Artificial Intelligence

The Practical Guide to Artificial Intelligence for Marketers

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Today’s marketers are in the early stages of transforming how they identify, execute, and evaluate campaigns leveraging new and innovative technology: artificial intelligence (AI). As digital-savvy customers expect more sophisticated and personalized experiences, marketers can lean on AI advancements to seamlessly deliver what they crave.

From AI-enabled chatbots that offer specific recommendations based on past behavior to intelligent solutions that improve decision making by analyzing reams of data, AI can be a critical driver of better conversions and marketing ROI.

But with all the hype surrounding AI, there are bound to be disappointments. It is not uncommon that a new technology will be subject to overpromising and under delivering, which leads to disillusionment and skepticism. In fact, research firm Gartner calls this the “trough of disillusionment” as part of their Hype Cycle model. And missteps, like data breaches and disconcerting levels of personalization, can turn potential customers cold.

Let’s take a look at potential opportunities where marketers can successfully improve their operations with AI.

It’s not about the technology—It’s about the strategy

The tools, coding, and the endless stream of technical jargon around AI can be intimidating for anyone without a computer or data science background. But marketers don’t need to know the specific algorithms used in an AI solution. They simply need to understand how to strategically leverage AI and how it helps drive business outcomes.

Once marketers realize the business applications, they can confidently buy and deploy the necessary tools or work with data science teams to develop the capabilities. For marketers, it’s all about identifying the problems that represent the biggest opportunity, not building the solutions.

Using AI in marketing 101

Marketing today can be boiled down to five basic steps, and AI can help enhance each one.

1. Audience

It can make you smarter about your customers and inform improved audience selection and segmentation. One common use case is by analyzing billions of data points, AI can predict lookalike audiences: individuals who share characteristics with those who’ve already converted. Armed with tools to identify a target audience based on past behaviors, marketers can offer tailored experiences that will resonate with potential customers.

2. Message

Customers want compelling and relevant experiences, and they expect all communications to demonstrate a deep understanding of their needs. AI can help marketers deliver greater value to potential customers by applying machine learning to the content selection and delivery process. This includes creative, formats, and offers. By creating personalized messages based on previous choices and behavior, marketers are able to engage in ways that resonate every time.

3. Channel

Channels aren’t just where you distribute your content—they’re also living organisms that can produce different outcomes. So, the same message in the same channel may be received differently depending on the timing and the context. AI can help marketers determine the best time and place to engage with potential customers based on past channel performance and what you know about the individual.

4. Analysis

An intelligent platform can track performance and attribute results across the marketing mix. With this feedback, marketers can quickly understand what’s working and what’s not so they can make adjustments to improve performance and drive a better return on their investments.

5. Optimization

With a constant focus on success rates and changing trends, AI can help marketers continually improve efficiency and performance—both immediately and in the long-term. By analyzing massive quantities of data over a substantial period of time, AI learns from historic data and gives marketers the tools to make smarter decisions faster.

4 Areas for Strategic Application

1. Identify opportunities to leverage AI

Rather than trying to understand the technology behind AI solutions, savvy marketers should focus instead on finding opportunities to use them. A red flag for efficiency is any data that’s not being used to drive insights or decision making—and AI can help. The more complex a data set, the more difficult it is to extrapolate any meaningful conclusions. So, if you catch yourself saying, “If only I could figure how to put all this data to use,” consider an AI application.

2. Automate manual applications

Marketers constantly have to decide what they should or shouldn’t do next. But what if there was a way to leverage historical data to automate that decision making? Using AI to automate manual or rules-based sequences can increase efficiency and reduce errors often created by manual processes. With a dizzying array of content options and parameters, AI can test every combination to determine the ideal track and optimize it over time.

3. Predict future behavior

Taking an AI approach to predicting behavior can forecast important figures like conversion rates and customer lifetime value while helping give customers more personalized experiences. Marketo ContentAI uses machine learning to deliver engaging content based on past buyer behavior. It can even predict the ten most interesting pieces of content for a particular audience in real time. And the more a customer engages, the smarter and more tailored the technology becomes.

4. Know when to say no

Despite everything that can be achieved by strategically implementing AI, there are still areas where AI solutions are not appropriate: those lacking data. It won’t matter how good the models are—if you don’t provide enough data with enough discrimination for AI to test different outcomes, the technology won’t be very accurate. AI is, after all, artificial intelligence. It’s only as good as the data you feed it.

AI can be an extremely powerful tool for any marketing engagement platform. Equipped with actionable, data-driven insights, organizations can optimize internal operations while driving more meaningful customer experiences. In the end, isn’t that what it’s all about?