A data analysis report for a juice and cocktail bar in eastern Nigeria that serves fresh “Parfaits, Milkshakes, smoothies and cocktails”. It's a popular spot for health-conscious people. The data set was collected for a year period and includes information on sales, and product. This analysis aimed to identify patterns and trends in the data that could inform decision-making and improve the business performance. To depict the distribution of customers and product preferences, tables and charts were made. To find any notable connections between sales and client preferences throughout several time periods (seasons), inferential analysis was carried out. Data must first be grouped by Product and Season, followed by a statistical test called the chi-square test. Excel was used to produce data visualizations, such as charts, graphs, and tables. Note: To ensure the confidentiality of the business data used in this report the data in the report was Aggregated to a higher level of granularity to prevent the identification of individual records. Also, data masking, data perturbation, or generalization were applied to obfuscate sensitive information while maintaining the overall integrity of the data.

1. : PERFORMANCE
EVALUATION OF DERA’S SHOP, “A JUICE AND
COCTAIL BAR”
Prepared
By
LOTANNA REMIGIUS OSIGWE
JANUARY, 2023

2. i
CONTENTS
CONTENTS i
FIGURES and TABLES ii
INTRODUCTION 1
METHODOLOGY 1
OVERVIEW 2
KEY FINDINGS 2
Seasonal Sales Trend and Pattern 2
Popular Products 2
Days of the week Sales Patterns 2
Upselling Opportunities 2
ANALYSES AND INTERPRETATIONS 3
Seasonal Sales Trend and Pattern 3
The Popular Product 6
Days of the Week Sales Pattern 8
RECOMMENDATIONS 9
Seasonal Marketing Campaigns 9
Menu Diversification 9
Enhance Customer Engagement 9
Optimize Staffing and Inventory 9
Monitor Competitors 10
Gather Customer Feedback 10
CONCLUSION 11

3. ii
FIGURES and TABLES
List of Figure
Figure 1: Seasonal Sales Trend 3
Figure 2: Seasonal Sales Trend with Peak Period 4
Figure 3: Level of Sales by Months 4
Figure 4: A bar chart of popular products from January to December 6
Figure 5: Pie chart showing percentages of Products Distribution 7
Figure 6: A bar chart showing products and their revenue 7
Figure 7: An Area chart showing Days of the Week Sales Pattern 8
List of Table
Table 1: Observed Values 5
Table 2: Expected Values 5

4. 1
INTRODUCTION
The purpose of this report is to present the findings of a data analysis conducted for Dera's
Shop - a juice and cocktail bar in eastern Nigeria that serves fresh “Parfaits, Milkshakes,
smoothies and cocktails”. It's a popular spot for health-conscious people. The data set was
collected for a year period and includes information on sales, and product. This analysis aimed
to identify patterns and trends in the data that could inform decision-making and improve the
business performance.
This report presents an overview of the data set and methodology used to analyze the data,
followed by a summary of the key findings. The analysis revealed valuable insights into customer
behaviour and preferences, which can be leveraged to enhance the business operations and
marketing strategies. Overall, this report provides actionable recommendations based on the
data analysis, which can help the business optimize their business performance and increase
customer satisfaction.
METHODOLOGY
The data for this analysis were collected for the whole year of 2022 from the Daybook. It
includes day and month of sales, price, quantity sold and product details. The data was cleaned
and preprocessed to remove any inconsistencies, missing values, or outliers that could impact
the analysis.
The key variables considered in this analysis include:
 Day and month
 Price (constant at #1000)
 Product/Quantity sold and,
 Seasons
Tables and charts were created to visualize the distribution of customers and product
preferences. Inferential analysis was conducted to identify any significant relationship between
sales and customer preferences across different periods (season). This involves grouping the
data by Product and various season and then using statistical test, chi-square test. Data
visualizations, including charts, graphs, and tables, were created using Excel. Finally, it’s
important to note that the analysis is based on the available data, and there may be limitations
or biases inherent in the data collection process.

