Our goal was to conduct a study on a research project using big data 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 on trend 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 big data 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 on trend 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 in the beverage industry 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 in the beverage industry 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 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.
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.
The term Asia is used here in a restricted sense to include the countries from India to the Pacific, bounded in the north by China, South Korea and Japan and in the south by Indonesia. This region contains approximately 2.7 billion people, about a half of the worlds population. If any single factor could be said to describe this research environment, it is diversity. At one end of the spectrum are countries where market research has yet to gain any real foothold, among them being Myanmar (Burma), Laos and Cambodia. Some, such as Vietnam, are at the emergent stage. But most have research industries of several decades standing, the full range of research services, and sampling and research methodology as sophisticated and variegated as western practice. The countries in this region certainly vary widely in the challenges they present for general population sampling, which are described below under five headings: 1. population characteristics; 2. information sources; 3. selection methods; 4. practical issues; 5. response rates.
This paper tells the story of how, using a number of already available surveys, a marketing and communications strategy was devised for an area that was historically better known as a coal and iron and steel industrial complex than as a touristic resort. The name of the area? The Leisure Coast, centred on the industrial city of Wollongong, some 80 kilometers south of Sydney, Australia.
The objectives of this paper are to detail the experiences in developing and using scanner-based systems and to demonstrate how these systems have influenced marketing and marketing research in general. The central focus of this paper is a discussion of the applications, advantages and disadvantages of scanner data and the ways in which this new information source can aid publishers in marketing decision making. A number of relevant, new developments and their possible effects on the media industry in the USA will also be highlighted.
Part I of this paper sets out by examining the external factors of change and how corporations are organisationally adjusting to the new reality. Set against this background, the new type of highly adaptable and flexible company has very specific information needs. Above all, it need less raw but more timely and value-added business information across very specific areas of corporate key concerns. Part II looks at both the risks and opportunities in the information industry of which research is a part.