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Welcome to the (Marketing) Machine: A Tech Loot Guide to AI and ML in Marketing

Posted on the 06 August 2019 by Techloot @tech_loot

Ever since the dawn of the internet, the marketing landscape has shifted dramatically – making the digital sphere the primary means by which businesses spread their messages. The internet, though, isn’t a single unified marketing channel. Within it, you’ll find sub-channels like social media marketing, content marketing, and any number of other related disciplines. For marketers, that means managing a complex web of related yet distinctly different marketing initiatives, each with its own requirements and best practices.

Needless to say, managing all of those separate initiatives isn’t easy. The good news is that there’s a whole new range of artificial intelligence (AI) and machine learning (ML) technologies entering into the digital marketing industry, promising to help relieve marketers’ burdens and amplify their efficacy in the medium. To help digital marketers make some sense out of these new tools – from how they are being used to why they’re important – here is a Tech Loot guide to AI and ML marketing technology.

What is artificial intelligence?

Artificial intelligence Machine Learning Business Internet Technology Concept.Photo: Sikov / Adobe Stock

To get started, it’s a good idea to first get a handle on what AI is, as well as what it isn’t. It’s a topic that isn’t very well understood by the public, owing largely to the popular culture references that paint it as some kind of future threat to humanity (insert sarcastic robot overlords comment here). The reality of AI is, at its core, far simpler than that. Most experts define the concept today as being systems that demonstrate the qualities of intentionality, intelligence, and adaptability.

That means they’re able to take deliberate steps to apply existing knowledge to solve problems as they arise, and not in a pre-programmed manner. In human terms, it’s akin to our ability to think and reason using the information we already know and understand. With it, such systems can automate decision making for routine processes with some level of irregularity (like a chatbot understanding how to answer several variations of the same question).

What is machine learning?

3d rendering robot learning or machine learning with education hud interfacePhoto: phonlamaiphoto / Adobe Stock

As its name implies, ML is a field of computer science dedicated to studying learning methodologies and how they may be integrated into computer programs. Put simply, it is the technology that may someday make computers capable of learning from new data without any human intervention – in other words, to get smarter with experience and exposure to new information.

It’s easiest to think of ML as a subset of AI. It’s the thing that will allow AI technology to improve itself, and to expand its own abilities without the need of a human programmer. In reality, ML is the technology that most people conflate with AI, in that it’s the closest analog to the portrayals of AI we see on television and in film. The truth is that ML is going to form the basis of what most experts believe to be the true potential of AI in a variety of applications in the future.

How are AI and ML used in marketing today?

At their current level of development, you won’t find many examples of marketing tools that employ true AI and ML at the same time. That doesn’t mean that there aren’t some amazing ways to apply the two leading-edge technologies to marketing tasks. In fact, there are more AI and ML-powered marketing tools going into use with each passing day, and they’re sure to proliferate even more in the future. Here are some of the places you’ll find them within the digital marketing field.

1. Conversational AI

The easiest place to begin is with the example of AI that most marketers should already be familiar with – conversational AI. It’s the technology that powered the high-end part of the wave of chatbots that flooded websites and social media channels over the past few years. It’s also at the core of the digital assistants like Alexa, Siri, Cortana, and Google Now that are becoming fixtures in consumer products of all kinds – which has led to some truly disturbing results that we don’t suggest you emulate in your AI deployments.

For marketers, conversational AI can be a game-changer in that it can be used as an always-on marketing representative across digital channels. It’s important to note, however, that not all chatbots use AI. True conversational AI in chatbots is still somewhat rare, and most current-generation chatbots make use of pre-configured dialog paths (like a tree of potential responses). There are some that make novel use of AI right now, though, like Mitsuku by Pandorabots.

2. Recommendation engines

A common use of ML in marketing today is through the deployment of recommendation engines. They’re an ML algorithm that keeps track of customers’ browsing and purchasing habits to build a profile of their likes and dislikes, which can then be used to make targeted suggestions for other products and services that they may like. It’s the technology that allows Netflix to suggest titles you may never have thought to watch, and how Amazon seems to know what you’re looking for before you do.

For the average marketer, a recommendation engine can supercharge customer engagement and drive sales, which are two of the primary goals of most marketing initiatives. There are already several open-source recommendation engines available for use today, and they should provide ample options for any company that wants to adopt the technology.

3. Churn prediction

One of the major goals of the digital marketer is to keep existing customers interested in a product or service, and engaged with a brand. The problem, however, is that not all customers need the same messages at the same time. It’s especially difficult for marketers to discern when a customer is about to jump ship to a competitor, or churn, in industry parlance. With an ML solution in place, marketers can get advance warning when they need to be concerned about a customer departing in the form of a prediction of future behavior.

On their own, systems that implement such predictive analytics aren’t ML in the strictest sense of the term. They merely analyze existing data and rely on pre-set data models to determine probabilistic outcomes – such as a customer disengaging from a business. With ML added to the mix, the same system can also learn from each intervention – like sending a sale offer to a customer before they go elsewhere – and figure out what kind of interventions work and which don’t. That enables marketers to automate such customer retention tasks, without fear that leaving it to the machine will lead to increased churn rates.

4. Customer segmentation

Today’s multi-channel marketing efforts are carefully designed to reach different audiences in different places and at different times. Figuring out how to do this has heretofore been something of a dark art practiced by marketing professionals. That’s because there’s a certain feel to figuring out what makes for an effective segment in a given customer group, and there are several ways to do it.

ML systems take the guesswork and intuition out of customer segmentation, substituting hard data and careful analysis in their place. By examining multiple available data points, from customer behavior to past purchased history, and any other relevant information available, an ML system can create customer clusters through an unsupervised learning process – in other words, you feed it the information and it will output the most effective and specific segmentation possible based on the data.

5. Content creation

Content marketing, online concept , Content Data Blogging Media Publication Content marketing , Content Strategy digital content and online webinar , Media Global Daily News Content Content marketingPhoto: adiruch na chiangmai / Adobe Stock

One of the most valuable and effective tools in the digital marketer’s toolbox today is content marketing. It’s a way of attracting attention to a product or brand, by connecting it with valuable information that’s related to what potential customers want. When done well, it can increase the reputation, traffic level, and search rankings of a given website, and provide a steady stream of visitors that should be primed to engage with the business.

The big problem, of course, is creating the content that makes such a strategy possible. Today, content creators have any number of AI-powered tools to help them churn out high-quality online content at scale, which lowers the cost in cash and time required to power a content marketing effort. In the near future, though, AI might even supplant the human army of writers all around the world that create all of the articles, blog posts, and tweets that make up the modern internet – that is, if AI developers deem it safe to allow that to happen.

AI and ML in marketing: it’s just the beginning

It should be obvious by now that AI and ML solutions are already very much available within the world of digital marketing. The earliest examples are more commonly used than you may have realized, and the latest technology is evolving at a rapid pace. For marketers, that portends a future where they will work almost side-by-side with their AI and ML solutions, creating value for the businesses they serve and driving new growth and bottom-line success – and that day may not be as far off as you might think.


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