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A competitive UX teardown of the ASICS mobile shopping experience against Nike, Adidas, and Skechers across five Southeast Asian markets, reconstructed entirely from secondary research when firsthand app access wasn't possible.

Project: Competitive UX analysis of ASICS vs. Nike, Adidas & Skechers

Markets: Thailand · Malaysia · Singapore · Philippines · Vietnam

Focus: Onboarding · Navigation · Visual design · Usability · SEA localisation

Goal: Locate where the ASICS app wins, loses, or fails to show up against the brands setting the regional standard

MY ROLE

Sole Researcher & Analyst

TYPE

Competitive UX Analysis

METHOD

Secondary research + AI-assisted synthesis

OUTPUT

15-point scorecard + opportunity map

Four brands scored across 15 UX dimensions. ASICS lands mid-pack — a competent, product-first app that under-invests in the things that matter most to first-time and regional users.

Brand

Score

Position

Nike

8.5 / 10

SEA UX leader

Adidas

7.6 / 10

Strong second

ASICS

6.7 / 10

Runner-focused, mid-pack

Skechers

5.6 / 10

Largest gap to close

SEA is mobile-first and diverse. Many users meet a brand for the first time on a 6"+ Android phone, on a slower network, in a language other than English.

A strong product ≠ a strong app. ASICS is trusted by serious runners, but reputation doesn't automatically translate into a strong digital experience.

The question I set out to answer: Where does the ASICS app experience win, lose, or simply fail to show up — relative to the brands setting the standard in the same markets?

I'm based in India and couldn't access the regional apps firsthand. The very onboarding flows I most wanted to evaluate were the ones that locked me out.

Region-specific accounts were required, tied to a local market.

2FA assumed a local mobile number, and phone verification blocked account creation from outside the region.

This created an honest tension: how do you produce a credible, specific competitive UX analysis when you can't personally use the products?

Reconstruct from primary-adjacent evidence. Reviews, walkthroughs, and annotated screenshots are accounts of the real experience, generated by people who did have local access.

Triangulate before trusting. No claim entered the scorecard until corroborated across independent sources or directly visible in evidence.

Use AI as a synthesis accelerator, not an oracle. I used Claude to cluster and structure a large, messy evidence base — then validated its output against the sources.

Be explicit about confidence. Thin or single-market findings were treated as directional, not definitive.

Every finding is built on secondary research and AI-assisted synthesis that I validated across multiple independent sources. Stated plainly, because the credibility of the work depends on it.

Sources: public app-store listings & reviews across all five markets, screenshots and recorded walkthroughs, UX teardowns and design commentary, and public info on regional payments, language support, and localisation.

Validation: each observation cross-checked against at least one independent source; conflicting signals flagged rather than averaged away; AI-clustered structure manually reviewed against the evidence.

Scoring: comparative — each brand rated relative to the others on the same dimension, reducing absolute-scoring bias.

Stated limits: secondary research can't fully replicate hands-on testing — micro-interactions and exact load times are inferred, not measured. Naming this transparently is the more rigorous choice.

Every brand scored 1–10 on each dimension, grouped into six experience categories.

Dimension

Category

Nike

Adidas

ASICS

Skechers

1

Onboarding flow length

Onboarding

9

7

6

4

2

Guest browse availability

Onboarding

10

6

3

2

3

Social / SSO login

Onboarding

9

8

5

4

First-screen clarity

Onboarding

9

8

7

3

5

Bottom navigation usability

Navigation

10

7

6

4

6

Search & filter UX

Navigation

9

10

7

5

7

Navigation depth (taps to product)

Navigation

9

8

5

6

8

Back navigation & gestures

Navigation

9

8

7

5

9

Visual hierarchy & typography

Visual

10

9

7

5

10

Colour contrast & accessibility

Visual

9

8

4

11

Dark mode support

Visual

10

6

3

2

12

Image quality & load performance

Visual

9

7

8

6

13

Language & SEA localisation

Localisation

9

7

4

14

Checkout & local payment UX

Checkout

7

5

4

15

Error & empty states

Usability

9

7

6

4

Where ASICS stands, broken down. It rarely fails outright — but it rarely delights, and in a few areas it simply doesn't show up.

Where ASICS wins — the foundation is sound

Gap 1 · The onboarding wall — the single biggest liability

Gap 2 · No dark mode — a usability gap with a running-specific edge

Gap 3 · English-only localisation — caps the addressable market

Gap 4 · Checkout misses the region's real payments

Secondary findings — navigation, visual & error states

What the competitors teach

As a self-directed analysis, the impact is clarity and prioritisation for decision-making — not a shipped feature or a measured conversion lift.

A defensible, evidence-backed scorecard that locates ASICS precisely against its competitors rather than on impression.

A prioritised opportunity map, sequenced by impact-to-effort:

Introduce guest browsing — removes the highest-friction barrier.

Add local e-wallets & shorten checkout — direct conversion recovery in PH/VN.

Localise language per market — lifts the strategic ceiling on reach.

Ship dark mode — closes a usability gap with brand-thematic resonance.

A reusable framework — the 15-point scorecard can be re-run as competitors evolve.

A proof point: a credible competitive analysis can be produced under genuine access constraints, given a rigorous and transparent method.

The most valuable lesson came directly from the constraint. Being unable to use the apps pushed me toward a more disciplined process — because I couldn't rely on my own first impression, I had to triangulate every claim, which made the findings more defensible than a single-perspective review. Constraints are a design brief, not a dead end. Re-architecting the research around reachable evidence was a more interesting problem than the original task. Transparency is a credibility multiplier. Stating plainly that this is secondary research — and naming its limits — makes the work more trustworthy, not less. AI is a synthesis tool, not a source of truth. It accelerated structuring a messy evidence base, but its value depended entirely on my validating its output. The judgement stayed human. The natural next step would be to pair these findings with moderated usability testing involving real users in each market, using this analysis to focus the test plan on the highest-stakes flows.