Article 3 of 3 about transforming research into insight. Published May 2012 in the Market Research Association’s Alert! magazine.
Part 1: The Four Places Insights Hide http://www.slideshare.net/tpulliam/alertmarketingresearchorg012012
Part 2: Qualitative Data
http://www.slideshare.net/tpulliam/marc-halertmarketingresearchinsightsqual
Sentiment Analysis on Twitter Dataset using R Languageijtsrd
Sentiment Analysis involves determining the evaluative nature of a piece of text. A product review can express a positive, negative, or neutral sentiment or polarity . Automatically identifying sentiment expressed in text has a number of applications, including tracking sentiment towards Movie reviews and Automobile reviews improving customer relation models, detecting happiness and well being, and improving automatic dialogue systems. The evaluative intensity for both positive and negative terms changes in a negated context, and the amount of change varies from term to term. To adequately capture the impact of negation on individual terms, here proposed to empirically estimate the sentiment scores of terms in negated context from movie review and auto mobile review, and built two lexicons, one for terms in negated contexts and one for terms in affirmative non negated contexts. By using these Affirmative Context Lexicons and Negated Context Lexicons were able to significantly improve the performance of the overall sentiment analysis system on both tasks. This thesis have proposed a sentiment analysis system that detects the sentiment of corpus dataset using movie review and Automobile review as well as the sentiment of a term a word or a phrase within a message term level task using R language. B. Nagajothi | Dr. R. Jemima Priyadarsini "Sentiment Analysis on Twitter Dataset using R Language" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd28071.pdf Paper URL: https://www.ijtsrd.com/computer-science/data-miining/28071/sentiment-analysis-on-twitter-dataset-using-r-language/b-nagajothi
Stock market prediction using Twitter sentiment analysisjournal ijrtem
ABSTRACT : In a study, it was investigated relationship among stock market movement and Tweeter feed content. We are expecting to see if there is connection among sentiment information extracted from the Tweets using a Vader in predicting movements of stock prices. As a result it was obtained strong positive correlation with a coefficient of correlation to be 0.7815.
KEYWORDS : correlation, financial market, polarity, sentiment analysis, tweets
Advances in technology and the rise of contemporary chaos has changed the advertising world. This deck shows how advertising agencies must change in order to remain relevant to clients and to audiences.
Artificial intelligence (AI) is everywhere, promising self-driving cars, medical breakthroughs, and new ways of working. But how do you separate hype from reality? How can your company apply AI to solve real business problems?
Here’s what AI learnings your business should keep in mind for 2017.
Study: The Future of VR, AR and Self-Driving CarsLinkedIn
We asked LinkedIn members worldwide about their levels of interest in the latest wave of technology: whether they’re using wearables, and whether they intend to buy self-driving cars and VR headsets as they become available. We asked them too about their attitudes to technology and to the growing role of Artificial Intelligence (AI) in the devices that they use. The answers were fascinating – and in many cases, surprising.
This SlideShare explores the full results of this study, including detailed market-by-market breakdowns of intention levels for each technology – and how attitudes change with age, location and seniority level. If you’re marketing a tech brand – or planning to use VR and wearables to reach a professional audience – then these are insights you won’t want to miss.
This chapter discusses primary and secondary research methods. Primary research methods outlined include interviews, observations, questionnaires, surveys, focus groups, and participation in online forums. Secondary research methods include books, journals, reference materials, periodicals, newspapers, archives, libraries, the internet, and government statistics. The chapter also provides a table comparing primary and secondary research, noting primary research involves original data collection while secondary research uses existing sources.
Sentiment Analysis on Twitter Dataset using R Languageijtsrd
Sentiment Analysis involves determining the evaluative nature of a piece of text. A product review can express a positive, negative, or neutral sentiment or polarity . Automatically identifying sentiment expressed in text has a number of applications, including tracking sentiment towards Movie reviews and Automobile reviews improving customer relation models, detecting happiness and well being, and improving automatic dialogue systems. The evaluative intensity for both positive and negative terms changes in a negated context, and the amount of change varies from term to term. To adequately capture the impact of negation on individual terms, here proposed to empirically estimate the sentiment scores of terms in negated context from movie review and auto mobile review, and built two lexicons, one for terms in negated contexts and one for terms in affirmative non negated contexts. By using these Affirmative Context Lexicons and Negated Context Lexicons were able to significantly improve the performance of the overall sentiment analysis system on both tasks. This thesis have proposed a sentiment analysis system that detects the sentiment of corpus dataset using movie review and Automobile review as well as the sentiment of a term a word or a phrase within a message term level task using R language. B. Nagajothi | Dr. R. Jemima Priyadarsini "Sentiment Analysis on Twitter Dataset using R Language" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd28071.pdf Paper URL: https://www.ijtsrd.com/computer-science/data-miining/28071/sentiment-analysis-on-twitter-dataset-using-r-language/b-nagajothi
Stock market prediction using Twitter sentiment analysisjournal ijrtem
ABSTRACT : In a study, it was investigated relationship among stock market movement and Tweeter feed content. We are expecting to see if there is connection among sentiment information extracted from the Tweets using a Vader in predicting movements of stock prices. As a result it was obtained strong positive correlation with a coefficient of correlation to be 0.7815.
