Longying Lai

• Utilized SQL to merge various data sources, created visualized geographic maps in Tableau, and optimized operations by clustering sites into new routes. • Identified key metrics such as distances and work hours to generate cost and revenue multipliers, leading to a 56% increase in revenue per mile served. • Contributed to the client company's 3-year curbside operation strategy at Simon Vision Consulting in Rochester, New York.

• Developed predictive models in Python—including time series (ARIMA), clustering (KMeans), and regression—to forecast monthly sales across product categories, regions, and customer cohorts with 92% predictive accuracy. • Defined and monitored digital commerce KPIs such as customer conversion rate, category growth, and monthly recurring revenue, built automated sales dashboards using SQL, Power BI, and Tableau for recurring reporting and actionable insights delivery. • Engineered and analyzed a customer dataset of 160K+ records from internal databases; extracted over 10 features (e.g., age, location, order frequency), applied RFM clustering to identify high-value customer segments for targeted engagement strategies. • Synthesized insights with data visualization and presented data storylines for stakeholders like sales, product, and marketing teams, contributing to a 10% lift in digital shelf performance and 15% regional ROI boost.

-Analyzed personal information, brand awareness, product needs, purchase and information acquaintance channels, and consumption behaviors, preference, & psychology of Leifheit and its three competitors’ customers, and proposed optimal strategies to boost customer conversion -Executed due diligence towards the integrated stove market, including investigating integrated stove market’ macro-economic environment, scale, latest supply & demand, competition pattern, and four leading firms’ major integrated stove products and target customer groups, and summarized all key findings into the market analysis report

• Utilized BLM model to conduct overall market research on the convenience store industry, analyzed business from 4 aspects, and provided key insights on instant food targeting, which were adopted by the client in their future operational strategy. • Calculated operational cost data, identified regional development pattern and summarized results with data driven outcomes, effectively communicated actionable next steps based on the findings.

Dean’s List for all semester (Top 5%) | Beta Gamma Sigma Honor Certification Coursework: Python Analytics, Programming in R, Core Statistics, Pricing Analytics, Accounting and Finance