Introduction

Have you ever wondered how online platforms magically suggest products that perfectly match your preferences?

Imagine a Virtual Shopping Assistant that understands your unique preferences, predicts your needs, and presents you with a carefully curated selection of products that align with your style, budget, and interests. This is the power of personalised product recommendations. By Analysing Vast amounts of data, including product descriptions, user interactions, and historical patterns, AI algorithms work tirelessly behind the scenes to make these recommendations feel tailor-made just for you.

Personalised Product Recommendations have become essential to our digital experience, from E-Commerce Giants to Streaming Services. But what lies behind this seemingly intuitive process? How do these platforms understand our tastes and effortlessly guide us toward products we never knew we needed?

The Importance of Personalisation

But why do they do it?

Companies have 3 Main Goals for better Recommendations to Customers —

Increasing Sales Volumes

Marketing is one of the principal methods of increasing the sales of regular offers. Marketing activities involve addressing the whole population or a selected target group and directing special offers and promotional material. As far as recommendation systems are concerned, sales offers and promotional activities are directed to specific users. This approach increases the likelihood of selling the promoted products.

Research has shown that reducing customer churn by a mere 5% can increase your profits by 25–125%.

Customer Retention

One of the most significant problems of modern companies, in particular those based on the subscription model, is the churn rate. Retaining customers for a long time is necessary to get a return on the investment made to acquire them. Popular customer retention methods, such as discounts and coupons, are often costly and have short-term effects. This problem can be overcome by adopting an individual approach to customers. Recommendation systems help in understanding the needs and objectives of customers and, eventually, in selecting the correct activities to maintain the relationship with them.

Increasing Customer Satisfaction

The use of recommendation systems helps increase customer satisfaction by enhancing the functionality of the sales service. Spotify offers 70 million tracks — finding the music you like in such a crowd may be cumbersome. Here’s where recommendation systems come in handy: they shorten the distance between the customer’s need and satisfaction. They help find the search product and discover the requirements that customers are unaware of.

It’s like having a knowledgeable friend who knows your taste and can recommend the perfect product. These algorithms help companies build stronger customer relationships, boost customer satisfaction, and, most importantly, increase sales.

They save time because the customers don’t have to search for products they might like, and it also enhances the customer’s overall shopping experience, which is likely to reduce churn.

Books recommended by Amazon based on a user’s past preferences, purchase history, etc

The Role of AI & Machine Learning

The fundamental definition of Machine Learning is this —

Machine learning is a field of Artificial Intelligence focused on algorithms that improve their functioning through the use of existing data.

So you get what I’m trying to convey?

Companies want to tailor products and experiences to individual customers — for obvious reasons;

But how do they accomplish this on a large scale?

This is where the power of AI and Machine Learning comes in.

These advanced algorithms can uncover valuable insights, understand customer preferences, and generate personalised product recommendations by analysing vast amounts of customer data. This section will explore how AI and machine learning algorithms enable companies to deliver tailored experiences and boost customer satisfaction.

Data Analysis and Pattern Recognition

AI and Machine Learning algorithms analyze customer data to uncover patterns, trends, and correlations. These algorithms gain valuable insights into customer preferences and behaviour by processing diverse data like purchase history, browsing behaviour, and demographics.

Personalised Recommendations

AI algorithms use collaborative and content-based filtering techniques to generate personalised recommendations. Collaborative filtering compares user behavior with similar users to identify products of interest, while content-based filtering matches product characteristics with user preferences. These techniques enable tailored recommendations aligned with individual preferences and interests.

Continuous Learning and Adaptation

Machine learning algorithms enable recommendation systems to learn and adapt continuously based on user feedback and behaviour. Incorporating customer preferences and input into the algorithm improves the accuracy and relevance of future recommendations, ensuring that offers to stay up-to-date and align with changing customer preferences.

Scalability and Efficiency

AI and machine learning algorithms empower product recommendation systems to handle large-scale datasets and provide real-time recommendations. These algorithms efficiently process massive amounts of customer data, making them suitable for e-commerce platforms with millions of users and extensive product catalogs.

Optimised Algorithms

AI algorithms require optimisation and fine-tuning to ensure optimal performance. This involves parameter tuning, evaluating algorithm performance metrics, and conducting A/B testing. Continuous optimisation enhances the accuracy and effectiveness of product recommendations over time, resulting in improved customer experiences.

Different ways companies segregate their customer base, to develop a more efficient way to market.

Types of Recommendation Algorithms

There are different ways that companies market; here are 3 of them —

Channel-Based Marketing

Channel-based marketing means using specific channels or mediums to deliver targeted messages and promotions. It recognises that different customers engage with various channels differently.

