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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.

Datasets for Image Classification in Marketing (The Language of Images)

Datasets for Image Classification in Marketing (The Language of Images)

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.

The uploaded “small sample” file contains the full data with references to all 18 datasets.

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).

Click-through-rate (CTR) data of conventional and AI-generated advertisements

Click-through-rate (CTR) data of conventional and AI-generated advertisements

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 encompases 20 rows of 10 conventional (actual_polestar) and 10 AI-generated ads geared towards luxury (gen_luxury), including impressions, clicks, and click-through rate (CTR).

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).

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.

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.

Consumer Responses to Radio Advertisements: Perceptual Sound Features and Attitudinal Outcomes

Consumer Responses to Radio Advertisements: Perceptual Sound Features and Attitudinal Outcomes

Tijmen
Open Dataset
Creators: Tijmen P. J. Jansen and Maximilian Witte
Publication Date: 19.11.2025
Creators: Tijmen P. J. Jansen and Maximilian Witte

This dataset contains perceptual, acoustic, and evaluative measures for a series of radio advertisements. Each ad was produced using different voice profiles, allowing analysis of how sound characteristics influence advertising effectiveness. The dataset includes subjective evaluations of the ad (liking, appealingness, purchase intent) alongside objective audio measurements such as loudness, brightness, pitch, vocal tract length, and harmonics-to-noise ratio. The dataset is suitable for research on auditory advertising effects, persuasion, sound engineering, and voice-based marketing.

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