Assignment Sample on Data Driven Decisions For Business
Introduction
Task 1: Introduction and project plan
Purpose of report
Decision-making is the process of opting for an appropriate course of action by analysing different available alternatives. For making sound decisions, an organisation is required to support those using relevant data. As a result, data-driven decisions are taken by the company. Data-based decision-making facilitates getting real-time insight and optimising overall business performance (Ivanov et al. 2019). The work aims to support data-driven business decisions of “Café On The Sea” (COTS). It is a UK-based company that runs chains of coffee shops. The work emphasises evaluating data quality issues at COTS, tabulating sales value & volume at different coffee shop locations of the company and conducting a graphical representation of its performance. A summarisation of COTS’ performance has been made in the conclusion section. Further, recommendations have been provided for dealing with data-related issues of COTS.
Overall project plan
The work focuses on evaluating the project plan of COTS corporate strategy manager. The company is currently planning for local expansion by focusing on three main areas. Firstly, it was to determine most profitable customer segment to invest in. Secondly, it wanted to determine most profitable coffee shop location. Thirdly, the company wants to assess impact of adding healthy, and breakfast menus at Plymouth locations. Hence, the work revolves around critical evaluation of these three issues.
Addressing core requirements
The work focuses on addressing core requirements of COTS which is supporting local expansion decisions while giving suggestions to deal with data quality issues. As a result, use of an appropriate “data analytics project framework” has taken place. As per Wang et al. (2020), a data analytics project framework facilitates wide-ranging performance assessment and making sound business-related decisions. Predictive analytics is data analytics project framework that can be used by company. As per views of Van Calster et al. (2019), predictive analytics is an analytical framework which facilitates usage of data for forecasting future outcomes. In this process, use of artificial intelligence, machine learning, and big data can take place.
Figure 1: Predictive analytics
(Source: Inspired from Van Calster et al. 2019)
COTS can use this framework as a forward-looking approach that can support its local expansion plan. However, the company needs to ensure the appropriate quality of data so that predictive analytics can take place soundly. Presence of errors in data can hamper outcomes obtained from predictive analytics.
COTS’s coffee shops Key Performance Indicators
“Key Performance Indicators” or KPIs act as performance measurement which helps in evaluating success of an organisation. This can be financial or non-financial (Zarzycka and Krasodomska, 2022). In case of COTS, main KPIs include “profit margin”, “revenue growth”, “customer satisfaction”, “customer retention rate”, “revenue per customer”, and “employees satisfaction rate”. Emphasising these KPIs can help COTS support its local expansion plan. Usage of data analytics frameworks such as predictive analysis can help make sound decisions and reach these KPIs.
Task 2: Data preparation quality issues and remedies
Generic data problems
Accurate data collection and analysis play an important role in the process of data-driven decision-making. Presence of data in an accurate format facilitates utilising them soundly for supporting upcoming operations and projects. However, presence of data-related issues hampers collecting, integrating and cleaning of data by data analysts. General data-related issues that can be encountered by a data analyst are presence of hidden data, inconsistent data, too much data on the file, and data downtime. Moreover, it includes presence of ambiguous data, inaccuracy in dataset, and duplication of data (Kotsios et al. 2019). Thus, data analysts are required to deal with these issues to maintain high-quality datasets.
Data quality issue in COTS dataset
Data quality issues can be assessed as presence of intolerable defects within dataset. This can result in reduction in reliability and quality of data. Three main types of issues can be found in dataset of COTS. Firstly, an error in city shop name has been found where “Poole” has been recorded as “Pooleham”. Secondly, an error in market segment name has been found. This includes recording “Married young couples” as “Married Y.P.”, “Retired people” as “Retired”, “Families with children” as “Fam. Children” and “Tourists” as “Turistas”. Thirdly, there was a record of sales value and volume for “2032”. However, the dataset is supposed to include records of last 3 years of data that is 2020 to 2022. Hence, it can be assumed that the year “2022” is mistakenly recorded as 2032.
