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anuj1. Managers use quantitative techniques like variance and regression analysis to make sales, customer performance, and futuristic predictions and decisions for organizations. 2. An educational institution in Jaipur uses mathematics to determine dimensions of spaces in its building like classrooms, stairs, gates, and wires to optimize use of space. 3. The institution analyzes applicant and student data like admission forms, cut-offs, performance in written tests, assignments, internships, and backgrounds to select and evaluate students.
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1. Data mining in marketing involves analyzing customer data to discover useful patterns that can help increase sales and profits.
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3. Decision trees are a common algorithm used that classify customers based on attributes and predict outcomes based on paths from the tree's root nodes to leaf nodes.
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Mis
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Marketing research involves systematically collecting, analyzing, and reporting data to help address marketing problems and opportunities. It identifies customer pain points - specific problems customers experience - so marketers can position their offerings as solutions. The marketing researcher's job is to produce insights that inform marketing decisions. This involves defining problems, developing research plans, collecting and analyzing information, and presenting findings. Research approaches include surveys, experiments, observations, and focus groups. The research plan specifies data sources, instruments, sampling, and contact methods. Analyzing and interpreting findings allows drawing conclusions to report to management.
Marketing information system
Marketing managers obtain information from three main sources: internal company data, marketing intelligence from external sources, and market research. Marketing intelligence encompasses all regular market information and can be purchased from external providers. When necessary information is not available internally or through intelligence, businesses conduct market research studies. An effective marketing information system collects data continuously from various sources, analyzes it, and shares it with decision-makers to provide a competitive advantage through informed decisions.
Nitesh synposis
1. The document provides details of a student project on the international operations of Nokia India Pvt. Ltd. including the student's name and program details.
2. The objectives and problem statement of the project are outlined, focusing on understanding Nokia's marketing, advertising, and international strategies as well as comparing them to competitors.
3. The methodology discusses using both primary research like interviews and secondary research from sources like reports and websites to analyze Nokia's consumer decision making and brand preferences.
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This document provides an overview of marketing research, including its role in strategic and tactical marketing decisions, sources of marketing information, and the typical marketing research process. It discusses how marketing research can help with segmentation, targeting, positioning, and the 4 Ps. The marketing research process involves identifying an information need, defining objectives, designing methodology, conducting secondary and primary research, analyzing results, and reporting findings to inform marketing actions. The document also covers types of research, ethical considerations, emerging issues like online research and data warehousing/mining.
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Marketing research (1)
This document discusses marketing research and the marketing research process. It begins by defining key customer groups and environmental factors that influence marketing. It then discusses the role of marketing research in providing information to marketing managers for decision making. The marketing research process involves problem definition, research design, data collection and analysis, and recommendations. Exploratory, descriptive and causal research designs are described. The document also covers sampling design and different types of marketing research data.
Mm unit 1point1
This document discusses marketing research and its key steps and methods. Marketing research involves collecting, analyzing and communicating information to make informed marketing decisions. There are 5 key steps in marketing research - defining the problem, collecting data, analyzing and interpreting data, reaching a conclusion, and implementing the research. Common data collection methods include interviews, surveys, observations, and experiments. The data is then analyzed using statistical techniques like frequency, percentage, and mean before reaching a conclusion and implementing the research findings.
Mm unit 1point1
This document discusses marketing research and its key steps and methods. Marketing research involves collecting, analyzing and communicating information to make informed marketing decisions. There are 5 key steps in marketing research: 1) define the problem, 2) collect data, 3) analyze and interpret data, 4) reach a conclusion, 5) implement the research. Common data collection methods include interviews, surveys, observations, and experiments. The data is then analyzed using statistical techniques like frequency, percentages, and means to interpret the findings and their implications for marketing decisions.
Mm unit 1point1
This document discusses marketing research and its key steps and methods. Marketing research involves collecting, analyzing and communicating information to make informed marketing decisions. There are 5 key steps in marketing research: 1) define the problem, 2) collect data, 3) analyze and interpret data, 4) reach a conclusion, 5) implement the research. Common data collection methods include interviews, surveys, observations, and experiments. The data is then analyzed using statistical techniques like frequency, percentages, and means to interpret the findings and their implications for marketing decisions.
Data Analytics in Industry Verticals, Data Analytics Lifecycle, Challenges of...
Banking and securities
Challenges
Early warning for securities fraud and trade visibilities
Card fraud detection and audit trails
Enterprise credit risk reporting
Customer data transformation and analytics.
The Security Exchange commission (SEC) is using big data to monitor financial market activity by using network analytics and natural language processing. This helps to catch illegal trading activity in the financial markets.
The Data Analytics Lifecycle is designed specifically for Big Data problems and data science projects. The lifecycle has six phases, and project work can occur in several phases at once. For most phases in the lifecycle, the movement can be either forward or backward. This iterative depiction of the lifecycle is intended to more closely portray a real project, in which aspects of the project move forward and may return to earlier stages as new information is uncovered and team members learn more about various stages of the project. This enables participants to move iteratively through the process and drive toward operationalizing the project work.
Phase 1—Discovery: In Phase 1, the team learns the business domain, including relevant history such as whether the organization or business unit has attempted similar projects in the past from which they can learn. The team assesses the resources available to support the project in terms of people, technology, time, and data. Important activities in this phase include framing the business problem as an analytics challenge that can be addressed in subsequent phases and formulating initial hypotheses (IHs) to test and begin learning the data.
Phase 2—Data preparation: Phase 2 requires the presence of an analytic sandbox, in which the team can work with data and perform analytics for the duration of the project. The team needs to execute extract, load, and transform (ELT) or extract, transform and load (ETL) to get data into the sandbox. The ELT and ETL are sometimes abbreviated as ETLT. Data should be transformed in the ETLT process so the team can work with it and analyze it. In this phase, the team also needs to familiarize itself with the data thoroughly and take steps to condition the data.
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