Sales and Customer Insights Analysis for RMS

As a Data Scientist, I conducted an in-depth analysis of sales, customer segments, and shipping data for RMS, a London-based office supplies and furniture retailer. The study leveraged advanced Excel functionalities, including Pivot Tables, INDEX-MATCH lookup functions, and data visualization techniques, to derive actionable business insights.

• The project aimed to analyze order data from 3 years and provide insights to help the Liverpool division of RMS make data-driven decisions. The analysis was structured into three case scenarios addressing various business aspects.



Case Scenario I: Sales Performance Analysis Top-Selling Product Categories: Identified the category with the highest sales volume? Regional Sales Analysis: Ranked the top three and bottom three sales-performing regions? Geographic Performance: Evaluated total sales for appliances in Ontario? Customer Revenue Optimization: Provided recommendations to increase revenue from the bottom 10 customers? Shipping Cost Evaluation: Identified the most costly shipping method? Customer Value Assessment: Analyzed the most valuable customers and their purchasing behavior?

Case Scenario II: Customer & Shipping Efficiency Analysis Shipping Cost Efficiency: Evaluated whether shipping costs were appropriately spent based on order priority? Small Business Performance: Determined the small business customer with the highest sales? Corporate Order Analysis: Identified the corporate customer with the highest number of orders from 2009-2012? Profitability Analysis: Determined the most profitable consumer customer? Returns Analysis: Examined customer returns and their segment classification?

The Pivot Tables Shipping Cost Efficiency: Evaluated whether shipping costs were appropriately spent based on order priority? Small Business Performance: Determined the small business customer with the highest sales? Corporate Order Analysis: Identified the corporate customer with the highest number of orders from 2009-2012? Profitability Analysis: Determined the most profitable consumer customer? Returns Analysis: Examined customer returns and their segment classification?

Technical Implementation Data Cleaning & Processing: Standardized data formats, handled missing values, and ensured consistency. Exploratory Data Analysis (EDA): Used Pivot Tables and statistical analysis to uncover trends. Data Visualization: Created dynamic charts and graphs for better insight presentation. Business Recommendations: Provided data-driven strategies for revenue growth, cost optimization, and customer retention. Returns Analysis: Examined customer returns and their segment classification? Key Outcomes & Business Impact Identified the most profitable customer segments, enabling targeted marketing strategies. Highlighted inefficiencies in shipping cost allocation, leading to potential cost savings. Provided actionable insights to improve sales performance and customer engagement.