Price changes
Who cut price first?
Track sequence, frequency, and depth of competitor price movement by platform and category.
Use case
Monitor competitor price changes, discount depth, and seller-led price pressure across Southeast Asia marketplaces before those moves distort your category plan.
What teams want to know
Price changes
Track sequence, frequency, and depth of competitor price movement by platform and category.
Discount pressure
See where persistent discounting is resetting buyer expectations and squeezing margin.
Competitive context
Link price behavior back to market share and assortment context instead of treating it as a separate dashboard.
Common pricing-monitoring questions
Related topics
Competitor price tracking works best when paired with broader pricing and platform context.
Coverage
Most brand teams start by watching their three or four direct category competitors. That is not enough. Magpie IQ tracks the full pricing environment: every brand with material category GMV, every SKU under each brand, and every seller type — Official Stores and C2C resellers measured separately. Coverage spans all five major Indonesia and SEA platforms: Shopee, Lazada, TikTok Shop, Tokopedia, and Blibli. Data is refreshed monthly, with weekly snapshots available during high-volatility periods (major campaign weeks, mega-sale buildups).
Each tracked SKU carries two price signals. The first is listed price — the headline number a buyer sees before any discount is applied. The second is average selling price, or ASP, calculated as GMV divided by units sold (Terjual). ASP is the effective post-discount price — the number that determines real competitive position. A competitor that posts a listed price of IDR 150,000 but regularly runs flash sales at 40% discount has an ASP far closer to IDR 90,000. Tracking only listed price gives a misleading picture. Magpie IQ surfaces both signals so brand teams can compare effective pricing, not headline positioning.
Across Magpie IQ's dataset, this means tracking more than 10 million SKUs and 78 billion data points accumulated since 2020. For Indonesia FMCG alone — the largest SEA ecommerce market at approximately $71B GMV — the pricing archive covers six years of category behavior, campaign cycles, and competitive moves.
Reseller risk
One of the most common failures in pricing intelligence is this: a brand tracks competitor Official Store prices and believes it has visibility. It does not. The real competitive threat is often C2C resellers — merchants buying at wholesale and selling below Official Store pricing — and these sellers are invisible to teams that only monitor OS-to-OS comparisons.
In Indonesian FMCG, C2C resellers regularly undercut Official Store pricing by 15–30% during mega-campaigns — 11.11, 12.12, and Harbolnas buildups. The mechanism is straightforward: resellers source at distributor prices, hold no brand equity obligation, and compete purely on price. During a major campaign window, a reseller listing at 25% below your Official Store price will absorb conversion that should have gone to your brand-controlled channel.
The consequence is not just lost revenue in that campaign — it is category floor erosion. When buyers consistently find a SKU at a lower price through C2C channels, their reference price for that product drops. Future Official Store promotions have to go deeper to achieve the same conversion lift. Magpie IQ tracks Official Store and C2C pricing separately for every monitored SKU, showing exactly when and where resellers are creating price gaps, and whether those gaps are growing or closing over time.
Pricing signals
Signal 1
The competitor's effective selling price after all discounts — calculated from GMV divided by units sold. This is the only price signal that reflects what buyers are actually paying. Listed price without ASP is promotional theater, not competitive intelligence.
Signal 2
The gap between listed price and ASP, expressed as a percentage. A competitor running persistent 30%+ discount depth is not managing a promotional calendar — it is structurally competing at a lower price point. Discount depth separates genuine promotional intensity from tactical flash activity.
Signal 3
Where a competitor sits in the category price ladder relative to all tracked brands. A brand moving from fourth to second cheapest in a category is a commercial threat regardless of whether its absolute price changed. Rank movement surfaces repositioning that absolute price data alone misses.
Signal 4
Month-on-month ASP change for a tracked competitor. A single month of low ASP could be a campaign. Three consecutive months of declining ASP is strategic repricing. The trend signal distinguishes temporary promotional noise from a structural shift in how a competitor is positioning in the category.
Signal 5
Whether a competitor maintains consistent pricing across Shopee, Lazada, TikTok Shop, Tokopedia, and Blibli — or segments aggressively. A brand running deep ASP discounts only on TikTok Shop is testing a price point for trial acquisition without committing to it category-wide. That is a different threat than uniform price reduction, and it requires a different commercial response.
Case study
In the lead-up to a major year-end campaign, a challenger brand in a high-value FMCG category reduced its pricing by 18% across Shopee sellers. That move contributed to a -2.1 percentage point market share loss for the category leader that month. The brands tracking competitive pricing in real time through Magpie IQ's data could see the move building — listed price changes appearing in October, ASP compression visible by early November, conversion signals shifting before the campaign window opened.
The brands relying on monthly distributor reports or quarterly agency research saw it only in the Q4 sales review — after the share had moved, after the category floor had shifted, and after the window to respond had closed.
This is the structural problem with lagged pricing data in high-velocity campaign environments. Shopee's 12.12 and 11.11 campaigns concentrate three to five months of category volume into a two-week window. A competitor making a significant pricing move in the buildup to that window is not adjusting price — it is setting the terms of the category's most commercially important period. Magpie IQ's monthly tracking, with weekly snapshots in high-volatility periods, surfaces these events in time for a commercial team to act. Not in the post-mortem.
Use cases
Use case 1
Monitor the 25th percentile ASP across all tracked competitors in your category. That number is the effective price floor — the minimum price at which the category sustains a premium positioning. If your ASP is consistently above the 75th percentile and your share is declining, you have a price positioning problem, not a product problem. Magpie IQ's category-wide ASP distribution surfaces this without requiring manual competitor audits across five platforms.
Use case 2
When a competitor launches a discount of more than 15% on an ASP basis, Magpie IQ's tracking makes that visible before the campaign window. Commercial teams typically have a two-to-four week lead time in which they can match, counter, or deliberately hold position. Acting on a competitor's promotional move after the campaign has run is almost always too late — the conversion has already shifted. Detecting at minus-three weeks changes the commercial decision entirely.
Use case 3
Identify which of your own resellers are undercutting your Official Store price and by how much. A distributor conversation about price discipline requires specific evidence — which seller, which platform, what discount depth, what volume. Without SKU-level C2C tracking, those conversations rely on anecdote and are easy to dismiss. With Magpie IQ data showing a specific reseller consistently 22% below Official Store ASP on Shopee, the conversation becomes a commercial negotiation with facts, not a complaint without evidence.
FAQ
Yes. That is often where marketplace price erosion becomes visible first, which is why seller context matters so much.
It is far more useful with market share context, because teams need to know not just that a competitor moved, but whether it changed category outcomes.