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Market Response Modeling

Market Response Modeling. Response Modeling Basics. Response Models. Aggregate response models Individual response models Shared-experience models Qualitative response models. Marketing Inputs:. Selling effort Advertising spending Promotional spending. Market.

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Market Response Modeling

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  1. Market Response Modeling • Response Modeling Basics

  2. Response Models • Aggregate response models • Individual response models • Shared-experience models • Qualitative response models

  3. Marketing Inputs: • Selling effort • Advertising spending • Promotional spending Market The Concept of a Response Model Idea: Marketing Outputs: • Sales • Share • Profit • Awareness, etc.

  4. Product design Price Advertising Selling effort etc. Awareness level Preference level Sales Level Input-Output Model Marketing Actions Inputs Observed Market Outputs Competitive Actions (2) Market Response Model (1) (4) (3) Environmental Conditions Control Adaption (6) Evaluation (5) Objectives

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

  6. } 1 A Simple Model Y (Sales Level) } b (slope of the salesline) a (sales level when advertising = 0) X (Advertising)

  7. — Q Phenomena P1: Through Origin P2: Linear Y Y X X P3: Decreasing Returns (concave) P4: Saturation Y Y X X

  8. Phenomena P5: Increasing Returns (convex) P6: S-shape Y Y X X P7: Threshold P8: Super-saturation Y Y X X

  9. Aggregate Response Models:Linear Model Y = a + bX • Linear/through origin • Saturation and threshold (in ranges)

  10. Aggregate Response Models:Fractional Root Model Y = a + bXc c can be interpreted as elasticity when a = 0. Linear, increasing or decreasing returns (depends on c).

  11. Aggregate Response Models:Exponential Model Y = aebx; x > 0 Increasing or decreasing returns (depends on b).

  12. Aggregate Response Models:Modified Exponential Model Y = a (1 – e–bx) + c Decreasing returns and saturation. Widely used in marketing.

  13. Xc d + Xc Aggregate Response Models:Adbudg Function Y = b + (a–b) S-shaped and concave; saturation effect. Widely used. Amenable to judgmental calibration.

  14. Aggregate Response Models:Multiple Instruments • Additive model for handling multiple marketing instruments Y = af (X1) + bg (X2) Easy to estimate using linear regression.

  15. Aggregate Response Models:Multiple Instruments cont’d • Multiplicative model for handling multiple marketing instruments Y = aXb Xc b and c are elasticities. Widely used in marketing. Can be estimated by linear regression. 12

  16. Spending Level Time Dynamic Effects 1. Marketing Efforteg, sales promotion

  17. Dynamic Effects 2. Conventional “delayed response” and “customer holdout” effects Sales Response Time

  18. Dynamic Effects 3. “Hysteresis” effect Sales Response Time

  19. Dynamic Effects 4. “New trier”“wear out” effect Sales Response Time

  20. Dynamic Effects 5. “Stocking” effect Sales Response Time

  21. Aggregate Response Models:Dynamics • Dynamic response model Yt = a0 + a1 Xt +l Yt–1 Easy to estimate. carry-overeffect currenteffect

  22. Aggregate Response Models:Market Share • Market share (attraction) models Ai Mi = –––––––––––––––––– A1 + A2 + . . . + An Ai = attractiveness of brand i. Satisfies sum (market shares sum to 1.0) and range constraints (brand share is between 0.0 and 1.0) Has “proportional draw” property.

  23. Individual-Level Response Models:Requirements • Satisfies sum and range constraints. • Is consistent with the “random utility” model. • Has the “proportional draw” property. • Widely used in marketing.

  24. Individual-Level Response ModelsMNL • Multinomial logit model to represent “probability of choice.” The individual’s probability of choosing brand 1 is: eA1 Pi1 = –––– åeAj j where Aj = åwk bijk k

  25. Logit Model Implications . . . High Marginal Impact of a Marketing Action Low 0.0 0.5 1.0 Probability of Choosing the Alternative

  26. Parking Store Variety Quality for Money Value 1 0.7 0.5 0.7 0.7 2 0.3 0.4 0.2 0. 3 0.6 0.8 0.7 0.4 4 (new) 0.6 0.4 0.8 0.5 ImportanceWeight 2.0 1.7 1.3 2.2 Attribute Ratings per Store

  27. Shares per Store (a) (b) (c) (d) (e) Share Share estimate estimate without with Draw Store Ai = wkbjkeiA new store new store (c)–(d) 1 4.70 109.9 0.512 0.407 0.105 2 3.30 27.1 0.126 0.100 0.026 3 4.35 77.5 0.362 0.287 0.075 4 4.02 55.7 0.206

  28. Objectives • Profit(= Sales ´ Margin – Costs) • Sales • Market share • Time horizon • Uncertainty • Multiple goals • Multiple points of view • Others ??

  29. Shared Experience Models • Base the response model on behavior observed at other leading firms: • Advisor model • PIMS model

  30. Qualitative Response Models • Rules to capture qualitative response: The retailer will accept the trade deal, but what he does with it is based on coop advertising dollars. If the deal includes coop money, the retailer will accept the deal and pass on all of the discount to the consumer. If the discount is greater than 30 percent, he will put up a big display. Otherwise, the retailer leaves the item at regular price and does not use an ad feature or a display. • ADCAD

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