Shopping is a necessary part of human survival today. Whether it be food, entertainment or household goods, all people need certain products and services to survive and thrive.
But how do people make buying decisions?
In the past, product recommendations came from trusted family members, friends or subject-matter authorities. Today, ecommerce has changed not only the way we shop, but the way we make purchase decisions.
Most online stores now use product recommendation engines, which are tools that use algorithms and user data to recommend relevant products to customers.
Companies like Netflix and Amazon use product recommendation engines to drive a large percentage of their sales. In fact:
- Amazon estimates that 35 percent of its revenue comes from its product recommendation engine
- Around 70 percent of everything customers watch on Netflix is a personalized recommendation
When configured correctly, recommendation engines can help increase revenue, conversion rates and customer lifetime value. In this blog, we’ll discuss the three main types of recommendation engines and answer the common question, “How do recommendation engines work?”
What are the Different Types of Recommendation Engines?
Although recommendation engine types can vary from business to business, most companies use one or more of the following systems:
- Collaborative filtering
- Content-based filtering
- Hybrid recommendation systems
This method involves collecting and analyzing user information based on behaviors, activities or preferences and making predictions based on similarities with other users.
The main advantage of collaborative filtering is its ability to make recommendations with no analyzable content and without understanding the item itself. Collaborative filtering runs off the assumption that people with similar interests should like the same products.
The main types of collaborative filtering algorithms are:
- User-User Collaborative Filtering: This method searches for customers with similar interests and offers product recommendations based on what the other customers like. This method of filtering is data intensive and takes more time, so systems can be difficult to implement.
- Item-Item Collaborative Filtering: This algorithm finds items similar to ones previously searched or purchased. By using an item look-alike matrix, you can suggest related products to anyone who has purchased from the store. Item-item collaborative filtering uses fewer resources and doesn’t require specific user history. Amazon uses this method when it displays related items on product pages.
There are simpler approaches available, but they do not generally have the same degree of accuracy as the algorithms described above.
As the name implies, content-based filtering methods are based on item descriptions and the user’s preferred choices. Using keywords, the algorithms recommend products similar to ones a user has liked in the past.
The premise of content-based filtering is that if you like item A, you will also like similar item B. One example of content-based filtering is when you like a song on Apple Music, and the platform provides a similar song from the same era.
The major drawback of content-based filtering is the lack of replicability across different content types. You can’t look at someone’s previously read news articles and make music or movie recommendations.
Hybrid Recommendation Systems
Recent research has shown that using both collaborative and content-based recommendation systems together can be more productive. You can implement a hybrid approach by separately making content-based and collaborative-based predictions and then combining the results.
Using a hybrid approach can provide a more accurate representation of user preferences than using collaborative or content-based methods alone.
Netflix is one company that embraces the hybrid recommendation system. Netflix provides recommendations by using watch history and searching habits of similar users (collaborative filtering) along with suggesting movies with shared characteristics (content-based filtering).
How Does a Recommendation Engine Work?
Typical recommendation engines process data using four phases: collecting, storing, analyzing and filtering.
Step 1: Collecting the Data
The first step in creating a recommendation engine is gathering the data. Explicit data consists of inputs such as ratings or comments on products. Implicit data includes order history, cart actions or search history. You will gather this data for every user who visits the site.
Behavioral data is easy to collect, but it can be difficult to distinguish useful from extraneous information. Each user’s data profile will become more distinct over time as it is fed more data and history.
Step 2: Storing the Data
As you collect more data, algorithms can provide more accurate recommendations. However, making product recommendations can quickly turn into a massive data project.
There are several storage options available depending upon the type of data used to make recommendations. You could use a NoSQL database, a standard SQL database or a type of object storage. Be sure to consider ease of implementation, data storage size, integration and portability when deciding on storage options.
Step 3: Analyzing the Data
The next step is analyzing the data. To offer immediate recommendations, you need to provide an instantaneous form of analysis. The most popular types of data analysis are:
- Real-time systems: The system uses tools that process and analyze events as they occur. If you want to provide instant recommendations, you should implement this type of system.
- Batch analysis: This is a periodic way of processing and analyzing data over a set period of time, such as daily sales. If you want to send emails with recommendations, you should perform this type of analysis.
- Near-real-time analysis: This method gathers data quickly so that you can refresh analytics every few minutes or so. The near-real-time system works best for providing recommendations while the user is still on the site.
Step 4: Filtering the Data
Once you gather all the data, you must accurately filter it to provide user recommendations. You must choose the appropriate algorithm to provide the right suggestion.
You can represent user data through sets of matrices with products and users as the dimensions. Assume two matrices are similar, but the second is deducted from the first by replacing existing ratings with the number 1 and missing ratings with the number zero.
The resulting matrix represents a truth table where number one represents user interaction with a given product. Recommendations are then provided to the user once all data is processed.
What are the Benefits of a Product Recommendation Engine?
Recommendation engines make life simpler for ecommerce businesses. Without having to rely on market research, you can provide instant, relevant product recommendations to customers based on their purchase history and preferences.
The main benefits of recommendation engines include:
- Revenue: Thanks to years of research and experiments by successful companies like Amazon, we know which recommendation engine algorithms work best. Recommending products based on each customer’s unique preferences is proven to drive higher conversion rates and revenue than non-personalized recommendations.
- Customer satisfaction: You can help customers discover products they may not have known about but will probably love – increasing customer satisfaction, brand loyalty and trust.
- Average order value: When you recommend similar products to a customer based on what’s in their cart, they’re more likely to tack additional items on to their order.
- Personalization: Each customer receives unique suggestions that make them feel like they’re getting special care and attention.
Not only are product recommendation engines useful for ecommerce business, but they’re virtually mandatory in today’s competitive marketplace.
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