How big data is used in recommendation systems to increase sales?

July 27, 2021.

In the past, a personalized shopping experience was a luxury, but these days, having a special offer for every customer at any time and empowering customers to feel that luxury has become a primary goal for companies in many industries. Today, thanks to the concept of big data, companies know their customers even better than customers know themselves. With that in mind, a topic that is impossible to avoid and that needs to be studied and discussed is the concept and impact of recommendation systems.

A recommendation system suggests products, services, and information to customers based on analysis of gathered data. A recommendation system, often called a recommendation engine, represents a type of data filtering tool that uses Machine learning algorithms and AI to recommend the most relevant product to a specific customer at the right moment.

The more data a company has, it will be easier to excel in the development of a recommendation engine. Hence, it can make recommendations that will generate more revenue and increase customer satisfaction due to proper offers and a personalized shopping experience.

In the development of a recommendation engine, the central point is data. Commonly, the recommendation engine processes data through four phases:

  1. Data collection

Customers’ data can be implicit or explicit. Implicit data include data such as cart events, page views, history activities, clicks, and search logs. Explicit data contains customer reviews and ratings, comments, likes, and dislikes. Age, gender, and customers’ interests, values, and other profile details are part of demographical data. This kind is also valuable and should be included in the data collection process.

  1. Data storing

There are different types of storage that can be used such as SQL database, NoSQL database, or object storage. Which storage you will use depends on the type of collected data, but, surely, storage should have enough capacity and be scalable.

  1. Data analysis

All collected data that is now in some storage have to be analyzed, so it can be used in an appropriate manner. Batch analysis, real-time analysis, and near-real-time system analysis are three essential types that are used when we talk about recommendation systems.

  1. Data filtering

The last but definitely not the least step in the recommendation engine data process is choosing which type of techniques for the recommendation system will be used. After finishing this last step, you will be able to provide the most appropriate recommendations for your customers.

There are various types of recommendation techniques, but here are the main three:

1. Collaborative filtering

Collaborative filtering is a type of recommendation system that focuses on collecting and analyzing user data (activities, preferences, and behavior) and then predicting what they would like to see based on the similarity with the other users. It assumes that if person A likes to watch tennis, volleyball, and golf, and person B likes to watch tennis and volleyball, then person B might also like golf as well.

2. Content-based filtering

Content-based filtering is a type of recommendation system that focuses on the description of products and user’s preferred choices and then recommends the other products that have similar characteristics. It works on the principle – if you like a particular product, you will also like this other item. Content-based filtering relies on the characteristics of the products themselves and does not include other users’ preferences.

3. Hybrid model

The hybrid model combines any two recommendation systems and creates a recommendation based on diverse rating and sorting algorithms – the two described above, but also it can use demographic-based recommendation systems, utility-based recommendation systems, or knowledge-based recommendation systems. Netflix is the perfect example of a hybrid recommendation system – it makes recommendations by comparing the searching and watching habits of similar users (collaborative filtering) as well as by offering content that has similar characteristics with the content that a user has watched and rated highly (content-based filtering).


Data and recommendation systems drive numerous industries led by e-commerce and streaming platforms. A lot of well-known brands such as Amazon.com, Alibaba.com, Netflix, Spotify, and YouTube use recommendation systems. As a matter of fact, thanks to their recommendation systems, these companies have been able to secure a leadership position in their industries.

Here are two of the most successful implementations of recommendation systems: the use case of Amazon.com and the use case of Netflix which we already mentioned.

  1. Amazon.com

Amazon excels in the e-commerce business thanks to the perfect combination of Big data usages and their recommendation system models. At every step of customers’ journey, Amazon targets their customers by providing relevant and useful product recommendations. That makes a shopping on website fully personalized and takes advantage of planned, but also impulsive shopping. The value of this approach is that the store can be changed due to customers’ interests and offer a perfect product with perfect timing.

  1. Netflix

Since 2000, Netflix has been using different ways of recommendations. Then, it was the recommendation of the videos that users should rent. Nowadays, there are about 1300 clusters that Netflix uses for customer recommendations that are based on gathered behavior and actions of the customers. Therefore, by providing its customers with relevant and personalized content that improves customer engagement and loyalty to the platform. In this way, Netflix is one of the biggest leaders in the Tech and Entertainment industry and the first one when it comes to streaming services.

Besides these successful examples, there is a wide application of recommender systems in other industries. Mainly, these are industries oriented to end-consumers – retail, transportation, healthcare, financial services, etc.

Team member
Ana Jacimovic
Product Owner
Team member
Aleksandra Vucicevic
Product Specialist
Team member
Ana Jacimovic
Product Owner
Team member
Aleksandra Vucicevic
Product Specialist

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