Have you ever purchased a product based on a recommendation from a friend or family member? Have you considered buying a product endorsed by your favorite celebrity?
Traditionally, we would get recommendations from friends, family, or salespeople. Nowadays, as commerce is becoming digital, recommendation algorithms have taken up this role. As a marketing and sales tool, these recommender systems are adept at driving revenue.
Netflix uses a recommendation engine to suggest movies and shows viewers might enjoy. The pioneer of recommendation engines, Amazon, uses them to recommend products to shoppers.
A product recommendation engine is a software used by e-commerce businesses to suggest products based on visitors' behavior. The goal is that customers will continue shopping and browsing when they see the recommendations.
Product recommendations can be made on e-commerce websites, social media ads, or sent out in marketing emails.
A timely product recommendation can lead buyers to make an additional purchase.
For example, an ecommerce business can make recommendations while the shopper is making a purchase. Personalized suggestions boost conversion rates while enhancing the customer experience of your e-commerce website.
What Is A Recommendation Engine for E-commerce?
A recommendation engine is a rules-based software that enables e-commerce brands to display different products to individual shoppers.
This way e-commerce businesses can personalize their offerings by displaying relevant items at critical stages of the customer journey. Using behavioral data of visitors, the engine recommends other items in which they may be interested.
When done right, this type of sales tactic enables e-commerce businesses to upsell or cross-sell, which increases the average value order.
Instead of making generic suggestions, product recommendation engines use sophisticated algorithms to deliver highly relevant recommendations to each visitor.
Brands That Have Mastered Recommendations
E-commerce businesses of all sizes can implement recommendation engines to make customized suggestions and drive revenue. Here are a couple of major brands that have taken product recommendations to the next level.
Amazon’s goal is to increase the average order value, which is why they use recommendation engines to help buyers discover new products rather than only buying what they initially intended.
Shoppers see recommendations based on what Amazon thinks they'll purchase. After viewing an item, visitors are shown similar products.
And once an item is added to the cart, products that buyers who purchased this item also bought are recommended.
Relevant recommendations are made at every stage of the customer journey to generate more revenue through upselling and cross-selling.
Rating-based recommendations are extremely useful to shoppers who are at the bottom of the sales funnel and are deciding between products. Items that have customer ratings end up being purchased at a far greater rate than un-reviewed products.
Best Buy's recommendation engine shows the highest-rated products to assist shoppers in making the right choice.
Are Product Recommendation Engines Really Worth the Hassle?
Latest studies prove the important role that product recommendation engines play in personalization strategies used by e-commerce businesses. A recommender system boosts conversion rates, average order value, retention rates, and the overall customer experience.
According to a recent study by Barilliance, product recommendations were responsible for up to 31 percent of ecommerce revenues.
Conversion rates for website visitors who clicked on recommended items were over 5 times higher than for users who didn't look at recommended products.
As consumers become more accustomed to personalization, they associate it with professionalism.
According to a report from Accenture, personalization increases the likelihood of a prospect making a purchase by 75%.
Why Choose a Product Recommendation System
Let's take a closer look at the many benefits of using recommender systems before we go over the most popular filtering options.
The main reason why e-commerce businesses use recommendation engines is to lock in more sales. They do so by:
- Offering more cross-selling and upselling opportunities.
- Boosting click-through and conversion rates. By displaying highly relevant products, an online store will convert customers than an e-commerce business that doesn't use a recommendation system.
- Contributing to customer satisfaction.
Let's now take a look at how recommendation systems generate more revenue for e-commerce businesses.
Recommendation engines help drive more sales and conversions by doing the following:
- Personalizing offers makes it more likely that visitors will stay longer on the page and keep browsing.
- Increasing qualified traffic. Marketing efforts become more effective because visitors are receiving emails or seeing ads for relevant products.
- Lowering bounce and cart abandonment rate. These are critically important metrics to ecommerce companies and recommendation systems are vital in improving them.
Finally, recommendation engines significantly increase average order value (AOV) and the number of items in carts.
Recommendation Engine Algorithms
There are three main types of recommender system algorithms used by most e-commerce websites.
E-commerce websites that want to properly use implement product recommendation engines need to have relevant customer data. After all, recommendation engines are only good as the data they filter.
Collaborative Filtering Method
This filtering method makes recommendations using similar behavior by different shoppers.
