In the post-pandemic world, the choice of individuals has become more personalized than earlier. Almost 83% of customers are happy to share their information to achieve a better-personalized shopping experience. Moreover, personalization in digital shopping has become a new anthem in marketing. In another analysis, the research found that 35% of amazon’s revenue and 75% of Netflix’s revenue comes from personalization. It happens because of recommendation systems in eCommerce. The product recommendation software finds the best suitable product based on individual characteristics. Today’s learning will discuss eCommerce product recommendation systems in detail.
What is Product Recommendation Systems?
Recommendation systems or recommendation engine is a subclass of mission learning. Similarly, machine learning is also a subset of artificial intelligence. The recommendation software predicts the most suitable product based on a rating of products and users. Based on the ranking of prediction, it is back to the user.
A product recommendation engine tracks all users’ activities like browsing history, purchase history, search pattern, frequency of transaction, demographic and age category, etc. After analyzing all of that data, it automatically recommends products to the user.
The recommendation system deals with large volumes of data. With the help of data science, artificial intelligence product recommendation systems recommend the most suitable items to the specific user. You can compare it to an experienced shop assistant who knows all the needs of a particular customer. It saves time for customers. Similarly, the business organization can sell goods faster, increasing their turnover and preserving data for future offerings.
Benefits of recommendation system
All the big organizations like Google, Instagram, Spotify, Netflix, Amazon, and Reddit use recondition engines to increase engagement with users. For example, Amazon recommends its products based on the collected data from the user. Spotify recommends music in the same way. So there is a massive benefit to using a product recommendation tool. Here is some help for you.
1. Increases sales conversation
The maximum cost of the production circle is from marketing activities. There are minimal options to increase sales without increasing the effort of marketing. After the automation of the recommendation engine, there is no repetitive cost. But based on customers’ data, the sales conversation rate will be higher day by day.
2. Maximum satisfaction of users
In the present era, time is money. When your engine can reduce the effort of your customer, then they will be happy. The engine recommends appropriate products and achieves maximum satisfaction.
3. Maintain good relationship
When the customer remains the maximum time on your website, trustability and familiarity will be higher. So more purchase is possible with the relationship.
4. Reduce Churn
The email application system powered by a recommendation engine is the best way to revisit customers. When you have coupons and discounts, you can easily reach your targeted customer in inappropriate ways.
5. Other benefits
- The recommendation engine allows Narrow Searches. It saves time in finding products and services.
- It helps the undecided customers in their product selection process.
- The business can offer personalized content and improve relevant search results.
- It contributes to a higher purchase rate and user loyalty.
- The user comes to know about the new products that serve the purpose of the newsletter, post notification, and advertisement.
Applicable Areas
Based on the insight of benefits, you can deploy a recommendation engine anywhere. How were there are two essential aspects to determine its business benefits. It is shown in tabular form.
| Breath of Data | Depth of Data |
|
Businesses serve only a handful of customers. The automated recommendation is not useful. Customers need direct conversation and interaction. For example, using sales girls. |
Having a single data point for each customer is not helpful. They purchase online. Moreover, they expect online recommendations. |
I want to classify the industries that can use product recommendation engines best on this framework.
1. Recommendation engine eCommerce business
Most of the recommendation systems are used in an eCommerce business. There are three types of e-commerce business to business, business to customer, and customer to customer. We can see business-to-customer eCommerce business uses recommendation tools. Amazon, eBay, Walmart, and Flipkart are the best example.
2. Media recommendation engine
You already have seen the recommendation system of YouTube, Netflix, and Spotify. It is like an eCommerce business. Moreover, you will not find any online news or media channel without a recommendation system.
3. Retail industry
If the retailer wants to build a robust database for reconding products to a customer, then they can develop a recommendation engine. It may not be like an artificial suggestion system, but it can start your purpose.
4. Banking sector
Financial data is essential for decision-making. The SME sector always needs financial data. Based on the economic behavior and transaction history, Bank can recommend their customers for father loans.
5. Telecom sector
Telecom securities are similar to banking because it has millions of customers. Based on the geographical and is group, the requirement is different. Someone may need a small data plan, and someone needs free Talktime. Recommendation systems can improve customer service for maximum sales.
6. Utilities
Besides the financial banking and telecom sector, artificial intelligence-based recommendation engines can make the product distribution channel narrower. You can add this system to any other solution if you want.
Tips before choosing a product recommendation engine
Before implementing a recommendation engine for your business, you need to know the actual requirement of your eCommerce platform. Hair solid backlog of valuable customer information is a must. Based on some other criteria, this engine can become fully optimized.
Your marketing data should be optimized for better customization. You can have a strategy of different channels like social media ads, email marketing, and onsite promotion.
