Task 1: Introduction and project plan
1.1 Report purpose and structure of the report
Performance analysis is the major purpose of this report regarding the Cafe on the Sea’s best three shops regarding coffee. This is located at Portsmouth, Southampton, and Blackpool. Through the performance examination of these shops of coffee, insight can be gained into the current position of the market. An informed decision can be made through this for the growth in the future of COTS, and regarding its plan of expansion.
1.2 Structure of the report
The structure of this report is the introduction, project plan, methodology, data collection, analysis of performance, insights, and findings, recommendations, conclusion, and plan of the project.
1.3 Overall plan of the project for the project delivery
There are various steps are followed overall for the delivery and these are data collection, data preprocessing, analysis of data, insights, and findings, and recommendations.
1.3.1 Data collection
From internal sources of COTS gathering data includes records of sales, feedback from customers, and operational metrics. Analysis of competitors, and research reports of the market are the external data sources considered also.
1.3.2 Data preprocessing
The data is collected that data must have to be organized, and clean to ensure its reliability and quality. The outliers’ removal, missing data handling, and data format that is standardized is included in it.
1.3.3 Analysis of data
Correct techniques of analytics are utilized for the performance analysis of the selected shops of coffee. It includes performance indicator identification, and with the benchmark of the industry doing the comparison of it with taking the data of the customer.
1.3.4 Insights and Findings
The data that is analyzed is interpreted for deriving results that are meaningful, and improvement areas are also identified through this (Carillo et al. 2019). In a concise and clear manner, these insights are going to be presented, that by statistical data, and visualization is supported.
1.3.5 Recommendations
The findings that are made depend on that, the recommendations that are actionable are provided for the enhancement of the coffee shops’ performance of the COTS. With the strategic objective of COTS the alignment of these recommendations will be noticed. It is also focused on growth in revenue, the satisfaction of customers, and on the competitiveness of the market.
1.4 Framework of data analytics
The comprehensive technique of data analytics framework will be adopted, and it is with the prescriptive analytics, predictive analytics, diagnostic analytics, and descriptive statistics is combined. Major questions regarding business are answered through this framework, and these are presented here (Fruhwirth et al. 2020). Compared to competitors how selected shops of coffee are performing? To the underperformance, and success what factors contribute? Can predictions be made regarding the preferences of customers and future trends of customers? For the improvement of the performance of the business, what actions are going to be taken?
Through this framework leveraging actionable insights, trends, and patterns are uncovered from the data, through which decisions that are based on evidence can be made by COTS, and strategic process planning is also optimized by COTS.
1.5 Improved analytics, and key performance indicator
In the form of major metrics, the presence of KPIs is noticed through which effectiveness, and success are gauged regarding a business. Some of the major indicators in this are the growth of sales, and revenue, loyalty, and customer satisfaction, the average value of transactions, rate of table turnover, and staff efficiency, and productivity.
The COTS will be provided the opportunity through the improved analytics for gaining a deeper understanding regarding these KPIs through the insights of real-time regarding the behaviour of customers, dynamics of the market, and operation efficiency.
Through these data analytics techniques leveraging, the areas are going to be identified by the COTS for improvements, resource allocation optimization, personalized experience of the customer, and data-driven decision-making that drive the growth of the business, and overall performance enhancement.
From the discussion above it is understood that the aim of this report is to analyze COTS coffee shops in Black analysis for the performance analysis with the analysis of the Portsmouth, and Southampton also. Through the structured plan of the project and data analytics framework utilization, valuable insights are uncovered, and for the business performance enhancement, the actionable recommendation is also provided (Heilig et al. 2020). The COTS will get empowered through the improved analytics leveraging for making strategic decisions that with its objective of growth gets aligned and competitive advantage is fostered also in the industry of coffee shop through this.
Task 2: Preparation of data, issues regarding quality, and remedies
2.1 Generic data problems and remedies
2.1.1 Incomplete data
The occurrence of incomplete data will happen when particular attributes or observations are missing. By checking missing or null values it can be identified in the dataset (Chatterjee et al. 2021). For looking after these issues the techniques of imputation can be used by an individual, and those techniques are mean imputation, multiple imputations, and regression imputation for the missing value imputation depending on the information that is available.
2.1.2 Inaccurate data
Erroneous and incorrect values are considered in the name of inaccurate data in the dataset. Through the checks regarding data validation, it can be detected. It includes checks regarding range, checks regarding consistency, and with external sources doing the cross-validation. For inaccurate data, the remedies are present in the name of correction of data, manual verification, and for clarification contacting the source of the data.
2.1.3 Inconsistent data
The rise of inconsistent data happens when there are present contradictions or discrepancies within the set of data (Elgendy et al. 2022). The identification of it can be done through the data relationship examination, data profiling performance, and use of tools of data quality. For this inconsistency resolution, transformation, standardization, and normalization techniques can be applied.