5. 2
OVERVIEW
The data set consists of 365-day record, capturing detailed information about each transaction,
including date, price, products, and quantity sold. By analyzing this data set, it was aimed to
identify patterns, trends, and relationships that can drive informed decision-making and help
the business optimize its operations, marketing strategies, and customer experiences.
Key variables considered in this analysis allowed us to understand revenue trends, identify peak
and off-peak periods, and evaluate the performance of specific products and preferences to shed
light on the most popular products aiding in inventory management and further development.
KEY FINDINGS
All products were sold at the same price of #1000, hence, this report couldn’t establish if there
could be a relationship between price and the quantity of products desired. Nevertheless, other
key findings include;
Seasonal Sales Trend and Pattern:
The analysis revealed distinct seasonal sales trend and pattern, with a significant increase in
sales during Rainy season and a gradual decline during the Dry season.
Popular Products:
The top two most popular products are Milkshake and Smoothie accounting for approximately
72% of total sales. Additionally, customers show a strong preference for Milkshake.
Days of the week Sales Patterns:
It highlights the peak sales day between Mondays and Sundays and off-peak day of Saturday.
Upselling Opportunities:
Analysis of sales data uncovered opportunities for upselling and increasing average order value.
By strategically promoting combo offers and special promotions the juice and cocktail shop can
encourage customers to spend more per visit, thereby boosting overall revenue.

6. 3
ANALYSES AND INTERPRETATIONS
Seasonal Sales Trend and Pattern
Figure 1: Seasonal Sales Trend
1715
2040
2551 2772
3302 3449
3810
3409
2918 2944
2424
1915
● Dry Season Rainy Season
Figure 1 displays a line chart illustrating the fluctuating seasonal sales for one-year period. The
chart clearly depicts the high sales pattern during the rainy season, followed by a gradual decline
in dry season. This visual representation emphasizes the importance of focusing efforts and
optimizing operations during the rainy season to maximize revenue. This indicates that
customers are more inclined to purchase these products during rainy season, aligning with
expectations for a seasonal product. To capitalize on this trend, the business should focus on
targeted marketing campaigns and promotions during the peak rainy period i.e. July as shown
in Figure 2 to maximize revenue. Additionally, efforts should be made to optimize operations
and inventory management during the off-peak seasons to maintain a steady flow of customers.

7. 4
Figure 2: Seasonal Sales Trend with Peak Period
The analysis of the seasonal sales trend with a peak i.e. figure 2, reveals a consistent trend of
higher sales during rainy season especially July, followed by a gradual decline in dry seasons.
This is further shown in figure 3, where there is a pattern in which sales consistently increase in
sequence in the first seven months and then decrease in sequence in the remaining five months
with December and January being the months with lowest sales.
Figure 3: Level of Sales by Months

8. 5
To further confirm if the relationship between the products and the seasons occurred by chance,
a Chi-square Test was conducted, analyzing the observed and expected values. If the P-values1 is
greater than the significance level of 0.05 then the relationship could have occurred by chance,
when less than its otherwise.
Table 1: Observed Values
Products Rainy season Dry season TOTAL
Milk Shake 8854 5850 14704
Smoothie 5602 3641 9243
Parfait 2114 1113 3227
Cocktail 3262 2813 6075
TOTAL 19832 13417 33249
Table 2: Expected Values
Expected
8770 5934
5513 3730
1925 1302
3624 2451
P-Values = 0.000000000000023
⸫ (P-Values = 0.000000000000023 < Significance Level of 0.052)
So, there is a significant relationship between the four product and the two seasons. Also, the
relationship didn’t occur by chance so, the increase and decrease of these product was as a result
of rainy and dry season respectively.
1
P-value- probability value measures how strong the evidence is against a null hypothesis with smaller P-value
showing stronger evidence & vice versa
2
Significance level is a threshold for making decision in hypotheses testing. It’s the level of rejecting null
hypothesis when its true

9. 6
The Popular Product
Figure 4 represents a bar chart illustrating in distribution popular products from January to
December.
Figure 4: A bar chart of popular products from January to December
Figure 4 represents a chart illustrating the distribution of the products, with milkshake and
smoothie constantly desired throughout the year. Also, there is a pattern- these products
consistently increase sequentially in distribution in the first seven months and then decrease
sequentially in the remaining five months. Milkshake and smoothie collectively make up
approximately 72% of total product distributed or sold as seen in the pie chart in figure 5 and
the products with most revenue as shown in figure 6.

