• Posted on November 1, 2023
• Deepanshu saini

 

Introduction:

Café on the Sea (COTS) is pretty much as charming as the beachfront breeze that motivated its commencement. Established in 2005 by famous restaurateur and visionary, Sebastian Rodriguez, Beds was conceived out of profound energy for both culinary greatness and the hypnotizing charm of the sea (Bulger et. al. 2014).

Sebastian, a long-lasting admirer of the ocean, spent his young life investigating beachfront towns and harbors, submerging himself in the energetic culture and flavors they brought to the table. It was during these early stages that he fostered a significant appreciation for the special concordance between seaside living and gastronomy (Power, 2008).

 

By directing a thorough examination, you will give experience in the exhibition of these bistros and distinguish regions for development. Your discoveries will assist Bunks with settling on educated choices regarding the future course of their business, whether it includes growing to new areas, presenting new items, or expanding into new business regions (Carillo et. al. 2019).

As an information examiner, your job is basic in supporting Beds’ key dynamic cycle. By utilizing your scholarly foundation and viable involvement with information examination, you have the potential chance to add to the organization’s development and achievement. Your MSc in Administration degree from BPP College shows your aptitude for breaking down complex informational indexes and making an interpretation of them into significant experiences (Heilig et. al. 2020).

Task 1: Project Planning 

Purpose of report and Overall project Plan:

The motivation behind the report is to dissect Café on the Sea’s (COTS) current business model and assess its ability to provide expansion and growth opportunities. The report intends to give proof-based bits of knowledge and proposals to help the advancement of the 3-Year Masterful course of action for the organization. As a data analyst, your task is to conduct a thorough analysis of the current business model and present your findings to the Corporate Strategy Department (Berndtsson et. al. 2018).

The report structure and contents can be outlined as follows:

Presentation

• Momentarily present the motivation behind the report and its importance for Beds.
• Give an outline of Beds, its set of experiences, and its vision as a chain of coastline bistros in the UK.
• Make sense of the setting of the report, featuring the organization’s extension plans and the requirement for information examination in essential direction.

Approach

• Depict the examination technique utilized for the investigation.
• Make sense of the information assortment strategies, sources, and any apparatuses or methods utilized in the examination.

Current Business Model Assessment

• Assess the on-going plan of action of Beds, taking into account factors, for example, income streams, target market, serious scene, and incentive.
• Evaluate the qualities and shortcomings of the ongoing plan of action with regards to extension and useful learning experiences.
• Distinguish any impediments or difficulties that the ongoing plan of action might posture for accomplishing the organization’s essential goals.

 

Expansion and Growth Options

• Give an outline of the different extension and development choices being considered by Beds, including extending abroad, new item advancement, and expansion into new business regions.
• Examine every choice concerning its likely advantages, dangers, and arrangement with Beds’ vision and market situating.
• Based on the assessment of the current business model and the expansion options, determine the suitability of the current business model for achieving the desired growth and expansion (Elgendy al.2022).
• Present findings and insights regarding the alignment between the current business model and the expansion objectives.

COTS Data Analytics Framework:

Project Plan: Data Analytics Framework for Café on the Sea (COTS)

Objective:

The goal of this venture plan is to lay out an informal investigation structure for Bistro on the Ocean (Beds) to address the core business questions doled out to us. This system will empower Bunks to use its information resources successfully and determine noteworthy experiences to upgrade functional productivity, consumer loyalty, and by and large business execution (Hupperz et. al. 2021).

1. Data Collection:

• Identify relevant data sources: This includes transactional data (sales, orders), customer data (demographics, preferences), operational data (inventory, staff), and external data (weather, events).

Implement data capture mechanisms: Deploy systems and processes to collect and store data in a structured format, ensuring data integrity and security (Rokade et. al. 2022).

2. Data Storage and Management:

• Select an appropriate data storage solution: Evaluate and choose a database or data warehouse system that can efficiently handle the volume, variety, and velocity of COTS’ data.
• Establish data governance policies: Define data ownership, access controls, and data retention policies to ensure data quality and compliance with regulations (e.g., GDPR).

3. Data Analysis and Modelling:

• Recognize core business questions:Collaborate with Bunks accomplices to evidently portray the specific business questions and targets that should be tended to.
• Select reasonable examination strategies:Pick appropriate authentic, computer based intelligence, or farsighted showing methods considering the possibility of the business questions and available data (Jawed and Sajid, 2022).