KEYWORDS : correlation, financial market, polarity, sentiment analysis, tweets
Advances in technology and the rise of contemporary chaos has changed the advertising world. This deck shows how advertising agencies must change in order to remain relevant to clients and to audiences.
Artificial intelligence (AI) is everywhere, promising self-driving cars, medical breakthroughs, and new ways of working. But how do you separate hype from reality? How can your company apply AI to solve real business problems?
Here’s what AI learnings your business should keep in mind for 2017.
Study: The Future of VR, AR and Self-Driving CarsLinkedIn
We asked LinkedIn members worldwide about their levels of interest in the latest wave of technology: whether they’re using wearables, and whether they intend to buy self-driving cars and VR headsets as they become available. We asked them too about their attitudes to technology and to the growing role of Artificial Intelligence (AI) in the devices that they use. The answers were fascinating – and in many cases, surprising.
This SlideShare explores the full results of this study, including detailed market-by-market breakdowns of intention levels for each technology – and how attitudes change with age, location and seniority level. If you’re marketing a tech brand – or planning to use VR and wearables to reach a professional audience – then these are insights you won’t want to miss.
This chapter discusses primary and secondary research methods. Primary research methods outlined include interviews, observations, questionnaires, surveys, focus groups, and participation in online forums. Secondary research methods include books, journals, reference materials, periodicals, newspapers, archives, libraries, the internet, and government statistics. The chapter also provides a table comparing primary and secondary research, noting primary research involves original data collection while secondary research uses existing sources.
Social Listening and Intelligence is Predictive! Now What?Rob Key
new approaches to filtering and annotating social listening data, together with more advanced modeling, shows clearly that this data has predictive value -- if done correctly. This presentation reviews key data issues and was presented at the Advertising Research Foundation's 2015 Rethink Conference.
The document outlines several challenges facing the market research industry, including societal shifts away from mass markets, technological changes like the rise of online research and mobile phones, a limited strategic role, and an image focused more on tactical data than insights or strategy. It also discusses how the term "market research" has been detrimental by focusing on data collection over value. The industry may be transcended by strategic foresight consulting or dispersed throughout organizations as data-driven decision making expands.
A picture is worth a thousand words_Mathilda EloffMathilda Eloff
This document discusses ways to improve how market research data and insights are communicated to clients. It argues that current methods, like lengthy PowerPoint presentations full of tables and charts, are not effective at engaging clients and helping them understand and act on the insights. The document advocates using more visuals, graphics, and storytelling to make research findings more interesting, memorable and actionable for clients. It also discusses how different people process information visually versus auditorily and suggests presentations should cater to both styles to better connect with all audience members.
The document discusses various topics related to market research including types, processes, definitions, examples, and potential issues. It provides examples of how market research was conducted on Agatha Christie novels and women's clothing sizes at N. Brown, leading to increased sales. It also summarizes common types of market research such as exploratory, descriptive, and causal research, as well as qualitative and quantitative methods. Finally, it outlines some potential problems with market research such as vague objectives, technical pitfalls, and its limitations for predicting radical innovations.
Overview of Data and Analytics Essentials and FoundationsNUS-ISS
As companies increasingly integrate data across functions, the boundaries between marketing, sales and operations have been blurring. This allows them to find new opportunities that arise by aligning and integrating the activities of supply and demand to improve commercial effectiveness. Instead of conducting post-hoc analyses that allow them to correct future actions, companies generate and analyze data in near real-time and adjust their operations processes dynamically. Transitioning from static analytics outputs to more dynamic contextualized insights means analytics can be delivered with increased relevance closer to the point of decision.
This talk will cover the analytics journey from descriptive, predictive and prescriptive analytics to derive actionable and timely insights to improve customer experience to drive marketing, salesforce and operations excellence.
