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1
Key Strategies In Marketing
Developing & Analyzing Strategies in
Uncertain Environments
Marketing Strategy Framework
Customer Analysis
CompetitorAnalysis
Targeting &Positioning
ExternalEnvironment
MarketingMix
CompanyAnalysis
Performance Metrics
ComplementorsAnalysis
3
The Effect of Uncertainty on Decision-making
Perceptions of uncertainty often dominate decision-making Perceptions of risk vary with the individual Perceptions of risk vary with the situation
Managers tend to be risk-averse when decision will be reviewed Managers tend to be risk-averse when things are going well
Perceptions of risk vary with the range of outcomes Managers tend to overweight potential losses
Yet, most managers often assess Uncertainty by “gut feel” 96% of managers alter or reject risk assessments provided to them (March and
Shapira 1987)
4
Strategy Under Uncertainty
1) Clear Enough Future Mature markets, low-technology
2) Alternate Futures Discrete events forthcoming from one uncertainty source: technology,
competitive, market
3) Range of Futures High levels of uncertainty from multiple sources
4) True Ambiguity Many sources of uncertainty that interact
Courtney, Kirkland, and Viguerie 1997
5
How Firms Should Respond to Uncertainty
1) Shape the Future Big bets -- big risks May come from large or small firms
2) Adapt to the Future No-regrets moves
3) Reserve the right to play Options, no-regrets moves Purpose is to transform big-bets into no-regrets moves
6
How Firms Actually Respond to Uncertainty
1) Head in the Sand
2) Toe in the Water
3) Take the Tiger by the Tail
7
How Firms Actually Respond to Uncertainty
Point estimates of strategy outcomes such as profits, sales, etc.
Sensitivity analysis to account for risk
Full range of outcomes associated with a major strategy
8
Systematic Ways to Evaluate Full Range of Outcomes
There are some useful tools to help assess risk Decision trees Sensitivity analysis
However, these can be inaccurate or difficult to use Don’t inspire managerial confidence Don’t capture interactions between multiple variables Often used in an unsystematic way
We need a better way to estimate strategic risk!
La
Shampoo
“For the look and feel of France”
10
La Shampoo -- Financials
Discount Strategy
Price $1.50Variable Cost $ .85CM $ .65
Fixed Costs $10MSales Volume 20M
Net Profit$ 3M
“Brand Building” Strategy
Price $2.25Variable Cost $ .95CM $1.30
Fixed Costs $30MSales Volume 28M
Net Profit$ 6.4M
Which is the better option?
11
La Shampoo -- A closer look at the numbers
Actual value of “Discount” strategy could be affected by: Competitive response Channel acceptance of new positioning Response of loyal customers Response of price sensitive customers
Actual value of “Brand Building” strategy could be affected by: Competitive response Channel acceptance of new positioning Promotional expenses (advertising, sales promotion) Promotional quality
12
La ShampooOutcome distribution -- “Discount” Strategy
Frequency Chart
.000
.006
.013
.019
.026
0
12.75
25.5
38.25
51
1,500,000.00 2,250,000.00 3,000,000.00 3,750,000.00 4,500,000.00
2,000 Trials 0 Outliers
Forecast: Net Profit
Most likely Value = $3.0 millionRange of Outcomes = $1.8 million to $4.2 million
13
La ShampooOutcome Distribution -- “Brand Building” Strategy
Frequency Chart
.000
.007
.014
.021
.029
0
14.25
28.5
42.75
57
-2,000,000.00 750,000.00 3,500,000.00 6,250,000.00 9,000,000.00
2,000 Trials 0 Outliers
Forecast: Nt Pr
Most likely Value = $6.4 millionRange of Outcomes = -$1.6 million to $9.0 million
14
Stage 1
Framing
Stage 2Data
Gathering
Stage 3
Modeling
Stage 4
Implement.
Post-hocAnalysis
Risk Analysis Process
15
Putting the Players in the Process
Decision Makers
The Analysts
FramingData
GatheringModeling Implement.
