Price movement
Who changed price first?
Track list-price, selling-price, and discount-depth changes by competitor and seller type.
Use case
Track competitor prices, discount depth, pricing architecture, and seller-led price pressure across Shopee, Lazada, TikTok Shop, Tokopedia, and Blibli.
What you can measure
Price movement
Track list-price, selling-price, and discount-depth changes by competitor and seller type.
Margin pressure
Identify when repeated discounting is resetting category price expectations on key platforms.
Commercial impact
Connect pricing events to market share movement instead of treating price monitoring as a separate workflow.
Common pricing questions
Related topics
Price moves mean more when they are read against share shifts and marketplace structure. That broader context is where Magpie IQ is strongest.
What is tracked
Pricing intelligence is not average category prices. It is not analyst estimates of market pricing. It is actual listed prices and average selling prices — the effective post-discount price at which transactions occur — for every tracked SKU, refreshed monthly across all five platforms.
The critical distinction is between listed price and average selling price (ASP). Listed price is what appears on the product page — the headline price a consumer sees before any promotion is applied. ASP is calculated as GMV divided by units sold: the real price at which units cleared. On Indonesian ecommerce platforms, these two numbers routinely diverge by 15–25% in promotional categories, because voucher mechanics, bundle pricing, and seller-funded discounts operate independently of the listed price.
The difference between listed price and ASP — what Magpie IQ calls discount depth — is one of the most strategically important signals in ecommerce pricing. It reveals the actual competitive pricing environment, which is often invisible to teams monitoring only listed prices.
Consider two competitors in the same category. Competitor A has cut listed price by 10% — visible to anyone checking the product page. Competitor B has maintained listed price but is running permanent vouchers that reduce the effective selling price by 20%. Competitor B's strategy is invisible to anyone who is not tracking ASP. Yet Competitor B is the more aggressive price actor: their effective price floor is lower, their margin sacrifice is greater, and they are putting more pressure on category pricing than Competitor A's visible price cut.
Magpie IQ's pricing intelligence captures both signals and makes the invisible strategy visible.
The key distinction
Signal framework
A complete picture of competitor pricing requires more than the current price. Magpie IQ tracks five distinct price signals for every monitored SKU across all five platforms.
Signal 1
The headline price displayed on the product page before any promotions are applied. This is the price that anchors consumer price expectations — the reference point against which all discounts are measured. Monitoring listed price is necessary but not sufficient. A brand that only watches listed prices is watching the shadow, not the substance, of competitor pricing activity.
Signal 2
The effective price after all discounts, vouchers, and bundle mechanics are applied — calculated as GMV divided by units sold. This is the price that consumers actually paid and the price that determines category price floor expectations over time. In categories with heavy promotional activity, ASP can run 20–30% below listed price for extended periods, reflecting a de facto repricing that is invisible to anyone monitoring only the product page.
Signal 3
The percentage gap between listed price and ASP: (listed − ASP) ÷ listed. A SKU with a 5% discount depth is operating close to its listed price — promotional activity is light. A SKU with a 28% discount depth is running permanent or near-permanent promotions that are structurally repricing the product. Discount depth tracked over time reveals whether a competitor's pricing strategy is tactical (promotional cycles) or structural (permanent below-listed-price positioning).
Signal 4
Where your brand's ASP sits relative to all tracked competitors in the category, sorted from lowest to highest effective price. Price rank tells you whether your brand is positioned at the premium end, the mid-tier, or the value end of the category — and whether that positioning is stable or drifting. A brand that was price rank 3 of 8 twelve months ago and is now price rank 5 of 8 has lost pricing authority without necessarily changing its listed price. The category around it repriced downward and the brand did not move with it — or chose not to, which is a different strategic decision.
Signal 5
Month-on-month ASP change for each tracked SKU, with a distinction between strategic repricing (a deliberate, sustained shift in the price level) and promotional cycling (a temporary drop followed by a return to baseline). These two patterns look identical in a single month's data but very different over six months. Price trend analysis over 3–6 months is what separates a competitor running an aggressive mega-campaign promotion from one that has made a strategic decision to permanently lower price and defend volume at the expense of margin.
The hidden signal
A competitor cutting listed price is visible. The price change appears on the product page, gets flagged in competitive monitoring alerts, and shows up in the next sales team briefing. Teams respond to it.
A competitor running permanent vouchers that reduce the effective selling price by 20% while maintaining listed price is invisible to anyone not tracking ASP. The product page shows the same headline price. The promotional banner says "20% off with voucher" — but this appears to be a temporary promotion, indistinguishable from a standard platform campaign. What is not visible is that this voucher has been continuously active for eight months and the brand has no intention of removing it. It is not a promotion. It is a price cut wearing a promotion's clothes.
