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Market Intelligence Session 2
Luna Beer, Hypothesis testing, Chi square, Appropriate Statistical Tests
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Agenda
• Luna Beer• Hypothesis Testing• Chi square• Appropriate Stats
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Luna Beer Case
• Summary• Decision alternatives?
– Vote• Luna Beer – customers, how purchased?• How will Gomez make decision?• Inputs needed?
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Approach to the Problem
• Calculate a Demand Forecast for the Company. Then calculate Break Even Volume and compare them.
• Demand Forecast = Industry Demand * Market Share for Luna Beer
• BEV = Fixed Costs / (Price – Variable Costs)
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What information do we need for demand forecast and BEV?
• Demand forecast:– Market size (industry demand)– Market share
• Break even:– Fixed costs– Price– Variable costs
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Luna Beer Case: Team Present
• Emphasis on:– What inputs did you need?– What reports did you buy to give you those?– How did you use the reports?– What was your recommendation?
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Calculation of Industry Demand
• Method 1: Uses Reports A and B.Per capita beer consumption * population
Population Per Capita Beer Consumption (gallons)**
Industry Demand in 2013
Based on Entire Population
70,100 31.3 gallons 2,194,130 gallons
Based on Population Over Age 21
45,500 47.5 gallons 2,161,250 gallons
**Assumes straight line growth.
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Calculation of Industry Demand
• Method 2: Uses Report E.“Taxes Paid Approach”
Taxes Paid (at $.21/ gallon)
Gallons Consumed
2011 $399,000 1,900,000
2012 $435,200 2,072,381
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Market Share Projection
• Market Share Estimates are available in Report C. We estimate 25% market share in 2013.
Demand Forecast = 25% * 2,161,250 gallons
=540,312 gallons
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Fixed Expenses (Year 1)
• Salaries: $450,000• Fixed, p. 3: $204,000• Interest on Loans at 10%/yr: $ 131,159 (see
next slide)
• Total fixed, yr. 1: $785,159– Note: does not include incentives, ads– Note: interest rate pulled out of hat to illlustrate
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Investments
• The investments given in the case (Table A) fail to include estimates of cash and accounts receivable. Report F provides an estimate of the percentage of total assets needed at 16.3%
$1,600,000 / (1-.163) = $1,911,589-- will need to borrow $1,311,590 of it
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Unit ContributionPrice can be estimated using Report I. We assume
that Luna is a premium beer, and can sell at a wholesale price equal to the average price of the top 4 beers listed ($3.11 for a 6-pack).
This translates into $5.53 / gallon (128 ounces per gallon, 12 ounces per beer).
In addition, kegs will be sold at a rate of 1/3 the gallons of bottles and cans. Price for kegs is 45% of bottle/can price.
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Unit ContributionClassification Revenue
WeightWholesale Cost / Gallon
Wholesale Price / Gallon
Bottles / Cans 3.0 $4.44** $5.53
Keg 1.0 $2.00 $2.49
Weighted Average
$3.83 $4.77
**The wholesale cost is calculated by multiplying the cost of goods sold(which from Exhibit F is 80.3% of sales) by the price per gallon.
Unit contribution is therefore $.94 ($4.77 - $3.83)
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Break Even Volume
BEV = Fixed Costs / Unit Contribution
= $785,159 / $.94 = 835,275 gallons
Our demand forecast was 540,312 gallons. We will most likely not break even.
Gomez should probably not invest in this business.
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Total Research Required…
1. Reports A,B,C,F,I for total of $6400
2. Reports C,E,F, I for a total of $7300
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Luna Conclusions• Feasibility studies need data on:
– industry demand, market share, investment, costs, margins. Break even analysis common.
• Know what will data look like before doing research (ask for dummy tables)
• Effort at problem formulation stage reduces later costs of doing research
• Secondary data is the place to start, but it’s usually flawed or not exactly what you need
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Luna Conclusions (cont.)
