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B. TECH Hyper-personalizes Key Touchpoints in Digital Commerce

Case Study

B. TECH Hyper-personalizes Key Touchpoints in Digital Commerce

Segment

Consumer Electronics

Objective

Drive sales with personalization across the buying journey on web, mobile site, Android and iOS apps

Product Used

The Results

0 %
attributable sales (compared to 11% earlier)
0 %
attributable revenue from cross-sell
0 X RPMV
(revenue per 1000 views) from cross-sell compared to recommendations avg
0 X RPMV
on cart page compared to recommendations avg

The Overview

B.TECH is the number 1 consumer electronics retailer in Egypt with over 100 stores and a growing online presence. Revenue from eCommerce saw significant growth in 2020 as consumers were home bound and relied on gadgets and electronics for not just professionally, but also for social and entertainment needs. In addition, the country’s growing purchasing power promises continued growth for the electronics industry, even as financing and monthly payments are popular with shoppers.

The retailer recognized the need to aid product discovery and help online shoppers deeply explore the product catalogue to make an informed buying decision. Electronics buyers want to compare product models, dive into the key features, explore related accessories, and they often make multiple visits before making a purchase.

B.TECH wanted to address these customer journey needs seamlessly with personalization and deployed Algonomy (now part of ADA) Recommend across their homepage, category pages, search results/no-results pages, item pages, add-to-cart and cart pages.

The electronics category pages start with ‘Top 10 best sellers’, helping shoppers to quickly start exploration, as they are likely to be open to suggestions early on. This merchandised placement also works well for cold start scenarios, i.e., for new shoppers that don’t have much behavioral data or known preferences.

B.TECH’s Commerce Personalization In Action

Item Page

As the shopper navigates to a specific item page, they are assisted with a placement ‘Compare with similar items’ that helps them evaluate other products. Here they can easily see all item specs without having to jump back and forth between pages, and can either navigate to the item page or add the chosen item to cart directly. This placement leverages Algonomy’s (now part of ADA) advanced merchandising that allows merchandisers to control recommendations using attributes such as brand, price range and compatibility. This enables upsell and cross-sell based on the seed product and without the need for extensive manual merchandising that is impractical for small teams.

Add to Cart Page

When a shopper decides on a product and adds it to the cart, they get highly relevant cross-sell recommendations for accessories compatible with the main product.

For a TV, for instance, they are reminded of the wall mount and home theatre system that they likely need. This strategy contributes to 5% of attributable revenue from cross-sell.

Cart Page

As the shopper progresses to the cart page, they are once again reminded of complementary products for their cart contents at the bottom of the page, allowing them to consider additional useful items without the experience being intrusive or pushy. The unique design also allows shoppers to switch between products in their cart and consider cross-sell recommendations for each of them separately.

Addressing Abandonment and Returning Shoppers

To address cart and search abandonment and help returning shoppers resume their exploration, B.TECH has designed some specific placements using Algonomy (now part of ADA) pre-built strategies and real-time customer profile.

Three effective examples of these placements are as follows.
First is for shoppers who interact with the search bar. Since searchers have a strong intent and are more likely to convert, 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.

Second, a clean homepage placement reminds returning shoppers of recently viewed products and products in cart, helping them resume their journey with ease and driving higher conversions.

In addition to personalized homepage placements shown above, the B.TECH business team retains control over merchandised placements showcasing seasonal promotions.

These could be products for specific occasions (e.g. Mother’s Day promotion), products with interest rate offers or even brands they’re promoting currently.

A third way to address abandonment is as follows – if a shopper empties their cart without making a purchase, instead of seeing a blank page, they immediately see alternatives based on their recent cart contents. This recommendation strategy for shoppers with clear intent contributes to the retailer now attaining 10X revenue per thousand views, powering their bottom of the funnel conversions.

In the future, B.TECH also plans to expand the personalized shopping experience to its 100+ retail stores and deliver on its vision of a customer-centric omnichannel experience.

Table Of Contents
The Results
The Overview

“Shoppers like to compare products from different brands and evaluate different models to be certain they’re making a sound decision. It is a very involved journey, and by providing personalized similar product comparisons on the item page, we’re able to reduce mid-funnel drops and improve our conversions.”

Hazem Salah
Principal Product Manager for E-Commerce & Innovation

“Products recommended on the cart page are akin to checkout counter displays in the store. The key difference, however, is that these can be much more tailored to your current cart contents. We can now show relevant, compatible accessories, products that are frequently bought together and this has contributed to 5% of revenue from cross-sell and 10X RPMV on the cart page. Our customers clearly value it, as they spend less time searching and are reminded of items they might’ve otherwise forgotten about.”

