Business Essentials Business Decision Making - Assignment Example

Business Decision Making Table of Contents Table of Contents 2 Task 3 Task 2 10 Task 3 14 Task 4A 21 Task 4B 28 28 Reference List 29 Task MarketResearch Brief To: Research Agencies chosen to submit a proposal From: “..............” (Name), Facelift Bungee INC, Market Research Manager Date: 4 June 2014 With due respect and humble submission, I want to state that our company has planned to conduct a major study regarding new product launch in UK. Although, guidelines regarding the study are being enclosed with this research brief but your agency is more than welcome to come up with fresh suggestions. According to our consideration, provided information would help you analyze the context of the study and develop suitable responses. This study is important as well as significant for Facelift Bungee because the research findings will help the company to develop new product launch strategy in UK. Findings of the study will provide valuable insight to Facelift Bungee regarding preference and purchase decision of UK customers towards anti-ageing products. As a Market Research Manager of Facelift Bungee, I am looking forward to receive your responses. Yours Sincerely “...................” (Name) Background In last 10 years, demand for natural as well as affordable anti ageing and age concealing products has increased manifold among target customers (McDougall, 2012). Demand for natural as well as affordable anti ageing products has become important growth driver for cosmetics market. Available high priced and chemical ingredient contained anti-ageing products were not able to satisfy the demand of customers for natural youthful looks. Therefore, gap in the market for anti ageing products has been created. In such context, Kimberly Aschauer invented the anti-ageing product named as "The Facelift Bungee" which is being positioned as innovative and affordable anti ageing product (Facelift Bungee, 2014). Primary target market for "The Facelift Bungee" is women over age of 35years facing problems like wrinkles in face and other signs of aging. The product is basically a device that is attached to small braids covered by hair. These braids need to be attached in either side of the face and ‘Pull’ effect created by the devise can dissolve wrinkles (Facelift Bungee, 2014). The devise is marketed in a face cream jar and each of the face cream jar is being priced at $24.99 in USA and £15 for UK. In the marketing communication campaign for the product, unique benefits of the product are being communicated as, natural anti ageing process, affordable, easily removable, adjustable as par requirements of customers and painless facelifts experience to customers (Facelift Bungee, 2014). Project Rationale Facelift Bungee has already achieved marketing success in USA and positive word of mouth has been created regarding the product among customers in USA. Driven by such marketing success, Kimberly Aschauer has decided to launch the product in UK and establish nationwide distribution by opening stores. Till date, Facelift Bungee is being only distributed to USA cosmetic market. Therefore, the company has very little idea about test and preferences of UK based target customers regarding anti ageing products. Before launching the Facelift Bungee at wide scale in UK cosmetic market, Kimberly Aschauer wants to conduct research to get idea about purchasing decision making process and behaviour of target population in UK. Objectives The company feels it is necessary to analyze the preference and expectations of UK based target population from anti-ageing cosmetic products. Understanding existing market gap will help Facelift Bungee to adjust the value proposition so that expectations of target population can be filled in efficient manner. It is expected that the market research findings will provide detailing profiling competitors and assessment of risk factors that create challenge during the phase of new product launch. Competitor profiling, industry trend analysis, market sizing, market gap analysis will be part of strategic objectives of the research. Study location will be London. Sub objectives of the market research can be briefed in the following manner; To conduct industry analysis (competitor profiling, industry trend analysis, market sizing and market gap analysis) for anti-ageing cosmetic products in UK. To understand purchasing decision process and consumption behaviour of target population (women over age of 35years facing problems like wrinkles in face and other signs of aging) in UK. Identifying potential marketing risks regarding launch of Facelift Bungee in UK. Possible Methodology It is being expected that the market research agency have previous project experiences regarding cosmetics (preferably ant-ageing division) market in UK. Furthermore, we expect that the market research agency should have strong command over qualitative market research tools (business database based secondary