Top 10 Ecommerce Recommendation Systems
As competition increases across industries, companies have realized that personalization is a major key to success. It doesn’t work to let people feel like just another customer. These days, people need to feel valued. One way we can inspire that feeling is through ecommerce recommendation systems. In this guide, you will learn:
- How ecommerce recommendation systems work
- The best systems/engines in the market
- Examples of companies who are mastering this art
What’s an Ecommerce Recommendation System?
Sometimes referred to as product recommendation engines, these systems allow businesses to personalize their offerings by showing all visitors relevant products. After tracking the behavior of visitors, the engine will suggest other products in which they may have an interest. The hope is that they see these suggestions and continue shopping. When done correctly, this type of ecommerce marketing can increase the average spend of visitors and help your brand to grow your Marketing ROI with AI.
Using algorithms and machine learning, they consider the most relevant products for every individual. Rather than delivering universal suggestions, the products are tailored to each visitor.
These algorithms originally started by showing products based on what others clicked on and the most popular products within certain time frames. As time goes on, the algorithms are becoming more specialized and complex. With contextual bandit algorithms, for example, promotions are offered to visitors before they even express their interests or leave a traceable footprint.
Elsewhere, you will find text analysis algorithms which use NLP (natural language processing) to pick up on keywords that work with each visitor. Fortunately, we don’t need to go too deep into the explanations because there are valuable recommendation systems that will do all the work for you. If you’re interested in e-commerce marketing and tailoring your experience to each visitor, we recommend one of the following suggestions.
Why Choose a Recommendation Engine?
Before we look at ten popular options, we should briefly mention the main benefits of investing in this area, including:
- Improved cross-selling and upselling techniques (increased sales)
- Increased click-through and conversion rates
- Boosted happiness in customers (and hopefully increased word of mouth and free advertising)
- Stronger brand image and a positive reputation in the market
10 Brilliant Recommendation Engines
1. Youchoose
It’s important to note that these recommendation engines work in more than one way: they make suggestions for your website, email campaigns, and even online advertisements. With Youchoose, businesses can set up tailored notifications so that customers get products that resonate with them through newsletters. Based on shopping patterns and their own buying behavior, these products should pique their interest, and this could lead to a sale you otherwise wouldn’t have earned.
2. Recolize
Recolize is great for not only product recommendations, but also content creation. Instead of guessing what the consumer needs, ecommerce store owners can assess shopping patterns and create blog content relevant to these patterns. What’s more, Recolize can even integrate with WordPress and Magento so everything is in one place.
3. Baynote
With Baynote, data is collected from various touch points and devices. In particular, Baynote shines with their ‘Smart customer data hub’ and real-time experience for all. Regardless of device, customers can look through their favorite products and have an experience molded to their needs.
4. Qubit
In the ecommerce marketing field, it would be difficult to find a tool more versatile than Qubit. It started as a solution for tag management, and has steadily added features that have grown in popularity. With all of their solutions, they have the aim of boosting both revenue per visitor and conversions. Overall, Qubit offers:
- Segmentation
- A/B testing
- Predictive analytics
- Personalization
- Abandonment recovery
- Customer insights
- Data collection
5. Unbxd
Using a clever machine learning algorithm, Unbxd tries to present every customer with the products that they are statistically most likely to purchase. In their own words, Unbxed is a platform built for merchandisers and tailored for consumers. To further increase conversion rates, they also provide users with cross-selling and upselling widgets.
6. Dynamic Yield
Working across a number of channels and in real-time, Dynamic Yield is another that has captured attention in recent years. Once an audience has been segmented using machine learning, each group is optimized via not only product recommendations, but A/B testing, real-time messaging, and other personalization techniques. Their main market is ecommerce, but Dynamic Yield also claims to be an expert in travel, gaming, B2C, and media.
7. Monetate
Monetate has a positive reputation and has been named a ‘leader’ in its field by Gartner Analysts. Why? For one reason, they allow businesses to personalize content, where it be emails, websites, online stores, or apps. With artificial intelligence at its core, this is a service that works with companies in all sorts of industries, from travel to retail.
Compared to others, Monetate can be expensive. However, users have commented on the simple setup process, which is always something we can get behind. If you lack experience or are nervous about getting started, an expert team is always ready and willing to help.
8. Sentient
In total, Sentient has three different services revolving around artificial intelligence, but we want to concentrate on two in particular. While Ascend focuses on the digital marketing side of things, Aware homes in on ecommerce. Essentially, Aware wants to transform the simple recommendation engine into something anybody can use by removing the manual work normally required. Over time, it will learn and continually make more accurate and reliable predictions for marketing purposes.
9. Magevolve
Magevolve is an extension of Magento. You’ll enjoy custom placeholder positioning, catalog synchronization, and admin control. If you already have a Magento online store, this is the sensible choice because you can automate many processes and optimize the experience for all visitors.
10. Evergage
Finally, Evergage works across several channels and aims for a 1:1 experience in real time. Whether you want to target mobile apps, email, websites, or search, the machine learning algorithms will track behavior, segment audiences, and set up a successful recommendation engine. If you want other services with your software, you will also get in-depth analytics, external input data, and triggered messages.
Three Brands Who Have Mastered Recommendation Systems
Amazon
It’s hard to ignore the world’s largest marketplace when talking about recommendation systems. If you shop on Amazon, you’ll remember seeing suggestions based on what the algorithm thinks you might like to buy. After clicking on a product, we are shown similar products and others that might take our interest. After adding a product to our basket, products that other customers have also bought are displayed clearly for us to see.
Amazon now has its own personalization system called Amazon Personalize. Using the same technology, those who sign up can enjoy the machine learning service and provide customers with individual recommendations while using their own app.
Amazon’s most important goal is to increase the amount people spend per order. Rather than just logging in and buying one thing, Amazon wants people to find other products they like and add them to the basket, too. If you need help getting started with a recommendation engine, use Amazon as an example of how to do it right.
Target
We also highly recommend learning from Target’s example. In fact, you can open another tab, click on any product on the home screen, and scroll down. No matter which product you choose, you will see three tabs below the product description:
- More to consider
- Similar items
- Guests also bought
Especially if you have a Target account, Target will learn your behavior and habits to ensure that these recommendations are accurate and worth your time.
L’Oréal
L’Oréal is our final example of companies doing ecommerce marketing and recommendation systems right. Over the years, this brand has become known for its continual updates of personalization efforts, and it all comes back to data. Not so long ago, the CMO of L’Oréal said that the company doesn’t have offline and online customers. Instead, the two are united. Shoppers don’t need to be treated differently across channels. Rather than espousing a digital strategy, L’Oréal has an overall strategy, and it seems to be working. They continue to evolve this year, as they push to create the right environment for voice searches and other new technologies.
