Apart from buying what they want, customers shop products that are recommended by friends and close ones to a great extent. As there is a trust factor involved, their choices are influenced by what people around them buy. Relatable?
Clearly buying behavior is influenced by recommendations. And your online business is not an exception to it! Be it e-commerce, audio/video streaming service, job portals, blogging platforms, websites, database forums, or any online business, a recommendation system helps you personalize your user’s browsing or purchase behavior that eventually drives more conversions.
Let’s begin with the question,
“What a product recommendation system is exactly?”
Basically, a product recommendation system is a filtering system that predicts and displays items that your user would like to purchase. You can think of a real-life “Counter guy” at a retail shop who suggests items related to the product that you are looking for or have already added to your shopping cart. They are very well trained in cross-selling and upselling. A Customized Product recommendation system is just an automated form of it.
In the age of Artificial Intelligence and machine learning, product recommender systems have become even more powerful as they take purchase behavior, browsing behavior, etc. to gather a huge sum of data and make use of algorithms to recommend the most relevant items to specific users. It analyzes a user’s choices and discovers data patterns to present the product offerings that are relevant to the interest and needs of the user.
Through machine learning, it searches, maps, and provides your users with the most relevant chunk of information as per their tastes and preferences.
Why should you consider it for your online business?
Product recommendations can make your profits multiply quite literally. Research suggests that product recommendations can engage prospects and happen to boost average order value by almost 369% with a single recommendation. And the numbers continue to spike with the clicks.
With the help of a robust recommender system that is backed by machine learning and predictive analytics, achieving such high revenue gets real.
Certainly, the more engaging and personalizing your recommendations are, the more your online business benefits from making larger purchase orders.
Now that we know what it brings to the table for you, lets now get to-
How a Product Recommendation Engine works for your Online Business
Let’s assume that you have an e-commerce platform. Once a product recommendation engine is integrated with your website, you can use it as a targeted marketing tool on your website pages as well as for your email campaigns.
It will recommend multiple products from different categories to your customers based on what they are browsing. You can offer a “frequently bought together” option that has the potential to entice customers into buying the combo. With an AI-driven recommendation engine, ratings and reviews of customers can be used for displaying products with a “best selling” option too.
The catch here is that your customers buy a package rather than a single product.
For example, one customer adds gym equipment to his cart.
Your Hybrid product recommendation engine recommends him in real-time to buy another equipment, fitness tracker, gym shoes, or clothing or combos of some or a package altogether.
Further, it will use personalized recommendations to email and engage your customers with current trends related to the product category as well. By bringing these products into your customers’ attention, you are creating a personalized user experience for each of your visitors with utmost accuracy.
And what does that ultimately bring to your online business?
Increased average order value and subsequent increased revenue!
A robust AI-powered recommending system is what needed to gain user engagement, deliver more user satisfaction, and drive more sales.
You too can take advantage of a powerful recommendation system like Alie for your online business platform to personalize your user experience.
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