AI Marketing Use Cases for Everyday Marketers

Machine Learning|9 Minute Read

AI Marketing Use Cases for Everyday Marketers

Examples of AI marketing are all around us.

In fact, just about every brand website, email, ad, and social media share today is developed with some help from artificial intelligence and machine learning:

  • Social listening tools use natural language processing (NLP) to help marketers understand brand sentiment by analyzing conversations about their business on social platforms like Twitter, which can then inform other facets of their efforts, such as content marketing.
  • Search engine optimization (SEO) tools provide marketers with recommendations as to how to create content for their websites so it ranks well in Google, Bing, and other search engines — which, ironically, also use machine learning in their search algorithms.
  • Many email marketers rely on AI-powered marketing tools to optimize the subject lines, body copy, and formatting of their campaign-based emails as well as determine the best days and times to send nurture emails to their top prospects and customers.

These are all instances of AI marketing that help marketers accomplish relatively important (if basic) objectives. Marketers’ jobs are undoubtedly easier with these resources and tactics at their disposal.

However, none of these resources or tactics moves the needle in ways that advanced marketing uses cases of AI and machine learning can for you, your team, and your company — specifically, the intricate use cases you can implement with a pure-play customer data platform like BlueConic.

Let’s break down how today’s brands have combined artificial intelligence and marketing to date, why these strategies have come up short for many of those organizations, and ways these businesses can take their AI marketing strategies to the next level by using a CDP to enable machine learning.

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Why an AI marketing strategy is essential for your business

“What’s the difference between AI and machine learning? What distinguishes one from the other?”

Everything seems to be “AI” and “machine learning” today, but it’s okay to admit you don’t know all the ins and outs of either technology and their respective use cases and benefits.

There are still many marketing pros and leaders who’ve admitted they’re unsure as well:

The best way to frame the terms? It’s not “AI vs. machine learning” — it’s “AI and machine learning.”

In short, the latter is a subset of the former — and both are now invaluable technologies that can elevate your marketing game (and marketing ROI) substantially when used right.

Just look at what those in the marketing industry had to say about how they incorporate AI marketing technology in their day-to-day efforts — and the big-time benefits of said solutions:

Simply put, there are lots of everyday marketers like you who don’t just leverage AI for marketing activities like augmenting content strategies (e.g., content creation for blog posts), growing their social media engagement (e.g., identifying engaged users in real time), or related use cases.

More specifically, there are several highly detailed, highly effective use cases of AI for marketing teams and their brands today, as outlined in the August 2019 edition of the CMO Survey:

  • 56.5% of companies use AI for predictive analytics regarding customer journeys
  • 38.3% of organizations leverage AI for programmatic ads and media buying
  • 40.9% of brands turn to artificial intelligence for customer segmentation
  • 25.2% of businesses implement conversational AI for customer service

But even brands who use machine learning for these use cases are limited by skills, processes, and tech. Simply put, they don’t have the experience or knowledge to best leverage AI tools.

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Where AI in marketing has failed most organizations today

Don’t worry: If you haven’t yet implemented AI marketing in sophisticated ways to do things like target audiences with personalized messaging, calculate customer lifetime value, and advance your marketing team’s efforts as a whole, you certainly still have the opportunity to do so in front of you.

But first, it’s important to know how, why, and where other organizations and their digital marketers have failed to take advantage of artificial intelligence and machine learning — and what solutions exist for these organizations to help them finally do so.

Issue #1: Skills

The perception one needs to know everything there is to know about artificial intelligence and machine learning algorithms to build an AI marketing strategy is a widespread one across industries.

The truth is you don’t need an advanced degree or specific technical skills to start working with and profit from AI. This mindset is what has prevented countless companies from securing martech with AI capabilities and functionality and training staff members to use it properly.

Having said that, a lack of hard-coding skills in your marketing team can lead to longer project timelines, especially if your organization is solely reliant on data science resources that are limited.

Solution: Read up on the basics of AI and machine learning to get a modest grasp on the subject (the Marketing AI Institute run by PR 20/20’s Paul Roetzer is another great resource). Also, brush up on core coding principles that help many marketers get going with machine learning on the ground level.

Once you know the fundamentals of both, you’ll be more confident when communicating your needs to your data science team and researching martech vendors and finding the one that offers the AI features you need for your use cases (e.g., dynamic segment creation based on customer scoring, lookalike models, and cluster analysis for more relevant targeting).

