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82score
PH · e-commerce
freemium
Build

Fake Sale Detector Extension

Build a consumer shopping assistant that verifies whether a discount is legitimate using historical price tracking shown directly on retailer pages. The strongest pull is immediate money protection at the moment of purchase, with clear evidence that users already value this more than generic coupon tools.

En hausse +118%5 canauxTendance des mentions sur 30 jours: latest 1, peak 4, 30-day series
Voir sur Reddit
Découvert 8 juil. 2026

Pourquoi c'est important

You are shopping online, see a dramatic markdown, and still feel unsure whether the deal is real. Existing tools pile on coupon codes or price-drop badges but rarely tell you if the current offer is actually better than the normal selling price. So you either buy with doubt or open multiple tabs to compare manually. That creates friction on everyday purchases and makes people vulnerable to urgency tactics. A simple, inline price-truth layer solves a highly repeated consumer problem because it works at the exact moment when purchase decisions are made.

  • · Conçu pour Frequent online shoppers who buy on large marketplaces and retail sites several times per month and want to avoid fake discounts without doing manual research..
  • · Monétisation la plus probable : freemium.

La douleur · Récit

You are shopping online, see a dramatic markdown, and still feel unsure whether the deal is real. Existing tools pile on coupon codes or price-drop badges but rarely tell you if the current offer is actually better than the normal selling price. So you either buy with doubt or open multiple tabs to compare manually. That creates friction on everyday purchases and makes people vulnerable to urgency tactics. A simple, inline price-truth layer solves a highly repeated consumer problem because it works at the exact moment when purchase decisions are made.

Détail du score

Intensité du problème9/10
Volonté de payer6/10
Facilité de réalisation6/10
Durabilité7/10

Signal du marché

Tendance des mentions sur 30 joursPic : 4
Sparkline: latest 1, peak 4, 30-day series
Canaux couverts
developer-toolsecommerceproductivitymarketingstartups

Mise sur le marché

Utilisateur cible exact

Desktop-first online shoppers who make at least 5 discretionary retail purchases per month on major marketplaces.

Nombre d'utilisateurs estimé

a few hundred thousand reachable early through browser-extension and deal-seeking audiences

Canal d'acquisition principal

SEO long-tail

Ancre de prix

$4.99/month

Premier jalon

100 weekly active users who save at least one product and 20 convert to paid alerts within 30 days

Périmètre MVP · 1–2 semaines

Semaine 1
  • Build Chrome extension that detects supported retailer product pages
  • Create product-page parser for title, current price, seller, and SKU-like fields
  • Set up database schema for daily price snapshots by product URL
  • Design simple inline widget showing current price versus historical median
  • Launch landing page with email capture and install flow
Semaine 2
  • Add fake-discount logic using rolling 90-day baseline and threshold rules
  • Implement saved-product watchlist with email alerts
  • Connect a second retailer to validate multi-site parsing
  • Instrument analytics for installs, widget views, and alert signups
  • Run a small beta with 20 shoppers and collect accuracy feedback
Fonctions MVP: Inline 90-day or 180-day price history on product pages · Fake-discount flag based on historical baseline and current seller context · Verified price alerts for saved products across retailers

Différenciation

Solutions existantes
Coupon browser extensionsMarketplace native seller ratingsGeneric price trackers
Notre angle
There is a gap between discount-focused shopping tools and a broader trust-focused decision layer that combines price truth, seller credibility, and duplicate-product detection in one interface.

Pourquoi cela pourrait échouer

Auto-contre-argument — le signal de confiance le plus important

  1. 1Retailers may change page structure often, making maintenance expensive for a small team.
  2. 2Consumers may like the feature but still expect it to be free because savings tools are often ad- or affiliate-funded.
  3. 3If the detector mislabels normal promotions as fake, users will stop trusting the product quickly.

Résumé des preuves

Comment l'IA a synthétisé cet aperçu — pas de citations textuelles

The discussion shows repeated enthusiasm for historical price visibility, with roughly ten comments emphasizing fake sales as a frequent problem. Several participants said price history changed buying decisions or would be valuable on its own, while others requested alerts and inline browsing support. This indicates a clear consumer wedge around price-truth verification rather than generic discount discovery.

1 1 publication analysée5 5 canauxAI · Synthétisé par IA · pas de citations

Plan d'Action

Validez cette opportunité avant d'écrire du code

Prochaine Étape Recommandée

Construire

Signaux de demande forts. Vraie douleur et volonté de payer détectées — commencez à construire un MVP.

Kit de Textes pour Landing Page

Textes prêts à coller, basés sur le langage réel de la communauté Reddit

Titre Principal

Fake Sale Detector Extension

Sous-titre

Build a consumer shopping assistant that verifies whether a discount is legitimate using historical price tracking shown directly on retailer pages. The strongest pull is immediate money protection at the moment of purchase, with clear evidence that users already value this more than generic coupon tools.

Pour Qui

Pour Frequent online shoppers who buy on large marketplaces and retail sites several times per month and want to avoid fake discounts without doing manual research.

Liste des Fonctionnalités

✓ Inline 90-day or 180-day price history on product pages ✓ Fake-discount flag based on historical baseline and current seller context ✓ Verified price alerts for saved products across retailers

Où Valider

Partagez votre landing page sur r/Product Hunt · e-commerce — c'est exactement là que ces points de douleur ont été découverts.

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Report & PRDBUSINESS

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Questions fréquentes

Qui rencontre ce problème ?
Frequent online shoppers who buy on large marketplaces and retail sites several times per month and want to avoid fake discounts without doing manual research.
Est-ce une réelle opportunité ?
Cette opportunité obtient un score de 82/100 selon la métrique composite de Pain Spotter (intensité du problème, propension à payer, faisabilité technique et viabilité). Validez-la davantage avant d'y consacrer du temps de développement.
Comment dois-je la valider ?
Menez 5 entretiens de découverte client avec le public cible, publiez une landing page avec une liste d'attente, et vérifiez l'activité récente sur le post source lié avant de commencer le développement.