Café On The Sea's (COTS)

?

Table of Contents

Introduction and Project Plan. 3

Data Quality Issues and Remedies. 3

Specific data quality issues in the provided dataset 3

Proposed Solutions. 4

Data Analysis and Commentary. 4

Table A: Sales Volume and Value Analysis. 4

Table B: Benchmark Comparisons of Market Segment Performance. 5

Table C: Benchmark Comparisons of Sales Volume and Value Between Coffee Shops. 6

Data Charting and Commentary. 6

Chart A: Comparison of Sales Value Trends Across Coffee Shops Over Time. 6

Regression Analysis. 7

Chart B: Market Segments Performance Comparisons Between Coffee Shops. 8

Chart C: Impact of New Product Ranges on Sales Performance in Plymouth and Comparison with Other Cities? 9

Conclusion. 9

Addressing Issues Raised by COTS's Top Management 10

Business Recommendations. 10

Leveraging Data Analytics. 10

References. 11

?

?

Café On The Sea's (COTS)

Introduction and Project Plan

The purpose of this report is to conduct a comprehensive analysis of Café On The Sea's (COTS) three coffee shops in Poole, Plymouth, and Newquay as part of the organisation's strategic planning process. The report is based on addressing specific research objectives that are outlined in having the corporate strategy manager, who is focused on sales analysis, customer segmentation, and the impact of new product ranges. Similarly, the report will include suggestions to improve operational efficiency and increase profitability based on the findings of the analysis. The project plan involves data collection through surveys, interviews, and financial record analysis to get a historical view of the shop's current performance and potential areas for growth and development.

The structure of the report is based on the introduction to provide an overview of the research objectives and a framework for the analysis. It is then followed by the section that is dedicated to data quality assessment, analysis of the data, data visualisation, conclusion, and recommendation. The project plan will be based to structured approach that is referenced in Gardner's Data Analytics Project Framework (2018). It is a systematic process that encompasses data collection, preparation, analysis, visualisations and interpretation. It is to ensure rigorous and comprehensive analysis that is linked to industry best practices.

Data analysis has the potential to improve business performance in coffee shops. ?gotnes et al. (2018) highlight it is based for moderating impact of leadership style to workplace conflict and emphasises the importance of data-driven insights for effective management. Allen (2018) proposing different conceptual approach for leadership theory that suggests having data analytics that provide valuable information in leadership decision making.

The shops have actionable information for sales trends, customer preference and operational efficiency to data analytics. It enables informed decision making in resource allocation, marketing strategies and product offering. Also, data analytics gives the target marketing efforts that enable the promotion for advertisement offerings for specific customer segments.

Data Quality Issues and Remedies

Specific data quality issues in the provided dataset

1.????? Negative Values in Financial Columns: Negative financial values in the dataset such as "-￡271" for "married young couples" in Newquay, December 2021 need to be addressed. These may indicate errors or discrepancies in data entry. To remedy this issue so thorough data validation processes should be implemented to catch and correct any instances of negative values for financial columns. Additionally regular audits and checks can help ensure the accuracy and reliability in dataset to effective decision-making.

2.????? Inconsistent Naming Conventions: The dataset exhibits inconsistent naming convention such as "families to children" and "families with children." Standardising category names is necessary in clarity and consistency to analysis.

3.????? Inconsistent Currency Formatting: Currency values are inconsistently formatted with some using commas as thousand separators and others not. Uniform currency formatting is essential in accurate analysis and visualisation.

4.????? Missing Values: Some entries have missing data such as the absence of "retired people" data for certain months for Newquay. Imputing missing values or investigate reasons behind their absence is crucial for completeness in analysis.

5.????? Data Discrepancies: Discrepancies between the same categories in different location such as "tourists" for Newquay and Plymouth may lead for incorrect comparisons or conclusions. Cross-verifying similar categories for locations are necessary in ensure data consistency.

Proposed Solutions

1.????? Data Cleaning: Removing and correct entries to negative financial value is in investigating potential error and anomalies for data collection or data entry process. There is an implementation in data validation checks for data entry that helping to prevent future errors and ensure accuracy for financial reporting. Regular updates and maintenance of the data quality and standard are useful for identifying and resolve discrepancies before they have impact for data analysis results.

2.????? Standardisation: Standardise category names for ensure consistency for dataset. For example rename "family children" to "family with children" in uniformity. Additionally establishing clear guidelines actually to data entry procedures can help minimising errors and maintain consistency in reporting. Regular training sessions in staff involved in data entry can also improve accuracy and efficiency in financial data management. Standardizing category is to ensure consistency for data set is there.

