SuperStore Sales Analysis & Forecasting

SuperStore Sales analysis in Power BI with forecasting of upcoming 15 days sales

Objective

To contribute to the success of a business by utilizing data analysis techniques, specifically focusing on "time series analysis" and provide valuable insights and accurate sales forecasting.

Tasks

• Performed Data Cleaning and Processing with the help of Power Query Editor.
• Transforming data in Power Query allows us to modify our data & select what data we want to bring in before we bring it into Power BI. Basically we are Shaping the data and building a Data model from it.
• Created new columns & new measures with the help of DAX (Data Analysis Expressions) queries in Power BI.
• Identified trends and patterns by making Data Visualisation with the help of map, donut charts, line & area charts and stacked bar charts.

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Insights

Key Performance Indicators (KPIs)

• Around 3000 orders were given which constitues for the sales of 1.57 M making profits of approx. 175 K
• It took atleast 4 days on an average to ship an order.

Charts

• Maximum sales has been generated from the Consumer segment.
• Most people prefer to pay by COD i.e. Cash On Delivery (~ 43% ).
• West region of US has got the maximum sales of around 33% .
• Monthly sales increases as we transition from January to December for both years 2019 and 2020.
• However its not the case with Monthly profits. It has seen slightly much profits in March 2020 and October 2019.
• Most of the products have been shipped by Standard Class (probably due to their cheap services).
• Office Supplies is the highest Sales category while Phones sub-category got the maximum amount in sales.
• California is the state with maximum sales of around 0.34 M.

Forecast Results

From 1 Jan 2021 to 15 Jan 2021, the average estimated sales will be around 5300 per day with lower and upper bounds varying each day accordingly as shown in Page 2 of report.

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Project Learnings

1. Dashboard Creation: Identified the KPIs, designed an intuitive and visually appealing dashboard along with interactive visualizations and filtering capabilities to allow users to explore the data at various levels of granularity.
2. Data Analysis: Provided valuable insights to business entities regarding effectiveness of their sales strategies through visualizations and charts.
3. Sales Forecasting: Leveraged historic data and applied time series analysis to generate sales forecasts for next 15 days.
4. Actionable Insights and Recommendations: Shared valuable insights and actionable information that can drive strategic decision-making and support the supermarket's goals for growth, efficiency and customer satisfaction.