For avoiding errors in the dataset, an organisation can adopt a data-cleaning tool. “IBM Infosphere Quality Stage” is one of leading data management tools that focuses on data quality and governance. It focuses on usual aspects like data quality, avoiding duplication, and data matching (Amaral et al. 2020). It consists of 200 in-build data quality rules that ensure sound data management. Furthermore, company can take assistance from data cleaning cycle. Data cleaning cycle includes six main stages from cleaning a dataset which include “removing irrelevant data”, “removing duplicate data”, “fixing structural errors”, “dealing with missing data”, “filtering out data outliers”, and “validating your data” (Huang et al. 2020). In essence, using such a tool and model can help COTS in dealing with data quality issues.
Figure 2: Data cleaning cycle
(Source: Inspired from Huang et al. 2020)
Task 3: Data analysis and commentary
Sales volume and value
Assessment of sale value and volume of an organisation can help in understanding current financial health. This supports taking relevant decisions regarding further increasing sales figures. As observed by Ikbal et al. (2021), keeping customers satisfied can influence their purchase decision-making and increase the company’s sales. The table below indicates yearly and monthly sales value and volume for last 3 years:
- Based on the above table, top 20% of areas have been highlighted where COTS has highest or lowest sales value and volume. Thus, The green areas highlight lowest but red areas highlight highest sales figures.
- The table highlights that in 2021, COTS has lowest sales and in 2020, it has highest sales. Both sales volume and value in 2020 were higher.
- Similarly, a favourable trend in sales value and volume can be seen from the above table. The company has had a gradual rise in revenue in passing months.
Benchmark comparisons of market segments
A market segment is category of customer who has similar likes and dislikes which influences their association with company. Conducting benchmark comparisons of market segments is useful for determining which customer group is more profitable (Sheoran and Kumar, 2022). Below table includes benchmark comparison of main customer segments of COTS which are “single professionals”, “young married couples”, “married couples with children”, “young people”, “tourists”, and “retired people”:
Table 3: Yearly & Quarterly Sales Value & Volume for Last 3 years
(Source: COTS dataset)
- The above table highlights both yearly and quarterly sales of COTS in which yellow areas indicate top 20% of highest sales and green areas indicate top 20% of lowest sales.
- As per the table, it can be stated that quarterly sales value and volume at end of year are higher than quarterly sales at beginning of year. Besides, sum value of sales was highest in 2020 and lowest in 2021.
- Computation of average sales highlights higher average sales in 2020, and even value of SD is higher in that year. This means data on sales volume and value is more spread out during period.
Benchmark comparisons of sales volume and value between coffee shops
Benchmark comparisons can be analysed as a process of comparing organisational processes and performance matrices with industry’s best practices and practices of other companies in market. Dimensions which are typically measured in benchmarking are cost, time, and quality (Anitha and Patil, 2022). Below table identified benchmark comparisons of sales volume and value between coffee shops of COTS:
Table 3: Yearly & Quarterly Sales Value & Volume in each Coffee Shops Location for 3 years
(Source: COTS dataset)
- The above table reflects both annual and quarterly sales of COTS for three main locations which are Poole, Newquay, and Plymouth. This defines that sales were higher in 2020.
- It can be inferred from table that both Plymouth and Newquay had similar performance in last three years with a similar number of total customers. However, Poole’s performance was slightly declined than Plymouth and Newquay’s.
- Based on comparisons, it can be stated that quarterly sales of Plymouth are higher than other two locations which is making it most profitable coffee shop location in all 14 locations at Poole, Newquay, Southampton, and Portsmouth.
Task 4: Data visualisation and commentary
Comparison of sales value trends
Trend analysis includes looking at current trend of a particular phenomenon for predicting future ones. As per Shiman (2023), trend analysis acts as a form of comparative analysis for evaluating performance in current year based on past years. Below chart reflects sales value and volume trends of COTS coffee shops:
- According to above graph, considerable fluctuation in sales value and volume has been observed. Based on the graph, young customers are market segment which is generating high sales value and volume.
- During 2020-2022, young people have created high sales value and volume. After that, married young couples are another market segment which is aiding in revenue.
- Answer to issue 2 of Corporate Strategy Manager can be found in this graph. The issue was to identify market segment that can create highest revenue. Hence, young people are customer segments that can create highest revenue.
