Customer Story
Footwear Company Builds Customer Experiences Across Global Brands
Featuring Amperity Bridge & Databricks
The challenge
Data siloed in on-prem SQL servers
Inconsistent customer IDs for 12 brands across 65 countries
No personalization across brands
Disparate schemas in decentralized systems
Complex ETL process
The strategy
Zero-copy data sharing to and from Databricks
Universal identity across all brands creates a unified buying journey
Personalized customer experiences with brand specific customer tables
Collaboration between marketing & data teams
PII remains secured
The results
The full story
The Challenge
ETL, on-prem servers, old CRM data, oh my
For years, this footwear company worked to combine data sources by migrating on-prem SQL servers to Databricks. Data teams spent weeks trying to combine it with their old CRM-based customer identities but ran into issues trying to merge new and reactivated customers or loyalty and non-loyalty customers. Each brand was still using disparate schemas in decentralized systems, creating silos dedicated to each brand, making it challenging to roll up customer buying journeys across brands. They hadn't been able to leverage the data sets together in a scalable way.
"We had a complex ETL process to get the data. Either we tried to scrape together support from our team, resulting in a far less robust result, or we waited for time in our IT team's workloads, a process that took months." said the brand's manager of advanced analytics.
The Solution
Universal Identity into lakehouse data paves the future
The game-changer was the creation of the Amperity ID, the AI-powered single universal customer identifier across all brands that united data sets regardless of schema, without sacrificing data quality. Amperity eliminated the need for complex ETL processes with Amperity Bridge by pushing centralized data from a lakehouse to various tools, giving all the brands zero-copy data sharing to and from their data lakehouse.
Having unified customer profiles across the business meant each team could use the data directly in their tools as needed.
The VP of Customer Analytics said, "If we wanted to get this data into Power BI or Databricks without Delta Sharing (with Amperity Bridge), we would have had to set up a ragtag ETL solution. The pivotal benefit with Amperity is that it unlocks our ability to handle data that was too granular to pull into our primary systems before."
The team gained confidence in the high-quality and reliable customer data from Amperity used for analysis. Collaboration between the marketing and technical teams improved efficiency in managing customer data, allowing the analytics team to shift their focus from unifying the data set to conducting actual analysis and gaining insights. Confidence in the data led to the creation of compelling retail offers aimed at customers of all brands.
The brands manager of advanced analytics continued, "The flexibility to take all data from everywhere is crucial. Once it's in Databricks, the sky's the limit."
"I have become 15 times more productive... Amperity has improved our decision-making process, helping to create more personalized customer experiences across our brand."
The Results
Collaboration across tech stack and teams is a miracle
Amperity fundamentally shifted everyone’s approach to day-to-day operations by making data accessible across teams and brands.
The VP of Customer Analytics stressed the efficiency gains: "I have become 15 times more productive because I don't have to change databases, make sure the tables match the same, and all the field names are the same for each brand, close to 10,000-line queries. Now, I flip a switch in my script and run it for a different brand. Amperity has improved our decision-making process, making it possible to create more personalized customer experiences across our brands."
Integrating external datasets from sources like Adobe Analytics and Qualtrics has enriched their analytical capabilities by a factor of ten. Adding new data sources is now easier, with Amperity automatically matching the raw data to existing customer profiles. Access to more data leads to more comprehensive insights and better strategic decisions.
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