data mining

(noun)

A technique for searching large-scale databases for patterns; used mainly to find previously unknown correlations between variables that may be commercially useful.

Related Terms

  • Marketing Research
  • business intelligence

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.
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