Assignment Sample on Data Driven Decisions For Business
Introduction
Task 1: Introduction and Project Plan
Purpose and structure of report
Data-driven decision-making (DDDM) can be stated as the use of metrics, facts and data that guide strategic decisions of a company related to the goal, initiative and objective of business. According to Pickup (2022), DDDM provides capabilities to businesses to predict future performance and test and implement different strategies for success. Purpose of this study increased understanding of DDDM by assessing the case study of Café On The Sea (COTS). Five types of tasks are performed here related to DDDM such as data preparation quality issues along with remedies to analyse here based on case study of COTS in task 2. Additionally, task 3 is related to data analysis and commentary for COTS based on its sales volume and value analysis. Task 4 is related to data visualisation for the trend of coffee shops and market segmentation performance analysis. A suitable recommendation is also given in task 5 related to Data analytics issues and business recommendations to the top management of COTS.
Overall project plan
Corporate strategy manager of COTS has focused on local expansion strategy based on analysing three types of issues. Issue 1 is performance of sales value along with volume analysis based on a dataset of three coffee shops of COTS that is important to identify the best shop for investment regarding future business expansion. Additionally, perform customer analysis and identify the best market segment that earns the highest revenue that is a part of issue 2. Issue 3 is related to analysis of whether adding two new product ranges of breakfast and healthy snacks have a positive impact on sales performance of Plymouth coffee shop. These three issues are analysed here that can help in the growth strategy and business expansion strategy of COTS.
Data Analytics project framework
Data analytics framework provides data to teams that process analysis of performance of organisations such as monitoring customer’s activity or profitability evaluation. Descriptive analytics, predictive analytics, diagnostic analytics and prescriptive analytics are four types of data analytics frameworks.
Figure 1: Predictive analytic framework
(Source: Influenced by Jiang et al. 2020)
Predictive analytics focuses on forward-looking information that estimates future outcomes based on reporting and monitoring (Jiang et al. 2020). This study is focused on predictive analytics based on a data set of COTS those analyses here to make decision making of local expansion strategy. By using predictive data analytic framework, management of COTS can address 3 issues by focusing on things that happen now with their reason and things that can happen in future.
Key performance indicators (KPIs) of COTS
KPI is a quantifiable measure of performance over a period related to specific objectives. In contrast with Dipura and Soediantono (2022), KPI helps in assessing business performance and making better decisions in future. The KPI of COTS indicates financial health, marketing efforts and customer satisfaction. It is an important part of COTS to improve its financial health by focusing on better marketing efforts that can increase consumer satisfaction optimally. A predictive data analytic framework can improve COTS’s KPIs as it prepares reports based on past performance and gives direction for better financial and non-financial performance in future.
Task 2: Data Preparation Quality Issues and Remedies
Generic data issue
Data analysis is the process of interpreting collected data for extracting meaningful information from it. It facilitates decision-making and provides a future course of action for a company (Awan et al. 2021). However, presence of generic data issues adversely impacts the data analysis process. Generic data issues are:
- Incomplete information
- Default values
- Inconsistency in data format
- Data duplication
- Drastic data changes
- Irrelevant data
- Redundant Data
- Ambiguous data
- Too much information
- Hidden data
- Records downtime
Presence of clean and good-quality data facilitates analytics machine learning and decision-making. As opined by Johnson et al. (2020), the inherent characteristic of data is its quality. However, presence of genetic data issues deteriorates data quality.
Data issue in COTS dataset and measures for improvement
Based on analysis of COTS dataset, some issues have been identified. The company has issues with default data, incorrect values, and inconsistency in data format. First of all, managers have recorded incorrect names of the coffee shop. Shop names which should be recorded as “Poole” are recorded as “Pooleham”. Besides, it can be seen from the table below that managers have recorded two customer segments wrongly which has impacted the data analytics process. Market segment “retired people” has been recorded as just “retired” in one place and market segment “married young people” has been recorded as “married Y. P.”. Another issue that has been found in the dataset of COTS is recording a year incorrectly. The dataset includes sales value and volume recorded for the last three years which are 2020, 2021, and 2022. However, in a few places, records of 2032 can be found. As sales data of 2032 do not come under the last three years, it can be assumed that these data belong to 2023 and 2023 is incorrectly recorded as 2032.
