Why Embedding Artificial Intelligence is Critical for Successful Digital Marketing

While digital has been a part of most organization’s marketing mix for some time, COVID-19 necessitated rapid digitization of our entire marketing process. This helps stay connected at a time when people are isolated. But simply digitizing marketing is not enough to overcome the challenges we face.

In fact, in some ways this digitization has created its own challenges. Everyone does digital marketing, and everybody is doing more of it these days. Consider these statistics:

• The Radicati Group reports that over 293 billion emails were sent and received per day in 2019. And that figure is forecast to grow to more than 347 billion by the end of 2023.
• According to Statistica, 4.4 million blog posts are published per day.
• Statistica also estimates ad spending on the social media will reach $110,628 million in 2021 and steadily increase to $138,412 million by 2025.

And those are just a few of the channels digital marketers use. All are becoming flooded quickly, and people are drowning in sea of digital noise. To rise above this and get outstanding results, you need to embed Artificial Intelligence (AI) into your digital marketing.

Here are just a few of the ways marketers are using AI to enhance their efforts and improve their Key Performance Indicators (KPIs).

Gain a deep understanding of each customer

Marketers today have access to more customer data than ever before. In fact, there is too much for traditional human methods or spreadsheets to effectively sort and analyze. Nearly every interaction with a brand offers valuable insights on that customer. And no two customers are going to be exactly alike.

With advent of big data solutions, you can combine massive amounts data from disparate online and offline sources to get a 360-degree view of each person in real time.

You no longer have to rely on old-fashioned segmentation that forced individuals into broad groups defined by one or two attributes. You can now have a segment of one, which allows you to…

Personalize communications at scale

Deep customer insights are only as valuable as what you do with them. And that’s again where AI comes in. Using all that data, you can deliver hyper personalized experiences across multiple touch points, including your websites, social media properties, email communications and marketing content, just to name a few.

If you’ve ever noticed ads for products or services show up in your social media feeds after viewing them online, then you’ve experienced the most basic form of this personalized approach first-hand. But simple retargeting is just the tip of the iceberg when it comes to personalization possibilities of digital marketing.

AI has advanced to the point where you can personalize the messaging beyond simple variable fields for name, city and the like within any given component. While more complex or sophisticated variable information often led to choppy or stilted sounding text in the past, AI can incorporate the messaging so that it fits within the natural structure and flow of the writing.

So rather than having to manually create and manage multiple variations of your marketing components, you can deliver personalized versions for every individual customer or prospective customer with minimal human involvement beyond the initial set up and base creative.

And because AI is able to process data in real time, you can optimize content for interactions that happen in real time, even for interactions using digital humans such as Amelia. This can be an effective way to engage with, qualify and up-sell prospects and customers who prefer more conversational communication.

Predict customer needs and behaviours

Once we understand the customer, we can also predict what he or she will need at any given point in the customer journey. And we can serve it up at the right time, in the right channels and in the right sequences to maximize value.

Considering how much emotions, intuition and other non-logical factors go into human decision making, it’s easy to underestimate AI’s ability to predict human behavior. But data doesn’t lie, and predictive models can be surprisingly accurate.

I’m reminded of the case where Target predicted a teen was pregnant before even her father knew. One of their predictive models – based on purchases of a combination of products within a specific timeframe – assigned a likelihood of pregnancy and the expected due date within a small window.

When the company sent her a mailer full of coupons for maternity clothes and baby products, her father complained to the store’s manager, irate that a girl still in high school would receive such offers. It was only after speaking with his daughter that the man discovered the targeted offers were indeed relevant.

That was about a decade ago, and the technology has only gotten better since then. With AI you can mine massive amounts of data and identify patterns that uncover useful insights. And you can use them to effectively plan the next best action.

Plus, you aren’t limited to one set of data or any specific marketing channel. AI allows you to seamlessly integrate it all into omni-channel marketing campaigns that are optimized in real time for any moment in the customer journey.

Real life examples

For a simple illustration of how AI can be used in the ways described above, take a look at Starbucks.

The popular coffee chain collects and analyzes data from a variety of sources to gain a better understanding of their customers. Data from its loyalty card and mobile app, for instance, offers insight into customers daily habits and routines, such as which items they purchase as well as the times and locations of those purchases.

The company applies predictive models to this data to deliver personalized marketing messages at opportune moments, such as recommendations when the customer is approaching a local store.

Amazon is another company that uses AI to deliver personalized marketing, and their recommendation engine is probably the most well known example. Given the number of products and customers Amazon has, let alone all the individual data points it collects on each, the data sets would be too big to use without AI.

But the value isn’t just in the ability to handle the quantity of the data. It’s also proven effective at generating revenue. The algorithm matches each item a user purchased and rated to similar items, then combines those into a personalized recommendation list for the user that is delivered on site and via email communication.

According to McKinsey, the recommendation lists accounts for 35% of Amazon’s sales. They also reported that a similar algorithm for Netflix drives 75% of what consumers watch on the platform.

How to get started

Transforming your digital marketing in such a dramatic way can seem overwhelming. And there is a lot that goes into designing an effective AI solution, but it doesn’t have to be intimidating. If you take a little time and effort to prepare and know what to expect, you can avoid complications and frustration later.

The first step is to increase your AI comprehension. 

You don’t have to be a data scientist or programmer yourself to develop marketing strategies that integrate AI, but it is important to know the concepts, capabilities and basic processes.

If you want an efficient and structured learning option, a training such as Eularis’ “AI Intelligence: From Understanding to Strategy to Implementation” can help people non-technical roles better understand AI and use a proven framework for creating a strategic plan.

Next, decide on use cases you want to prioritize. 

Start with your key challenges that you wish to solve. So many companies get caught up with ‘shiny toy syndrome’ seeking the next best AI tool before they analyze the problem they are trying to solve. The key thing is starting with your challenge, then identifying what the optimal solution would look like, then start looking at technology. So many companies have this back to front and start with the tech and then go looking for the problem it can solve.

Once you have your problem, look outside your industry to see if someone else has solved it. There are so many possibilities when it comes to AI, so it may be helpful to seek out examples from other companies, even if they are outside your industry. Then consider how you can adapt these to your situation.

Use cases can include ways to improve upon existing processes, campaigns or efforts as well as new ways to bring value to your digital marketing. Consider your most important KPIs and where you have the potential for the greatest impact from AI.

Discover whether there is a tool available for your use case or do you have to build one

When you have a solid understanding of AI and how you want to use it in your digital marketing, you’ll want to assess and plan your custom solution, or whether you can find an existing solution. If you are building it yourself, data science projects don’t always need a tech stack, but some do. You need to understand if you need one for your strategy, and if you do, what will be required of it.

You may not need to build something as so many great AI solutions already exist and from what I am seeing in my evaluation of the ecosystem, so many incredible ones are slipping under the healthcare radar when they would be a valuable asset. 

You’ll also want to find out what the capabilities of your existing marcom stack include in regards to AI. Maybe it an be easily added.

Finally, you’ll need someone (or a team of people) with the technical skills and knowledge to make your AI strategy a reality. 

This could include partnering with digital marketing agencies or consultants that have AI experience and capabilities, hiring AI professionals to work in-house or a combination of the two.