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Leveraging AI and Machine Learning for Personalization and Engagement

Leveraging AI and Machine Learning for Personalization and Engagement

Thanks to today’s technology, businesses have access to various sophisticated AI and machine learning solutions that can help them enhance the customer experience through more nuanced personalization.

The following guide will introduce you to some of these solutions and show you how they deliver personalization at scale. It will also address the ethical challenges of using AI and machine learning and how to address them.

AI and Machine Learning’s Role In Personalization

Traditionally, products and promotional campaigns were tailored to appeal to a specific audience or group. Thus, marketing materials were typically static and unchanging, which made them inefficient.

Your business can’t thrive if it doesn’t know who its customer is. Thorough market research is essential to catering to each customer’s needs and building your customer experience around them. But creating individual custom experiences for consumers can be tricky. Not only does it require the amassment of large sets of data, but this data must be applied in meaningful ways.

This is the role of AI and machine learning in personalization and AI in personalization. They are data-centric tools that work by sifting through large sets of acquired data, sorting it and presenting it back to you based on an input, instruction (prompt), or configuration.

6 Examples of Successful Machine Learning and AI-driven Personalization

When the internet was still in its infancy, there was no personalization; everyone was exposed to the same information.

Then designers introduced templates with blank spots that could be filled with a site visitor’s details, such as their name or location. Soon marketers used strategies such as loyalty cards and programs to gather information about their customers. This information could then be used for personalization.

But we’ve come a long way since then. Here are a few examples of how AI and machine learning have been used to deliver more optimal personalization.      

Targeted Advertising

Targeted advertising is likely the most popular application of AI and machine learning in marketing. Companies like Google and Meta use customer search history and usage behaviors to deliver personalized ads.

They also deploy AI-powered ad trackers that can determine how well an ad is performing, allowing them to adjust and improve their strategies.

Dynamic Web Design

An AI can learn about site visitors’ or clients’ preferences by observing their usage habits and behavior. This includes tracking their time spent on certain pages, products they frequently search for, etc. It can then dynamically shift the visual elements of your website, including fonts, colors, and themes.

Your website’s look and feel aren’t the only aspects of your website that machine learning and AI can improve. They can also gather information about suboptimal processes and web elements that may impede your website’s performance and ruin the customer experience. They can also determine which sections or parts of your website visitors spend the least time on or bounce away from quickly.

Enhancing Accessibility

Marketers and designers have become more attentive to the accessibility of their marketing campaigns and promotional materials. Not only does accessible marketing open you up to new customer bases, but it also has the potential to improve your brand’s reputation.

Content Recommendations

Your product and its delivery can be influenced by AI and machine learning. Streaming services are the most evident examples of this.

Machine learning algorithms are used to gather information about what users like to watch or listen to. They can then make listening or watch recommendations. They use the information gathered from other users as well to make these predictions.

Machine learning and AI also track actual viewing and listening habits. For instance, it will track if users prefer to listen to entire uninterrupted albums or mix-and-match playlists. They can also analyze how clients watch videos. For instance, do they prefer to watch movies in daily intervals or single sittings?

Personalized Customer Relations

Customer Relationship Management (CRM) software is where business intelligence meets customer experience. And, of course, CRM software has not been spared by AI-driven modernization.

Artificial intelligence can be harnessed to gather and process data from both internal and external sources. Predictive analytics offered by this system can provide organizations with unrivaled levels of customer intelligence.

Thanks to ChatGPT, more software companies have begun integrating generative AI into their software. Microsoft Co-Pilot and Salesforce’s Einstein GPT are two of the most famous examples. Generative AI can be used to relay faster responses to customers and determine the best ways to communicate with them.

This isn’t the only example of how AI is used in CRM software. Zendesk is one of the well-known software-as-a-service (SaaS) CRM software solutions. They use AI and cloud computing to deliver AI at scale. Whether through conversational AI and customer analytics, they’ve used this technology to revamp and revitalize their products by adding more personalization.  

However, they’re not the only ones. There are a litany of Zendesk alternatives using AI to deliver truly innovative products, from AI translating messages and transcribing audio in real-time to AI sending custom message responses.

Artificial intelligence and machine learning have elevated nearly every business area and will continue to do so in the foreseeable future.

Computer Vision and Facial Recognition

Organizations can use tools such as facial recognition and computer vision systems to learn things about customers. For instance, if given permission, a machine learning algorithm could cluster all the photographed images on a customer’s phone to form a profile. These tools could potentially conclude that a customer enjoys certain hobbies or likes to eat out at certain restaurants frequently.  

