1. Marketing Engineering Model
Marketing Actions
Inputs
Observed Market
Outputs
Competitive Actions
(2)
Product design
Price
Advertising
Selling effort
etc.
Market
Response
Model
Awareness level
Preference level
Sales Level
(1)
(4)
(3)
Environmental
Conditions
Control / Adaptation
(6)
Evaluation
(5)
Objectives
e.g., Profits 4

2. Steps in Creating a Marketing Response Model
Develop a relationship between sales and marketing variables
Sales = f(marketing variables)
Calibrate the model
Statistically or judgmentally
Create a profit model
Profits = unit volume x contribution margin – fixed costs
Optimize
What if or optimum

3. Linear Response Model:
Y = a + b1X1 + b2 X2
Examples:
Medical advertising
Conjoint analysis
Bookbinders Book Club
Price, cart, and coupon exercise
Easy to estimate, robust, good within certain ranges
Optimum is either zero or infinity
Judgmental – sales at current level of effort and change in sales for a one unit change in effort. 8 9

4. Weight Loss Response and Profit Model
Response Model
Profit Model

5. Linear Models - Statistical Concepts Least Squares

6. Linear Models - Statistical Concepts R2

7. Nonlinear Response Models: ADBUDG
b – minimum Y
a – maximum Y
c – shape, 0 < c < 1 concave; c > 1 s-shaped
d – works with c to determine specific shape 12 13

8. Response function: Expected Sales Relative to Base
R(X )
R(X )
1.5
Expected Sales
Relative to
Base
R(X )
1.0
R(X )
Base
1.5 ´ Base
Effort Relative to Base 12

9. ADBUDG Model Examples:
Response Modeler: units of marketing effort and sales
Conglomerate: four cities responding to sales promotion
Spreadsheet Exercise: (Blue Mountain Coffee) sales response to advertising
Syntex: 7 products or 9 specialties responding to number of sales calls
John French: 4 accounts responding to call frequency

10. Using Solver to Estimate Response Functions
Locate parameters and choose starting values
Create columns for independent and dependent variables. Calculate mean of dependent variable.
Create column of predicted dependent variables based on parameters and independent variables.
Create column of squared errors between actual and predicted dependent variable. Sum this column.
Use solver to search over parameters to minimize sum squared errors.

11. Judgmental Calibration of ADBUDG
Data: R(Xminimum), R(Xsaturation), R(X1.0), and R(X1.5)
Parameters:
a = R(Xsaturation)
b = R(Xminimum)
d = (a-R(X1.0))/(R(X1.0)-b)
c = ln((d*(R(X1.5)-b)/(a-R(X1.5))/ln(1.5)

12. Judgmental Calibration of ADBUDG
Data: R(Xminimum), R(Xsaturation), R(X0.5), R(X1.0), and R(X1.5)
Parameters:
a = R(Xsaturation), b = R(Xminimum)
d = (a-R(X1.0))/(R(X1.0)-b)
Solve for c using least squares over R(X0.5) and R(X1.5)

13. Profit Models Unit Sales = f(marketing variables) Response Function
Profits = Unit Sales(margin) – fixed costs
Example: Example on page 38

14. Different Shapes of Multiplicative Model : Y= aXb

15. Linearizable Response Models: Multiplicative Model

16. Multiplicative Models Cont’d
Estimate judgmentally
Sales at current level of marketing variable(s)
Percent change in sales for a percent change in marketing variable i = exponent bi
Yc=a Xcb
Solve for a

17. Multiplicative Models Cont’d
Examples:
Allegro: Sales = a price-b . Advc
Nonlinear Advertising Sales Exercise
Forte Hotel Yield Management: Sales = a price-b
Constant elasticity – exponents are elasticities
Models both increasing (adv) and decreasing (price) functions as well as both increasing (positive feedback) and decreasing (adv and price) returns

18. Other Linearizable Models
Exponential Model: Y = aebx; x > 0
Ln Y = Ln a + bX
Models increasing (b>1) or decreasing (b<1) returns .
Semi-Log Model: Y = a + b Ln X
Reciprocal Model: Y = a + b/X = a + b (1/X)
Models saturation
Quadratic Model: Y = a + bX + c X2
Supersaturation
Ideal points in MDS
Bass Model

19. Choose model based on: Theory Fit Pattern of error terms
Signs and T-statistics of coefficients

20. Response Function Sales Response Effort Level Max Response Function
Current
Sales
Min
Current
Effort
Effort Level 5

21. Elasticity - Percent change in the dependent variable divided by the percent change in the independent variable
 = (Y/Y)/(X/X) = (Y/X) (X/Y) = (dy/dx)(X/Y)
If Y = bX then  = 1 For example, if we double X (from x to 2x), Y also doubles (from bx to 2bx), so the percent change in X is always the same as the percent change in Y.
If Y = a + bX, then Y/X = b(x)/ x = b and X/Y = X / (a + bX) and  = (Y/X) (X/Y) = bX/(a+bX) <1 if a>0

22. Elasticities with a Multiplicative Model
Y = aXb
 = (dy/dx)(X/Y)
dy/dx = a bXb-1
 = (a bXb-1) (X/aXb) = (a bXb-1 X)/aXb = b 14 15

23. Elasticity – A way to compare various marketing instruments
 = (Y/Y)/(X/X) = (Y/X) (X/Y) = (dy/dx)(X/Y)
(Adv Existing Product) =
(Adv New Product) =
Advertising Long Term = 2X Short Term
(Price) = -2.5
 (Coupons) = .07
Source:Bucklin and Gupta, 1999

24. Elasticity in Product Classes where P&G Competes
(Adv) = .039
(Price) = -.541
 (Deals) = .092
 (Coupons) = .125
Source: Ailawadi, Lehmann, and Neslin 2001

25. Effect of Increasing Advertising
Assume 100 units sold at $1.00/unit, 50% contribution margin, advertising elasticity of .22, and 10% A/S ratio
No change in advertising:
Profit = (100 * $.50) - $10 = $40
A 50% increase in advertising – sales increase by 11%
New Profit = (111 * $.50) - $15 = $40.50

26. Conglomerate Market Share Calculations – New York
Promotion Level E4
Response Multiplier
P56
Non-Deal Prone Share
Deal Prone Share
Total Share
E10 .4
69% x .05 = 3.45
31% x .05x .4 = .62
4.07
100% 1
3.45
31% x.05 x 1 = 1.55
5.0
150%
1.64
31% x .05 x 1.7 = 2.635
6.0
Saturation
2.7
31% x .05 x 2.7 =4.185
7.635

27. Aggregate Response Models: Dynamics
Dynamic response model
Yt = a0 + a1 Xt + l Yt–1
Easy to estimate.
Difficult to interpret correctly
carry-over effect
current effect 15 21