Customer Experiences
Consumer opinion plays a crucial role in the evaluation of products and services.
Comprehensive surveys in the form of online panel surveys are conducted regularly by the German Institute for Product and Market Evaluation. The aim is to collect systematic feedback from various areas of products and services. The data obtained in this way provides valuable insights into consumer preferences and perceptions, facilitates the identification of market trends, and promotes improved alignment with customer needs.
Online panel surveys are a quantitative research method used to systematically collect data on consumer attitudes, opinions, and behaviors. This process uses the internet to access a pre-recruited group of participants who are representative of a larger target group. The method makes it possible to collect a large amount of data quickly. The process of online panel surveys is described in detail below, including the methodological steps involved.
Methodology
Concept and design of the study
The first step is to precisely define the research objectives and develop an appropriate study design. This involves determining the variables and hypotheses to be investigated and defining the target group for the study. Based on this, a questionnaire is developed that is tailored to the specific information requirements of the study.
Selection and recruitment of the panel
The selection of panel participants is crucial for the representativeness of the results. Recruitment is usually carried out via online platforms that offer access to a broad base of potential participants. Various strategies are used to ensure a demographically and geographically representative sample. These include random selection procedures, quota selection, or targeted approaches to specific target groups.
Conducting the survey
After recruitment, participants receive an access link to the online questionnaire. AI-supported software solutions enable flexible design of the questionnaire, including various question types (e.g., multiple choice, Likert scales, open-ended questions) and multimedia content.
Data analysis
After checking for completeness and consistency, statistical analysis is performed, which may involve different methods depending on the research question, ranging from descriptive statistics to complex multivariate analyses. The results are interpreted in the context of the original research objectives and taking into account possible limitations of the study.
Pool of test subjects
Structure of the test subject pool
The pool of test subjects comprises 9,000 individuals covering a wide range of demographic categories in order to represent the diversity of the population in Germany.
Panel distribution
Depending on the study and survey objective, survey panels are compiled from the pool of respondents in order to achieve valid results and keep wastage to a minimum.
Demographic characteristics
Age distribution
The pool reflects the age structure of the population, with an even distribution across the following age groups: 18-24 (12%), 25-34 (18%), 35-44 (17%), 45-54 (20%), 55-64 (17%), 65+ (16%).
Gender:
The aim is to achieve a balanced distribution between men (49%) and women (51%).
Geographical distribution:
Participants are recruited from all federal states. The distribution is 72% from the new federal states and 28% from the old federal states.
Level of education:
The pool reflects the distribution of education in Germany with a mix of secondary school diplomas (20%), intermediate school diplomas (40%), high school diplomas (30%), and university degrees (10%).
Socioeconomic characteristics
Professional status:
A mix of full-time (50%), part-time (20%), self-employed (10%), unemployed (10%), and retired (10%).
Income distribution:
The income distribution in the pool reflects social stratification, with lower (20%), middle (50%), and upper income quartiles (30%).
Psychographic characteristics
Lifestyles and values:
The pool encompasses a wide range of lifestyles and values, which are identified through specific screening questions in order to cover diverse consumer segments.
The percentages are based on data collected on January 30, 2024, and may vary at the present time. We attach great importance to protecting our test subjects and their privacy in accordance with the GDPR. All information provided in our online surveys is treated anonymously and confidentially, used solely for statistical purposes, and protected by strict security measures.
Recruitment and maintenance of the pool
Recruitment takes place through various channels, including online platforms, social media, and partnerships with educational institutions, in order to reach a broad and diverse participant base. Regular updates and maintenance ensure that the pool remains up to date and panel fatigue is avoided. This includes regular communication with participants, incentive systems to encourage participation, and quality assurance measures such as checking the consistency of responses.
Are you interested in participating in panel surveys as a respondent? We invite you to contact us without obligation so that we can add you to our survey panels. It is important that all participants in our surveys guarantee their independence. Please note that we cannot consider every application. Inclusion in our survey panels depends on various factors, including demographic, socioeconomic, and psychographic characteristics.
AI-based opinion polling
Big data for opinion research
At the core of this research method is the collection and analysis of large amounts of data from the internet. AI models, specifically natural language processing (NLP) algorithms, are used to analyze text content in terms of sentiment, topics, and specific opinions. These algorithms are capable of identifying and classifying both positive and negative statements, as well as recognizing trends and patterns in public opinion.
Screening process and data analysis
The screening process begins with the systematic collection of content from preselected platforms. This data is then filtered to remove irrelevant information and focus on high-quality, meaningful data. A crucial step in this process is the elimination of extreme outliers that could distort the results. Extremely positive or negative statements that are not representative of the general public or could have manipulative intentions are carefully identified and excluded from the analysis. This ensures that the final results provide a realistic picture of general brand and product perception.