Aveneu Park, Starling, Australia

ABSTRACT Marketing has an important part in








This aim of this paper is to give the  key  information regarding artificial neural
networks (ANNs),  With the advancement of
computer  technology, there has been a
drastic change in management applications , to determine the effective solution
is the main target in todays context. Artificial Neural Networks(ANNs) are one
of these tools that have become a critical component for business intelligence.

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Artificial neural networks are machine learning techniques which integrate a
series of features upholding their use in financial and economic applications.
Backed up by flexibility in dealing with various types of data and high
accuracy in making predictions, these techniques bring substantial benefits to
business activities. This paper investigates how consumer behavior can be
identified using artificial neural networks, based on information obtained from
traditional surveys. Results highlight that neural networks have a good
discriminatory power, generally providing better results compared with
traditional discriminant analysis. The purpose of this paper is to describe the
basic behavior of neural networks as well as the works done in application of
the same in management sciences and stimulate further research interests and
efforts in the identified topics.

Key words: Artificial Neural Networks, Marketing

because of Globalization competition occurs among companies and even countries.
companies need to maintain and sustain competitive advantage that can results
with profitability. Marketing has an important part in competition and
companies which can effectively manage costs can make more  profits. Forecasting sales quantity and sales
revenue is very important  for a company
to take action for the next period. Sales forecast enable companies to manage
their budget effectively, to reduce 
uncertainty, to reduce risks, to speed up the decision making process
etc. For this reason some techniques are used for decades.

Artificial neural networks (ANN)
can be defined as a highly connected array of elementary processors called
neurons . According to Specht, ANN is usually defined as a network composed of
a large number of simple processors (neurons) that are massively
interconnected, operate in parallel, and learn from experience (examples) . At
information processing and pattern identification, ANN?s are used. It takes its
roots from the working mechanism of human brains. At this model, like human
brains, there are neurons which are computing units, and they are
interconnected with each other in an organized fashion. Neurons process
information and convert inputs into outputs. According to the relationship
between these neurons,   information can be generated 1. It is a easiest
 form of the neural system at human
brains, but if inputs have a strong connection than notable information can be
achieved. One major application area of ANNs is forecasting for both researchers
and practitioners. The neural network approach gives  better classification, handles complex  relationships better, and is stronger for  interpolation. ANNs have a reliable modelling
flexibility and adaptability, as they can deal the learning process. On the
other hand it can adjust their parameters if new input data are available .

 Using ANN in studies has  benefits to some extent. Different neural
system models have been created for different applications. The most important
and frequent neural network is feed forward neural network. At this type there
are three layers, which are; input layer, hidden layer, and output layer. At
input layer, different independent variables are used to forecast output layer,
which consists of dependent variable. At hidden layer, neurons are
interconnected to input and output layers. This is used for examining the
pattern at nonlinear relationships between output and input layers . Because at
our study there is linear relationship between inputs and outputs, hidden layer
is not used. The type which doesn’t contain hidden layer in ANN is called
single-layer perceptron. Inputs are simply connected with outputs via weights.
According to Sexton and Dorsey, criticisms are generally focused on the
inability to adequately identify identify unnecessary weights in the solution .
This limits to reach a strong output but some algorithms are created to
eliminate this problem.  






Forecasting problems occur in so
many different disciplines and the literature on forecasting that use ANNs is
scattered in so many diverse fields. It is hard for a researcher to be alert of
all the work done to date in the area . ANNs have been used in many disciplines
from the early 1980s to recent years such as engineering, medical diagnosis,
data mining, and corporate business 
available . For example ANN is used in data mining . There are other
areas that ANN?s are successful, and these include competitive market
structuring, market segmentation analysis, identification of loyal and
profitable customers, also forecasting brand shares etc . ANN models are
increasingly being used as a decision aid. Number of areas such as
manufacturing, marketing, and retailing used it , Several authors have given
comprehensive reviews of neural networks, examples of its applications, and
comparisons with the statistical approach .

Neural networks for marketing :

Neural networks technology  became 
a preferable destination  with its
influence over number of domains. Researchers designed different kinds of
neural nets systems , perceptron is a feed-forward network with one layer of
learnable weights connected to one or more units, which is the basic element of
neural network. Perceptron is a linear classification algorithm of supervised
learning. An activation function is used to reach the goal of nonlinearity3.
It combines a set of weights with the feature vector to make predictions.
Perceptron can date back to the middle of last century and therefore it’s
regarded as one of the earliest machine learning algorithms in the world.

 main areas of application of ann  is in marketing research is the market
segmentation. calculated customer lifetime value, loyalty and consequently
identified client segments. In most of segmentation studies auto maps were
utilized and authors often stressed their advantage in interpreting the
informational value of input data.  They
confronted self-organizing map with multilayer feedforward network and argued
that map allows an intuitive representation of results, therefore is more
straight forward to understand. On the otherhand, feedforward network was more
powerful, since its generalization process was more robust than in case of
self-organizing map. 