2.1.4 Duplicate data
The occurrence of duplicate data happens when various records are present that are highly similar or identical in terms of information. Through the attributes’ comparison it can be detected that are major or record linkage is also used in this regard. The duplicate data remedies involve duplicating or merging records for the retention of relevant, and unique information.
2.2 Data problems that are specific, and solutions that are proposed for the dataset of COTS
2.2.1 Sales record missing
Missing sales record identification through cross-checking with the expected number, regarding the provided data with a particular transition number happens missing sales records. If gaps are detected the respective shops regarding coffee are contacted for any data that is missing.
2.2.2 Inconsistent product codes
The inconsistencies are needed to be checked in the codes of the product through the comparison of them with the list of product codes that is standardized. Through the alignment, and mapping these issues are going to be resolved regarding the codes of the product with a format that is consistent.
2.2.3 Inaccurate figures of regarding revenue
The figures regarding revenue are going to be validated through the comparison of them with the appropriate receipts of sales, and records of finance. If the presence of inconsistency is noticed, at that time discrepancies are investigated, and the values that have errors are needed to be corrected.
2.2.4 Incomplete feedback of the customer
Missing feedback from customers is needed to be identified through the complete analysis of the dataset feedback. The coffee shops are going to be contacted for missing feedback gathering. If required the missing feedback is imputed through the use of the technique, named analysis of sentiment or on available data the presence of topic modelling.
2.2.5 In operational metrics presence of outliers
The outliers are needed to be identified in the operational metrics, named turnover rate table or productivity of staff using the methods of statistics named z scores or box plots (Breidbach and Maglio, 2020). The reason behind the outliers is needed to be investigated, and from that it is also identified whether in the data there are any errors are present or whether the data are valid or not. According to that the outliers handling is done by applying them or removing them through the transformation that is appropriate.
2.2.6 Inconsistencies in format of data
In the formats of data, the inconsistencies are needed to be checked. It includes the measurement of units or formats of data. The formats of data are standardized for the analysis easing, and ensuring consistency.
From the above discussion, it has come to know that the particular problems regarding data in the dataset of COTS provide incomplete feedback from customers, in the operational metrics presence of outliers, and inconsistencies in the format of data (Hannila et al. 2022). Through the validation of data, these issues can be addressed. The issues can also be addressed through external sources cross-referencing, coffee shops communication, manipulation of data, detection techniques of outliers, and standardization (Mahmood et al. 2023). Through this, it is ensured that a more accurate, and reliable dataset for analysis will be obtained.
Task 3: Data analysis
Table A: Sales volume and value by month, by year and across the year period
The data that are calculated are the “average”, “ranges”, “standard deviations”, top to bottom performing product categories, and top to bottom performing time period.
3.1 Sales volume and value by month
| Average | 71 | 230 | Average | 79 | 231 | Average | 86 | 270 | ||||||||
| standard deviation | 41.85963 | 161.114784 | standard deviation | 44.7622 | 195.5096372 | standard deviation | 51.08518036 | 184.6467 | ||||||||
| Range (min) | 15 | 90 | Range (min) | 17 | -540 | Range (min) | 17 | 0 | ||||||||
| Range (max) | 190 | 760 | Range (max) | 206 | 824 | Range (max) | 220 | 880 |
| Average | 99 | 315 | Average | 82 | 254 | Average | 89 | 282 | ||||||||
| standard deviation | 59.75766 | 242.093896 | standard deviation | 50.47655 | 201.0137456 | standard deviation | 53.29090178 | 191.7311 | ||||||||
| Range (min) | -1 | -344 | Range (min) | -1 | -385 | Range (min) | 18 | 98 | ||||||||
| Range (max) | 290 | 1160 | Range (max) | 240 | 960 | Range (max) | 252 | 1008 |
| Average | 96 | 309 | Average | 103 | 332 | Average | 84 | 273 | ||||||||
| standard deviation | 55.02269 | 206.19686 | standard deviation | 60.23643 | 223.364767 | standard deviation | 47.9979314 | 179.3951 | ||||||||
| Range (min) | 23 | 108 | Range (min) | 23 | 116 | Range (min) | 20 | 84 | ||||||||
| Range (max) | 256 | 1024 | Range (max) | 270 | 1080 | Range (max) | 230 | 920 |
| Average | 86 | 277 | Average | 86 | 271 | Average | 88 | 271 | ||||||||
| standard deviation | 50.41683 | 193.417352 | standard deviation | 48.32453 | 199.8315191 | standard deviation | 54.26589282 | 211.6013 | ||||||||
| Range (min) | 20 | 94 | Range (min) | 18 | 88 | Range (min) | 21 | -266 | ||||||||
| Range (max) | 250 | 1000 | Range (max) | 270 | 1080 | Range (max) | 256 | 1024 |
3.2 Sales volume and value by year
| Average | 87 | 272 | Average | 92 | 289 | ||||
| standard deviation | 52.06624 | 203.65081 | standard deviation | 55.41399 | 211.0976748 | ||||
| Range (min) | -1 | -540 | Range (min) | -1 | -385 | ||||
| Range (max) | 290 | 1160 | Range (max) | 270 | 1080 |
| Average | 103 | 328 |
| standard deviation | 65.72922226 | 233.5244 |
| Range (min) | 15 | 88 |
| Range (max) | 356 | 1425 |
The table above is regarding the sales volume and value across the 3-year period by month and by year.
