272 Resources

Open Dataset

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Marketing Technology Survey

Marketing Technology Survey

Julia
Open Dataset
Creators: University of Hamburg
Publication Date: 2025-02-01
Creators: University of Hamburg

A survey of marketing decision-makers sheds light on how marketing processes can be successfully automated. The results show which activities are best suited for automation and which mix of technologies is particularly promising. To achieve these research objectives, a survey of marketing decision-makers was conducted. This includes 18 semi-structured interviews with decision-makers from the areas of sales, marketing, and business intelligence, as well as members of top management. Based on the findings, the elements of the subsequent survey were developed. The participants in this preliminary study and the actual survey were contacted via the business-to-business (B2B) panel in order to achieve the greatest possible representativeness for the German-speaking region and to cover all sectors in both B2B and business-to-consumer (B2C) marketing. The participants are decision-makers from marketing and business intelligence who are responsible for relevant software decisions, as well as employees who are responsible for the operationalization of automation software in their companies in the areas of marketing, sales, and business intelligence. A total of 124 companies based in Germany, Austria, and Switzerland were reached.

In addition to general information on marketing automation and its future prospects, the study is divided into two main areas: marketing analytics and communications. Marketing analytics encompasses real-time analysis, target group analysis, the creation of forecasts, and the controlling of marketing activities. The second area of focus, marketing communications, concentrates on automated campaign management and relates to paid and owned media activities, social media campaigns, and customer service.

Open Political Statements Germany 2021 General Election (2021 Bundestagswahl)

Open Political Statements Germany 2021 General Election (2021 Bundestagswahl)

Maximilian
Open Dataset
Creators: Maximilian Witte
Publication Date: 2025-11-21
Creators: Maximilian Witte

This dataset consists of two complementary components capturing both the official positions of the major political parties in the 2021 German general election and the public perception of these positions.

The first component contains pre-processed short versions of the election programs from the six major parties that competed at the federal level in 2021: CDU/CSU, SPD, Bündnis 90/Die Grünen, FDP, Die Linke, and AfD. All statements are processed for text classification and were sourced from party publications released for the 2021 campaign.

The second component includes 7,500 individual statements from consumers describing what they believe these political parties stand for and do not stand for. Participants were asked to freely express their perceptions without constraints on length or structure. Each statement is linked to the referenced party where applicable and contains metadata on anonymized participant ID, timestamp, and language. Statements include both supportive and critical assessments and therefore represent a wide range of public interpretations of party identity and political priorities.

Together, the two components enable the study of the relationship between the official communication of political parties and the way citizens mentally represent party beliefs. The dataset can be used for research in political communication, perception gaps, misinformation, narrative framing, and natural language processing applications such as stance detection and text similarity.

Car Design Ratings for Text Analysis

Car Design Ratings for Text Analysis

Maximilian
Open Dataset
Creators: Maximilian Witte
Publication Date: 2025-11-21
Creators: Maximilian Witte

This dataset captures consumer evaluations of car design wireframes along three perceptual dimensions relevant to automotive styling research: aggressiveness, complexity, and typicality. It contains no visual material and is therefore designed exclusively for text-based analysis.

The dataset comprises 232 distinct car design wireframes, each represented through text descriptions detailing the visual form of the design. For every wireframe, consumers rated perceived aggressiveness, perceived complexity, and perceived typicality. Each dimension includes 20 independent ratings per wireframe, resulting in more than 13,000 numeric evaluations. The ratings are stored in separate CSV files, one for each dimension, and include the wireframe ID, anonymized rater ID, and the numeric score.

In addition to the numeric evaluations, a free-text description provided by participants accompanies every wireframe. These statements capture how consumers interpreted individual design elements and why they formed their perceptions. Together, the rating data and participant statements enable quantitative and qualitative analyses of design perception.

The dataset supports a wide range of applications including text-based modeling of aesthetic impressions, computational analysis of design language, semantic feature extraction, prediction of numeric perception ratings from text, and research on variability in consumer interpretations of automotive forms.

Datasets for Image Classification in Marketing

Datasets for Image Classification in Marketing

Maximilian
Open Dataset
Creators: Maximilian Witte
Publication Date: 2025-11-21
Creators: Maximilian Witte

This dataset provides a curated list of 18 publicly available image classification datasets selected for their relevance to marketing and consumer insight research. Each dataset contains labeled visual material that reflects consumer-facing content, product perception, or brand-related context. The collection covers a broad range of marketing-relevant domains including product packaging, retail environments, shelf placement, food and beverages, fashion, automotive, advertisements, and social media imagery.

Innovative Ideation Sample

Innovative Ideation Sample

Claus
Open Dataset
Creators: Claus Hegmann-Napp, Tijmen P. J. Jansen
Publication Date: 2025-11-18
Creators: Claus Hegmann-Napp, Tijmen P. J. Jansen

This dataset contains 336 unique innovation ideas for a product(lap desk). The ideas were collected as part of an experimental study, potentially involving human participants and/or an artificial intelligence/Large Language Model for comparative idea generation.

The dataset contains 336 observations (rows). The purpose of the dataset is to provide a repository of diverse innovation concepts for subsequent analysis (e.g., novelty, feasibility, and impact assessment).

Innovation Idea Generation for a Bookstore's Holiday Promotion

Innovation Idea Generation for a Bookstore's Holiday Promotion

Claus
Open Dataset
Creators: Claus Hegmann-Napp, Tijmen P. J. Jansen
Publication Date: 2025-11-18
Creators: Claus Hegmann-Napp, Tijmen P. J. Jansen

This dataset contains 460 observations from an experimental study focused on idea generation. The goal of the study was likely to examine how individual differences and attitudes toward artificial intelligence (AI) influence the ideas produced for a specific task (holiday promotions for a bookstore).

The dataset features 460 rows (observations) and 9 variables, including the text of the generated ideas and a suite of participant metrics such as personality scores, self-reported task metrics, and ratings of AI opinions.

AI and conventional marketing images + Human ratings for AIDA, brand dimensions, and other metrics

AI and conventional marketing images + Human ratings for AIDA, brand dimensions, and other metrics

Tijmen
Open Dataset
Creators: Mark Heitmann, Tijmen P. J. Jansen, Martin Reisenbichler, and David A. Schweidel
Publication Date: 19.11.2025
Creators: Mark Heitmann, Tijmen P. J. Jansen, Martin Reisenbichler, and David A. Schweidel

This dataset includes participant-level ratings, or conventional and AI-generated marketing images gathered in the research paper “Picture Perfect: Engaging Customers with Visual Generative AI (https://doi.org/10.1177/00222429251356993). It encompasses 39,040 rows with ratings on variables such as AIDA, brand dimensions Rugged and Luxury, aesthetic scores measured by the LAION Aesthetic Predictor, fluency, utilitarian and hedonic perception, and perceived humor. Additionally, all images are numbered by a unique identifier and, if available, a link to the image as well as brand and product information.

Click-through rate (CTR) dataset on conventional and AI-generated ads including demographical information

Click-through rate (CTR) dataset on conventional and AI-generated ads including demographical information

Tijmen
Open Dataset
Creators: Mark Heitmann, Tijmen P. J. Jansen, Martin Reisenbichler, and David A. Schweidel
Publication Date: 19.11.2025
Creators: Mark Heitmann, Tijmen P. J. Jansen, Martin Reisenbichler, and David A. Schweidel

This dataset encompasses click-through rate (CTR) information on 5 conventional and 5 AI-generated car advertisements, including impressions, results (clicks), euros spent, cost per click (CPC), and CTR for several demographical groups (gender and age groups).

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