research, Focus group interview, document analysis, working knowledge of NVivo software) and quantitative market research tools (close ended questionnaire survey, online survey, web data analysis and others). Qualitative market research should be used for analyzing industry trend, competitor profiling and market sizing (Davies, 2007; Gray, 2009). While, quantitative technique should be used for understanding consumer purchase behaviour and market gap analysis (Zikmund et al., 2012). From our side; previous sales data, product details, supplier information and previous market research report conducted by the company will be provided to the market research agency. We will also provide access of existing USA based sales force of the company to the market research agency. 5 of the sales managers can be contacted through Skype chatting while rest of the 30 sales executives can be contacted through e-mail. Data Analysis Requirement Following this brief, market research agency will need to submit a detailed research proposal. Discussion on the detail research proposal will be done through formal business meetings. In case of any confusion, market research agencies should contact us through e-mail and telephonic conversations. Statistical analysis of the data should be done through SAS/ R/ Python programming languages. Customer survey data should be managed through Oracle or SQL Server. Data visualization should be done through Tableau or Crystal Reports. Advanced Excel (e.g., pivot tables, look-up functions, conditional formatting, what-if analysis etc) and VBA techniques should be used for reporting purpose (McKinney, 2012). Time Line Timeline for the market research project should consider the timing of new product launch by the company. Important dates for the project can be highlighted as; 23rd June 2014: Proposal Deadline 14th July 2014: Go ahead given 3rd November 2014: Presentation of Market Research Report 24th November 2014: Presentation of Market Research Report in front of sales channel partners. May 2015: Official Product Launch Budget We require robust market research report covering all the above mentioned research objectives. It is being also expected that the market research agency will be able to deliver accurate statistical modelling and forecasting estimates. A budget of £25000 has been allotted for the market research project. In the market research proposal, market research agency should provide breakdown of direct and indirect cost elements. Administrative costs of the market research agency will not be covered under the mentioned budget. Sample Questionnaire Added in the appendix section. “..............” (Name) Market Research Manager Facelift Bungee INC, 1128 Royal Palm Beach Blvd Suite 292 Royal Palm Beach FL 33411 Tel: Email: surname@faceliftbungee.com Task 1: Questionnaire Qualifying Question Are you experienced in using Anti-ageing cosmetics product? Yes No 5-Point Likert scale questionnaire No. Statement Strongly Disagree Disagree Neutral Agree Strongly Agree 1. Offerings of anti-ageing creams available in the cosmetics market in UK can precisely match requirements of customers. 2. Natural anti-ageing creams are far more skin friendly in contrast to chemical enriched anti-ageing creams? 3. Price of the Anti-ageing creams available in the cosmetics market needs to be decreased in order to make them affordable. 4. I want to get rid of painful facelifts operations. 5. Existing anti-ageing creams are not easily removable. 6. Existing anti-ageing creams cannot be adjusted as par my requirements. 7. Existing anti-ageing creams cannot dissolve ageing wrinkles properly. 8. I am ready to buy natural as well as innovative anti-ageing devises being offered by relatively new brands. 9. I am ready to buy anti-ageing devises being priced at affordable range but offered by a less known brand. 10. I am ready to buy anti-ageing devises offers painless face lift experiences but offered by a less known brand. 11. I am ready to buy anti-ageing devises offers easily removable anti-ageing solution but offered by a less known brand. Task 2 Table 1: Central Tendency Amount Spent (£) No. of Clothing Items(f) Cumulative Frequency (cf) x(Mid points) fx x2 fx2 20-30 12 12 25 300 625 7500 30-40 15 27 35 525 1225 18375 40-50 16 43 45 720 2025 32400 50-60 20 63 55 1100 3025 60500 60-70 14 77 65 910 4225 59150 70-80 10 87 75 750 5625 56250 80-90 7 94 85 595 7225 50575 90-100 6 100 95 570 9025 54150 ∑f= 100 ∑fx= 5470 ∑fx2= 338900 [25th and75th percentiles are being marked in green] 1. Cumulative Frequency (∑f) = 100 Median is lying at ∑f/2= 100/2= 50 or 50th numbers of clothing items Median class= (£) 50-60 L= Lower class boundary of the median class= £50 CW= class width of the median class= 10 fmed= frequency of the median class= 20 Cfp= Cumulative frequency of preceding class= 43 Median= L + [(∑f/2- Cfp)/ fmed* CW] = 50+ [(50- 43)/ 20*10] = 53.5 Mean=∑fx/∑f = 5470/100= 54.7 fm= 20, fp= 16, fs= 14 Mode= L + [(fm- fp)/ (2fm- fp- fs)* CW]= 50+ [4/10*10]= £54 