Machine Learning — 6 Things to Know For Marketers

Machine learning (ML) and artificial intelligence (AI) have taken the marketing world by storm, enabling brands to optimize campaigns like never before. 

However, as this technology becomes essential to many marketers’ everyday operations, understanding machine learning for marketing is no longer a “nice to have”— it’s a must to maximize results.

Whether you’re part of a digital marketing agency or an in-house team, here are some of the top things marketers need to know about ML to help them understand and make the most of this technology.

Key Takeaways (TL;DR)

• Machine learning enables computers to learn from data.
• There are three main types of machine learning: supervised, unsupervised, and reinforcement learning.
• ML requires lots of relevant, high-quality data to make accurate predictions, and expert help is often needed to create models.
• Marketing tools like AI tools, AI chatbots, and AI detectors may use ML technology.
• The benefits of ML for marketing include scalability, cost-effectiveness, and data-driven decision-making.
• Challenges of ML include potential bias, overreliance on ML tools, and ethical and privacy concerns.

1. Machine Learning Enables Computers to Learn From Data

Machine learning is a branch of AI technology that helps computers learn from algorithms and AI training data. 

Instead of being programmed to perform specific tasks, ML uses algorithms to identify patterns within datasets and uses them to make predictions and decisions. 

One way to picture it is to visualize how humans learn things when they make connections between new experiences and concepts.

One of the best things about ML is that it enables systems to learn, analyze, and store massive amounts of historical data, and use it to improve their decision-making capabilities over time. 

This can help marketers in various ways, especially when it comes to:

• Identifying customer preferences.
• Predicting buying behaviors.
• Personalizing content.

Of course, marketers could go in and crunch the numbers themselves, but ML systems are a fantastic choice to improve efficiency.

2. There Are Three Main Types of Machine Learning

Not all ML systems receive the same kind of training. 

There are three main types of machine learning, including:

Supervised learning

This approach trains ML models with labeled datasets to improve accuracy over time. For example, a model trained to recognize positive vs negative reviews can help marketers assess brand reputation.

Unsupervised learning

Here, ML models train with unlabeled datasets and work to identify patterns or group data into clusters. Marketers may use this approach for customer segmentation, where models group customers based on similar behaviors or preferences instead of labels.

Reinforcement learning

With this type of machine learning, models learn through trial and error, receiving positive or negative reinforcement to help them determine the best action. Marketers can optimize dynamic ad bidding with this method, as the model can learn where to spend the budget to maximize clicks.

These three types of machine learning may train models differently, but they share one thing in common: the need for quality data, and lots of it.

3. You Need the Right Data and Team for the Job

For an ML model to make accurate predictions, it needs to be fed lots of high-quality data. 

However, it’s important to ensure that this data aligns with your marketing goals to get the best possible results. 

Just because an ML model can process massive amounts of data doesn’t mean it’s always necessary. 

For example, data that can help with customer segmentation may not be best used for ad optimization goals. Irrelevant data can throw off your results, so hone in on what you’re trying to achieve with your ML model.

If you’re unsure how to approach and implement the right model for your needs, working with data scientists and machine learning experts can help get you on the right track. Working with the experts can be key to setting realistic expectations and goals with ML.

4. ML Powers Many Marketing Tools and Techniques

Many marketers already use ML tools and techniques in both their professional and personal lives, including:

• Generative AI tools: From brainstorming content ideas to generating ad copy, marketers may use generative AI tools like Jasper to help create marketing materials.
• Chatbots: Instead of having a person available 24/7, many companies use AI chatbots that leverage ML technology to support customer service teams.
• AI content detectors: Since AI writing can result in Google penalties, some AI detectors use ML and AI innovations to help marketers verify content authenticity.

Learn more about AI detection, how AI detection works, and AI detection accuracy.

5. Using Machine Learning for Marketing Has Benefits

Sure, many marketers found ways to optimize marketing campaigns without any kind of AI technology in the past.

However, there are several benefits to using ML for marketing, such as:

• Scalability: It can be easy to analyze user data when you only have a few customers, but it becomes more difficult when you get into the thousands or millions. ML enables you to scale your efforts if you experience rapid business growth.
• Cost-effectiveness: Using ML can optimize your ad spend or campaign focus and improve return on investment.
• Data-driven decision-making: ML can help you find patterns and trends in raw data and turn them into actionable insights, enabling you to make informed decisions

That being said, you need to weigh the benefits against the limitations of ML for the best results.

6. ML Is Not Without Its Challenges

It's important to note that ML isn’t a perfect technology. 

There are some limitations and challenges:

• Potential for bias: ML models can perpetuate or even amplify bias if there’s any present in the training data.
• Overreliance on ML tools: ML and AI technology aren’t perfect. They’re still evolving, and require human oversight to be used effectively.
• Ethical and privacy concerns: There are concerns surrounding ML and AI, like the ethics of AI-generated content and the collection and analysis of customer data.

Considering the challenges and limitations around AI and ML, it’s best practice to keep a human in the loop whether you’re reviewing data, writing content, or planning a marketing campaign.

At Originality.ai we believe that transparency is essential. That’s why we’ve shared our guide on how Originaltiy.ai treats your content.

Final Thoughts

Although machine learning marketing tools are getting increasingly user-friendly, a solid understanding of how they work and their benefits and limitations can help you make the most of them. 

Continuous learning and adapting have always been a part of an effective marketer’s toolkit, so remember to keep an eye on the latest developments in AI and ML.

If you work to understand and implement the latest technology before anyone else, it may just give you the competitive advantage you’ve been looking for.

Get more insight into AI and marketing innovations:

• What Is Responsible AI?
• How to Grow a Digital Marketing Agency
• Best Practices For Using AI Tools as a Writer
• 7 Considerations When Choosing an AI Detector
• Google’s SynthID AI Watermark is Going Public