AI Marketing Apps
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:
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?”
Although recommendation engine types can vary from business to business, most companies use one or more of the following 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:
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.
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).
Typical recommendation engines process data using four phases: collecting, storing, analyzing and filtering.
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.
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.
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:
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.
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:
Not only are product recommendation engines useful for ecommerce business, but they’re virtually mandatory in today’s competitive marketplace.
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