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Researching, Reporting, Planning Essay


Collection of Market Related Data Using Primary and Secondary Research

Decision-making process facilitates the growth and development of business organisations. The primary responsibility of a business manager is to conduct, manage, and proffer decisions that support the firm’s vision and objectives (Browne & Keeley 1998). As a result, the business decision process relies on decision models, data collection, sampling technique and analysis.

The features of an effective business decision making process includes choice, continuous process, intellectual activity, data reliability, feedback, problem specific, timeline, effective communication, pervasive process, and job responsibility (Chapman, Hopwood, & Shields 2006). Business decisions can be tactical, operational, strategic, non-programmed, and programmed.

However, the challenges of the decision-making process can be internal or external. Internal constraints include finance, business policy, consumer profile, attitude, taste, preference, and buying behaviour. External constraints include legislation, competitor’s behaviour, technology, and economy (Davenport & Brooks 2004). Data collection and sampling techniques influence a firm’s decision-making process.

Thus, the business decision process facilitates organisational management and the decision making process. For each scenario, I will analyse the market data using different data collection and sampling techniques. Consequently, I will summarise the primary and secondary data of the organisation using appropriate sampling techniques. Finally, I will present a report to the management to facilitate the development of a new project plan for the new coffee product.

Company profile

The London Coffee Company began in 1971 with staff strength of six. The management introduced various components of the beverage formulation to ensure customer’s satisfaction. As a result, the organisation launched its distribution nationwide. The components of the tea formulation influenced the company’s sustainability and development. As a result, the mission of the company is to stimulate the human mind towards ground coffee.

Thus, the goal of the organisation is one cup of coffee per customer. The London Coffee Company produces the finest tea brands in the country. Consequently, the organisation collaborates with various retail and wholesale agents to provide business expertise, customer feedback, and effective brand formulation. The company has a hand full of fine coffee blends to suit the market demographics.

London coffee corporate governance and social responsibility influence its competitive advance. As a result, the organisation recorded a high return on investment. However, the desire to meet customer’s satisfaction requires regular production innovation. As a result, the company is introducing a coffee-based drink to boost sales. As the product development coordinator, I will determine the customer’s taste and preference using various data collection and sampling methods. Data collection facilitates the decision making process.

Consequently, data collection assists managers to share ideas with stakeholders and administrators. Data collection techniques include survey, inspection, observation, published book, and the Internet. Data analyst must decide the effective tool for the given problem. An effective data collection technique depends on the research problem, research design, and information collected.

However, methods of data collection depend on objectivity, obtrusiveness, quantifiability, and structure. Data can be primary or secondary depending on the method of collection. Data collected from an original source are called primary data.

The features primary data include validity, efficiency, and reliability. Sources of primary data include observations, questionnaires, interview, and experiments. For the purpose of this evaluation, I will use three primary sources to determine consumer’s taste, preference, and buying behaviour. Primary sources include interviews, observation, focus group, and questionnaires.

Advantages of primary data

The advantages of primary data include problem specific, time management, data collection procedure, data interpretation, proprietary issues, problem-solving ability, cost efficiency, and control. Primary data can address specific research problems. As a result, the London Coffee Company can utilise different sources of primary data to determine the market trends of the new coffee product.

Primary data can be interpreted without challenges. Thus, simple statistical tools support the firm’s business market. Methods of collecting primary data are not expensive compared to secondary data collection. Consequently, the processes of data collection are decent and easy compared to secondary data collection process. Primary data solve specific business issues without conducting long-term research.

Disadvantages of primary data

The disadvantages of primary data include cost, time consuming, inaccurate feedbacks, and sample size. Data analysts require more sample size to analyse the research problem. As a result, the cost of data collection is expensive. Time management affects the collection of primary data. Consequently, customer feedback is difficult when conducting primary research. Thus, the disadvantages of primary data research affect the validity of the analysis.

Secondary data

Secondary data can be retrieved from a primary source or published material. The sources of secondary data include published material, Internet, databases, online survey, journals, magazines, letters, literatures, government, and public records.

Advantages of secondary data

The advantages of secondary data research include access, cost efficiency, classification, research design, and problem-solving ability. Access to secondary data is easy and cheap (Glasow 2005). Most online databases provide information that facilitates the research problem. As a result, the challenge of individual consent is eliminated. Secondary data can be classified with specific objectives. As a result, a data analyst can easily collect samples that correspond to the research question.