The document discusses qualitative and quantitative consumer research methods. Quantitative research uses experimental and survey techniques to descriptively and empirically measure consumer behavior. Qualitative research uses depth interviews, focus groups, and projective techniques administered by trained interviewers to subjectively understand consumption practices. Positivism aims to predict behavior through quantitative methods, while interpretivism seeks to understand experiences through qualitative research. The consumer research process involves defining objectives, collecting secondary and primary data, designing qualitative and quantitative studies, analyzing results, and reporting findings.
Fern Halper is an analyst who has observed growing interest in predictive analytics from companies seeking competitive advantages and deeper customer insights. While the technology has existed for decades, businesses are now recognizing its value. Vendors are developing easier to use tools in response, hoping both statisticians and regular business users can build basic models. Open source is also becoming more important, with ecosystems of support emerging around languages like R.
Marketing L5: Marketing Research & Guest SpeakerAhmed Eid
The document discusses marketing information systems and marketing research. It covers several key topics in 3 points:
1) Marketing research provides a basis for rational decision making. Firms can analyze internal data from sales records and customer accounts, as well as buy external syndicated or commissioned primary research.
2) Primary research includes qualitative methods like focus groups, observations, and interviews to generate insights, as well as quantitative methods like surveys, experiments, and scanner data for statistical analysis. Both types of research are typically used.
3) Good marketing research defines clear objectives, uses appropriate sampling and methods to avoid bias, analyzes and interprets results carefully, and ensures the information is disseminated and used to inform decision making
RES724 v6Observation GuideRES724 v6Page 2 of 2Analysisanitramcroberts
RES/724 v6
Observation Guide
RES/724 v6
Page 2 of 2
Analysis and Interpretation WorksheetPart I: Analysis and Interpretation
Interview Data:
1. Select an appropriate analytic procedure to code your interview data.
https://www.sagepub.com/sites/default/files/upm-binaries/24614_01_Saldana_Ch_01.pdf
2. Code your data using a qualitative data analysis program or by hand in a separate document. This process must identify: (1) codes and themes, (2) sub-codes, (3) categories, and (4) subcategories.
a. Discuss your approach to coding the interview data. What procedure(s) did you select? Justify your response.
Click or tap here to enter text.
3. Examine the results of your analytic procedure(s).
a. Identify 2 or 3 initial assertions based on your interpretation of the interview data. Discuss how you arrived at each assertion.
Click or tap here to enter text.
b. Synthesize your initial assertions into a key assertion about the central phenomenon in your mock study.
Click or tap here to enter text.Observation Data:
1. Review the Observation Guide you completed in Week 6, paying attention to your field notes and detailed narrative passages.
2. Compose 2 or 3 analytic memos triggered by your field notes.
a. Memo 1:
Click or tap here to enter text.
b. Memo 2:
Click or tap here to enter text.
c. Memo 3:
Click or tap here to enter text.
3. Compose a meta-memo that synthesizes the primary elements from your analytic memos into a new whole.
Click or tap here to enter text.
4. Examine the results of your analytic procedure(s).
a. Identify 2 or 3 initial assertions based on your interpretation of the observation data. Discuss how you arrived at each assertion.
Click or tap here to enter text.
b. Synthesize your initial assertions into a key assertion about the central phenomenon in your mock study.
Click or tap here to enter text.Part II: Reflection
Write a 700- to 1,050-word reflective essay about your experience applying qualitative research methods and the knowledge you have gained from this course. Include your essay in the space below.
The strength of qualitative research method gained from this course is the ability to provide complex textual descriptions of how people experience a given research issue. It provides information about the “human” side of an issue that is, the often-contradictory behaviors, beliefs, opinions, emotions, and relationships of individuals. It seeks to understand a given research problem or topic from the perspectives of the local population it involves. Qualitative research is especially effective in obtaining culturally specific information about the values, opinions, behaviors, and social contexts of populations. In the qualitative methods are also effective in identifying intangible factors, such as social norms, socioeconomic status, gender roles, ethnicity, and religion, whose role in the research.
Although findings from qualitative data can often be extended to people with characteristics similar to th ...
This document provides an overview of developing a research design. It discusses defining a research topic and evaluating whether it is worthwhile. It also covers developing research approaches, including secondary and primary research. Quantitative and qualitative research methods are introduced. Different types of research like exploratory, descriptive, and causal research are explained. The document stresses the importance of validity and reliability in research and discusses using sampling to represent a population.
An online community intelligence audit follows a 4-step scalable process to deliver actionable insights for organizations:
1. Discovery - Define audit parameters mapped to objectives through keyword research and data modeling.
2. Data Modeling - Build and test a data model to ensure data integrity and validity.
3. Measurement - Measure community presence, reach, engagement and influence using benchmark metrics.
4. Analysis - Provide quantitative metrics and qualitative insights through measuring conversations and identifying themes.