The logic behind the algorithm is that if customer A likes certain types of products and customer B also likes this line of products, then the assumption is that customer A will be interested in other products that customer B browsed and purchased.
The way this filtering system works is best described by Amazon, "Customers Who Bought This Item Also Bought."
Collaborative filtering algorithms predict what a visitor will like by comparing them to others who have bought the same or similar products.
This filtering system is a great choice for businesses that have large amounts of data on their customers and visitors.
Simply put, content-based recommenders rely on user preferences to display products.
One of the ways these algorithms gauge peoples' interests is by asking them to upvote or downvote various types of content.
Content-based filtering systems analyze customer preferences over the course of several visits(based in part on the likes and dislikes ratio) to make relevant product recommendations.
The idea behind this filtering method is that if a user likes a certain product, they’ll likely also like a similar product.
So, if a customer recently bought Nike running shoes, the recommendation system will show more running shoes.
Hybrid Filtering Algorithm
Leading e-commerce brands use hybrid recommender systems to provide highly-personalized suggestions. It combines both the collaborative filtering system and content-based filtering system to make suggestions.
Netflix uses this type of filtering to make recommendations: the interests of individual users along with the descriptions of tv shows or movies. It takes into account the viewing habits of similar viewers (collaborative filtering) and tv shows and movies the individual viewer has liked or watched (content-based filtering) to offer new and relevant recommendations.
Best Practice Tactics
Ecommerce businesses use product recommendation engines to experiment with different tactics in order to come up with the most effective suggestions. We've put together a short list of recommendation tactics that have worked for our clients. Pick the ones that are the most suitable for your business model.
- Including a 'Recommended For You' is a popular tactic that uses the shopper's browsing history to make relevant suggestions. Add the customer's name to make the recommendation more personal.
- ‘Frequently Bought Together’ recommendations are proven to boost the average order value (AOV).
- 'Similar Products' tactic displays items that are similar to the item on the page. It's an effective strategy because shoppers can see the full range of a product line, which helps them select the one that best suits their preferences.
- Improve your digital marketing results. Customize email campaigns with product recommendations. Send marketing emails to your website visitors and customers with product recommendations based on their recent purchase history.
- ‘Featured recommendations’ introduce visitors to enticing products they haven't considered.
- 'People who purchased this product also bought this' recommendations offer relevant products the customers may like.
- Let visitors know about products that have been upgraded with by ‘ there is a newer version of this product’ notification.
- Enable shoppers to view their history of purchases with your brand. Some e-commerce businesses make the, "since you've bought this item, you may also like to get this product" suggestions.
- Displaying best-selling products on the homepage or popular pages is a highly-effective tactic for grabbing visitors' attention as soon as they land on your site. This tactic acts as a kind of social proof that makes the purchase easier.
- Bundling products together under the recommendation, "items frequently purchases together" and offer a special discount for the offer.
- Feature highest rated products. Take it one step further and include products with the best customer reviews.
- Recommend accessories to products that require them. For example, drones require batteries.
- Remind your customers and visitors about upcoming special events and discounts.
The last chance that online stores have to make recommend a product is on the shopping cart page, right before the checkout page.
However, you have to be careful not to display any products that might sidetrack the visitor from the shopping journey. The best tactic here is to recommend products that complement those placed in the shopping cart.
Getting Started with Product Recommendation Systems
It may take some time to properly configure product recommender systems, but once operational they do all the heavy lifting by themselves.
Remember, the great thing about personalized recommendations is that they frequently lead to unplanned purchases. What makes product recommendations exciting for consumers is the discovery of new and interesting items.
Also, always A/B test; you can't just set it and forget it. Analyze the conversion rates of your recommendations and make adjustments to algorithms accordingly. This will allow you to see which filtering algorithm works best for your e-commerce business.
Experimenting with personalized recommendations and social proof enables e-commerce businesses to replicate the level of personalization once only available in brick-and-mortar stores.
Implementing a recommendation engine into your e-commerce website is not difficult. Hyperon is a rules engine on top of which you can build a recommendation engine that works well with your e-commerce platforms.
It enables e-commerce brands to develop the type of algorithms that suit their business needs the best. The tool is designed to be user-friendly, which means that non-technical subject matter experts can design filtering algorithms on the fly without any help from IT.
Get in touch with us today to learn how product recommendations can help your e-commerce business grow at unprecedented rates.