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 yielded strong conversion but became a bottleneck due to the time and resources required to create them at scale, and the company lacked an efficient way to harness domain expert knowledge for algorithmically generated ad assets.

Workflow diagram · grounded in source

Algorithmic ad generation trigger

trigger

“One of the earliest data science teams to adopt Labelbox focused on innovating on personalized ads algorithmically”

NLP and computer vision generation

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 annotation infrastructure

integration

“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 review

human_review

“Key questions typically answered inside Labelbox included: "Would this layout be acceptable?" and "Would this outfit combination be acceptable?", while tapping into subject matter experts for inputting their judgment and expertise”

Expert feedback trains models

feedback_loop

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

Scaled personalized ad output

output

“Having a central platform to automate the data import and export process, speed up human QA review, and simplify the management of a high number of users allowed the company to scale this algorithmically-driven approach for creating pers…”

Reported outcome

Ad asset production time dropped from 2 weeks to three days, and the company now manages nearly a thousand users across multiple teams and roughly 90 projects on a scalable, algorithmically-driven personalized ad pipeline.

Reported metrics

Ad asset production time2 weeks to three days

Platform users managednearly a thousand users

Active projectsroughly 90 projects

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 time dropped from 2 weeks to three days, and the company now manages nearly a thousand users across multiple teams and roughly 90 projects on a scalable, algorithmically-driven personalized ad pipe…

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 users; Active projects: roughly 90 projects; Efficiency gains: striking (source-reported, not independently verified).

How is this marketing ops AI workflow structured?

Algorithmic ad generation trigger → NLP and computer vision generation → Labelbox annotation infrastructure → Domain expert review → Expert feedback trains models → Scaled personalized ad output.