For dealing with data quality issues, the company can use a data cleaning cycle. Data cleaning cycle includes editing, correcting, and structuring information for removing irrelevant and corrupted data (Munawar et al. 2022). For managing data, data cleaning acts as a pre-processing stage which helps in smoothing the data. As a result, using such processes can help COTS maintain correctness in its data and use them in making management decisions. It will help in effectively utilising data for developing new strategies that will help increase sales value and volume in the market.
Task 3: Data Analysis and Commentary
Monthly & yearly sales volume and value
Sales analysis is crucial aspect of running a business successfully. Through sales analytics, an organisation can decide which product to focus on, how to reach best customers or where to sell (Atrill et al. 2020). Below table includes data analysis of sales volume and value of cots based on data from last 3 years which are 2020 2021 and 2022:
- This tabular representation is efficient for evaluating areas where the company has made higher or lower sales based on sales value and volume. The green area has highlighted lower sales value and red area shows highlighted where sales are higher.
- From the presentation, it can be seen that sales value and volume in 2020 are higher in the next two years. This means performance of COTS in chain of 15 cafes in locations such as Poole, Southampton, Portsmouth, Plymouth and Newquay are lower during these two years.
- Based on data analysis results, it can be observed that during 2021 and 22 customer traffic could have been lower in COTS’ cafes. Besides, computation of sum, average and SD is showing higher results in 2020 than in the next 2 years.
Benchmark comparison of monthly & quarterly sales value and volume of market segments
Main goal of market segment analysis is to help organisations understand distinct groups of consumers from which overall market is made up. By grouping these customers with similar characteristics and attributes, organisations can effectively target the most valuable customer segment (Slomanet al. 2019). Below is tabular representation of sales value and volume of COTS quarterly and yearly basis in last 3 years:
- The quarterly analysis shows that quarterly sales performance in 2020 is better than in 2021 and 2022. As this performance is better in 2020, it can be utilised as a benchmark by the company for enhancing performance in coming year.
- From the table, it can be inferred that total sum of sales in 2020 is higher along with average and SD. This means sales data of COTS is more spread out in 2020.
- Based on the data, it can also be assessed that sales values & volume in quarters 3 and 4 are higher than in quarters 1 and 2. This means the company has a gradual increase in sales during the year.
Benchmark comparison using quarterly & yearly sales value and volume of coffee shops
Benchmark comparison acts as a performance comparison of an entity with performance of past year or performance of a similar entity. It allows understanding if the company has favourable or unfavourable business performance (Weetman, 2019). Below is tabular representation of benchmark comparison of COTS of its three main coffee shops based on quarterly and yearly data:
- The table above highlights that sales volume and value are higher in Plymouth locations in the last 3 years as compared to Poole and Newquay locations.
- It can be stated that Plymouth is highest performing location and Newquay is lowest performing location for COTS.
- By comparing performance of all three locations, it can be stated that performance of Plymouth can be used as a benchmark that will help in boosting sales value and volume of the other 14 locations in UK.
Task 4: Data Visualisation and Commentary
Sales value trends across coffee shops
Sales value identifies the number of sales made by the organisation within a particular period. Making higher or lower sales in market considerably impacts the company’s financial position (Susanto et al. 2021). Below is a graphical presentation of sales value trends across three main coffee shops of COTS:
- Based on graph above, fluctuating trend in sales value of COTS has been found. In both the three main shops, higher sales have been found from young customer segment followed by single professional people.
- Both sales value and volume from young customer segments are higher in last three years.
- According to issue two in which the company wanted to identify most profitable customer segment, it can be stated that young customers are most profitable customer segment. Focusing on this segment can bring higher revenue to COTS.