The process begins with data collection and sampling to understand where target audiences are active online and validate strategies. Both quantitative and qualitative analysis are used to understand perceptions and conversations to develop effective online strategies. Ongoing audits are recommended
This document provides a project report on research methodology for comparing the Apple and Samsung smartphone brands. It includes an introduction outlining the purpose and structure of the report. The structure section lists topics that will be covered such as objectives, literature review, data collection methods, data analysis methods, and conclusions. The objectives are to understand student preferences and the role of brand equity and identity in smartphone preferences. Both primary data collection through questionnaires and secondary data collection through literature are discussed. The report will analyze the data to understand brand loyalty, awareness, and identity between the two brands.
Gather DATA to identify business requirements.pptxgashawmekonnen4
This document provides information on identifying business requirements by gathering data from key information sources. It discusses gathering both qualitative and quantitative data through formal processes like interviews, questionnaires, workshops and document analysis. Specific internal sources mentioned include employees, annual reports, sales figures, procedures manuals, and completed forms. The importance of representative sampling is also covered.
Social Media Measurement by Daniel Backhaus at InfuzDaniel Backhaus
Presented October 2nd at Digital East 2012 in the Advanced Social Media Track.
Discusses the need for robust SM metrics while emphasizing the importance of rigorous analysis and sound business strategy as guiding principles for social media.
The document provides an overview of research methods topics covered in a foundation course, including:
1. The objectives of the course are to provide background on the nature of research processes and methods used in interdisciplinary research projects.
2. The course covers topics like research design and strategies, problem definition, data collection and analysis methods, sampling techniques, and report writing.
3. Key research design concepts discussed include exploratory vs conclusive research, descriptive vs causal research, and types of experimental designs. Measurement methods like scales, reliability, and validity are also summarized.
The document provides an overview of research methods topics covered in a foundation course, including:
1. The objectives of the course are to provide background on the nature of research processes and inquiries as well as expose students to methodological problems in interdisciplinary research.
2. The course units cover introduction to research, data measurement, collection, processing and analysis, interpretation and reporting.
3. Specific topics discussed include research definitions and types, the research process, experimental design, sampling theory, statistical analysis, and report writing.
The document provides an agenda for a webinar on leveraging social media data mining. It introduces Jim Schwab from Alterian who will discuss why social media should be of interest to marketers and how to find the right tools to mine social media. It outlines that Schwab will cover listening to, learning from, engaging with, and participating in social media. He will also provide examples of how to leverage social media content.
This slideshow presentation includes information about the nature, methods and purpose of research, including how research is collected, as well as examples.
A good research practice is to collect secondary data and analyze it completely before analyzing primary research data. This is because primary data addresses the issue at hand, unlike secondary data which is used to analyze all the other ‘related’ issues to the problem under concern. To be more precise, secondary data is mostly related to background information and can only add to or support the information that is later generated from primary research.
Prescriptive analytics BA4206 Anna University PPTFreelance
Business analysis - Prescriptive analytics Introduction to Prescriptive analytics
Prescriptive Modeling
Non Linear Optimization
Demonstrating Business Performance Improvement
More Related Content
Similar to Making the Leap from Market Research to Insight Part Three: Quantitative Data
Social Listening and Intelligence is Predictive! Now What?Rob Key
new approaches to filtering and annotating social listening data, together with more advanced modeling, shows clearly that this data has predictive value -- if done correctly. This presentation reviews key data issues and was presented at the Advertising Research Foundation's 2015 Rethink Conference.
The document outlines several challenges facing the market research industry, including societal shifts away from mass markets, technological changes like the rise of online research and mobile phones, a limited strategic role, and an image focused more on tactical data than insights or strategy. It also discusses how the term "market research" has been detrimental by focusing on data collection over value. The industry may be transcended by strategic foresight consulting or dispersed throughout organizations as data-driven decision making expands.
A picture is worth a thousand words_Mathilda EloffMathilda Eloff
This document discusses ways to improve how market research data and insights are communicated to clients. It argues that current methods, like lengthy PowerPoint presentations full of tables and charts, are not effective at engaging clients and helping them understand and act on the insights. The document advocates using more visuals, graphics, and storytelling to make research findings more interesting, memorable and actionable for clients. It also discusses how different people process information visually versus auditorily and suggests presentations should cater to both styles to better connect with all audience members.
