MRKT 354 Marketing Management II Session 7 Market Simulator
Jun 09, 2015
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