<br /> <b>Warning</b>: Missing argument 2 for issart_title(), called in /var/www/blog.issart.ru/wp-includes/class-wp-hook.php on line 288 and defined in <b>/var/www/blog.issart.ru/wp-content/themes/seashell/functions.php</b> on line <b>289</b><br /> <br /> <b>Warning</b>: Missing argument 3 for issart_title(), called in /var/www/blog.issart.ru/wp-includes/class-wp-hook.php on line 288 and defined in <b>/var/www/blog.issart.ru/wp-content/themes/seashell/functions.php</b> on line <b>289</b><br /> 5 use cases of AI based recommendation systems - ISS Art Blog | AI | Machine Learning | Computer Vision

5 use cases of AI based recommendation systems

Artificial intelligence solutions are widely used in a variety of businesses. With opportunities they provide, it becomes possible to optimize processes and bring revenues to a new level.

E-commerce is not an exception. Lots of companies are now looking for ways to cross-sell and up-sell effectively. This is where an AI based recommender system can help. 

As McKinsey reports have shown, 75% of content that Netflix users consume and 35% of products that Amazon users buy come from recommendations. After implementing a recommender system, Amazon reported a 29% increase in sales. Alibaba group managed to drive the conversion rates by 20% when it applied ML based recommendation algorithms to provide shoppers with personalized offers during the sales festival in 2016.

Actually, most online shoppers expect companies to provide them with personalized recommendations. According to Evergage, 56% of users will come back to the sites that offer recommendations again and again.

Wondering what kind of an intelligent recommendation engine to implement for your business? Or probably you are interacting with people who need to implement such a system? If any of these is the case, you definitely need to look through the possible use cases below.

1. Related products recommendations

Say, a user has already purchased a hat. Why not offer buying a scarf that matches this hat, so that the look will be complete? This is about related products recommendations, and this use case is very popular among online stores. It is often implemented by means of machine learning algorithms as “Complete the look”  or “You might also like” sections in online fashion stores like ASOS, H&M, Pandora and many others.

Intelligent recommendation system

In addition, such AI based recommender engine can analyze the individual purchase behavior and detect patterns that will help provide a certain user with the suggestions of products that will match his or her interests most likely. This is what Netflix actively applies when recommending movies and TV shows.

2. Alternative products recommendation

Sometimes it happens that a certain product is currently out of stock. This shouldn’t become a reason to let a user leave without any purchase. To prevent this, companies implement a smart recommendation system which suggests alternative options. Check this example from Urban Outfitters.

Out of stock recommendations

Combined with a feature for “notify me” when a product appears in stock again, such ML based recommender engine demonstrates markable results. See the Skechers example below.

In stock notification

3. Location based recommendations

With location based recommendation engine, it is possible to detect customers who are nearby the physical stores or restaurants and send them an invitation to come in.

For instance, Sephora sends the app users a push notification when they are nearby their store and offers them an incentive to come in (such as free makeover). This approach certainly drives customer engagement and significantly increases chances that a user will finally make a purchase.

4. Recommendations on 404 page

It happens sometimes that users encounter 404 error when browsing your website. Why not minimize the level of disappointment and provide something relevant there by applying a personalization engine

A good idea is to display items that are similar to those he/she has recently checked on your site. Alternatively, you can show top products within certain categories. 

personalized recommendations on 404 page

Coursera offers a user to browse the course catalog on its 404 page.

Personalized recommendations on 404 page

The point is to prevent a user from leaving the site with the feeling of frustration from not having found what he/she was looking for.

5. Recommendations of weekly/monthly top products

Recommending the top items is another popular tactic. This is what music streaming services actively apply when offering top charts and playlists. A well-known example of a music recommendation engine is Discover Weekly by Spotify.

So, we can see that embedding a system of personalized recommendations into online stores can not only boost sales. It can also increase customer satisfaction and retention level, so that a user will return to the online shop again and again.

***

Considering adding an intelligent recommendation system to your online store? Reach out to us. Together we will find the ways to increase your online sales with personalized product recommendations.