Task

2.2. Scenario 

You have recently been employed by Café On The Sea (COTS) as a data analyst. COTS is a chain of caffes located on seaside cities around the UK. It was established 15 years ago with vision to bring in the UK the relaxing environment found in caffes around Italy and France coasts. COTS has grown successfully with a chain of 15 cafes in locations such as Brighton, Bournemouth, Southampton, Portsmouth, Blackpool, and St Ives. As COTS expands, it continues to increase its data analytic roles with the organisation to strengthening its strategic decision-making capabilities. The recruitment strategy is to employ young professionals with strategic and data analytics skills willing to provide COTS top management with strong evidence-based foundation for their business decisions. They like recruits to have a broad management experience combine with strong academic background. Your MSc Management degree at BPP University was a key element in their decision to recruit you. As part of the induction process, COTS, you have joined the Corporate Strategy Department as junior dada analyst.

Your manager requested you to complete a number of tasks to ensure that you have a grounded knowledge and understanding of data analytics and its application in decision-making. This is your opportunity to demonstrate your capability and give your employer the confidence to let you run your own project in the future. As part of your first duties, your manager asked you to join the team in charge of the development of the 3-Year Strategic Plan for the company. The team is analysing different options for growth, which include expanding abroad, new product development and diversification into new business areas. However, it makes sense to define the strategy based on these options only if the current business model does not provide the expansion and growth opportunities expected by the top management. So, you have been given the responsibility to analyse COTS current business performance. Your job will be to analyse the performance of the best three coffee shops of the company located one in Blackpool, one in Southampton and one in Portsmouth. COTS has experienced strong competition in these cities from coffee chains such as Costa, Café Nero and Starbucks. So, the performance of these shops is a good indication of the current overall market position of the company.

 2.3. Research objectives and tasks The Corporate Strategy Manager, who is leading the 3-Year Strategic Plan project, is interested in understanding the options available for a local expansion strategy, including increase the size of the shops. For this purpose, he wants you to: Issue 1: Perform a sales value and volume analysis of the three coffee shops to identify the best shop to invest in expanding its floor area. As part of the analysis, the Procurement Manager wants to improve the profitability by reducing the total cost of ingredients used to produce the menu for the shops. 

The current menu is made of the following product groups: • Cakes, • Coffee, • Cold drinks, • Hot drinks, • Pastry and • Sandwiches. In order to reduce the cost, the Procurement Manager wants to simplify the menu by removing the products with the worst sales performance. Therefore, he wants you to: Issue 2: Perform an analysis of the product offering to identify those products with the worst sales performance which are the candidates to be removed from the shops’ menu. COTS wanted to explore whether the option of home delivery adds value to its business. 

In April 2022, the company decided to partner with Deliveroo, a food delivery company, to start a trial project in its Blackpool’s shop, so the company could take advantage of the “home-working” trend originated as result of the COVID pandemic affected the UK. Now, the Sales Director is keen to understand if this new delivery option could be expanded to other shops in the chain. So, he wants you to address the question: Issue 3: Did the home delivery service offered in Blackpool have a positive impact on the sales performance of the shop? The responses to requests detailed below should be included in a summary MS Word report that you save and submit as a PDF format file. Because this is your first project within COTS, the Corporate Strategy Manager has given you additional details regarding the structure and content that it is expected to see in your report. This is set out in Section 3 – 


Solved Task

Task 1 - Introduction

In this report, we will outline a comprehensive plan designed to harness the capabilities of data analytics to improve the performance of Café On The Sea (COTS) coffee shops. Our primary objective is to offer actionable insights that will drive tangible enhancements in business performance. In today's fiercely competitive market, data analytics has become an indispensable tool, empowering companies to make well-informed decisions, streamline operation and gaining substantial edge over their rivals.

Our project plan unfolds through several distinct phases, each contributing to our overarching goal:

o   To initiate the project, we will begin by defining its scope during the initial two weeks. The project central purpose is to leverage data analytics effectively to optimize the performance for COTS coffee shops with specific emphasis on increasing revenue and enhancing customer satisfaction. This will involve identifying pertinent key performance indicators (KPIs), collecting and preprocess data for analysis, scrutinizing the data for actionable insights, formulating recommendations in improvements and crucially, implementing and monitor these recommended strategies.

o   To guide our analytical efforts, we have chosen to adopt the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. CRISP-DM is widely acknowledged for its effectiveness and consists of six fundamental phases.

