π Data Projects
Jordan 2.0 Store Study
Analysed customer behaviour and store performance to inform Jordan 2.0 flagship store investments. Used FPA (Full-Path Analysis) data to evaluate trade zones and support site-selection decisions.
View Project βIn-store A/B Testing on Signage Design
Innovative in-store A/B testing experiment at the Shanghai Live store. Tested two signage variants (orange vs. multi-color) using dwell time as the key metric. Sample size: 1,109 customers across 5 days.
View Project βInline Scalper Detection Machine
Built a machine-learning model to identify scalpers ("yellow cattle" / huangniu) in customer data. Used unsupervised learning to cluster behaviour patterns and separate high-value customers from scalpers based on frequency, cadence, and purchase history.
View Project βNike Fit Service Study
Analysed the effectiveness of the Nike Fit service in stores. Evaluated customer adoption rates, service efficiency, and impact on conversion.
View Project βLive Store Traffic Analysis
Analysed in-store traffic patterns using FPA sensor data. Generated insights on customer flow, dwell time, and peak hours to inform store-operations optimisation.
View Project βMember Day Analysis
Analysed member engagement during special promotional events. Evaluated member acquisition, retention, and spending patterns.
View Project β365 Experience Study
Analysed customer-experience metrics across Nike Live stores. Evaluated service quality, customer satisfaction, and the relationship with repeat visits.
View Project βLive Store Learning Program
Analysed the effectiveness of in-store learning programmes. Measured employee training outcomes and knowledge retention.
View Project βNike Live Unlock Box Study
Analysed customer behaviour and engagement with the Unlock Box promotional campaign. Evaluated conversion rates and customer satisfaction.
View Project βSanlitun Rise Store Analysis
Analysed the Nike Rise store at Sanlitun (δΈιε±―), Beijing. Evaluated store performance, customer engagement, and trade-zone dynamics for one of China's most iconic retail locations.
View Project βRise at Xujiahui β Potential-Consumer Data
Data analytics project for the Rise store at Xujiahui. Identified potential customers and analysed conversion patterns.
View Project βπ½οΈ Presentations
Geospatial Analysis and Store Location Selection
Presentation on using geospatial data and machine learning for retail-store site selection. Covered LBS / POI analysis, trade-zone evaluation, and predictive modelling.
View Presentation βResearch Presentation 07/15
Internal research presentation covering store-analytics methodologies and findings. Shared with stakeholders on data-driven decision-making.
View Presentation βResearch Presentation 02/17
Presentation on analytical methodologies and research findings for Nike Greater China store operations.
View Presentation βResearch Presentation 11/19
Additional research presentation covering advanced analytics techniques and business insights.
View Presentation β