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Proof A Live confirmed

Shopee

AI automation of seller ad campaigns

IndustryRetail & e-commerceLeverMonetizationFamilyOptimization / automationImplementationMartech platformStagepurchase
Pattern proven in 4 industries still untouched in Banking, insurance & fintech, Luxury & beauty, CPG & D2C +9 See the pattern map
8%
Rise in the purchase conversion rate on Shopee (Q2 2025)
"We saw an 8% uplift in Shopee purchase conversion rates" S1

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

8%
Rise in the purchase conversion rate on Shopee (Q2 2025)
"We saw an 8% uplift in Shopee purchase conversion rates" S1
pres de 70 points de base
Improvement in ad take rate over one year (Q2 2025)
"improved our ad take rate by almost 70 basis points this quarter, year-on-year" S1
environ 20%
Growth in the number of sellers buying ads (Q2 2025)
"the number of sellers using our ad products rose by around 20%" S1
plus de 40%
Average quarterly ad spend per seller, year over year (Q2 2025)
"ad-paying sellers' average quarterly ad-spend grew by more than 40% year-on-year" S1

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 approach

The internal detail is not public. Here is a proven approach that leads to the same result, to adapt to your stack.

conversion observeeboucle d'optimisation Vendeur Shopee(activation GMV Max) Moteur d'optimisationpublicitaire GMV Max (Shopee) Signaux de conversion etde GMV Annonces sponsoriseesShopee Acheteurs Shopee

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
CadenceContinuous campaign optimization, driven by the algorithm in near real time
Operated byShopee's ad-tech team that maintains the algorithms; the seller activates the tool and sets the budget
  1. 1
    Activation by the seller customer

    The seller activates GMV Max and sets budget and goal, without managing each bid.

  2. 2
    Traffic allocation AI

    The algorithm distributes traffic and sets bids to maximize the seller's GMV.

  3. 3
    Continuous optimization AI

    The system continuously readjusts based on the conversion observed on the ads.

  4. 4
    Monetization tracking data team

    The ad-tech team tracks take rate, conversion, and seller adoption to improve the algorithms.

The signal that drives it

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 studies

Le 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).

68%
Consommateurs qui se sentent leses (taken advantage of) quand les marques utilisent le pricing dynamique (2024)
80%
Consommateurs d'accord pour dire que les marques aux prix constants sont plus dignes de confiance (2024)
79%
Consommateurs ayant vecu des situations de prix inattendues sur un an (surge pricing, frais caches, hausses imprevues) (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

See full acceptance: by country, by use, by generation

How to replicate

Inference, not sourced

Data 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
Team to operate1 dedicated ad-tech team (3-6 ML/data engineers) + 1 monetization PM + 1 seller support team

The plan, step by step

  1. Step 1
    Make attribution reliable: validate conversion and GMV tracking attributed to the ads, clean up product inventory taggingDeliverable: Audited attribution loop, clean conversion signal
  2. Step 2
    Build v1 of the engine: traffic allocation and automatic bidding on a GMV goalDeliverable: Engine in test on a subset of categories
  3. Step 3
    Open a seller pilot: beta on a panel, with settings reduced to budget + goalDeliverable: Pilot campaigns compared to manual management (conversion, seller feedback)
  4. Step 4
    Industrialize self-serve for non-expert sellers and simplify onboardingDeliverable: Product opened to all, seller adoption tracked
  5. Step 5
    Close 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

  1. S1 Sea Second Quarter 2025 Earnings Call Transcript Primary cdn.sea.com · 2025-08-12 · accessed 2026-07-11 archive pending
  2. S2 SE Q1 Deep Dive: Shopee Logistics and AI Drive Revenue Established press stockstory.org · 2025 · accessed 2026-07-11 archive pending