3.????? Currency Normalisation: Convert all currency values in consistent format such as removing commas and ensure proper currency symbol to facilitating accurate analysis.

4.????? Imputation: Fill to missing values using appropriate methods like mean, median or interpolation based for nature of the data and the context to missing values.

5.????? Cross-Verification: A comparison for having similar categories to different locations is there to identify and resolving any discrepancies. The investigation reason should be there for discrepancies and ensuring data consistency and verification for have reliable sources or additional data collection effort.

Data Analysis and Commentary

Table A: Sales Volume and Value Analysis

Month

Year

Category

Sales Volume

Sales Value

January

2020

Young people

179

￡726

January

2020

Single professional people

79

￡141

December

2022

Married young couples

151

￡363

December

2022

Families with children

53

￡252

December

2022

Tourists

41

￡258

The analysis of sales volume and value to different months and years shows interesting trends and fluctuation in consumer behaviour. For instance in January 2020 sales to "young people" in Plymouth were notably high for 179 units sold and ￡726 in sales value. This indicating potential preference or demand in young consumers during that period. Similarly in December 2022, sales to "married young couples" in Plymouth saw significant increase in 151 units sold and ￡363 in sales value. These fluctuations may influenced for various factor such as seasonality, marketing campaign or economic conditions.

Table B: Benchmark Comparisons of Market Segment Performance

Market Segment

Average Sales Volume

Average Sales Value

Young people

215

￡856

Single professional people

101

￡203

Retired people

152

￡308

Comparing the performance in different market segments is giving important data to consumer preferences and behaviour. For example "young people" consistently demonstrate higher average sales volume and value compared in other segments. This suggests that they might more lucrative target demographic for businesses. Conversely segments like "single professional people" show lower average sales volume and value indicating potential areas for improvement in marketing strategies or product offerings. By understanding these variations in segment performance thus businesses can tailor their approaches to better meet the needs and preferences for different customer groups (Francis et al., 2022).

Table C: Benchmark Comparisons of Sales Volume and Value Between Coffee Shops

Coffee Shop

Average Sales Volume

Average Sales Value

Newquay

125

￡421

Plymouth

202

￡814

The benchmark comparisons between coffee shops for Newquay and Plymouth highlight significant differences to average sales volume and value. Plymouth coffee shops demonstrate notably higher average sales volume and value compared to those for Newquay. This discrepancy may be attributed to various factor such as population density, local competition or tourist influx. For instance Plymouth being larger city actually to more residents and tourists might naturally lead to higher sales volume and value. Understanding these differences can help coffee shop owners identify opportunities for growth and improvement whether to marketing initiatives, operational enhancement or customer experience strategies (Gackowiec et al., 2020).

Data Charting and Commentary

Chart A: Comparison of Sales Value Trends Across Coffee Shops Over Time

The line chart illustrates the trends in sales value in two coffee shops thus one in Newquay and one in Plymouth over three-year period. In Newquay sales value showing gradual increase from January 2020 to December 2022 to occasional fluctuations. Conversely for Plymouth sales value demonstrating more pronounced upward trend so indicating consistent growth over the same period. For instance in January 2020, the sales value for Newquay was ￡726, while in Plymouth, it was ￡141. By December 2022 the sales value for Newquay had risen to ￡962, whereas in Plymouth, it surged to ￡1,016. This chart highlights the significant difference for sales performance between the two locations to Plymouth consistently outperforming Newquay in terms of sales value (Ghahremani-Nahr & Nozari, 2021). These trends suggest that Plymouth may have a more robust market or a stronger consumer base than Newquay. It would be beneficial for businesses in consider these factors when making decisions to expanding or investing to these locations.

Regression Analysis

SUMMARY OUTPUT

Regression Statistics

Multiple R

0.091295

R Square

0.008335

Adjusted R Square

0.0068

Standard Error

3.442954

Observations

648

ANOVA

?

df

SS

MS

F

Significance F

Regression

1

64.36139

64.36139

5.42954

0.020106

Residual

646

7657.639

11.85393

Total

647

7722

?

?

?

?

