Global fashion leader increases revenue with better data-Databricks
Burberry is a global luxury brand with a rich British heritage. The company has 413 stores worldwide and plans to keep growing by providing an outstanding, personalized customer experience. Until recently, this goal was difficult to achieve due to extensive data processing delays which prevented the company from analyzing clickstream data from Burberry.com in a timely fashion. By implementing the Databricks Data Intelligence Platform and Snowplow’s Behavioral Data Platform, Burberry formed a complete view of each customer (AI-Ready Customer 360). In-store client advisors can now pull up information about opted-in customers’ latest online behavior on their phones and tablets — which helps them deliver a truly superior customer experience (NextGen CX).
Daily clickstream data batches arrived too late to be useful
Whether they’re looking for the iconic Burberry trench coat or another luxury clothing item, shoppers around the world flock to Burberry stores for a personalized experience. But many browse the company’s offerings online first, a practice known as webrooming. Burberry analyzes online user behavior to determine how they can best improve their service and increase sales. For years, the company pulled clickstream data from a cloud-based data warehouse — but wanted more precise control over data delivery.
“With our data warehouse, we would receive the previous day’s data anywhere between 2:00 and 7:00 PM GMT,” recalled Benjamin Stephens, Senior Manager, Decision Analytics at Burberry. “Although that delivery schedule works for many businesses, we at Burberry are in a constant race to activate our web data so that we can pass it along to our client advisors in stores, worldwide. That kind of speed is essential to offering the level of service for which Burberry is known.”
Burberry also needed greater visibility into their data. The company’s data warehouse automatically pre-aggregated and sanitized Burberry’s data, which made it difficult to trust the details about customer visits. Burberry also needed to validate their web data regularly to ensure it was actionable.
“The more deeply we dug into our data, the less confident we felt in our ability to form conclusions,” said Stephens. “Simple requests such as, ‘What happened on the website yesterday?’ were going unanswered. We often faced the choice of either making a quick decision or making a good one.”
Better, faster data enables machine learning models to drive NextGen CX
To address these challenges, Burberry implemented the Databricks Data Intelligence Platform and Snowplow’s Behavioral Data Platform (BDP). The solutions enable Burberry to deliver a NextGen CX based on reliable, high-quality data. As clickstream data flows in from Burberry.com, the company uses Snowplow BDP to realize real-time AI-Ready Customer 360s from across their digital touchpoints. From there, the data travels into the Databricks Data Intelligence Platform, which contains 40 personalized models covering tasks such as product recommendations, propensity scoring and lifetime value. These models instantly recalculate the data before sending it to the company’s action system.
“A NextGen CX program is only as good as the speed and accuracy of the data you can get from it,” Stephens explained. “With the Databricks Data Intelligence Platform and Snowplow BDP, we’ve broken free from daily data batches and formed an accurate real-time view of our customers’ digital activity. Our personalized models automatically stay up-to-date by consuming behavioral data. And throughout the process, Snowplow helps us safeguard data privacy by giving us total control over how much information we collect from customers and enabling us to track user consent.”
Burberry also gained a more accurate view of their marketing ROI by refining their approach to marketing attribution. The company used Snowplow to refine their business definitions for all their marketing activities and taxonomies. Using the dbt plug-in for Databricks, Burberry gathered additional detail from their referral sources, consent banners and server-side cookies. As a result, the company has migrated onto a rich and fully owned marketing foundation that uses a simple attribution model to understand what’s working in near-time.
“Using behavioral data from Snowplow, we’ve formed a last-click attribution view that incorporates some of the many variables from our work,” Stephens added. “We’ve gotten much smarter about how we create our data lead attribution models, which will help us make better decisions about where to invest our marketing spend in the future.”