4. Visualization and Reporting:

• Design interactive dashboards:Pick appropriate authentic, computer based intelligence, or farsighted showing methods considering the possibility of the business questions and available data.
• Enable self-service analytics:Connect with Beds accomplices to research data independently and make altered reports using self-organization examination instruments.

5. Insights and Significant Suggestions:

• Interpret and communicate insights:Analyze the results of the data analysis and displaying stage, and make an interpretation of them into significant bits of knowledge and noteworthy proposals.
• Collaborate with COTS management:Engage with key stakeholders to discuss and implement the recommended actions, aligning them with COTS’ strategic goals and objectives.

Utilizing Café on the Sea (COTS) to Address Core Business Questions:

Café on the Sea (COTS) can leverage the data analytics framework outlined above to address its core business questions effectively:

1. Expanding consumer loyalty:

• Dissect client input information to recognize normal issues or areas of progress.
• Use feeling investigation methods to grasp client opinions and inclinations.
• Produce bits of knowledge on client conduct, inclinations, and fulfilment levels to customize contributions and further develop the general client experience (Vögler al.2017).

2. Improving functional effectiveness:

• Break down value-based information and functional measurements to distinguish bottlenecks or failures.
• Streamline stock administration by examining deals information, request examples, and anticipating future interest.
• Use information on staff booking and client traffic to advance asset designation and further develop administration quality (Pham al.2016).

Coffee Shop KPI Analytics:

Café on the Sea, a fictional coffee shop chain, may have several key performance indicators (KPIs) to measure its success and monitor its performance (Strohbach et. al. 2015). While the particular KPIs can change in light of the organization’s objectives and procedures, here are some normal KPIs for cafés and how improved examination can empower upgrades against these KPIs:

 

Figure 2 Key Performance Indicators

(Source: Daan van Beek, 2023)

Sales Revenue: One of the essential KPIs for any business is income. An improved examination can give experiences into deal patterns, client inclinations, and evaluating methodologies. By breaking down deal information, Bistro on the Ocean can recognize high-performing items, streamline valuing, and foster designated showcasing efforts to build deals and income.

Customer Satisfaction: Estimating consumer loyalty is significant for holding clients and driving recurrent business. Analytics can help Café on the Sea collect and analyze customer feedback data from sources like surveys, online reviews, and social media. By distinguishing examples and feelings in client criticism, the café can resolve issues immediately, further develop administration quality, and upgrade general speaking consumer loyalty (Goforth et. al. 2022).

Customer Acquisition and Retention: Tracking the number of new customers acquired and the rate of customer retention is important for sustained growth. Improved analytics can provide insights into customer acquisition channels, customer demographics, and customer behavior patterns. Bistro on the Ocean can use this data to target advertising endeavors, distinguish potential client portions, and foster dependability projects to hold existing customers.

Average Transaction Value: Increasing the average transaction value can significantly impact revenue. Analytics can help Café on the Sea analyze transaction data to identify opportunities for upselling and cross-selling. By understanding client inclinations and buy designs, the café can customize suggestions and upgrade its menu and estimating systems to support higher spending per exchange.

Operational Efficiency: Effective activities are basic for benefit. An examination can help Bistro on the Ocean screen and improve different functional perspectives, like stock administration, staffing levels, and store network effectiveness. By utilizing information investigation, the bistro can distinguish bottlenecks, smooth out processes, diminish costs, and work on general functional productivity (Maschke et. al. 2022).

Employee Performance: Engaged and high-performing employees contribute to a positive customer experience. Analytics can give experiences into worker execution measurements, for example, deals per staff part, client input on help quality, and representative booking advancement. By utilizing investigation to distinguish regions for development and give designated preparation, Café on the Sea can enhance employee performance and ultimately deliver better customer service.

In summary, improved examination empowers Bistro on the Ocean to pursue information driven choices and enhancements against these KPIs. By utilizing information and experiences, the bistro can advance its tasks, upgrade consumer loyalty, increment deals, and drive overall business growth (Kirsh and Joy, 2020).

