This dataset contains the historical sales of a supermarket company XYZ, obtained from 3 different branches of the company in 3 cities of the Asian country of Myanmar. The growth of supermarkets in most populated cities is on the rise and market competition is also high. This analytics seeks to draw insights from existing data and also make predictive forecasts that could help XYZ in driving operational efficiency and profitability. By analyzing this dataset, XYZ supermarket sales can be optimized, inventory managed better, dynamic pricing strategically optimized, and marketing tailored to specific customer segments. The analysis also enhances supply chain management, aligns operations with customer demand and market dynamics, ensuring long-term success and ultimately fostering business growth.
Here are some of the Business questions that this analysis would be finding answers to: Does gross income generated vary between gender, and product line. How much income is generated in the cities of operation? How does tax affect quantity of goods purchased per customer ? How are the customer types distributed per city and branch of XYZ’s operation? Which payment method is most frequently used across the genders? Which is the least used? Does payment method influence income generated by each gender type? Is there a significant difference in the average gross income across different customer types?
Insights from the Analysis: There are 2 unique customer types (Normal, Member), and 3 unique payment options (E-wallet, Cash, Credit card) in the dataset. Product categories of goods cold across the various locations are Electronic accessories, Fashion accessories, Food and beverages, Health and beauty, Home and lifestyle, Sports and travel. The cheapest good costs $10.17, and the most expensive, $993. On the average, goods sold cost $323. Taxes on goods was on average, about 15%. Mean score on customer satisfaction rating was 6.9. The highest and lowest ratings read 10 and 4 respectively.
The analysis reveals that the 3 branches of operation of XYZ supermarket have similar unit prices across the different product categories. All of the branches have 25% of their products sold for at least $32 per unit. Three-quarter of goods sold in branch C go for prices below $80. The health and beauty category yields the lowest gross income of $13.75 in females. Female customers of XYZ generated the company’s highest income ($18) by purchasing Home and lifestyle products, doubling as the highest income overall category of customers’ products sold.
On the other hand, the supermarket records the highest income ($16.5) from purchases of health and beauty products made by males, and the lowest income ($12.5) is generated by Food and beverage categories in males. It may help XYZ Supermarket to maximize the opportunity the female Home and lifestyle customer segment presents by tailoring their value propositions to boost sales and improve the income generated. Food and beverages in Naypyitaw city yields the highest income for the business, giving a 31.4% and 40.7% difference in income gotten from the same product line in Yangon and Mandalay cities respectively. Interestingly, Naypyitaw is the lowest income generating city in home and lifestyle products, giving returns of $662, against Mandalay’s $1067 and Yangon’s $835.
In the given dataset, females contribute to 52% of the total income, while males contribute to the remaining 48%. This indicates a slight majority in income contribution from females. The chart shows that male customers have a higher preference for payment by E-wallet than the other types of payment methods used at XYZ supermarket. i.e cash and credit cards. In females however, cash payments were most prevalent and E- wallets were least preferred. While there were variations across genders and payment types, there is minimal or no difference observed in the total income generated per payment type made by female and male customers of XYZ supermarket. Inferential statistical analysis using Chi square, shows that there is no significant relationship between Gender, City, and Time of Purchase.
The correlation plot shows that There is strong positive correlation between gross income, COGS, Tax, Quantity, and Total Income. This implies that the observed strong positive correlations highlight interdependencies among key financial variables, emphasizing the interconnected nature of revenue, costs, taxes, and quantity sold in the business's financial ecosystem. On the other hand, the analysis reveals a weak negative correlation between ratings and all the other variables. This is in agreement with the F-statistics carried out on the dataset which reveals that there is no significant relationship between Customer Ratings, Branch, and City.
Each region has its areas of strengths and weaknesses, which affect the performance of their product lines. These peculiarities can be analyzed in order to increase XYZ’s total income. Income realization in Mandalay and Yangon is equal, contributing 32.9% each to the total gross income. This suggests a balanced economic performance between these two cities. Naypyitaw is the greatest contributor to the company’s gross income, accounting for 34.2%. The city's economic performance surpasses that of Mandalay and Yangon, making it pivotal for the company's financial success.
Summarily, understanding the factors contributing to Naypyitaw's higher income can help XYZ Supermarket to make strategic decisions and improve resource allocation, effectively analyze market dynamics, and customer behavior. It could provide insights into the city's economic dominance, It could also be good to replicate this across the other cities. Future business strategies could be drawn from identifying whether this dominance is caused by demographics, economic policies, or other factors.