data_entry_ops · travel · workflow
Leading vacation rental company automates ML labeling pipelines with Labelbox to enrich unique property listings
A large vacation rental company faced the challenge of classifying and labeling vast volumes of unstructured listing image data across multiple business units with different formats, and needed to standardize ML infrastructure to run effective data science initiatives at scale while reducing human labeling costs.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Unstructured listing images submitted
Multiple business units submit unstructured listing image data in different formats for classification and labeling.
Tools used
LabelboxAnnotateCatalogactive learning
Outcome
After three months, ML pipelines were fully automated with the majority of labels model-generated and accepted by subject matter experts. Human labeling costs fell to a fraction of their original level, over nine million annotation tasks were completed, and the enriched unique listings collectively earned more than $300 million globally since the start of the pandemic.
Results
Time savedthree months
Volumeover nine million
Cost replacedmore than $300 million globally
Grounding & classification
Source type: vendor customer story
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computer visionproduct cataloghuman review describedmetric backedproduction runtime claimedtools describedworkflow describedtravelcost reductionthroughput increasetime savedvendor customer storyback office opsdata entry opsdocument to recordhuman review queue