Trial –Repeat Model

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Assessor Model
(Silk and Urban 1978)
• A pretest market model;
• Giving market share projection for the new
brand and the source of the new brand’s
share;
• Use both trial-repeat and preference
models;
• Adopting a research design combining
laboratory and use test.
Assessor Research Design
Design Procedure
Measurement
O1
O2
Respondent screening Target-group criteria
X1
O3
Exposure to ad
Premeasurement for
established brands
Responses to Ad
Relevant set, attribute
weights, ratings and
preferences
Assessor Research Design
Design Procedure
Measurement
O4
X3
O5
Brands purchased
purchase Opportunity
Home Use
Post-usage
Measurement
new brand usage rate,
satisfaction, repeat
purchase propensity,
attribute ratings and
preferences
Likability and
Believability
Long-Run Trial
Trial –Repeat Model
Parfitt and Collins ( 1968)
M(t)=TS
M: ultimate market share
T: ultimate penetration rate
S: ultimate repeat rate among triers
T=FKD+ CU
those
who
try
•
•
•
•
•
-(FKD)(CU)
those
sampled
adjustment
for double
counting
F: long-run trial rate conditional on K and D;
K: long-run prob.of awareness;
D: long-run prob.of availability;
C: prob.of consumer receiving sample
U: prob.of that a consumer who receives sample uses it
1
Preference Model
Repeat Rate S
S =
Pi ( j ) =
q(k , z )
1 + q(k , z ) − q( z, z )
q( k,t ) : Prob.[switch from k to the new band t]
q( t,t ) : Prob.[ repeat purchase of t ]
q(k, t) and q(t,t) are estimated from O5
Preference Model (Cont’d)
mi
( Ai (t )) + ∑ ( Ai (k ))
b
b
Draw and Cannibalization
Estimates
Two subgroups
• Those include t in their evoked set;
• Those do not include t in their evoked set
∑
P (j)=prob.that consumer i chooses brand j,
Vi(k)= brand preference for brand k, k=1,..j,..m
mi =number of brands in i’s relevant set,
b = parameter to be estimated
i
i
Preference Model (Cont’d)
M (t ) =
∑ L (t )
i =1
i
N
M(t) : estimated market share for brand t
E(t) : proportion of consumers evoking the
new brand t
N : # of consumers measured
Draw and Cannibalization
Estimates (Cont’d)
M ( j ) = E (t ) M ( j )'+ (1 − E (t )) M ( j )' '
( Li ( j ))
M(j): the expected market share for brand j after
the introduction of the new brand t;
N
∑
M ( j )' ' =
b
i
N
Li(t)= prob.that i chooses brand t after trying it
t = index for the new brand
k = index for established brands
Ai(j)=i’s estimated preference for brand j
after trying new brand, j=1, 2,…mi, t
M ( j )' =
k =1
E (t )
k =1
i∈r ( j )
mi
∑ (V (k ))
( Ai (t )) b
Li (t ) =
(Vi ( j )) b
i∈ r ( j )
( Pi ( j ))
Draw from brand j= M(j)’’- M(j)
N
2
Two Contributions
• Convergent Methodology
(1) Trial-Repeat Model
(2) Preference Model
• Pretest-Market Evaluation
Urban and Katz (1983)
• Report on 44 products that had used
ASSESSOR and gone to test market;
• The actual average test market share
was 7.16%
• The average predicted by ASSESSOR
was 7.15% (s.d. 1.12% )
• Benefit-Cost Ratio = 6:1 (assuming
$50,000 investment)
3
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