How Recommendation System Can Increase Sales in Your eCommerce Business
The e-commerce market is facing drastic changes brought on by new technologies. And those retailers stay ahead of the competition, who implement new marketing technologies, test and analyse data they get from their customers. Nowadays, these are the key components of sales success and greater income. For example, Amazon generates 35% of the revenue by its own recommender engine (RE). So how to boost your electronic commerce sales using a similar solution?
What Is a Recommendation System?
The definition of a recommendation engine is reflected in its name. RE is a set of algorithms that provides users with recommendations of products they are likely to buy. It is a data filtering technology that uses a specific recommender algorithm to determine the items a particular visitor may like.
How Recommender Systems Work
These systems generate recommendations by analyzing previous purchases, interests, and web-pages viewed by a user. It is all about “smart” processing of big data arrays that helps to make the most accurate customer profile as possible. That is why data science plays a primary role in building recommender systems (RS).
The whole process passes through three basic steps before a user gets a supplementary item he or she may consider to buy. These are:
- Information collection;
- Processing collected information;
- Suggesting recommendations.
The system requires as much information about a user as possible in order to offer relevant products. It may use different consumer preference elicitation or other techniques to collect data. The more accurate data is, the more relevant recommendations customer gets.
Processing collected information
This is a phase when the system analyses collected data by applying specific learning algorithms.
Since their invention, recommender systems have mainly applied two filters to suggest items users may like: content-based filtering and collaborative filtering. The first one means making predictions by finding similarities in the different content of the same type. Another model compares products based on ratings that are given by users with similar interests. Recently, with the development of new technologies, RS started to use more advanced approaches – today they all are identified by the term “Deep Learning”. Let’s briefly go through them.
Context-aware approach. This deep learning approach utilises the information about user environment at different time periods: when and how he or she changes preferences. There are various ways how it can collect contextual information – with the help of device built-in components (GPS, transaction timestamp etc.) and data mining techniques (analysing the content of user’s textual reviews by means of AI algorithms).
Semantic-based approach. The standard content-based approach mostly uses tags and keywords to find similar content. Semantic-based RSs use a knowledge base that is defined as a concept diagram or taxonomy. In movies-related RSs, there is a complex hierarchy that consists of genres, movie names for better understanding of user preferences. In photo stocks, users add tags to images and the most frequently utilised tags become distinct taxonomies or categories.
Cross-domain based approach.
The traditional collaborative principle provides recommendations using the following hypothesis: similar appreciations in one domain can be similar in another one. Although, that is not always true. Such RS compares users without splitting items of different domains.
Cross-lingual approach. This approach-based RS recommend the items that have descriptions in other languages even if a user does not speak any of them. The system takes keywords from multilingual descriptions, translates them using dictionaries, and generate recommendations.
Existing Recommender Systems
Google Cloud Prediction API
This is a cloud-based machine learning tool that helps analyse data and make predictions. However, it does not have any user-friendly web interface. Google announced that the project will be closed on April 30, 2018.
Amazon Machine Learning
This is a service that allows to visualise data, thus simplifying the usage of machine learning technologies by providing comprehensive tools.
SLI Systems Recommender
An RS that was created especially for eCommerce entrepreneurs. It contains learning-based searching, merchandising, navigation, and various SEO algorithms to help users buy products they need.
This RS uses a semantic-based approach. It collects the information about user’s favorite TV channels and show preferences, and recommends a place or a location to visit.
New York Times RS
NYT recommender engine applies an advanced hybrid filtering approach to offer content users may like. It provides the specific content modelling algorithm that compares two documents on the basis of their topic weight. In other words, the system is able to understand what this particular article is about.
This is an eCommerce focused RS. Its principle of operation is similar to Google Analytics: in order to create recommendations, special scripts are needed to be installed on the website.
Advantages of Using a Recommender System for eCommerce Business
There is no doubt that recommendation system makes a positive impact on the growth of eCommerce business and here is how personalized content can get you more clients.
More traffic means higher CR, which in turn results in higher sales. REs apply mass customization to the emails sent to customers. Every single email will be automatically created for a particular user specially and suggests products he or she may like. It will significantly increase a click-through rate (CTR) of your emails.
Personalized product recommendations make users view more and more pages on your website by offering them highly relevant content. It saves users the trouble of looking for the products they need on other websites.
There are three possible ways how RE can convert your visitors into customers:
- By increasing customer loyalty and making them return offering only useful and relevant products;
- By encouraging users to make a purchase suggesting things they really need;
- By increasing User LTV.
RS helps to increase an average order value (average paycheck) by offering related products that can improve particular item characteristics, or its alternative (to do a cross-sell or an up-sell).
How Much Does It Cost to Implement a Recommender System?
The cost of an RS implementation depends on the requirements you have regarding data analysis. For small data arrays, you can use an existing open source solution and integrate it into your system i.e. Apache Mahout. If you need a simple custom RS for ElasticSearch or your eCommerce website, it will cost you approximately $10,000. The cost of more advanced system development can reach up to $1.5M including paychecks for data scientists, programmers, and engineers.
The main goal of any RS is content personalization and delivering relevant suggestions to the target audience. This principle significantly changes eCommerce business in a technological way. RS is equally beneficial for customers, who want to get useful recommendations and retailers, who use it to drive higher sales.