Hazem Salah
Principal Product Manager for E-Commerce & Innovation

“Optimizing for various abandonment scenarios is crucial to our commerce business because shoppers typically purchase only after multiple visits and doing their research. With Algonomy’s real-time customer profiles and AI decisioning, 1:1 personalization is a breeze – and we’re no more wasting opportunities but getting the most value from our visitors.”

Hazem Salah
Principal Product Manager for E-Commerce & Innovation

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Game-changing DeepRecs NLP Improves Product Discovery of 2M+ Catalog with 96% Long-tail Items

Case Study

Game-changing DeepRecs NLP Improves Product Discovery of 2M+ Catalog with 96% Long-tail Items

Product Used

The Results

+ 0 %
Average Order Value (AOV)
+ 0 %
Click-through Rate (CTR)
0 %
More Items Per Order

Neowing and CDJapan are popular Japanese online shops that offer a wide array of entertainment products — including CDs, DVDs, games, books, comics, and character merchandise.

The Challenge

Neowing wanted to improve the experience on their digital channels with a focus on personalization at different touchpoints across the buying journey. With Algonomy (now part of ADA), Neowing planned to:

  • Increase customer satisfaction and loyalty with integrated references, personalized browse and content experiences.
  • Improve product discovery of new and long-tail products, which form over 96% of the catalog.

Move from Segmentation to Hyper-Personalization at Every Customer Touchpoint

Complete, unified commerce personalization
Connect all digital path-to-purchase touchpoints — search, navigation, recommendations, and content — to deliver one personal experience that supports the complete customer journey.
Most comprehensive library of 150+ personalization strategies
Accelerate time to market with our strategy library that ranges from wisdom of the crowd and collaborative filtering approaches to deep learning visual discovery and NLP approaches.
Advanced merchandising
Cross-sell and upsell seamlessly by leveraging product attributes and compatibility data. Create automated recommendations and bundles that take the load off your merchandisers.
Algorithmic decisioning for every user with real-time context
Leverage AI to detect each shopper’s stage in the buying process, and combine it with their affinities to pick the best strategy that delivers the most relevant 1:1 experience while meeting your revenue or engagement goals.
Table Of Contents
The Results
The Challenge
Move from Segmentation to Hyper-Personalization at Every Customer Touchpoint

“With Algonomy DeepRecs NLP, recommendations are based on product descriptions rather than past purchases or historical browsing data. As a result, even for highly specialized and seasonal products, we can now recommend products with similar affinities, which makes shopping very convenient, highly relevant, and valuable for our shoppers.”

Katagiri Fumio
CEO
Neowing

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Reduction in out-of-stock across stores
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Época Cosmeticos Pivots to Individualized Shopper Experiences, Improves SEO with Personalized Search, Content and Recommendations

Case Study

Época Cosmeticos Pivots to Individualized Shopper Experiences, Improves SEO with Personalized Search, Content and Recommendations

The Results

0 X
Engagement
+ 0 %
Conversions
+ 0 %
AOV

The Client

Época Cosméticos is among the  largest beauty and cosmetics retailers in Brazil, with over 16,000 SKUs of more than 740 brands.

The Challenge

Época Cosméticos had aggressive growth plans but was unable to deliver results with legacy technologies.
They sought Algonomy’s (now part of ADA) help to:

  • Ensure that shoppers find what they are looking for from 16,000+ SKUs
  • Go beyond segments and deliver individualized, real-time experiences
  • Reduce spends on customer acquisition through inorganic PPC

Move from Segmentation to 1:1 Personalization at Every Customer Touchpoint

1 Complete, unified commerce personalization

Connect all digital path-to-purchase touchpoints — search, navigation, recommendations, and content — to deliver one personal experience that supports the complete customer journey.

2 Most comprehensive library of 150+ personalization strategies

Accelerate time to market with our strategy library that ranges from wisdom of the crowd and collaborative filtering approaches to deep learning visual discovery and NLP approaches.

3 Advanced merchandising

Cross-sell and upsell seamlessly by leveraging product attributes and compatibility data. Create automated recommendations and bundles that take the load off your merchandisers.

4 Algorithmic decisioning for every user with real-time context

Leverage AI to detect each shopper’s stage in the buying process, and combine it with their affinities to pick the best strategy that delivers the most relevant 1:1 experience while meeting your revenue or engagement goals.

Want to learn more about our commerce solutions or personalization offerings?

Book a consultation
Table Of Contents
The Results
The Client
The Challenge
Move from Segmentation to 1:1 Personalization at Every Customer Touchpoint

Algonomy helps us understand our customers better because we have all our site’s behavioral information on a single platform — something that allows us to analyze and act to further improve the experience according to their evolving needs.

