In the digital-first world, you can find recommendation engines (or the outputs actually) – everywhere — from Tinder to Netflix, The growth of this recommendation engine market is expected to be phenomenal, a CAGR of 37.46% over the next five years! With AI and machine learning making these recommendations even more effective, it’s common to see them used in almost every type of website, ecommerce stack, and app.
The mere deployment of a recommendation system doesn’t mean you are doing it right, however. It’s possible you could actually be annoying your shoppers rather than helping them, We’ll go through some common techniques to help guide you to the most personalized recommendation experience – on that will seamlessly direct shoppers on to their next purchase.
What Is a Recommendation Engine
A recommendation engine is also known as a “recommendation system,” “recommender system” or just plain, recommender. The terms tend to be driven by geography (soda vs pop), but they all mean the same thing. In general, a recommendation engine is an advanced filtering tool that sorts through data to predict what a person might find useful.
A Virtual Personal Shopper and Librarian
If you’ve been in ecommerce for more than a few minutes, you’ve likely heard the stat that more than a third of Amazon’s purchases are a result of product recommendations. Conscious of it or not, a recommendation engine has most likely been your shopping companion every time you have completed a purchase online.
But products are not the only items a recommendation system can suggest. Machine learning is serving up content throughout workplaces, social media, and customer service sites.
Recommendations can be made at any time:
- From the moment you log onto a site (ala Netflix)
- In the search bar (with drop-down suggestions)
- After you have selected something to purchase (i.e., you might like this, too!)
In fact, a whopping 80% of entertainment consumed on Netflix is because of its content recommendation engine. That shouldn’t be surprising, says Louis Têtu, CEO of Coveo. “Netflix wants you watching — not searching.”
What Makes a Recommendation Engine So Powerful?
Why do people take such stock in recommendations? It’s machine learning.
A good recommendation engine predicts what item(s) a visitor might be interested in and guides that person to the most relevant content — whether it be products, services, or information. The beauty of this is that the visitor is exposed to content they might not have known about — but thanks to artificial intelligence (AI) may find the perfect match regardless.
With a goal of delivering the best user experience, from getting users to swipe right selling romance or add to cart for stick-on-tile, a recommendation engine powered by artificial intelligence uses machine learning to analyze big – and little – data – but with a narrower set of approaches.
Predictions Based on Ratings
Think of a recommendation system like a ratings engine. The higher the rating the more likely it will be “liked” by a consumer. High ratings signal highly relevant product recommendations. And while ratings can be explicit or implied, often, they are binary:
Purchase = 1 No purchase = 0
To generate meaningful, personalized product recommendations, the recommendation system must have a fair amount of data to determine a recommendation strategy. That data is then filtered accordingly so that the user is exposed to the most relevant product he/she is most likely to buy.
So, what kinds of data will predict behaviors? Previous buyers’ histories is one of the most common types of data. But a good recommender should not stop there. You can use contextual data, business-related, user profile-based, product-based, or content-based data.
Really, any type of data can be added. (In fact, here are the five categories of recommendation elements that can be offered.)
But once you have the data, then what?
Collaborative Filtering: Unicorn Baby Items for Days
Many recommender systems, such as Amazon’s and Netflix’s, work by using artificial intelligence models to aggregate the behavior histories of all of their shoppers. They analyze that data so that the system can predict the behavior of what similar users have done in the past.
In marketing, this is called “persona-based personalization.” Whereas in data science and computer science, researchers call this “collaborative filtering.”
Advantages to CollaborativeFiltering
- Gives a great starting point for most visitors
- Provides serendipity — a way to explore and find what you weren’t looking for
Disadvantages to CollaborativeFiltering
- Cannot handle new items such as products or information (since no one else has looked at it)
- Does not include variants or accessories based on similarity for queried items
- Reliant on big data; requires a lot of user preference and behavior data
- It also may turn out to be just plain wrong
Collaborative filtering/persona-based merchandising works… kind of. It can also introduce a lot of noise. I once bought a unicorn blanket for a friend’s new baby from an online retailer. Now, I am constantly deluged with unicorn-themed baby items — across all of my devices. Luckily, there are other ways to provide a user with an accurate recommendation that has a positive impact on the shopper experience.
Content-Based Filtering: Reduce Annoyance of Unwanted Products
The best recommendation engines avoid annoying customers in this way through content-based filtering. This real-time analysis of a shopper’s behavior offers the most precise and personalized way to handle relevant recommendations.
Prior user history is considered but is weighted much lower than what a shopper is doing while in the current shopping session. With a content recommendation engine, everything a shopper or visitor is looking at counts, including product attributes – such as size, description, color, and price point. All of this is funneled by the algorithm into compiling similar products, more likely to be added to the cart and purchased.
An effective content-based system is like an astute personal shopper. The customer is happy because it is easier to make smarter choices, faster. And the retailer is happy because the recommendation has driven more sales by pointing the customer to a similar item they might have otherwise missed!
Advantages to Content-Based Filtering
- Collaborative approach that can provide truly personalized recommendations
- Does not rely on aggregate user behavior data
- Reduces cold start problems with new products
Disadvantages to Content-Based Filtering
- Requires a good amount of product data (brand, variants, colors, size, descriptions, accessories, etc.)
- Can limit recall if narrowly tuned to user search
Machine Learning Recommendations
Traditionally, creating a recommendations engine has been a highly manual process involving intensive analysis of shoppers’ behavior that then gets hardcoded into an algorithm. Think of it as “if-then” situations from school tests. The thinking goes that, if shoppers buy baby blankets, then they may want to buy another similar product.
The recommendation algorithm or set of rules may work sometimes (although not all of the time, per my unicorn baby example from above, or this poor person’s struggle with toilet seats ). However, it’s not unusual for legacy systems to have literally thousands of algorithms. And you likely won’t know when they conflict.
The best recommendation engine then is based on advanced machine learning algorithms, says Simon Langevin, director of product management for Coveo.
Hybrid Recommendation Systems: Collaborative and Content-based Filtering
Recommendation engines don’t just feed on data about user behavior, though. As I noted before, they also savor all of the rich information in product catalogs and descriptions – plus all the business data you might want to further tweak the results.
For example, if you are selling women’s golf slacks, size 4, the attributes of the slacks you have selected are identified.
Womens, size 4, golf, slacks
If you then were to search for gloves, the prior attributes would dynamically order the autosuggest to:
Sporting gloves → golf
And if you were really savvy, you could also add in margin data – so that the slacks that have the greatest margin are weighted higher in the results!
The Future of Recommendation Systems
It’s only recently that machine learning has enabled recommendation engines that can suss out these more subtle relationships. With natural language processing and understanding, it’s possible to “vectorize” a product catalog. You can see dynamic auto suggestions in detail by checking out Jacopo’s blog on how to grow a product tree.]
Understanding the product catalog is ideal when launching a new product, line of business, website or app. If, for example, your retailing website has a new, cutting-edge TV that’s debuting, you want it to show up in recommendations before shoppers have bought or even browsed it. This is a great solution to the cold-start problem.
Content-based filtering on product catalogs are so useful, in fact, that recommendation engine algorithms can work solely on catalog information when there’s an absence of user behavior.
Of course we don’t advise that. A hybrid filtering approach is best, because all that rich buyer history from all touch points should never go to waste!
Want to start creating more personalized recommendations? Learn how Coveo delivers relevance in commerce.
Learn more about what makes a great recommendation engine in this 6-minute read.
Looking for a recommendation engine? Learn all about The Recommendation Periodic Table.
Ready to test out a collaborative filtering-based recommendation platform like Coveo? Try out a demo today.