Research
Ein Bild sagt mehr als tausend Worte (planung&analyse) (German only)
DownloadGaining Marketing Knowledge from Social Media Images
The flood of images
The lavish party with friends, the holiday on the beach, the newly acquired smartphone – all of this is captured on camera and shared via social networks. The flood of images is large: The photo-sharing platform Flickr, for example, already includes 8 billion images. Around 3.5 million are added every day. 1.15 billion Facebook users upload on average of 350 million photos per day. The entire photo database of Facebook already counts 250 billion photos.
Highlights
- Brand love study with 148,393 social media photos from 503 Germans and Americans
- Social media image study of 41 FMCG (Fast-Moving Consumer Goods) brands with 47,988 photos, 57,984 textual posts, online survey with 1,000 people and purchasing data from a panel of 30,000 households
- Study on consumer-brand interactions with 941,731 social media images
- Study on beauty trends with 16,568 social media photos
- Study on confectionery brands with 49,074 social media photos
Social media images as a source of knowledge
These snapshots do not only provide insight into the lives of users, they also reflect their attitudes and experiences towards brands and products. And they influence a potentially large circle of viewers. Images often have a greater impact than text because they are perceived more subtly and have a stronger influence on the emotions of the viewer. User-generated photos also have a high level of credibility compared to professional photos. Social media images, therefore, represent a rich source of data for market research – which, however, has so far hardly been usable because existing tools for social media analysis focus on textual postings.
Computer vision for gaining marketing knowledge
Due to the large number of images on social networks, manual evaluation is only possible to a limited extent, and automated methods for image analysis are necessary. For this reason, we developed the PictureScan tool, which gains marketing-relevant knowledge from user-generated photos. The content of the image will first be recognized using methods from the field of computer vision. Awareness, popularity, usage situations of brands, products and consumers and interactions with them can be determined through further analyses. These key figures are evaluated in comparison to the competition and over a period of time. In this way, trends can be uncovered, and opportunities and risks for corporate image and sales can be estimated.
Awards
- Innovation Award 2016 of the Professional Association of German Market and Social Researchers (BVM)
Events
- IEEE Conference on Multimedia Information Processing and Retrieval, March 2019, San Jose
- General Online Research, March 2019, Cologne
- IEEE Conference on Multimedia Information Processing and Retrieval, April 2018, Miami
- Predictive Analytics World, November 2018, Berlin
- South African Marketing Research Association Annual Conference, Oct 2017, Cape Town
- International Colloquium on Corporate Branding, Identity, Image and Reputation, Sept 2017, London
- International Conference on Image Analysis and Processing, Sept. 2017, Catania
- Bayreuth Economic Congress, May 2017, Bayreuth
- Photo Industry Association Congress, March 2017, Frankfurt
- AMA Summer Marketing Educators' Conference, Aug. 2016, Atlanta
- ESOMAR Congress, Sept. 2016, New Orleans
- Ludwig Ehrhard Symposium, August 2016, Nuremberg
- Research Plus, July 2016, Nuremberg
- BVM Congress, April 2016, Berlin
- European Marketing Academy Conference, June 2014, Valencia
- Social media conference of the Federal Statistical Office, June 2013, Wiesbaden
Project team
- Dr. Carolin Kaiser, Head of Artificial Intelligence, NIM, carolin.kaiser@nim.org
Cooperation partner
- Prof. Dr. Rainer Lienhart, Universität Augsburg
- Prof. Dr. Aaron Ahuvia, University of Michigan-Dearborn
Publications
- Kaiser, C., Ahuvia, A., Rauschnabel, P. A., & Wimble, M. (2020). Social media monitoring: What can marketers learn from Facebook brand photos? Journal of Business Research, 17, 707–717. https://doi.org/10.1016/j.jbusres.2019.09.017
- Harzig, P., Zecha, D., Lienhart, R., Kaiser, C., & Schallner, R. (2019). Image captioning with clause-focused metrics in a multi-modal setting for marketing. 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), 419–424. San Jose, CA, United States. https://doi.org/10.1109/MIPR.2019.00085
- Kaiser, C., Schallner, R., & Manewitsch, V. (2019). Revealing Consumer-Brand-Interactions from Social Media Pictures – A Case Study from the Fast-Moving Consumer Goods Industry. Proceedings of the 21st General Online Research Conference, Cologne, Germany.
