Solution
Every competitor price move, captured the moment it happens
Real-time competitor prices across web, app, and geo. SKU-level deltas detected within minutes. Built for pricing teams that cannot afford to react last.
Pricing competition is no longer monthly. It is hourly.
Competitor prices shift on flash sales, member tiers, vouchers, and dynamic pricing models. By the time a quarterly report tells you what changed, your customers have already shifted basket.
Most pricing teams see 10 to 20% of the picture.
Web prices miss app-only deals. Single-geo extraction misses regional differences. Daily scrapes miss flash windows. Most providers ship one of these slices and call it pricing data.
We capture every price, on every surface, in every market you compete in.
Web, app, voucher-applied, member-tier, geo-specific. Cycles as low as every 15 minutes. Delivered structured, deduplicated, and validated, in your schema.
Price moves, in minutes
Detect SKU-level price changes within minutes of competitors making them. Repricing teams act inside the same trading hour, not the same week.
Every channel a buyer sees
Web, mobile app, member-only views, voucher-applied prices, geo-specific listings. The same SKU at three prices is captured as three records.
Built into your pricing stack
Records pushed directly to your warehouse, schema mapped to your repricing engine. Your team uses the data, not moves it around.
In pricing, the team that sees a competitor move at 10 AM is competing against the team that sees it at 2 PM. The team that sees it on Monday is already losing in Wednesday's basket. Speed of detection is no longer a nice-to-have.
How it works
The extraction pipeline
From target spec to your warehouse, every competitive pricing record passes through these stages. You see the output. We run everything in between.
Target spec
Categories, SKUs, geos, cadence, and schema locked from your pilot scope.
Source orchestration
Web, app, and API extraction across every market in scope, in parallel.
Capture
Signed requests, anti-bot bypass, geo-routed sessions, app-layer where needed.
Validation
Schema, range, deduplication, decoy detection, currency normalization.
Delivery
CSV, JSON, REST, or direct push to your warehouse, in your spec.
Coverage
Platforms we monitor
Pricing intelligence is only as good as the platforms you cover. Each platform has its own pipeline, its own pace, its own defenses.
Marketplaces
Shopee, Amazon, Flipkart, Walmart, Lazada, Tokopedia. Sponsored, member, and voucher pricing across global and regional players.
Quick commerce
Blinkit, Zepto, Swiggy Instamart, BigBasket, dark-store level pricing
Food delivery
DoorDash, Uber Eats, Zomato, Swiggy menu and surge pricing
Travel and hotels
Booking.com, MakeMyTrip, Agoda, Expedia, airline direct fares
Telecom self-care apps
Plan pricing, recharge offers, member-tier rates
D2C brand storefronts
Direct-to-consumer pricing and bundle structures
Data landscape
The data we extract
Every price record from every monitored platform, normalized into one schema, delivered on your cadence to your warehouse.
Pricing
List, sale, member, voucher-applied, currency, geo, capture timestamp
Promotions
Type, value, time window, eligibility, voucher code, bundle structure
Sourcing
Platform, surface (web or app), market, capture geo, capture timestamp
Identity
Member tier, segment, language, app version where exposed
Comparison
Your matched SKU, your active price, delta vs competitor, delta percent (when matching enabled)
Volume signals
Units sold where exposed, stock signal, demand proxy
This is a representative sample of the data we extract. We customize every extraction to your exact requirements. If you need a data point not listed here, we will add it to your pipeline.
Sample output
What a single record looks like
This is a representative payload from a real competitive pricing extraction job. Field names, schema, and delivery format are scoped to your spec at pilot time.