A good database management system will give you additional support. You can customize an eCommerce dashboard from your vendor to get live insight and predictive visualization.
What to the procedure before offering a recommendation strategy?
The ultimate goal of a business organization is to provide the right product to the right person. This strategy will create a good user experience and increase the possibility of cells. This recommendation system follows several strategies and rules. Here are some procedures that you can follow:
1. Focus on algorithm
The filtering algorithm is essential to recommend any product to your customer. Many filtering algorithms like collaborative filtering, content-based filtering, and hybrid recommendation.
Out of the three hybrid algorithms, it is the most suitable. It combines all the rules and data and suggests the best product for your customer.
2. A/B testing
A/B testing is comparing several engines to find the best suitable product. There are a lot of comparing tools for A/B testing. However, VWO optimized, and Google optimized the popular testing tools.
3. Study your case
Own business case study is critical. Track your recommendation engine to find out the responses from customers. You have to ensure your return on investment and marketing matrix. If it does not get any testimonials from the users, you can switch to other options.
4. Customer support
Customer support and after-sales service are essential for users and businesses because of many technical issues, such as software errors like wrong recommendations and unresponsiveness. For an online eCommerce business, 24/7 support is vital.
Types of recommendation system
The recommendation engine is classified based on various criteria and algorithms. Here are some types of this system:
1. Popularity based system
The popularity system works based on popular topics and recent trends. Movies, songs, and videos are recommended based on popularity algorithms.
I can site examples like new year sales, geographical festival sales, YouTube movie recommendations, and Netflix episode suggestions.
Pros:
- You do not need any historical data for the popularity algorithm.
- Day one business also can recommend products.
Cons:
- The popularity algorithm system is not personalized based on the user.
- It recommends products based on the liking of other users.
Example:
- The trending videos on YouTube.
- Google News feed on Chrome browser.
2. Classified model
The classified model uses the feature of products as well as users. It predicts whether the user will choose the product or not. The outcome will be 0 or 1. 0 represents the user will not select the product. When a user would like it, the output will be 1.
Pros
- Classified models ensure more customization.
Cons
- Collecting data is a rigorous process. Without a high volume of information, correct recommendations will be complex.
- Classification of the data is also difficult.
- You may face flexibility issues.
3. Content-based recommendation engine
Content-based recommendation works based on the algorithm of similarity. When you watch a movie of adventure, the system automatically recommends another adventure movie.
Content best recommendation system uses the Euclidean distance model. The system checks the similarity of different products and computes the distance between them. Based on the different scenarios, it finds a parallel. Moreover, it uses a different matrix. The matrix mainly follows Cosine similarly and Jaccard similarly.
Pros
- There is no requirement for users’ much data.
- We only need the item data to recommend users.
- It is not dependent on the user’s data. You can recommend any new user if they do not have any profile.
- It’s free from the cold start problem.
Cons
- You need a good volume of item data.
- The features may not be available for finding similarities.
4. Collaborative filtering
The eCommerce websites and online movie websites use a collaborative filtering recommendation system. This smart recommendation engine checks the taste of similar users and searches for the similarities of products. The system is more efficient if we have a large volume of data.
Pros
- Collaborate filtering is a smarter algorithm than any other recommendation algorithm.
- The chances of selling conversation become higher.
Cons
- The system requires enough users to find the match.
- Due to sparsity problems, there may arise the issue of recommending items.
- Knowledge-based system
When the system needs a complete decision, you can use a knowledge-based system. It requires knowledge regarding subject data. It has a set of rules or guidelines.
When your customer buys any camera, they need a camera bag. Your system may offer 5 to 10% discount on the camera bag. The rules may be based on diversity, privacy, serendipity, trust, long tail, and user demographic.
5. K Fold Cross-Validation
You can use the K Fold cross variation model if you need more accuracy. This algorithm makes a model predict the demand of users based on a set of features. It trains the recommendation system. This system is very costly.
6. Hybrid recommendation system
The hybrid recommendation engine combines both collaborative filtering and content best filtering method. It can overcome the limitation of the above two algorithms.
The 10 Best Product Recommendation Engines
In your E-Commerce business, you have to deploy a recommendation system. So it is necessary to find out the best solution for your beloved organization. I will give you a brief idea of the best 10 systems.
1. Clerk.io
More than 18000 websites are using Clerk.io. It provides a personalized experience. Site search, email, and social media ads are the popular services of this organization.
Clerk.io has more than 15 pre-built recommendation logic. The standard logic is customer order history, hot products, and cross-selling products. This system automatically changes recommendations based on your business needs by calculating trends and seasons.