Based on the mean, median and mode value, it can be said that average spending of customers lies in the range of £50-£60. Mean, median and mode values are showing that most of the frequency of clothing items being purchased by consumers is being centred on the value range of 53 to 55 (Watkins, Scheaffer and Cobb, 2010). This frequency value range represents the amount being spent by customers such as £50-£60. Therefore, the best pricing range will be £50-£60 for the clothes being offered by the retail shop. 2. Range: Highest upper class boundary- Lowest lower class boundary= 20 – 6= 14 Standard Deviation (SD) = √ = √ = √ (3389- 2992.09) = 19.9226 3. N= Number of observed values= 8 First quartile position: 0.25 (N+1) = 0.25*9= 2.25th position Third quartile position: 0.75 (N+1) = 0.75*9= 6.75th position 25th Percentile= L + [(P*∑f/100- Cfp)/fpercentile class* CW] Percentile Class= 40-50 & Cfp=27 25th Percentile= 40+ [(25-27/16)*10] = 38.75 [Amount Spent (£)] 75th Percentile= L + [(P*∑f/100- Cfp)/fpercentile class* CW] Percentile Class= 80-90 & Cfp=87 75th Percentile= 80+ [75-87/7*10] = 62.85 [Amount Spent (£)] It is evident from the percentile scores that 50% of transactions are being done within the amount spent range of £38.75 to £62.85. 0 to 25% of transactions by customers are being done within the amount spent range of £20 to £38.75. 51% and 75% of transactions by customers are being done within the limit of £62.85. Therefore, it can be said 75% of transactions by customers are being done within the amount spent limit of £62.85. 4. The inter quartile range (IQR) = Q3 - Q1= 62.85- 38.75= 24.10 5. Correlation analysis will be done on Sales (Units) treated as X and Returns (Units) treated as Y. Correlation coefficient (r) lies in the range of +1 to -1. Table 2: Correlation Range Correlation coefficient (r) -1 -0.7 -0.3 0 0.3 0.7 1 Perfectly negative Strong negative Weak negative Perfect independence Weak positive Strong positive Perfect positive (Source: Cohen, West and Aiken, 2014) Product moment correlation coefficient (r): n(∑xy)- (∑x)(∑y)/√[n∑x2- (∑x)2]* √[n∑y2- (∑y)2] Table 3: Correlation Matrix Sales (Units)(x) Returns (Units)(y) x2 y2 xy 20 1 400 1 20 40 4 1600 16 160 50 6 2500 36 300 55 6 3025 36 330 60 10 3600 100 600 70 12 4900 144 840 80 13 6400 169 1040 90 14 8100 196 1260 100 15 10000 225 1500 ∑x = 565 ∑y = 81 ∑x2= 40525 ∑y2= 923 ∑xy= 6050 N= Number of observations= 9 Manually calculated Product moment correlation coefficient (r) = [(9*6050) – (565*81)]/ √[9*40525 - (565)2]* √[9*923 - (81)2]= [54450- 45765]/ √[364725-319225]* √[8307-6561] = 8685/213.307*41.7852= 8685/8913.07=0.9744 According to Cohen, West and Aiken (2014), correlation coefficient only depicts magnitude of linear association between variables but does not explain direction of relationship. Similarly, from the calculated correlation coefficient, it can be said that sales and return are strongly positively related. With rise of one parameter, other parameter also gets increased. However, it cannot be said whether sales is working as predictor for return or return is working as predictor for sales. In order to understand the direction of relationship, regression analysis will be needed (BPP Learning Media, 2013). Correlation analysis provides multiple advantages to business such as, 1- positive relationship between two variables can help managers regarding to decisions regarding adjustment of these two variables, 2- based on correlation coefficient value, two business variables can be separated from each other and 3- correlation analysis can be used as base for further regression analysis or time series forecasting. Calculated Correlation through MS Excel= 0.99784035 Task 3 Table 4: Advertising & Sales Data Year ending 31st March Advertising Cost (£000s) Sales(£000s) 2000 200 1200 2001 240 1400 2002 300 1500 2003 340 1800 2004 400 2200 2005 480 2300 2006 520 2600 2008 760 2800 2009 880 2900 2010 920 3100 2011 980 3450 2012 1020 3600 2013 1100 4100 2014 1170 4670 3.1 Figure 1: Line Graph for Advertising versus Sales Table 5: Branchwise Net Income Branch Net Income(£000s) London 240 Manchester 180 Birmingham 160 Bristol 140 Leeds 130 Figure 2: Branchwise Net Income Figure 3: Branchwise Net Income Figure 4: Sales versus Advertising Cost Figure 5: Sample Sales in London Store 3.2 Figure 6: Time series Data [Note: 2000 being coded as 1 and 2014 is being coded as 14; code value of 2 to 13 represents the respective years from 2001 to 2013] Table 6: Regression Statistics Multiple R 0.989054124 R Square 0.97822806 Adjusted R Square 0.976413732 Standard Error 159.5524877 Observations 14 Table 7: Coefficients Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 844.945 90.070 9.381 .000 Time_Code 245.626 10.578 .989 23.220 .000 a. Dependent Variable: VAR00002 Forecasted Sales (£000s) (Y) = 844.945 + 245.626 (Time) Forecasted Sales (£000s) 2015= 844.945 + 245.626 (15) = 4529 Forecasted Sales (£000s) 2016= 844.945 + 245.626 (16) = 4774 Forecasted Sales (£000s) 2017= 844.945 + 245.626 (17) = 5020 Forecasted Sales (£000s) 2018= 844.945 + 245.626 (18) = 5266 Forecasted Sales (£000s) 2019= 844.945 + 245.626 (19) = 5511 3.4 To: Board of Directors From: Report Presenter “..............” (Name) Subject: Business Scenario of the Company Status: Confidential Date: 4 June 2014 Introduction Respected managing director gave the opportunity to this report presenter to access data regarding business operation of the company. Data regarding year wise advertising versus sales performances, branch wise net income and sales data on London store on day of promotion were being accessed by this report presenter. Above charts and tables are summarizing the above mentioned data sources. Discussion It is evident from the above diagram that both advertising cost and sales have shown increasing trend. Throughout the period of 2000 to 2014, sales volume has always been greater than advertisement cost. More detailed estimation of the relationship between variables can be done through linear regression analysis. It can be said that highest net income is being generated by the London branch (28% of total) while lowest net income is being generated by the Leeds branch (15% of total) (source: 2014 net income data). Manchester branch is the second highest (21% of total) net income generator for the company. Birmingham branch generates 19% of total net income and Bristol branch generates 17% of total net income. Scatter graph is showing year wise periodic rise of sales and advertising cost. From regression data, it can be said that increase in advertising spending has influenced the rise in sales revenue for the company. It is evident from the histogram, distribution for sales data on London store are asymmetrical and right skewed or positively skewed in nature. Most of the sales data on London store are being concentrated towards left side of the mean or lower end of the value range (£30 to £50). Conclusion From time series analysis, it is being predicted that for next 5 years, positive increase in sales data will be achieved. At the year 2019, it is being predicted that the company will achieve sales value of £5,511,000. Overall, it can be concluded that the company is in growth path both in terms of both branch wise net income and rise in annual sales revenue. Task 4A A 1. Figure 7: Network Diagram A 2. 4th June, 2014 has been selected as the start date for the project while 26th September, 2014 has been selected as project finish date. Total duration of the project will be (26th September, 2014 -1st June, 2014) = 115 days. A 3. Figure 8: Critical Path (marked by Red lines) Critical Path Duration= Physical Preparation (A) + Organizational Planning (B) + Equipment Installation (D) + Personnel Training (E) + Detailed System Designing (F) + File Conversion (G) + Finishing Documentation (K) + Follow Up (L) = (4+2+15+4+9+9+20+20) Days= 83 Days A.4. Figure 9: Gantt Chart There are three benefits of using Gantt chart as project management toll such as, 1- it provides date wise breakdown of tasks which helps project managers to visualize duration of the project, 2- total duration of the project can be calculated from the Gantt chart and 3- predecessor activities can also be highlighted through Gantt chart and it helps project manager to visualize the dependency of task. A.5. Figure 10: Earliest Start Time (EST) & Latest Finish Time (LFT) Task 4B Table 8: NPV & IRR Year Cash Flow 0 (-) £1,000,000.00 1 280000 2 424000 3 600000 4 288000 NPV (+) £198,107.56 IRR 21% 12% has been considered as rate of return. Due to positive NPV (Net present value), benefits from the investment on a new machine by Draco Co exceeds the cost of investment. By investing £1 million on the machine can provide opportunity to Draco Co to earn £198,107.56 that the company can spend today. The internal rate of return (IRR) for the investment is 21%. By investing £1 million on the machine, Draco Co can earn 21% per year on their investment for four years. Reference List BPP Learning Media., 2013. Business essentials business decision making: Study text. London: BPP Learning Media. Cohen, P., West, S. G. and Aiken, V. R., 2014. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Oxon: Psychology Press. Davies, M. B., 2007. Doing a successful research project: Using qualitative or quantitative methods. Basingstoke: Palgrave Macmillan. Facelift Bungee., 2014. Facelift Bungee Story. [online] Available at: [Accessed 4 June 2014]. Gray, D. E., 2009. Doing Research in the real world. 2nd ed. London: Sage Publications Ltd. McDougall, A., 2012. US cosmetics market to be driven by demand for naturals and anti-aging. [online] Available at: [Accessed 4 June 2014]. McKinney, W., 2012. Python for data analysis. California: OReilly Media, Inc. Watkins, A. E., Scheaffer, R. L. and Cobb, G. W., 2010. Statistics: From data to decision. 2nd ed. New Jersey: John Wiley & Sons. Zikmund, W., Babin, B., Carr, J. and Griffin, M., 2012. Business research methods. New Jersey: Cengage Learning. Read More