An Application of Neural Networks in Market Segmentation

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1 1 & Marketing An Application of Neural Networks in Market Segmentation Nikolaos Petroulakis 1, Andreas Miaoudakis 2 1 Foundation of research and technology (FORTH), Iraklio Crete, 2 Applied Informatics and Multimedia Dep., TEI of Crete, miaoudak@epp.teiher.gr Abstract Neural Networks (NN) have acquired great success, as they are one of the most rapidly expanding areas. Marketing and specifically Market Segmentation (MS) is one topic that NN can be a useful tool. This work presents an application of NN in MS. A Case Study (CS) in the market of mobile phones is used. In this scenario, a Mobile Company wants to predict what type of mobile phone people desire, in order to construct and promote. For this reason, a NN was developed using Matlab in order to predict the most suitable mobile phone for different mobile users. A great number of data were collected to train the NN. The decision capabilities of this NN were evaluated. Results show the potential of NN application in MS. Keywords: Market Segmentation, Neural Networks, Marketing, Artificial Intelligence. 1. Introduction Artificial Intelligence (AI) is the science and engineering of making intelligent machines, especially computer programs [McCarthy, John (2004)]. One of the most important parts of AI is Neural Networks (NN). NN is the element that makes computers think. For this reason, NN have acquired great success, becoming one of the most rapidly expanding areas. NN can be utilized into many application areas such as pattern and image recognition where much research has been done. In addition, NN can be used in business applications [Haykin, Simon (1999)]. However, there are some fields that these networks have not been applied enough yet. One topic that NN can be a useful tool is in Marketing and more specifically in Market Segmentation (MS). In this work, the application of NN in MS is examined. For this reason, a Case Study (CS) in the market of mobile phones is used. In this scenario, it is assumed that a Mobile Company wants to predict what kind of mobile phone people desire, in order to construct and promote. A NN was developed using Matlab in order to predict the most suitable mobile phone for different mobile users. Several training methods were explored. A great number of collected data were used to train the NN. After training, the ability of the NN to decide was evaluated. The evaluation presents satisfactory results, which demonstrate the potential of the NN usage in Marketing and especially in MS. The rest of this paper is organised as follows. In section 2 an introduction to NN is given. In section 3 the MS method is presented. In section 4 the CS is described. In section 5 the construction and training of the Neural Network is analyzed. In section 6 the Evaluation of the NN decision capabilities is shown. Finally, in section 7 conclusions are given. 2. Neural Networks The first idea for development of this type of network was done in 1943 by the neurophysiologist Warren McCulloch and the mathematician Walter Pitts by writing a paper about how neurons work [Haykin, Simon (1999)]. The construction of one network that it can think in the same way of a human brain, attracted many researchers because it can provide huge capabilities. Based in simple mathematic calculations, a NN tries to simulate learning capabilities of neurons that exist in the human brain. Figure 1 depicts a representation of a Brain Neuron. With the development of this type of network, the idea for thinking machines became a reality.

2 186, Figure 1. Neurons of Brain Figure 2. Architecture of Neural Network A NN is consisted of three basic parts; the input layer, the network layer (one or more hidden layers) and the output layer (see Figure 2). The input signal enters the network, weighted biased summations are performed inside the hidden layer and the results are exported in the output layer. The training of the NN is a procedure where the network weights and biases are adapted every time that a new training data is fed into the network. Figure 3 shows the way that NN make calculations inside the network. The input signal matrix is composed of the signals P1, P2,..PK that are received by the hidden layers. Each element Pi is multiplied by is corresponding weight factor wi to produce weighted sums in each node of the hidden layer. After that, every sum is biased by the value bi to produce an output signal. Thus the output αι can be expressed as: f(ai) = Σ ( wi,i x Pi) + bi., where bi f is the transfer or activation function. In Figure 3 a generic neural network topology is presented. Figure 3. Inside the Neural Network There are many areas that NN can be used such as: Pattern Recognition, Medicine, Sports applications, Science, Manufacturing, Stocks Commodities and Futures, Business Management, Finance and Marketing [Active8 (2005)]. The subject of this work is the application of NN in Marketing and more especially in Market Segmentation. 3. Market Segmentation Market Segmentation is a part of marketing theory. It is the process of partitioning market into smaller subgroups of potential customers with similar needs and characteristics [Weinstein, Art (2004)]. The purpose of segmenting a market is to allow your marketing and sales plan to focus on the subset of prospects that are most likely to purchase your offering. Right segmention will help to the highest return of marketing and sales expenditures. Depending on whether you are selling your offering to individual consumers or a business, there are definite differences in what you will consider when defining market segments [CED (2004)]. There are four basic categories of variables in MS: Geographic Variables, Demographic Variables, Psychographic Variables and Behavioural Variables [Brassington and Pettitt (2003)].

