Modelling and analysis of consumer’s multi-decision process: a new integrated stochastic modelling framework.
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Date
26/06/2012Item status
Restricted AccessAuthor
Adnane, Alaoui M'Hamdi
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Abstract
Interest in understanding Human Beings’ behaviour can be traced back to the early days of
mankind. However, interest in consumer behaviour is relatively recent. In fact, it is only
since the end of World War II and following economic prosperity of some nations (e.g.,
U.S.A.) that the world witnessed the rise of a new discipline in the early 1950s; namely,
Marketing Research. By the end of the 1950s, academic papers on modelling and analysis
of consumer behaviour started to appear (Ehrenberg, 1959; Frank, 1962). The purpose of this research is to propose an integrated decision framework for modelling
consumer behaviour with respect to store incidence, category incidence, brand incidence,
and size incidence. To the best of our knowledge, no published contribution integrates
these decisions within the same modelling framework. In addition, the thesis proposes a
new estimation method as well as a new segmentation method. These contributions aim at
improving our understanding of consumer behaviour before and during consumers’ visits
to the retail points of a distribution network, improving consumer behaviour prediction
accuracy, and assisting with inventory management across distribution networks.
The proposed modelling framework is hybrid in nature in that it uses both non-explanatory
and explanatory models. To be more specific, it uses stochastic models; namely,
probability distributions, to capture the intrinsic nature of consumers (i.e., inner or built-in
behavioural features) as well as any unexplained similarities or differences (i.e.,
unobserved heterogeneity) in their intrinsic behaviour. In addition, the parameters of these
probability distribution models could be estimated using explanatory models; namely,
multiple regression models, such as logistic regression.
Furthermore, the thesis proposes a piece-wise estimation procedure for estimating the
parameters of the developed stochastic models. Also proposed is a three-step segmentation
method based on the information provided by the quality of fit of stochastic models to
consumer data so as to identify which model better predicts which market segments. In the
empirical investigation, the proposed framework was used to study consumer behaviour
with respect to individual alternatives of each decision, individual decisions, and all
decisions. In addition, the proposed segmentation method was used to segment the
panellists into infrequent users, light to medium users, and heavy users, on one hand, and
split loyals, loyals, and hardcore loyals, on the other hand. Furthermore, the empirical
evidence suggests that the proposed piece-wise estimation procedure outperforms the
standard approach for all models and decision levels. Also, the empirical results revealed
that the homogeneous MNL outperforms both the heterogeneous NMNL and DMNL when
each one of these distributions is applied to all decisions, which suggests the relative
homogeneity in consumer decision making at the aggregate or integrated decision level.
Last, but not least, through the use of the proposed framework, the thesis sheds light on the
importance of consumer choice sequence on the quality of predictions, which affects the
quality of segmentation. The reader is referred to chapter 3 for details on these
contributions.