10. 7
Figure 5: Pie chart showing percentages of Products Distribution
Figure 6: A bar chart showing products and their revenue
These visuals convey the importance of maintaining a well-stocked inventory of these popular
products while also exploring opportunities to introduce new and unique product to attract a
broader customer base. To cater to this demand, the shop should ensure a consistent availability
of these products while also exploring opportunities to introduce new variations of the seasonal
product to add excitement and variety to the menu.

11. 8
Days of the Week Sales Pattern
Figure 7: An Area chart showing Days of the Week Sales Pattern
4861
4658
4702
4728
4742
4649
4909
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Figure 7 showcases a stacked area chart depicting the day of the week sales pattern. The analysis
revealed distinct fluctuating sales patterns based on Days of the week. Sales are specifically at its
highest during the first and second day of the week and appear to be consistently stable and
close in magnitude in other days and with lowest sales on Saturdays. This visual enables better
operational planning and resource allocation to meet customer demand during the busiest days.

12. 9
RECOMMENDATIONS
Since all products were sold at the same amount of price this report couldn’t establish if there
was a relationship between price and the quantity of products desired by customers. However,
to establish a relationship, it was recommended that the prices of less desired/popular products
should be reduced. Based on further analysis and interpretation of the data, the following
recommendations were suggested to optimize the operations and maximize the performance of
the business:
Seasonal Marketing Campaigns:
Develop targeted marketing campaigns that align with the seasonal sales trend. Increase
promotional activities and advertising efforts during the peak rainy months to attract more
customers. Consider offering special deals or seasonal promotions to capitalize on increased
demand. Additionally, during the dry seasons, focus on creative promotions, and price discount
to maintain customers and boost sales.
Menu Diversification:
The business should introduce new and unique flavors to cater to a broader range of customer
preferences (cocktail and smoothie). Conduct regular market research to identify emerging
flavor trends and incorporate them into the menu. Offer a platform for customer reviews and
suggestions. Additionally, regularly review the menu to ensure a balance between all products.
Enhance Customer Engagement:
Implement a customer loyalty program to reward and incentivize repeated visits. Leverage
technology, such as a mobile app to make it convenient for customers to participate in the
program. Additionally, engage with customers through social media platforms.
Optimize Staffing and Inventory:
Utilize the insights from days of the week sales pattern analysis to optimize staffing levels during
peak days. Ensure that there are enough staff members available to serve customers efficiently.

13. 10
Additionally, closely monitor sales trends and adjust inventory management accordingly to
prevent stock outs during Sundays and Mondays.
Monitor Competitors:
There was no data about competitors, so there is need to Conduct regular competitive analysis to
stay informed about their offerings and strategies. Identify areas where the business can
differentiate itself and capitalize on its unique selling points.
Gather Customer Feedback:
Implement mechanisms to collect and analyze customer feedback consistently. Encourage
customers to provide feedback through online surveys or suggestion boxes.

14. 11
CONCLUSION
In conclusion, the analysis of the data set for Dera’s Shop provides valuable insights into the
shop's performance, customer dynamics, and opportunities for growth. By leveraging these
insights, the Shop can make informed decisions to optimize its operations, enhance customer
experiences, and drive business success. The analysis revealed several key findings, including
seasonal sales patterns, popular product and day of the week sales patterns. These findings have
informed the recommendations outlined in this report to help the Shop improve its strategies
and outcomes.
By implementing targeted marketing campaigns aligned with seasonal trends, diversifying the
menu with flavors, and enhancing customer engagement through social media engagement the
Shop can attract a broader customer base and encourage repeat visits. Optimizing staffing and
inventory management based on day of the week sales patterns will enable efficient operations
and enhance the overall customer experience. Monitoring competitors and gathering customer
feedback will provide valuable insights for maintaining a competitive edge and improving
offerings.
It is important for the Shop to regularly review and analyze the performance metrics, keeping a
pulse on the changing market dynamics and customer preferences. Continual evaluation and
adjustment of strategies will be key to ensuring sustained growth and success in the industry.
By harnessing the power of data and applying the recommendations presented in this report, the
Shop can enhance its operational efficiency, customer satisfaction, and financial performance.
Embracing a data-driven approach will enable the shop to make more informed decisions, stay
ahead of the competition, and deliver delightful ice cream experiences to its customers.