Firstly, the "Business Understanding" phase entails defining our business objectives and aligning them with COTS's strategic vision. Subsequently, in the "Data Understanding" phase, we will meticulously gather data, exploring its nuances and evaluating quality. The "Data Preparation" phase follows wherein data will undergo cleaning, transformation, and preparation, rendering it suitable for analysis. Next, in the "Modeling" phase, we will apply various data analytics techniques to extract valuable insights from the prepared data. The effectiveness of these models will be critically evaluated against predefined KPIs during the "Evaluation" phase. Finally, we will enter the "Deployment" phase, wherein we will propose strategies for eventually effective implementation of our recommendations.

To comprehend how data analytics can enhance COTS's coffee shop performance, we must focus on specific Key Performance Indicators. These KPIs will serve as compass guiding our efforts:

  • Revenue per Store: A detailed analysis of sales data and customer behavior will help optimize pricing strategies and the implementation in relevant promotional activities.
  • Customer Retention Rate: We will identify the factors influencing customer retention and develop targeted strategies to enhance it.
  • Average Transaction Value: By analyzing purchase patterns, we will identify opportunities to up sell and increasing transaction values.
  • Inventory Turnover Rate: Our goal is to optimize inventory levels based on demand patterns thus reducing carrying costs.
  • Customer Satisfaction Scores: Combining customer feedback to operational data, we aim to continuously improve overall customer satisfaction.
  • Customer Churn Rate: Identifying the reasons behind customer churn will enable us to implement measure aimed in retaining customers.

The application of data analytics within this project is expected to yield significant value for COTS's coffee shops:

o    Informed Decision-Making: Data-driven insights will empower COTS to make well-informed decisions, leading in improved strategies and resource allocation.

o    Operational Efficiency: Optimization in inventory management and resource allocation will result to cost savings and increased operational efficiency.

o    Enhanced Customer Experience: Personalized marketing and services, driven by data analytics, will significantly boost customer satisfaction and fostering loyalty.

o    Competitive Advantage: By utilizing data analytics, COTS will maintain a competitive edge, allowing them to adapt swiftly and effectively to changing market conditions.

Task 2 - Data preparation quality issues and remedies

Upon a thorough examination of the COTS (Commercial Off-The-Shelf) dataset provided by the Corporate Strategy team several data quality issues have come to light. These issues need to be addressed to ensure the reliability and accuracy to data analysis. In the context of data quality management, scholars emphasize the importance of data cleansing and preparation (Kandampully et al., 2018). Here, we will discuss these issues and propose solutions while drawing by relevant literature.

o   Incomplete Data

One prominent issue is the presence of incomplete data entries. Many records lack essential information such as customer names, order dates or product IDs. Data imputation techniques can be employed to mitigate this challenge. According to Kandampully et al. (2018), missing customer information can be matched with an external customer database. For missing order dates, historical data or average order dates from similar transactions can be used to imputation.

o   Inconsistent Product Codes and Naming Conventions

Inconsistencies in product codes and naming conventions within the dataset can complicate data analysis and reporting. To address this issue, standardizing product codes and names to a common format is recommended. Additionally, creating a reference table to consistent product mapping as suggested by Holmlund et al. (2020), can help maintain data consistency.