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Stage 1: Framing the Decision
Framing is simply the way that individuals think about a decision Example: The proverbial half full (half empty) glass of water One of the most critical factors in decision-making
Proper framing involves developing a common understanding of the decision, its key elements, and the decision-making process
17
Stage 1: Framing the Decision
Signs of a poor decision frame Poor quality information is disowned Real decision-maker is not involved or doesn’t understand Manger needed for implementation not included in process New issues raised at conclusion Assumptions challenged after the fact
Managers need a tool to facilitate framing process Influence diagrams
18
Two Approaches to Decision-making
Facts &Assumptions
Forecast Modelwith Risk Discount
Traditional Business Case Approach
“What will it take for this strategy to be successful
Cashflow Modelwith Risk Modeling
Influence Diagram
An Inductive Approach
“What should I know to make the best decision?
The Influence Diagram Shows the Relationships of all Uncertainties for a Decision
NPV
RevenuesSales
Volume
Fixed Costs
S,G & A
MarginalCosts
PriceLevel
New ProductDesign
It illustrates the relationships among uncertain quantitiesand shows their influence on the focal decision criterion
Investment
Costs
20
Influence Diagrams
Focus on what we need to learn, rather than what we already know
Working back from the decision criterion produces a different type of analysis model than working forward from available facts
Starts with outcome measure Identifies major uncertainties that need to be understood Directs efforts toward resolution of key uncertainties rather than more detailed
analysis of existing data
21
What is “Influence”
One factor influences another when it is a major determinant of the possible future outcomes
For example, we know that the volume of next years sales will be “influenced” by our competitor’s pricing strategy as well as our own pricing strategy
CompetitorPricing
ExpectedSales
Our Price
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Building the Diagram
Decisions
Uncertainties
Influence Indicator
Calculation
Outcomes
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An Example: Comparing Two Strategies
InputsSales Forecast 35,000,000Price 1.50Variable Cost 0.75Consumer Promotions 0.15
Revenues 52,500,000Operating Exp. Production Cost 26,250,000 Consumer Promotions 5,250,000
21,000,000
Fixed Expenses Set-up Costs 1,000,000 Advertising 8,500,000 Slotting Allowances 3,500,000Net Profit 8,000,000
Revenues 63,000,000Operating Exp. Production Cost 26,600,000 Consumer Promotions 8,400,000
28,000,000
Fixed Expenses Prod. Develop. Costs 1,500,000 Advertising 13,500,000 Slotting Allowances 5,000,000Net Profit 8,000,000
InputsSales Forecast 28,000,000Price 2.25Variable Cost 0.95Consumer Promotions 0.30
Brand Building Strategy Price Discount Strategy
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LaShampooKey Uncertainties for Brand Building Strategy
Advertising Management approves base plus contingency spending
Channel push Some retailers will require quarterly slotting allowances for shelf space
Sales promotions Coupon redemption depends on quality of advertising
Product development costs Depends on results of consumer research
Competitive response Affects sales volume
25
Influence Diagram for Brand Building Strategy
EBIT
Capital
Prod.Develop.
Advertising
SlottingAllowances
Sales Increase
Compet.Response
Adv.Quality
Price
Sales Promotions
ProductQuality
Production Costs
Revenues
OperatingExpenses
26
Influence Diagram for Brand Building Strategy
EBIT
Capital
Prod.Develop.
Advertising
SlottingAllowances
Sales Increase
Compet.Response
Adv.Quality
Price
Sales Promotions
ProductQuality
Production Costs
Revenues
OperatingExpenses
27
Influence Diagram for Brand Building Strategy
EBIT
Capital
Prod.Develop.
Advertising
SlottingAllowances
Sales Increase
Compet.Response
Adv.Quality
Price
Sales Promotions
ProductQuality
Production Costs
Revenues
OperatingExpenses
28
Influence Diagram for Brand Building Strategy
EBIT
Capital
Prod.Develop.
Advertising
SlottingAllowances
Sales Increase
Compet.Response
Adv.Quality
Price
Sales Promotions
ProductQuality
Production Costs
Revenues
OperatingExpenses
MarketCharact.
29
Completing the Diagram
The objective is to continue dis-aggregating until we have specific quantities to be estimated
Avoid getting bogged down in minutia The influence diagram is NOT the model
The process of making the diagram is as important as the result Creates a common frame of reference to view the problem Develops consensus at an early stage Can be revised as the analysis team learns more about the decision
30
Influence Diagram
Final outcome of stage 1 is a listing of experts to be interviewed
about key uncertainties:
Uncertainty Expert Department
Prod. Development Cost Benny Fitz EngineeringAdvertising Expense Natalie Drest MarketingSlotting Allowances Ima Goober SalesSales Forecast Mary-Kay Mart Market ResearchSales Promotions Exp. Natalie Drest Marketing
Stage 2: Data Gathering
32
Expert Interviews
Data gathering involves seeking out the experts identified in Stage 1 and conducting interviews
Traditional approach attempts to pin the expert down to a single number
Focus on what must go right for success Ad Hoc approach to risk assessment
Risk analysis approach attempts to define the full range of values that might occur
Explore the factors that could create extreme outcomes Understand what could go wrong / right
33
What if There are Two Experts?
