Ray-Ban
AI-automated sales campaign optimized on purchase value, combined with a consideration objective, measured incrementally
In 2025, Ray-Ban (EssilorLuxottica) achieved 2.08x incremental ROAS and +32% average order value in the United States by tuning its Meta Advantage+ campaigns on purchase value, combined with a consideration objective, measured by Conversion Lift.
Key points
- A/B/C test of settings on Meta Advantage+ sales campaigns.
- Value optimization plus a consideration objective, measured by Conversion Lift.
- Incremental ROAS of 2.08x and average order value up 32%.
- Evidence B, status confirmed on standard Meta formats.
Objective
Increase the yield of sales campaigns by pushing the AI to seek high-value buyers rather than plain purchase volume.
The deployment
Ray-Ban, the eyewear brand of EssilorLuxottica, tested how to better tune its Meta sales campaigns in the United States. The brand compared three approaches in an A/B/C on Advantage+ Sales campaigns: standard optimization, value optimization (where the AI targets high-basket buyers), and value optimization combined with a consideration objective. The test, from February 4 to March 22, 2025 on an adult audience, was measured by a Conversion Lift with a search-lift method. Value optimization alone brought 9 percent more ROAS and 32 percent more average order value. By adding the consideration objective, Ray-Ban achieved 2.08x incremental ROAS across all purchases, 2.1x on prescription lens purchases, and an 80 percent lower cost per incremental conversion on search visits.
Results Proof B
Official Meta case study, quantified, backed by an A/B/C test and a Conversion Lift (search lift), with a named person; single source published by the platform.
How it works
Documented architectureThe stack in detail
- plateforme Meta Advantage+ Sales Campaigns AI-automated sales campaign: audience, placements, and budget managed by Meta.
- outil Value optimization (Meta) Optimization setting where the AI targets high-basket buyers rather than gross purchase volume.
- outil Meta Pixel + Conversions API Server-side reporting of the purchase and its amount, a signal essential to value optimization.
- outil Meta Conversion Lift Incremental measurement with a search-lift method, used to arbitrate between the three tested settings.
How it runs, concretely
For ops teams-
1Report purchase value Data team
The Pixel and Conversions API send not only the purchase but its amount, to feed value optimization.
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2Choose value optimization Media team
The Advantage+ Sales campaign is tuned to target high-basket buyers rather than gross purchase volume.
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3Add the consideration objective Media team / Meta AI
Combining value optimization and a consideration objective dropped the cost per incremental conversion and doubled the incremental ROAS in the test.
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4Decide on incrementality Media team
The choice between the three settings was made on an A/B/C test and a Conversion Lift (search lift), not on the reported ROAS.
The purchase value reported server-side (not just the purchase event). Without reliable value, value optimization has nothing to distinguish a large basket from a small one.
How your customers perceive this type of use
Sourced studiesLe pricing algorithmique est le terrain le plus inflammable : 68% des consommateurs disent se sentir leses quand les marques utilisent le pricing dynamique et 80% jugent plus dignes de confiance les marques aux prix constants (Gartner, 2024). L'equite percue varie selon le secteur : le pricing dynamique n'est juge juste que par 33% a 40% des repondants selon qu'il s'agit de concerts ou de cinemas (YouGov, 17 marches). Le prix personnalise par les donnees individuelles est le plus rejete : 47% des Americains s'y opposent fermement (Consumer Reports, 2024).
Acceptance conditions
- La constance des prix comme signal de confiance : 80% jugent plus fiables les marques aux prix stables (Gartner 2024)
- Le secteur conditionne l'equite percue : le pricing dynamique est mieux tolere pour les cinemas (40% le jugent juste) que pour les concerts (33%) (YouGov 2024)
Red lines
- Le pricing dynamique percu comme abus : 68% se sentent leses (Gartner 2024)
- Le prix individualise a partir des donnees personnelles : 47% d'opposition ferme (Consumer Reports 2024)
- Les frais caches et hausses imprevues, vecus par 79% des consommateurs sur un an et associes a la perte de confiance (Gartner 2024)
Sources: Gartner 2024 · YouGov 2024 · Consumer Reports 2024
How to replicate
Inference, not sourcedData prerequisites
- Purchase value reported server-side (amount, not just the event)
- Reliable Pixel + Conversions API
- Sufficient purchase volume for value optimization and a Conversion Lift
Org prerequisites
- Objective oriented to margin / basket rather than gross volume
- Ability to read an A/B/C test and an incrementality study
Possible stack
- Meta Advantage+ Sales Campaigns + value optimization + CAPI
- Conversion Lift or geo-test as arbiter
The plan, step by step
- Step 1Make the Pixel and Conversions API reliable by reporting the purchase amount, not just the event.Deliverable: Purchase events with value verified in Events Manager.
- Step 2Set up the A/B (or A/B/C) test: standard optimization vs value optimization, possibly plus a consideration objective, on Advantage+ Sales.Deliverable: Test structure with defined cells, budgets, and duration.
- Step 3Deliver with an active Conversion Lift to measure incrementally, without reworking the campaigns during the test.Deliverable: Campaigns in delivery with incremental measurement connected.
- Step 4Read incremental ROAS, average order value, and cost per incremental conversion, then switch to the winning setting.Deliverable: Documented decision and campaign in production on the chosen setting.
First step: Activate value optimization on an Advantage+ Sales campaign and compare it in an A/B to standard optimization, with purchase value reported via CAPI.
The same pattern, proven in other industries
adidas
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Allwyn
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Nivea
genAI testing and optimization of creative elements + automated localization at scale
Sources
- S1 Ray-Ban: Facebook ads case study - Advantage+ sales, value optimization, Conversion Lift Interested party archive pending
- S2 Meta Advantage+ Sales Campaigns: AI Automated Shop Ads Interested party archive pending
An error, newer info, a source?
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