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Consumer Behaviour A presentation on strategic marketing models By Group 1
Group Members  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Foundations of Consumer Behaviour Models ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Nature of Consumer Behavior  models ,[object Object],[object Object],[object Object]
Chapter Overview -Variety Seeking Models -Satisfaction Models -Commu and Network models Post-Purchase -Multinominal Discrete Choice  -Markov models Purchase -Perceptual mapping  -Attitude models Evaluation -Individual Awareness Models -Consideration Models -Information Integration Models Information Search -Stochastic Models of Purchase Incidence -Discrete Binary Choice Models Need Arousal
Stochastic Models Brand Choice Model
Consumer Behavior ,[object Object],[object Object],[object Object]
Stochastic Model-Brand Choice ,[object Object],[object Object],[object Object],[object Object]
Purchase Feedback Models ,[object Object],[object Object],[object Object]
 
[object Object],[object Object]
Purchase Probability are related as follows: ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Zero Order Model ,[object Object],[object Object]
Moderately  Heterogeneous  Population (2) Heterogeneous  Population (1) Extremely  Heterogeneous  Population ( Mostly Brand Loyal (3) f (p), Distribution Actors Population  P, Probability of Purchase  Bernoulli Model
Simple Multiple Brand model ,[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
1-∑p (i,i) k=  1- ∑mi² ∑ p (i,i)= diagonal values of Joint Probability Table   = 0.267 +0.349+0.09   =  0.706 k = 0.294/0.614 = 0.479 Example Contd. Brand Shares ( m i ) m i *(1-  m i ) A 0.39 0.238 B 0.46 0.248 C 0.15 0.128
Markov Model ,[object Object],[object Object],[object Object],[object Object],[object Object],Markov Model  assumes that only the previous brand  purchase affects the present purchasing choice
Markov Models  ,[object Object],ij ,[object Object],[object Object],m  = ∑ p  m  i,t + 1 ij it
Markov Models – Example  Consider two brand A and B with following switching matrix t + 1 t ,[object Object],[object Object],[object Object],A B A 0.7 0.3 B 0.5 0.5
Markov Models  Price shift  Limitations  Stationarity  - unrealistic a firm loosing market position will take corrective action.
Post Purchase and purchase feedback ,[object Object],[object Object]
[object Object],[object Object],[object Object]
[object Object],Post Purchase and purchase feedback + Indicates Positive Effect - Indicates Negative Effect Disconfirmation Expectations Attitude 1 Intention 1 Satisfaction/ Dissatisfaction Attitude 2 Intention 2 Complain Reports + + + + + + + - T1 Current T2 Future
Lattin and McAlister modeled a consumer’s utility for brand on a given consumption occasion as a diminishing proportion of the value of the features it shares with the brand the consumer chose on previous occasion. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
P i  =  V i (Σ k V k   ) Applying Luce Model to this formulation gives the probability of purchase of  i  given a previous purchase of  j, P i
~ Previous consumption alters the unconditional brand  choice probability,  V i,  that is,  ~ P i  – V i  < 0, then  j  is a substitute for product  i  (the consumption of brand  j  lowers the probability of choosing brand  i ) ~ While P i  – V i  > 0, then  j  is a compliment for product  i  (the consumption of brand  j  increases the probability of choosing brand  i )
(B)  Noncompensatory Models ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
3)  Lexicographic Model  :- ,[object Object],[object Object],[object Object],[object Object],[object Object]

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Rdx 230907

  • 1. Consumer Behaviour A presentation on strategic marketing models By Group 1
  • 2.
  • 3.
  • 4.
  • 5. Chapter Overview -Variety Seeking Models -Satisfaction Models -Commu and Network models Post-Purchase -Multinominal Discrete Choice -Markov models Purchase -Perceptual mapping -Attitude models Evaluation -Individual Awareness Models -Consideration Models -Information Integration Models Information Search -Stochastic Models of Purchase Incidence -Discrete Binary Choice Models Need Arousal
  • 6. Stochastic Models Brand Choice Model
  • 7.
  • 8.
  • 9.
  • 10.  
  • 11.
  • 12.
  • 13.
  • 14. Moderately Heterogeneous Population (2) Heterogeneous Population (1) Extremely Heterogeneous Population ( Mostly Brand Loyal (3) f (p), Distribution Actors Population P, Probability of Purchase Bernoulli Model
  • 15.
  • 16.
  • 17. 1-∑p (i,i) k= 1- ∑mi² ∑ p (i,i)= diagonal values of Joint Probability Table = 0.267 +0.349+0.09 = 0.706 k = 0.294/0.614 = 0.479 Example Contd. Brand Shares ( m i ) m i *(1- m i ) A 0.39 0.238 B 0.46 0.248 C 0.15 0.128
  • 18.
  • 19.
  • 20.
  • 21. Markov Models Price shift Limitations Stationarity - unrealistic a firm loosing market position will take corrective action.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26. P i = V i (Σ k V k ) Applying Luce Model to this formulation gives the probability of purchase of i given a previous purchase of j, P i
  • 27. ~ Previous consumption alters the unconditional brand choice probability, V i, that is, ~ P i – V i < 0, then j is a substitute for product i (the consumption of brand j lowers the probability of choosing brand i ) ~ While P i – V i > 0, then j is a compliment for product i (the consumption of brand j increases the probability of choosing brand i )
  • 28.
  • 29.
  • 30.