Disadvantages of secondary data

The disadvantages of secondary data include research quality, research need, incomplete information, and time management. However, secondary data can be evaluated based on accuracy, sufficiency, relevance, and availability.

Primary and secondary data collection plan for London Coffee Company

I have established different methods used for data collection. To determine the emerging trends of the new coffee drink, I will analyse sample data from the primary and secondary sources. As a result, I will classify the each data collection method with the source, and schedule. The sources of primary data include an interview, observation, focus group, and questionnaires.

The interview data require face contact with the customer or telephone. As a result, time management must be enforced. The time schedule plan must be allocated to each customer. Observation data require the physical presence of the customer. As a result, I will allocate specific time to the observation session. The focus group data collection requires audio recording.

Questionnaires will also be distributed through online channels, postal services, and social media. Time management will determine customer feedback. The survey methodology for the research depends on the research question.

However, the sample frame controls the sample size and technique. The features of survey design include sample frame and sample types. Sample types include stratified sampling, systematic sampling, simple random sampling, cluster sampling, purposive sampling, convenience sampling, snowball sampling, and quota sampling.

Analysis and Summary of the Collected Data

Data collection facilitates the decision making process of an organisation (Creswell 2003). Statistical tools can alter, analyse, and summarise data that support a firm’s research question. As a result, I will use different statistical tools to summarise the primary and secondary data. Methods of statistical dispersion include mean, percentile, mode, median, quartiles, and average. Each representative data can be summarised for decision makers.

Data collected from the survey questionnaires will summarise different representative values for the London Coffee Company. As a result, the research questionnaire was structured to receive responses from 100 coffee customers. The feedback was classified based on five variables. Variables for the research include age, gender, price, occupation, and income status. The responses from the participants facilitate the decision making process of the London Coffee company.

No Gender Frequency/week Age Occupation Income Price Total
1 Male 12 18-25 Student 600$ x x
2 Male 14 36-45 Government 1000$ x x
3 Female 7 36-45 Government 2500$ x x
4 Female 26 36-45 Government 2500$ x x
5 Female 14 55+ Government 2500$ x x
6 Male 13 26-35 Private 1500$ x x
7 Male 23 36-45 Private 1000$ x x
8 Female 12 55+ Retired 2500$ x x
9 Male 14 26-35 Unemployed 800$ x x
10 Male 21 36-45 Government 6500$ x x

The data above revealed the responses of 10 participants. As a result, data can be analysed with different statistical analysis. The mean frequency of coffee consumption per week can be computed from the data above.

Mean frequency = 12 + 14 + 7 + 26 + 14 + 13 + 23 + 12 + 14 + 21 / 10

Thus, the mean frequency = the sum of frequencies divided by the number of respondents.

Mean frequency = 156/10 = 15.6

The median is the sum of the middle numbers divided by two.

Thus 14 + 13 / 2 = 13.5

The mode is 14. The mode is the number that appears most often from the frequency of the respondents. The percentile range describes the responses in percentages. However, the quartile mark descries three positions that classify the responses of the participants.

Data analysis with valid conclusion of the London Coffee Company

The data above revealed that age, income status, and occupation affected the frequency of coffee consumption in the London Coffee Company. The results revealed that government workers consumed more quantities of coffee per week compared to students. Thus, the frequency of consumption of private workers, retired and unemployed was lower than the responses from government workers. Gender distribution across the respondents revealed that male customers consumed more coffee compared to female customers.

The results show that sixty percent of coffee customers in the London Coffee Company were males. As a result, the project manager can alter the buying behaviour of the customers using factors that affect consumer taste and preference. The age distribution of respondents revealed that the variable could not influence the frequency of coffee consumption.

Data analysis using different measures of statistical dispersion for the London Coffee Company

The values of standard deviation, coefficient analysis, and correlation analysis can be used to determine the market trend of the new coffee drink. As a result, the business trends of the new coffee drink can be determined with the measures of dispersion. The standard deviation for the above data revealed that customer’s taste, preference and buying behaviour affects the frequency of coffee consumption. Thus, the target market target can be altered to suit government workers.

