The Gist

  • Term clarity. Machine learning and generative AI in marketing often get conflated, leading to confusion for brands evaluating tools.
  • User focus. Generative AI for marketers mainly offers time-saving benefits, while machine learning primarily enhances campaign performance.
  • Email significance. Machine learning algorithms have revolutionized email marketing optimization, from send times to subject lines.

The most popular classification models have generative AI being a subset of machine learning, and machine learning being a subset of AI. While this may technically and architecturally be the correct way to think about these tools, it has led to significant confusion.

Let’s take a look at machine learning and generative AI in marketing.

Considering Machine Learning and Generative AI in Marketing

First, because of our natural tendency to drop words to simplify and economize our language, this has led to many people referring to generative AI simply as AI. Muddling things further, because generative AI and machine learning are both lumped under AI as a big umbrella, all of these terms are being used somewhat interchangeably.

All of this makes it difficult for brands to evaluate these tools and understand how to use them to accomplish their goals. After all, most people aren’t building these tools. They’re using them.

So, let’s consider a more user-centric framework, one that focuses on what brands — and in particular, marketers — care about. Let’s also focus in on just machine learning and generative AI, which comprise most of the AI tools that marketers use. From a user standpoint, the biggest differences between machine learning and generative AI are twofold:

  1. Where the data comes from that fuels the tool’s output.
  2. The primary benefit of the tool.

Let’s first examine how machine learning looks when viewed through these two lenses…

Oracle -- Machine Learning vs. Generative AI
Oracle — Machine Learning vs. Generative AI


Machine Learning and the Impact on Email Marketing

Used by marketers for around a decade, machine learning fuels a variety of common email marketing features and applications, including:

  • Send time optimization, which determines the best time to send email, SMS, and other digital campaigns to individual subscribers based on their past engagement times.
  • Subject line optimization, which helps determine the best words and other elements to include in the subject line of a particular campaign based on the historical performance of campaigns using those subject line words and elements.
  • RFM modeling, which uses the recency of a subscriber’s last engagement, the frequency of their engagement and the monetary value of their engagements to classify subscribers into groups, helping to determine the best contact and content strategies to use with each group.
  • Fatigue analysis, which determines which subscribers are the most at risk of opting out if they are included on your next send based on the historical engagement patterns of subscribers who opt out.
  • Product and content recommendations, which determines the best products or content to include in a campaign based on each subscriber’s past engagement with products and content.
  • Channel selections, which determines the best channel or channels to use to reach each subscriber for a particular campaign or journey.

Where the Data Comes From

Machine learning algorithms are fueled almost entirely by your company’s data — whether it’s the performance of your campaigns, the engagement of your subscribers or other aspects of your business.

However, there are instances where machine learning results may be informed by data from other brands that also use the machine learning tool. For example, sometimes subject line optimization tools prioritize your subject line data, but also use data from other users to supplement. This kind of data supplementation works best when limited to users who are similar to your brand, such as in your same industry.

The Primary Benefit of Machine Learning in Marketing

Marketers use machine learning because it improves the performance of their campaigns. The return on investment is seen in higher engagement and conversions, and in lower opt outs.

At its core, machine learning helps marketers fulfill our long-standing goal of sending the right message to the right person at the right time via the right channel — and doing that with precision at scale.

However, when used for things like product and content recommendations, you could argue that machine learning also saves time. Indeed, many of our clients have been excited to redesign their campaigns so that such recommendations have a steady presence — or even constitute the entirety of a recurring campaign — so they save time on writing, designing and coding. But for the most part, machine learning allows marketers to perform tasks that would be time-prohibitive otherwise.

Generative AI in Marketing

Bursting onto the scene in the fall of 2022 with the debut of ChatGPT, generative AI in the form of large language models (LLM) and image-generation engines can help marketers with:

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شركة النمر هي شركة متخصصة في تصميم وادارة المواقع الالكترونية والارشفة وكتابة المحتوى والتسويق الالكتروني وتقدم العديد من خدمات حلول المواقع الالكترونية والتطبيقات وهي شركة رسمية ومسجلة منذ عام 2015.

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