A stochastic programming approach for marketing campaign optimisation
Abstract
A marketing campaign is a series of activities that effectively promote an organisation's
service or product. Optimising a marketing campaign by providing the right product to
the right customer at the right time is challenging since there are multiple products and
complex business constraints. One of these constraints is demand uncertainty, which deals
with customer behaviour and is continuously impacted by numerous factors.
Operations research focusing on optimisation under uncertainty is part of an ongoing effort
to solve and optimise these types of complex problems. Optimisation under uncertainty
consists of three primary approaches, and one of these approaches is known as stochastic
programming. Stochastic programming models are developed to provide optimal solutions
hedged against uncertainty. From a systematic literature review (SLR) conducted in the
literature, it was concluded that there is no stochastic programming model that addresses
the problem identified in this study. This study addresses the gap by proposing a two-stage
stochastic programming model called a recourse model.
First, two deterministic integer linear programming (ILP) models are identified from the
literature for campaign optimisation. These two models are considered base models. Second,
a uniquely defened deterministic model is formulated based on the marketing fundamentals
of Model 1 and Model 2, respectively. The proposed deterministic model aims to
maximise the profitability of a campaign while excluding uncertainty. Last, a deterministic
model's counterpart, a recourse model, is developed. The proposed recourse model
seeks to maximise the profitability of a campaign while providing solutions hedged against
uncertainty.
All four optimisation models are verified by critically evaluating each constraint's impact
on the model's functionality and results. Subsequently, the model performed as expected in
each instance, confirming that the models are correctly formulated and coded in CPLEX.
The proposed recourse model was validated by showing that the model maximises the
campaign's profitability while providing solutions hedged against uncertainty.
Many factors continuously in
uence customer behaviour, and retailers need to adopt approaches
that accommodate uncertainty while maximising profitability. However, there
is a limit to stochastic applications in the literature. Therefore, the main contribution
of this study is the formulation of the recourse model and the value that this approach
adds when dealing with uncertainty in the decision-making process. The recourse model
proposed in this study will provide retailers with an opportunity to make decisions hedged
against uncertainty. Furthermore, the recourse model revealed a new horizon of future
possibilities that should be investigated in the retail industry.
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