nme app

A pan‑India local business and services app redesigned for fast discovery and direct contact.

Client

Cybexel Technologies Private Limited

Service Provided

UX Research, Information Architecture, UI Design

The Goal:

The goal was to help users across India quickly find nearby businesses and services, view offers, and contact providers directly without intermediaries. The redesign prioritizes search‑first navigation, a clearer category structure, and action‑focused listings to reduce friction and speed up decisions.

Objectives

Reduce time‑to‑action for core tasks (Call, Directions).
Improve findability across 60+ categories and 400+ subcategories.
Design for mixed digital literacy and future multilingual support.

The Challenge:

The legacy 2022 app showed 60+ top‑level categories in one dense grid and 400+ subcategories with minimal guidance, causing choice overload and frequent backtracking. Listing cards hid critical details and lacked a clear CTA hierarchy, while search offered no assistance for errors or intent.

Pain points

Category overload and poor scannability.
No search assistance (misspellings, suggestions, recent).
Unclear card hierarchy; small tap targets for older users.

The Approach

Research

Stakeholder interviews and user surveys/interviews to define top tasks and failure points.
Heuristic audit of legacy flows to isolate navigation and decision bottlenecks.

Information Architecture

Consolidated 60+ categories into clearer family groups; split domain into Business and Services.
Added multiple low‑effort entry points: Nearby, Recent, Popular, and Offers.
Designed progressive disclosure and breadcrumb‑like cues to reduce cognitive load.

Key flows

Home: search‑first layout with location‑aware quick actions and chips.
Category browse: grouped families, drill‑down, sticky filters.
Search: autocomplete, spelling tolerance, recent queries, applied filter chips.
Offers: nearby offers with distance and expiry; category filters.
Listing: clear hierarchy (name, rating, distance, status) with primary Call, secondary WhatsApp, tertiary Share/Directions.

Design system and accessibility

Larger type scale and 44px+ tap targets for inclusive touch ergonomics.
High‑contrast palette and consistent spacing system for legibility.
Empty, error, and loading states for slow networks and no‑results.
Multilingual readiness (Malayalam, Tamil, Hindi, Arabic).

The Result

The nme app redesign turns a sprawling catalogue into a clear, search first experience that helps people across India quickly discover nearby businesses and live offers and take action without intermediaries. By consolidating the information architecture, guiding search with smart assistance, and clarifying the CTA hierarchy, the product supports confident decisions for users of varied digital literacy while giving merchants a direct line to customers. Built with accessibility and scalability in mind ready for multilingual rollout and personalization the new system lays a strong foundation for sustained engagement and growth. The impact of these changes is reflected in prototype testing, with meaningful gains in speed, success, and accuracy outlined below.

Prototype test, pre‑release, n=8 (moderated)

Headline metrics

−45% time‑to‑primary action (58s → 32s median)
+25 pts task success (63% → 88% across 3 core tasks)
−61% errors per task (wrong taps/backtracks: 3.1 → 1.2)

Per‑task improvements

Task 1 — Call a salon within 2 km: Success 5/8 → 7/8; time 54s → 30s (−44%); steps 6 → 4 (−33%).
Task 2 — Get directions to an electronics repair shop: Success 5/8 → 7/8; time 62s → 36s (−42%); no‑results 2/8 → 0/8.
Task 3 — Open a textiles offer near me: Success 6/8 → 8/8; time 58s → 31s (−47%); filter use 2/8 → 6/8.

Navigation and search quality

No‑results frequency: 25% → 4% (−84%).
Backtrack rate: 38% → 12% (−68%).
Category bounce: 41% → 19% (−54%).

CTA effectiveness and perception

First‑try CTA accuracy: 62% → 90% (+28 pts).
Tap precision issues: 15% → 5% (−67%).
SUS: 61 → 78 (+17); clarity rating: 2.7 → 4.2/5.
All figures are from moderated prototype testing prior to release; production analytics will be added post‑launch.