The document discusses various topics related to market research including types, processes, definitions, examples, and potential issues. It provides examples of how market research was conducted on Agatha Christie novels and women's clothing sizes at N. Brown, leading to increased sales. It also summarizes common types of market research such as exploratory, descriptive, and causal research, as well as qualitative and quantitative methods. Finally, it outlines some potential problems with market research such as vague objectives, technical pitfalls, and its limitations for predicting radical innovations.
Overview of Data and Analytics Essentials and FoundationsNUS-ISS
As companies increasingly integrate data across functions, the boundaries between marketing, sales and operations have been blurring. This allows them to find new opportunities that arise by aligning and integrating the activities of supply and demand to improve commercial effectiveness. Instead of conducting post-hoc analyses that allow them to correct future actions, companies generate and analyze data in near real-time and adjust their operations processes dynamically. Transitioning from static analytics outputs to more dynamic contextualized insights means analytics can be delivered with increased relevance closer to the point of decision.
This talk will cover the analytics journey from descriptive, predictive and prescriptive analytics to derive actionable and timely insights to improve customer experience to drive marketing, salesforce and operations excellence.
The document discusses qualitative and quantitative consumer research methods. Quantitative research uses experimental and survey techniques to descriptively and empirically measure consumer behavior. Qualitative research uses depth interviews, focus groups, and projective techniques administered by trained interviewers to subjectively understand consumption practices. Positivism aims to predict behavior through quantitative methods, while interpretivism seeks to understand experiences through qualitative research. The consumer research process involves defining objectives, collecting secondary and primary data, designing qualitative and quantitative studies, analyzing results, and reporting findings.
Fern Halper is an analyst who has observed growing interest in predictive analytics from companies seeking competitive advantages and deeper customer insights. While the technology has existed for decades, businesses are now recognizing its value. Vendors are developing easier to use tools in response, hoping both statisticians and regular business users can build basic models. Open source is also becoming more important, with ecosystems of support emerging around languages like R.
Marketing L5: Marketing Research & Guest SpeakerAhmed Eid
The document discusses marketing information systems and marketing research. It covers several key topics in 3 points:
1) Marketing research provides a basis for rational decision making. Firms can analyze internal data from sales records and customer accounts, as well as buy external syndicated or commissioned primary research.
2) Primary research includes qualitative methods like focus groups, observations, and interviews to generate insights, as well as quantitative methods like surveys, experiments, and scanner data for statistical analysis. Both types of research are typically used.
3) Good marketing research defines clear objectives, uses appropriate sampling and methods to avoid bias, analyzes and interprets results carefully, and ensures the information is disseminated and used to inform decision making
RES724 v6Observation GuideRES724 v6Page 2 of 2Analysisanitramcroberts
RES/724 v6
Observation Guide
RES/724 v6
Page 2 of 2
Analysis and Interpretation WorksheetPart I: Analysis and Interpretation
Interview Data:
1. Select an appropriate analytic procedure to code your interview data.
https://www.sagepub.com/sites/default/files/upm-binaries/24614_01_Saldana_Ch_01.pdf
2. Code your data using a qualitative data analysis program or by hand in a separate document. This process must identify: (1) codes and themes, (2) sub-codes, (3) categories, and (4) subcategories.
a. Discuss your approach to coding the interview data. What procedure(s) did you select? Justify your response.
Click or tap here to enter text.
3. Examine the results of your analytic procedure(s).
a. Identify 2 or 3 initial assertions based on your interpretation of the interview data. Discuss how you arrived at each assertion.
Click or tap here to enter text.
b. Synthesize your initial assertions into a key assertion about the central phenomenon in your mock study.
Click or tap here to enter text.Observation Data:
1. Review the Observation Guide you completed in Week 6, paying attention to your field notes and detailed narrative passages.
2. Compose 2 or 3 analytic memos triggered by your field notes.
a. Memo 1:
Click or tap here to enter text.
b. Memo 2:
Click or tap here to enter text.
c. Memo 3:
Click or tap here to enter text.
3. Compose a meta-memo that synthesizes the primary elements from your analytic memos into a new whole.
Click or tap here to enter text.
4. Examine the results of your analytic procedure(s).
a. Identify 2 or 3 initial assertions based on your interpretation of the observation data. Discuss how you arrived at each assertion.
Click or tap here to enter text.
b. Synthesize your initial assertions into a key assertion about the central phenomenon in your mock study.
Click or tap here to enter text.Part II: Reflection
Write a 700- to 1,050-word reflective essay about your experience applying qualitative research methods and the knowledge you have gained from this course. Include your essay in the space below.
