- Case Study
International fashion retailer boosts new customers 22% using data activation
About the Retailer
This retailer is a multinational lifestyle retailer. It’s famous for its well-curated mix of on-trend women’s and men’s apparel, as well as impressive homeware, music and tech collection.
The Challenge
Retailer X had long optimized its paid search campaigns for basic revenue goals. It used a third-party bidding tool to drive this strategy, which had resulted in consistently solid, albeit short-term results. But this approach undermined the retailer’s ultimate goal: profitability and future growth.
Crealytics tackled the root of this conflict by examining its existing system. Because its solution was oriented toward revenue goals, its day-to-day optimizations lacked the data needed to take it to the next level. Retailer X needed to identify – and activate – this new data within its bidding solution to address any growth ambitions.
Crealytics tackled the root of this conflict by examining its existing system. Because its solution was oriented toward revenue goals, its day-to-day optimizations lacked the data needed to take it to the next level. Retailer X needed to identify – and activate – this new data within its bidding solution to address any growth ambitions.
Crealytics' Approach
Retailer X’s bidding solution excelled at efficient performance. Crealytics reasoned that it could maintain the tool’s efficiency while changing its incentives to make it more compatible with long-term goals.
Incentivizing the tool to prioritize new customers, combined with exact margin data, underscored this approach. Once ingested by the system, this combination of values would steer performance toward the gold standard of long-term KPIS: Customer Lifetime Value.
In practice, the retailer implemented a tag which informed Crealytics whether a purchase came from a new or existing customer at the time of transaction. To respect user privacy, Crealytics received no other information that might identify them in real-life. Crealytics assigned additional credit to all purchases generated by new customers, reflecting this audience’s higher lifetime value.
Crealytics also built the retailer a custom feed, simplifying the flow of all SKU-level margin data.
Merging Retailer X’s margin and customer uplift data created a comprehensive CLV value. Feeding its bidding algorithms with these CLV-values steered future bidding in favor of margin and new customer acquisition. The retailer also benefited from smart campaign prioritization and structuring.
Incentivizing the tool to prioritize new customers, combined with exact margin data, underscored this approach. Once ingested by the system, this combination of values would steer performance toward the gold standard of long-term KPIS: Customer Lifetime Value.
In practice, the retailer implemented a tag which informed Crealytics whether a purchase came from a new or existing customer at the time of transaction. To respect user privacy, Crealytics received no other information that might identify them in real-life. Crealytics assigned additional credit to all purchases generated by new customers, reflecting this audience’s higher lifetime value.
Crealytics also built the retailer a custom feed, simplifying the flow of all SKU-level margin data.
Merging Retailer X’s margin and customer uplift data created a comprehensive CLV value. Feeding its bidding algorithms with these CLV-values steered future bidding in favor of margin and new customer acquisition. The retailer also benefited from smart campaign prioritization and structuring.
The Results
Shifting the retailer’s bid rationale to prioritize Customer Lifetime Value drove spectacular results.
Download full case study
Download Case Study
More Case Studies
Google won the bid management war. Now retailers can take the spoils with CLV-based advertising
Measuring for incrementality makes Facebook Ads 90% more effective
