How to Choose a Recommendation System
A recommendation system or recommendation engine is basically an information filtering system. It helps you offer the most relevant product or service recommendation to each of your customers. Now, the choices and interests of various customers are different, and it’s next to impossible to provide them apt recommendations without the help of a recommendation engine. In today’s digital era, nearly all digital stores and websites use recommendation systems. Statistics says that nearly 80% of Netflix’s stream time is achieved by its recommendation system, while nearly 35% sales in Amazon is driven by its recommendation engine. But still, why do customers prefer some digital stores more over the others? Definitely the recommendations they get over various digital stores play a vital role here.
So, if you are thinking of having a recommendation system for your online store or website & app, you need to know some important factors. Here we have listed it out for you-
1. Type of Business
A recommendation system can be very good for a particular business, but may not be that much effective for another business provider. Because every digital store has a different kind of target audience and content. So, it’s better to keep in mind what kind of content are you offering and who your target audience is before choosing a recommendation system.
2. Type of Algorithm
Recommendation engines are based on different types of algorithms and you must have the basic knowledge about each to set your preferences right.
There are three types of algorithms a recommendation system use-
- Collaborative Filtering
In collaborative filtering, a recommendation system makes predictions about the interests of a user by collecting the information regarding the preferences of other larger number of users. It is important to know about the one major drawback of a recommendation system with collaborative filtering which is cold-start problem. As the collaborative filtering is based on the data related to user preferences such as user ratings, thumbs ups and downs and other feedback, the products with no feedback can’t be recommended to any customer by this algorithm. Furthermore, it is not effective for a new user who hasn’t given any feedback to any product yet. If your online business deals with a lot of new customers every day, a recommendation engine with only collaborative filtering will not be of much use to you. Also, for any digital store, every product is expected to have good reach and visibility, so this filtering may not let you accomplish it fully.
- Content-based Filtering
Content-based filtering is based on the features of a content. If a user likes a specific digital product, content-based filtering enables a recommendation engine to recommend the digital products with similar features to that user. Not only it eliminates the drawbacks of collaborative filtering but also helps you enhance the reach of your digital products further and improve customer experience significantly.
- Hybrid filtering
A recommendation engine with hybrid filtering uses both collaborative and content-based filtering to recommend your customers the most relevant and accurate contents. Most of the leading OTT platforms such as Hulu, Netflix, or Hotstar use advanced recommendation engines equipped with hybrid filtering algorithms.
3. Usage of Artificial Intelligence
While adopting a recommendation engine for your business, you will come across a lot of options. But, how advanced are they? Or, which one will help you stand out from the crowd in the long run? Well, the answer lies in Artificial Intelligence (AI) which can drastically improve your customer experience by providing them with real-time recommendations. A recommendation engine with AI not only focuses on the product content or description but the visual preferences of your customers. So, if you want your customers to get pragmatic recommendations, AI will serve the purpose.
This is one of the most crucial factors you should consider while opting for a recommendation engine. You need to be aware of how effective a recommendation engine will be for your OTT platform, if it is popular among the e-commerce stores. Or, will a recommendation engine designed for a job portal, be equally efficient for your B2B SaaS business? A good recommendation system comes with great adaptability. But then again, not every recommendation engine will meet the criteria. And that’s why you need to keep this checklist ready before finalizing the recommendation system for your business.
So, that’s it! There are a few other factors such as how easily integrable it is, how well the previous users got benefited from it, etc. you should check before having a recommendation engine for your business!
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