(Tiểu luận) business analyticsbusiness report for final evaluation

FOREIGN TRADE UNIVERSITY SCHOOL OF ECONOMICS AND INTERNATIONAL BUSINESS -*** BUSINESS ANALYTICS BUSINESS REPORT FOR FINAL EVALUATION Classcode: VJPE205(HKI-2324)1.1 Group: 11 Instructors: PhD Nguyen Thi Hong Vinh PhD Pham Thi Cam Anh List of team members No Name Student ID Contribution Hoàng Năng Đức 2113150022 17.5% Nguyễn Nhật Mai 2111110178 16.5% Nguyễn Đặng Nhật Minh 2119090015 16.5% Nguyễn Vũ Duy 2112150042 16.5% Kiều Ngọc Dung 2113150014 16.5% Vũ Minh Phương 2111510069 16.5% TABLE OF CONTENTS INTRODUCTION I THEORETICAL FRAMEWORK AND LITERATURE REVIEW 1.1 Income and Expenditure 1.2 Income effect theory 1.3 Consumer theory 1.4 Literature review 1.4.1 The impact of Family size on the amount of consumption 1.4.2 The impact of Income on people’s expenditure II RESEARCH MODEL AND RESEARCH HYPOTHESIS 2.1 Research Model 2.2 Research Hypothesis III DATA PROCESSING 3.1 Handling Values 3.2 Clustering 3.2.1 Overview 3.2.2 Details 4.1 Result 4.2 Diagnostics test 4.2.1 Testing statistical significance of an individual regression coefficient 𝛽i 4.2.2 Testing the significance of the model 4.2.3 Multicollinearity testing 4.2.4 Heteroscedasticity test 4.2.5 Omitted value test 5.1 Recommendations towards Cluster 5.2 Recommendations towards Cluster REFERENCE INTRODUCTION In today's highly competitive business landscape, understanding the factors that influence consumer spending behavior is paramount for companies aiming to maximize their profits and achieve success in their marketing campaigns This business analysis report delves into the examination of the key factors that affect consumers' spending patterns, with the primary objective of providing insights that will guide the development of a highly effective marketing campaign for the company To conduct the analysis, a regression model will be utilized to test the hypothesis and examine the relationship between consumer spending, income, marketing campaigns, frequency and family size By applying this model to the available dataset provided, we can extract meaningful insights and draw conclusions that will inform the company's marketing strategy This is the first time our team has worked on a business analysis report, so there may be some mistakes during the process We would like to express our gratitude to the teacher for guiding us and being open to feedback I THEORETICAL FRAMEWORK AND LITERATURE REVIEW 1.1 Income and Expenditure Income refers to the money that a person or entity receives in exchange for their labor or products Household income generally refers to the combined gross income (the total amount of a person’s or organization’s income in a particular period before tax is paid) of all members of a household above a specified age Household income includes every member of a family who lives under the same roof, including spouses and their dependents Expenditure is the total amount of money that a government, organization, or person spends during a particular period of time In this report, expenditures are counted by Spent (The money people spent for goods and services) 1.2 Income effect theory The income effect is the change or shift in the level of consumption of goods and services when the purchasing power of consumers changes This can be due to the fluctuations in the consumer’s income, which changes their consumption patterns which in turn changes the prices of goods If a consumer’s income rises, they are more likely to buy more goods and services as long as other factors remain constant The demand for normal goods rises when the consumer’s income increases The demand for inferior goods decreases as the income of the consumer increases When considering the given database, for general items, there is no differentiation in terms of categories We examine all variables in the article as Normal goods to determine whether income truly affects the consumption/expenditure of consumers on that item This means that the higher the income, the more spending on these items, or income is the determining factor for consumer spending 1.3 Consumer theory Consumer theory is the study of how people decide to spend their money based on their individual preferences and budget constraints Consumer theory seeks to predict their purchasing patterns by making the following three basic assumptions about human behavior: - Utility maximization—The combination of goods or services that maximize utility is determined by comparing the marginal utility of two choices and finding the alternative with the highest total utility within the budget limit The decision is influenced by the option that produces a higher level of satisfaction - Non-satiation—People are seldom satisfied with one trip to the shops and always want