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Consumer Behaviour
A presentation on strategic marketing models
By
Group 1
Group Members
Sameer Sanghani
Neha Chaudhary
Atit Shah
Deesa Kamdar
Joanna Barsey
Sagar Thukral
Samina Papeya
Nirav Shah
Foundations of Consumer Behaviour Models
Consumer behaviour models are based on certain traditions:
• Behavioural Learning Under this theory consumers’ prior experience is the primary
determinant of future behaviour
• Personality Research Consumer attempts to reconcile his behaviour, others’ behaviour,
the state of his environment etc with prior beliefs.
• Information Processing
The characteristic nature of the individuals customers’ decision making process and changing belief
• Attitude Models Product attributes are drivers of the consumer decision process
Nature of Consumer Behavior models
•Consumers are Different
•Choice Decisions differ
•Context of Purchases Differ
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
Multiple approaches for modeling consumer behavior
For low involvement products- little conscious random decision making takes place; STOCHASTIC models are appropriate
Concentrate on Random nature of the choice process than on a deterministic explanation
Stochastic Model-Brand Choice
B.C.model can be differentiated by how they deal with
Population heterogeneity
Purchase event feedback
Exogenous market factors
Purchase Feedback Models
Zero order model- assume no feedback
Markov model- assume only previous brand choice affect present event feedback
Learning model- assume entire purchase history affects current choice (recent having more effect)
Example Purchases of Brands Table Brand bought on Occasion 2 Brand bought on Occasion 1 Joint Probability Table Brand bought on Occasion 2 Brand bought on Occasion 1 Conditional Probability Table Brand bought on Occasion 2 Brand bought on Occasion 1
A B C Total A 137 47 19 203 B 41 179 12 232 C 22 10 46 78
Total 200 236 77 513
A B C Total A 0.267 0.092 0.037 0.396 B 0.080 0.349 0.023 0.452 C 0.043 0.019 0.09 0.152
Total 0.39 0.46 0.15 1.00
A B C Total A 0.674 0.232 0.094 1.00 B 0.177 0.772 0.051 1.00 C 0.283 0.128 0.59 1.00
p(i,j) = joint probability that a consumer will purchase brand i on the second purchase occasion and brand j on the first purchase occasion
P(i/j) = conditional probability that a customer will purchase brand i on the second purchase occasion given that brand j was purchased on the first occasion
Purchase Probability are related as follows:
p(i/j) = p(i,j) ----- (1) p(j) where p(j) = probability of purchasing brand j on
the first purchase occasion so p(j) = mj , the market share of brand j ∑p(i,j) = mi ----- (2) j ∑ p(i/j) = 1 i
Zero Order Model
1. The assumptions they make about consumer preferences and choice
2. The number of brand they consider
Moderately Heterogeneous Population (2)
Heterogeneous Population (1)
Extremely Heterogeneous
Population ( Mostly Brand Loyal (3)
f (p
), D
istr
ibu
tion
Act
ors
Pop
ula
tion
P, Probability of Purchase
Bernoulli Model
Simple Multiple Brand model
Joint probability of a consumer purchasing brand i and j on successive purchase occasions
p(i,j) = kmimj ----- (3)
where mi and mj are the market share of respective brand
p(i,i) = mi – kmi(1 - mi) ----- (4)
From (4),k = 1 - ∑p(i,i) ----- (5) 1 - ∑mi2
p(i) = mi ----- (6)
p(i/j) = kmi ( j = i ) ----- from (1) = 1 – k(1 - mi) ( j = i ) ----- from (1,4,6)
Brand Shares (mi) mi*(1- mi)
A 0.39 0.238
B 0.46 0.248
C 0.15 0.128
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.
Markov Model
Assumptions – Stationary (probabilities do not change) Homogeneity One purchase per time period
Zero Order Model – Brand choice is independent
Other models assume – Post purchase event feedback .
Markov Model assumes that only the previous brand purchase affects the present purchasing choice
Markov Models
Brand Choice – Pij
Given current market share , a Markov Model can be used to to predict how market shares change over time.
m = ∑ p m i,t + 1 ij it
Markov Models – Example
Consider two brand A and B with following switching matrix
A B
A 0.7 0.3
B 0.5 0.5
t + 1
t
2 Uses of Markov Model –
Forecasting of the market share with the help of transition matrix
How the effect of change in market structure can be evaluated
Markov Models
Price shift
Limitations
Stationarity - unrealistic a firm loosing market position will
take corrective action.
Post Purchase and purchase feedback
After purchasing and experiencing a product, a consumer’s reaction is important for future purchases.
Biehal (1983) showed that, for auto repair services, the outcome of prior experience is more important than external search in choosing the next service provider.
Post purchase behavior affects attitude which in turn affects the consumer’s behavior.
Post purchase affects how the consumer communicates about the product through WOM.
A purchase can affect future purchases through variety seeking.
Bearden and Teel (1983) used structural equation model to explain the post purchase effects.
Post Purchase and purchase feedback
Disconfirmation
Expectations
Attitude 1
Intention 1
Satisfaction/ Dissatisfaction
Attitude 2
Intention 2
Complain Reports
+
+
+
+
+
+
+
-
T1 Current T2 Future
+ Indicates Positive Effect
- Indicates Negative Effect
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.
Vi\j = Vi – λSijWhere,
Vi\j = Utility of i given that j was chosen previously
Vi = unconditional utility of i
λ = discount factor indicating consumer’s variety- seeking intensity.
Sij = value to the customer of all want-satisfying features shared by i and j
Pi\j = Vi\j(ΣkVk \j )
Applying Luce Model to this formulation gives the probability of purchase of i given a previous purchase
of j, Pi\j
~ Previous consumption alters the unconditional brand choice probability, Vi, that is,
~ Pi\j – Vi < 0, then j is a substitute for product i (the consumption of brand j lowers the probability of choosing brand i)
~ While Pi\j – Vi > 0, then j is a compliment for product i (the consumption of brand j increases the probability of choosing brand i)
(B) Noncompensatory Models1) Conjunctive Model :-
In a conjunctive model a consumer prefers a brand only if it meets certain minimum, acceptable standards on all of a number of key dimensions. If any one attribute is deficient, the product is eliminated from contention. Let
YJK = perceived level of attribute k in brand j
Tjk = minimum threshold level that is acceptable (negatively valued attributes such as price that have a maximum level can be multiplied by -1)δjk = 1, if brand j is acceptable on attribute k
0, otherwise
Aj = 1, if it is a preferred brand overall 0, otherwise
under the conjunctive model, we have δjk = 1, if Yjk ≥ Tjk
0, if Yjk < Tjk
Aj = Πk δjk
Thus, Aj will be nonzero if and only if Yjk ≥ Tjk for all attributes.
2) Disjunctive Model :-In this model, instead of preferred brands having to satisfy all of a number of criteria, they have to satisfy one of a number of criteria. The conjunctive model is often called the “ and ” model, while the disjunctive model is called the “ or ” model. Under the conjunctive model the consumer may insist on a product that has lots of memory and software. Under the disjunctive model the consumer may want to settle for either a product with a lot of memory or a lot of software.
Mathematically, we can express this as
Aj = min(Σkδjk, 1)
Where Aj and δjk are defined as before.
3) Lexicographic Model :- This model assumes that all attributes are used, but in a stepwise
manner. Brands are evaluated on the most important attributes first; then a
second attribute is used only if there are ties, and so forth.
Mathematically, if we assume that the attributes are arranged in order from most important to least important, then brand j is the preferred brand if:
yji > yjk for all brands k,k = 2,…,k