U/X Coordinator
Época Cosméticos

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Reduction in out-of-stock across stores
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Denmark’s Leading Retailer Drives AOV with Personalization

Case Study

Denmark’s Leading Retailer Drives AOV with Personalization

Segment

Food/Grocery

Objective

Enhance conversion, engagement, and end-shopper experience

The Results

+ 0 %
increase in Average Order Value
+ 0 %
Sales Per View on intelligent cross-sell
0 %
Click-Through Rate
+ 0 %
Sales Per View on mobile
  • Intelligent cross-sells at checkout led to a significant increase in average order value and items per order (over 17%).
  • Mobile, in particular, experienced a remarkable lift of over 70% in sales per view.

The Client

The client, a major player in Denmark’s shopping cooperative, initiated efforts to meet elevated customer expectations. With a vast network and over 1.7 million local members, their focus was on elevating the end-shopper experience and driving increased conversion.

The Challenge

To address the challenge of personalization throughout the customer lifecycle, the client extensively evaluated providers. They chose Algonomy (now part of ADA) for its robust merchandising, AI-driven recommendations, and a platform conducive to continuous innovation.

Move from Segmentation to 1:1 Personalization at Every Customer Touchpoint

Complete, unified commerce personalization
Connect all digital path-to-purchase touchpoints — search, navigation, recommendations, and content — to deliver one personal experience that supports the complete customer journey.
Most comprehensive library of 150+ personalization strategies
Accelerate time to market with our strategy library that ranges from wisdom of the crowd and collaborative filtering approaches to deep learning visual discovery and NLP approaches.
Advanced merchandising
Cross-sell and upsell seamlessly by leveraging product attributes and compatibility data. Create automated recommendations and bundles that take the load off your merchandisers.
Algorithmic decisioning for every user with real-time context
Leverage AI to detect each shopper’s stage in the buying process, and combine it with their affinities to pick the best strategy that delivers the most relevant 1:1 experience while meeting your revenue or engagement goals.
Table Of Contents
The Results
The Client
The Challenge
Move from Segmentation to 1:1 Personalization at Every Customer Touchpoint

Want to learn more about our commerce solutions or personalization offerings?

Personalization is something that customers in the Danish market simply expect when they visit an e-shop. To meet our customer needs, we started by first implementing the most important part of the Algonomy platform: Recommend™ for Personalized Product Offers. We did this throughout our website. Soon after, we looked into implementing Discover™ for personalized product pages and lastly, Engage™, for personalized content and placements. All three modules work great together.

Digital Marketing Manager

Want to learn more about our commerce solutions or personalization offerings?

Wine.com Drives AttributeBased Recommendations for Personalization
Food & Beverage US

Wine.com Drives AttributeBased Recommendations for Personalization

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0 %
Reduction in out-of-stock across stores
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Blue Tomato Leverages ADA’s AI-powered Recommendation Engine

Case Study

Blue Tomato Leverages ADA’s AI-powered Recommendation Engine

Product Used

Algonomy’s (now part of ADA) Personalization Suite:

The Results

0 X
Revenue from Orders With Recommendations
0 %
Increase in Avg. Basket Value

The Client

Blue Tomato is a European retailer for snowboarding, freeskiing, surfing, and streetwear. They own more than 55 shops in Austria, Germany, Switzerland, Finland, and the Netherlands. Their product range has over 400,000 items across 500 brands.

The Challenge

The growing breadth of Blue Tomato’s product range became a challenge for their existing recommendation engine. With Algonomy (now part of ADA), they aimed to:

  • Reduce manual effort for merchandising and improving recommendation results
  • Recommend matching items from the same as well as different product categories and collections
  • Personalize on mobile devices as well, overcoming the challenges of a small screen

Move from Segmentation to 1:1 Personalization at Every Customer Touchpoint

Complete, unified commerce personalization
Connect all digital path-to-purchase touchpoints — search, navigation, recommendations, and content — to deliver one personal experience that supports the complete customer journey.
Most comprehensive library of 150+ personalization strategies
Accelerate time to market with our strategy library that ranges from wisdom of the crowd and collaborative filtering approaches to deep learning visual discovery and NLP approaches.
Advanced merchandising
Cross-sell and upsell seamlessly by leveraging product attributes and compatibility data. Create automated recommendations and bundles that take the load off your merchandisers.
Algorithmic decisioning for every user with real-time context
Leverage AI to detect each shopper’s stage in the buying process, and combine it with their affinities to pick the best strategy that delivers the most relevant 1:1 experience while meeting your revenue or engagement goals.
Table Of Contents
The Results
The Client
The Challenge
Move from Segmentation to 1:1 Personalization at Every Customer Touchpoint

Want to learn more about our commerce solutions or personalization offerings?

The value of the shopping baskets resulting from product recommendations has increased by an average of 20 percent, with an average of one more product purchased by each customer. The numbers apply as well for the recommendations shown on mobile devices, where significantly less products can be listed. But thanks to Algonomy, these are the most relevant.

Andreas Augustin
Head of Digital Customer Experience
Blue Tomato

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0 %
Reduction in out-of-stock across stores
0 %
increase in revenue

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