Burberry harnesses Labelbox and Databricks to curate strategic marketing assets
Burberry uses thousands of images from multiple sources for global marketing campaigns and must classify them precisely to drive the right action by the right audience. Training its object detection and classification models required labeling massive volumes of images — a task that was not feasible to do manually at the company's scale.
Burberry first tried an open source image annotation tool that lacked an interface to read from their data sources, required storing all images locally on a data scientist's machine, and could only save progress in a JSON file — making it impractical at the company's scale.
Image annotation projects that previously took months now take hours, and insight generation was reduced from two months to a two-hour self-service process.
Burberry saved 10 headcount compared to building in-house, achieved a 70% improvement in time savings for generating insights from images, and has seen 4 years of decreasing total cost of ownership with Databricks.
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Frequently asked questions
What did this team achieve with this AI workflow?
Image annotation projects that previously took months now take hours, and insight generation was reduced from two months to a two-hour self-service process.
What tools did this team use?
Labelbox, Databricks Lakehouse Platform, Amazon S3, CRANE, Databricks.
What results were reported?
Headcount saved vs. building in-house: 10; Time savings for generating insights from images: 70%; total cost of ownership (TCO) decrease period with Databricks: 4 years; Image annotation project time: months to hours (source-reported, not independently verified).
What failed first in this deployment?
Burberry first tried an open source image annotation tool that lacked an interface to read from their data sources, required storing all images locally on a data scientist's machine, and could only save progress in a…
How is this marketing ops AI workflow structured?
Image import from Amazon S3 → Image annotation via Labelbox → Edge case detection and label correction → CRANE engagement scoring → Image ranking and insights output → Revenue tracking for continuous improvement.