2. Emarsys
Emarsys has more than 15000 users and actresses in different industries. It is an Omnichannel customer engagement platform. Out of the market share, it represents 6%. Moreover, its leverage is online and outlines customer data.
Emarsys is a product recommendation service through searches, shopping charts, product descriptions, emails, and ads. You will love the feature of real-time recommendations on different channels and devices.
3. Nosto
Nosto offers a variety of recommendation algorithms like best-selling products, new arrival products, trending products, and many more. It utilizes a machine-learning algorithm to serve relevant products based on customers’ behavior.
Nosto has multiple customization options to make the journey of purchasing procedure smooth. It applies A/B testing to get insight from customers.
4. Adoric
Adoric is the perfect option to increase the conversion rate of your sales. The recommendation engine of this platform is powerful. It shows recommendations on your home, product pages, and any other pages where you visit.
Adoric sets the correct target by segmenting the audience. Wix, WooCommerce, and Shopify at the user of this platform.
5. Qubit
Qubit uses deep learning technology to recommend the product. Google cloud AI powers it. Moreover, both YouTube and Google use the same recommendation algorithm.
Qubit automatically changes the recommendation strategy in real-time to make better sales conversion. Its health check feature finds the missing product, missing data, and errors from the data source. Inherit learning from older is another exciting feature of Qubit.
6. Retail Rocket
Retail Rocket is also an excellent tool for recommending products through shopping charts, product listings, product descriptions, and emails. It quickly finds out the following purchase product of customers. So it can recommend very accurately.
7. Salesforce
Salesforce offers real-time personalization of your eCommerce. Many organizations are using this platform to increase the conversion rate of sales.
The most exciting factor of Salesforce to me is its user interface. It knows the needs of every customer. So the success rate of Salesforce is very high.
Retail Rocket is powered by artificial intelligence for complex customer segmentation. It contributes to a smart communication strategy on sells funnel.
8. Dynamic Yield
Dynamic Yield enables multiple recommendation strategies into one widget. This technology automatically finds the best next purchase option for customers. Moreover, it set the new attribute based on machine learning and deep learning.
Dynamic Yield uses a statical engine, cross-channel support, and merchandising control. It is an advanced-level eCommerce recommendation system.
9. Criteo
Criteo is suitable for its retargeting capacities. It’s dynamically re-engaged its site visitors at different stages. It is a partner with separate networks like Google, Facebook, Taboola, etc.
10. AdRoll
AdRoll is a perfect eCommerce marketing platform for recommending products in various ways. Multiple channels like email, social media platforms, and browsing data.
The best four companies that use product recommendation tools
Now almost all the eCommerce websites are using any sort of recommendation system. I am discussing the best 10 that will help you in better assimilation.
1. Amazon
When I discuss the contribution of recommendation software, the first name that comes to my mind is Amazon. Almost 35% of Amazon purchase comes from the recommendation tool. It follows the item-to-item collaboration-free caring technology along with email campaigns.
2. Netflix
Though Netflix is a video watching channel, it is a data-driven company. Research by McKinsey found that about 75% of Netflix viewing comes from recommendations. In a data science competition, Netflix won the price of $1000000.
3. Spotify
Based on the unique music test Spotify generates are new playlist of 30 songs. This recommendation tool you just three types of models collaborative filtering, natural language processing, and audio file analysis.
4. LinkedIn
If you are a LinkedIn user, you are very acquainted with that terminology ” You may also know. ” LinkedIn does the thing with recommendation technology.
How to set a recommendation system for my business?
While most eCommerce companies use recommendation technology, your organization should have this technology. Here are some ways to deploy a suggestion engine.
Out of the box solution
The recommendation engine works based on AI use cases. You have to collect historical and live data to get the right system.
Building own solution
You can build your solution in a niche domain where any vendor is yet to work there. In that case, you need a large amount of transaction history. The more data you will get, the recommendation will be more accurate.
What is an artificial intelligence-based recommendation engine?
The artificial intelligence-based recommendation engine is a combination of different algorithms. Moreover, the algorithm is based on Machine learning, deep learning, and natural language processing. Those are one of the portions of artificial intelligence. Furthermore, AI algorithms work based on historical data, popularity trends, and other business rules.
Final Thought
There is a need for perfection in the world of eCommerce business competition. The recommendation system is to support businesses in a customized way. So, the customers do not get annoyed. However, artificial intelligence-based recommendation engines may play a perfect role in achieving your target.
Nasir H is a business consultant and researcher of Artificial Intelligence. He has completed his bachelor’s and master’s degree in Management Information Systems. Moreover, the writer is 15 years of experienced writer and content developer on different technology topics. He loves to read, write and teach critical technological applications in an easier way. Follow the writer to learn the new technology trends like AI, ML, DL, NPL, and BI.