Market segments performance comparisons
Market segmentation helps in creating a subset of market according to demographics, priorities, needs, common interests, and other psychographic or behavioural criteria (Davras and Caber, 2019). It facilitates giving equal importance to each market segment and ensuring their satisfaction. Below graph are market segments performance comparisons covering sales volume and value:
- The graph above highlights that coffee shop at Plymouth location has efficient performance followed by Newquay and Poole. Hence, Plymouth has highest and Poole has lowest performance.
- Based on performance of Plymouth location it can be observed that at this location there was high customer traffic. This is favourably impacting sales volume and value of COTS at this location.
- Answers to issue 1 can be found in this graph. Corporate Strategy Manager wanted to identify best shops to invest in for future expansion. Hence, the best coffee shops for investment are either Plymouth or Newquay.
Impact of addition of two new product ranges
Adding new product ranges based on requirements of customers helps in attracting a higher number of customers. It positively affects total number of customers of the company and enhances market share as well. As per Mikalef et al. (2019), offering adequate products and services can help a company gain a competitive edge in market. Below is assessment of impact of addition of two new product ranges at Plymouth location:
- According to graph above, higher sales value and volume can be found in Plymouth locations due to higher visits by customers.
- As both sales value and volume at this location are high, it is the most profitable location for making future investments.
- Thus, answers to issue 3 can be found in this graph. Corporate Strategy Manager wanted to assess impact of adding two new product ranges (breakfast and healthy snacks) to Plymouth’s menu on sales performance.
- As majority of customers who visit Plymouth locations are young people and young married people, so adding healthy snacks and breakfast can help in making them satisfied. This can give competitive edge against potential competitors “Costa, Café Nero, Pret A Manger and Starbucks”.
- Therefore, inclusion of “hearty blueberry oatmeal, bacon & gouda breakfast sandwich, spinach & feta breakfast wrap and slow roasted ham & Swiss cheese on croissant bun” as breakfast will attract young married couples. Besides, a fruit bar, protein bistro box, omega 3 bistro box and cheese & fruit bistro box” as healthy snacks will attract young people.
Task 5: Conclusions and recommendations
Conclusions
According to overall analysis, it can be concluded that COTS runs chains of coffee shops in UK in 14 different locations. The company has main competitors like Café Nero, Pret A Manger, Costa and Starbucks. Maintaining a sound amount of data within dataset and efficiently utilising them can aid ability to make decisions. This can facilitate both financial and non-financial performance of company. However, it needs to deal with general data quality issues by using data analytics framework and using data management tools like IBM Infosphere Quality Stage. The company needs to ensure avoidance of data inconsistency, duplications, data irrelevance, and any hidden information in the file and should ensure standard language and format in dataset. Based on dataset, some of errors which are impacting their usage in decision-making process are errors in market segment recording, errors in recording year, and errors in recording coffee shop location names at some places. Based on tabulation of data, it can be observed that sales value and volume were higher in 2020 than next two years. Besides, gradual rise in sales has been found with passage of months. In the ending quarters of year, sales are higher as compared to beginning quarters. As per graphical presentation, it has been found that most profitable market segment is young people, so investment should be made in them for boosting profits. Coffee shop location which brings the highest revenue is Plymouth. Further, there will be a positive impact of including healthy snacks and breakfast items on menu of Plymouth.
Recommendations
According to overall analysis, it can be recommended that COTS is required to deal with its data quality issue. Maintaining good quality data is important for a company to utilise in decision-making processes (Andronie et al. 2021). Hence, it can be recommended that use of data management tools such as analytical tools, customer relationship management systems, marketing technology systems, and data warehouse systems should be used. The company needs to ensure that there is a good flow of data from higher to operational management and operational to higher management. Hence, it is recommended that focus should be made on both formal & informal communication processes in both online & offline modes. In ensuring that there is a good flow of information, conflicts among the workforce should be avoided. Needless to say, the company is required to ensure good leadership by managers and other higher executives that can aid overall organisation’s performance. As the company is currently focusing on its local expansion plan, managing data soundly and using them for decision-making is important. Hence, knowledgeable staff members in the field of data analytics must be hired. Besides this, training and development sessions should be arranged for existing employees for educating them in the field of data handling. Further, using ERP systems may also help by using both qualitative and quantitative data soundly. Hence, desired performance development and growth can be obtained by COTS in the foreseeable future.
References
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