The Power of AI-driven Personalization in Modern Marketing

Introduction

With the rise of AI in recent years, there has been a vast increase in AI-driven personalization in marketing. Companies have realized that by using AI to personalize the customer experience, they can increase customer satisfaction and conversion rates. People want to feel special when purchasing a product; they don't want to feel like the company is treating everybody the same. Especially in the US, a country driven by individualism, personalization is a strong selling point. In this blog, I would like to introduce you to a few typical AI tools running in the background, explaining why you will maybe have more items in your online shopping cart than intended, spend more time browsing social media instead of doing anything else, and that, at the end of the night, you wonder why you watched so many movies again.

"AI is the future of personalized marketing. By leveraging data and algorithms, businesses can create personalized experiences that are tailored to each individual customer, improving engagement and driving sales." - Sarah Pearson, Director of Marketing at IBM Watson.

AI Tools and Methods for Personalization

Recommendation Engines

A recommendation engine is a type of software or algorithm that analyzes the data based on behavior, preferences, and historical interactions with a product or service to make personalized recommendations to users. These engines are mainly used in e-commerce, media streaming, and social media platforms. One example we are all familiar with is TikTok. They are at the forefront of social media by recommending tailored videos to the user. It feels like you are sucked into the algorithm. Another example would be Netflix, which is known for recommending movies or shows that fit the users' preferences. Recommendation engines can use various techniques to make recommendations; the most common ones are collaborative and content-based filtering.

Collaborative filtering uses similar users to find overall preferences. When purchasing an item on Amazon, you might have noticed their recommendation based on what other consumers have bought. This would be an example of collaborative filtering. Collaborative filtering relies on the notion that consumers who have enjoyed a content or product in the past will have similar preferences in the future.

Content-based filtering analyzes the attributes of items the user has enjoyed in the past. It doesn't focus on other customer preferences but rather looks at the contents or product attributes. Netflix uses a mix of content-based filtering and collaborative filtering to recommend movies and shows. The content-based approach analyzes the movie's genre, director, or cast to recommend movies with similar features.

Many companies choose a hybrid approach, combining the two techniques to allow for an optimized user experience. Often collaborative filtering is used to recommend similar products, and content-based filtering further refines the selection based on the item's attributes. Almost every successful company combines both methods. Examples are Amazon, Netflix, YouTube, and LinkedIn.

Natural Language Processing (NLP)

NLP focuses on interactions between computers and human languages. It analyzes and generates natural language text. It has applications in various fields, such as customer service, marketing, and healthcare. Some examples of NLP are chatbots, text summarization, or speech recognition. There are three common techniques in NLP: sentiment analysis, keyword extraction, and content summarization.

Sentiment analysis analyzes text to determine the overall sentiment or emotion in the text. Companies use this for social media monitoring or customer feedback analysis. There are several companies that focus on this type of analysis. One example is Hootsuite Insights, which is a social media monitoring platform. It allows users to track social media mentions and sentiments on their brand, competitors, or industry. This is especially helpful in finding the right influencers for business cooperation in marketing brands to a certain target audience.

Keyword extraction is a technique that involves automatically identifying and extracting the most important keywords and phrases from a piece of text. This is often used in search engines. The most well-known example of this would be Google. Keyword extraction is a part of their integral algorithm, which identifies the most important keywords in a search query.

Lastly there is content summarization, which involves automatically generating a summary of a certain text. This is used in news aggregation and email triage. Bloomberg uses content summarization to provide their readers with a brief summary of a news article.

Machine Learning Algorithms

Machine learning algorithms are able to learn from data and make predictions based on their findings. This method is used in almost every industry today. By using data from the past, these algorithms are constantly improving their accuracy and providing valuable insights for companies. Again, there are several common types of machine learning: clustering, classification, and regression.

Clustering groups similar data points based on their characteristics. The machine is left alone, leading to the algorithm discovering patterns on its own. Clustering is commonly used in customer segmentation or image recognition. Facebook uses clustering to group users based on their similarities in interests or behaviors. By doing so, Facebook can run more customized ads, leading to a higher return on investment for the ads.

Classification assigns labels or categories based on the data characteristics. This is often used in supervised learning, in which the data is clearly labeled, and the algorithm uses the existing data to make predictions for the future. This is often used in fraud and spam detection. PayPal uses classification to identify fraudulent transactions. The algorithm analysis every transaction and gives it different risk categories.

Regression predicts a continuous value based on the relationship between the input and output variables. This is used to predict stock or forecasting demand. Uber uses regression to predict demand for their rides and adjust prices accordingly.

Prime Example Netflix

Netflix has been a leader in using AI effectively to provide personalized recommendations to its users. The company uses many of the AI tools I mentioned above to analyze data, such as viewing history, search queries, and ratings, to generate personalized recommendations. Netflix’s algorithms are constantly improving through machine learning; the more a user uses the platform, the better the recommendations become. I have felt the effects of this first hand, as I am tempted to stay on the platform by always getting the next perfect movie recommended. This personalization has helped Netflix in retaining its customers and increase engagement on the platform. The AI-powered recommendations are not only limited to content. Netflix also uses AI to personalize marketing messages and promotional offers. A unique feature of Netflix is the “Taste Profile”; every user has a profile that describes their viewing preferences. Netflix has been able to build its following steadily through AI-powered algorithms and has increased its market share in the streaming industry consistently.

Personalized Marketing Strategies

Several personalized marketing strategies involve tailoring marketing efforts to individuals based on their preferences. Many of these strategies use techniques discussed in the first chapter.

Dynamic email marketing involves tailoring email content, subject lines, and send times based on the consumer. Different consumers might prefer different emails. Think about a stay-at-home parent; the best time to reach them might be right after they bring the kids to school or after their kids are in bed, whereas a working parent might read their emails in the evening after coming home from work. Marketers analyze data such as open rates or click-through-rate to determine the success of an email.

Ads are targeted to certain groups of consumers by using AI-driven targeting. Data such as demographics, interests, and behaviors are stored and analyzed to present ads to the target customer. What is the point of showing a teenager an ad for a new house or car insurance? Those ads will be significantly more effective when shown to an adult.

Lastly, marketers can analyze search queries, buying behavior, and social media interaction to create more relevant content for their audience. One of the most successful personalized content marketing campaigns is “Share a Coke” by Coca-Cola. By replacing the logo with a name, the consumer feels a more personalized connection when they find their name in the store. They will share this experience with others on social media, creating buzz around the brand.

Preparing for a Future of AI-Driven Personalization

To prepare for the future of AI-driven personalization in marketing, businesses should embrace new technologies such as AI chatbots and virtual assistants, stay updated on emerging trends and best practices in AI-driven marketing, and adopt a customer-centric approach to personalization. By leveraging new technologies, businesses can enhance their personalization efforts, create more personalized experiences for their customers, and improve customer service. Staying informed on emerging trends and best practices in AI-driven marketing can help businesses better understand how to leverage AI technologies to personalize their marketing efforts and stay ahead of their competitors. Finally, adopting a customer-centric approach to personalization can help businesses focus on meeting the individual needs and preferences of each customer, improving customer satisfaction and loyalty.