to consume more - Decreasing marginal utility—Consumers lose satisfaction with a product the more they consume it The utility maximization is directly related to the issue we are researching, with variables such as Marital Status and Family size, consumers will have to consider carefully when making decisions that depend on others and aim to maximize benefits for their families The consumer may consider purchasing more of one item and less of another Through maximizing utility, the consumer will buy an item that produces the greatest marginal utility with the least amount of spending 1.4 Literature review 1.4.1 The impact of Family size on the amount of consumption Most of the existing studies are of the view that family size affects both the savings and consumption expenses of the individual, but in opposing directions (Rehman et al., 2010) Consumption expenditure is regarded as a positive function of household size as proposed by a number of consumption theories Every addition to the family size results in incremental burden on the current income levels of the household which leads to the diversion of income towards consumption (Dornbusch et al., 2004) and the gratification of day to day consumption needs of the additional family member results in increased consumption income ratios of the individual The effect of family size on consumption expenditure in real terms is assessed through examining the pattern of proportion of income spent on consumption (consumption income ratios) in response to increase in the number of members in a family A number of studies unanimously agree that existence of additional family members in a household result in increased propensity to consume, thereby implying that consumption expenses are positively impacted by the family size (Kelley, 1988) 1.4.2 The impact of Income on people’s expenditure Theoretical aspects of household expenditure and private consumption function have been an object of research in lots of studies and research Many theoretical studies in econometrics have been directly or indirectly devoted to these issues or cover these economic processes (as Bardsen et al 2005; Intriligator et al 1996; Mills 2003; Klein et al 2005) The researches solely devoted to con-sumption behavior and estimation of income changes are relatively in small numbers (for in-stance, Garratt et al 2009; Lo et al 2007; Mar-quez 2006) According to Astra, Remigijs, 2010 research results and main-stream economic theory, one percent income increase or decrease has a different impact on expenditures by purpose – thus elastic and inelastic expenditure purposes can be determined Values of elasticities vary in large amplitude if statistics of average Document continues below Discover more Introduction to from: Business… VJPE205 Trường Đại học… 7 documents Go to course 10 27 Phân tích liệu Vở ghi Phân tích dữ… Introduction to Business… None VJPE205 HK1-23241 - assignment docs Introduction to Business… None Final Report Guideline Introduction to Business… None ECO231 Syllabus 9 Introduction to Business… None Bài viết Hướng dẫn cài đặt Azure Data… Introduction to Business… None De 212 - Bài tập vật household budget is replaced with data of households by income quintile and a more lý sophis-ticated study has been performed Income changes have significant impact on overall consumption and saving process and, in re-sult, on structure of consumption Introduction None expenditure to Business… II RESEARCH MODEL AND RESEARCH HYPOTHESIS 2.1 Research Model Based on the theoretical basis as well as previous studies, the group has built a function to study the relationship and influence of different factors on the amount of customer’s household spending on purchases: Spent = f(Income, Cmp5, Cmp4, Cmp1, Frequency, Family_Size) In which: ● Spent: Amount of customer’s household spending on purchases (USD) ● Income: Customer’s yearly household income (USD) ● Cmp5: Result of the 5th campaign ● Cmp4: Result of the 4th campaign ● Cmp1: Result of the 1st campaign ● Frequency: Total number of purchases made (purchasing unit) ● Family_Size: Total number of people in customer’s household (person) With the data provided, the dependent variable y is related to more than one independent variables Therefore, we use Multiple Linear Regression According to Natural Resources Biometrics (Kiernan, 2023), Multiple Linear Regression is basically the extension of Simple Linear Regression For that reason, the Population Regression Model is constructed as: y = β0 + β1x1 + β2x2 + + βkxk + ui with the mean value of y given as: µy = β0 + β1x1 + β2x2 + + βkxk The Sample Regression Model could be constructed as: D = b0 + b1x1 + b2x2 + b3x3 + + bkxk Whereas: ● k = the number of independent variables (also called predictor variables) ● y = the random response variable/ dependent variable ● W = the estimated mean value of the dependent variable y given values for x1, x2, …, xk (computed by using the multiple regression equation) ● x1, x2, …, xk = the independent variables ● β0 is the y-intercept (the value of y when all the predictor variables equal 0) ● b0 is the estimate of β0 based on that sample data ● β1, β2, β3, …, βk are the coefficients of the independent variables x1, x2, …, xk ● b1, b2, b3, …, bk are the sample estimates of the coefficients β1, β2, β3, …, βk ● ui is the random error, which allows each response to deviate from the average value of y The errors are assumed to be independent, have a mean of zero and a common variance (σ2), and are normally distributed Using the data provided, the Population Regression Model and Sample Regression Model for this work would be: Population Regression Model: PRF: Spent = β0 + β1Income + β2Cmp5 + β3Cmp4 + β4Cmp1 + β5Frequency + β6Family_Size + ui In which: ● Spent: dependent variable ● Income, Cmp5, Cmp4, Cmp1, Frequency, Family_Size: independent variables ● β0: the intercept term of the model ● β1, β2, β3, β4, β5, β6: the regression coefficient of each independent variables it follows ● ui: the disturbance term of the model Sample Regression Model: SRF: Spent = b0 + b1Income + b2Cmp5 + b3Cmp4 + b4Cmp1 + b5Frequency + b6Family_Size In which: ● Spent: dependent variable ● Income, Cmp5, Cmp4, Cmp1, Frequency, Family_Size: independent variables ● b0, b1, b2, b3, b4, b5, b6: the estimator of β1, β2, β3, β4, β5, β6 2.2 Research Hypothesis H0: Regression coefficients of independent variables are equal to H1: Regression coefficients of independent variables are different from Research Hypothesis: Regression coefficients of independent variables are different from zero, which means there is a positive or negative correlation between each independent variable and the dependent variable In other words, the customer’s yearly household income; the number of customers accepting the offer in the 5th campaign, 4th campaign, 1st campaign; the total number of purchases made and the total number of people in the customer’s household have remarkable impacts on the amount of customer’s household spending on purchases, whether in a positive or negative way To be more specific, if a customer’s yearly household income changes, the amount of customer’s household spending on purchases would alter in the same or opposite direction Similarly, if the number of customers accepting the offer in the 5th campaign, 4th campaign, 1st campaign; the total number of purchases made or the total number of people in the customer’s household varies, the amount of customer’s household spending on purchases would also differ remarkably ● Cluster also has the highest average income with about 76000 USD and Cluster has the lowest income with only 35000 USD ● There is a huge difference in the Family Size of all the clusters While Cluster and Cluster are almost married and have children, which can be shown in their average family size with 2.87 and 2.88, almost all customers in Cluster are still single ● Cluster and Cluster are the most frequent buyers in all the channels of our firm in the period 2012-2014 which is about three-time higher than Cluster with about 4.58 to 6.45 times ● The success rate of marketing campaigns focusing on Cluster outperform others In other words, Cluster seems to be the most potential target customers for the firm In conclusion, a glance at different values by Clusters, we can conclude that all the statistics show the dominance of Cluster 1, which helps firms make better decisions in choosing target customers 3.2.2 Details a Purchase by channel Figure Purchase by channel In general, all the Clusters choose the Store as the main channel for purchasing with the largest proportion with 36.99%, 40.48% and 40.39% respectively While the Web is the second-best choice of Cluster and Cluster 2, Cluster frequently buy from the Catalog On the other hand, the proportion of Web chosen by Cluster is still higher compared to the Deals with 24.45% b Purchase by Product 13 Figure Purchase by products The dominant choice of all Cluster from our firm is Wine with over 40 percent of total spending The second choice of all customers is Meat The largest spent for this product is from Cluster as about 33.81% of their total spending paid for meat, while Cluster spent 23.54% and Cluster spent about 19.62% It can be concluded that Wine and Meat will be the two strategic products of the firm 14 IV RESULTS AND DIAGNOSTIC TESTS 4.1 Result We have already had the Sample Regression Function (SRF) as below: SRF: Spent = b0 + b1Income + b2Cmp5 + b3Cmp4 + b4Cmp1 + b5Frequency + b6Family_Size In which: ● Spent: dependent variable ● Income, Cmp5, Cmp4, Cmp1, Frequency, Family_Size: independent variables ● b0, b1, b2, b3, b4, b5, b6: the estimator of β1, β2, β3, β4, β5, β6 To find out the estimated coefficients of the independent variables, we use Stata to get the following results: Source SS df MS Number of obs = 2212 F(6, 2205) = 1367.75 105442481 Prob > F = 0.0000 Model 632654887 Residual 169987537 2205 77091.8535 R-squared = 0.7882 Total 802642424 2211 363022.354 Adj R-squared = 0.7876 = 277.65 Root MSE Spent Coef Std Err, t P>|t| [95% Conf Interval] Income 0111029 0003921 28.32 0.000 0.010334 0.0118717 Cmp5 335.9963 26.89932 12.49 0.000 283.2456 338.747 Cmp4 54.34595 24.04062 2.26 0.024 7.201318 101.4906 Cmp1 122.4446 27.21803 4.50 0.000 69.06898 175.8203 15 Frequency 31.41909 1.01926 30.83 0.000 29.42028 33.4179 Family_Size -115.3183 6.885278 -16.75 0.000 -128.8206 -101.816 _cons -76.33504 26.8591 -2.84 0.005 -129.0068 -23.66325 Table 4.1 Regression model According to the Table, we can obtain the SRF as: Spent = -76.34 + 0.011Income + 335.996Cmp5 + 54.35Cmp4 + 122.44Cmp + 31.42Frequency – 115.3183Family_Size Model explanation:  The constant term of the model is estimated as 𝛃1 = -76.34 This value means that when all independent variables equal to 0, the estimated value of spent is 191.6533 USD  The regression coefficient of Income is estimated as 𝛃2 = 0.011 It illustrates that when other independent variables are constants, if the Income increases by 1USD, the estimated value of Spent will increase by 0.011 USD  The regression coefficient of Cmp5 is estimated as 𝛃3 = 335.996 Therefore, when other independent variables are constant, if one more person accepts Campaign 5, the estimated value of Spent will increase by 335.996 USD  The regression coefficient of Cmp4 is estimated as 𝛃4 = 54.34595 Therefore, when other independent variables are constant, if one more person accepts Campaign 4, the estimated value of Spent will also increase by 54.34595 USD  The regression coefficient of Cmp1 is estimated as 𝛃5 = 122.44 Therefore, when other independent variables are constant, if one more person accepts Campaign 1, the estimated value of Spent will also increase by 54.34595 USD  The regression coefficient of Frequency is estimated as 𝛃6 = 31.42 Therefore, when other independent variables are constant, if one more customer buys the goods in any channel, the estimated value of Spent will increase by 31.42 USD  The regression coefficient of Family_Size is estimated as 𝛃7 = -115.3183 Therefore, when other independent variables are constant, if one more customer buys the goods in any channel, the estimated value of Spent will increase by 115.3183 USD Correlation coefficient: Run the command cor Spent Income Cmp5 Cmp4 Cmp1 Frequency Family_Size, we can obtain the Correlation Matrix below: 16 Spent Income Cmp5 Cmp4 Cmp1 Frequency Family_Size Spent 1.0000 Income 0.7927 1.0000 Cmp5 0.4687 0.3956 1.0000 Cmp4 0.2491 0.2196 0.3126 1.0000 Cmp1 0.3814 0.3275 0.4094 0.2427 1.0000 Frequency 0.7596 0.6612 0.2470 0.1672 0.2579 1.0000 -0.2866 -0.2257 -0.0767 -0.1857 -0.2313 Family_Size -0.4245 1.0000 Table 4.2 Correlation Matrix From the Correlation Matrix, we can see that: - Spent and Income; Spent and Frequency; Income and Frequency have a strong positive linear relationship - Spent and Cmp5; Spent and Cmp1; Income and Cmp5, Income and Cmp1; Cmp5 and Cmp4; Cmp5 and Cmp1 have a moderate positive linear relationship - Spent and Cmp4; Income and Cmp4; Cmp5 and Frequency; Cmp4 and Cmp1; Cmp4 and Frequency; Cmp1 and Frequency have a low positive linear relationship - Spent and Family_Size have a moderate negative linear relationship - Income and Family_Size; Cmp5 and Family_Size; Cmp4 and Family_Size; Cmp1 and Family_Size; Frequency and Family_Size have a low negative linear relationship Mean: Run the command sum Spent Income Cmp5 Cmp4 Cmp1 Frequency Family_Size, we can obtain the table below: 17 Variable Obs Mean Std Dev Min Max Spent 2,212 607.2681 602.5134 2,525 Income 2,212 51,958.81 21,527.28 1,730 162,397 Cmp5 2,212 0.0727848 0.2598417 Cmp4 2,212 0.074141 0.2620595 Cmp1 2,212 0.0641953 0.2451559 Frequency 2,212 11.75723 7.758412 71 Family_Size 2,212 2.593128 0.9062364 Table 4.3 Mean, standard deviation, max, of variables From the table, it can be seen that: - The average amount of customer’s household spending on purchases is 607.3 USD - The average customer’s yearly household income is 51,958.8 USD - The average results of the 1st, 4th and 5th campaigns are 0.064; 0.074 and 0.073 respectively - The average frequency is nearly 12 purchases made - The average number of people in a customer’s household is about people 4.2 Diagnostics test 4.2.1 Testing statistical significance of an individual regression coefficient Hypothesis statement: 𝛽i { H : β =0 H 1: β1≠ 18