The importance of quartiles, percentiles correlation, and dispersion in the London Coffee Company

Quartile value divides business data into equal parts. The quartile values assist decision makers quantify the market demographics. Thus, quartile values are divided into four groups. As a result, the middle range is called the median. Percentile values describe the class value in percentages. Thus, business data can be classified in percentages. The measures of dispersion facilitate the distribution of resources in the market segment.

Business Data Report

Data analysis using graphs, tables, and charts

Business data can be represented with graphs and diagrams. Pictorial diagrams include charts, histograms and other measures of central tendency. However, data analysts simplify statistical analysis to avoid misunderstanding. The graphs, tables, and charts below represent the responses from the customers.

The responses from the customers
Figure 1: The responses from the customers

The chart diagram revealed that occupation influenced the frequency of coffee consumption. As a result, business trends for the new coffee drink will grow in government areas. Consequently, the age distribution of the chart revealed that customers between 46 and 55 years consumed more coffee compared to other class limit. As a result, business advertisements that support the target market will improve coffee sales in the London Coffee Company.

Frequency of cofee consumption
Figure 2: Frequency of cofee consumption

The data analysis in Fig. 2 revealed that the occupation influenced the consumer’s taste preference and buying behaviour. As a result, government works consumed more coffee than students and private workers. The frequency distribution revealed that the firm must align with the goals of government workers to facilitate higher sales. Thus, the performance of the new coffee drink will surpass previous brands.

Table 2

No Gender Frequency/week Age Occupation
1 Male 12 18-25 Student
2 Male 14 36-45 Government
3 Female 7 36-45 Government
4 Female 26 36-45 Government
5 Female 14 55+ Government
6 Male 13 26-35 Private
7 Male 23 36-45 Private
8 Female 12 55+ Retired
9 Male 14 26-35 Unemployed
10 Male 21 36-45 Government

Project Plan for the Development of the New Project

Information processing tools for the London Coffee Company

Information processing tools influence the cognitive development of human thinking. However, information-processing models describe the critical path of human cognitive development (Davila & Foster 2005). The components of an information processing model influence the decision making process of business markets. Information processing tools used in the decision making process include databases, enterprise systems, server, accounting information systems, search engine, and geographical locations (Kraemer 1991).

However, the London Coffee Company can use primary, secondary data and accounting information systems to support the decision making process. Information tools must align with the business area. As a result, information-processing tools depend on the research problem. Research problems include project management, investment appraisal, inventory management, tools, marketing, organisational behaviour, economics, and statistics.

Financial tools

Financial tools provide the framework for data analysis. Thus, financial tool is the bedrock of business investments and forecast. Business coordinators collate data analysis using various financial tools. Financial tools used for decision making include income statements, comparative statement, cash flows, and the balance sheet (Quinlan 2010).

However, the component of each financial tool depends on the research analysis. Income statement analysis for the London Coffee company requires revenue sales, cost of goods, total expenses, gross profit, insurance, depreciation, operating costs, operating income, interest expense, interest revenue, tax, and net income.

Income statement report for London Coffee Company
Comparative statement Income statement
2013 % 2014 %
Sale revenue 800,000 100 750,000 100
Cost of products 300,000 33 455,000 34
Operating income 10,000 5 11,500 7
Interest expense (9000) 4 9,300 5
Gross profit 200,000 30 207,000 28
Interest revenue 45,000 11 48,000 15
Insurance 34,000 9 44,000 11
Coffee Adverts 50,000 13 54,000 13
Assets expense 106,000 15 130,000 17
Loss on sale (23,000) 11 27,5000 4
Net income before Tax 20,000 11 23,000 6
Tax 37,000 12 40,000 8
Net Income 300,000 33 400,000 30

The income statement for the London Coffee Company revealed that projected sales revenue increased in 2014. Consequently, the projected gross profit increased by 4 percent. However, the total operating cost decreased by 0.5 percent. The factors include price, quality, taste, environmental concerns, health concern, gender, occupation, social concern, and promotion schemes. However, promotion schemes, taste, occupation, and income altered the buying behaviour of customers.

The net income increased by 3.4% in 2014. The projected revenue for the new drink showed that net income after tax increased in 2014. The firm’s ratio analysis includes profitability, liquidity, and investment ratios. Profitability ratios include gross profit margin, net profit margin, and return on capital. Liquidity ratios include acid test ratios, creditor payment period, and debtors collection period.