{
"extracted_at": "2026-05-07T08:14:22Z",
"platform": "shopee",
"surface": "app",
"market": "ID",
"competitor_sku_id": "SHP-18472913",
"matched_sku_id": "OUR-SKU-3942",
"product": {
"name": "Sony WH-1000XM5",
"category": "Audio > Headphones",
"brand": "Sony"
},
"competitor_price": {
"list_price": 5499000,
"sale_price": 4799000,
"voucher_applied_price": 4399000,
"currency": "IDR"
},
"your_price": 4599000,
"price_delta": -200000,
"price_delta_percent": -4.35,
"voucher": {
"code": "SHOPEEPAY100K",
"type": "platform"
},
"captured_geo": {
"city": "Jakarta",
"lat": -6.2088,
"lng": 106.8456
}
}Schema
Field-level reference
Every record conforms to a stable schema. Your engineering team can integrate against this spec before the pilot starts.
extracted_atISO 8601UTC capture timestamp2026-05-07T08:14:22Zextracted_atISO 8601UTC capture timestamp
2026-05-07T08:14:22ZplatformstringSource platform nameshopeeplatformstringSource platform name
shopeesurfaceenumWhere extracted (web, app, api)appsurfaceenumWhere extracted (web, app, api)
appmarketISO-3166Market codeIDmarketISO-3166Market code
IDcompetitor_sku_idstringSource-platform SKU identifierSHP-18472913competitor_sku_idstringSource-platform SKU identifier
SHP-18472913matched_sku_idstringYour SKU mapped to this listingOUR-SKU-3942matched_sku_idstringYour SKU mapped to this listing
OUR-SKU-3942product.namestringListing display nameSony WH-1000XM5product.namestringListing display name
Sony WH-1000XM5product.brandstringBrand labelSonyproduct.brandstringBrand label
Sonycompetitor_price.list_pricenumberPre-discount competitor price5499000competitor_price.list_pricenumberPre-discount competitor price
5499000competitor_price.sale_pricenumberActive competitor price4799000competitor_price.sale_pricenumberActive competitor price
4799000competitor_price.voucher_applied_pricenumberBest post-voucher price4399000competitor_price.voucher_applied_pricenumberBest post-voucher price
4399000competitor_price.currencyISO-4217Currency codeIDRcompetitor_price.currencyISO-4217Currency code
IDRyour_pricenumberYour active price for the matched SKU4599000your_pricenumberYour active price for the matched SKU
4599000price_deltanumberYour price minus competitor-200000price_deltanumberYour price minus competitor
-200000price_delta_percentnumberDelta as percent of competitor-4.35price_delta_percentnumberDelta as percent of competitor
-4.35voucherobjectActive voucher details{code, type}voucherobjectActive voucher details
{code, type}captured_geoobjectGeo of the request origin{city, lat, lng}captured_geoobjectGeo of the request origin
{city, lat, lng}Delivery formats
How you receive the data
You define the format. We handle the rest.
Use cases
How teams put competitive pricing data to work
From pricing teams to category managers to operations leads, here are the most common ways competitive pricing data drives decisions.
Pricing team, automated repricing
Feed competitor price deltas into your repricing engine. Rules fire on real signals, not stale weekly reports. Margin and competitiveness, balanced in real time.
Revenue ops, elasticity modeling
Build price-elasticity models on actual market response data, not historical assumptions. Test pricing hypotheses against real competitor moves.
Category managers, assortment positioning
See where your category is priced too high, too low, or out of position relative to direct competitors at SKU level. Identify gap categories where competitive pressure is rising.
Trade marketing, promotional response
Catch competitor flash sales, bundles, and voucher pushes the moment they go live. Decide to match, undercut, or ignore inside the same trading window.
Brand managers, premium positioning
Track how your brand is priced versus private label and competitor brands across markets. Defend price ladders. Spot where your premium is eroding.
Leadership, board-ready intelligence
Monthly price-position reports across categories, markets, and competitors. Trends, deltas, response times. Without anyone on your team running scrapers.
Tech specs
What we run at scale
Every competitive pricing engagement runs against these baseline specs. Your scope can move freshness, throughput, or geo coverage to whatever you need.
