AI Product Recommendation For Your Ecommerce Store

When you casually browning through Amazon, you notice that the “Recommendation Deals For You” section has all the products you are interested in. 5 minutes later, you realise you have just spent over a hundred bucks on several products. That’s how a recommendation system or a recommender increases the conversion rates of your store.

According to a report from McKinsey, 35% of Amazon’s sales in 2012 were generated from product recommendations and Amazon has continued to use this system to drive sales. The modern recommendation system is powerful, however, it’s not perfect. In this post, we will how AI can enhance product recommendation and why you should implement it in your business.

How does the modern recommendation system work?

Modern recommenders use filtering methods to discover what a buyer is interested in and then suggest products that are attractive to them. There are two common filtering techniques used in modern recommendation systems.

Collaborative filtering

The system collects customer purchases and browsing history to identify their interests and preferences and predict what a buyer might like by analysing similar behaviour.

For instance, when two customers buy the same product, the system might categorize them as having the same interest. Therefore, when one of them purchases another product, the recommender system will recommend the same product to the other customer. When you see “Customer who bought X also bought Y, that’s the system recommendating products through collaborative filtering.

Content-Based Filtering

Content-based filtering suggests products based on product attributes instead of buyer behaviours and interests. They can be:

Features
Specifications
Category
Description
Reviews

For example, if you are a fan of thriller books and purchase them regularly. The system might suggest other thriller books you haven’t purchased with high ratings and good reviews. You might recall seeing “You may also like” on websites after purchases, that’s how the system knows what you might like, based on the products you have recently purchased.

How AI can improve product recommendations

Modern product recommendation system has its limitations. It can sometimes struggle to recommend what buyers may really want by only recommending popular items. Apart from that, if you are a new customer, there will be a cold start problem. The system has no data about you to know what to recommend. Sol, how can AI change product recommendations?

Hyper-Personalization

AI has the ability to not just consider customers’ preferences and interests but also combine real-time data such as customers’ location, time, and weather to provide the best recommendations. For example, if are in Canada and it’s winter, the AI may recommend coats with specific designs and colours based on your previous purchasing history.

Hyper-personalization can also help increase the average order value by recommending complementary items (cross-selling) or higher-priced or premium items (upselling) that are tailored to customers’ preferences.

Natural Language Processing

Natural language processing, which is a subfield of AI will be an important aspect of the AI recommendation system. With the capability of understanding human language, AI can analyse user-generated text, such as search queries to get an idea of what your customer is searching for instead of just using product attributes to discover buyers’ interest. That allows more accurate recommendations.

Predictics Analytics

AI recommendation systems have the ability to predict and forcast by collecting the previous historical data and analysing the patterns of a process. In inventory management, it’s called predictive replenishment.

Predictive replenishment involves collecting previous historical data, such as how frequently customers buy a product, how quickly a product is used and the amount of product a company holds at a time to predict when it needs to be restocked.

This predictive capability of AI can be translated into product recommendations. For example, if you have purchased a printer from a store, the recommender may recommend ink cartridges for your printer at the right time by analyzing your previous purchase patterns and average ink usage. By anticipating customers’ needs, you can reduce the likelihood of customers looking for alternatives from other brands.

Personalised Pricing

When you bargain with a shop owner, asking for a discount and eventually the owner says “Fine, if you buy 3 of them I will give you a 10% discount”, that is a personalised offer or personalised pricing. The same thing can happen in online stores. Sometimes when you add items to your cart but do not complete the purchase, you can receive an email with a discount.

Traditionally, personalised pricing needs to be done manually and oftentimes time the price is not optimised. However, AI can process and analyse a large amount of data, and optimise the price based on their purchasing history, behaviours and how much they are willing to pay.

For instance, if a customer has been purchasing multiple products from your business and has more buying power, the recommender can recommend more premium products and at the same time, offer some discounts. Customers might have been desperate to purchase a product from you but hoping that it can be cheaper, and AI can predict that and offer what they want and more money to your business.

Conclusion

The benefit of using AI for product recommendations is enormous. AI recommenders have become increasingly common and they have the potential to increase sales, improve customer satisfaction and make customers come back for more. By providing hyper-personalized recommendations, you can bring your store to the next level.