Business can be used for a varied application.


Business Research Methodology


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Applications of Artificial
Neural Networks in Marketing




TO:                                     SUBMITTED

Ritanjali                                                                 Joga

of Management                                               Roll
no.: 178910

Warangal                                                              MBA 1st year

of Management

NIT Warangal


A neural network is an interconnected assembly of simple
processing elements, units or nodes, whose functionality is loosely based on
the animal neurons. Artificial neural networks, which are in essence, computing
systems modeled on our very own biological neurological systems, have made the
concept of a self-thinking AI entity a reality, or a close approximation of it,
rather. This paper presents various applications of artificial neural networks
in Marketing.


            An Artificial
neural network is a form of computer program modelled on the brain and nervous
system of humans. Neural networks are composed of a series of interconnected
processing neurons function simultaneously to achieve certain outcomes. Using trial
and error learning methods neural networks detect%patterns existing within a data set
ignoring data that is not significant, while emphasizing the data which is most
influential. Neural networks are progressively learning systems that continuously improve their function over time.
The network is made of millions of neurons called units arranged in three
interconnected layers:

Input units, which receive information and data
from an external source that the network needs to process or learn about.

Output units, which produce a response to the
information processes or learned by the network.

Hidden units, which sit between the input and
output units and form the bulk of the network that processes or learns the
tasks it’s supposes to perform.

            From the
marketing perspective, neural networks are a form of software tool used to
assist in decision making. Neural networks are effective in gathering and
extracting information from large data sources and have the ability to identify
the cause and effect within data. These neural nets through the process of
learning, identify relationships and connections between data bases. Once
knowledge has been accumulated, neural networks can be relied on to provide generalizations
and can apply past knowledge and learning to a variety of situations.

networks help fulfill the role of marketing companies through effectively
aiding in market segmentation and measurement of performance while reducing
costs and improving accuracy. Due to their learning ability, flexibility, adaption
and knowledge discovery, neural networks offer many advantages over traditional
models. Neural networks can be used for a varied application.

Various applications of
Artificial Neural Networks:

Pattern Classification

            Classification of customers can be facilitated through
the neural network approach allowing companies to make informed marketing
decisions. For Example, Spiegel Inc., a firm dealing in direct-mail operations
who used neural networks to improve efficiencies. Using software developed by
Neuralware Inc., Spiegel identified the demographics of customers who had made
a single purchase and those customers who had made repeat purchases. Neural
networks where then able to identify the key patterns and consequently identify
the customers that were most likely to repeat purchase. Understanding this
information allowed Spiegel to streamline marketing efforts, and reduced costs.


            Estimating a business’s future performance, both long
and short-term, based on historical data, competitor and industry analysis, and
economic trends is essential to its success. Insights drawn from the sales
forecasting can help a business make informed marking decisions pertaining to their
growth and increase in their sales revenue. An example of forecasting using
neural networks is the Airline Marketing Assistant, an application developed by
Behabheuristics which allows for the forecasting of passenger demand and consequent
seat allocation through neural networks. This system has been used by USAir.

Marketing Analysis

            Neural networks provide a useful alternative to
traditional statistical models due to their reliability, time-saving
characteristics and ability to recognize patterns from incomplete or noisy
data. Examples of marketing analysis systems include the Target Marketing
System developed by Churchull Systems for Veratex Corporation. This support
system scans a market database to identify dormant customers allowing
management to make decisions regarding which key customers to target. When
performing marketing analysis, neural networks can assist in the gathering and
processing of information ranging from customer demographics and credit history
to the purchase patterns of customers.

Predictive Analytics

            Predictive analytics is a confluence of two
statistical methodologies, data mining and predictive modelling, which can be
augmented by the machine learning capabilities of neural networks. By learning
to recognize the current and past trends and behaviors, artificial neural
networks can make predictions in future outcomes within a campaign. For Example,
Microsoft used Brainmaker neural network to fine-tune its direct mailing
campaign, increasing its mail response rate from 4.9% to 8.2%. The network
analyzed data associated with 25 variables such as the recent product purchase
and the time elapsed between the release of a new product and the purchase of
the product. By analyzing behavioral patterns associated with each of these
purchases, the neural network was made to score each of the users according to
the likelihood of them opening a mailer. This allowed Microsoft to incisively
target only those users with a higher likelihood of opening a second mailer
from them, and thereby increase their mail response rate.

Market Segmentation

            Segmentation and micro-targeting are key tactics in any marketing
campaign. Marketers need to be able to single out the customers that will
respond positively to a product or service. A customer’s response is influenced
by a number of factors, including specific characteristics associated with
them, such as their demographics, socio-economic status and geographic
location, and more importantly, by their attitude and emotions at any given
time. Neural networks can be used
effectively to segment the audience into distinct groups based on the
above-mentioned qualifications.