Not too long ago, consumers used to buy products from their preferred shops. Primarily because the shop owner knew the preferences of each customer they sold items to. The shop owners manually tracked the buying habits of their customers to recommend suitable products. It helped them build good relations with their customers and increase brand loyalty.
Leading businesses now use technology to strengthen their customer relationships. With the ever-growing customer base, it is a challenge for companies to track their customers’ buying history. However, AI and its derivative technologies have simplified their efforts. Consumer-facing businesses use recommendation systems to analyze customer preferences and recommend products accordingly. The demand for recommendation systems has increased significantly due to the value they provide.
Anticipating the needs of your customers increases customer satisfaction and improves revenues. You can engage your customers in a more meaningful and effective manner with a recommendation system. You can reach out to your target audience based on their preferences. It increases your revenue rapidly as customers will have to spend less time browsing through a long list of products.
Leading companies like Netflix, Amazon, LinkedIn are speeding up their users’ decision-making process with personalized recommendations. The recommendation algorithm narrows down the pool of available choices to a few options using big data analysis. Artificial Intelligence and Big Data analysis play a vital role in this algorithm.
One recommendation engine cannot suit every type of business. It is necessary to choose the most accurate type of recommendation system to extract maximum benefits.
Here’s a quick look at the available options -
Collaborative Filtering:- As the name suggests, this recommendation system takes recommendations from other users to make suggestions for specific items. People making similar selections in the past would probably agree on additional choices in the future.
Content-based Filtering:- It solely depends on a particular user's interaction with the business. This type of recommender system creates a user profile using deep learning. Through this technique, the system determines items that a customer would like. For example, the keyword system is used to show results based on similar keywords in the items previously bought.
Demographic Based Filtering:- Customers get suggestions based on their demographic info. The system will suggest only those items selected by other users of the same demographics.
Utility-Based Filtering:- This type of recommender system suggests products as per their utility. The items are displayed based on vendor reliability and availability to make sure they are suitable for the customer.
Knowledge-Based Filtering:- A thorough study of the user's preferences and buying patterns enables this system to expert suggestions and recommendations. The system analyzes past purchases to suggest items that you will possibly buy.
Hybrid Filtering:- It combines two or more different techniques to create a more precise recommendation system, thereby generating better output.
A wide range of recommendation systems is available for businesses to choose the right one.
Leading businesses credit recommendation engines for their growth and high customer satisfaction. Since AI-based solutions are becoming more common in consumer industries, businesses should adopt them to be on par with the competition. Moreover, a business can save a lot in other areas of business, like marketing.
Companies like Netflix, Spotify, Amazon are already setting examples for all kinds of businesses. As reports suggest, they are earning huge revenues using recommendation systems. Discovering customer preferences to make the correct suggestions leads to better sales.
Get in touch with us if you want to improve your cross-selling and up-selling capabilities. Our developers have developed recommendation systems for multiple businesses with significant results.