Data Analyst - E-commerce Analytics

Data Analyst - E-commerce Analytics - New Vine Holdings LLC - O'Fallon, MO.

Present quantitative analyses and models, sales performance analysis, customer behavior analysis, and performance indicator analysis to management and users using MS Excel charts, SQL datasets, cross-tab analysis, period-over-period (PoP) variance analysis, and comparative analysis across reporting periods to explain order trends, revenue, conversion, fulfillment efficiency, promotions, and operational issues. Define data requirements for e-commerce analysis, including order volume, GMV, net sales, refunds, cancellations, customer acquisition, repeat purchase rate, conversion rate, AOV, inventory turnover, fulfillment status, delivery timeliness, category performance, and campaign results; gather and validate data through MS Excel checks, SQL queries, reconciliation, cross-tab analysis, and variance review. Validate e-commerce models, sales reports, inventory reports, customer reports, and dashboards with MS Excel formulas, SQL extracts, reconciliation checks, PoP variance analysis, comparative analysis across reporting periods, and performance indicator analysis to identify incomplete data, duplicate orders, incorrect refund treatment, abnormal sales movements, and inconsistent KPI definitions. Prepare management reports evaluating declining conversion, cart abandonment, rising returns, delayed fulfillment, inventory shortages, weak repeat purchase, underperforming promotions, category weakness, customer complaints, and abnormal sales variance; recommend solutions based on MS Excel analysis, SQL extraction, cross-tab analysis, and performance indicator analysis. Collaborate with operations, marketing, customer service, inventory, finance, warehouse, management, and decision makers to clarify sales, margin, customer, inventory, and fulfillment objectives; align KPI definitions; confirm SQL outputs; review MS Excel results; and track whether actions improve sales, fulfillment, inventory, customer, and promotional performance. Formulate quantitative and comparative models by defining traffic, conversion, order count, AOV, discount rate, ad spend, refund rate, return rate, inventory availability, delivery cycle time, customer retention, and gross margin; break ecommerce systems into components, assign values, and use MS Excel and SQL datasets to compare pricing, promotion, assortment, replenishment, fulfillment, advertising, retention, and service alternatives. Observe e-commerce operations through order systems, storefront data, payment records, inventory records, service logs, listings, ad reports, warehouse fulfillment data, refund records, and management feedback; analyze sales fluctuation, listing inefficiency, stockouts, fulfillment delay, return-rate increases, and reporting inconsistency through SQL, MS Excel, cross-tab analysis, comparative analysis across reporting periods, and PoP variance analysis. Specify computational methods for e-commerce models, including SQL logic for order and customer extraction, MS Excel formulas, pivot tables, cross-tab analysis by category or customer segment, PoP variance calculations, comparative analysis across reporting periods, cohort comparisons, sales funnel calculations, and performance indicator calculations. Design and evaluate pilot analyses and structured comparisons with e-commerce data, including pilot promotion analysis, listing tests, customer segmentation, pricing comparisons, fulfillment trials, Excel simulations, SQL data pulls, and performance indicator tracking; develop campaign planning analyses for launches, seasonal campaigns, marketplace expansion, replenishment, fulfillment improvement, and retention programs. Develop procedures for order review, sales reporting, inventory tracking, escalation, refund and return analysis, listing review, promotional evaluation, and fulfillment monitoring; review marketplace standards, order datasets, product trends, promotional results, inventory r cords, and benchmarks to support KPI selection and recommendations.

Master's in Information Technology with 24 months as a data analyst with experience with Microsoft Excel, SQL, Cross-tab analysis, Period-over-period variance analysis, Comparative analysis across reporting periods. Performance indicator

analysis