Project plan for the new coffee drink

The London coffee project plan aligns with the vision and mission of the organisation. As a result, the new coffee drink will require a project implementation plan to facilitate sales. The features of a project plan include team building, expected problem, project plan, project analysis, project estimate, critical path, project schedule, revised plan, implementation, and monitoring plan.

Project management tools include a work breakdown structure, network analysis, event times, and network node. The new coffee project will require careful implementation of the project plan. As a result, the modalities for the project plan will guide the implementation process.

Project description

The new coffee drink will require market segmentation and promotion schemes. The survey conducted in Task 1, 2, and 3 revealed that occupation, age, income, and gender influenced the consumer buying behaviour. As a result, adverts and promotion schemes will improve the sale of the new coffee drink. The costs of daily business operations will influence sales. The project plan for the new coffee drink will be summarised below.

Items £ Quantity Daily Weekly Total
Delivery of coffee 2685 7 28 100 18795
Entertainment screens 2000 5 37 128 10000
Comfortable sets 5594 2 154 888 11188
Entertainment 8,000 3 172 246 24000
Workers salary 6599 5 139 745 32995
Marketing cost 6650 11 145 839 73150
Turning tables 16499 3 3999 5695 49497
modern tea mugs 4500 1 27 142 4500
New uniforms for staff 4500 1 33 132 4500
Innovative dining rooms 3000 15 31 142 45000
Promotion cost 2700 1 14 99 2700
Project expense 2100 2 12 118 4200
Total 69,827 57 5,103 11,613 285,525

The project management plan includes work packages, dependencies, financial requirements, and resource management. Useful financial tools will be used to monitor and evaluate a business plan. Thus, the organisation will use the investment appraisal tool to evaluate the progress of the project plan. Other financial tools for decision making include the return on investment (ROI), payback technique, and the accounting rate of return (ROCE).

Conclusion

Business decision-making process facilitates the growth of an organisation. Data sources and sampling methods influence the decision making process. Consequently, pictorial diagrams, graphs, and tables assist managers to evaluate the progress of the project plan. The net income after tax will determine the financial status of the company.

References

Browne, M. N., & Keeley, S. M., (1998). Asking the right questions: a guide to critical thinking 5th ed. Upper Saddle River, N.J Prentice Hall.

Chapman, C., Hopwood, A. & Shields, M. 2006, Handbook of management accounting research, Elsevier Science, New York.

Creswell, J. 2003, ‘Research design: qualitative, quantitative and mixed method approach’, Journal of Management, vol. 8, no. 4, pp. 8-10.

Davenport, T. & Brooks, J. 2004, ‘Enterprise systems and the supply chain’, Journal of Enterprise Information Management, vol. 17 no. 1, pp. 8-19.

Davila, A. & Foster, G. 2005, ‘Management accounting systems adoption decisions: evidence and performance implications from early-stage/startup companies’, The Accounting Review, vol. 80 no. 4, pp. 1039-1068.

Glasow, P. 2005, Fundamentals of survey research methodology, Mitre Product, New York.

Kraemer, K. 1991, Introduction paper presented at the information systems research challenge: survey research methods, Cengage Learning , England.

Quinlan, C. 2010, Business research methods, Cengage Learning, England.

This Essay on Researching, Reporting, Planning was written and submitted by user Branden Hensley to help you with your own studies. You are free to use it for research and reference purposes in order to write your own paper; however, you must cite it accordingly.

Branden Hensley studied at Stevens Institute of Technology, USA, with average GPA 3.46 out of 4.0.

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Hensley, B. (2020, March 24). Researching, Reporting, Planning [Blog post]. Retrieved from https://ivypanda.com/essays/researching-reporting-planning/

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Hensley, Branden. "Researching, Reporting, Planning." IvyPanda, 24 Mar. 2020, ivypanda.com/essays/researching-reporting-planning/.

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Hensley, Branden. "Researching, Reporting, Planning." IvyPanda (blog), March 24, 2020. https://ivypanda.com/essays/researching-reporting-planning/.

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Hensley, Branden. 2020. "Researching, Reporting, Planning." IvyPanda (blog), March 24, 2020. https://ivypanda.com/essays/researching-reporting-planning/.

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Hensley, B. (2020) 'Researching, Reporting, Planning'. IvyPanda, 24 March.

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