 Relationship between marketing and operations,
One of the first authors to discuss the relationship between marketing and
operations was Shapiro (1977). He mentioned that, in order to reduce the amount
of conflict between marketing and operations, these two must understand each
other’s characteristics. The marketing professionals should impart their
strategies around the existing operating characteristics. It is clear that
marketing people should understand operations management challenges and not
only market needs. To Karmarkar (1996), a greater interaction between marketing
and operations occurs through interaction between the functions of both areas.
These interactions are represented by joint decisions that can result in improved
performance of factors, such as quality, lead time, cost and flexibility. For
Sawhney and Piper (2002), an important interface between the functional areas
of marketing and operations involves structuring and managing the production
capacity.   Inconsistent actions between areas of
marketing and operations in terms of management capabilities result in negative
impacts on delivery time, quality and cost. According to McGaughey (1988), the
market is always shifting demand towards products of ever greater complexity.
This increased complexity becomes critical in order to face competition. We
have observed that constant adaptation to market demands requires greater
coordination between marketing and operations. According to Mollenkopf et al.
(2011), operations can meet the objectives of the marketing area and offer
competitive market differences, if properly designed.4 The relationship
between marketing and operations has prospered in the last 20 years (Tang,
2010). Yet, there are still topics included in this interface that deserve
attention. In other words, there is a need for more knowledge about the
interface between the areas of marketing and operations. One factor that
requires greater knowledge about the interface between these two functional
areas is the number of elements involved. The following section aims at
presenting the relationship between marketing and operations, and will discuss
the attributes of the marketing and competitive criteria of the operations.   Relationship between marketing and operations
One of the first authors to discuss the relationship between marketing and
operations was Shapiro (1977). He stated that, in order to reduce the amount of
conflict between marketing and operations, these two areas must understand each
other’s characteristics. The marketing professionals should develop their
strategies around the existing operating characteristics. It is clear that
marketing people should understand operations management challenges and not
only market needs.


  Proposed design

The design proposed in this study
is based on the framework developed by Tang (2010). In this   framework, the authors propose a series of
decisions in the marketing area that relate to the operations area. For Tang (2010),
the main objective is to attain the coordination of demand and a supply chain
that maximizes profit. According to Tang (2010), from the moment that the
marketing and operations areas conduct their activities together, the company
obtains better performance .The proposed design is based on the areas not
highlighted in gray.4 It should be noted that Tang’s approach (2010) is
different from that of Shapiro (1977), which defines the existence of possible
conflicts between marketing and operations due to the characteristics of their
responsibilities. For Shapiro (1977), it is not possible to eliminate
conflicts, but they can be minimized through greater knowledge,  Our model attempts to unify these two
approaches: (i) a line of study proposed by Tang (2010) that states that the
decisions of the marketing field can be synchronized with the operations area
in order to seek better performance, (ii) and a line of study proposed by
Shapiro (1977) which states that there is no way of avoiding the conflict
between marketing and operations, requiring assimilation of knowledge about
this relationship in order to keep track of activities and to minimize
conflicts. The input variables are grouped according to the framework proposed
by Tang (2010): product, service, quality, price, promotion, and delivery channel.
The output variables are grouped according to coordination and collaborative
demand, since the objective is to achieve the best delivery performance, that
is, on-time. Tang (2010) ranked different marketing decisions and operations
performance in these categories. The relationships among variables presented in
our model have the intention of representing those in Shapiro’s study (1977).
According to this author, there is an interface between marketing and
operations activities that relate to one another. There are a number of
relations among these decisions, also called processing activities, that impact
the outputs of our model, which constitute the delivery performance of the
operations area. In short, marketing decisions are the input variables of the
model, and the processing stage of these decisions determines the relations
between them, while delivery performance is the output variable.  


 Results  indicate that the ANN enables efficient
management of investments. The preparation and establishment of the model of a
distinguished group of 6 high-risk factors: financial, scientific-technical,
manufacturing, company, market and external-the eco. Within each group, the
identification of risk factors 5. There were three clusters are : aggressive,
conservative and moderate. The aggressive risk cluster of companies 30
different risk factors to the ANN model is able to include all the factors. The
conservative risk cluster of companies to the ANN model could add only 12 risk
factors. The remaining 18 risk factors must be eliminated. The moderate risk
cluster of companies may include 19 risk factors. 11 risk factors must be
eliminated. Thus, every company must know what level of risk the company may
assume by investing in technological innovation using ANN model



1. Rytis Krušinskasa , Management
Problems of Investment in Technological Innovation, Using Artificial Neural
Network, 20th International Scientific Conference Economics and Management –
2015 (ICEM-2015)

2. Michal , Robert Verner
,Artificial neural networks in business: Two decades of research, Applied Soft

3. Ashkan Zakaryazad, Ekrem Duman
n, A profit-driven Artificial Neural Network (ANN) with applications to fraud
detection and direct marketing, Neurocomputing

4. https://pdfs.semanticscholar.org



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