Frequently the decision-makers will appoint two experts Often competing factions will want their expert to have input How would you handle this?
Expert #1
Expert #2
25 50 75 100
34
Conducting the Interview
1) Qualify the expert
2) Search for bias (expert or motivational)
3) Define quantity precisely (determine units)
4) Have expert explain factors behind extreme optimistic/pessimistic outcomes
5) Explore likelihood of outcomes (5/50/95 values) Most likely value Symmetry Uniformity
6) Have expert review big picture and make adjustments
35
Objectives of the Interview
Identify the appropriate distribution of possible outcomes A few distributions will cover most scenarios Identify any bias Identify drivers of extreme values
Major distributions include Normal / Lognormal Triangular Uniform
36
The Normal Distribution
Normal distribution with parameters:10% - tile 9,000.0090% - tile 11,000.00
Selected range is from -Infinity to +InfinityMean value in simulation was 9,983.26 7,659.09 8,829.54 10,000.00 11,170.46 12,340.91
A4
Normal distribution is most common Not bounded on high or low sides Must be symmetric Not appropriate for some values that must always be positive
e.g., unit sales
37
The Log-Normal Distribution
Lognormal distribution overcomes some limitations of normal Distribution is always positive Can be skewed
Lognormal distribution with parameters:5% - tile 9,00095% - tile 11,000
Selected range is from 0 to +InfinityMean value in simulation was 9,963 8,286 9,201 10,117 11,032 11,948
B4
38
The Triangular Distribution
Triangular distribution is powerful, and easy to use Covers most situations Can be skewed
Triangular distribution with parameters:Minimum 9,000Likeliest 10,000Maximum 11,000
Selected range is from 9,000 to 11,000Mean value in simulation was 9,995
9,000 9,500 10,000 10,500 11,000
C4
39
The Triangular Distribution
Triangular distribution is powerful, and easy to use Covers most situations Can be skewed
Triangular distribution with parameters:Minimum 9,000Likeliest 10,747Maximum 11,000
Selected range is from 9,000 to 11,000Mean value in simulation was 10,242
9,000 9,500 10,000 10,500 11,000
C4
40
The Uniform Distribution
The uniform is also simple and intuitive Useful when there is not a “most likely” value
Uniform distribution with parameters:Minimum 9,000Maximum 11,000
Mean value in simulation was 9,998 9,000 9,500 10,000 10,500 11,000
D4
41
The Custom Distribution
The custom distribution is a variant of the uniform Useful when there is a contingent factor affecting the decision
9,000 10,500 12,000 13,500 15,000
D4
DistributionUncertainty Units Function Assumptions
Prod. Dev. Costs $ Triangular 0% - $1.0M50% - $1.5M100% - $2.3M
Advertising Exp. $ Uniform 0% - $12M100% - $15M
Slotting Allowances $ Triangular 5% - $2M50% - $5M95% - $8M
Sales Forecast Units Triangular 0% - 23M
50% - 28M100% - 30M
Cons. Promotions $ / unit Normal 5% - .2050% - .3095% - .40
Production Costs $ / unit Uniform 0% - 0.900% - 1.05
LaShampoo: Assessing Key Variables
Stage 3: Modeling the Strategy
44
Monte Carlo Simulation
A process that creates an artificial model of the “real” world by modeling the distribution of important and related variables and then sampling from these distributions many times to estimate the impact on an outcome variable
Conceptually simple Computationally intensive Commercial packages only recently available
45
The Monte Carlo Principle
Revenues
Costs
Profits
0.12 0.21 0.30 0.39 0.48
Consumer Promotions
0.12 0.21 0.30 0.39 0.48
Consumer Promotions
0.12 0.21 0.30 0.39 0.48
Consumer Promotions
46
LaShampoo: Brand Building Model
Revenues 63,000,000Operating Exp. Production Cost 26,600,000 Consumer Promotions 8,400,000
28,000,000
Fixed Expenses Prod. Develop. Costs 1,500,000 Advertising 13,500,000 Slotting Allowances 5,000,000Net Profit 8,000,000
InputsSales Forecast 28,000,000Price 2.25Variable Cost 0.95Consumer Promotions 0.30
EBIT
47
Stage 3: Primary Outcome
Frequency Chart
Mean = 6,195,928.000
.008
.015
.023
.030
0
7.5
15
22.5
30
-2,500,000 1,875,000 6,250,000 10,625,000 15,000,000
1,000 Trials 1 Outlier
Forecast: Net Profit
The primary outcome of the modeling stage is a probability distribution of the focal variable
48
Supplementary Analyses
The modeling approach allows the analyst to probe the data to answer important questions regarding the key drivers of the final outcome
Supplementary Analyses include: Probability analysis Quantitative sensitivity analysis Analysis of key drivers Qualitative sensitivity analysis
49
Probability Analysis
The goal of probability analysis is to provide insights into the downside risk and upside potential of a strategy
Probability analysis allows the analysts to answer strategic questions such as:
What is the probability that this strategy will be profitable? What is the probability that this strategy will achieve its target level of profits? What is the probability that the strategy will exceed expectations?
