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HowtoIncreaseConversionRatewithSmartProductRecommendations

Apr 10, 2017

Making the customer’s decision process easier is one way to increase your conversion rate on your eCommerce website.

Making the customer’s decision process easier is one way to increase your conversion rate on your eCommerce website. Working with a UX design agency that focuses on improving the user experience, even at the simplest level of optimizing the recommendation engine. Ultimately, having a recommendation engine in your eCommerce store becomes a must, in order to survive in such a competitive and dense space.

Utilizing a recommendation engine is a good strategy to engage your customers and offer them relevant options during their searches.

All in all the recommendation engine helps your users explore new content and products in the site, recommends relevant content, increases the user’s experience and makes the customer’s journey more unique and personalized.

86% of consumers say personalized recommendation plays an important role in their purchasing decision.

Some of the biggest e-commerce websites, such as Amazon and Ebay, are using product recommendation tools to be an effective means to increasing conversions on their sites. Those not yet utilizing this feature should work with a UX design company to implement this critical feature on the site.

How does a recommendation engine work?

Recommendation engines are algorithms that provide relevant products or content suggestions to the users. The recommendation engine tracks users’ purchase history, browsing history, analyzes customer behavior, what customers viewed or items in their shopping cart.

By using effective recommendation engine and offering such recommendations, visitors will explore new content or products that they may not even take a look at.

In general, these search engines encourage the user to spend more time on the site, increase engagement, and overall makes the shopping process easier.

Types of recommendation engines

Collaborative filtering method

Collaborative Filtering is the method that collects and analyzes large amounts of data based on transaction history, user behaviors, preferences of users, and ratings among others. It is then that this big data is used to recommend items to other users and measure the similarity of two different customers.

For example, Amazon uses item-to-item collaborative filtering algorithms for effective recommendations.

Content based filtering method

Content-based filtering focuses on descriptions of products or services and the user’s profile. This method finds the similarity between the description of items and user’s previous likes, which then provides a relevant recommendation at the right time and the right place.

Tactics to increase your conversion rate

Popular products

Recommending popular products is a widely used recommendation strategy. First off, it’s easy to create this type of recommendations and very effective.You can simply determine the popularity of the item by looking at how many time the product has been purchased.

Popular items

The above image is a screenshot of how Amazon shows some of their best selling items. As you can see, all of their recommended products have nearly 5/5 stars, and a lot of reviews. If you’re into technology and electronics, chances are that you’re interested in some of these products.

Personalized recommendations

Based on visitors’ previous actions and behaviors, recommendation engines offer related products or content to the visitor by building personalized pages. Recommendation engine displays personalized widgets like “Selected For You”, “Recommended For You” and related items based on the browsing history.

These actions will increase user engagement, and will also make them feel unique.

Complementary products

These type of product recommendation is very effective in order to sell more than one product to your customers and fill their shopping carts. You can offer items by displaying widgets like “Customers who bought this also bought” or “Frequently bought together”.

By offering relevant complementary products, you can increase your order value and boost your revenue.

Another Amazon example (above), based on a book this user bought, Amazon gives suggestions of other similar books. If you buy books on Amazon, I’m sure you know how well they match you with more books you will like!

Shopping cart recommendations

Shopping cart recommendations is one of the best performing recommendation types. When you offer products your customers after they add some items to their cart, you’ll encourage them to buy more. Because they already added items to their cart, so they may accept your new offer and increase the overall transaction value.

Conclusion

It's time to implement smart recommendations

When it comes to optimizing your conversion rate on your site, implementing a recommendation engine is a must. In the process of determining which recommendation type fits in your strategy, hire UX company to help implement the best alternatives.

This is a guest post by Omer Tayfur, from Segmentify