---
license: cc-by-4.0
language:
  - en
tags:
  - generative-engine-optimization
  - geo
  - market-research
  - singapore
  - ecommerce
  - web-measurement
pretty_name: "Singapore Commerce Index: Who Pays To Be Found, and How the Market Segments"
size_categories:
  - 1K<n<10K
configs:
  - config_name: search_spend_companies
    data_files: data/search_spend_companies.csv
  - config_name: shopify_merchants
    data_files: data/shopify_merchants.csv
  - config_name: segments
    data_files: data/segments.csv
  - config_name: industry_to_segment
    data_files: data/industry_to_segment.csv
---

# Singapore Commerce Index

**Who in Singapore pays to be found on search, who sells on Shopify, and how 166 industry
labels collapse into 16 buying situations.**

Canlah Research · Singapore · Captured 2026-09-01 → 2026-09-03 · Segmented 2026-09-09 · CC BY 4.0

---

## TL;DR

Two registers built from live Singapore search captures and Shopify's own public store records,
graded by **what kind of proof each row actually has**, plus a segmentation that rolls all 166
industry labels into 16 buying situations with no orphans and no duplicates.

- **3,308 companies** in the search-spend register. **748 ad captures** on live SG SERPs — of which
  only **555 are Singapore-verified**; 107 are foreign entities or global portals of multinationals.
- **1,026 Shopify stores**, Singapore-registered per Shopify's own `/meta.json` record, in a market
  of roughly 17,220. **Only 3.9% were caught buying Google Ads.**
- **16 segments over 166 labels**, each label in exactly one, verified programmatically.

The headline finding is uncomfortable and is the reason this is published: **only 1,341 of 4,009
companies (33%) have an *observed* search-investment signal at all.** The rest are inferred from
organic ranking, which is a much weaker claim, and the dataset labels which is which per row rather
than blending them into one number.

## Read this before quoting any number

- **Tier A is the only payment-shaped evidence.** A captured ad impression (A), a Google Ads
  conversion tag in the page source (B), a verified agency case-study mention (C) and an organic
  ranking (D) are four different things. Column `tier` says which. **Do not sum them.**
- **All ad counts are floors.** Captures came from a datacenter IP that sees roughly two ad slots
  per query, and tags loaded via GTM are invisible to a static fetch.
- **`748` is captures, not Singapore advertisers.** Use `sg_entity` to filter. An adversarial audit
  found multinationals' global `.com` portals being counted as local companies in 16.7% of a
  sample; that was fixed across the whole set, and the correction is why these two numbers differ.
- **The Shopify universe figure (~17,220) is a third-party crawler's attribution**, not a census.
  Published estimates for this market disagree by more than 2×.
- **The segmentation is a taxonomy, not a ranking.** `rank` is the order segments were proposed in.
  Nothing here says a segment is attractive.

## Files

| File | Rows | What it is |
|---|---|---|
| `data/search_spend_companies.csv` | 3,308 | One row per company, with its evidence tier and what was observed |
| `data/search_spend_by_industry.csv` | 145 | Industry aggregates for the above |
| `data/shopify_merchants.csv` | 1,026 | SG-registered Shopify stores, classified from their own product data |
| `data/shopify_by_industry.csv` | 39 | Industry aggregates for the above |
| `data/segments.csv` | 16 | The segments, their size and observed-signal count |
| `data/industry_to_segment.csv` | 166 | Every industry label and the one segment it belongs to |

## Method, in short

- **Captured, not surveyed.** Every ad impression comes from a live Singapore Google result
  (`gl=sg`), saved raw. Every Shopify store is confirmed against Shopify's public `/meta.json`
  store record — HTML fingerprinting alone let 37 false positives through, so the store record is
  the judge, not a heuristic.
- **Industry classification** for Shopify stores comes from each store's own
  `/products.json` `product_type` and `vendor` fields; a hand audit put the error rate at ~9%.
- **Audited adversarially.** Samples went to reviewers instructed to refute. When they found a
  systematic error, it was fixed across the whole set and disclosed rather than patched in the
  sample.
- **Segmentation** was produced by four independent roll-ups from different angles (buying trigger,
  deal economics, search-demand structure, competitive state), scored by three judges including one
  auditing the arithmetic against the file, then recomputed from the source rows.

## What was deliberately removed

This dataset is a **subset** of a working register, and the omissions are decisions, not gaps:

- **All contact data.** The working register holds business emails, WhatsApp numbers, phone numbers
  and the names of real people. None of it is here, and none of it will be. Two residual
  contact strings that had survived inside free-text fields (an email inside a store's own meta
  description, a phone inside an ad headline captured as a company name) were redacted before
  publication.
- **Vendor relationships.** The working register names which SEO/SEM agency serves which company,
  inferred largely from those agencies' own published case studies. Aggregate counts appear in
  `search_spend_by_industry.csv`; **no agency is named**, because a supplier's client book is not
  ours to publish.
- **Our own commercial judgements.** Which segments we consider worth pursuing, at what price, with
  what reach — none of that is here. It says nothing about the companies and everything about us.

## Known limits

- One capture window (2026-09-01 → 09-03). A register is a snapshot; it becomes a measure the
  second time it runs.
- Tier D (organic-only, 1,742 rows) is **inferred** investment. It is the largest tier and the
  weakest evidence in the file.
- No company size, revenue or headcount data exists in this dataset.
- `notes` and `description` are free text of mixed provenance, including text the companies
  published about themselves. Treat as descriptive, not as measured fields.

## Citation

See `CITATION.cff`. Corrections are welcome and will be published in `ERRATUM.md`
rather than quietly patched: <hello@canlah.ai>