From the table above the sales volume and sales value of 2020, 2021, and 2022 is calculated. The value of the average in 2020 is 87 and 272, “standard deviation” is 52.06624, and 203.65081. The range value is -1 and -540 which is minimum, and 290 and 1160 is the range value that is maximum (Dotye 2021). The second year is 2022, and accordingly in that year average value determined 92 and 289, “standard deviation” value determined 55.41399, and 211.0976748, the range value which is minimum is -1 and -385, and the range value that is maximum is 270 and 1080 (Shah and Murthi, 2021).
- From this table it is understood that from year 2020 to 2021 values are higher of COTS
- Rather than 2021 the values of these variables are higher in 2022 of COTS.
Table B Benchmark comparison of product groups performance, covering the sales value, and volume by quarter by year.
| Average | 86 | 269 |
| standard deviation | 52.54778 | 204.838547 |
| Range (min) | -1 | -540 |
| Range (max) | 290 | 1160 |
| Average | 92 | 294 |
| standard deviation | 55.01582 | 206.4085067 |
| Range (min) | -1 | -385 |
| Range (max) | 270 | 1080 |
| Average | 86 | 273 |
| standard deviation | 49.93653449 | 187.291 |
| Range (min) | 18 | -266 |
| Range (max) | 256 | 1024 |
| Average | 113 | 362 |
| standard deviation | 61.77367 | 243.125572 |
| Range (min) | -1 | -344 |
| Range (max) | 290 | 1160 |
| Average | 84 | 255 |
| standard deviation | 44.40001 | 191.5107719 |
| Range (min) | 21 | -540 |
| Range (max) | 201 | 804 |
| Average | 58 | 187 |
| standard deviation | 31.98777901 | 122.903 |
| Range (min) | 15 | 0 |
| Range (max) | 145 | 580 |
| Average | 137 | 437 |
| standard deviation | 76.23985 | 295.65344 |
| Range (min) | -1 | -385 |
| Range (max) | 356 | 1425 |
| Average | 97 | 311 |
| standard deviation | 50.13039 | 187.1931985 |
| Range (min) | 26 | 132 |
| Range (max) | 228 | 912 |
| Average | 119 | 380 |
| standard deviation | 60.7053 | 229.513754 |
| Range (min) | 36 | 176 |
| Range (max) | 256 | 1024 |
| Average | 89 | 277 |
| standard deviation | 43.52877 | 174.8139693 |
| Range (min) | 26 | -266 |
| Range (max) | 179 | 714 |
| Average | 61 | 199 |
| standard deviation | 33.43416553 | 130.75 |
| Range (min) | 15 | 0 |
| Range (max) | 145 | 580 |
| Average | 115 | 369 | Average | 122 | 387 | Average | 145 | 464 | ||||||||
| standard deviation | 62.38522 | 247.464317 | standard deviation | 64.01448 | 252.3714531 | standard deviation | 77.91663133 | 283.8447 | ||||||||
| Range (min) | -1 | -344 | Range (min) | -1 | -385 | Range (min) | 36 | 160 | ||||||||
| Range (max) | 290 | 1160 | Range (max) | 270 | 1080 | Range (max) | 356 | 1425 |
Table 2: Benchmark comparisons of product groups’ performance covering sales volume and value by quarter, by year and across the 3 years period
(Source: In MS Excel self created)
The table above is regarding the Benchmark comparison of group performance of product that covers volume of sales and value of sales by year and by quarter across period of 3 years (Elfindah 2021).
The contents of this table are in this table the value of the “average”, “range”, and standard deviation is calculated.
- From this table it is come to know that variable from by quarter, and years are remaining constant (Akter 2021).
- But from 2021 to 2022 the values of the variables are gone higher.
