The Advantages Of A Recommendation Engine In e-Commerce

the advantages of a recommendation engine in ecommerce
The real-time recommendation engine is undoubtedly one of the most popular marketing technologies for e-commerce. This is indeed a big current trend in the sector, which takes part in particular in the personalization of the commercial and marketing offer.
What is a recommendation engine?
A recommendation engine on an online store is really essential, especially if it offers many references. It is still necessary that it works in a relevant way by responding perfectly to user requests. How does a recommendation engine work and what are its advantages for an online sales activity?
As its name suggests, a recommendation engine therefore issues recommendations to the Internet user who uses it. Amazon was one of the first online sales platforms to offer it. The principle is as follows: to offer, in real time, the products which are likely to interest the user. For this, several criteria are generally used, in isolation or in combination:
• First, the history of the user: his previous purchases, his searches, the clicks he makes on the site, his browsing habits, etc.
• Then, the references which are the most marketed at present, the “best-sellers”;
• The products that most interested internet users, those that were the most bought, the best evaluated, etc;
• References linked to those already chosen by the visitor: cross-selling, up-selling, down-selling;
• The profile of the Internet user, their socio-demographic data;
• The seasonality of products (holiday periods, weather, etc.);
• Stock data, etc.
The recommendation engines in e-commerce are therefore based on dedicated algorithms or on artificial intelligence systems. Their main goal is as follows: to detect and propose, in real time, the references which are most likely to interest the Internet user in question, at the most favorable moment.
How does a recommendation engine work?
The recommendation basically works on three key stages:
1. The data collection;
2. Classification of information;
3. Finally, extracting the recommendations.
• Data collection
Since the user is at the center of the recommendation system, the engine must imperatively determine its expectations. To do this, it collects various information which may have been explicitly communicated by the Internet user (note, opinion, etc.). It will also take into account implicit data such as the visit of a page, the purchases already carried out, the time spent on the page of a product, etc.
• Classification of information
A step which consists of correlating the data collected on the user, with the references available on the online store. This allows the construction of a data model.
• Extracting the recommendations
Starting from the previously constructed data model, the recommendation engine will be able to extract a list of relevant products in order to offer them to the Internet user.
The different types of recommendation
The recommendation engines can be classified into three categories:
1. The object recommendation, or content based;
2. Social recommendation, or user based;
3. The hybrid recommendation.
The object or content based recommendation
the object or content based recommendation
This type of recommendation is mainly based on the characteristics of the references (example: sizes, colors, brands, etc.), as well as on the preferences already known by the Internet user.
The main advantage of the content based recommendation is that it does not require a large community of users to be effective. However, it requires a certain rigor in the information of the characteristics of the products by the e-merchant, in its database.
Social recommendation, or user based
Its principle is simple: if Internet user A buys product X and Y, then the person who bought product X will be likely to be interested in product Y.
The social recommendation thus takes into account the preferences of all visitors to an e-commerce platform. This therefore requires a large community of users to function properly. This type of recommendation engine is also greedy in server resources. Other disadvantages: it is not able to offer a new product (this is called cold start), as well as a very rarely consulted product (sparsity).
It is therefore the most suitable recommendation engine for cross-selling. Its advantage is also that it follows trends, since its recommendations evolve with the interests of users, in real time.
The hybrid recommendation, Holy Grail of the recommendation
Hybrid recommendation can be considered the most relevant recommendation technique. Indeed, the goal of this approach is to bring together the best of the two types of recommendations explained above. With hybrid recommendation, which uses object recommendation and social recommendation techniques, there is no more concern about cold start or sparsity.
The giant Amazon uses this type of recommendation in particular. It also has one of the most sophisticated recommendation engines on the web.
What are the benefits of a recommendation engine?
what are the benefits of a recommendation engine
The advantages of a recommendation engine are numerous. This type of technology thus greatly helps the internet user in his choices. Recommendation engines are especially essential on e-commerce sites that have a catalog provided. This technology makes it possible to collect, in real time, multiple data in order to analyze them to understand the expectations of a user. Based on these, the recommendation engine will offer the right product, at the right time, in the most appropriate way. For the e-merchant, the objective is therefore twofold. Apart from a good user experience, this will increase the average cart while optimizing the conversion rate; which is fundamentally important in e-commerce.
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In other words, a recommendation engine allows you to sell more, thanks to personalized suggestions. This technology therefore has several advantages for an e-commerce store:
• Generate greater product visibility;
• Arouse a real interest in the Internet user;
• Improve the visitor's journey;
• In the end: generate more sales.
In conclusion, it is thus possible to affirm that if it is difficult to imagine for a successful store to do without good sellers, it is just as difficult for an e-commerce site not to include an effective recommendation engine. Regarding the type of recommendation engine to install, there are no predefined solutions, a solution adapted to one merchant site will not necessarily be for another. Everything therefore depends on the type of customers and the products sold and a data science company( can help you in this regard.
Harnil Oza

Harnil Oza is a CEO of HData Systems - Data Science Company & Hyperlink InfoSystem a top mobile app development company based in USA & India having a team of best app developers who deliver best mobile solutions mainly on Android and iOS platform and also listed as one of the top app development companies by leading research platform.


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