data mining

Examples of data mining in the following topics:

• Analyzing Data

  • Data Analysis is an important step in the Marketing Research process where data is organized, reviewed, verified, and interpreted.
  • Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes.
  • In statistical applications, some people divide data analysis into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA).
  • All are varieties of data analysis.
  • Summarize the characteristics of data preparation and methodology of data analysis

• Channel Integration

  • With omni-channel retailing, marketing is made more efficient with offers that are relative to a specific consumer determined by purchase patterns, social network affinities, website visits, loyalty programs, and other data mining techniques.
  • Real-time data may be necessary when moving towards an omni-channel approach.

• Cross-Channel Customer Experience

  • It facilitates data mining and makes decisions relating to products (pricing, development), management (financial forecasting and customer profitability) and marketing (customer acquisition, cross-selling and up-selling) more efficient and effective.
  • The business is able to fine-tune its message and refine the data it receives in return by sending campaign-related material (e.g. on special offers) to selected recipients using various channels (e.g. e-mail, telephone, post).

• Rebates

  • The information given in the rebate form, such as name, address, method of payment, can be used for data mining studies of consumer behavior.
  • Thus, a rebate can be thought of as being paid to do this paperwork and provide one's personal data to the company.

• Measuring the Market

  • The demand for cars in India creates demands for steel, tires, forgings, castings, and plastic components which in turn create demands for mining, rubber, forging machines, casting sand and polymers.
  • A customer value model (CVM) is a data-driven representation of the worth, in monetary terms, of what a company is doing or could do for its customers.
  • The amount and detail of customer data is now mined for its value to supply chain decisions and the bottom line.
  • The CVM uses data from customer interaction, on-site interviews, customer service data, sales force reports and all the other types of input and observations about product benefits and the bottom line.
  • Companies are looking beyond traditional assumptions and adopting new frameworks, theories, models and concepts based upon customer data and input.

• Effects of Technology on Services

  • Customer service is now based on data garnered from multiple channel technology so that "real" customer needs and desires are communicated each time a channel or touch point is accessed.
  • Technologies such as mobile commerce,electronic funds transfer, supply chain management, Internet marketing, online transaction processing, electronic data interchange (EDI), inventory management systems, e-mail, mobile applications, social media, telephones and automated data collection systems are commonplace and the new normal for the business world.
  • On the institutional level, big corporations and financial institutions use the internet to exchange financial data facilitating domestic and international business dealings.
  • Data integrity and security are very hot and pressing issues for electronic commerce.
  • Automation technology has been introduced to many different industries such as food service, mining, retail, industrial, home land security, building services, medical procedures and more.

• Collecting Data

  • In marketing research, an example of data collection is when a consumer goods company hires a market research company to conduct in-home ethnographies and in-store shop-alongs in an effort to collect primary research data.
  • This is especially important in the data collection phase.
  • The data collected will be analysed and used to make marketing decisions.
  • Hence, it is vital that the data collection process be free of as much bias as possible.
  • There are many sources of information a marketer can use when collecting data.

• Plan the Research Design

  • This process is guided by discussions with management and industry experts , case studies and simulations, analysis of secondary data, qualitative research, and pragmatic considerations.
  • Decisions are also made regarding what data should be obtained from the respondents (e,g,, by conducting a survey or an experiment).
  • The research plan outlines sources of existing data and spells out the specific research approaches, contact methods, sampling plans, and instruments that researchers will use to gather data.
  • Secondary data analysis is one of the steps involved in formulating a Research Design

• Developing Insights and an Action Plan

  • An example of a presentation is a PowerPoint document supported by graphs, media, or visual elements that showcase the research objectives, data collection, insights, and conclusions/recommendations.
  • During the Report Preparation & Presentation step, the entire project should be documented in a written report that addresses the specific research questions identified; describes the approach, the research design, data collection, and data analysis procedures adopted; and presents the results and the major findings.
  • Final conclusions (based on the insights gathered from data collected) that effectively meet the initial objectives of the research

• Categories of Business Products

  • Agriculture and mining businesses are concerned with the production of raw materials, such as plants or minerals.