Nowadays e-commerce and retail businesses use different techniques and channels to promote shops and goods. Most of them keep the focus on advertisement. In fact, relevant product recommendations can not only increase revenue but also can have positive effects on the user experience.
In this article we want to review product recommendation engines for e-commerce. How do they work work? What value can they bring to a business? How can recommendations boost sales and marketing? What filters can be used? It may be relevant not only for e-commerce business owners, but also for sales analysts, business analysts and marketing managers.
A product recommendation is a filtering system that tries to foresee and show the goods that a user may likely buy. Almost every person has seen such a recommendation while doing online shopping: when you view or add an item to your basket, you can see suggestions like "you may also like these products". Sometimes it can show inaccurate suggestions and become annoying for you. However, if it shows an appropriate item, it becomes a win-win situation where a client receives a needed product and a shop increases a revenue.
In offline shops, you may meet a shop-assistant who is responsible for the customer satisfaction and for the company's upsales. E-commerce businesses don't have the benefit of having a friendly sales manager to assist your client with each step of their shopping journey. In online stores this role is done by AI algorithms, that create recommendation systems for each client.
Recommender systems have become increasingly popular in the past few years. Nowadays they are implemented in various industries: filming, music streaming services, news platforms, bookstores and online libraries, research articles, and of course in E-commerce businesses. They can work as generators of playlists for video and music services like Netflix, YouTube or Spotify, product recommenders for services such as Amazon or AliExpress, or content recommenders for social media such as Facebook and Instagram. Mostly used in the digital domain, the majority of today's E-commerce websites like eBay, Amazon, Alibaba make use of their recommender systems for serving the customers better with the products they may need.
The recommender systems work in the following way:
In most cases for e-commerce businesses, product recommendations are made directly on the website while purchasing. Also, it can be done through email campaigns or on advertising banners. With advanced software, you can get more accurate predictions. As a result, the best e-commerce recommendation systems will have a significant impact on the conversion rate, sales flow and increase revenue.
A product recommendation system is a software or a tool. Usually, it's based on various machine learning algorithms that are used to conduct the data filtering process. There are a few different types of recommendation systems. Let's review some of them:
Another successful usage of recommendation systems is shown by Amazon. 35% of the company's revenue is generated by the recommendation engine. There is information that the company uses to provide relevant recommendations to its customers:
The most obvious benefit of using recommender systems is that your company can increase revenue without dramatical changes in advertisement expenses. Amazon is an inspiring example of it. Along with the revenue, you can increase the number of users and the level of their satisfaction.
You need different kinds of data to create the best filtering systems and try different approaches. Recommendation systems can be easily implemented with relevant tools. If your company does not have enough storage or computation capacity to work with huge amounts of data from visitors and items in your online store, you can use cloud consider service providers.
If you're interested in implementing a product recommendation system, you can contact Datrics team. Our experts have an extensive experience in recommender systems implementation. With Datrics platform you can easily deal with analytics in different way. Platform allows user various integrations with custom visualization and accesses to API.
If you're interested in recommendation system implementation, contact our team to discuss further.
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