data unless you have the support of AI/ML tools. We interviewed David Edelman, former Fortune 50 chief marketing officer, Harvard Business School professor, executive advisor, and AI marketing expert. Edelman shares information on the most pressing questions for marketers looking to incorporate AI into their digital marketing strategy, including:

  • How marketers can navigate the plethora of AI solutions available
  • How AI can help brands deliver exceptional customer experiences
  • Why AI can bridge the gap between online and offline data to enable attribution

Q: How can brands navigate the plethora of AI solutions available?

David: “AI is now being used to bring together fragmented channels by creating a unified picture of the customer journey. Advanced algorithms can create an identity map that can link information about a customer across systems, even if they not all have the same identifying ID.In doing so, AI provides marketers with support in four key areas.

  1. Analytic: Simply put, what’s going on? AI analysis provides insight into where customer journeys take place and what those journeys entail, then derives attribution. For example, it can help marketers understand what is driving costs.
  2. Automating: There are now AI tools, such as those from a company called Stanley.ai, that can create 100 different variations of a particular email you want to send, test those variations, and label them. Large-scale marketers are starting to use these tools rather than wasting their creative energy doing it manually. This is one example of automation – there are of course many others.
  3. Personalization: What is the best outreach and message for each customer? It’s about testing, experimenting and constantly finding the right combination of creativity, timing and channel. Based on these smaller tests, you can then leverage the learning and use it to personalize what you send out to a larger group of people. There are so many variables you can play with, but traditional A/B testing just doesn’t get you there. A company called OfferFit, for example, has tools that allow you to fully configure all test cells, figure out which personalization works for whom, and then send the right message to the right person at the right time. Climb the demand curve with the right incentive for each person, delivered in the best way for them to engage.
  4. Optimization: Over time, all of these systems learn from what is constantly happening in terms of customer behavior. Based on this learning, they constantly update their models.

You also have to think about integration. None of these tools will work perfectly and will do everything out of the box. All of these AI tools need to fit into your environment because they extract the data, do something with that data, and then often feed the data back into the runtime or into a reporting tool.”

Q: How should brands use AI to manage the customer journey?

David: “Marketers can use AI to gather large amounts of data into customer journeys and then make predictive judgments. Pointillist is an example of a vendor that can do this. It provides customer journey analytics, using AI in two ways:

  1. Matchmaking: It filters a company’s channel databases and assembles each customer’s journey. It timestamps data and provides information about the full longitude of a customer’s journey.
  2. Pattern and Anomaly Detection: Shows the most common trajectory of customer journeys and where variations from the journey occur. For example, a company experienced a spike in calls to the call center. Customer journey analysis showed that callers had just used the Android version of the app within 15 minutes of calling and were trying to pay a bill. This showed a bug in real time and allowed them to make a change.

Systems like this, which are used upstream and in customer journeys, can bring all this information together. Whether it’s showing you a problem you need to solve or a customer to contact, these AI-powered tools allow marketers to manage customer journeys at a micro and macro level.

Q: How can this help brands personalize post-cookie?

David: “Thanks to AI, you can now create a better identity graph. By linking the different contacts you have with the customer, you can use it not only to identify someone, but also to have a richer idea of ​​the path he has taken.

If, for example, I’m sending a simple email, I would have to look at the image background, font, font color, and word spacing, to name a few variables. I should look at when the customer responds, what time of day they respond, if they click once or come back, how long, and if they kept it on screen.

Personalization is all about variation. The more data you can create or gather, the more variations you will have and the more you can tag. You can then model your future interaction with the customer. The previous basic cookie environment did not provide this capability and data. Now you can create more granular data and a much more powerful arsenal to customize with. »

Q: Why is this an important part of the marketing attribution formula?

David: “Marketers have been constantly challenged to find data links to close the loop and connect channels (online and offline). Loyalty cards and programs are often offered as a solution, but that doesn’t only applies to a number of industries, and unless you put money on the loyalty card to get people to use it, penetration won’t necessarily be high.

The other thing to consider is the decision-making timeline. Sometimes, especially for high-end products, many steps are required. B2B products, buying a car, buying appliances because you are remodeling your kitchen. They all take time. So for measurement and attribution, how long should this lag be? What other influences come along the way? Over a longer period, it is difficult to weigh different influences and get a longitudinal view of performance.

So, despite all the digital development of the last twenty years, marketing still has smoke and mirrors. This is not always perfectly attributable. Some techniques can bring you closer, but you won’t be able to completely come full circle. AI can help you get much closer.

Q: What impact could AI have on marketing in the future?

David: “When I look to the future, what excites me the most is innovation. Marketing is always about emotional connection. You have to find a way to make that connection, and humans are going to have to be part of the picture to understand how we can better elicit emotion and come up with new ideas.

Companies I’ve worked with that use these tools are changing the way they think about marketing. Instead of just having standard campaigns where they gradually run a simple A/B test and push for improvement, they can now launch 20 new ideas every two weeks.

An amazing personal example happened when I was buying solar panels for my house. I had communications from various vendors, but one stood out above the rest. I received a personalized physical mail to my home address.

It had a custom URL that took me to a site that showed my house from Google Earth with solar panels on top of it to show me what it would look like. They used AI to determine how many panels could fit on my roof and, based on the angle of my house and its tree cover, how much energy those panels could generate. Using Zillow, they could get data on the square footage of my home and estimate how much energy I was using. With these real-time calculations, they could tell me how much I could save. It was completely transparent and remarkably powerful.

This is the future of AI. This will allow marketers to innovate and create more impactful personalization than we can imagine. »


A sought-after advisor on digital transformation and marketing, as CMO, David Edelman guided Aetna (now part of CVS Health) in becoming a digital-first, customer-centric brand. Recognized repeatedly by Forbes as one of the “World’s Most Influential CMOs” and by AdWeek as one of the “Top 20 Marketing & Technology Executives”, his work has attracted more than 1.1 million followers on his LinkedIn blog.

Currently, David teaches marketing at HBS and advises CEOs and CXOs in healthcare and marketing services, focusing on AI and personalization.

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William L. Hart