• Trade off: can usually get 2 of 3: cost, speed, quality
• “Nice to know” info can not only add expense but be misleading
• Understand dummy tables and action standards
• My Excel version of solution on Sakai
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Dummy Tables• Two kinds of dummies:
– Raw Data Dummy Table: require analysis, no action standard
• Example: Luna Reports A-I
– Dummy Analysis: organizes output so that an action standard can direct a decision, conditional on data
• Example: profit in year 1 = [Total Volume in Market * Market Share * (Price – Marginal Cost)] - Year 1 fixed expenses
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Action Standards• Action Standards:
– Prescribe actions on the basis of results from analysis dummy tables
– Example: in Luna Beer, Go if• NPV > 0, or• 1st year Rev. > Fixed Expenses • Break even by Year 5
– Other examples of action standards:• send coupons to a segment if the expected response is 10% or
higher• Use new commercial if awareness is less than 60%• Launch brand extension if we will break even in 5 years
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Coming up…• National Insurance Case: case is due on Thurs in
class – individual assignment. Handwritten answers on handout in coursepack fine.– Wed 4-6, Danielle available in PC lab to help
• Colgate Oral Care Focus Group Case – Read “Using Focus Groups …”– Read case “Colgate Oral Care”– View steaming video on Sakai.
• Submit 2 slides by Wed. 10pm• First quiz on Monday. Study guide coming soon.
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Agenda
• Luna Beer• Hypothesis Testing• Appropriate Stats• Chi square
22
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Statistics / Hypothesis Testing: Step 1
• State a null hypothesis, Ho • Common nulls:
– There is no demographic difference between the sample and the population
– There is no difference between 2 groups– There is no association between 2 variables – Variable A has no effect on Variable B
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Statistics / Hypothesis Testing: Step 2
• Pick a significance level, e.g., a critical “p-value” at which you will reject the null H:– The P-value is the probability of finding the
particular observed data assuming the null hypothesis is true
• “Standard” cutoffs for significant p-values are frequently cited as the following:– Significance: p <= 0.05– Marginal Significance: 0.05< p <= 0.10
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Statistics / Hypothesis Testing: Step 3
• Observe your data, calculate your statistic and p-value
• Reject null or not– If the p-value is smaller than .05, we reject the null
hypothesis– If the P-value is larger than .05, we “fail to reject”
the null hypothesis.
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A example with t-test• Ho: There is no difference between men
and women on attitudes toward Dove soap
• Test Results– Women average 6.2, men average 4.8 on 9
point scale– T-test statistic = 2.429, df=38 – P-value = 0.02
• What should be our conclusion?
Is this random sampling error or is there a significant
difference?
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A example with t-test• Ho: There is no difference between men
and women on attitudes toward Dove soap
• Test Results– Women average 6.2, men average 4.8 on 9
point scale– T-test statistic = 2.429, df=38 – P-value = 0.02
• What should be our conclusion?
Is this random sampling error or is there a significant
difference?
The prob that we would observe this large of a difference when Ho is true is the p-value. If
the p-value is small, we reject Ho.
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Agenda
• Luna Beer• Hypothesis Testing• Chi square• Appropriate Stats
28
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Chi-square Test
• Chi-square test is used for nominal data, to compare the observed frequency of responses to what would be “expected” under some specific null hypothesis.
• Two types of tests:– Goodness of fit: 1 factor, H0 on category
proportions– Test of independence: H0 of independence in
crosstabs
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Nominal Data -- Observed vs. Expected Frequency
Expected if random from customer base 54% M, 46%F
Chi-Squared Goodness of Fit from National Insurance
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categoriesk
i i
ii
E
EO_
1
22 )( df = k-1
P>0.05, not significant543.0,37.021 pdf
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Conclusion: Fail to reject H0
Conclude no evidence of sample bias Appears the variation is due to chance alone
32
categoriesk
i i
ii
E
EO_
1
22 )( df = k-1
P>0.05, not significant543.0,37.021 pdf
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Chi-squared Test of Independence
• In crosstab data, one type of null hypothesis is that there is no association between 2 categorical variables. Rejecting the null means the observed association is larger than would be expected if there is no association in the population
• Expected Proportions under independence, P(Row i AND Col j) = P(Row i) * P(Column j).