The strength of qualitative research method gained from this course is the ability to provide complex textual descriptions of how people experience a given research issue. It provides information about the “human” side of an issue that is, the often-contradictory behaviors, beliefs, opinions, emotions, and relationships of individuals. It seeks to understand a given research problem or topic from the perspectives of the local population it involves. Qualitative research is especially effective in obtaining culturally specific information about the values, opinions, behaviors, and social contexts of populations. In the qualitative methods are also effective in identifying intangible factors, such as social norms, socioeconomic status, gender roles, ethnicity, and religion, whose role in the research.
Although findings from qualitative data can often be extended to people with characteristics similar to th ...
This document provides an overview of developing a research design. It discusses defining a research topic and evaluating whether it is worthwhile. It also covers developing research approaches, including secondary and primary research. Quantitative and qualitative research methods are introduced. Different types of research like exploratory, descriptive, and causal research are explained. The document stresses the importance of validity and reliability in research and discusses using sampling to represent a population.
An online community intelligence audit follows a 4-step scalable process to deliver actionable insights for organizations:
1. Discovery - Define audit parameters mapped to objectives through keyword research and data modeling.
2. Data Modeling - Build and test a data model to ensure data integrity and validity.
3. Measurement - Measure community presence, reach, engagement and influence using benchmark metrics.
4. Analysis - Provide quantitative metrics and qualitative insights through measuring conversations and identifying themes.
The process begins with data collection and sampling to understand where target audiences are active online and validate strategies. Both quantitative and qualitative analysis are used to understand perceptions and conversations to develop effective online strategies. Ongoing audits are recommended
This document provides a project report on research methodology for comparing the Apple and Samsung smartphone brands. It includes an introduction outlining the purpose and structure of the report. The structure section lists topics that will be covered such as objectives, literature review, data collection methods, data analysis methods, and conclusions. The objectives are to understand student preferences and the role of brand equity and identity in smartphone preferences. Both primary data collection through questionnaires and secondary data collection through literature are discussed. The report will analyze the data to understand brand loyalty, awareness, and identity between the two brands.
Gather DATA to identify business requirements.pptxgashawmekonnen4
This document provides information on identifying business requirements by gathering data from key information sources. It discusses gathering both qualitative and quantitative data through formal processes like interviews, questionnaires, workshops and document analysis. Specific internal sources mentioned include employees, annual reports, sales figures, procedures manuals, and completed forms. The importance of representative sampling is also covered.
Social Media Measurement by Daniel Backhaus at InfuzDaniel Backhaus
Presented October 2nd at Digital East 2012 in the Advanced Social Media Track.
Discusses the need for robust SM metrics while emphasizing the importance of rigorous analysis and sound business strategy as guiding principles for social media.
The document provides an overview of research methods topics covered in a foundation course, including:
1. The objectives of the course are to provide background on the nature of research processes and methods used in interdisciplinary research projects.
2. The course covers topics like research design and strategies, problem definition, data collection and analysis methods, sampling techniques, and report writing.
3. Key research design concepts discussed include exploratory vs conclusive research, descriptive vs causal research, and types of experimental designs. Measurement methods like scales, reliability, and validity are also summarized.
The document provides an overview of research methods topics covered in a foundation course, including:
1. The objectives of the course are to provide background on the nature of research processes and inquiries as well as expose students to methodological problems in interdisciplinary research.
2. The course units cover introduction to research, data measurement, collection, processing and analysis, interpretation and reporting.
3. Specific topics discussed include research definitions and types, the research process, experimental design, sampling theory, statistical analysis, and report writing.
The document provides an agenda for a webinar on leveraging social media data mining. It introduces Jim Schwab from Alterian who will discuss why social media should be of interest to marketers and how to find the right tools to mine social media. It outlines that Schwab will cover listening to, learning from, engaging with, and participating in social media. He will also provide examples of how to leverage social media content.
This slideshow presentation includes information about the nature, methods and purpose of research, including how research is collected, as well as examples.
A good research practice is to collect secondary data and analyze it completely before analyzing primary research data. This is because primary data addresses the issue at hand, unlike secondary data which is used to analyze all the other ‘related’ issues to the problem under concern. To be more precise, secondary data is mostly related to background information and can only add to or support the information that is later generated from primary research.
Similar to Making the Leap from Market Research to Insight Part Three: Quantitative Data (20)
Prescriptive analytics BA4206 Anna University PPTFreelance
Business analysis - Prescriptive analytics Introduction to Prescriptive analytics
Prescriptive Modeling
Non Linear Optimization
Demonstrating Business Performance Improvement
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Profiles of Iconic Fashion Personalities.pdfTTop Threads
The fashion industry is dynamic and ever-changing, continuously sculpted by trailblazing visionaries who challenge norms and redefine beauty. This document delves into the profiles of some of the most iconic fashion personalities whose impact has left a lasting impression on the industry. From timeless designers to modern-day influencers, each individual has uniquely woven their thread into the rich fabric of fashion history, contributing to its ongoing evolution.
