In this presentation you will learn how to use internet data and machine learning to derive actionable insight into employee health and wellness, with tangible outcomes and best in class practice when it comes to the ?new normal? of working health.For this webinar, Quilt.AI is joined by special guest Steve Stine who is CEO / Board Advisor for Korn Ferry, an organisational consultancy unlocking the potential of organisations through people.
How connected data can unlock contextual understanding of your brand?s target audiences.Your audience is leaving vast amounts of data in its wake, but it is often fragmented and inaccessible due to various regulations, trends and challenges in the MarTech landscape. How can you connect it all together - in a scalable, repeatable and compliant way - for greater understanding and better decision making? You need the right infrastructure and technology to future-proof your approach to holistic consumer insights.In this webinar:Uncover what connected data means now and for the future of consumer intelligenceWalk through new and existing marketplace trends that are creating challenges for connecting the data dotsExplore use cases of companies that are connecting data for ad measurement, survey data enrichment, audience segmentation and moreUncover practical ways to connect data for greater understanding in today?s complex consumer landscape
The biggest recent development in the market research/insights profession is the explosion of secondary data resources.Companies today have more data than ever, from traditional syndicated studies to government databases to vast digital data warehouses but are struggling to synthesize it all into actionable insights.Join secondary research expert Paul Hunter to explore the latest developments in this rapidly evolving sub-field of market research. Topics will include:The Secondary Data Landscape: An overview of the secondary data resources available today, from ?old-school? syndicated sources to ?new wave? digital data repositoriesSecondary + Primary: How to combine insights from secondary research and primary research to ?triangulate on the truth? -- so that the whole is greater than the sum of its partsEthical Issues in Secondary Research: Learn the latest about privacy and other ethical considerations, including details on what laws like GDPR (Europe - EU) and CCPA (and several other states beyond California) mean for market researchers
Our goal was to conduct a study on a research project using âbig dataâ and to compare the outcome of the analyses with traditional survey research. Online shopper ratings and review data in social media is an exciting and âon trendâ data source and was compared to traditional survey data. The survey data included product tests, i.e. products were placed in-home and consumers evaluated the products using a standardized questionnaire. The objective was to derive substantive insights about the core drivers for a five-star-rating of consumer reviews in online shops or platform ratings for pet care products and compare these insights with drivers of liking from traditional research on pet care products already existing in the market. We validated the hierarchy of drivers for the overall product rating by conducting a meta analyses on previous product tests and assessed the drivers of overall liking.
Our goal was to conduct a study on a research project using âbig dataâ and to compare the outcome of the analyses with traditional survey research. Online shopper ratings and review data in social media is an exciting and âon trendâ data source and was compared to traditional survey data. The survey data included product tests, i.e. products were placed in-home and consumers evaluated the products using a standardized questionnaire. The objective was to derive substantive insights about the core drivers for a five-star-rating of consumer reviews in online shops or platform ratings for pet care products and compare these insights with drivers of liking from traditional research on pet care products already existing in the market. We validated the hierarchy of drivers for the overall product rating by conducting a meta analyses on previous product tests and assessed the drivers of overall liking.
Research departments are under pressure. They are expected to deliver faster, cheaper and more impactful insights than ever before. Instead of doing more and faster research, insight departments are also able to revisit existing data sources. Often, companies have plenty of valuable data sources at their disposable, without being aware of their full potential. Moreover, relevant databases are often publicly available via APIs or sold via data brokers. Yet, the biggest hurdle lies in making sense of this abundance of data. Companies are struggling to connect different sources due to different structures, missing values and other complexities. In the last years enormous advancements have been made in machine learning and data science. Although demystifying is needed in order to better understand this discipline in context. With a creative and pragmatic mind-set, the problem can be solved by borrowing techniques from this field of data science. We show a case â in the beverage industry â where we exploited existing data sources to uncover a hidden layer of insights.
Research departments are under pressure. They are expected to deliver faster, cheaper and more impactful insights than ever before. Instead of doing more and faster research, insight departments are also able to revisit existing data sources. Often, companies have plenty of valuable data sources at their disposable, without being aware of their full potential. Moreover, relevant databases are often publicly available via APIs or sold via data brokers. Yet, the biggest hurdle lies in making sense of this abundance of data. Companies are struggling to connect different sources due to different structures, missing values and other complexities. In the last years enormous advancements have been made in machine learning and data science. Although demystifying is needed in order to better understand this discipline in context. With a creative and pragmatic mind-set, the problem can be solved by borrowing techniques from this field of data science. We show a case â in the beverage industry â where we exploited existing data sources to uncover a hidden layer of insights.
In medieval times people sought a universal elixir. They failed, just as modern insighters fail if we proselytise one approach is âthe futureâ. We search for truth through data â but history tells us there is no universal solution.Rather, we believe research, tech, insight entrepreneurs and intrapreneurs should join forces to unlock new truths and create a stronger ecosystem - where each collaborates to create a whole that is greater than the sum of its parts.
In medieval times people sought a universal elixir. They failed, just as modern insighters fail if we proselytise one approach is the future. We search for truth through data but history tells us there is no universal solution.Rather, we believe research, tech, insight entrepreneurs and intrapreneurs should join forces to unlock new truths and create a stronger ecosystem - where each collaborates to create a whole that is greater than the sum of its parts.
This paper reviews the key ethical, legal, technical and data quality challenges researchers face when working with these new data sources. Its goal is to start a conversation among researchers aimed at clarifying their responsibilities to those whose data we use in research, the clients we serve and the general public. It uses the term secondary data to mean data collected for another purpose and subsequently used in research. It expands on the traditional definition of secondary data to account for new types and sources of data made possible by new technologies and the Internet. It is used here in place of the popular but often vague term, big data, and is meant to include data from various sources, such as transactions generated when people interact with a business or government agency; postings to social media networks and the Internet of Things (IOT). It is distinct from primary data, meaning data collected by a researcher from or about an individual for the purpose of research.
This presentation reports on early, but very promising, research results toward an innovative media selection factor. Media Ad Xponent(SM) isolates a program's contribution to a commercial's ability to motivate incremental brand sales, above and beyond its ability to deliver recent category purchasers. This new learning evidences the economic value of single source data and supports our belief that it will soon return to the marketplace.