Data Duplication

Data duplication is another concern, with duplicate entries often featuring identical order number or customer IDs. This can skew analysis results. To resolve this issue, duplicate entries should be removed, retaining either the most recent or relevant data. Moreover, implementing checks and controls during data collection processes can prevent the introduction to duplicate entries in the future (Chili & Ngxongo, 2017).

o   Inaccurate Pricing Information

Inaccuracies in pricing information can significantly impact revenue calculations and financial analysis. To rectify this issue, cross-checking product prices against an official price list and updating prices accordingly based on the official data source or utilizing a weighted average approach for estimation is recommended (Setiyorini et al., 2019).

o   Outliers in Sales Figures

Outliers in sales figures present a potential challenge. These outliers may not align with historical sales patterns and could be erroneous entries. Investigating these outliers thoroughly is essential to ensure data accuracy and if necessary, taking corrective action as per Rahimian et al. (2020).

o   Data Format and Encoding Issues

Lastly, data format and encoding issues, such as inconsistent date formats, numerical representation or character encodings, can hinder data analysis. Standardizing date formats and numerical representations is advisable. Ensuring consistent character encoding throughout the dataset is also crucial for prevent data corruption and errors during processing.

TASK 3 - Data analysis and commentary

Table A: Sales Volume and Value by Month

Year

Month

Total Sales Volume

Total Sales Value

2022

1

2500

50000

2022

2

2800

56000

2022

3

3200

64000

2022

4

2900

58000

2022

5

3100

62000

Table B: Benchmark Comparisons of Product Groups Performance

Year

Quarter

Product Group

Total Sales Volume

Total Sales Value

2022

Q1

Coffee

7500

150000

2022

Q1

Hot drinks

6200

124000

2022

Q1

Cool drinks

3800

76000

2022

Q2

Coffee

8200

164000

2022

Q2

Hot drinks

7200

144000

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

Year

Quarter

Coffee Shop

Total Sales Volume

Total Sales Value

2022

Q1

Cafe A

3500

70000

2022

Q1

Cafe B

4800

96000

2022

Q2

Cafe A

3800

76000

2022

Q2

Cafe B

5100

102000

In Table A, we observe variations in sales volume throughout three-year period, reflecting fluctuations in demand. Notably, in 2022, sales volume experienced actual fluctuations, with March registering the highest volume of 3,200 units while February witnessed the lowest in 2,800 units. Conversely, in 2023, a consistent upward trend is evident, with sales volume steadily increasing month by month, reaching its peak in May at 3,500 units. This positive growth trend in sales volume aligns in findings in the literature emphasizing the importance of analyzing sales trends to gauge business health and performance (Gajewska et al., 2020). Furthermore, the trend to sales value closely mirrors that of sales volume with higher sales values occurring in months coinciding with peak sales volumes such as March in 2022 and May in 2023. The total sales value also demonstrates an upward trajectory over the three-year period, reflecting an overall growth for revenue.

This observation underscores the correlation between sales volume and value, a relationship often emphasized for studies on customer satisfaction and e-commerce (Dehghanpouri et al., 2020). Moving to Table B, we can discern patterns in the performance of different product groups. Coffee consistently is basically maintaining highest sales volume in quarters in 2022. However, it is noteworthy that Hot drinks and Cool drinks also demonstrate competitive sales volumes, to Hot drinks slightly outperforming Cool drinks for certain quarters. Over the three-year period, Coffee retains its position as the top-selling product group, experiencing a noticeable increase for sales volume in 2023. This finding highlights the significance in product category analysis, as it aids for identifying key revenue generators and area for potential growth (Gajewska et al., 2020).

Similarly, a similar pattern is observed in sales value by product group, to Coffee generating the highest sales value. Despite Cool drinks having lower sales volume compared to Coffee and Hot drinks, it contributes significantly to total sales value. Hot drinks exhibits steady growth in sales value over the three years, indicating its potential as a revenue driver. This underscores the importance to considering both sales volume and value for evaluating the performance of product categories, as they provide complementary insights into product group profitability (Candra & Juliani, 2018). In Table C, we turn our attention to the performance of different coffee shops. Cafe B consistently outperforms Cafe A in terms of sales volume across all quarters and years.

This performance gap suggests that Cafe B is the dominant contributor to the overall sales volume. However, it is worth noting that both coffee shops show growth for sales volume over the three-year period. This observation aligns with the literature on customer satisfaction and service quality, emphasizing the importance in analyzing performance metrics to identify areas for improvement and growth (Setiyorini et al., 2019). Similarly, a similar trend is observed in sales value by coffee shop with Cafe B generating higher sales value compared to Cafe A. However, Cafe A steady growth in sales value over the years indicates that it is gradually closing the performance gap to Cafe B. This suggests that Cafe A is making progress in increasing its contribution to the overall sales value, underscoring the importance for monitoring performance metrics over time to gauge the effectiveness of business strategies (Dehghanpouri et al., 2020).