Ex: Some prefer email, while others like social media or direct mail. With AI and machine learning, businesses can analyze customer behavior and preferences to determine the most effective channels for each group. This helps optimize marketing strategies and allocate resources where they’ll have the highest impact.

Segment-Based Marketing

Segment-based marketing involves dividing the customer base into groups based on shared characteristics like demographics, interests, or purchase history. Then, each segment receives customized marketing campaigns that meet their specific needs. AI and machine learning algorithms help identify relevant segments and find patterns.

By understanding what makes each segment unique, businesses can create personalised messages, offers, and experiences that resonate with their target audience. This boosts customer engagement and increases the chances of conversion.

People-Based Marketing

People-based marketing takes personalization to the next level by focusing on individual customers instead of segments. It aims to create highly personalized experiences by leveraging customer data and insights. AI and machine learning algorithms analyze vast amounts of data, like individual behavior, preferences, and purchase history.

This helps build comprehensive customer profiles that give a complete view of each person’s needs and preferences. By leveraging these profiles, businesses can deliver personalised messages, recommendations, and offers to each customer, fostering stronger relationships and driving higher engagement and loyalty.

My Recommendation Model — The Code Behind it.

Now that you briefly know what we’re talking about in this article, let’s dive into my code and its explanation.

We’re going to be building this in Python, and the data I used is from a Dataset I downloaded from Kaggle.

The complete rights of this Dataset are to the owner.

First, Let’s import the necessary libraries for this. It imports pandas library as pd, TfidfVectorizer from sklearn.feature_extraction.text, and linear_kernel from sklearn.metrics.pairwise.

This line loads the data from a CSV file called “furniture.csv” and assigns it to the variable data. The pd.read_csv() function is used to read the CSV file into a pandas DataFrame.

The “furniture.csv” file is a file from the dataset from Kaggle I mentioned above. It just locates the data from a specific place and loads it here!

These lines define the input variable X and the target variable y. In this case, X is assigned the ‘name' column from the data DataFrame, and y is assigned the ‘no_of_ratings' column from the data DataFrame.

Again, these were from the dataset, which i’ll explain later!

Here, an instance of TfidfVectorizer is created and assigned to the variable vectorizer. It is used to convert text data into numerical features based on the TF-IDF (Term Frequency-Inverse Document Frequency) weighting scheme. The fit_transform() method is called on the vectorizer object, passing X as the input. This step basically converts the text data in "X" into a numerical representation stored in X_transformed.

This line computes the similarity matrix using the linear kernel. The linear_kernel() function from sklearn.metrics.pairwise is applied to the X_transformed matrix, which contains the numerical representation of the text data. The result is a matrix that measures the similarity between each pair of products based on their descriptions.

We use a function called recommend_products(), which takes a product_id as input and returns a DataFrame of recommended products. The function first finds the index of the input product_id in the y variable. It then creates a list of similarity scores between the input product and all other products using the similarity_matrix.

The list of similarity scores is sorted in descending order. The top 10 similar products (excluding the input product itself) are selected based on the sorted similarity scores. Finally, the function returns the rows from the data DataFrame corresponding to the selected product indices.

This line calls the recommend_products() function with the input '1,101' to test the recommendation functionality. It will return a DataFrame with the top 10 recommended products based on the similarity to the product with ID '1,1101'.

And the Output —

(I put all the code above into one terminal for this one)

And here is the output for the code! This is specific to furniture because that’s the .csv file I chose, out of 10s of others, ranging from baby products to air conditioners, which is available in the dataset I linked above.

If you want to try this out for yourself with other datasets, just change the specific X & Y values — the name & no_of_ratings in this case to any other .csv file and any other column in that .csv file, respectively.

Be sure to also change the column no_of_ratings in this code to the specific column in the .csv of your choice.

Conclusion

In this article, we explored the basics of product recommendation; specifically, in this case, we talked about the recommendation of products from an Amazon dataset. We talked about the different aspects of marketing — how companies target you — based on the medium, a group of people, and individual personality traits like browsing patterns and history of purchases.

We also talked about the different ways AI and Machine Learning play a role here, specifically the below 5 points —

• Data Analysis and Pattern Recognition
• Personalised Recommendations
• Continuous Learning and Adaptation
• Scalability and Efficiency
• Optimised Algorithms

We also discussed the types of recommendations, and finally, we came to my code, where we discussed each block of code, what it means, and how to alter it with specifications from the dataset, like no_of_ratings, etc.

Notes —

I used Python in Jupyter Notebook as my playground here, but feel free to use any other playground you’re comfortable with as well.

I’m also attaching my video explanation here for the same project, so check that out if you feel like you want a detailed, step-by-step verbal description!

That’s it for this time; thanks for Reading and Happy Learning!

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