AI & Machine Learning: Where Should A Digital Marketer Start?

AI and machine learning technologies have their hands in just about every new and exciting trend facing digital marketers in 2019.

These technologies have been brewing for years (even decades), but they’ve really blossomed into a big topic of discussion now that they’ve reached that vital stage of maturation.

As this technology continues to develop and become more and more sophisticated, the market becomes increasingly saturated with AI products.

Which can make things difficult, and even overwhelming, for a digital marketer to understand where to start and what tools offer the most promise and value for their clients and businesses.

Automation is powerful, but also a little scary. In this discussion, we’ll break apart some of the most important types of tools and why they should be the first to implement into your strategies.

Sentiment Analysis Tools

Customer sentiments are extremely valuable, in terms of understanding how your customers are feeling. In the Dark Ages, before the Internet, businesses had to practically knock on customers’ doors to receive this potent feedback.

Now, between social media platforms, review sites and other sources, organizations have more feedback than they know what to do with, especially when you consider that businesses may have between 7 or 8 social media accounts active at one time.

By design, social media is made for customer feedback and comments. While each individual’s comment is important in its own right, getting the big picture is much more critical.

You need to have a full understanding of what your collective audience is feeling, not just the thoughts of a small sample of people.

This is where sentiment analysis tools come in handy. They take all of the raw text, from social media comments, customer reviews and other sources of feedback, and mine it for words and phrases that indicate a sentiment is being shared, whether positive, neutral or negative. Then, it detects what the target, or subject, of that sentiment is.

This is an example of a sentiment analysis of data from Uber’s Facebook page:


For a digital marketer, this is an extremely efficient way to navigate a large volume of comments, reviews and customer feedback. You can immediately see the customer pain points and correct potential issues. It gives a clear view of what’s working and what’s not.

As your AI tool continues to gather customer sentiments and study these attitudes, it will become more adept at detecting early warning signs of major issues, like defective products or bad customer service incidents, which gives a business more time to avoid potential crises.

Content Topic Generation

Generating ideas for new content assets is relatively easy. Coming up with good content ideas, on the other hand, is about as easy as finding your way through a corn maze with a blindfold on.

Artificial intelligence helps lift that blindfold up and allow marketers to better understand the content ideas and topics that are currently desirable by audiences, as well as validate the marketer’s own suggestions for new content.

AI-empowered content strategies will often see higher engagement metrics. When you’re able to strategically target the topics and questions that your audiences are currently facing, they’ll be much more enticed to click and consume that content.

This will skyrocket your blog’s status as a valuable resource for thought leadership.

These tools work by analyzing topic clusters, trending search queries and other dynamic data to see what topics are most relevant at any given time. Sophisticated content strategy solutions will use AI capabilities to take the analysis a step further by also studying competitors and their content efforts.

Adding this competitive research angle allows content creators to then determine which of these valuable topic opportunities have the least amount of competition.

These are the golden nugget topics, where target audiences are desperate to learn more about them, but no one is offering that content, yet.

Chatbots For Customer Communication

This is where AI gets a little troubling for a lot of businesses because the prospect of putting a machine in charge of corresponding with customers, instead of a real, human being, sounds like a recipe for trouble.

Chatbots, despite these apprehensions, are gaining significant traction. According to an Oracle survey of 800 marketing and strategist specialists, 80% have plans to implement chatbot technology by 2020.

Why? Because Chatbots offer a lot of convenience:

  • Much faster than waiting for someone to answer a phone or respond to an email
  • Available 24/7, so businesses can stay open after the human employees have left work
  • A familiar text-message-like interface that is universally familiar
  • Cuts staffing costs for customer service representatives

While business owners have their fears about chatbots, in some ways, these tools are actually safer than a human. There’s an unpredictability factor with human agents, where they can lose their temper, misinterpret a customer’s request or provide an incorrect solution.

Chatbots, on the other, don’t have a temper to lose. And, they have every ounce of information available to answer customer questions, which means they don’t have to pause to look anything up or transfer customers to another department.

As time goes on, and a chatbot is exposed to more conversations, it only becomes more sophisticated. This is the essence of machine learning. The chatbot learns from past conversations and gains a better understanding of what customers are asking and how to provide the right solutions.

From a marketing standpoint, chatbots can be used to connect customers with materials that help solve their problems. Sephora’s “Visual Artist” bot is a great example of this. It links users to the marketing materials that best correspond to their text responses.


The chat logs created by interactions with a chatbot are also saved and reviewable, which can be another source of those valuable customer sentiments we discussed earlier!

AI-Powered Bidding And Testing Of Ads

Digital advertising is an important dimension in the online efforts of businesses. Ads on search engines and social media platforms help companies put their products in front of target customers.

However, this has traditionally been one of the more challenging aspects of digital marketing, especially when you consider that there’s real dollars on the line.

Advertisers have to make the most out of their ad budgets; a particularly hard task if that budget is minute. Some online advertisers find themselves banging their heads against the walls, as they witness only small returns from their campaigns. It’s a demoralizing feeling to waste your ad spend on poor targeting and ineffective campaigns.

Luckily, AI has helped quell some of these Internet advertising woes. Facebook Advertising, Google Ads and other popular search/social advertising platforms have already begun including AI-enabled tools to help marketers optimize their advertising efforts.

Google has especially embraced the AI revolution. Their Smart Bidding feature uses machine learning to detect shifts in search trends and automatically retarget ads to accommodate for these changes.

For ad testing, Google has introduced responsive search ads. These AI-enabled ads ask marketers to write a number of different headlines and ad copies. Then, it runs campaigns and automatically tests which combinations work best with certain audiences.

Not only does this save digital advertisers a lot of time and eliminates a lot of the need for traditional A/B testing, but it also gives marketers some clues into what certain audience segments are interested in. These insights can be applied to other strategies as well.

Personalized Experiences

Personalization is becoming an important competitive differentiator. Consumers are increasingly interested in, and looking for, businesses that are able to customize the experience to their preferences.

It’s easier to just let the numbers do the talking:

  • 75% of consumers prefer to buy from retailers that recognize their name and preferences, while 74% find unpersonalized web content a point of frustration
  • 78% of consumers are more likely to utilize offers and engage with content that have been personalized based on prior interactions
  • Personalization has been linked to improved revenue in 79% of organizations

The key to personalization and unlocking these powerful advantages is artificial intelligence. These tools learn user behaviors overtime and become better equipped to deliver those custom-tailored experiences and offers that customers are looking for from businesses.

This is what ecommerce companies like Amazon do so well, in their ability to recommend products based on past user behaviors.

Another great example is ASOS clothing, which adapts their website based on previous visits. If you’ve shopped for men’s clothing in the past, your next visit to ASOS will immediately redirect you to the men’s clothing department.

Personalization, however, is a tricky tightrope walk. If you personalize the experience too much, especially in the beginning of a business-customer relationship, it can feel invasive and quickly scare prospects away.


Now that you have a better grasp on the best AI tools for digital marketing, the hard task is knowing which solutions offer the most value to your business.

The key is to really evaluate the features and capabilities your business needs from these tools, in order to solve its unique challenges. Then, find products that meet those criteria.

The good news is that the majority of AI service providers have some form of demo or trial period available. It’s wise to utilize these offers and really test each solution out. Not only will this help you understand how these tools work, but you’ll be able to better determine which one offers that Goldilocks fit for your company’s needs.

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