Shopee
AI automation of seller ad campaigns
In the second quarter of 2025, Shopee's AI ad automation (GMV Max) coincided with an 8% rise in the purchase conversion rate, a gain of nearly 70 basis points in ad take rate, and growth of more than 40% in average ad spend per seller.
Key points
- AI automation of seller ad campaigns (traffic allocation and bids).
- GMV Max tool on Shopee's in-house ad engine.
- Purchase conversion +8%, ad take rate +70 basis points, ad spend per seller +40% year over year.
- Evidence level A, confirmed status, figures from Sea Limited's Q2 2025 earnings call.
Objective
Raise Shopee's ad revenue by making advertising accessible to sellers who cannot manage campaigns: the AI optimizes traffic allocation and bids on their behalf, which increases both their conversion and the share of monetized GMV.
The deployment
GMV Max is Shopee's automated ad tool: the seller delegates campaign steering to the AI, which handles traffic allocation and optimization to maximize GMV. It is one building block of a dedicated ad-tech team that, since 2024, has been reworking the algorithms, traffic-allocation efficiency, and service to advertising sellers. In the second quarter of 2025, Shopee attributes to these technical improvements an 8% rise in the purchase conversion rate and a gain of nearly 70 basis points in its ad take rate over one year. The number of sellers buying ads rose about 20% and their average quarterly ad spend more than 40% year over year.
Results Proof A
Figures drawn from the official transcript of Sea Limited's earnings call for the second quarter of 2025 (a primary document from the listed company, T1), confirmed by an established press analysis (T3). These are figures communicated in financial results to investors.
How it works
Inferred typical approachThe internal detail is not public. Here is a proven approach that leads to the same result, to adapt to your stack.
The stack in detail
- plateforme GMV Max Shopee's automated ad tool: the seller sets budget and goal, the AI handles traffic allocation and bids to maximize GMV
- infra Moteur publicitaire in-house Shopee bidding, traffic-allocation, and matching algorithms reworked by the dedicated ad-tech team since 2024; proprietary ML, not detailed publicly
- plateforme Shopee Ads (annonces sponsorisees) sellers' self-serve ad inventory on which the optimization is applied
How it runs, concretely
For ops teams-
1Activation by the seller customer
The seller activates GMV Max and sets budget and goal, without managing each bid.
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2Traffic allocation AI
The algorithm distributes traffic and sets bids to maximize the seller's GMV.
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3Continuous optimization AI
The system continuously readjusts based on the conversion observed on the ads.
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4Monetization tracking data team
The ad-tech team tracks take rate, conversion, and seller adoption to improve the algorithms.
The GMV and conversion attributed to the ads. If the conversion signal is noisy or the inventory poorly tagged, traffic allocation optimizes on nothing.
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
- Conversion signals attributed to the ads
- Well-tagged product inventory
- Campaign performance history
Org prerequisites
- Ad inventory and bidding on the platform
- A transparency rule on ad ranking
- An ad-tech team to maintain the algorithms
Possible stack
- A bidding and traffic-allocation optimization engine
- A self-serve ad platform for sellers
- A conversion attribution loop
The plan, step by step
- Step 1Make attribution reliable: validate conversion and GMV tracking attributed to the ads, clean up product inventory taggingDeliverable: Audited attribution loop, clean conversion signal
- Step 2Build v1 of the engine: traffic allocation and automatic bidding on a GMV goalDeliverable: Engine in test on a subset of categories
- Step 3Open a seller pilot: beta on a panel, with settings reduced to budget + goalDeliverable: Pilot campaigns compared to manual management (conversion, seller feedback)
- Step 4Industrialize self-serve for non-expert sellers and simplify onboardingDeliverable: Product opened to all, seller adoption tracked
- Step 5Close the continuous-improvement loop: track take rate, conversion, and spend per seller, iterate on the algorithmsDeliverable: Monetization dashboard and regular engine releases
First step: Make conversion attribution reliable on the ads before automating traffic allocation.
Sources
- S1 Sea Second Quarter 2025 Earnings Call Transcript Primary archive pending
- S2 SE Q1 Deep Dive: Shopee Logistics and AI Drive Revenue Established press archive pending
An error, newer info, a source?
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