The importance of a recommendation system has increased over the years and will continue owing to its various features. A recommendation system can help you improve your revenue by suggesting the customer a perfect product or service; it can also help a business in marketing campaigns and track their customers. Needless to say, a recommendation system has smoothly become a part of the majority of digital stores and the SaaS industry. However, there are a few challenges that come along with a recommendation system, which are –
Lack of User Activity
A recommendation system majorly depends on the activities performed by multiple users. It studies different users’ behavior and then lists the products or services according to their activity and feedback. However, if many users buy the same product but do not leave any feedback or ratings, it becomes difficult for the recommendation system to recommend that particular product.
Lack of Data
The availability of abundant data is what a recommendation system needs. It can only recommend a product to a user when it has enough information about a user’s past activity and preferences. But when a new business appoints a recommendation system, user data is usually low. So, a recommendation system fails to give a recommendation. Even if it does, the chances are that it will not be as effective because it doesn’t have enough data to learn the user behavior.
New Item Introduction
When a new item is introduced on a website that is already using a recommendation system, it will not recommend it until and unless a user buys it. This shelves down the possibility of a good product coming into the limelight.
When a business decides to expand its market, it will want to scale its recommendation system. However, some recommendation systems are too rigid with their algorithms, and to achieve what the business desires, they will have to build or buy a new recommendation system. The only possible solution to this problem is to buy a recommendation system that supports scalability.
We all know-how in a blink of an eye, trends can change. One day it could be blue jeans that are trending, and one day suddenly, there is a trend of cargo pants. A recommendation system is accustomed to such a quick change in trend.
Like any other product, even a recommendation engine has its pros and cons. But what’s important is that most of its challenges can be tackled by thinking wisely while investing in a recommendation system. Such as the above-mentioned challenges of scalability and new item introduction can be avoided if use Alie. It provides you the flexibility to scale your recommendation system when you want, and you can yourself add parameters and filters to it, like ‘Newly added Item.’
Try the 14-day free trial of Alie to know more about the recommendation engine, challenges, and solutions.
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