The key to converting users to potential buyers is to use real-time product recommendations. This is because real-time product recommendation engines gather the required data and are refreshed automatically. – Tweet This!
One of the major challenges faced by webshop owners is finding out how to offer the best online shopping experience to their customers. Sadly, eCommerce stores are not that fortunate to have an amiable sales executive to assist customers with each step of their shopping journey.
Likewise, product recommendation engines offer a solution to this issue – that is providing your customers with a look at your latest editions, products and best sellers to offer an enhanced online shopping experience like never before.
Why do we need a product recommendation engine?
If you have ever searched for smartphones on Amazon or browsed through posts on social media channels (Facebook and Instagram), you have used the product recommendation engine without even knowing it.
With online shopping, customers have nearly countless options. And no one has enough time to try and assess every product that is displayed in the store. Product recommendation engine plays a significant role in assisting online shoppers to find products and services relevant to their tastes and preferences.
Product recommendation engines have become increasingly popular in the last few years, and are used in different domains including music, movies, books, emails, search queries and online shopping. Mostly popular in the eCommerce domain, major eCommerce giants like eBay, Amazon and Alibaba are making use of their own customized version of product recommendation engines to serve their customers better.
A product recommendation engine is a software program that assists users (online shoppers) in the discovery of products and services by predicting the shopper’s rating of each item and showing them relevant items they would like to purchase.
Earliest algorithms used in building product recommendation engines
In this day and age, many organizations make use of big data, machine learning and AI to develop product recommendation engines. Among a variety of product recommendation algorithms, software engineers and data scientists need to select the best according to a webshop’s requirements.
The algorithmic approaches are mainly divided into two categories — collaborative and content-based filtering. However, modern recommender systems are usually a mix of both algorithms, also known as hybrid recommendation systems. They also tend to go by product recommendation machine learning algorithms.
A product recommendation engine is a software program that assists users (online shoppers) in the discovery of products and services by predicting the shopper’s rating of each item and showing them relevant items they would like to purchase. – Tweet This!
The most widely used recommendation algorithm is the Collaborative Filtering.
- Collaborative Filtering:
Collaborative filtering (CF) is one of the most commonly used product recommendation machine learning algorithms. It can learn to give better recommendations as more data about shoppers is collected.
Websites like Amazon, YouTube, Netflix, eBay etc. use this technique as part of their product recommendation systems. Data scientists can use this technique to build recommenders that offer suggestions to shoppers based on the likes and dislikes of similar shoppers.
There are many ways to decide which shoppers are similar and group their choices to create a list of possible recommendations. Be it product recommendation machine learning, Big Data or AI, this technique is compatible with all programming languages.
Some of the earliest recommendation algorithms used were:-
- Content-based filtering:
These filtering techniques are purely based on the description of a product or a profile of a shopper’s favored choices. In this recommendation system, keywords are used to describe the products. Moreover, a shopper profile is created to state the type of products this shopper likes.
In simpler terms, the algorithms in this recommendation technique try to recommend products, which are similar to the ones that a shopper has purchased or liked in the past. The underlying approach of this recommender system is that if a shopper likes a product, they will also like a ‘similar’ product.
2. Hybrid recommendation filtering
Research studies show that a combination of product-based (collaborative) and content-based recommendation algorithms can be highly effective. Hybrid recommendation systems can be implemented by building collaborative-based and content-based algorithms separately and then fusing them.
Furthermore, hybrid recommendation systems can offer more precise recommendations than single algorithms. This algorithm can be used to overcome the challenges in recommendation systems such as data paucity and cold start.
Netflix is an excellent example of the use of hybrid algorithms. It makes recommendations by comparing the searching and watching habits of its viewers (collaborative filtering) as well as recommendations based on the ratings given for a movie (content-based filtering).
How Amazon had success in creating its own recommendation engine?
We all know about Amazon and how it catapulted to being one of the best online eCommerce stores in the world. The eCommerce giant generated $141.92 billion in product sales in 2018, up 19.7 per cent from $118.57 billion in 2017.
Several factors contribute to Amazon’s skyrocketing product sales, but recently, artificial intelligence (AI) is increasingly being publicized as the reason for being this eCommerce giant’s competitive advantage. And one of the many best AI applications of Amazon is in its product recommendation engine.
Amazon aims to create a customized shopping experience to anyone who visits and buys through its site. The “Your Amazon.com” page on the site recommends online shoppers a unique selection of products based on their past shopping behavior. As per McKinsey research, 35 per cent of sales are generated from such recommendations.
While the details of Amazon’s customized product recommendation algorithm are highly confidential, its abstracts can be recovered in their patent application. Basically, Amazon employs its “item-to-item collaborative filtering” concept to pairs of similar products.
Let’s see how Amazon boosts its sales with its product recommendation engine and how could it help you to design your own product recommendations.
- “Frequently bought together” recommendation
This recommendation suggests a combination of complementary products and is commonly found below every product listing. This recommendation focuses on cross-selling products.
Takeaway: This recommendation can maximize orders and sell packages. Customers need not search for products elsewhere.
- “Customers who bought this also bought” recommendation
This recommendation focuses on product discovery by offering varied choices. It motivates shoppers to add more items to their shopping carts, instead of replacing the products in their cart.
Takeaway: The mix of choices helps customers to plan their shopping. However, this is not a very reliable recommendation as it offers too many choices to customers. This can also confuse customers and prevent them from buying the current item. In addition to that, when product recommendations are not “context aware” and display products from other categories than the originally viewed item (i.e. recommending hard-disks to viewers of body sprays), relevancy may be lost leading to dissatisfaction on the consumer’s side.
