A Leading UK-based
Department Store Chain
Increases Sales By Delivering
attributable revenue from winter email campaigns
clicks from personalized campaigns
Overview The client is a high-end UK-based department store, with a focus on Fashion and Home categories. They have over 42 stores in the UK, Republic of Ireland, and Australia alongside a growing online business.
They have been trading in London since 1864 and went online in 2001, with their website now offering over 300,000 products and recording over 500 million annual visits. They aim to be trading 70% online by 2025. They are also the UK’s largest employee-owned business with 78,000 partners.
The client has been partners with Algonomy for over five years now.
The pandemic changed the focus from offline to online, leading the client to revisit their business goals. Their online business went up by 73% between 2020 and 2021. They realized the potential of digital channels and decided to up their game to adapt to evolving customer expectations and beat competition which was ahead in terms of digital maturity.
They aim to replicate their in-store service differentiators on their digital channels by recommending the right products and accessories based on affinities so that customers can make the right purchase decisions and complete their purchases.
The client wanted a strong product recommendation engine to support this goal and drive up their revenues and customer engagement by providing hyper-personalized cross-channel experiences across eCommerce, mobile app, email.
They deployed Algonomy’s Recommend™ engine to amplify their efforts to deliver hyper-personalized omnichannel product recommendations at various stages of customer journey.
Transforming Product Recommendations
Algonomy’s Recommend™ aligned perfectly with the client’s requirement—that of targeting customers with the right recommendations on multiple channels based on a combination of user purchase data and merchandising data. The client uses the recommendation engine on their website, mobile app, and in email campaigns.
With Recommend™, the client was able to replicate the in-store experience that service partners would provide customers—suggest suitable alternative products, recommend the right complementary products, and help customers make the right buying decisions.
The platform is able to respond to the behaviors and affinities of individual customers in real time in addition to using historical data, including in-store purchase data, which allows Recommend™ to get a 360 degree view of the customer across all channels.
This helps the engine build rich product recommendation models and show the right recommendations at different points of the customer journey, such as after a product is added to cart, upon cart or browse abandonment, and in personalizing emails.
Recommend™ leverages Algonomy’s Xen AI to provide context-aware recommendations and continuously optimize weights and parameters to increase relevance and revenue for each customer. Marketers can choose to deploy various configurable strategies such as visually similar products, compatible products, cross-sell, upsell, top sellers, and Wisdom of Crowd.
How Recommend™ Helped Them Achieve Their Goals
With Recommend’s out-of-the-box strategies such as Similar Items, Top Sellers, Frequently Bought Together, and Configurable Strategies, the client was able to create individualized product recommendations for their customers.
Some of the ways in which these functionalities were used are:
When a customer adds a product to cart, an add-to-cart confirmation pop-up appears, with a cross-sell strategy that displays complementary products based on the purchase co-occurrence data from the bespoke report.
Product and category recommendations are placed in search boxes based on the user’s recent searches.
Emails in the client’s retargeting campaign contain product recommendations based on the individual customer’s affinities to a brand or style or product family.
Upon cart or browse abandonment, emails are sent to customers with recommendations of products alternative to or similar to the ones they viewed on the site.
The client sends post-purchase emails to their customers, containing recommendations of products complementary to the ones the customer bought most recently.
When a customer signs up for notifications for products that are out of stock on the website, the client runs an email campaign around it. These emails contain not just alerts about the product in question but also recommends alternatives and similar products they could consider instead. The same strategy is also used on the website, where alternatives to out-of-stock products are shown in a panel on the product page.
Being able to constantly recommend the right and most relevant products to customers—whether using similar products, compatible products, Wisdom of Crowd, or affinity-based products strategy—the client is able to increase revenues of personalized email campaigns by over 350% and increase the overall online revenue by over 2%.
Bespoke Analytics Empowers Business Decisioning
Algonomy creates several bespoke reports for the client’s Analytics department; one of them is purchase analysis by Buying Group (main category) and Buying Office (sub-category within each main category). Using this data, they are able to gather more focused insights for each business unit.
Another bespoke report Algonomy produces shows purchase co-occurrence data by attributes, which reflects how the client’s merchandising team is organized. The client uses these insights to build cross-sell Advanced Merchandising rules that helps the platform recommend the right complementary products to new users on the site or in a product segment.
Algonomy continues to help the client create strategies to suit their business goals and add value to their marketing and optimization campaigns.