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

6.072677

0.22787

26.64973

4E-106

5.625222

6.520133

5.625222

6.520133

X Variable 1

0.001449

0.000622

2.330137

0.020106

0.000228

0.00267

0.000228

0.00267

The regression analysis conducted on the dataset reveals valuable data in the factors influencing sales performance across COTS's coffee shops. The coefficient of determination (R-squared) indicates that only about 0.83% in variation in sales volume can be explained by the independent variable studied. However, the regression model is statistically significant as evidenced to ANOVA results (F-statistic = 5.43, p-value = 0.020). This suggests that at least one independent variable is affecting the sales volume. Further examination in regression coefficients reveals that the intercept term is statistically significant (t-statistic = 26.65, p-value < 0.001) indicating baseline level of sales volume even for absence of other factors. Additionally coefficient in independent variable (X Variable 1) is also statistically significant (t-statistic = 2.33, p-value = 0.020), suggesting that it has modest positive effect on sales volume. These findings highlight the importance of considering various factor both internal and external that may influence sales performance for COTS coffee shops.

Chart B: Market Segments Performance Comparisons Between Coffee Shops

The stacked bar chart compares the performance of different market segments, such as "young people," "single professional people," and "married young couples," between coffee shops in Newquay and Plymouth. Each segment is represented by a different colour, and the bars are stacked to show the total sales volume or value for each segment in both locations. The chart reveals that certain segments, such as "young people," contribute significantly more for sales volume and value to Plymouth compared to Newquay. For example in Plymouth, sales volume from "young people" in December 2022 was 253 units whereas in Newquay so it was only 194 units. This disparity is eventually giving really different consumer preferences or market dynamic for two locations. Understanding these differences since coffee shop owners can tailor their marketing strategies and product offering to better cater in needs for their target demographics (Silva & Mendis, 2017). This can help increase sales and overall success to each location. It is important to coffee shop owners for regularly analyse and adapting these market dynamics for stay competitive for industry.

Chart C: Impact of New Product Ranges on Sales Performance in Plymouth and Comparison with Other Cities

The bar chart illustrating impact of introducing new product ranges to sales performance for Plymouth compared to other cities such as Newquay. The chart displays the sales value generated for new product ranges for Plymouth alongside the sales value from existing products. Additionally it is comparing this data with the sales value of similar product ranges to Newquay. For instance in January 2022 and so we actually have sales value of new product ranges in Plymouth was ￡748, while the sales value in existing products was ￡173. Comparatively in Newquay during the same period, the sales value from new product ranges was ￡42 also that is in from existing products so we have it was ￡111. This comparison highlights the relative success in introducing new product ranges in Plymouth as evidenced by the higher sales value compared for Newquay. Understanding the impact of new product introductions for sales performance can informing no doubt future product development and marketing strategies (Silva & Mendis, 2017).

Conclusion

In conclusion, there is sales volume and value trends across coffee shops for Newquay and Plymouth exhibit fluctuations in time and it is having really different performance across different market segments. Market segment performance also varies between coffee shops, with differences observed in sales volume across segments such as young people, single professional and married young couples. The impact of new product ranges to sales performance particularly in Plymouth highlighting potential for boosting revenue in strategic product introductions.

Addressing Issues Raised by COTS Top Management

o??? Issue 1: Lack of Sales Growth in Certain Segments - To address this, COTS should focus to market segmentation strategies tailored to the preference and need for different customer groups. Targeted marketing campaign and product offering can help capture untapped segments.

o??? Issue 2: Fluctuating Sales Performance across Locations - Implementing standardised operational procedures and performance monitoring system for all coffee shops can helping mitigate performance disparities. Sharing best practices and optimising resource allocation based for location-specific demands can also improve efficiency.

o??? Issue 3: limited Visibility into Product Impact - Investing in robust data analytics tools and systems can provide deeper data to performance of new product ranges. Conduct thorough market research and customer feedback analysis can inform product development and marketing strategies thus ensure link for customer preferences.

Business Recommendations

o??? Market Segmentation Strategy: Develop and implementing targeted marketing strategies tailored in specific market segments to maximising sales growth potential.

o??? Operational Standardisation: Establish standardised operational procedure and performance metrics to all coffee shops to ensuring consistency and efficiency to operations.

o??? Product Innovation and Testing: Continuously innovate and testing new product ranges based in market trends and customer feedback to drive sales growth and enhancing customer satisfaction.

Leveraging Data Analytics

o??? Investment in Analytics Tools: Allocating resources towards acquiring and implement advanced data analytics tools and system in facilitate data-driven decision making.

o??? Training and Skill Development: Provide training and skill development opportunities for employees to enhance data analysis capabilities for organisation.

o??? Cross-functional collaboration: Foster or actually having collaboration between different department including marketing, operation and product development for leverage data analytics to strategic planning and execution.

?

References

?