Performance comparisons between coffee shops
According to Corporate Strategy Manager, COTS should implement a local expansion strategy by increasing the size of shops. As opined by Khan et al. (2022), local expansion strategy includes offering exciting services & products to a new market or new products & services to existing markets. Below is a graphical presentation of Plymouth, Poole, and Newquay coffee shops of COTS:
- As per evaluation of data, coffee shops in Poole, Newquay, and Plymouth have sound performance, but sales value in Plymouth is higher than the remaining two shops.
- Strong sales value in Plymouth has taken place due to higher visitation of young customers and single working professionals are most profitable customer segments.
- Based on issue one, the company wanted to assess best shop for future expansion which is Plymouth. Investing in this location can help COTS in boosting sales volume & value.
Plymouth’s comparison with Poole & Newquay
Current offerings of COTS are highly appreciated by customers whose positive impact can be found in its sales figures. Maintaining customer satisfaction positively aids in performance and growth of an organisation (Dehghanpouri et al. 2020). In case of COTS, the company is considering adding healthy snacks and breakfast to menu for which comparison between three main shop locations has been made with below graph:
- As per the graph above, a considerably similar performance can be found between Poole, Newquay and Plymouth. However, overall performance of Plymouth is higher than the other two locations.
- Both sales volume & sales value and total number of customers are higher in Plymouth than in Poole and Newquay.
- As per issue three, it has been found that the company wanted to add healthy snacks and breakfast to the Plymouth menu. This will have a positive impact on performance of Plymouth coffee shops. As the most profitable market segment of the company is young customers and single working professionals, these new offerings will be positively adopted by them.
Task 5: Conclusions and Recommendations
Conclusions
Based on the overall evaluation, it can be stated that data management plays a crucial role in making sound business decisions. With effective use of data, an organisation can make relevant strategies that can be used for future performance improvement. COTS, which is a chain of cafes located in different cities of UK, has maintained a dataset regarding coffee shop locations, customer segments, monthly & yearly sales values and volume. As per the COTS dataset of last three years, some of the data-related issues have been found. Presence of such issues adversely impact on interpretation of those data and utilising them in future decision-making process. Therefore, usage of data cleaning cycle can be ensured to maintain sound data quality. As per tabular presentation, it can be inferred that sales value and volume were higher in 2020, so performance of 2020 can be considered as a benchmark for coming years. Quarterly performance analysis is showing a rising trend in sale value and volume on a gradual basis. Moreover, the sales position in Plymouth location is high. According to the graphical presentation, the most profitable market segment for COTS is young customers. The most profitable coffee shop location for COTS is Plymouth. Furthermore, adding two new menus in the Plymouth location will be profitable. As this organisation is focusing on expanding business in the local market, investing in Plymouth location will be a profitable decision for it.
Recommendations
Overall performance evaluation of COTS is showing good performance due to maintaining a satisfied customer base. Along with this, the company has maintained 15 successful coffee shop chains across the UK which is adding to its market share and growth. Based on such performance, below is the recommendation for further improving the performance:
- Firstly, the company needs to deal with its data quality issue. For this, the company can use new technologies and approaches especially developed for data management. This includes applications like ERP, cloud computing, and big data analytics. Sound data management can help an organisation deal with data quality issues (Lähnemann et al. 2020). Therefore, COTS need to use such applications to avoid data-related issues.
- Secondly, the company needs to ensure that employees are trained enough for using new technologies and approaches adopted for the data management process. As a result, higher executives of COTS are required to arrange skill improvement sessions and workshops. This has enhanced knowledge of workforce and data handling abilities will enhance.
- Thirdly, good communication is needed throughout the company for ensuring a smooth flow of information. This will have a significant impact on the way data is being recorded, and interpreted by employees. This can help in making sound decisions regarding the future of the company.
- Fourthly, COTS needs to avoid conflicts in the workforce. Presence of conflicts among employees can harm sound flow of information and data recording process may get hampered. Hence, leaders are required to take proactive measures to avoid conflicts.
- Fifthly, good leadership is required from the higher authorities of COTS like the Corporate Strategy Manager, Marketing Manager, Financial Manager, Human Resource Manager and Senior Data Analyst to support expansion plan.
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