Analysing Sales Performance and Strategic Planning for COTS Café

Task 1: Introduction and Project plan

Purpose of the report 

The purpose of this study is to analyse Café On The Sea's (COTS) current company performance and give insights to help construct the 3-Year Strategic Plan. The report will be divided into three sections: sales performance analysis, customer segmentation analysis, and the impact of new product lines. The study will give evidence-based suggestions to enhance greater business performance for COTS's coffee shops by employing data analytics.

Report structure and contents

The report will manage in an organized manner to deal with the current investigation and discoveries associated with the execution of COTS café business performance and strategic planning. It will consist of several segments that provide a broad understanding of the data analysis and subsequent pieces of knowledge and suggestions. The report will begin with a introduction outlining the purpose and objectives of the report (Luca, 2021).

Overall Project Plan 

Data analytics project framework in the project plan 

The project plan for investigating the ongoing business execution of COTS and addressing the business issues of the relegated center will clearly use the structure of the AARRR data analytics framework. Representing Acquisition, Activation, Retention, Revenue, and Referral, the AARRR system provides an organized way to deal with the investigation and upgrading of the various stages of the client lifecycle (Luca, 2021). 

Figure 1: AARRR data analytics framework (Muzyka, 2022)

Here's how the system can be used to address all three business questions:

Issue 1: Perform a sales value and volume analysis of the three coffee shops to identify the best shops to invest for future expansion (Acquisition)

In this phase we will examine the business value and volume data of three coffee shops (Poole, Plymouth and Newquay) to examine their presentation and distinguish the best deals for future speculation and expansion. By reviewing the metrics obtained, such as revenue and business volume, we can identify which coffee shops have the greatest potential for development and expansion (Soltanifar and Smailhodżić, 2021).

Issue 2: Perform a customer analysis to identify which market segment provides the highest revenue (Activation)

To address this issue, we will conduct a customer survey to determine which market groups provide the most outrageous money for COTS cafés. By looking at customer data and purchasing behavior, we can see which parts produce the highest revenue and are generally important to the business. This investigation will allow us to put in place targeted demonstration systems and improvements to really pull in and retain these high revenue market sections (Xu et al., 2019).

Issue 3: Did the two new product ranges (breakfast and healthy snacks) added to Plymouth’s menu have a positive impact on the sales performance of the shop? (Retention)

To examine the effect of the new entry on sale execution, we focus on the maintenance phase of the AARRR structure. By analyzing store data and customer reviews, we can evaluate maintenance metrics such as consumer loyalty, repeat purchases, and overall Plymouth store performance. This research will assist us in deciding whether breakfast and solid candy presentations have significantly affected business execution and client maintenance, and guide choices to expand these assortments into different areas. By applying the AARRR framework to these three business questions, we can effectively break down part of acquiring, operating and maintaining COTS bistros (Arafat et al., 2020).

Analysis of Key Performance Indicators (KPIs) of COTS coffee shop

 KPIs are core measurements used to quantify business performance and results. By dissecting these KPIs and applying enhanced research, COTS can recognize regions for development and make informed choices that lead to positive change. Here are some key KPIs for COTS Cafes:

Sales revenue - Revenue from stores is an urgent KPI that affects the monetary presentation of bistros. By examining store revenue data, COTS can recognize patterns, examples, and revenue differences, allowing them to make informed decisions regarding item ratings, advances, and contributions (Luca, 2021).

CLV (Customer lifetime value) - CLV is a metric that measures the total revenue a client should generate during their lifetime as a COTS client. By examining CLV, COTS can distinguish the most important clients and focus on techniques to improve client reliability and maintenance. 

Customer satisfaction - Customer loyalty and NPS are significant KPIs that influence the overall client experience and reliability. By collecting and investigating client critiques, COTS can gain knowledge about consumer loyalty levels and distinguish regions for development (Xu et al., 2019). 

Operational efficiency -  The performance KPIs, for example, require processing time, standby time and staff efficiency, measure the adequacy and effectiveness of bistro tasks. Improved examination can distinguish bottlenecks, failures and areas of progress in functional cycles. By upgrading these cycles with data pieces of knowledge, COTS can improve efficiency, reduce costs and deliver a smoother, more productive customer experience (Arafat et al., 2020).

By leveraging these KPIs, COTS can gain additional experience with these KPIs, enabling them to make informed decisions and go to proactive lengths to further develop business execution (Arafat et al., 2020).

Task 2: Data preparation quality issues and remedies

Explanation of generic data problems and their identification as well as resolution  

The generic data problems are: 

Fragmented or missing Data:

Identification - One common problem with Data quality is fragmented or missing Data. This problem can occur when certain Data centers are not recorded or are incorrectly excluded when sorting Data. As for COTS, it's conceivable that some relevant data is missing for bistros in Poole, Plymouth and Newquay that you might be interested in, such as marketing projections or client data (Arafat et al., 2020).

