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Extra Increases AOV From Product Recommendations

Case Study

How eXtra Used Recommend™ to Get 52% Higher AOV from Recommendations



Consumer Electronics


To increase Conversion Rate and AOV using the right product recommendation strategies



higher Items Per Order (IPO)
from recommendations


increase in
Conversion Rate


higher AOV
from recommendations

The eXtra Story eXtra is one of the most popular consumer electronics and home appliances marketplace in the Gulf region, with both online and offline operations in Saudi Arabia, Bahrain, and Oman. It was started in 2003 by United Electronics Company (UEC).

eXtra now has over 45 stores in Saudi Arabia and 3 stores each in Bahrain and Oman, in addition to three websites serving customers in both English and Arabic. They offer over 12,000 different products, including leading international brands, and cater to over 12 million shoppers.

They also offer comprehensive after-sales service such as extended warranty, free home delivery, product installation, and 24×7 remote assistance through three dedicated service centers across Saudi Arabia.

The Challenge eXtra has been working with Algonomy for close to seven years now. They realized the need to aid their customers in product discovery early on, and wanted to recommend the most relevant products to customers at every step of their journey. A search for the right platform to put this vision into action led them to Algonomy Recommend™.

The electronics retailer continues to place their trust in the platform because of its proven ability to significantly and consistently improve key metrics such as Conversion Rates, Revenue per Click, Average Order Values, and Attributable Sales.

The Solution Recommend™ makes it very easy for eXtra to deploy different strategies, assign weights to them as per business KPIs, and decide how and where to display product recommendations on a page.

They use product recommendation placements across different pages on their websites and apps. They optimize the impact of these recommendations by using a variety of recommendation strategies, including but not limited to:

  • Advanced Merchandising Strategy
  • Movers and Shakers
  • New Arrivals
  • Site-wide Top Products
  • Popular Products
  • Category Top Sellers
  • Brand Top Sellers
  • Top Offers
  • Frequently Bought Together
  • Related to Cart
  • Related to Cart Category
  • Recently Viewed
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The top five strategies that have worked well for them are: recently viewed, others also viewed (category level), others also bought (category level), advanced merchandising strategy, and personalized as per users’ pageview history.

Here are some examples of these placements:

1. Related Items and Category Top Sellers on the Product Detail Page

2. Popular Products and Top Products on the Category Page

3. Advanced Merchandising Strategy on the Add to Cart Confirmation Page

4. Top Sellers on the Cart Page

As a customer-first business, we are always looking to improve digital experiences for our customers. Through Algonomy Recommend™, we are able to add value to our customers’ shopping journeys by showing products most relevant to them.

Imran Khan e-Commerce Director, eXtra

The Results

eXtra has seen the Average Order Value go up by 52% when product recommendations are present on a page, versus when there are no product recommendations on a page. Similarly, the number of Items per Order is 50.5% higher when there are product recommendations on a page than when there are none.

The electronics retailer’s Revenue per Click grew by 100%, and they have seen a year-on-year uplift of up to 73% in conversion rates (2020-2021) by leveraging the capabilities of Recommend™.

Here’s a quick summary of the growth:

Looking Ahead

eXtra is keen to leverage more of the capabilities of the Algonomy platform to implement more nuanced product recommendation strategies, such as Advanced Merchandising.

They also plan to explore the features of DeepRecs Natural Language Processing (NLP) and deploy it on their websites and mobile app. They believe NLP will not only give them competitive advantage, but also make it easier for their customers to discover more relevant products as well as niche products.

Recommend™ has been consistently delivering 5% to 7% conversion rates for us, in addition to higher AOVs and IPOs. This has encouraged us to explore more nuanced product recommendation strategies within the platform. We’re also looking forward to exploring DeepRecs NLP, which will help us further individualize experiences for our customers.

Shahin Riaz Head of Product, eXtra

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Extra Increases AOV From Product Recommendations

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