Recommendation engines are advanced data filtering systems that predict which content, products, or services a customer is likely to consume or engage with. One doesn’t need to look far to see one in action. Every time someone chooses a TV show using Netflix’s “You May Also Like…” feature or buys a product Amazon recommends, they’re using powerful recommendation engines.
Recommendation engines (sometimes called recommenders) are win-win features for both customers and the businesses that deploy them. Customers enjoy the level of personalization and assistance a well-tuned recommendation engine provides. Businesses build them because they fuel engagement and encourage sales.
Accurate recommendations don’t appear out of thin air. Businesses must invest in data solutions capable of analyzing a high volume of products and identifying patterns in customer behavior. Only then can they unlock the true value of their customer data and make recommendations that positively impact revenue.
- Recommendation engines are advanced data filtering systems that use behavioral data, computer learning, and statistical modeling to predict the content, product, or services customers will like.
- Customers are drawn to businesses that offer personalized experiences.
- The three main types of recommendation engines include collaborative filtering, content-based filtering, and hybrid filtering.
- Recommenders improve revenue by encouraging cross-selling, suggesting product alternatives, and drawing attention to items abandoned in a digital shopping cart.
What is a recommendation engine?
Recommendation engines are tools that leverage predictive analytics to help companies anticipate their customers’ wants and needs. The engines use machine learning and statistical modeling to create advanced algorithms based on a business’s unique historical and behavioral data. The resulting recommendations are based on some combination of:
- A customer’s past behaviors and history
- A product’s ranking by consumers
- The behaviors and history of a similar cohort
Recommendations are most accurate when there’s a great volume of data at a company’s disposal. The more active users a product has, the more data there is to compare behaviors and preferences across demographics.
However, not every bit of data collected will be relevant or even reliable. Building recommendations on bad data results in recommendations that are inaccurate and unhelpful. The first step in creating a workable recommendation engine is adopting a proper data management strategy and analytics stack that collects and verifies data before it is put to use.
Types of recommendation engines and how they work
Not every recommendation engine uses the same methodology to form predictions. Recommenders typically achieve results using one of three types of data filtering: content-based, collaborative filtering, or a combination of the two.
This type of filtering is used in “Similar items include…” recommenders. Content-based filtering creates predictions on the actual qualities of the products and services being offered. Products in this system are assigned attributes that can be compared to other products directly. Companies choose the types of attributes used by the engine based on the type of products being consumed.
For instance, an ecommerce website that specializes in selling groceries might tag their products with the following attributes:
- Type of food (e.g., “fruit” or “cereal”)
- Established taste (e.g., “bitter” or “sweet”)
- Container (e.g., “box” or “can”)
The recommender would then compare items historically purchased by the user or those currently in their shopping cart to other similar or linked items. Attributes are weighted by the number of items in the database that share the tag with more common tags receiving higher rankings than uncommon ones. This weighting determines which items appear first in a list of recommendations.
Content-based filtering doesn’t require the input of other customers to make predictions. It bases its predictions on similarities within a customer’s own behavioral and historical profile. A well-designed content-based filtering engine will identify specific quirks and interests that may not have broad appeal to other customers.
A major drawback with this type of recommendation engine is it requires a great deal of maintenance. Attributes must be added and updated constantly to keep recommendations accurate—a daunting task for businesses with a high volume of product. Additionally, the attributes themselves must be accurate. Labeling a Honeycrisp apple “red” is easy, but more complex content may require a dedicated team of subject matter experts to correctly label each individual product.
This method of filtering is what’s used in “People who watched this show also watched…” types of recommenders. Collaborative filtering uses behavioral data to determine what a person will like based on how their preferences compare to other users. Whereas content-based filtering focuses on linking products to other products, collaborative filtering builds predictions by linking similar customer profiles.
For example, imagine using a video streaming platform that uses collaborative filtering. When you go to find a movie, you create data based on a number of behaviors, including:
- Movies you watch
- Titles you select but ultimately do not watch
- Selections you hover over
- Searches you make
- Rankings you give films
The recommender then effectively builds a user profile for you based on this data set. It then compares your profile against a cohort of users who behave similarly. The resulting predictions are based on the movies this cohort has consumed and enjoyed versus the actual content of each film.
Collaborative filtering doesn’t require product feature information. This makes maintenance less time-consuming than that of a content-based engine. However, a reliance on other customers’ behaviors can create data gaps. Say no one interacts with your favorite movie on a streaming service. A movie that’s perfectly suited to your interests won’t be recommended because the recommendation engine won’t have any behavioral data with which to form a prediction.
