The shoe and handbag designer uses machine learning to optimise ads and boost conversions by 900%
Women all around the world buy shoes and handbags made by London-based designer Sophia Webster. Facing stiff competition in e-commerce, the brand wanted to drive incremental growth and bolster sales volume, and revenue. Ahead of their autumn/winter product launch, they created a strategy to increase sales by finding new customers likely to be interested in their collections, as well as reaching previous customers who might be interested in purchasing from Sophia Webster again.
The Sophia Webster team worked with their agency Croud to launch Smart Shopping campaigns, an approach that leveraged Google’s machine learning and automation to showcase the brand's products through search, remarketing, and prospecting. By using Smart Shopping campaigns, Google’s machine learning was applied to the existing product feed with the goal of maximising conversion values at a budget set by the team.
The campaigns ran a variety of ad formats across multiple Google networks. Taking clues from search history and demographics amongst other data signals, the campaigns dynamically generated ads showing relevant products most likely to appeal to consumers’ interests.
Comparing the Smart Shopping campaign with Sophia Webster’s standard shopping activity, several key metrics surged. Because they were able to capture a greater amount of upper-funnel traffic, they saw the number of conversions rise by 900%. Cost per click dropped by 57% and return on investment grew by 408%.
“This campaign allowed us to capture more upper-funnel traffic at an effective ROI, which allows us to drive incremental growth through our top markets,” says Shivali Bennion, Global CRM Manager for Sophia Webster. Moving forward in partnership with Croud and Google, the business stands ready to adopt new strategies designed to combat the challenges of e-commerce as they seek to continue their growth on a global scale.