3 187 MS can be made with many techniques. Neural is the one of the most recent method for segmenting markets. Moreover, there are some other methods that are used such as Regression Analysis and Multiple Linear Regression Analysis. When compared to the above methods, NN are proved to give better performance, in most of the times [DeTiennen, Kristen Bell (2003)]. There is the need to develop an application to prove so. 4. The Case Study For this work a CS regarding mobile phone market is considered: A very big communication company wants to classify and predict the consumer s needs in purpose to construct and calculate the number of new mobile phones. In addition, the company wants to map out the strategy of promotion and advertise its products. To produce such a prediction a NN can be used. After the research on how to segment the market for the above example, a final selection of variables was done. Fifteen final variables were selected as questions/opinions regarding to Geographic (Region, Country, and City), Demographic (Gender, Financial Status, Occupation, and Age), Psychographic (Active Life, New Technology, Travelling, and Gaming) and Behavioural (New Products, Mobile Phone, Brand and Regularity) criteria. The 15 variables/questions are used as input data for the NN. An additional 16th variable/question about the preferred type of mobile phone corresponds to the NN output. This variable/question is the target of each input vector for the training mode. Collection of data is a very important and difficult task but it is essential for training the network. In order for training data to have a real base, there was the need for collecting data through the help of volunteers. For this reason, an online program was used [Web Surveyor (2005)]. This software can create surveys for collection data through a web interface. The survey had three basic phases: formulation of questions, publication of survey and collection of results. A questionnaire combined of 16 questions was created. An was sent to many people through group mail addresses of Sussex University. There were a big number of responses. More than 300 people entered the survey page and 200 people completed the questionnaire. Finally, there was a selection of answers for training and testing the network. From the selected data, the 80% were used for training and the rest 20% for evaluating the network. 5. The application of the Neural Network Since the training data were collected, they were converted into numerical by assigning each String a number. This is necessary in order that data can be used in calculations. In this way, each answered questionnaire was converted into a numerical matrix, which is used as an input vector. For the presented scenario, a NN was designed and implemented in Matlab. Regarding the network type, the Feed-Forward Backpropagation (FFB) was chosen. FFB is the most common and effective Network type for predicting and classifying inputs. Several training types were selected such as the Levenberg-Marquardt optimization (TrainLM) and the Scaled Conjugate Gradient optimization (TrainSCG). For the Adaption Learning Function the Gradient Descent with Momentum weight and bias learning function (LearnGDM), was used [Matlab User s Guide (2000)]. The implemented NN has one hidden layer. The number of neurons that were used in the hidden layer can be varied. Different numbers of neurons were examined in order to have the best performance. In the output layer 5 neurons were used (as many as the outputs-types of mobile phones). Since the system is nonlinear, the transfer function that had the better performance was Tansig (tangential) for the hidden layer and Logsig (logarithm) for the output layer. After the NN creation, the training of the NN is a very important issue. The training data vectors were used with both training methods and with different number of neurons in the hidden layer. Three matrixes are needed for training the network: the input matrix, the target matrix and the input range matrix. For the input matrix, the results of the 14 questions were used (finally, the city question was not included as not applicable). Concerning the target, the 16th question (type of preferred mobile phone) was utilized. Finally, a matrix containing the range of each element of the input matrix was constructed.

