When you continuously visit a local shop to buy 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. Everything you see online will be as per your interest in your previous searches in this personalization era.
The online survey suggests that 35% of the people that make purchases on amazon store are based on the products their clients found via recommendations. 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 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 recommendation engine’s central concept is to show your users the appropriate results and make it easier for them to make better decisions.
Different Approaches for Product Recommendation Engine
There are four different types of approaches employed for filtering the information 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 gain the knowledge of 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 trousers or pants, you’ll see the shirts and scarf recommendations. Implementing collaborative filtering is difficult where the intent is 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 recommend things similar to it.
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 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 and the analogous user’s preferences. An excellent example of this type of approach is NETFLIX. The recommendations given by users are based on the previous ratings and the attributes of their similar users.
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 store the individual visitors’ data 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. The product recommendation engine narrows down the store visitors’ choices so that you can only focus on the merchandise you are interested in.
There are many 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 based on the buyer’s search history, likes, page views, and search. The precise data includes the users’ information on your websites, such as product reviews or comments.
This system also analyses the user’s behavior while scrolling on your website, but analyzing this type of data can be 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 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 system’s number of tasks, 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. The engine system uses four different approaches. 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.
Companies can only implement this product recommendation engine 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 open-source tools.
Advantages of Using Product Recommendation Engine for An Online Store
As the marketer implements various marketing strategies to promote their brand’s products online, the recommendations engine makes it easier for the marketers to know what they are 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, their recommendations help them make 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
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 down the user’s preferences and shows them what they need.
All the website owners wish to increase their user’s engagement. These recommender systems will immensely help improve your buyer’s experience that compels them to visit your store repeatedly. The success of your eCommerce store is wholly based on the preferences you offer to your clients. This plays a huge role in the long-term value of your e-store.
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 area. Her blogs stand out among others. Stella possesses the ability to deal with clients online and advising them regarding eCommerce.