Summary
This report analyses the sales dataset for INSIGHT EDGE in-depth, covering records on stores, business lines, locations, rankings, and total revenue over two years. The goal of this project is to evaluate sales performance and uncover valuable insights.
Introduction:
This analysis project uncovered insights through the analysis of historical data provided by my coach. The data set covers the period between April 2014 and June 2015. The primary aim of this analysis is to comprehend the distribution of overall revenue by utilizing various metrics, such as best-performing product-optimized sales and overall business performance. The data revealed how each store is performing based on ranks, the line of business that is performing the best, and generating more revenue.
Data Overview
To enhance my workflow, it's important to first create a backup copy of the provided dataset for future reference. Additionally, organizing the dataset into a table format will greatly improve clarity and efficiency in my work. The dataset consists of 12 columns and contains approximately 30,000 rows, as shown in the image below which will be worked on further to generate data for the missing columns.
Data Processing
The first step is to make sure that the data is clean, which involves formatting the entire data set and creating uniform data for analysis. I was able to fill in the missing field in the dataset provided by applying the detailed formula below.
To populate the region column, I use the VLOOKUP function. =VLOOKUP($A2,Region!$A$2:$B$23,2,FALSE)
To calculate the revenue, I multiplied the price per unit by the number of units sold. =[@[Price/unit]]*[@[Units Sold ]]
For the rank column, I utilize the IFS functions.
\=IFS([@[Revenue ]]>30000000,"Excellent Performance",[@[Revenue ]]>10000000,"Good Performance",[@[Revenue ]]>2000000,"Average Performance",[@[Revenue ]]<2000000,"poor Performance" )
The first requirement from INSIGHT EDGE is to populate the summary table of Revenue per State/Line of Business. This was solved using the SUM IF function with the formula below
\=SUMIFS(Data!$K:$K,Data!$A:$A,Sumifs!$D4,Data!$G:$G,Sumifs!E$3)
Exploratory Data Analysis (EDA)
The task at hand requires creating a dashboard using the historical sales data displayed.
Total Revenue
Total Units Sold
Revenue by region
Revenue by location (pivot table)
Trend of Revenue by month
Revenue by rank (doughnut) chart)
Average Sales Performance
Average Price/unit
Top 5 Stores by Revenue
Sales Performance by Day Category
Include a timeline using the Date column and it must be connected to every chart and table.
Add a slicer using the location column and it must be connected
Total Revenue
Working with the revenue generated for each store from the table, I was able to generate the total revenue for the whole data set.
Total Units Sold
Revenue by region
The revenue distribution across regions shows the Northeast as the highest-generating region, followed by the Southwest and South-South. North Central recorded the lowest revenue, indicating weaker sales performance. Northwest and Southeast had similar mid-range revenue levels. The strong performance in the Northeast and Southwest suggests high business activity or market demand. Strategies should focus on boosting sales in lower-performing regions while maintaining growth in high-revenue areas.
Revenue by location (pivot)
Analysing sales revenue by state provides deeper insights into geographical sales variations, highlighting high-performing regions with strong demand and identifying potential market opportunities. It was ranked from the highest state down to the lowest.
The Trend of Revenue by Month
The revenue trend from January 2014 to June 2015 shows fluctuations with notable peaks in February, May, and October. February 2015 recorded the highest revenue, suggesting seasonal factors or promotions. July and August 2014 saw a decline, possibly due to lower demand. A recovery was observed in October 2014, followed by a steady start in 2015. Businesses should leverage high-performing months and implement strategies to counteract slow periods. Continuous monitoring is essential for optimizing sales strategies.
Revenue by rank (doughnut chart)
The revenue distribution by performance rank shows: that average performance contributes the highest (54%), good Performance follows with 29%, poor performance holds 16%, and excellent Performance has the least share (1%). This suggests a need to improve the Excellent and Good Performance categories to boost overall revenue.
