B.Tech makes a smart shift to Algonomy’s personalization solution

During the pandemic, B.TECH, Egypt’s leading consumer electronics retailer, realized that the debate in the retail world was no longer about physical stores versus online stores, but that the real differentiator was retailers consistently delivering innovative and personalized experiences to customers. .

To support demand through personalized experiences for their consumers, B.TECH used an AI-powered omnichannel personalization tool from Algonomy, a customer engagement solutions company built for retail, based in Bengaluru. and in San Francisco.

The recommendation engine uses machine learning algorithms and NLP to create contextual products and provide recommendations to shoppers. Other than that, there are also great opportunities for cross-selling and up-selling. As a result, online shoppers on B.TECH sites are easily directed to the products of their choice.

Helping customers make informed choices

Usually, electronics buyers compare product models, delve into key features, explore related accessories, and make multiple visits before final purchase. The whole process can be quite overwhelming and buyers would definitely need prompt assistance.

Over the past two years, B.TECH has realized the need to help customers discover products and help online shoppers explore the entire product catalog to make informed purchasing decisions.

A Gartner survey found that 82% of consumers are influenced by a personalized purchase recommendation. But personalization isn’t easy, and retailers need to have advanced systems in place to deliver it. To solve this problem, B.TECH deployed Algonomy Recommend on its home page, category pages, search results/no results pages, item pages, add to cart pages and shopping cart pages to provide a seamless personalized experience throughout the buying journey. B.TECH attributes 18.6% of its sales to the use of Algonomy’s recommendation engine.

“We are really pleased with the results Algonomy is able to deliver on our online channels. Recommending relevant similar and complementary products is crucial.Today, personalized recommendations directly contribute 5% of cross-sell revenue and we are seeing 10x more revenue on the cart page,” said Hazem Salah, Head of main product for e-commerce and innovation at B.TECH.

Algonomy Recommend provides contextually relevant product recommendations with a library of over 150 predefined strategies. Product recommendation for each shopper is handpicked by a decision engine based on a real-time ensemble model, taking into account business goals and the shopper’s stage in the purchase funnel . Thus, B.TECH now has the ability to recommend relevant similar and complementary products to customers.

The way it works

• The electronics category pages begin with a “Top 10 Best Sellers” slot, which helps shoppers get started quickly. This merchant placement also works well for cold start scenarios, i.e. new buyers with little behavioral data or no known preferences.

• When shoppers navigate to a specific item page, they are assisted with “Compare to Similar Items” placement, which helps them review other products. This allows merchandisers to control recommendations using attributes such as brand, price range, and compatibility; as well as upselling and cross-selling without the need for extensive manual merchandising.

• Once shoppers add their favorite product to cart, they get highly relevant cross-sell recommendations for products and accessories compatible with the main product. For example, TV customers are reminded of the wall mount system and home theater system they might need.

• B.TECH leverages specific search terms used by an individual to generate personalized recommendations. For example, a search for “dishwasher” and “dustbags” is captured and used to display relevant products within the same session.

• Buyers also receive complementary product recommendations at the bottom of the page, without being intrusive or pushy.

“B.TECH’s success in their market is impressive and we are thrilled to be part of their incredible journey,” said Amit Agarwal, SVP Business Development APAC & MEA at Algonomy. “Over the years, our team constantly thinks of ways to strengthen B.TECH’s position in the market, retain its buyers and ultimately help them accelerate their growth”

B.TECH also designed specific placements using pre-defined strategies and a real-time consumer profile to address abandoned cart and search issues and help returning shoppers resume their exploration. Clean placement on the homepage reminds returning shoppers of recently viewed products and products in cart, to help them easily resume their journey, driving higher conversions. This recommendation strategy for shoppers fuels bottom-of-funnel conversions, with the retailer reaching 10x revenue per thousand views.

“Thanks to the success of Algonomy Recommend, B.TECH also plans to expand the personalized shopping experience to its more than 100 retail stores,” added Hazem Salah.

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