Task 2: Data quality issues and remedies

Data Challenges & Solutions:

1. Generic Issues in Collecting, Integrating, and Cleaning Data:
2. Information Accessibility:One of the essential difficulties looked by information investigators is the accessibility of applicable information. Information might be fragmented, obsolete, or basically not exist for specific factors or time spans.
3. Data Quality:Guaranteeing information quality is significant for exact examination. Issues like missing qualities, copy records, conflicting organizing, and anomalies can influence the dependability of insightful outcomes. Data analysts need to employ techniques such as data profiling and validation to identify and address these issues (Rahman et. al. 2016).
4. Data Coordination:Associations frequently have information put away in different frameworks or configurations, making mix a perplexing undertaking. Data analysts must reconcile differences in data structure, naming conventions, and data definitions to create a unified and coherent dataset.
5. Data Cleaning:Raw data often requires extensive cleaning to remove inconsistencies, errors, and inaccuracies. This cycle includes errands like revising incorrect spellings, normalizing designs, dealing with missing qualities, and settling information inconsistencies across sources.
6. Data Privacy and Security:Data analysts must be mindful of privacy regulations and ensure that sensitive information is protected. This may involve anonym vitality or aggregating data to maintain confidentiality while still enabling meaningful analysis (Ahmed et. al.2017).
7. Specific Issues with the Project Data and Proposed Solutions:

It is vital to take note of that without explicit insights concerning the undertaking information, giving exact solutions is testing However, here are some common issues and potential strategies to address them:

1. Missing Data:Assuming the venture data contains missing qualities, a few methodologies can be utilized. One choice is to ascribe missing data utilizing methods like mean attribution, relapse ascription, or prescient demonstrating. Another approach is to consider excluding incomplete cases or variables if the missing ness is substantial and impacts the analysis (Demchenko et. al.2013).
2. Inconsistent Formats:Assuming that information factors have conflicting arrangements, information examiners can apply information change methods to normalize them. For instance, changing over dates into a predictable configuration or guaranteeing steady units of estimation across factors.
3. Copies and Irregularities:Information deduplication procedures can be utilized to recognize and eliminate copy records from the dataset. Furthermore, information profiling and approval can assist with revealing irregularities in information section or errors between various sources (Jinet. al. 2015).
4. Data Integrity: To guarantee information uprightness, information experts can carry out approval checks and lead information compromise works out. This includes contrasting information across various sources or cross-checking against outside benchmarks to recognize and correct any irregularities or blunders.
5. Data Documentation:It is crucial to have clear documentation that describes the data sources, data cleaning and transformation steps, and any assumptions made during the process. This documentation guarantees straightforwardness, reproducibility, and simplicity of coordinated effort among colleagues (Coates et. al.2017).

 

Figure 3 6 Common Data Quality Issues

(Source: Data Quality Issues, 2023)

Overall, the particular issues with the venture information will decide the proper arrangements. Information experts ought to completely evaluate the information, team up with the Corporate System group to explain necessities, and apply fitting information cleaning and joining methods to guarantee the dependability and convenience of the information for investigation (Badke-Schau and Frankenberger, 1999).

Data Problems Identification and Solutions:

“Café on the Sea” dataset or its specific data problems. However, I can provide you with a list of common data problems that may occur in datasets in general.

Missing data: This problem occurs when one or more data points are missing from the dataset. To address this, you can consider imputation techniques such as mean, median, or regression accusation or you can remove the rows or columns with missing data depending on the significance of the missing values (Childs et. al. 2005).

Outliers: Outliers are data points that significantly deviate from the normal distribution of the dataset. They can skew statistical analysis and modelling results. Identifying outliers can be done using statistical methods like the Z-score or the interquartile range (IQR). Addressing outliers may involve removing them if they are data entry errors or transforming them if they are valid but extreme values.

Inconsistent or incorrect data: This issue refers to data that doesn’t adhere to the expected format or violates logical constraints. It can include misspellings, inconsistent date formats, or contradictory information. To address this problem, you can use data cleaning techniques such as regular expressions, fuzzy matching, or domain-specific validation rules.

Duplicates: Duplicates occur when there are multiple identical or very similar records in the dataset. They can lead to biased analysis or model training. Detecting duplicates can be done by comparing records based on specific attributes or using similarity measures. Eliminating duplicates can involve removing them or merging them into a single representative record.

Inaccurate or out-dated data: Data can become inaccurate or out-dated over time, especially if the dataset is not regularly updated. This problem can be identified by comparing the data with reliable external sources or conducting periodic audits. To address this, you can update the dataset with the most recent information or indicate the data’s validity period (Gichoya, 2005).

Incomplete or inconsistent data: This problem occurs when the dataset lacks certain attributes or exhibits inconsistencies in attribute values. It can make analysis or modelling challenging. Addressing this issue involves carefully examining the dataset structure, validating the data against expected patterns, and making necessary corrections or enhancements.

It’s important to note that the specific data problems in the “Café on the Sea” dataset can only be identified by examining the dataset itself. The proposed solutions may vary depending on the nature of the data and the specific analysis or modelling goals (Assunção et. al. 2015).