4 188 Ierapetra, May 2007 Figure 4. The applied Neural Network Figure 4 depicts the designed NN. As it can be seen, the 14-row vector enters the network. The weights are adjusted inside the hidden layer. In the above figure, there are 10 neurons inside the hidden layer. The vectors are summed together with the bias values and the results are fed to the output layer. By using the tangential function, the values are limited between -1 and 1. Finally, the output layer adjusts the new weights and biases in order to reach the desired targets. Since the function that was used is the logarithm, the output can take values from 0 to 1. The output was a 5row vector where the nearest value to 1 is the desired target. 6. Evaluation of the Network For the evaluation of the network, 40 data vectors were used. The answers (regarding the type of the mobile phone) produced by the network were compared with the real ones. The results of the evaluation are presented in Table 1 and 2 for TrainSCG and TrainLM method respectively for different number of neurons. The first column contains the number of neurons, the second the training performance (Mean Squared Error), the third the number of epochs (training iteration) and the forth the succession percentage. As it can be seen, for the TrainSCG approach, the success is ranged from 26.7% to 50%. The 50% correct outputs were produced by a network using 35 neurons. In addition, the test shows that the number of neurons in the network is a very important parameter but it is not proportional to the success percentage. The second method that is presented (TrainLM) produced better results. As it can be seen, correct answers were more than 33.3% in all different number of neurons. The higher number of correct answers was reached for 37 neurons in the hidden layer, which is 70% or 28 over 40 giving great efficiency for this kind of network. However, this method involves higher processing complexity. Table 1 and 2: Evaluation of the NN Performance TrainSCG Method TrainLM Method Neurons MSE Epochs Correct % Neurons MSE Epochs Correct % % % % % % % % % e % e % % e % % % % % % % e % % % e %

5 189 There are some comments about the results of the Evaluation: Results can be assumed successful since a big percentage of correct answers were accomplished. Even for the TrainSCG method, the results were satisfying enough. TrainLM proved to be better in comparison to the TrainSCG method. The number of neurons that gave best results was close for both training methods. (35 neurons 50% for TrainSCG, 37 neurons 70% for TrainLM) The number of neurons is of very importance for the training of the network. It is not possible to know the correct answer of the neurons before the test. The training of the network was proved a time-consuming procedure. Especially for TrainLM method. 7. Conclusion In this work, the application of NN in MS is proposed. In order to examine this, a case study regarding the mobile phone market is presented. Several NN designs and training methods were investigated. Two training methods produced satisfactory results dictating the potential of NN usage in MS, which can be a powerful tool in the Marketing Science. The presented work is an example to show the methodology for future research and implementation. This method can be used in a very big variety of applications of Marketing. It can be used in every market that has to be segmented. In order to improve further the performance of such application several issues have to be taken into account: Selection of questions (which are used as training inputs) is critical. Different formulations for the questions may give different results. Range of possible answers also affects the results. Variation of the statistical sample is very significant. The amount of training data is important. A greater database can give better results. The network proved to be sensitive. The size and type of NN as well as the training method is critical. The application of NN in MS requires a good background in many different scientific fields such as marketing, neural network, sampling and statistics. The capability of neural networks to take decisions is very hopeful for developing more applications on the field of market segmentation. It provides to the future scientist a great area for research and development. References Active8 (2005), Predictive Technologies, The History and Application of Artificial Neural Network, Human Resources White Paper. Bloom Jonathan Z (2004), Market Segmentation, A Neural Network Application, doi: /j.annals Brassington Frances and Pettitt Stephen (2003), Principles of Marketing, 3rd, Printed in Italy, ISBN DeTiennen Kristen Bell, Lewis Lee W (2003), Artificial Neural Networks for the Management Researcher: The State of the Art, Marriott School of Management Brigham Young University. Euroregional Center for Democracy (CED) (2005), Specific Issues Relevant For SME Cross-Border Business Development, Timisoara Romania Haykin, Simon (1999), Neural Networks, A Comprehensive Foundation, 2nd edn, Printed in USA, ISBN Matlab 7 (2000), Neural Networks Toolbox, User s Guide, MathWorks McCarthy, John (2004), What is Artificial Intelligence? Computer Science Department, Stanford University. Sentient Machine Research B.V., Neural Networks Applied to Direct Marketing, Amsterdam, The Netherlands Vriens Marco and Brown Millward (2001), Market Segmentation, Analytical Developments and Application Guidelines,Technical Overview Series. WebSurveyor (2005), University of Brighton Wedel, Michel (2002), Introduction to the Special Issue on Market Segmentation, Intern. J. in Research in Marketing 19 (2002)

6 190 Ierapetra, May 2007 Weinstein, Art (2004), Handbook of Market Segmentation, Strategic Targeting for Business and Technology Firms, 3rd edn, Printed in USA, ISBN