market intelligence session 2 luna beer, hypothesis testing, chi square, appropriate statistical...

Market Intelligence Session 2

Luna Beer, Hypothesis testing, Chi square, Appropriate Statistical Tests

Agenda

• Luna Beer• Hypothesis Testing• Chi square• Appropriate Stats

2

Luna Beer Case

• Summary• Decision alternatives?

– Vote• Luna Beer – customers, how purchased?• How will Gomez make decision?• Inputs needed?

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)

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

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?

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.

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

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

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

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

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.

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)

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.

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

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

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

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

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

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.

Agenda

• Luna Beer• Hypothesis Testing• Appropriate Stats• Chi square

22

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


• 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


• 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.

Agenda


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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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31

categoriesk

i i

ii

E

EO_

1

22 )( df = k-1

P>0.05, not significant543.0,37.021 pdf

Conclusion: Fail to reject H0

Conclude no evidence of sample bias Appears the variation is due to chance alone

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categoriesk

i i

ii

E

EO_

1

22 )( df = k-1

P>0.05, not significant543.0,37.021 pdf

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

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PurchaseNot

purchasePromotion seen 48 6 54Promotion not seen 27 19 46

75 25 100

Seen promotion x Purchase

ExpectedProportionsAssumingIndependence

Pij = Pi x Pj

Need to Calculate Expected Frequencies

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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

# 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


Promotion Seen

Promotion not seen

# Cars x Income

(Observed – Expected) Frequencies

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Promotion Seen

Promotion not seen

# Cars x Income

Chi-Square Statistic

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Promotion Seen

Promotion not seen

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

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v = df = (# rows – 1) x (# columns -1)

P-val = 0.001

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

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).

Conclusion Now?

Chi-square = 19.01, p <.0001

Agenda


43

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


• 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


• 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:


• 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:


• 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:


• 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:


• 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?


• 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?

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?

Examples

• Are awareness numbers for AudioTechnica head phones higher in Charlotte or Raleigh?

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?

Examples

• Are there enough Asian Americans in your study?

Examples

• Are people willing to pay more for Bratz dolls when they see it in a red package, blue package, or yellow package?

Examples

• Do men and women differ on brand of pizza purchased?

Examples

• Do customers report liking strawberry Jello or lemon Jello more?

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)

Agenda

• Luna Beer• Hypothesis Testing• Appropriate Stats• Chi square

60

61

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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Laddering exercise: in pairs

• Recent purchase • Over $10• Went to store to purchase• Not food• Underlying emotional or social benefit?

Laddering

• Initial reason vs. deeper reason?• Laddering up versus down (“why” vs. “how”)?

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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

market intelligence session 2 luna beer, hypothesis testing, chi square, appropriate statistical...

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