- “Compare to similar items” recommendation
In this section, Amazon lists out competitor products or similar products of a category and facilitates a comparison chart.
Takeaway: Comparison of products is valuable while purchasing an item provided that the user’s taste profile is taken into consideration. When individual tastes are not analyzed, the recommendations may not entirely be applicable to all products like books, clothes etc.
- “Recommended for you” recommendation
This section offers recommendations based on customers’ purchasing data. This section is only available to users who have already made a purchase. A new user who has not made any purchases will have their recommendations page populated with best sellers as alternatives.
Takeaway: Customers will definitely return to purchase additional items to supplement their purchased product. This section can certainly help them to make quick choices without wasting their time browsing through the entire site.
The key to converting users to potential buyers is to use real-time product recommendations. This is because real-time product recommendations gather the required data and are refreshed automatically.
Many product recommendation algorithms design recommendations based on what a shopper has recently purchased. Real-time recommendations go one step ahead. They critically evaluate what products shoppers are clicking on the site, what categories they are going through and which ads they are clicking on. This recommendation is highly helpful to customers as it offers quick suggestions on the shopper’s interaction on the webshop.
Role of AI in product recommendation and how has it changed webshop preferences in the last 5 years
Many product recommendation eCommerce algorithms are gradually moving away from mainstream recommender algorithms. Artificial Intelligence is taking over and building smart product recommendation software systems.
Thanks to AI, product recommendation engines can make fast and accurate recommendations customized to each shopper’s needs and preferences.
With the use of AI, online searching is improving tremendously, since it makes recommendations related to a shopper’s visual tastes rather than item descriptions. These recommendation engines can sense what the user requires and quickly recommend items as per their tastes.
Apparently, AI product recommendation systems may become options of search fields for most eCommerce stores since they help shoppers find products and content they might not find in another way.
How is our approach different in building product recommendation engines?
Keeping up with the current trends in the industry, we built a unique approach while developing our product recommendation engine at Perzonalization. Unlike mainstream product recommendation machine learning algorithms, we use AI powered real-time predictive technology to deliver a customized shopping experience.
Our platform, which is a successful example to hybrid algorithmic approaches coupled with real-time micro-segmentation technology and machine learning, is perfectly capable of predicting online shoppers’ purchase intentions in real time and providing relevant product recommendations.
Our recommendation engine displays personalized product recommendations and content based on each shopper’s unique shopping need and preference. Here is how our product recommendation engine is different from the rest.
- Instantly operational
Recommendations are delivered to shoppers once the products are catalogued. The experience gets more customized as Perzonalization’s product recommendation software learns about shopper’s tastes and preferences.
- Prediction of shopper preferences
Perzonalization’s product recommendation engine is quick to learn about shopper’s intent. It takes into account the kind of pages shoppers visit, the products added to cart and the orders placed.
- Flawless Design
Recommendations created are designed to match the look and feel of your eCommerce store. You can also preview and tweak the design as per your choices.
Perzonalization’s advanced options offer you the authority to define your filters and personalize what items will be displayed to your shoppers on the site.
- Real-time filtering
All recommendations are re-calculated once a shopper leaves or visits the website. This ensures the most recent history is used for estimating the required data for a recommendation.
- Individual Tracking
Perzonalization’s recommendation engine can track the performance of each widget individually so you know what is the best recommendation and option for your eCommerce site.
- Omni-channel Analysis
User data is unified across web, mobile and emails to come up with a coherent taste profile of the shopper. This attains accuracy and consistency for personalized recommendations on different channels.
10 reasons why you should get product recommendation software for your webstore
Product recommendation engines improve the shopping experience of shoppers. Here are the top 10 reasons why you should get it for your webstore:
- Drives traffic to your website
A product recommendation engine drives traffic to your website. It achieves this with product recommendation email links and targeted blasts to audiences.
- Engage shoppers
Product recommendations engage shoppers by helping them delve deeper into the product line without having to perform repeated searches.
- Offers relevant content
Recommendation engines can analyze shopper’s current preferences and their previous purchase history. With this real-time data in hand, a recommendation engine can deliver relevant product recommendation to shoppers as they shop.
- Converts users to shoppers
Recommendation engines help users to find a product or content of their choice. It learns about user behaviors and recommends options based on their preference.
- Offers relevant reports
Recommendation engines offer relevant reports about customer behavior to online entrepreneurs. This further empowers them to make decisions about their website and design marketing campaigns.
- Enhance average order value
Average order value increases when a recommendation engine displays customized options to shoppers. It gives them recommendations on similar products thereby prompting shoppers to purchase more products.
- Increases customer satisfaction
Product recommendation engines perform a really good job offering customer satisfaction. It gives customers the choice to select products without having to search elsewhere. Besides, the customer feel that they are valued more when the shopping experience on a webshop is personalized to their needs.
- Ad retargeting benefits
By combining product recommendation engine and digital marketing efforts (paid ads), webstore owners can target their customers on competitor websites or other websites with products they have liked on the webstore.
- Trigger events
The data generated from the website lets triggering emails based on shopper’s behavior. For instance, a webstore owner can send emails to shoppers who viewed three pages of a particular product with a discount or provide more information on the product.
Alongside developing marketing strategies for eCommerce stores, an online merchant must make efforts to enhance the shopping experience of their customers. Hence, the best way to do this is by implementing product recommendation tools on your webstore.
Since machine learning for product recommendation techniques is getting outdated, it is best to opt for product recommendation engines that are built using AI techniques. We suggest going with our beloved recommendation software as it combines AI and machine-learning techniques to give customers enhanced shopping experience.