Most ecommerce teams in Southeast Asia are running on two kinds of data: a dashboard someone else built, and a spreadsheet someone on the team maintains by hand. Both go stale, and neither answers the specific question you have on a Tuesday afternoon.
The alternative is not a bigger dashboard. It is building the small, exact tool you actually need — which is now a day's work rather than a quarter's project, because the hard part (collecting, cleaning and brand-labelling marketplace data) is already done.
Here are nine tools worth building, what each one answers, and roughly what it costs to run. Every figure is measured, not estimated.
Tools that read history
These use the Data API — a maintained monthly panel across Shopee, Tokopedia, TikTok Shop, Lazada and Blibli. Indonesia goes back to November 2020 with 121 categories; the other five Southeast Asian markets start November 2022. Every export is priced before you commit to it, and the estimate is free.
1. A category share tracker. Rank every brand in a category by GMV, month by month, and watch the shares move. Not a snapshot — a trend line built from full-category coverage rather than a sample. This is the tool most brands think they already have and mostly do not, because their existing view covers only the SKUs they sell.
2. A price-band map. Compute realised price per unit for every brand as GMV divided by units, then bucket them into deciles against the category average. You are looking for a band that holds real demand but few competitors. It is the single most useful analysis before a launch, and it takes one export.
3. A category-entry scorecard. Combined share of the top ten brands, how many brands it takes to reach half of category GMV, and the price index of the leaders against the average. Those three numbers tell you whether a category is concentrated and premium, concentrated and cheap, or genuinely fragmented — and each implies a completely different cost of entry.
4. A variant demand profiler. Which size, shade or pack of a product actually sells. Demand that concentrates in two variants out of nine is a range that needs pruning; demand spread evenly is a range that is working. Most assortment decisions are made without this.
Tools that read the present
These use the Scraping API, which fetches live marketplace pages on request. One of them runs unattended; the rest are approval-gated and process 08:00–18:00 GMT+8, which matters enormously if you are designing a scheduled job.
5. A competitor price monitor. The one that runs unattended. Eight credits per product per check — about a cent — so watching 400 SKUs nightly costs roughly USD 120 a month. We wrote the full walkthrough separately.
6. A channel-control audit. Search your own brand name and its common misspellings, aggregate the results by shop, and compare against your authorised distributor list. What comes back is every shop selling your product that should not be, with its observed GMV and location. Report what the listing data shows; do not accuse anyone of counterfeiting on the strength of a price.
7. A competitor catalogue map. Enumerate a rival's entire shop — listing count, total observed monthly GMV, the twenty listings carrying most of it, and their price distribution. One caution that matters: a fully enumerated shop cannot be compared against a market total built from keyword search, because the shop is complete and the market is not. Any share computed that way is a ceiling, not an estimate.
8. A new-launch watch. Same search as the channel audit, sorted by newest instead of by sales, run weekly with last week's item list kept. What is new since the last run, grouped by shop. A new listing that takes meaningful volume immediately is almost always a launch with paid support behind it — which is a useful thing to know in week one rather than week six.
9. A review miner. Pull reviews for a product, group by star rating, and summarise the recurring complaints in the one and two-star reviews. Product teams consistently underrate this. It is the cheapest qualitative research available and it is sitting in public.
What it actually costs
The honest answer is that it depends entirely on scope, which is why every Data API export accepts a free dry-run estimate that returns the exact row count and dollar price before anything is charged.
A real example, measured: six months of one Indonesian category across Shopee came to 374,169 rows and USD 7,015 in full. The same scope restricted to the top 100 brands per month came to 600 rows and USD 37.50. That is a factor of 187 for a difference in question, not a difference in quality — which is exactly why you estimate first.
Most of the tools above want the second shape, not the first.
The part that has changed
None of these tools is conceptually new. What changed is that building one no longer requires a data engineer, a scraping stack and a maintenance burden. The collection, cleaning, brand-labelling and deduplication are done; you are writing the analysis, which is the part you actually have an opinion about.
And if you do not write code at all, the ten recipes behind these tools are published as a plain-text skill file. Hand it to an AI assistant with your API key and describe what you want. It reads the documentation, works out whether your question is a panel question or a live one, prices it, and comes back with the numbers.