[To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This PowerPoint compilation offers a comprehensive overview of 20 leading innovation management frameworks and methodologies, selected for their broad applicability across various industries and organizational contexts. These frameworks are valuable resources for a wide range of users, including business professionals, educators, and consultants.
Each framework is presented with visually engaging diagrams and templates, ensuring the content is both informative and appealing. While this compilation is thorough, please note that the slides are intended as supplementary resources and may not be sufficient for standalone instructional purposes.
This compilation is ideal for anyone looking to enhance their understanding of innovation management and drive meaningful change within their organization. Whether you aim to improve product development processes, enhance customer experiences, or drive digital transformation, these frameworks offer valuable insights and tools to help you achieve your goals.
INCLUDED FRAMEWORKS/MODELS:
1. Stanford’s Design Thinking
2. IDEO’s Human-Centered Design
3. Strategyzer’s Business Model Innovation
4. Lean Startup Methodology
5. Agile Innovation Framework
6. Doblin’s Ten Types of Innovation
7. McKinsey’s Three Horizons of Growth
8. Customer Journey Map
9. Christensen’s Disruptive Innovation Theory
10. Blue Ocean Strategy
11. Strategyn’s Jobs-To-Be-Done (JTBD) Framework with Job Map
12. Design Sprint Framework
13. The Double Diamond
14. Lean Six Sigma DMAIC
15. TRIZ Problem-Solving Framework
16. Edward de Bono’s Six Thinking Hats
17. Stage-Gate Model
18. Toyota’s Six Steps of Kaizen
19. Microsoft’s Digital Transformation Framework
20. Design for Six Sigma (DFSS)
To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations
Cover Story - China's Investment Leader - Dr. Alyce SUmsthrill
In World Expo 2010 Shanghai – the most visited Expo in the World History
https://www.britannica.com/event/Expo-Shanghai-2010
China’s official organizer of the Expo, CCPIT (China Council for the Promotion of International Trade https://en.ccpit.org/) has chosen Dr. Alyce Su as the Cover Person with Cover Story, in the Expo’s official magazine distributed throughout the Expo, showcasing China’s New Generation of Leaders to the World.
The Steadfast and Reliable Bull: Taurus Zodiac Signmy Pandit
Explore the steadfast and reliable nature of the Taurus Zodiac Sign. Discover the personality traits, key dates, and horoscope insights that define the determined and practical Taurus, and learn how their grounded nature makes them the anchor of the zodiac.
The APCO Geopolitical Radar - Q3 2024 The Global Operating Environment for Bu...APCO
The Radar reflects input from APCO’s teams located around the world. It distils a host of interconnected events and trends into insights to inform operational and strategic decisions. Issues covered in this edition include:
The APCO Geopolitical Radar - Q3 2024 The Global Operating Environment for Bu...
Making the Leap from Market Research to Insight Part Three: Quantitative Data
1. Alert!
Marketing Research Association
Technology & Software Directory
MAY 2012 · Vol. 52 · No. 5
Tomorrow’s Technology in Today’s Research
Navigating Trends and Technology
MRA 2011 Annual Report
2. Keeping Informed
Making the Leap from Market Research to Insight
Part Three: Quantitative Data
By Thom Pulliam
This is the final of a three part series that has provided practical counsel on
the subject of insights and where they hide. In January’s issue, we looked at
The Four Places Insights Hide, using the “Don’t Mess With Texas” campaign
as an example of how a few words can communicate so much and to so many
– effectively. The second article, published in March, focused on drawing
insights from qualitative data.
As mentioned in earlier editorials, decision makers. Thus, one of the major with clear research objectives. When
as market researchers and brand roles of the researcher and strategist is to using quantitative research to test
strategists, we hunt for insights – unique examine, organize, and statistically test hypotheses, concepts, copy, products,
combinations of information that give the data so that meaningful conclusions etc., it is very clear what your research
meaning to the marketplace. We are and insights can be drawn. All too objective is: figure out how an audience
familiar with a myriad of methodologies often, clients conduct quantitative responds to the work. Do they like it?
