Personalized product recommendations at Baby&Tiener online shop: 13,4% revenue increase

Personalized product recommendations at Baby&Tiener online shop: 13,4% revenue increase

Ecommerce market grows exponentially. Day by day the number of competitors becomes bigger and the visitors` expectations become higher. In these circumstances personalized product recommendations are not an object of desire but a “must have” instrument for online retailers.

Visitors want to see only the products they are interested in, so personalization is one of the ways to show the attention and care to the loyal audience. Personalized product recommendations engage people further into the store and help them find what is relevant to them.

Baby&Tiener is an expert in a child commodities` industry. According to Similarweb, the website has more than 68 thousand of visitors per month. The Dutch webshop offers everything you may need for pregnancy, newborn babies, toddlers and teenagers.

Baby&Tiener was seeking a solution that will help to significantly increase their online revenue, and personalized product recommendations is a reliable booster.

With the help of Retail Rocket the company launched a website personalization project and in this Case Study we will look into the implementation results.

Efficiency Analysis Process Description

The website personalization system efficiency analysis was performed using the A/B testing approach: all visitors of were randomly split into two segments in the real time. Only one visitor segment was shown the Retail Rocket’s real-time personalized product recommendations.

The goal of the experiment was to identify the statistically significant difference in the conversion rate between both visitor segments.

The product recommendation blocks were located on the following pages within the website:

  • Homepage
  • Category Page
  • Product Page
  • Cart Page
  • 404-Error page


As an “everything for babies” retailer, the webstore has the homepage that shows the shop window through which the buyers will look for what they need. They have several seconds to decide whether it’s good enough to make a purchase, so it’s important that the first impression is a good one.

Real-time personal recommendations

Personal product recommendations are based on the visitors` interests and the website behavior in the real time. Our research shows that ~50% of the visitors who end up buying, make their purchase within 24 hours from the first interaction with the website, which makes the fast generation of personalized content very important.

Most popular products in the category of interest

The most popular products from the category where the visitor showed interest in.

Category Page

One of the most important pages of the website is the category page. To avoid a visitor`s leaving it automatically highlights the most likely converted products with the most popular products for each category recommendations.

These are updated in the real-time, automatically reflecting and leveraging any changes in behavior.

Product page

The aim of personal recommendations on the product page is preventing visitors from leaving if they think that this wasn’t the product they were looking for.

The best way to avoid it is showing the most similar products to the one that visitors are currently looking at.

Similarity is based on the product properties (price, brand, category, text description, etc.) and other customers` behavior (what they also viewed, what they ultimately ended up buying, etc.). It helps customers to find the products they need.

Another purpose of recommendations on the product page is to increase the average order value by offering related products. These are complementary items that visitors can add to the cart with the current product.

Cart page

If there are items in the cart, customers are about to cross the finishing line and they are a step away from being redirected to the checkout.

This is the perfect moment to increase the average order value, suggesting complementary items from the inventory, allowing customers to indulge an impulse purchases.

Baby&Tiener encourages customers to make further purchases with Related products recommendations based on the interests they have demonstrated and purchases of other customers.

404-Error Page

Have you ever thought of seeizing the maximum benefit out of the page that shows a 404 error? Recommendations are a great way to use the 404 space as a potential conversion booster.
In cases when this page appears, Baby&Tiener shows Personal recommendations. It presents a list of products that users are most likely to buy, based on their preferences, website behavior and order history.


The A/B test showed the following results:

Conversion Rate Average Order


Retail Rocket Implementation Results +7.1% +4.5% +13.4%

According to the A/B test data, Retail Rocket product recommendations improve the conversion rate by 7.1% with a statistical significance of 90.7%.

The average order value improved by 4.5% and, as a result, overall revenue led to a 13.4% increase.

Retail Rocket

Retail Rocket’s product recommendation system takes a data-driven approach and uses a powerful machine-learning engine to serve up the most contextually-relevant recommendations for each customer based on the behavior, intent, past purchases and many other variables.

It relies on the highly customizable algorithms to determine what products and a block of products to display. You have the full control over the look and the placement of recommendations and the full transparency through a dashboard access to see all the metrics and business results.

Jordy Schipper | Ecommerce Manager from Baby&Tiener

“We chose Retail Rocket due to its ease of use and implementation, but even more importantly because of the power of Retail Rocket’s algorithms. We’ve been really happy with the improvement in the conversion rate we’ve seen so far and the level of customer service we’ve been met with”.

Learn more about Retail Rocket’s product recommendation system here!

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How to Increase Online Store Conversions by Using Product Recommendations: Quelle Case Study

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