How an eCommerce Product Recommendation Engine Works
- eCommerce, Understanding eCommerce

How an eCommerce Product Recommendation Engine Works

When you continuously visit a local shop for buying stuff, the storekeeper will start to know your preferences, and next time you’ll enter there, you will be directly shown the things according to your needs. This same concept is applied in eCommerce stores for boosting sales. In this era of personalization, everything you see online will be as per your interest on your previous searches.

The online survey suggests that 35% of the people that make purchases on amazon store is based on the products their clients found via recommendations. The users today are more interested in personalized and accurate guidance. Any eCommerce website or store that fails to deliver this to its visitors will quickly begin to lose leads. That’s where the idea of product recommendation comes in.

The product recommendation engine refers to a system that gathers the customer’s data and uses algorithms for offering suggestions. The information on every user is collected separately and evaluated based on recent searches, past purchasing, browsing history, and demographics. The central concept behind the recommendation engine is to show your users the appropriate results and make it easier for the buyers to make better decisions.

Different Approaches for Product Recommendation Engine

There are four different types of approaches that are employed for filtering the information that is collected by the algorithms. These filters attempt to envisage the user’s preferences in an eCommerce store.

1.      Collaborative Filtering

This approach is based on two notions, i.e., user-to-user filtering and item-to-item filtering. In user to user filtering, by analyzing the user’s preferences, they can be harmonized with other similar clients that can help in gaining the knowledge what more they would prefer that they haven’t purchased yet.

The second notion is called item to item filtering, where the objects with the same underlying attributes are searched for a user, and then the store recommends the same products to its customer. For example, if you buy a trouser or pant, then you’ll see the recommendations for shirts and scarfs.  Implementing collaborative filtering is a bit difficult where the intent in narrow such as in session search or the site search.

2.      Content-Based Filtering

In this approach, the product filtering for the eCommerce store users is done based on keywords. It evaluates the gathered information found on the users’ dislikes, users’ visits, and likes. In content-based filtering, if you like specific merchandise, you will be recommended things that are similar to it.

Content-Based Filtering

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3.      Demographic Filtering

This method is a bit different. The recommendations are not based on browsing history, past searches, or user likes and dislikes. An audience is a group based on their attributes, and the recommendations are shown to the diverse demographic profiles.

4.      Hybrid Recommendation Method

The hybrid recommendation approach is an amalgamation of collaborative and content-based filtering methods for integrating group decisions. The endorsements are based on the user’s previous searches as well as the preferences of the analogous user found. An excellent example of this type of approach is NETFLIX. The recommendations given users are based on the previous ratings, along with the attributes of their similar users.

eCommerce Hybrid Recommendation Method

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How Does It Work on An Ecommerce Store?

You have been buying stuff from an eCommerce store, and the next time you visit them, some items will be recommended to you based on similar customer searches or past purchases. You might wonder how it works, right? This whole product recommendation system works based on machine learning techniques and algorithms.

The AI algorithms are employed on these e-stores that stores the data of the individual visitors and the purchase they made on your website. This stored data is then evaluated, and the system is developed that gives recommendations to the clients by building a link among the products you purchase and similar users.

The principal goal of teaching such a kind of engine system in your eCommerce store is to make it easier for your buyers to find the relevant products. Products recommendation engine narrows down the choices for the store visitors so that you can only focus on those merchandise that you are interested in.

There are a lot of benefits that an eCommerce store will receive when a recommendation engine system is implemented. Here is the complete detail about how this whole system works.

1.      Data Collection

The very first step carried out by the recommendation engine system is collecting the user’s data. This data is divided into two types, i.e., explicit and implicit data. The implied data is the one based on the buyer’s search history, likes, page views, and search. The precise data includes the information entered by the users on your website, such as product reviews or comments.

This system also analyses the user’s behavior while they are scrolling on your website, but analyzing this type of data can be a bit difficult. Every individual or client of your eCommerce store will have different data sets created based on their likes and dislikes, and with time as you feed more, the recommendations get much more accurate and smarter

2.      Data Storage

After collecting the data, the next step is storing that information. The greater the amount of data you feed to the algorithms, the more improved will be the user’s recommendation. The form of data that is used to generate the recommendations will aid in deciding the storage type.

The data storage bases available are standard SQL database, NoSQL database, and other storage. When the system stores the user ratings and comments, the accessible and managed database reduces the number of tasks performed by the system, which helps in focusing on the user recommendation. Most of the eCommerce stores prefer to use Cloud SQL as it makes the data storage feasible.

3.      Data Analysis

You might wonder how the system finds the relevant data according to the users? It is done through different analysis methods. The user data is filtered and analyzed in the following ways.

  • Real-time analysis is done to provide immediate recommendations to the users.
  • Near real-time analysis, work to offer recommendations to the users while they are browsing the website. User analytics are refreshed.
  • The batch analysis systematically evaluates the data.

4.      Data Filtration

The fourth step of the product recommendation engine is filtering out the gathered data. Data filtration helps in separating the data required to offer relevant recommendations to the users. There are four different approaches used by the engine system. These are collaborative, content-based, demographic, and hybrid. Every eCommerce store uses the algorithm that best fits its site. The filtered data is then presented to the clients on their timelines in the form of recommendations.

5.      Implementation

Companies can implement this product recommendation engine only when they have enough storage capacity to store a considerable amount of user data. The most effective systems that can help enterprises in data storage are Spark and Hadoop. Companies can even develop their product recommendation engine system by using various opensource tools.

Advantages of Using Product Recommendation Engine for An Online Store

As the marketer implement various marketing strategies to promote their brand ‘s products online, the recommendations engine makes it easier for the marketers to know what the customer is willing to purchase. When the clients are only shown the necessary item, they are more likely to engage in your online store and return soon. Another significant benefit that eCommerce store receives is higher customer satisfaction levels and more significant generation of revenue.

Implementing a product recommendation engine will help you get higher conversions as a recommendation system will enhance your user experience. When the individual browses your online store, the recommendations offered to them help them in making the right decisions. This indirectly makes your users satisfied with your e-store services.

Best Recommendations Engines Software to Use

You have probably understood the whole concept of how this recommendation system works and how it can bring considerable benefits to your eCommerce website. Here are some of the efficient recommendation software that provides the best money/ time ratio. These are:

  • Suggest grid
  • SLI system
  • Episerver

This Engine system works dazzlingly for your eCommerce store. One of the reasons many organizations prefer it because it is not based on assumptions. It follows a completed process for data collection to filtration that narrows downs the user’s preferences and shows them what they would need.

All the website owners wish to increase their user’s engagement, and these recommender systems will immensely help in improving your buyer’s experience that compels them to visit your store time and again. The success of your eCommerce store is wholly based on the preferences you offer to your clients. This plays a huge rule in the long-term value of your e-store.

Stella LincolnAuthor Bio

Stella Lincoln is working as an eCommerce Specialist at Dissertation Assistance. She began her career as a freelance writer in eCommerce and has written many articles in this field of area. Her blogs stand out among others. Stella possesses the ability to deal with clients online and advising them regarding eCommerce.

How an eCommerce Product Recommendation Engine Works

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