• Expected Frequency = Exp. Proportions*N= RowTot/N * ColTot /N * N = RowTot*ColTot / N
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Example: 2 for Promotion x Purchase
Promotion x purchase
Observed Frequencies
34
PurchaseNot
purchasePromotion seen 48 6 54Promotion not seen 27 19 46
75 25 100
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Seen promotion x Purchase
ExpectedProportionsAssumingIndependence
Pij = Pi x Pj
Need to Calculate Expected Frequencies
35
Step 1 : Calculate the Expected Proportions
Prob of having seen promotion = .54; not seen promotion = .46Prob of purchasing = .75; not purchasing = .25
Purchase Not Purchase
Promotion Seen
Promotion not seen
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# Cars x Income
Expected Frequencies
e.g., 0.54 x 0.75 = 0.405 x 100 = 40.5
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Step 2 : Calculate the Expected Freq from Proportions and N
Expected Proportion = Overall Row % x Overall Column % x N
Purchase Not Purchase
Promotion Seen
Promotion not seen
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# Cars x Income
(Observed – Expected) Frequencies
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Purchase Not Purchase
Promotion Seen
Promotion not seen
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# Cars x Income
Chi-Square Statistic
38
Purchase Not Purchase
Promotion Seen
Promotion not seen
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Value of χ2 compared to critical value of χ2 for v degrees of information
In this example = (2-1) x (2-1) = 1 x 1 = 1 df
Since chi-squared = 12.08, and df=1 p < .05,
Chi-Square Statistic Test
39
v = df = (# rows – 1) x (# columns -1)
P-val = 0.001
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Value of χ2 compared to critical value of χ2 for v degrees of information
In this example = (2-1) x (2-1) = 1 x 1 = 1 df
Since chi-squared = 12.08, and df=1 p < .05,
Reject H0 of no association between seeing promotion and purchase
*Direction of effect?
Chi-Square Statistic Test
40
v = df = (# rows – 1) x (# columns -1)
P-val = 0.001
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Back to gender bias in admissions…
• If these were a sample, how would I feel about drawing a conclusion from these numbers? I have 140 males accepted (14% of males) and 60 females accepted (7.5% of females accepted).
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Conclusion Now?
Chi-square = 19.01, p <.0001
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Agenda
• Luna Beer• Hypothesis Testing• Chi square• Appropriate Stats
43
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Know when to use these statistics in market research:
• Chi Square (2 types)– Goodness of fit– Test of independence
• T-test– Paired sample– Independent samples
• Analysis of Variance (ANOVA)• Regression
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Know when to use these statistics in market research:
• Chi Square (2 types)– Goodness of fit: is a sample representative of
population?– Test of independence
• T-test– Paired sample– Independent samples:
• Analysis of Variance (ANOVA)• Regression
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Know when to use these statistics in market research:
• Chi Square (2 types)– Goodness of fit: – Test of independence: is there a relationship
between 2 nominal variables?• T-test
– Paired sample– Independent samples:
• Analysis of Variance (ANOVA): • Regression:
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Know when to use these statistics in market research:
• Chi Square (2 types)– Goodness of fit– Test of independence
• T-test– Paired sample: is there a difference between 2
means? (means come from 1 group)– Independent samples:
• Analysis of Variance (ANOVA):• Regression:
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Know when to use these statistics in market research:
• Chi Square (2 types)– Goodness of fit– Test of independence:
• T-test– Paired sample– Independent samples: is there a relationship
between 1 nominal variable (2 levels) and 1 continuous (interval or ratio) variable?
• Analysis of Variance (ANOVA):• Regression:
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Know when to use these statistics in market research:
• Chi Square (2 types)– Goodness of fit: – Test of independence:
• T-test– Paired sample:– Independent samples:
• Analysis of Variance (ANOVA): is there a relationship between nominal variable(s) (>2 groups) and 1 continuous (interval or ratio) variable?