The Potential Ethical Challenges of AI and Machine Learning

Personal data and privacy were always concerns even before the advent and popularization of modern AI. Companies originally used strategies such as loyalty cards to extract data from customers and to understand their spending habits.

These companies would then use this data to offer customers personalized products and deals. Then smartphones became widespread, allowing companies to use metrics such as location (geolocation) and other data to deliver personalized ads.

All these forms of data gathering were introduced before current AI. Many of them can be considered unethical. So if these problems have always existed, how does AI make a difference?

Unethical Data Acquisition

AI can enhance the data acquisition process through monitoring and other techniques. It can ultimately amass and sort this data faster than a human operator, which, of course, may raise questions of privacy.


Unfortunately, machines and algorithms aren’t free from bias. After all, they’re made by human beings, and we’re naturally biased. As such, it’s only natural that algorithms built and trained by us would be as flawed.

An ML/AI model trained using data from a specific group is likelier to give unreliable predictions for people outside that group.  


Marketing is cited as one of the many industries that will be impacted by AI, causing many of those working within it some concern for their job security. AI can post on social media, interact with clients, target potential customers, etc. It can perform these tasks more efficiently than human operators.

4 Potential Solutions for Ethical Implementation of AI and Machine Learning

Data privacy and the rules and regulations that govern them continue to evolve. The best way for companies to protect themselves completely is by not capturing personal data.

However, this isn’t technically possible, especially if you want to implement AI-driven personalization to drum up engagement. The next best step is to get informed consent or gather data in such a way that the user is always aware of it.

Giving Visitors Options

Not everybody’s comfortable with the idea of an Orwellian-like software program lurking behind the scenes, watching every move they make. Visitors must be made aware of your AI and machine learning software upon visiting your website. You can do it similarly to how most modern websites notify visitors of cookies and other privacy policies.

However, many websites do not always provide users with a way to opt-in or out of certain rules or settings. By using the website, you agree to all policies, including being monitored. Users can only opt out by choosing not to engage or use your website. Of course, this isn’t ideal as you want to direct more people to your website and keep your conversion rates healthy.

Instead, you can inform users of your policies and allow them to choose which portions they can opt in or out of. This will also allow you to acquire fully informed consent.

Even if they decide they’d rather disable your monitoring tools, other more ethical ways exist to extract information about them. In these instances, AI and machine learning may not play a part in the data acquisition process. However, it can still be used to apply personalizations dynamically.

Using Surveys and Quizzes

If you can’t use AI to gather data about visitors because they’ve found a way to block it or have opted out, there are still other creative ways to do this.

For instance, you can use surveys and quizzes to learn more about your potential customers. Now defunct UK-based fashion company Thread was a great example of how this strategy could be implemented well.

Their AI would send their clients weekly style recommendations based on information acquired from these quizzes. Clients would rate the recommendations, and Thread’s AI could then use these ratings to improve their suggestions. It’s no surprise that Mark and Spencer purchased Thread’s technology to enhance their own personalization capabilities.  

Staying Up to Date With Rules and Regulations

As we previously mentioned, companies must adhere to many rules, standards and regulations when working with data. Some of the most well-known and significant include the EU’s General Data Protection Regulation (GDPR) and The American Data Privacy and Protection Act (ADPPA).

Non-compliance and infringement of the rules set out by these regulations can result in heavy fines or imprisonment. Thus, organizations must be cognisant of the data protection laws and regulations of their regions.  

This can be tricky as both the technology and the rules that govern it continue to change. Fortunately, AI can help with this and ensure that your organization is updated on the latest news and regulation changes.

Moreover, it can automatically update your security and policies based on these changes. Ultimately, machine learning and AI can be used to tackle some of the ethical challenges they present.

Upskilling Employees

Businesses must remember not to dehumanize their employees. They must be proactive to ensure that they invest in the morale of their human staff, which includes coaching and upskilling them.

Companies should also consider hiring in-house counselors to help calm and quell the fears and anxiety of their employees.    


As advancements in AI continue to accelerate, we’ll begin to see more discourse concerning the ethics of its usage. Many of the questions surrounding the ethics of using AI and machine learning tend to be philosophical. However, there are ways to approach these matters pragmatically.

Organizations must ensure guards are in place to protect customers’ privacy when using machine learning models and AI to extrapolate personalization data. Customers need to know what information is being recorded and what it’s used for.

AI and machine learning are great tools but should not be leveraged with near-reckless abandon. We can expect to see more literature and laws regulating their use in the future.          

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