There are several steps to solve this problem are:

• Review Data Sources: Data sources and assortment techniques to guarantee that all relevant Data has been captured. Assuming any holes are discerned, they will work with the appropriate groups to recover the missing Data.
• Data attribution: In situations where Data is missing, the company use appropriate attribution strategies to assess the missing qualities with respect to examples and relationships within the available Data (Xu et al., 2019).

Inconsistent format of data

Identification - Organizations with conflicting Data may have difficulty investigating the Data. For example, the Data provided by COTS may have conflicting data appearances, such as different views of the date or varying degrees of detail (eg daily, week-by-week or month-to-month Data).

To solve this problem, there are sets to solve the issue are:

• The company completely converts all data to a stable organization such as YYYY-MM-DD to guarantee consistency and simplicity of examination (Camilleri, 2020). 
• There is choosing to aggregate Data on a week-by-week or month-by-month basis, depending on the exam's objectives.

Accuracy of Data and Exclusions

Identification - Accuracy of Data is the basis for a solid investigation. Be that as it may, it is fully expected that there will be exceptions or errors in the Data that can fundamentally affect the results and objectives resulting from the examination.

To solve this problem, there are several steps the organisation can take are

• Anomaly discovery: They use measurable techniques such as box plots, z-scores or cluster calculations to recognize and investigate anomalies in Data. Anomalies that are not fully resolved as incorrect or conclusive will be appropriately addressed, either by editing them or excluding them from the investigation (Xu et al., 2019).
• Cross-validation: They compare and cross-validate Data and external sources or autonomous Data collections to ensure its accuracy and reliability. This step will help identify and correct any errors or discrepancies (Chiang et al., 2019).

Data quality problems that identified with the COTS dataset

There are three data issues identified in the COTS dataset: 

First Issue: There is an error in the name of the city, i.e there is Pooleham instead of Poole.

Identification of the issue – The issue got identified when analyzing the data and compiling the data. The name of the city got identified as Pooleham and there is no city exist in the name of Pooleham (attach screenshot in Appendix).

Resolution - Correct the city name to the correct name (eg Poole) in light of the correct data. Update the dataset to reflect the exact city name for further investigation.

Problem: Negative properties in sale valuation section

Second Issue: There are various negative values given in the sales value part whereas the value can be zero or positive (can’t be negative). 

Identification - The issue got identified when looking at the sales value that there are various negative values given in the dataset (attach screenshot in Appendix). 

Resolution - Determine the cause of negative values in the sales value column. If the negative values are erroneous or inconsistent with the value of sales, they should be corrected. This can be done by investigating the source of the data or contacting the data provider for clarification.

Third Issue: There is an error in the year, i.e given year is 2032 which is not relevant to the data.

Identification - The issue got identified when compiling the data in excel sheet, there was an year of 2032 which was providing issue in the dataset and didn’t matched the data (attach screenshot in Appendix).

Resolution: Exact year to correct year in light of setting data. If the given year 2032 is an error of the data section or does not correspond to the time period of the data set, it should be replaced with the correct year. 

Task 3: Data analysis and commentary

The table presents data specifically related to the UK (Poole, Newquay, Plymouth) campaign of the COTS. It allows for a comparison of sales volume and sales value within the UK campaign over a three-year period (2020, 2021 and 2022) (Xu et al., 2019). The table provides data on the sales volume and sales value for each month within these years.

Table A: Sales Volume and value by month, by year and across the 3 year of period

Summary

The maximum remarkable amount of valued offers is found in month 8th, expressly in 2022, valued at 7833,294.

The lowest sales value is found in month 4th, specifically in 2020, at 5473.5. The sales values increasing at the rate of 12% in the year of 2021 and then increasing at the rate of 15.5% in th year of 2022. 

Table B: Benchmark comparisons of market segments performance covering sales volume and value by quarter, by year and across the 3 years period

Table: 

Table 2: Table showing comparisons of market segments performance covering sales volume and value by quarter, by year and across the 3 years period

Summary

The table illustrates the sales value in various categories and quarters. The highest sales value was recorded in the 5-8 quarter range, totaling £69,170.13, which accounts for 46.8% of the overall sales. Conversely, the lowest sales value was observed in the 1-4 quarter range, amounting to £58,372.25, representing 12.7% of the total sales. In the 5-8 quarter, the highest sales were generated by young individuals, reaching a value of £26,079.536. On the other hand, no sales were recorded in the first quarter of 2020 for the retired population. The cumulative sales for all market segments over the course of three years sum up to £190,693.61.