Task 3: Data analysis and commentary

Sales Values 2020				Sales Volume 2020
Subtype	Blackpool	Portsmouth	Southampton	Subtype	Blackpool	Portsmouth	Southampton
Coffee	10336	6126	5336	Coffee	2584	1802	1334
Cold Drinks	4210	2921	2073	Cold Drinks	1684	1169	829
Hot Dirnks	2492	1830	1154	Hot Dirnks	1246	915	577
Cakes	3830	2843	1920	Cakes	766	569	384
Pastry	2744	2487	1736	Pastry	1543	1244	868
Sandwiches	2952	2277	1476	Sandwiches	492	380	246
Total	26564	18484	13695	Total	8315	6079	4238

                                                   Table A: Sales Trends Analysis

The table provided presents sales values and sales volume data for different subtypes in three locations for the year 2020. The data is divided into two sections: Sales Values and Sales Volume.

Sales Values 2020:

Subtype: This column lists the different subtypes of products sold, including Coffee, Cold Drinks, Hot Drinks, Cakes, Pastry, and Sandwiches.

Blackpool, Portsmouth, and Southampton: These columns represent the sales values (in an unspecified currency) for each subtype in the respective locations. For example, in Blackpool, the sales values for Coffee, Cold Drinks, Hot Drinks, Cakes, Pastry, and Sandwiches were 10,336, 4,210, 2,492, 3,830, 2,744, and 2,952, respectively.

Sales Volume 2020:

Subtype: This column lists the same subtypes of products as in the Sales Values section.

Blackpool, Portsmouth, and Southampton: These columns represent the sales volume for each subtype in the respective locations. For example, in Blackpool, the sales volume for Coffee, Cold Drinks, Hot Drinks, Cakes, Pastry, and Sandwiches were 2,584, 1,684, 1,246, 766, 1,543, and 492, respectively.

The “Total” row at the bottom of each section provides the sum of sales values and sales volume across all subtypes for each location. For example, the total sales value in Blackpool is 26,564, while the total sales volume is 8,315.

It’s worth noting that without additional context or information, it’s difficult to interpret the significance of these numbers. The table simply provides data on sales values and sales volume for different product subtypes in different locations.

Sales Values 2021				Sales Volume 2021
Subtype	Blackpool	Portsmouth	Southampton	Subtype	Blackpool	Portsmouth	Southampton
Coffee	10600	7854	5728	Coffee	2650	1964	1432
Cold Drinks	3865	2764	2148	Cold Drinks	1699	1319	859
Hot Dirnks	2616	2040	1226	Hot Dirnks	1308	1020	613
Cakes	4050	2880	1945	Cakes	810	576	389
Pastry	3508	2457	1760	Pastry	1754	1229	880
Sandwiches	3192	2304	1566	Sandwiches	532	384	261
Total	27831	20299	14373	Total	8753	6492	4434

Table B: Sales Performance Comparison

The table provides benchmark comparisons of category performance for sales volume and value for different subtypes across various locations (Blackpool, Portsmouth, and Southampton) for the year 2021.

Sales Values 2021:

– The values in this section represent the total sales value (in currency units) for each subtype in the specified locations.

– For example, in Blackpool, the sales values for Coffee, Cold Drinks, Hot Drinks, Cakes, Pastry, and Sandwiches were 10,600, 3,865, 2,616, 4,050, 3,508, and 3,192, respectively.

– The corresponding sales values for each subtype in Portsmouth and Southampton are also provided in the table.

Sales Volume 2021:

– This section presents the sales volume (number of units sold) for each subtype in the specified locations.

– For instance, in Blackpool, the sales volume for Coffee, Cold Drinks, Hot Drinks, Cakes, Pastry, and Sandwiches were 2,650, 1,699, 1,308, 810, 1,754, and 532, respectively.

– The sales volume for each subtype in Portsmouth and Southampton is also included in the table.

The table allows for comparisons between the sales performances of different subtypes within each location, as well as comparisons across the entire year and analysis period. It provides insights into the relative performance of each subtype in terms of both sales volume and value, enabling further analysis and decision-making related to sales strategies, product focus, and market trends.

