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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 databases. 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.
Neural 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, adaptation and knowledge discovery, neural networks offer many advantages over traditional models. Neural networks can be used for a varied application.
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.
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 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.
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.
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