Table C: Benchmark comparisons of sales volume and value between coffee shops by quarter, by year and across the 3 years period

Table 3: comparisons of sales volume and value between coffee shops by quarter, by year and across the 3 years period

Summary

The table displays sales value and volume information, both on a quarterly and yearly basis, for different coffee shop locations within the organization. The highest sales value was recorded in Plymouth during the year 2022, reaching a total of £87,540.36, which accounts for 57.9% of the overall sales. Conversely, the lowest sales value was observed in Poole during the year 2020, amounting to £39,371.5, representing 17.21% of the total sales. The combined sales for all three years amounted to £187,635.11, with a total sales volume of 59,121.92.

Task 4: Data visualisation and commentary

Chart A: Comparison of sales value trends across coffee shops over time

Figure 2: Comparison of sales value trends across coffee shops over time

Summary

In the year 2020, the Poole area experienced the lowest sales value, amounting to £14,767. On the other hand, the Plymouth area achieved the highest total sales value in 2022, reaching approximately £33,315. Notably, the Plymouth area witnessed the highest increase in sales value, with a growth rate of 39.4% during the year 2022.

Chart B: Market segments performance comparisons between coffee shops

Figure 3: Market segments performance comparisons between coffee shops

Summary: 

The Poole area recorded the lowest sales value within the retired people segment, amounting to £136, which represents 0.72% of the total sales. In contrast, the Plymouth area saw the greatest sales value in the young people category in 2022, reaching £33,385.768, accounting for 42.3% of total sales.

Chart C: Impact of the addition of the two new product ranges to the Plymouth’s menu, and in comparison with the other two cities

Figure 4: Impact of the addition of the two new product ranges to the Plymouth’s menu, and in comparison with the other two cities

Summary: 

Based on this data, the top performer among all three areas of COTS is Plymouth. It has the highest average sales value (£412000) compared to Poole and Newquay (£243). 

Maximum Sales Value: 87,540.36 (Plymouth) after adding the two new products in the year of 2022.  The percentage of Maximum Sales Value is 46.66% (Chiang et al., 2019).

Task 5: Conclusions and recommendations

This report is to explore the performance of company behind the COTS café and provide bits of knowledge to help build a 3 year strategic plan. The report is organized into three issues: an examination of business execution, an investigation of the client division, and the effect of new product offerings (Liu et al., 2020). The target market segments identified in the dataset are young people, married couples, and families with children. Plymouth has recorded the highest sales among the three locations. This can be attributed to the launch of two new products in Plymouth, which have contributed to increased sales.

The overall trend in sales from 2020 to 2022 shows a consistent increase. Month-wise, there is a general upward trend in sales across the three-year period. However, there is a noticeable dip in sales specifically during the 3rd quarter of each year. This drop in sales can be attributed to potential mobility issues during the winter season, affecting customer traffic and sales performance. The maximum Sales Value is 87,540.36 of Plymouth after adding the two new products in the year of 2022.  The percentage of maximum Sales Value is 46.66%.

The COTS dataset highlights the target market segments of young people, married couples, and families with children. Plymouth stands out with the highest sales, likely due to the launch of new products. While there is an increasing sales trend overall, a dip in sales during the 3rd quarter suggests the impact of seasonal factors, potentially related to winter mobility challenges. The AARRR data analytics framework is used to resolve business issues and conduct investigations. 

Given this data, it can be very well seen that the sales value and volume over the three-year period for each area and aggregate are increasing. This shows a positive development in the COTS café. The absolute sale for all areas combined over three years is £35,048. The total volume of sales is 12,715. By analyzing metrics such as revenue and business volume, it worked out which café had the best potential for expansion (Arafat et al., 2020). Hence, by leveraging data research and solving data quality problems, COTS can make informed choices, streamline some assets, focus on high-income market segments, and improve overall business execution.

Recommendations 

From the above findings and a greater understanding of the COTS business and market area, here are some ideas related to reviewing the data and making better use of it in COTS Bistros, along with potential actions that COTS could take:

Improving the data collection and integration: This includes data about deals, client data, reviews and other related measurements (Hair et al., 2019). Put resources into data within the leadership or CRM (Customer relationship management) stage, which can collect and combine data from various sources. 

Use Predictive Analytics - COTS can use predictive investigation methods to estimate trades, discern client shifts and streamline stock management. By examining authentic data, market patterns, and external elements, insightful research can help COTS make informed decisions regarding item contributions, evaluating methodologies, and asset designations (Liu et al., 2020).

Utilizing data analytics enables COTS coffee shops to make informed decisions based on data, leading to improved operational efficiency, heightened customer satisfaction, and a competitive advantage in the coffee shop market. It allows for a comprehensive comprehension of the business and its customers, facilitating the implementation of effective strategies and well-informed decision making to drive growth and achieve success.

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