• Regression:
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Know when to use these statistics in market research:
• Chi Square (2 types)– Goodness of fit: – Test of independence:
• T-test– Paired sample: – Independent samples:
• Analysis of Variance (ANOVA): • Regression: is there a relationship between 2
or more continuous variables?
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Know when to use these statistics in market research:
• Chi Square (2 types)– Goodness of fit: is a sample representative of population?– Test of independence: is there a relationship between 2 nominal
variables?• T-test
– Paired sample: is there a difference between 2 means? (means come from 1 group)
– Independent samples: is there a relationship between 1 nominal variable (2 levels) and 1 continuous (interval or ratio) variable?
• Analysis of Variance (ANOVA): is there a relationship between nominal variable(s) (>2 groups) and 1 continuous (interval or ratio) variable?
• Regression: is there a relationship between 2 or more continuous variables?
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Examples
• Are awareness numbers for AudioTechnica head phones higher in Charlotte or Raleigh?
• Which of the following variables is the biggest driver of intention to buy Jif peanut-butter: self-reported attitude toward Jif, attitude toward Skippy, customer age, number of children?
• Are there enough Asian Americans in your study?• Are people willing to pay more for Bratz dolls when they see it
in a red package, blue package, or yellow package?• Do men and women differ on brand of pizza purchased?• Do customers report liking strawberry Jello or lemon Jello
more?
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Examples
• Are awareness numbers for AudioTechnica head phones higher in Charlotte or Raleigh?
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Examples
• Which of the following variables is the biggest driver of intention to buy Jif peanut-butter: self-reported attitude toward Jif, attitude toward Skippy, customer age, number of children?
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Examples
• Are there enough Asian Americans in your study?
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Examples
• Are people willing to pay more for Bratz dolls when they see it in a red package, blue package, or yellow package?
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Examples
• Do men and women differ on brand of pizza purchased?
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Examples
• Do customers report liking strawberry Jello or lemon Jello more?
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Examples• Are awareness numbers for AudioTechnica head phones higher
in Charlotte or Raleigh? (independent samples t-test)• Which of the following variables is the biggest driver of intention
to buy Jif peanut-butter: self-reported attitude toward Jif, attitude toward Skippy, customer age, number of children? (regression)
• Are there enough Asian Americans in your study? (goodness-of-fit chi square)
• Are people willing to pay more for Bratz dolls when they see it in a red package, blue package, or yellow package? (ANOVA)
• Do men and women differ on brand of pizza purchased? (test of independence chi square)
• Do customers report liking strawberry Jello or lemon Jello more? (paired samples t-test)
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Agenda
• Luna Beer• Hypothesis Testing• Appropriate Stats• Chi square
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Qualitative research
• Focus groups• In-depth interviews (one-on-one)• Ethnography/observational
– Overt– Covert
• All considered “Exploratory”, not decision research
• Outside bounds of BMR
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Roles of qualitative research
• Insights• Hypothesis generation• Questionnaire development• Underlying emotional benefits (“laddering”)• Screening and refining ideas/concepts, etc
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Roles of qualitative research
• Insights• Hypothesis generation• Questionnaire development• Underlying emotional benefits (“laddering”)• Screening and refining ideas/concepts, etc
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Laddering exercise: in pairs
• Recent purchase • Over $10• Went to store to purchase• Not food• Underlying emotional or social benefit?
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Laddering
• Initial reason vs. deeper reason?• Laddering up versus down (“why” vs. “how”)?
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Roles of qualitative research
• Insights• Hypothesis generation• Questionnaire development• Underlying emotional benefits (“laddering”)• Screening and refining ideas/concepts, etc
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For next time
• SPSS online tutorial – self-paced.– Do in computer lab with SPSS open to go through
analyses as you listen to tutorial• Get started on National Insurance Individual
assignment