Published in the IWSiB 2026 proceedings. The PDF hosted here is the earlier preprint; use the publication record for the published version.
Original snapshot study.
A study of scale, concentration and entry timing.
The paper describes the Shopify app ecosystem and tests how market structure and entry timing relate to adoption. It analyses a September 2025 snapshot; its findings are evidence about that sample and period.
Evaluate the category before the timing of entry.
The study challenges a universal first-mover advantage: later entrants had stronger recent growth in 44 of 50 categories. Use the category-level analysis to frame further investigation. It does not forecast the success of a particular app.
Use ecosystem evidence to question assumptions.
Market leadership and app age are not complete measures of suitability for a store. The study gives context for periodically reviewing an app portfolio; it does not compare individual product quality or recommend particular vendors.
One snapshot, two analytical samples.
The concentration analysis covers 4,213 active apps across 55 categories. Growth analysis covers 3,456 apps aged at least 180 days across 50 categories, measured over July–September 2025. Storefront detection undercounts backend integrations; longitudinal stability remains untested.
Editorial context · The source paper remains the reference.
Based on the supplied preprint · Data collected 28 September 2025
24,826
apps catalogued across the Shopify App Store
$706B
in estimated annual merchant sales represented
88%
of 50 categories favouring later entrants
47.7%
of detected product-reviews installs held by Judge.me
This study examines the scale and structure of the Shopify app marketplace using a Store Leads snapshot collected on 28 September 2025. It asks how adoption is distributed across apps and categories, and how the timing of market entry is associated with cumulative adoption and recent growth.
More than half of the 55 categories analysed had low concentration.
In 44 of 50 growth-analysis categories, later entrants had stronger recent growth on both measures.
Market structure varies substantially by category; the results describe a snapshot and do not predict individual app performance.
Dataset and observed adoption
The dataset covers 24,826 applications and more than 2.7 million Shopify stores, representing an estimated $706 billion in annual merchant sales. Of roughly 16,700 apps listed as live, 4,213 had at least one detected install. Absence of a detected install does not establish absence of customers: storefront-based measurement misses some backend and private integrations.
The median store had three detected apps, with over half recording between one and five. App creation increased more than twentyfold between 2015 and its 2024 peak in this dataset. These measures describe the supply of apps and observable adoption, rather than the revenue or commercial viability of individual vendors.
Concentration differs across categories
Across 55 categories, the mean Herfindahl–Hirschman Index (HHI) was 0.190, with a median of 0.144. More than half were classified as low-concentration under the thresholds used in the paper. Categories with more active apps were associated with lower concentration. This association alone does not show that additional entrants cause markets to become more competitive.
Product reviews: a category example
The product-reviews category had an HHI of 0.253, just above the paper’s high-concentration threshold. Judge.me accounted for 47.7% of detected installs within that category in the September 2025 snapshot. Install share measures observed adoption; it does not establish relative product quality or explain the causes of market leadership.
Entry timing and recent growth
Cumulative adoption and recent growth show different relationships with entry timing. Older apps have had longer to accumulate installs, while later entrants showed stronger recent growth in 44 of the 50 categories analysed for growth. The growth sample includes 3,456 apps aged at least 180 days.
Although the sample includes apps created over more than a decade, growth is measured in a single 90-day window. This does not establish that the pattern persisted across that decade. Longitudinal data would be needed to evaluate its stability over time.
Interpretation and implications
For app founders, the findings support examining category-specific conditions rather than assuming that early entry confers a universal advantage. Category concentration and recent growth can frame further investigation, but neither measure forecasts the success of a new app.
For merchants, the study provides context for the ecosystem their stores use. It does not compare individual apps’ suitability, reliability or service quality. Those questions require evidence beyond install share and entry date.
How we measured it
The analysis uses a 28 September 2025 snapshot from Store Leads’ weekly crawl of Shopify storefronts and the app store, filtered to active apps with at least one detected install. Concentration was computed with the Herfindahl–Hirschman Index and top-five market share; entry-timing effects used Spearman correlations between an app’s creation-date rank and its recent growth, with false-discovery-rate control across categories. The concentration analysis covers 55 categories; the growth analysis covers 3,456 apps aged at least 180 days across 50 categories. Install figures are detected from storefront scripts, so backend and private integrations are under-counted — a limitation we treat explicitly in the paper.
Cite this paper
Fabrizio Assabese and Giuseppe Destefanis. (2026). Scale, Concentration, and Entry Timing in the Shopify App Ecosystem. Proceedings paper · IWSiB 2026, pp. 17–24. https://doi.org/10.1145/3786158.3788552
FA
Judge.me
Fabrizio Assabese is Chief Growth Officer at Judge.me. His work connects data, growth strategy and commercial decision-making, drawing on experience across fintech and SaaS. He holds a bachelor’s degree in Computer Science and an Executive MBA from SDA Bocconi, and co-authors research on the Shopify app ecosystem.
GD
University College London
Giuseppe Destefanis is an Associate Professor in UCL’s Department of Computer Science. His research spans empirical software engineering, mining software repositories and large language models. He uses data mining, machine learning and natural language processing to study how people, technical systems and economic incentives interact across digital platforms.