Marketing ops · Production

Leading fashion ecommerce company reduces ad asset production from 2 weeks to 3 days with Labelbox

The problem

Professionally-produced editorial ads drove strong conversion but became a bottleneck due to the time and resources required to produce them at scale. The company wanted to generate assets algorithmically but needed a human quality-assurance layer to ensure quality.

Workflow diagram · grounded in source

Intent to generate algorithmically

trigger

“the team wanted to find ways to generate these creative assets algorithmically and pair them with the right copy and text”

NLP and CV generate assets

ai_action

“the company utilized natural language processing (NLP) and computer vision for both creating the copy, as well as the images of outfit combinations that were being algorithmically generated”

Labelbox human QA layer

human_review

“The Labelbox platform was used as the core infrastructure for annotation and collaboration in order to ensure that there was a human QA layer to review all of these text and visual assets”

Domain expert validation

validation

“Key questions typically answered inside Labelbox included: "Would this layout be acceptable?" and "Would this outfit combination be acceptable?"”

Expert feedback trains models

feedback_loop

“"quiz" these domain experts - stylists in this instance - to offer their recommendations and train ML models faster in order to deliver better predictions”

Scaled ad asset output

output

“which used to take 2 weeks - now just takes three days”

Reported outcome

Ad asset production that previously took 2 weeks now takes three days, allowing the company to scale its algorithmically-driven approach.
The platform now supports nearly a thousand users across roughly 90 projects.

Reported metrics

Ad asset production time2 weeks to three days

Platform users managednearly a thousand

Projects managedroughly 90

Efficiency gainsstriking

Reported stack

LabelboxLabelbox AnnotateNLPcomputer vision

Source

https://labelbox.com/customers/ecommerce-customer-story/

Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Ad asset production that previously took 2 weeks now takes three days, allowing the company to scale its algorithmically-driven approach.

What tools did this team use?

Labelbox, Labelbox Annotate, NLP, computer vision.

What results were reported?

Ad asset production time: 2 weeks to three days; Platform users managed: nearly a thousand; Projects managed: roughly 90; Efficiency gains: striking (source-reported, not independently verified).

How is this marketing ops AI workflow structured?

Intent to generate algorithmically → NLP and CV generate assets → Labelbox human QA layer → Domain expert validation → Expert feedback trains models → Scaled ad asset output.

Ad asset production that previously took 2 weeks now takes three days, allowing the company to scale its algorithmically-driven approach.The platform now supports nearly a thousand users across roughly 90 projects.