<15 min
Detection latency on flagged SKUs
100M+
SKU prices monitored daily
99.9%
Pipeline uptime
200+
Geos and markets covered
<5 min
p95 delivery latency post-extract
99%+
Records passing validation
Challenges
Why competitive pricing data extraction is hard
If extraction were easy, you would do it yourself. Here is why it is not.
Competitor pricing lives across many surfaces
Web, mobile app, member-only flows, voucher-applied checkout, geo-specific listings. A single SKU has five or more visible prices, and your decision has to factor all of them.
Every platform fights extraction differently
Anti-bot stacks, signed requests, certificate pinning on apps, CAPTCHAs, behavioral analysis. One pipeline is hard. Twenty pipelines, every 15 minutes, is an infrastructure operation.
Sales events break everything
Flash sales, mega events (9.9, 10.10, 11.11, 12.12), regional festivals. Volume spikes 5 to 10x. Endpoint behavior changes. Teams that miss these windows lose the most expensive competitive moments.
Currencies, geos, and SKU matching multiply the problem
The same product appears under different titles, attributes, and SKU IDs across platforms. Matching your SKU to a competitor's listing across 20 platforms and 8 markets is a data-engineering problem most teams underestimate.
Stale data is worse than no data
A pricing decision built on yesterday's competitor prices moves your margin in the wrong direction. Reliable detection latency matters more than raw scale.
Building it in-house costs more than the data
Engineers, proxies, anti-bot tooling, monitoring, on-call rotation. Most companies that try to build internal price-monitoring spend 6 to 12 months and end up with a fragile system that misses the events that matter most.
Why us
Why Clymin for competitive pricing
We are not a tool. We are the team you call when the data matters too much to get wrong.
We solve what others can't
Pricing intelligence at this scale is what built our reputation. App-layer extraction, anti-bot bypass at scale, geo-distributed capture across 200+ markets. Where other providers ship partial feeds, we deliver the complete competitive picture.
We prove it before you pay
Free pilot on your specific competitors, categories, and markets. Sample data within 1 to 3 days. You evaluate against your own benchmarks before any commitment.
You pay only for data delivered
Per record, no setup fees, no per-platform charges, no per-market charges. One metric: cost per record. If we don't deliver, you don't pay.
Your identity stays protected
We do not display client logos or name-drop. Pricing intelligence is sensitive. Your competitors should never know you are watching.
100B+
Data points extracted
24/7
Pipeline uptime
Real-time
Data delivery
100K+
Points of interest covered
Proven at enterprise scale. We operate continuous competitive intelligence infrastructure for one of the world's largest quick commerce platforms.
Industries served
Who buys competitive pricing data
The verticals where competitive pricing extraction creates the most leverage.
Stop reacting to last week's prices
Tell us your competitors, your categories, your markets. Pilot data in 1 to 3 days. No commitment.
FAQ
Competitive Pricing Intelligence data extraction FAQ
Every major e-commerce, quick commerce, travel, marketplace, and brand site we are scoped to. Web, app, and API. We add new platforms as part of the pilot at no additional cost.
Cycles as low as every 15 minutes. For flash sales and mega events, higher cadence on the SKUs you flag.
Yes. SKU matching is part of the pipeline. We map by attributes, brand, model, pack size, and image hash where needed. Match accuracy is verified during pilot.
Yes. We capture list, sale, member-tier, and voucher-applied prices as separate fields. Decisions made on list price alone are decisions made on partial data.
We run extraction across 200+ cities globally. The same SKU at different prices in different markets is captured as separate records, geo-tagged.
Higher-cadence cycles on flagged SKUs during 9.9, 10.10, 11.11, 12.12, regional festivals, and platform mega events. Capture rate scales with volume.
CSV, JSON, REST API, or direct push to your data warehouse: BigQuery, Snowflake, Redshift, S3. You define the schema.
Schema validation, range validation, deduplication, decoy detection, currency normalization. Every record passes through a 4-layer validation pipeline before delivery.
We extract publicly available data. We do not extract authenticated user-level data without explicit account ownership. Use of extracted data is the customer's responsibility under their jurisdiction.