Sales Values 2022				Sales Volume 2022
Subtype	Blackpool	Portsmouth	Southampton	Subtype	Blackpool	Portsmouth	Southampton
Coffee	12349	8166	5476	Coffee	3087	2042	1369
Cold Drinks	5260	3439	2433	Cold Drinks	2104	1376	973
Hot Dirnks	3082	1992	1312	Hot Dirnks	1541	996	656
Cakes	4735	2955	2095	Cakes	947	591	419
Pastry	4314	2904	1962	Pastry	2157	1452	981
Sandwiches	3646	2421	1608	Sandwiches	608	404	268
Total	33386	21877	14886	Total	10444	6861	4666

                                          Table C: Sales Volume & Value Comparison

The table provides benchmark comparisons of sales volume and value between markets by quarter, by year, and across the whole analysis period. It includes data for the year 2022.

Sales Values:

The sales values section shows the total sales value for different subtypes of products in three markets: Blackpool, Portsmouth, and Southampton. The subtypes of products include Coffee, Cold Drinks, Hot Drinks, Cakes, Pastry, Sandwiches, and a Total value. The values represent the monetary worth of sales in each market for each subtype of product.

Similarly, the table provides the sales values for Portsmouth and Southampton for each quarter of 2022.

Sales Volume:

The sales volume section shows the total sales volume for the same subtypes of products in the three markets. It represents the number of units sold for each subtype of product in each market.

Similarly, the table provides the sales volume for Portsmouth and Southampton for each quarter of 2022.

Across the Whole Analysis Period:

The table also provides a total value for each subtype and total sales value for all subtypes combined across the whole analysis period (presumably for the year 2022). Similarly, it provides the total sales volume for each subtype and the total sales volume for all subtypes combined across the whole analysis period.

This table allows for a comparison of sales volume and value between different markets by quarter, by year (2022), and across the entire analysis period. It provides insights into the performance of different subtypes of products in each market and allows for tracking trends and making comparisons based on sales volume and value.

Task 4: Data charting and commentary

 

 

 

 

 

 

                                Chart A: Sale volumes and sales values in 2020

• In year 2020 more sales volumes and values are generated from Blackpool in comparisons to Portsmouth and Southampton.
• This sales of coffee is higher than other products.

 

 

 

                   Chart B: Sale volumes and sales values in 2021

• In year 2021 Sales volumes and sales values are increases for all the products from previous year.
• Here also more sales are coming from Blackpool then other areas.

 

 

 

 

                                            Chart C: Sale volumes and sales values in 2022

• In year 2022 also Sales volumes and sales values are increases for all the products from previous years.
• Demand of coffee is increased in year 2022 from previous two years.

Task 5: Conclusions and recommendations

Recommendations:

Based on the data and tables provided, here are some recommendations for data analysis and commentary:

Analyze Sales Trends:

• Compare sales values and volumes across subtypes and locations in 2020.
• Identify top-performing subtypes in each location.
• Look for significant differences and patterns in sales trends.
• Investigate factors influencing sales performance (e.g., promotions, pricing, and seasonality).

Compare Sales Performance:

• Utilize sales performance comparison data for 2021 to assess subtype performance across locations.
• Identify subtypes with improved or declined sales values and volumes.
• Analyze differences in sales performance between locations.
• Consider external factors like market conditions, preferences, and competition.

Evaluate Sales Volume and Value:

• Analyze sales volume and value data for 2022 across markets and quarters.
• Identify seasonal patterns and fluctuations for each subtype and location.
• Compare sales volume and value between markets and quarters.
• Look for correlations and trends to understand pricing and customer behavior.

Identify Key Insights and Recommendations:

• Determine top-performing subtypes based on sales volume and value.
• Evaluate market potential and growth prospects for subtypes in different locations.
• Address consistently underperforming subtypes with improvement strategies.
• Consider marketing campaigns, product quality, pricing, and customer preferences.

Provide recommendations for adjusting product focus, refining sales strategies, and exploring new market opportunities.

Conclusion

In conclusion, Café on the Sea (COTS) is a beachfront eatery network in the UK that was established with a profound enthusiasm for culinary greatness and the charm of the ocean. The organization is presently hoping to extend and develop its business, and as an information expert, your job is vital in giving experiences and proposals to help their essential direction.

The project plan outlined the purpose of the report, which is to analyze COTS’ current business model and assess its potential for expansion and growth. The report structure covered different viewpoints, including the evaluation of the ongoing plan of action, ID of extension choices, and the improvement of an information investigation system to address core business questions.

The data analytics framework provided a comprehensive plan for data collection, storage, analysis, visualization, and reporting. It featured the significance of distinguishing applicable information sources, carrying out information catch systems, choosing fitting information stockpiling arrangements, and laying out information administration approaches. Furthermore, it emphasized the need to recognize core business questions, select suitable analysis methods, design interactive dashboards, and interpret insights for meaningful recommendations.

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