The metric you're using to track market share may be pointing you to the wrong competitor, the wrong pack size, and the wrong platform.
The Measurement Problem Nobody Talks About
Indonesia's e-commerce market is generating more data than ever. With the market forecast to reach USD 94.5 billion in 2025 and growing at a CAGR of 15.5%, brands and their commercial teams are swimming in dashboards — GMV by platform, units sold by SKU, value share by category. More data should mean better decisions.
But in FMCG categories where the same product sells in pack sizes ranging from 120 grams to 3,000 grams, treating every unit as equal is not a data problem. It is a measurement problem. And measurement problems compound quietly — until a brand finds itself losing household penetration to a competitor it thought it was beating.
The Number That Looks Fine Until You Look Closer
Consider a hypothetical illustration from the formula milk category on Indonesia's major e-commerce platforms in H2 2025. A brand tracking value share might show the following: Brand A at 30% value share, Brand B at 22%, Brand C at 16%, Brand D at 13%.
On the surface, Brand A looks dominant. But if you convert those same sales to consumption share — adjusting for what volume of product consumers actually received — the picture changes. Brand A's consumption share might be closer to 40% while Brand B, which sells predominantly in larger pack sizes, could hold a significantly different consumption position. A competitor that appears to be "behind" in units sold may actually be winning in grams consumed, household penetration, and repurchase rate.
Why the Unit-Based Metric Fails Here
When a parent buys a 1,200g pack of formula, the platform records one unit sold. When another parent buys a 150g pack, the platform also records one unit sold. The GMV difference is captured, but the consumption difference — the real measure of brand presence in the household — is obscured.
In categories where pack size is a deliberate strategy (brands use smaller packs to lower the trial barrier, larger packs to lock in loyal buyers), unit sales and GMV both systematically misrepresent competitive position. A brand driving high unit volume through small pack promotions may be acquiring trialists while a competitor converting the same buyer to a 2,400g replenishment cycle holds the actual share of stomach.
The Two Errors That Cost the Most
Error 1: Misidentifying your primary competitor. Because pack size distribution differs by brand, value share rankings and volume-weighted consumption rankings can diverge substantially. Commercial teams respond to the competitor that looks biggest in their dashboard — which may not be the competitor that is most deeply embedded in their target household.
Error 2: Misallocating promotional investment. If peak-season promotions are pulling consumers toward smaller trial packs while a competitor locks the same consumer into a large-pack subscription, the brand with the higher reported units sold may be losing the long-term household battle. TikTok Shop's growing role — up from 40% to 46% platform usage year-on-year — is compounding this: the live-stream environment drives impulse unit purchases that may not reflect long-term consumption patterns.
Volume Consumption: A More Honest Starting Point
Magpie approaches FMCG category analysis by tracking consumption volume alongside GMV and unit sales. For formula milk, this means normalising all SKU sales data to grams-per-transaction, then building a consumption share view that sits alongside the standard value and volume metrics.
The insight this produces is consistently different from the unit or GMV view. It shows which brands are winning at the household replenishment level, not just at the trial acquisition level. It shows where pack strategy is being used to inflate apparent share. And it shows which platform and campaign mechanics are producing loyal buyers versus promotional responders.
The Bottom Line
GMV and units sold are not wrong metrics. They are incomplete ones. In FMCG categories where pack size variance is high, they produce systematically misleading pictures of competitive position. The brands that are making the most accurate strategic decisions in Indonesia's e-commerce market are those that have added a consumption-weighted layer to their analytics — and are making investment decisions at that level of specificity.
With 95% of Indonesian respondents now making online purchases (up 4% year-on-year), the data available is sufficient to build this picture. The question is whether your measurement framework is built to surface it.