Cannes Lions
McCANN, Toronto / BLACK & ABROAD / 2024
Overview
Entries
Credits
Background
Black & Abroad offers luxury travel experiences for an underserved and under-represented customer base: young, upwardly mobile members of the Black community. The brand’s destinations include Paris, Ghana, Colombia and South Africa. With 30% of revenue coming from repeat business, and the pandemic’s industry impact finally fading, 2024 was the perfect opportunity to re-engage with past guests to drive new bookings.
BRIEF
Drive new bookings from past Black & Abroad guests with an uplifting, tech-forward engagement platform.
OBJECTIVES
Lift in engagement among previous Black & Abroad customers
High open rates in email
Percentage increase in repeat bookings.
Strengthen leadership position as an innovative, purpose-driven brand.
Idea
SEE YOU THERE
A project that began as a 1:1 digital experience for Black & Abroad customers – and became a global data solution for bias in generative AI models.
https://seeyouthere.ai/
A PROJECT IN THREE PARTS
1. Personalized digital experiences for Black & Abroad customers, in which we used generative AI to show past guests at future destinations.
2. A mass media campaign highlighting the racial bias problems we encountered along the way.
3. The Generative AI Bias Reporting System – our data solution to the problem of bias in generative AI.
Strategy
GENERATING PAST GUESTS INTO FUTURE DESTINATIONS
We began by collecting +11,000 untagged images of guests from previous Black & Abroad trips. We hand-sorted by customer, prioritizing facial clarity and removing unusable or irrelevant images – to arrive at 5 to 10 images per customer, which we ingested into a secure cleanroom environment to test, train and refine our AI models. We documented problematic images, then uprezed and fine-tuned outputs, for a long list of selects. Human review determined final image selects.
STRUCTURING UNSTRUCUTRED BIAS DATA
The Generative AI Bias Reporting System is our data solution to bias in generative text, image and video. By entering instances of bias, users are creating structured data at scale, for use by anyone, anywhere in the refinement and development of gen AI models.
TARGETING
First party data – customer email, photos and purchase
Third party data – MRI Simmons, Acxiom Infobase, Kantar Mars
Execution
GENERATING PAST GUESTS INTO FUTURE DESTINATIONS
We began by collecting +11,000 images of guests from previous Black & Abroad trips. We hand-sorted by customer, prioritizing facial clarity – to arrive at 5 to 10 images per customer, which we ingested into a cleanroom to test, train and refine our models. We documented problematic images, then uprezed and fine-tuned , for a long list of selects. Human review determined finals.
OUR GEN AI TECH STACK
Google Colab – Testing, prototyping
Dreambooth/Autotrain/SDXL1.0, PyTorch – LoRA weights training, raw image generation
sdxl-outpainting-lora (batouresearch) – Outpainting, upscale
SDXL-Magic-Image_Refiner, MagnificAI – Refine, upscale
HuggingFace – Temporary hosting of (private) trained weights, access control
Pillow – Compositing, image processing
Pandas – Data management (client approvals)
CREATING THE GENERATIVE AI BIAS REPORTING SYSTEM
Modelled after the FDA’s Adverse Event Reporting System (FAERS), our reporting system is a disciplined technical solution to bias in generative AI across text, image and video. It collects unstructured, observed instances of bias and converts them into structured data at scale.
BIAS REPORTING STACK
Python backend
Postgres database
Python Front End
Hosted on Google Cloud Platform
Data wrangling SQL, Pandas
APPLICATION ACROSS INTERSECTIONALITY
Go to any gen AI platform. Prompt: “two trans women”. How old are the women it generates? What are they wearing? Ethnicity? Are they hypersexualized? Our utility provides a systematic way to consider questions such as these, while delivering structured, open data back to the source.
DATA USE CASES INCLUDE:
Testing and training new gen AI models on prompts causing biased outputs on other platforms. Benchmarking different models on inclusivity themes.
Observing trends across intersectionality.
TOUCHPOINTS
1:1 email
1:1 pages featuring past customers at 5 upcoming destinations
Paid social, OOH
Launch film
https://seeyouthere.ai/
TIMELINE
Four-month development cycle.
Outcome
DRIVING CHANGE
3,462 biases reported
81,293,895 impressions – The most seen campaign in Black & Abroad’s history
“How the ad industry in making AI images look less like AI” – Wall Street Journal
FILLING THE FUNNEL
81% email open rate – of emails using generative imagery of past customers
218% clickthrough rate vs. benchmark from digital media (AdTheorent)
77.68% view rate on top performing creative shown on Meta platforms
53% higher video completion rate vs. YouTube Benchmark (ZEFR)
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