Using technology like BlueConic’s AI Workbench that has built-in features for marketers to take advantage of AI, like parameters and scheduling models to run, can also help.

Issue #2: Process

Data scientists spend nearly all of their time sorting, scrubbing, and organizing customer data before they are actually able to analyze it.

Given these data scientists’ hours are taken up by these tasks, it leaves little time to use their skills to help marketers with that they actually need — building, training, and deploying machine learning models to get detailed insights about prospects and customers to drive better business outcomes.

The most common barrier to the process is marketing technologies that don’t communicate with one another. Data scientists are stuck manually pulling lists from various marketing technology sources to get a complete data set before even starting to build models.

For example, if they’re trying to create smarter segments with the outcomes of a customer lifetime model, by the time those segments get uploaded to various marketing technology, it’s already too old to be useful. This creates data latency and hinders marketers from seeing meaningful outcomes from machine learning models.

Solution: Leverage a profile database that is connected to the rest of your martech stack so you can get going with the advanced marketing use cases above involving AI and machine learning.

This way, you’ll get better input because you’re using real-time data (read: No more data latency), while outcomes of machine learning models can enrich profiles to help you create smarter segments that you can then activate as needed via real-time marketing activities and messaging.

Issue #3: Technology

There are two main facets of martech that can hinder marketers from fully realizing AI’s potential:

  • The number of places customer data exists (and marketers’ and data scientists’ ability to access it)
  • Black-boxed technology that incorporates machine learning, but operates within a single solution

Much like we mentioned in process barriers, customer data exists in so many disparate systems, forcing data scientists or marketers to pull manual lists to get a unified set of data before even starting to create models to understand that customer data. Without a comprehensive view of their customer base (i.e., all pertinent info about their behaviors and actions), models can’t be nearly as effective.

Secondly, email service providers that provide AI to personalize subject lines or social listening tools that help you understand sentiment about your brand are only able to offer insights based on the data within the channel they operate. What’s more, they often black-box their algorithms so brands can’t adjust the models or understand what’s driving these predictions to optimize the model.

Solution: The CDP was built to unify customer data from across channels so that you have a real-time view of customer preferences, behaviors, and attributes. You’re not processing models based on a single channel of data.

With open-source machine learning models that are directly connected to your profile database, marketers and data scientists are free to test and learn from models and make adjustments to them as the analyze whether or not models are driving the right business outcomes.

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How to combine AI and marketing with the ideal CDP

Securing a customer data platform that unifies all your first-party data for easy cross-channel activation is crucial. But it’s worth your while to get a CDP that can also help you leverage AI for marketing that provides you with rich and comprehensive customer intelligence you otherwise would’ve never had — and the ability to act on that data in real time.

As mentioned, you don’t need to be an artificial intelligence expert. You just need some moderate knowhow and the right marketing technology in place. Once you can check these two items off your list, it’s time to develop an AI marketing plan that streamlines your work, with tactics like:

  • Optimizing all your paid advertising campaigns: If you build the propensity model mentioned earlier, you can determine which individuals are likely to buy and target your ad dollars solely to those likely-to-purchase prospects. Likewise, you can also avoid targeting ads to those who are probably — based on the machine learning algorithm — unlikely to purchase (until they exhibit behaviors or actions that prove otherwise, at least).
  • Personalizing targeted messaging to segments: With AI Workbench in BlueConic, you can segment customers based on CLV and RFM (recency, frequency, and monetary value) scores and combine those attributes with other preferential, demographic, psychographic, or behavioral data in their profile. For instance, you can create a premium experience for customers with high lifetime value and low recency to keep them engaged. You can use the same segments across your martech stack, meaning fewer discrepancies across systems and better control over customer experience.
  • Targeting likely-to-churn customers: Using AI in conjunction with a unified profile database that is connected to all your marketing tech allows you to make use of predictive scores at the moment you need. For example, create a smart segment of customers who are likely to churn based on recent behaviors, then automatically send individualized messaging with specific discounts to prevent them from churning or offer them an on-site offer the next time they visit your site.

If you want additional advice on how you can get started with an advanced AI marketing strategy, watch our on-demand AI webinar, which features even more in-depth examples, insights, and ideas.

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See what BlueConic can do for you.

Whether you’re looking for operational efficiencies or improved marketing effectiveness through data activation, our customer data platform can help.