50
LaShampoo: Brand Building Strategy
What is the probability that LaShampoo will achieve its target profitability of $8,000,000?
Frequency Chart
Certainty is 26.50% from 8,000,000 to +Infinity
Mean = 6,195,928.000
.008
.015
.023
.030
0
7.5
15
22.5
30
-2,500,000 1,875,000 6,250,000 10,625,000 15,000,000
1,000 Trials 1 Outlier
Forecast: Net Profit
Approx.25%
Approx.25%
51
LaShampoo: Brand Building Strategy
What is the probability that the brand building strategy will lose money?
Approx.2%
Approx.2%
Frequency Chart
Certainty is 98.40% from 8,333 to +Infinity
Mean = 6,195,928.000
.008
.015
.023
.030
0
7.5
15
22.5
30
-2,500,000 1,875,000 6,250,000 10,625,000 15,000,000
1,000 Trials 1 Outlier
Forecast: Net Profit
52
Quantitative Sensitivity Analysis
The purpose of quantitative sensitivity analysis is to identify the critical variables that drive strategy outcomes
Essential part of analysis Provides insights into sources of downside risk Enables decision team to direct attention where it will do the most good
53
LaShampoo: Brand Building Strategy
Target Forecast: Net Profit
Consumer Promotions -.49
Sales Forecast .47
Variable Cost -.44
Slotting Allowances -.41
Advertising -.31
Prod. Develop. Costs -.09
-1 -0.5 0 0.5 1
Measured by Rank Correlation
Sensitivity Chart
Consumer PromotionsSales ForecastProduction CostSlotting AllowancesAdvertisingProduct Dev. Costs
-.49.47-.44-.41-.31-.09
54
Tornado Diagram
Net Profit
0.22
24,870,829
3,341,641
0.92
12,300,000
1,254,951
0.38
28,816,784
6,658,359
1.04
14,700,000
1,977,510
2,000,000 4,000,000 6,000,000 8,000,000 10,000,000
ConsumerPromotions
Sales Forecast
SlottingAllowances
Variable Cost
Advertising
Prod. Develop.Costs
Downside
Upside
55
Qualitative Analysis
The qualitative analysis is intended to yield insights into the key variables identified by the quantitative analysis
Relies on the expert interviews
Purpose is to identify specific actions that could be undertaken to manage downside risk or improve upside potential
Stage 4: Decision / Implementation
57
Making the Decision
The analysis is not the only factor to be considered in the decision. Other factors include:
Strategic considerations Risk environment
Individual tolerance Size of project Financial stability of division, company
Social / political environment
58
Implementation
Implementing a decision means translating the outcomes of the analysis made in our pseudo-world into concrete actions in the real world
Good decisions can still be derailed by poor implementation
Implementation will be broader than the analysis New issues will emerge But key issues should have already been resolved
59
Developing Strategies in Uncertain Environments
We have talked about a new approach to evaluating strategies Its powerful Its flexible Its thorough
At this point, you should be able to go out and do your own analyses
Be cautious, however, this type of modeling can be subtle Stick to the basics