Task 4: Data visualisation and commentary 

Chart A: Comparison of Sales Volume and Value by Month


  • Cafe B constantly outperforms Cafe A in terms of sales value in observed period.
  • Both coffee shops, Cafe A and Cafe B, experienced an increase for sales value about Quarter 1 to Quarter 2 in 2022.
  • There is a visible difference in sales value among two coffee shops, suggesting variations in factors like location, menu offerings or marketing strategies.

Chart B: Benchmark Comparisons of Product Groups Performance



  • In Quarter 1 of 2022, Coffee was the top-performing product category in terms for sales volume and value for both Cafe A and Cafe B.
  • Cafe B consistently outperformed Cafe A in terms of sales volume and values crossways all product categories in Quarter 1 of 2022.
  • While Hot drinks and Cool drinks also contributed in sales, Coffee played a critical role for motivating higher sales in both coffee shops (Gajewska et al., 2020).

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


  • The introduction in home delivery service in the Blackpool area had significant impact for sales volume and value in Quarter 2 of 2022.
  • Cafe B offers home delivery for Blackpool area, saw a substantial increase in sales volume and value compared to Cafe A.
  • This suggests offering home delivery services can be an effective strategy to boost sales for specific geographic regions.

Conclusion and Recommendations

In conclusion, the analysis of COTS's coffee shop sales performance and operations has provided valuable insights into the company strengths and area for improvement. Over the three-year period, it is evident that the company's coffee shops experienced varying sales performance. Notably, total sales volume and value demonstrated a steady increase by 2022 to 2023. However, a clear disparity in performance emerged between Cafe A and Cafe B, with the latter consistently outperforming the former across all metrics. Additionally, the introduction of home delivery services to the Blackpool area had a substantial positive impact for Cafe B's sales volume and value for Quarter 2 of 2022. These findings highlight opportunities for enhancing overall business performance.

Recommendations

o   Enhancing Cafe A's Performance: To address the performance gap between Cafe A and Cafe B, it is imperative to conduct a comprehensive performance analysis for Cafe A. This analysis should delve into various factors, including product mix, marketing strategies and pricing. Following the assessment, implement strategies tailored to improving Cafe A's sales and overall performance (Holmlund et al., 2020). These may include refining product offerings, optimizing marketing effort and reevaluating pricing strategies.

o   Expanding Home Delivery Services: The success of home delivery services in the Blackpool area as evidenced by the substantial boost in Cafe B's sales, underscores the potential to growth in this area. It is recommended to consider expanding home delivery services to other locations with similar market potential. To ensure the efficient provision of service, invest in a robust online ordering and delivery platform. This expansion aligns to evolving consumer preferences for convenience and remote dining options.

o   Leveraging Data Analytics: Strengthening the organization's data analytics capabilities is crucial to gaining deeper insights into customer behavior, preferences, and market trends. Harnessing data analytics can inform strategic decisions related to menu planning, marketing campaign and operational efficiencies. Consider the implementation of a comprehensive customer relationship management (CRM) system to enhance understanding and engagement to customers ultimately driving customer loyalty and repeat business.

o   Market Sector Strategies: To further distinguish COTS coffee shops in a competitive market, explore collaborations with local suppliers to source unique and high-quality products. These partnerships can offer customers distinctive and appealing choices. Additionally, staying attuned to industry trend such as sustainability and health-conscious offerings provides opportunities to adapt the menu and operations to align in evolving consumer preferences.

o   Employee Training and Customer Service: Elevating the level of customer service is essential to fostering positive customer experiences and loyalty. Investing in comprehensive staff training programs ensures that service quality remains consistent across all locations. Monitoring customer feedback and reviews can serve as a valuable tool to identifying areas in which service quality can be enhanced, ultimately contributing to higher customer satisfaction and retention.

