toolathon123/ecommerce-ux-monthly-sa-eg

Ecommerce UX Monthly — South Africa & Egypt (SA-EG)

Monthly tracking dataset of e-commerce user-experience (UX) feedback collected from a small panel of online retail shoppers in South Africa and Egypt. This repository is maintained as an academic research resource for studying e-commerce retail UX across African markets, with one dataset file per calendar month under data/YYYY-MM/.

Data Collection

• Region: Africa — South Africa & Egypt
• Cities: Johannesburg, Cape Town (South Africa); Cairo, Alexandria (Egypt)
• Collection frequency: Monthly
• Sample size per month: < 100 records (this month: 92 records)
• Collection period: 1–12 August 2026
• Data type: Small-sample, de-identified user feedback records
• Language: English

Current Month File

Month	Path	Records
2026-08	data/2026-08/ecommerce_ux_sa_eg_2026-08.csv	92
2026-08	data/2026-08/ecommerce_ux_sa_eg_2026-08.json	92

Historical months are kept in place; each new month adds a new file under its own data/YYYY-MM/ sub-directory, producing a clear monthly version history.

Field Dictionary

Field	Type	Description
record_id	string	Unique, non-identifying record identifier (e.g., SAE-2026-08-0001)
session_date	date	Calendar date of the browsing session (YYYY-MM-DD)
country	string	Country of the session: South Africa or Egypt
city	string	City of the session: Johannesburg, Cape Town, Cairo, or Alexandria
user_id_anon	string	Pseudonymized (salted-SHA256) user key; not linkable to any real identity
device_type	string	Device used: mobile, tablet, or desktop
page_dwell_time_sec	integer	Time spent on the page (seconds)
click_zone	string	Click heat-map zone of the main interaction (e.g., search_bar, add_to_cart, checkout_button)
page_load_time_ms	integer	Page load time (milliseconds)
checkout_abandonment_reason	string	Checkout abandonment reason, or none if the session did not abandon checkout
satisfaction_score	integer	User satisfaction rating, 1 (very dissatisfied) – 5 (very satisfied)

Anonymization & Privacy

This dataset is shared under academic research data-sharing norms:

• No direct identifiers: No names, emails, phone numbers, or exact addresses are collected or stored.
• Pseudonymized users: user_id_anon is a salted SHA-256 hash of an internal surrogate key; it cannot be reversed to identify a person and is not shared across datasets.
• Coarse geolocation: Only country and city are retained; no street-level or precise location data.
• Aggregated behavioral metrics: Dwell times, load times, and click zones are session-level aggregates, not raw tracking streams.
• No free-text comments are captured in the current schema, further reducing privacy risk.

Business Objective

This dataset supports e-commerce retail UX optimization in African markets, enabling:

1. Checkout abandonment analysis — identify the most frequent abandonment reasons (e.g., shipping cost, long checkout flow, untrusted payment page) and prioritize fixes.
2. Performance tuning — correlate page_load_time_ms with satisfaction and abandonment to set load-time budgets per device and market.
3. Heat-map & navigation insight — understand which click zones drive conversions vs. drop-off.
4. Device & market segmentation — compare mobile-first behaviour across South Africa and Egypt to inform localised storefront design.
5. Longitudinal tracking — monitor UX metric trends month-over-month to measure the impact of design and infrastructure changes.

Usage

import pandas as pd

df = pd.read_csv(
    "https://huggingface.co/datasets/toolathon123/ecommerce-ux-monthly-sa-eg/resolve/main/data/2026-08/ecommerce_ux_sa_eg_2026-08.csv"
)
print(df.info())

Versioning

Each calendar month adds a new file under data/YYYY-MM/. Existing monthly files are never overwritten, preserving a clean, append-only monthly version history for longitudinal research.

License

This dataset is released under the CC-BY-4.0 license for academic research and industry collaboration, with attribution required.