I help e-commerce businesses understand what is actually happening across their products — and what they should consider doing next.

This service goes beyond creating charts or summarizing sales numbers. I review multiple product-performance signals together, such as historical sales, orders, revenue, traffic, pageviews, pricing, categories, location, and available demand signals to identify patterns, opportunities, and underperforming areas.

My background includes hands-on e-commerce operations and merchandising experience managing a catalog of more than 25,000 healthcare SKUs. Product positioning decisions were supported by historical sales performance, pageviews, pricing, location, category demand, and ongoing SKU review.

Depending on the available data, the analysis can help answer questions such as:

• Which products are performing well? • Which SKUs may deserve more exposure? • Which products receive traffic but generate weak sales? • Which categories show stronger demand? • Are there meaningful pricing differences between stronger and weaker products? • Which locations appear to perform better? • Which products may need repositioning or further testing? • Where are the most practical opportunities to investigate next?

My approach follows a simple framework:

I focus on practical recommendations rather than presenting numbers without context. Depending on the findings, recommendations may include maintaining exposure, testing additional visibility, reviewing pricing or positioning, comparing alternative SKUs, or deprioritizing weak candidates.

• Product / SKU performance ranking • Category-level analysis • Sales and historical performance review • Traffic vs. sales analysis when traffic data is available • Pricing and location observations • Identification of stronger and weaker products • Opportunity and demand-pattern observations • Merchandising or exposure recommendations • Recommended next actions • An organized analysis file in Excel or Google Sheets • A concise written summary of the main findings

Recommendations are based on the quality, completeness, and time range of the data provided. The analysis identifies patterns and useful business signals, but I will not present observational relationships as proven cause-and-effect.