Computer-mediated communication and market research Role of IT Tools in market research

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1 E-commerce, CMC, IT and Knowledge Management: Finding new horizons in e-research Dr Varuna Godara, Lecturer in Information Systems, University of Western Sydney, Sydney, Australia Abstract E-commerce, Computer-mediated communications, IT tools and knowledge management have the capability to play a bridging role in managing the huge growth in businesses and information. This capacity can lead to an increased understanding of the nature of customers, markets and communication through e-researches. The combination of these new emerging high-tech disciplines can help identify new targets and test new strategies for e-businesses. High performance computers, software, appropriate algorithms, and integrated databases can also play a key role in e-research and identifying key innovative strategies. In this paper a conceptual model of the flow of data and information to knowledge management has been suggested and uses of various fields in e- research are explained. Keywords: E-commerce, Computer-mediated communications, e-business, e- marketing, competitive advantage, data, databases, information, knowledge management and e-market. Introduction There has been a dramatic change in the way companies do business. We are living in a surge economy dominated by Information Technology and are moving towards digital society in which information will be the basic resource for survival of organizations and individuals. Almost all the workers will be knowledge workers who will gather, store, analyse and disseminate information. All the organizations will be learning organizations that accumulate and analyse knowledge and learn from the knowledge warehouses. This is the reason why databases have become the core infrastructure of e-business for storing data and information. Organizations are using different types of databases like analytic, Operational, Hierarchical, Network, Relational or Client Server Databases. These databases contain different types of data, information and knowledge. It is important to differentiate clearly between data, information and knowledge (Cader, 2001). Data includes recorded measures of certain events or

2 transactions. For example, Customer name and address are data. Information on the other hand is processed data that has some meaning and is in a suitable form for interpretation. For example, if we can see the address against the customer name it becomes information. If Knowledge on the other hand, provides further understanding of this information, which comes from interpreting the information with experience, insight, reflection, perspective, context and being able to take decisions and actions (Davenport et al 1988). For example, knowledge is being able to predict the demand of product; identify target market for a particular product; and predict the competitor s behaviour for any action. Computer-mediated communication and market research Computer-mediated communication may be defined as the communication in which the computer is the media, such as electronic mail, computer conferencing, chat, bulletin boards, discussion lists, World Wide Web and offline mail readers etc. For e-research the information can be collected with help of online questionnaire, s and online interviews (chat or computer conferences). After considering related ethical and legal issues, Computer-mediated communication tools can be used for contacting respondents, collecting data and storing directly into the hierarchical, network or relational databases. Every message send by individuals or groups in s, chats, computer conferencing, discussion forums etc can be recorded and categorized in the databases and knowledge bases for further analysis by the researchers. Any communication happened between the customers and employees, customers and customer can be captured for further use in decision-making after taking permission from them. More over companies can also place a program on the hard disk of the customer (after taking permission of customer) that monitors and records every action of individuals on the net. This information of customer behaviour can be further used for product development, website development, providing customisation and forming marketing strategies. Role of IT Tools in market research A new synergistic approach towards market research is developing from multidiscipline working together Marketing, computer Science, statistics, mathematics, knowledge management and Information Systems. This approach is developing to organise statistically, process speedily and store safely market research data so that it can be further used for research and development. Highly complicated data is generated while doing high-level researches that is quite difficult to be dealt with traditionally available tools.

3 This is the reason why latest Information technology tools are used to satisfy the needs of the researchers in marketing area. More and more Data warehouses are created for keeping the records of the customers, suppliers and other stakeholders. Data warehouses and online analytical processing engines can be widely used for the purpose. The cleansed, integrated and preprocessed data in the data warehouses can be used for efficient and effective data analysis. Researchers can take help of data mining and utilise it for analysing statistically the information available. Data mining can be performed on different platforms like transaction databases, spatial databases, text databases, time series databases, relational databases, flat files etc (Han, 1998). Customer behaviour, the market trends and relationships between the factors can be easily analysed with these databases and datamining. The speed of data processing has also increased that enables researchers to identify sequences, compare them other sequences in very short time. This ultimately helps e-businesses to take decisions in less time that is most important in this surge economy. A.C. Nielsen's Spotlight is a good example of a DM tool. Nielsen clients use Spotlight to mine point-of-sale databases. These terabyte-size databases contain facts (e.g., quantities sold, dates of sale, prices) about thousands of products, tracked across hundreds of geographic areas for at least 125 weeks. Spotlight transforms tasks that would take a human from weeks to months to do into tasks a computer can do in minutes to hours. Nielsen says it has sold about 100 copies of Spotlight (DOS and Windows) to U.S. clients, who have in turn deployed it to field-sales representatives in multiple regional centers. The software frees analysts to work on higher-level projects instead of being swamped by routine, laborious chores (http://www.byte.com/art/9510/sec8/art3.htm). Online analytical processing is also used in researches these days to verify the hypothesis. According to Herb Edelstein(1997), online analytical processing and data mining complements each other. OLAP is user-driven; the analyst generates an hypothesis and uses the OLAP tool to verify the hypothesis. In contrast, in data mining the tool is used on the data to generate a hypothesis. Similarly, when users employ OLAP and other query tools to explore data, they guide the exploration. However, when users employ data mining tools to explore data, the tools perform the exploration. For example, an analyst might hypothesize that people with high debt and low incomes are bad credit risks. The analyst would use OLAP in various ways to verify or disprove that hypothesis. A data-mining tool, however, would be used to find the risk factors

