During the pandemic, B.TECH, Egypt’s leading consumer electronics retailer, realized that the debate in the retail world was no longer about brick-and-mortar stores vs online stores but the real differentiator lay in retailers consistently delivering innovative and personalized experiences to customers.
To sustain demand through personalized experiences for their consumers, B.TECH used an AI-based omnichannel personalization tool from Bengaluru and San Francisco headquartered Algonomy, a made-for-retail customer engagement solutions company.
The recommendation engine uses machine learning algorithms and NLP to make context-aware product and offer recommendations to shoppers. Apart from this, there are also great possibilities for cross-selling and upselling. As a result, online shoppers on B.TECH’s site’s are smoothly navigated to products of their choice.
Aiding Customers Make Informed Choices
Usually, electronics buyers compare product models, delve deep into the key features, explore related accessories, and make multiple visits before the final purchase. The whole process can be quite overwhelming and shoppers could certainly do with timely assistance.
In the last couple of years, B.TECH realized the need to aid customers with product discovery and help online shoppers explore the entire product catalogue to make informed buying decisions.
A Gartner survey found that 82% of consumers are influenced by a personalized shopping recommendation. But personalization is not easy and retailers have to put in place advanced systems to deliver it. To address this issue, B.TECH deployed Algonomy Recommend across its homepage, category pages, search results/no-results pages, item pages, add-to-cart and cart pages to offer a seamless personalized experience through the shopper journey. B.TECH attributes 18.6 per cent of its sales to the usage of the recommendation engine of Algonomy.
“We are really excited with the results that Algonomy is able to deliver on our online channels. Our customers are very particular about specific product features and a gadget’s compatibility with their existing devices, hence the ability to recommend relevant similar and complementary products is crucial. Today personalized recommendations directly contribute to 5% revenue from cross-sell and we are seeing 10 times more revenue on the cart page”, said Hazem Salah, Principal Product Manager for E-Commerce & Innovation at B.TECH.
Algonomy Recommend delivers contextually relevant product recommendations with a library of 150+ pre-built strategies. Product recommendation for every shopper is handpicked by an ensemble model-based decisioning engine in real time, considering the business goals and shopper stage in the purchase funnel. So, B.TECH now has the ability to recommend relevant similar and complementary products to customers.
The way it works
• The electronics category pages start with a ‘Top 10 best sellers’ placement, which helps shoppers to quickly start their survey. This merchandised placement also works well for cold-start scenarios, i.e., new shoppers with little behavioral data or no known preferences.
• As shoppers navigate to a specific item page, they are assisted with ‘Compare with similar items’ placement, which helps them evaluate other products. This allows merchandisers to control recommendations using attributes such as brand, price range and compatibility; as also upsell and cross-sell without the need for extensive manual merchandising.
• After shoppers add their preferred product to the cart, they get highly relevant cross-sell recommendations for products and accessories compatible with the main product. For instance, TV customers are reminded of the wall mount system and home-theatre system that they may need.
• B.TECH leverages specific search terms used by an individual to generate personalized recommendations. For e.g, a search for ‘dishwasher’ and ‘dust bags’ is captured and used to surface relevant products in the same session.
• Shoppers are also given complementary product recommendations at the bottom of the page, without being intrusive or pushy.
“The success of B.TECH 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 is constantly thinking of ways to boost B.TECH’s market standing, inspire loyalty amongst their shoppers, and ultimately help them fast track their growth”
B.TECH has also designed specific placements using prebuilt strategies and real-time consumer profile to address cart and search abandonment issues and help returning shoppers resume their exploration. A clean homepage placement reminds returning shoppers of recently viewed products and products in cart, to help them resume their journey with ease, thus driving higher conversions. This recommendation strategy for shoppers powers bottom-of-the-funnel conversions with the retailer attaining 10X revenue per thousand views.
“Driven by Algonomy Recommend’s success, B.TECH plans to expand the personalized shopping experience to its 100+ retail stores too”, added Hazem Salah.