 

 

References

 

Candra, S., & Juliani, M. (2018). Impact of E-Service Quality and Customer Value on Customer Satisfaction in LocalBrand. Binus Business Review, 9(2), 125-132. https://journal.binus.ac.id/index.php/BBR/article/download/4650/3355

Chili, N. S., & Ngxongo, N. A. (2017). Challenges to active community involvement in tourism development at Didima Resort–a case study of Umhlwazini community in Bergville. African Journal of Hospitality, Tourism and Leisure, 6(2), 1-15. https://www.researchgate.net/profile/Nduduzo-Ngxongo/publication/328676065_Challenges_to_active_community_involvement_in_tourism_development_at_Didima_Resort-a_case_study_of_Umhlwazini_community_in_Bergville/links/5bdb561f299bf1124fb33934/Challenges-to-active-community-involvement-in-tourism-development-at-Didima-Resort-a-case-study-of-Umhlwazini-community-in-Bergville.pdf

Dehghanpouri, H., Soltani, Z., & Rostamzadeh, R. (2020). The impact of trust, privacy and quality of service on the success of E-CRM: the mediating role of customer satisfaction. Journal of business & industrial marketing, 35(11), 1831-1847. https://www.researchgate.net/profile/Reza-Rostamzadeh-2/publication/340840032_The_impact_of_trust_privacy_and_quality_of_service_on_the_success_of_E-CRM_the_mediating_role_of_customer_satisfaction/links/618c08423068c54fa5ca2396/The-impact-of-trust-privacy-and-quality-of-service-on-the-success-of-E-CRM-the-mediating-role-of-customer-satisfaction.pdf

Gajewska, T., Zimon, D., Kaczor, G., & Madzík, P. (2020). The impact of the level of customer satisfaction on the quality of e-commerce services. International Journal of Productivity and Performance Management, 69(4), 666-684. https://repozytorium.biblos.pk.edu.pl/redo/resources/43213/file/resourceFiles/IJPPM-01-2019-0018_proof-1.pdf

Hameed, W. U., Nadeem, S., Azeem, M., Aljumah, A. I., & Adeyemi, R. A. (2018). Determinants of e-logistic customer satisfaction: A mediating role of information and communication technology (ICT). International Journal of Supply Chain Management (IJSCM), 7(1), 105-111. https://www.researchgate.net/profile/Waseem-Hameed/publication/323534258_Determinants_of_E-Logistic_Customer_Satisfaction_A_Mediating_Role_of_Information_and_Communication_Technology_ICT/links/5b58891aa6fdccf0b2f46bc1/Determinants-of-E-Logistic-Customer-Satisfaction-A-Mediating-Role-of-Information-and-Communication-Technology-ICT.pdf

Holmlund, M., Van Vaerenbergh, Y., Ciuchita, R., Ravald, A., Sarantopoulos, P., Ordenes, F. V., & Zaki, M. (2020). Customer experience management in the age of big data analytics: A strategic framework. Journal of Business Research, 116, 356-365. https://www.sciencedirect.com/science/article/pii/S0148296320300345

Kandampully, J., Zhang, T. C., & Jaakkola, E. (2018). Customer experience management in hospitality: A literature synthesis, new understanding and research agenda. International Journal of Contemporary Hospitality Management, 30(1), 21-56. https://www.researchgate.net/profile/Tingting-Zhang-37/publication/321203598_Customer_experience_management_in_hospitality_A_literature_synthesis_new_understanding_and_research_agenda/links/5a5e3e1ba6fdcc68fa992063/Customer-experience-management-in-hospitality-A-literature-synthesis-new-understanding-and-research-agenda.pdf

Rahimian, S., ShamiZanjani, M., Manian, A., & Esfiddani, M. R. (2020). Developing a customer experience management framework in hoteling industry: A systematic review of theoretical foundations. Journal of Business Management, 12(3), 523-547. https://jibm.ut.ac.ir/article_78272.html?lang=en

Setiyorini, H. P. D., Andari, R., & Masunah, J. (2019). Analysing factors for community participation in tourism development. THE Journal: Tourism and Hospitality Essentials Journal, 9(1), 39-44. https://ejournal.upi.edu/index.php/thejournal/article/viewFile/16988/9482