Top 5 Stores by Revenue
The top 5 revenue ranking by store shows Ankpa leading with 25%, followed by Ajaokuta (21%) and Arochukwu (20%). Ekiti South-West (18%) and Nembe (16%) had lower revenue contributions. The dominance of Ankpa suggests strong market demand or better sales strategies. Nembe and Ekiti South-West may require targeted interventions to boost performance. Maintaining momentum in high-performing stores while improving low-ranking ones can optimize overall revenue.
Sales Performance by Day Category
Workdays generate the highest revenue, significantly outperforming all other day categories. Public Holidays, Local Holidays, Observances, and Seasonal days contribute very little to total revenue. The vast difference suggests that business activity is heavily concentrated on regular workdays, with minimal revenue generated on holidays or special observances. This indicates a strong reliance on workday sales, highlighting potential opportunities to improve holiday or seasonal revenue strategies.
The Dashboard
The Historical Sales Data Performance Dashboard provides a comprehensive overview of sales metrics and trends. It includes key performance indicators such as Total Revenue ($73 billion), Total Units Sold (786,678), Average Price per Unit ($171,791), and Average Sales Performance (2,371,246).
Key Insights and findings:
Revenue Analysis: Sales are broken down by Region, Location, and Rank, highlighting the North East as the top-performing region.
Trend Analysis: A monthly revenue trend graph shows fluctuations over 2014 and 2015.
Store Performance: The Top 5 Stores by Revenue are visualized using a pie chart.
Day Category Performance: Sales performance is significantly higher on Workdays compared to other categories like Holidays or Observances. The dashboard effectively visualizes historical sales performance, helping stakeholders identify trends, top-performing locations, and key business drivers.
Revenue Distribution by Region: The North East region has the highest revenue, while North Central has the lowest. There is a clear disparity in sales across regions, indicating potential market saturation or underperformance in some areas.
Top-Performing Locations: Ekiti, Ibadan, and Aba are among the highest revenue-generating locations. Some locations, such as Borno and Ogun, have significantly lower sales.
Monthly Revenue Trends: Sales exhibit seasonal fluctuations, with peaks observed in certain months. There is noticeable revenue growth from early 2014 to mid-2015, suggesting market expansion.
Store Performance: The top five stores contribute a significant portion of total revenue, with Ankpa and Ajaokuta leading. Other stores contribute less, indicating the need for performance optimization in lower-ranking stores.
Sales by Day Category: Workdays drive the majority of sales, while sales on holidays, observances, and public holidays remain low. This suggests that business performance is more aligned with regular operational days.
Recommendations for Insight Edge
Boost Sales in Low-Performing Areas:
Identify reasons for low sales in Borno and Ogun, such as low market penetration, competition, or economic conditions.
Implement targeted marketing campaigns or promotional strategies in these areas.
Leverage Seasonality for Sales Growth:
Conduct detailed analysis on seasonal peaks and create promotional offers aligned with high-sales periods.
Develop off-season strategies to maintain steady revenue.
Optimize Store Performance:
Analyze operational efficiency and customer preferences in top-performing stores to enhance weaker stores.
Consider staff training, inventory management improvements, or location-specific marketing.
Maximize Workday Sales & Improve Holiday Performance:
Enhance weekday sales by optimizing promotions, discounts, and targeted outreach.
Develop holiday-specific promotions to drive sales on non-workdays, potentially offering holiday discounts, limited-time deals, or online shopping incentives.
By implementing these recommendations, Insight Edge can optimize its sales strategy, expand in high-revenue regions, and improve underperforming areas for sustained business growth.
Conclusion
The analysis of Insight Edge's historical sales data has revealed key trends, regional performance variations, and store-specific insights. The findings indicate that revenue is highest in the Northeast region, with Ankpa leading among stores. Workdays significantly outperform holidays in sales, and seasonal fluctuations affect revenue trends. While certain locations thrive, others, such as Borno and Ogun, require targeted strategies to boost sales.
To drive sustainable growth, Insight Edge should focus on improving underperforming regions, leveraging seasonal trends, optimizing store operations, and enhancing sales on non-workdays. Implementing these strategies will help maximize revenue, strengthen market presence, and improve overall business performance.
Your feedback is welcome. Thanks for reading!