Table C Benchmark comparison of sales value, and sales volume between coffee shops by quarter, by year
| Average | 113 | 362 | Average | 84 | 255 | ||||
| standard deviation | 61.77367 | 243.125572 | standard deviation | 44.40001 | 191.5107719 | ||||
| Range (min) | -1 | -344 | Range (min) | 21 | -540 | ||||
| Range (max) | 290 | 1160 | Range (max) | 201 | 804 |
| Average | 58 | 187 |
| standard deviation | 31.98777901 | 122.903 |
| Range (min) | 15 | 0 |
| Range (max) | 145 | 580 |
| Average | 137 | 437 | Average | 97 | 311 | ||||
| standard deviation | 76.23985 | 295.65344 | standard deviation | 50.13039 | 187.1931985 | ||||
| Range (min) | -1 | -385 | Range (min) | 26 | 132 | ||||
| Range (max) | 356 | 1425 | Range (max) | 228 | 912 |
| Average | 63 | 203 |
| standard deviation | 33.17712512 | 130.9418 |
| Range (min) | 18 | 98 |
| Range (max) | 135 | 540 |
| Average | 119 | 380 |
| standard deviation | 60.7053 | 229.513754 |
| Range (min) | 36 | 176 |
| Range (max) | 256 | 1024 |
| Average | 89 | 277 |
| standard deviation | 43.52877 | 174.8139693 |
| Range (min) | 26 | -266 |
| Range (max) | 179 | 714 |
| Average | 61 | 199 |
| standard deviation | 33.43416553 | 130.75 |
| Range (min) | 15 | 0 |
| Range (max) | 145 | 580 |
| Average | 115 | 369 | Average | 122 | 387 | Average | 145 | 464 | ||||||||
| standard deviation | 62.38522 | 247.464317 | standard deviation | 64.01448 | 252.3714531 | standard deviation | 77.91663133 | 283.8447 | ||||||||
| Range (min) | -1 | -344 | Range (min) | -1 | -385 | Range (min) | 36 | 160 | ||||||||
| Range (max) | 290 | 1160 | Range (max) | 270 | 1080 | Range (max) | 356 | 1425 |
Table 3: Benchmark comparisons of sales volume and value between coffee shops by quarter, by year and across the 3 years period
(Source: In MS Excel self created)
The table above is regarding the Benchmark comparison of sales value, and sales volume between coffee shops by year and by quarter across a three-year period (Camilleri 2020).
The variables, range, average, and “stand deviation” are calculated.
- From this table, it has come to be known that the coffee shops’ market values are changing according to year and by a quarter of each year (Akhtar et al. 2019).
- The variable that is calculated is range, average, and standard deviation.
Task 4: Data valuation and commentary
Chart A: Comparison of sales value trends across coffee shops over time
Figure 1: Comparison of sales value trends across coffee shops over time
(Source: In MS Excel self created)
Chart B: Product category performance between coffee shops
:
Figure 2: Product category performance between coffee shops
(Source: In MS Excel self created)
Chart C: Home delivery services impact in the black pool area and in the comparison with other cities
Figure 3: Home delivery services impact in the black pool area and in the comparison with other cities
(Source: In MS Excel self created)
Task 5: Conclusion and recommendation
From the discussion above it are concluded that in various months, quarters, and years the COTS has operated its coffee shops in the markets that are mentioned, and the market that is performing well for COTS is the Blackpool market in terms of the sales value, and in terms of the sales volume (Grandhi et al. 2021).
For COTS the business recommendation is that except in these markets, COTS must expand its business in the other parts of the world for its increase in sales volume, and sales value where the market is open and not present in a saturated state.
Reference list
Journal
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Fruhwirth, M., Ropposch, C. and Pammer-Schindler, V., 2020. Supporting Data-Driven Business Model Innovations: A structured literature review on tools and methods. Journal of Business Models, 8(1), pp.7-25.
Grandhi, B., Patwa, N. and Saleem, K., 2021. Data-driven marketing for growth and profitability. EuroMed Journal of Business, 16(4), pp.381-398.
Hannila, H., Silvola, R., Harkonen, J. and Haapasalo, H., 2022. Data-driven begins with DATA; potential of data assets. Journal of Computer Information Systems, 62(1), pp.29-38.
Heilig, L., Stahlbock, R. and Voß, S., 2020. From digitalization to data-driven decision making in container terminals. Handbook of terminal planning, pp.125-154.
Mahmood, A., Al Marzooqi, A., El Khatib, M. and AlAmeemi, H., 2023. How Artificial Intelligence can leverage Project Management Information system (PMIS) and data driven decision making in project management. International Journal of Business Analytics and Security (IJBAS), 3(1), pp.180-191.
Shah, D. and Murthi, B.P.S., 2021. Marketing in a data-driven digital world: Implications for the role and scope of marketing. Journal of Business Research, 125, pp.772-779.
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