MRKT 354Marketing Management II

Session 7

Market Simulator

Overview

• Building Blocks• Choice Rules• Market share forecasting• Profit forecasting

M e n u

Market simulatorExcel Solver Manual Input

Product Specification Competing products Individual partworths

Market Shares

Price Product Characteristics

DemandCost

Profit

Market share forcasting

M a r k e t S i m u l a t o r

Market share forecasts

• Market simulators– What-if scenarios to evaluate marketing strategies– Select a set of products to represent the market

• Often start with the current market as base case– Each product is represented by its levels on each

feature– Use each respondent’s utility function to calculate

his/her utility for each product in the choice scenario– use a decision rule to predict choice for each consumer– Aggregate the predicted choice (probability) across

respondents to calculate predicted market shares

U s U s + t h e m

Computation steps

step 2: multi-attribute utility model

step 3: choice model

step 4: straight addition

step 1: conjoint analysis

Partworths for attribute levels

Utilities for competing products

Probabilities of choice

Market share forecasts

individual level

aggregate level

U s U s + t h e m

• Profile space

• Suppose we are Apple, and our product is:

User interface

Touch screen

Keyboard

Brand

Apple

Blackberry

Samsung

Price

Any price$99 to $399

Apple Touch screen $249

Building blocks: Profile space

• Who are the important competitors? – Customer view: look for substitutes for your product– Perceptual maps helpful – Better to include too many rather than too few: conjoint will deal with lack of actual competition, but it

cannot magically account for an excluded competitor

• Simplistic example: 2 competitors

Blackberry Touch screen $199

Samsung Keyboard $149

Building blocks: Competitors

• Market = your product + competitors’ products

Blackberry Touch screen $199

Samsung Keyboard $149

Apple Touch screen $249you

competitor 1

competitor 2

Building blocks: Market

• Customer = partworths (each customer is a row of numbers)

Utility function = sum of product’s partworths

Customer Brand: Apple

Brand: Blackberry

Brand: Samsung

User interface: keyboard

User interface:

Touch screen

Price: Utility

$99 vs. $399

Alex 20 10 0 0 10 30

Bonnie 10 10 0 0 10 30

Colin 0 10 10 0 20 15

Danielle 0 0 0 20 0 15

Ella 0 20 0 0 0 15

Building blocks: Customers

Customer Brand: Apple

Brand: Blackberry

Brand: Samsung

User interface: keyboard

User interface:

Touch screen

Price: Utility $99 vs. $399

Alex 20 10 0 0 10 30

Exercise: Would Alex buy your product?

Brand User interface Price Utility

You Apple Touch screen $249

Competitor 1 Blackberry Touch screen $199

Competitor 2 Samsung Keyboard $149

• Alex: partworths (each customer is a row of numbers)

• Market: choice-sets

• Reminder on how to interpret the price partworths– Partworth for $99 is 30 utils– Partworth for $399 is 0 utils– Partworth Gap = 30 uitls

• For other price points: use interpolation– Partworth for $L is (MaxPrice – L) * (partworth gap)/(MaxPrice – MinPrice)– In this case this interpolation formula becomes: Partworth for $L = (399 – L) * 0.1

• Now calculate utility for each product

Customer Brand: Apple

Brand: Blackberry

Brand: Samsung

User interface: keyboard

User interface:

Touch screen

Price: Utility $99 vs. $399

Alex 20 10 0 0 10 30

Help: Would Alex buy your product?

• Alex is most likely to buy ( )– Key idea: utility maximization

(We can predict what each customer will buy)

Brand User interface Price Utility

You Apple Touch screen $249

Competitor 1 Blackberry Touch screen $199

Competitor 2 Samsung Keyboard $149

Solution - Utility Calculation for Alex

• Question: Highlight the product chosen by each customerin Table 1. Assume that customers choose the product which gives the maximum utility with the probability of 1 (deterministic choice rule.)

Customer

Brand:

Apple

Brand: Blackber

ry

Brand: Samsu

ng

User interfac

e: keyboar

d

User interface:

Touch screen

Price: Utility $99 vs. $399

Alex 20 10 0 0 10 30

Bonnie 10 10 0 0 10 30

Colin 0 10 10 0 20 15

Danielle 0 0 0 20 0 15

Ella 0 20 0 0 0 15

Utility of

your product

Utility of

Comp 1

Utility of

Comp 2

45 40 25

35 40 25

27.5 40 22.5

7.5 10 32.5

7.5 30 12.5

you Comp 1 Comp 2

Exercise #2 – Choice Prediction

Highlight the product Table 1

Apple Blackberry

Samsung

Touch screen

Touch screen

Keyboard

$249 $199 $149

• Forecast:

Customer

Brand:

Apple

Brand: Blackber

ry

Brand: Samsu

ng

User interfac

e: keyboar

d

User interface:

Touch screen

Price: Utility $99 vs. $399

Alex 20 10 0 0 10 30

Bonnie 10 10 0 0 10 30

Colin 0 10 10 0 20 15

Danielle 0 0 0 20 0 15

Ella 0 20 0 0 0 15

Product # persons buying

% share

Your product ( ) ( )%

Competitor 1 ( ) ( )%

Competitor 2 ( ) ( )%

you Comp 1 Comp 2

Exercise #3 – Market Share ForecastQuestion: Add the number of customers purchasing each product and compute market shares in Table 2.