4 for granting credit - for example, it might discover that people with high debt and low incomes were bad credit risks. But data mining might also discover a pattern that the analyst did not even conceive, such as that debt-to-income ratio and age indicate risk. There is one extraordinary type of data mining called Online Analytical Mining (OLAM) that is in its infant stage. OLAM (also called OLAP mining) integrates on-line analytical processing (OLAP) with data mining and mining knowledge in multi-dimensional databases. It uses cleansed high quality data and provides the facility to select the range of data for analysis from the database. The researcher can easily traverse through the data warehouse and select the portion of data he/she wants to use for the study and analyse them at different granularities and present knowledge/results in different forms. On-line analytical mining provides facilities for data mining on different subsets of data and at different levels of abstraction, by drilling, pivoting, dicing and slicing on data cube and on some intermediate data mining results (Han, 1998). Thus, together with data/knowledge visualization tools data mining can greatly enhance the power and flexibility of exploratory market research. E-market research makes use of Computer-mediated communication tools, information technology, marketing and statistics in firstly collecting data. Secondly information technology tools are used to process data with help of statistics and mathematics. In the third phase comes knowledge management techniques and information technology to form databases and properly using them.we can expect an outburst of information and knowledge in next five years. Information and knowledge management will be a challenge to those working in the field of market research and knowledge management. Together with principles of market research, Knowledge management will play a key role in helping to manage and make use of this outburst of information in functional areas of business like marketing etc. ROLE OF KNOWLEDGE MANAGEMENT IN E-RESEARCH We should define Knowledge management to understand its important role. In simple words knowledge management is a process through which organisations generate value from their research based or transaction based intellectual assets. According to Stewart (1997), "Knowledge has become the preeminent economic resource-more important than raw materials; more important, often than money. Considered as an economic output, information and knowledge are more important than automobiles, oil, steel, or any of the products of the industrial age". Knowledge management is "a set of practices that includes identifying and mapping intellectual assets within organizations,

5 generating new knowledge for competitive advantage, making vast amounts of corporate information accessible, sharing best practices, and applying management strategies and technology that support all of the above." --CAP Ventures( Businesses and their research and development departments are struggling to manage the huge growth in the customer information and other records which is stored in their own or proprietary databases. They have their own databases and therefore have a free access to high quality customer and market information that makes the situation even more complex. E-businesses are facing biggest challenges in managing the information and knowledge. In many respects, this is the same challenge faced by any organisation in any industry. The more relevant knowledge a company can gather and then use it wisely, the better the competitive advantage it can have. For example by analysing the information about the taste of customers about a product, a company can become a leader by producing and marketing that product. There can be two types of knowledge, which researchers generally deal with, one is explicit and the other is tacit. Explicit knowledge is that which can be codified and can be put in form that can be read, written, seen, heard or felt. For example these can be large customer data that can be codified and stored with help of information technology for further use. Tacit data is generally in mind of the respondents and researchers. It is hard to recognise, generate, store and share the tacit data. Knowledge management helps in identifying ways to tackle with this data also.knowledge management can foster market researchers by fostering free flow of ideas, using these ideas with reduction of time, recognising useful information and streamlining research operations such as product development etc. Eresearch model for retrieving knowledge from data warehouses In fig 1 the flow of customer data and information to knowledge management has been conceptualised. Data (customer records) from a data warehouse (disk) is entered into a database server, where it is dynamically analysed using the appropriate software for sequence analysis. This can then be mined for meaningful information such as previously undetected patterns and relationships (pattern recognition). For example, buying habits of particular set of customers can be linked to particular season. Also graphs and image if data can be visualised. If researchers can see a graph picture, they are likely to find that information more meaningful. Researchers can also look for differences and similarities between all the segments of customers. They could also study the problems arrays of customers at different stages in product life cycle.

6 Information leads to knowledge, which tries to make sense of this information. The knowledge that can be gained from the information is used in predicting the demand; customer behaviour, competitor s behaviour, identifying target market for a particular product; prediction of the structure of market and finding out impact of advertising campaign. Although data mining can provide us with exciting trends and information, even then sometimes it is very difficult to analyse this information manually. Therefore a new technology that takes help from Artificial intelligence is developing, which is called KDD. According to Peggy Wright, 1998, Knowledge discovery is defined as ``the non-trivial extraction of implicit, unknown, and potentially useful information from data''. The knowledge discovery process takes the raw results from data mining (the process of extracting trends or patterns from data) and carefully and accurately transforms them into useful and understandable information. This information is not typically retrievable by standard techniques but is uncovered through the use of AI techniques. KDD technique uses probabilistic, statistical, classification, deviation and trend analysis and hybrid approaches to analyse information and provide knowledge.

7 Knowledge Research queries KDD TECHNIQUES Researcher Computer-mediated Graphic communication interface API OLAM Engine OR OLAP Engine Cube API Data cube Database API Data warehouse OR Database A Conceptual E-Research Model for retrieving knowledge from data warehouses

8 REFERENCES Cader, Yoosuf "Knowledge Management and Marketing Information Systems".Journal of the Raffles Education Group. 5 (1): Carver, Tim J and Mullan, Lisa J "Website Update: A new graphical user interface to EMBOSS". Comparative and Functional Genomics. 3: Davenport, Thomas H., De Long, David W. and Beers, Mike C "Successful Knowledge Management Projects". Sloan Management Review. 39 (2): Edelstein Herb. Data Mining: Exploiting the Hidden Trends in Your Data.DB2. Spring 1997 Evans, Philip B and Wurster, Thomas S "Strategy and the new economics of Information". Harvard Business Review. Sept-Oct: Han, J Towards On-line analytical mining in large databases. ACM SIGMOD Record, 27: Porter, Micheal E and Miller, V.E "How Information gives you Competitive Advantage". Harvard Business Review. July-Aug: Stewart, T.A Intellectual Capital: The New Wealth of Organizations. Nicholas Brealey Publishing. Wright Peggy Knowledge Discovery In Databases: Tools and Techniques. WEBSITES

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