This dynamic is particularly acute during Shopee and Lazada mega-campaigns. During 11.11 and 12.12, voucher stacking — platform vouchers, brand vouchers, and seller vouchers applied simultaneously — can push the effective price 30–40% below listed price for a 48-hour window. That is a legitimate promotion. The problem occurs when the mega-campaign discount mechanics get extended into the following weeks or months, gradually becoming the permanent pricing reality rather than an event-specific activation.
In practice, the most aggressive price competitors in Indonesian FMCG categories are frequently not the brands with the lowest listed prices. They are the brands with the deepest voucher activity — running discount depths of 22–30% month after month while their listed prices remain at or above the category average. This creates a structural illusion: the brand appears premium (high listed price) while operating as a value competitor (low ASP).
Magpie IQ's ASP tracking (GMV ÷ units sold) surfaces this strategy directly. The discount depth metric is calculated, tracked historically, and presented alongside brand share and units sold — so the connection between aggressive voucher strategy and share gain is explicit rather than inferred.
Mega-campaign dynamics
Cross-platform pricing
The same SKU can carry meaningfully different prices across Shopee, Lazada, TikTok Shop, Tokopedia, and Blibli. This is not a data error — it reflects the reality of how ecommerce pricing works in Southeast Asia. Brands set prices on their Official Store or LazMall storefront, but they do not control every reseller on every platform. The result is a price landscape that is fragmented across channels, platforms, and seller types.
Three price disparity scenarios are commercially significant:
Scenario 1
If a reseller is selling your product on Lazada at 25% below your Shopee Official Store price, that undercuts your brand positioning on your primary platform. Consumers comparing prices across platforms will see the Lazada reseller price as the reference point — not your Shopee Official Store price. The Official Store price loses its anchoring function, and you are effectively training the market to price-shop across platforms for your own product.
Scenario 2
Some brands operate Official Stores on multiple platforms with different price architectures — either by design (deliberate platform-specific pricing) or by accident (pricing set independently by different regional or channel teams). Deliberate price differentiation by platform can be a valid strategy, but it needs to be intentional. Magpie IQ surfaces the price gap across all five platforms so that brand teams can distinguish between strategic price differentiation and unmanaged pricing drift.
Scenario 3
Competitors do not necessarily price consistently across platforms either. A competitor may be price-leading on TikTok Shop (driving awareness and first-purchase conversion via live commerce discounts) while maintaining a higher listed price on Shopee and Lazada (protecting margin on repeat purchase channels). Tracking this strategy requires simultaneous visibility into competitor ASPs across all five platforms — which is exactly what Magpie IQ provides.
Magpie IQ tracks the same SKU identifier across all five platforms — matching products by name, brand, variant, and pack size — so that cross-platform price comparison is based on genuine like-for-like SKU equivalence rather than approximate category-level matching.
Commercial use cases
Pricing intelligence is only useful if it drives commercial action. Below are three use cases that represent the most common applications across Magpie IQ's 30+ FMCG brand clients operating in Indonesia and Southeast Asia.
Use case 1
Premium positioning in FMCG is maintained by price floors — the minimum effective selling price below which the brand should not be transacting if it wants to sustain its premium perception. Setting that floor requires knowing where every tracked competitor's ASP sits, how stable those ASPs are, and whether the category is drifting downward in aggregate. Magpie IQ provides the competitor ASP landscape at SKU level across all five platforms, so the price floor decision is based on measured competitive reality rather than distributor feedback or sales team estimates. For a brand protecting premium positioning in a category where 4–5 competitors are running aggressive voucher programs, this is the difference between setting a price floor that holds and one that erodes within three months.
Use case 2
Mega-campaign pricing decisions — how deep to discount, which SKUs to promote, which platform to prioritise — are better when informed by what competitors did in the previous campaign cycle. Magpie IQ's historical ASP data shows the exact discount depths competitors deployed during 11.11 and 12.12 in prior years: which brands went deeper than 20%, which held price, and what the share outcome was for each strategy. More immediately useful: detecting when a competitor launches a deep discount (more than 15% ASP drop) in the weeks before a mega-campaign allows a brand to prepare a response before the campaign window opens, rather than reacting during the campaign when promotional budgets are already committed.
Use case 3
The most structurally damaging pricing pattern in ecommerce is a SKU that has been in continuous promotional pricing for 3 or more months. At that point, the promotional price is the consumer's reference price — not the listed price. Returning to the listed price will feel like a price increase, which will cause a visible drop in units sold and a corresponding visible drop in platform conversion metrics. Identifying which SKUs have entered this state — and quantifying the volume risk of reverting to full price — requires exactly the kind of historical ASP tracking that Magpie IQ provides. Brands using this capability can distinguish between SKUs where promotional dependence is recoverable (promotional activity less than 60 days) and those where a price reset requires a more gradual strategy.
FAQ
Manual checks show a moment in time. Pricing intelligence shows pattern, pressure, recurrence, and competitive context across marketplaces.
Yes. It is often most useful when it includes the seller ecosystem around the official store, because that is where hidden price pressure often starts.