Table 2

Utility of

your product

Utility of

Comp 1

Utility of

Comp 2

45 40 25

35 40 25

27.5 40 22.5

7.5 10 32.5

7.5 30 12.5

Apple Blackberry

Samsung

Touch screen

Touch screen

Keyboard

$249 $199 $149

• How sure are you Colin will buy Comp 2?

• How sure are you Alex will buy your product?

• Alex gets 0.1 utils per dollar saved (5 utils / $50). What if Blackberry (comp 1) discounts by $50? What will Alex buy? Apple Black

berrySamsung

Touch screen

Touch screen

Keyboard

$249 $199 $149

you Comp 1 Comp 2

What was the choice model used here?

Utility of

your product

Utility of

Comp 1

Utility of

Comp 2

45 40 25

35 40 25

27.5 40 22.5

7.5 10 32.5

7.5 30 12.5

Alex

Bonnie

Colin

Danielle

Ella

• Maximum utility rule (deterministic): predict that an individual will always buy the option with the highest estimated utility– Simple to apply– Puts too much confidence in our utility measurement, not empirically valid– Unstable: the entire prediction can tip with a miniscule discount

Improvement idea: assign probability of choice instead of 0/1!

• Logit model (probabilistic): predict that an individual will most likely buy the option with the highest fitted utility, but there is some uncertainty.

Choice rules

• Robust, industry standard

• Theoretically sound: related to maximizing utility, Nobel price (2000) to Daniel McFadden for developing this model

• c = confidence parameter ~ how confident are you in your utility estimates?

Logit Model Rule

• Suppose we take c = 0.1

Utility (U) c*U Exp(c*U) Choice probability

You 45 4.5 90.02 90.02/[90.02+54.6+12.18]=

0.57

Competitor 1 40 4 54.60 54.60/[90.02+54.6+12.18]=

0.35

Competitor 2 25 2.5 12.18 12.18/[90.02+54.6+12.18]=

0.08

Logit Model Rule Example: Alex

• Low confidence (c=0.01)

Customer

You Comp 1

Comp 2

Alex 36% 34% 30%

Bonnie 34% 36% 31%

Colin 32% 37% 31%

Danielle 30% 31% 39%

Ella 30% 38% 32%

Share 33% 35% 32%

• Medium confidence (c=0.1) • High confidence (c=1)

Customer

You Comp 1

Comp 2

Alex 57% 35% 8%

Bonnie 33% 55% 12%

Colin 20% 68% 12%

Danielle 7% 9% 84%

Ella 8% 78% 14%

Share 25% 49% 26%

Customer

You Comp 1

Comp 2

Alex 99% 1% 0%

Bonnie 1% 99% 0%

Colin 0% 100% 0%

Danielle 0% 0% 100%

Ella 0% 100% 0%

Share 20% 60% 20%

The Role of Confidence ParameterUtility

of your

product

Utility of

Comp 1

Utility of

Comp 2

Alex 45 40 25

Bonnie 35 40 25

Colin 27.5 40 22.5

Danielle 7.5 10 32.5

Ella 7.5 30 12.5

• It is possible to use a choice-task as part of your ratings-based conjoint

• In practice, we often use– c = 100/ [12 * Max of Rating Scale]

• With 100 point rating scales, this gives– c = 100/1200 = 0.083 (a reasonable value based on dozens of past studies)

How can we determine c?

• Works for arbitrary number of products:

• Interpretation: exp(c*Uia) ~ attractiveness of product A to person I, and logit is just ratio of attractiveness to total attractiveness of market offerings

Logit model choice rule: Summary

Shape of Logit

Market shares

• Prediction of market share is the average of the individual level probabilities of choice

i j ij

iAA Uc

Uc

N )exp(

)exp(1S

U s U s + t h e m

Profit Forecast

• We need marginal cost function and size of the market in addition to market share forecast

• 1. Compute predicted market share s(P,p)• 2. Compute predicted marginal costs c(P)• 3. Compute predicted profit

= {# of customers x s(P,p)} x {p – C}

U s U s + t h e m

Exercise #4 – Profit Forecast• A medical equipment manufacturer is looking into a new testing device. It has identified a number

of key product characteristics among which price and accuracy are deemed the most important. • The company issued a conjoint analysis which you carried out. It turned out only two types of

customers exist in this market. Segment 1 is 60% of the market, segment 2 is 40% of the market. The following table of partworths at the segment level was obtained.

• The total size of the market is 100 units. The competition consists of only one firm. It offers a mid-priced (i.e. price = $13,000) testing device that delivers 99% accuracy. Your costs to manufacture and develop the various levels of accuracy are as follows

 

• If you decide to launch the me-too option the best you will be able to do is to get half of the market and you can maximally charge the price of your competitor.

• Consider two product launch options: (A) 99.9% accuracy at price of $15,000, (B) 95% accuracy at price of $11,000. Which product would be more profitable for you to launch in this market? Show your work by calculating expected profit for each option. (Note: Assume that the utility differences are large enough to use the deterministic maximum utility rule.)

Attribute Price Accuracy

Level $15,000 $13,000 $11,000 99.9% accuracy

99% accuracy

95% accuracy

Segment 1 0 20 40 55 25 0

Segment 2 0 15 30 15 10 0

Costs Variable Fixed99.9% accuracy 11000 20000099% accuracy 10000 15000095% accuracy 9500 50000