used in the collection of data, but research only to have the work result Does it resonate? Is the brand linkage
oftentimes struggle with making the in a “data dump” with no discernible there? Clients adverse to risk frequently
information we find useful. How do we conclusions, implications, or direction for rely on this type of testing to validate
transform information into insight? This implementation of the results. Likewise, their direction before bringing something
third and final piece for Alert! magazine clients frequently spend significant sums to market. But, what are the best ways
will focus on answering this question as it of money not on actionable and insightful to use quantitative research early on, as
relates to quantitative data. research, but on reports and binders an input of the discovery process; when
Quantitative research measures queries of quantitative data that sit – largely you are searching for a leading insight to
on a numerical scale and encompasses unused – on shelves or in filing cabinets. inspire strategy? The key first insightful
three types of research techniques: This happens, not because quantitative and actionable step to quantitative
observation, physiological measurement research is inherently unable to surface research is for both researcher and client
(i.e. eye tracking and brain wave insights, but because the approach is to define and agree upon what they
studying), and survey research. Raw often not optimized to do so. want to learn. By always setting research
data derived from a quantitative study is If you put garbage in, you get garbage objectives first, before determining what
useless by itself; only data that have been out. When embarking on any research questions to ask or how to ask them, you
thoroughly analyzed is of any value to project it is of vital importance to start set yourself up for success.
MRA’s Alert! Magazine – May 2012 39
3. What are your research objectives? If
you want to learn what an audience’s
top motivators for visiting the Biltmore
Estate in autumn are, then you can
count on asking questions about price,
travel distance, weather, attractions, and
holiday vacation time. If you are looking
to understand, by market, how consumers
compare ULTA to Sephora, then your
line of inquiries would involve selection,
proximity, price, customer experience,
store environment, and networks of
communication.
Once your research objectives are
established, your questions developed, Paper Surveys
and your study fielded, it is time to I Full print and mail capabilities
analyze the information and uncover
insights. In quantitative research, I Automated inbound mailroom
insights live in the juxtaposition of data I Postage discounts
with surrounding observed associations.
These associations, represented as I Scanning/data entry
Paper Surveys
correlations, indexes, and scales provide I Litigation surveys
I Full print and mail capabilities
context and meaning to the data and I Automated inbound mailroom
allow us to make statements about I Verbatim open end capture
I Postage discounts
the likelihood of an event given the I Print and mail within Canada
I Scanning/data entry
occurrence of another event. Quantitative
research allows us to zoom out and I Litigation surveys
see patterns that are not obvious from Open End Comment Coding
Verbatim open end capture
I
our usual vantage point. For example, I Computer assisted traditional
I Print and mail within Canada coding
data mining helped retailer Victoria’s
I Text End Comment Coding
Open analysis
Secret find geographic differences in
preferences for bra sizes and colors that I Computer assisted traditional coding
I
Over 30 languages
were previously unnoticed. As a result, I Text analysis
the company improved its purchasing
I
I
Healthcare, financial and technical specialists
Over 30 languages
and inventory management efficiency. Healthcare, financial and technical specialists
Transcription
I
In a different example of zooming out
with quantitative analysis, Berger and Transcription
I IVR and recorded CATI open ends
Le Mens looked at the prevalence of first IIVR and recorded CATI open ends
names going back to 1880 (as recorded I Focus groups
I Focus groups
by the US Census). They found that the I 1-on-1 interviews
I 1-on-1 interviews
faster a name becomes popular, based on
the number per one million children, the
faster it declines to “pre fad” levels. Only Contact Dave Koch
by “zooming out” in a temporal sense
and looking at data over a long period
Contact Dave Koch
dkoch@adaptdata.com
(and a large number of names) could the
(952) 939-0538 ext.114
dkoch@adaptdata.com
researchers discover this pattern.
Another technique for making
(952) 939-0538 ext.114analysis, the raw data from quantitative
quantitative survey helps provide a more
quantitative research more actionable exhaustive set of variables to test. This research yields meaningful insights.
is to set it up with discovery qualitative qualitative discovery provides the people Furthermore, the power of quantitative
research. In other words, use methods writing the quantitative research with research lies in the safety of statistical
such as qualitative focus groups to more knowledge about the consumer significance – knowing at a specific
expand your thinking on a research mindset and marketplace, and helps confidence level that your insight is
objective and better articulate your inform the creation of quantitative representative of the larger population,
quantitative methodology. For example, methodology. and not an anomaly reflected by a few
when the research objective is to Qualitative research provides rich outliers (as is the risk with qualitative
understand what consumer wants and understanding of consumers and the research).
needs are driving the shift from agent- marketplace, while quantitative also has
based auto insurance providers to its place and is more than a tool used to Thom Pulliam is an independent
direct-to-consumer providers, talking post-test ideas. When set up with the strategic communications professional
with consumers before setting up the right research objectives and thoughtful newly residing in Los Angeles, CA.
40 MRA’s Alert! Magazine – May 2012