Hybrid filtering attempts to address the shortcomings of both content-based filtering and collaborative filtering by combining the two methods. As such, it’s the most effective of the three types of recommendation systems.
Content-based filtering works well for suggestions that appeal to a user’s current interests. However, they can’t accurately predict what users may like outside of their documented preferences. In a hybrid filtering system, this deficit is covered by collaborative filtering. Collaborative filtering can suggest related content that falls outside of a user’s established profile by basing recommendations on the preferences and behaviors of a similar cohort. Alternatively, content-based filtering helps fill in the gaps created by collaborative systems. If no comparative data exist for similar cohorts, the recommender will default to seeking a match based on attribute tags to find a suitable result.
How recommendation engines are used
Recommendation engines do more than improve the product experience for customers. In 2021, an estimated 39% of businesses of all sizes engaged in predictive analytics to enhance operations—an 11% increase over 2018. More businesses than ever before are embracing recommendations as customers increasingly prefer personalized experiences. A survey by Epsilon determined that 80% of consumers are more willing to buy from businesses that offer personalized experiences.
A properly built recommender also provides an opportunity for companies to target customers with products they’ve either expressed interest in or are highly likely to enjoy. Recommenders help businesses take advantage of predictions through the following methods:
Providing cross-selling opportunities
A recommendation engine can entice customers with products that are complementary but not necessarily similar. A winter hat and gloves are two completely different articles of clothing, and yet someone ordering one could very easily find a use for the other. A recommender identifies these relationships and makes data-based suggestions that help increase the value of individual orders.
Addressing cart abandonment
Items abandoned in digital shopping carts are excellent recommendation opportunities. Customers were interested enough in an item to place it in their cart. Their incomplete sale could be a change of mind or an external disruption of the buying experience.
Suggesting the items again to a customer at a later time can push them across the finish line. A customer may have temporarily talked themselves out of purchasing every Wham! song in the catalog. However, a gentle reminder that “Wake Me Up Before You Go-Go” is gathering cobwebs in their cart might be enough to change their minds. These reminders can be displayed both within the product itself or even as an email message after the initial session.
Recommendation engines provide “backup” suggestions for cases where the option determined by the algorithm to be the “most likely” isn’t one the customer wants. Your recommender might be perfect, but it’s always at the whim of the human brain. For instance, a recommendation engine can’t know that a customer had a bad interaction with a specific brand in 1987.
There’s also no way for machines to understand the finer aspects of human intent. A viewer may want to ironically enjoy the infamous 2003 movie “The Room,” but their search may instead return results for the critically acclaimed 2015 Oscar winner “Room.” Recommended alternatives help get the customers where they wanted to go instead of searching for what they actually wanted in frustration.
Examples of recommendation engines in action
Recommendation engines have become especially popular in the ecommerce world for their use in suggesting related products. Many other industries have created digital products that either heavily feature or are built on recommenders. Prominent examples include:
Amazon is the home of one of the most famous recommendation engines on the planet. The ecommerce giant sells tens of millions of unique products, and every one of them is cataloged for use by its recommender. In fact, Amazon was one of the first major ecommerce companies to pioneer content-based filtering and filed a patent for their system as far back as 2001. Two decades later, Amazon’s recommendations account for as much as 35% of their total sales.
Chik-fil-A might be famous for their good ol’-fashioned fried chicken, but their online ordering experience benefits from the application of a modern recommender. Online shoppers may find that the Chik-fil-A menu does not always display the same products at the top of the menu with each visit. Instead, the team built a recommendation engine using Amplitude Recommend that suggests new or popular items based in large part on similar past orders.
Wantable describes itself as a “try-before-you-buy” online retailer. A new customer fills out a personal survey based on their style preferences and measurements. Their recommender uses this information to predict which articles of clothing best fit the customer’s profile. Clothing is then shipped to the customer, where they view, try on, and decide whether they’d like to keep each article or return it. The success of Wantable is entirely dependent on the accuracy of both their recommendations and the attribute tags required to make them.
Bring the power of recommendations to your company
Now that you’ve learned the basics about recommendation engines, it’s time to explore how these tactics can improve conversion and retention metrics at your company. Download the Mastering Retention playbook today or take a tour of Amplitude to continue your learning about personalized digital experiences.
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GlobeNewswire, Dresner Advisory Services Announces 2018 Advanced and Predictive Analytics Market Study
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SiliconANGLE, Amplitude uses personalization to satisfy Chik-fil-A’s appetite for success