?gotnes, K. W., Einarsen, S. V., Hetland, J., & Skogstad, A. (2018). The moderating effect of laissez‐faire leadership on the relationship between co‐worker conflicts and new cases of workplace bullying: A true prospective design.?Human Resource Management Journal,?28(4), 555-568. https://onlinelibrary.wiley.com/doi/pdf/10.1111/1748-8583.12200

Allen, W. E. (2018). Leadership Theory: A Different Conceptual Approach.?Journal of Leadership Education,?17(2). https://journalofleadershiped.org/wp-content/uploads/2019/02/17_2_Allen.pdf

Bassen, A., & Kovács, A. M. (2020).?Environmental, social and governance key performance indicators from a capital market perspective?(pp. 809-820). Springer Fachmedien Wiesbaden. https://www.ssoar.info/ssoar/bitstream/handle/document/34886/ssoar-zfwu-2008-2-bassen_et_al-Environmental_social_and_governance_key.pdf?sequence=1&isAllowed=y&lnkname=ssoar-zfwu-2008-2-bassen_et_al-Environmental_social_and_governance_key.pdf

Francis, C., Hansen, P., Guelaugsson, B., Ingram, D. M., & Thomson, R. C. (2022). Weighting Key Performance Indicators of Smart Local Energy Systems: A Discrete Choice Experiment.?Energies,?15(24), 9305. https://www.mdpi.com/1996-1073/15/24/9305

Gackowiec, P., Podobińska-Staniec, M., Brzychczy, E., Kühlbach, C., & ?zver, T. (2020). Review of key performance indicators for process monitoring in the mining industry.?Energies,?13(19), 5169. https://www.mdpi.com/1996-1073/13/19/5169/pdf

Ghahremani-Nahr, J., & Nozari, H. (2021). A Survey for Investigating Key Performance Indicators in Digital Marketing.?International journal of Innovation in Marketing Elements,?1(1), 1-6. https://www.ijime.ir/index.php/ijime/article/download/4/29

Silva, S., & Mendis, B. A. K. M. (2017). Relationship between transformational, transaction and laissez-faire leadership styles and employee commitment.?European Journal of Business and Management,?9(7), 13-21. https://www.researchgate.net/profile/Kanchana-Mendis/publication/334494910_Relationship_Between_Transformational_Transaction_and_Laissez-faire_Leadership_Styles_and_Employee_Commitment/links/5d2e2fb0458515c11c36b829/Relationship-Between-Transformational-Transaction-and-Laissez-faire-Leadership-Styles-and-Employee-Commitment.pdf

Singgalen, Y. A. (2023). Culture and heritage tourism sentiment classification through cross-industry standard process for data mining.?International Journal of Basic and Applied Science,?12(3), 110-120. https://ijobas.pelnus.ac.id/index.php/ijobas/article/download/299/103

Tosunoglu, H., & Ekmekci, O. (2016). Laissez-faire leaders and organizations: how does laissez-faire leader erode the trust in organizations?.?Journal of Economics Finance and Accounting,?3(1). https://dergipark.org.tr/en/download/article-file/757422

Uslu, O. (2019). A general overview to leadership theories from a critical perspective.?Маркетинг ? менеджмент ?нновац?й, (1), 161-172. https://www.irbis-nbuv.gov.ua/cgi-bin/irbis_nbuv/cgiirbis_64.exe?C21COM=2&I21DBN=UJRN&P21DBN=UJRN&IMAGE_FILE_DOWNLOAD=1&Image_file_name=PDF/Mimi_2019_1_15.pdf

Van Wart, M. (2016). Public-sector leadership theory: An assessment. In?Administrative leadership in the public sector?(pp. 11-34). Routledge. https://drh.tecnico.ulisboa.pt/files/sites/45/PA-PublicSectorLeadershipTheory1.pdf

Wong, S. I., & Giessner, S. R. (2018). The thin line between empowering and laissez-faire leadership: An expectancy-match perspective.?Journal of Management,?44(2), 757-783. https://www.researchgate.net/profile/Sut-I-Wong/publication/271537531_Wong_S_I_W_Giessner_S_R_in_press_The_thin_line_between_empowering_and_laissez-faire_leadership_An_expectancy_match_perspective_Journal_of_Management/links/54cf7a230cf298d656637cbc/Wong-S-I-W-Giessner-S-R-in-press-The-thin-line-between-empowering-and-laissez-faire-leadership-An-expectancy-match-perspective-Journal-of-Management.pdf

Zhang, Y. (2021). Sales forecasting of promotion activities based on the cross-industry standard process for data mining of E-commerce promotional information and support vector regression.?Journal of Computers,?32(1), 212-225. https://www.csroc.org.tw/journal/JOC32-1/JOC3201-18.pdf

?