- Kaiser, C., Enzingmueller, L., & Schallner, R. (2018). Analyzing the temporal development of brand-related social media photos: A case study from the confectionary industry. GfK Verein Working Paper Series, 2018(6).
- Harzig, P., Brehm, S., Lienhart, R., Kaiser, C., & Schallner, R. (2018). Multimodal image captioning for marketing analysis. Proceedings of the IIEEE Conference on Multimedia Information Processing and Retrieval, Miami, FL, United States. https://doi.org/10.48550/arXiv.1802.01958
- Paolanti, M., Kaiser, C., Schallner, R., Frontoni, E., & Zingaretti, P. (2017). Visual and textual sentiment analysis of brand-related social media pictures using deep convolutional neural networks. In S. Battiato, G. Gallo, R. Schettini, & F. Stanco (Eds.). Lecture Notes in Computer Science. Presented at the International Conference on Image Analysis and Processing, 402–413, Springer. https://doi.org/10.1007/978-3-319-68560-1_36
- Kaiser, C., Ahuiva, A., Rauschnabel, P., & Wimble, M. (2016). Sind Facebook-Markenbilder ein Zeichen von Markenliebe? Proceedings of the 3rd International Colloquium on Corporate Branding, Identity, Image and Reputation, London, United Kingdom.
- Kaiser, C., & Wildner, R. (2016). Gaining marketing-relevant knowledge from social media photos – A picture is worth a thousand words. Proceedings of the 2016 ESOMAR Congress, New Orleans, LA, United States.
- Kaiser, C., Ahuiva, A., Rauschnabel, P., & Wimble, M. (2016). Sind Facebook-Markenbilder ein Zeichen von Markenliebe? Proceedings of the AMA Summer Marketing Educators' Conference 2016, Atlanta GA, United States.
- Kaiser, C., Frey, L., & Ivens, B. (2014). Characterizing consumer-brand-relationships in social media pictures. Proceedings of the 43rd Annual European Marketing Academy Conference, Valencia, Spain.
- Kaiser, C. (2014). Soziale Medien als Mittel der Produktgestaltung (Co-Creation). In C. König, M. Stahl & E. Wiegand Soziale Medien (pp. 171–194). Springer VS. https://doi.org/10.1007/978-3-658-05327-7_10
- Kaiser, C. (2017). Ein Bild sagt mehr als Tausend Worte – Neues Marketing-Wissen aus Social Media Fotos ziehen. Planung&Analyse 1/2017, 51–53.
- Kaiser, C., Ahuiva, A., Rauschnabel, P., & Wimble, M. (2016). Visual eWOM: Are Facebook Brand Photos a Sign of Brand Love? 2016 AMA Summer Marketing Educators' Conference, Atlanta.
- Buder, F., Couronné, T., & Kaiser, C. (2017). Digitalization and the value of new (meta) data sources for market insights: How increasing options and new decision requirements can impact the market research value chain. Proceedings of the SAMRA Annual Conference, Cape Town.
- Kaiser, C., & Wildner, R. (2016). Gaining Marketing-Relevant Knowledge from Social Media Photos - A picture is worth a thousand words. Proceedings of the 2016 ESOMAR Congress, New Orleans.
- Kaiser, C., Frey, L., & Ivens, B., (2014). Characterizing consumer-brand-relationships in social media pictures. Proceedings of the 43rd Annual European Marketing Academy Conference, Valencia.
- Harzig, P., Zecha, D., Lienhart, R., Kaiser, C., & Schallner, R. (2019). Image captioning with clause-focused metrics in a multi-modal setting for marketing. 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), San Jose, CA, USA, 419–424.
- Kaiser, C., Schallner, R., & Manewitsch, V. (2019). Revealing Consumer-Brand-Interactions from Social Media Pictures – A Case Study from the Fast-Moving Consumer Goods Industry. Proceedings of the 21st General Online Research Conference, Cologne.
- Harzig, P., Brehm, S., Lienhart, R., Kaiser, C., & Schallner, R. (2018). Multimodal image captioning for marketing analysis. Proceedings of the IEEE Conference on Multimedia Information Processing and Retrieval, FL, USA.
- Paolanti, M., Kaiser, C., Schallner, R., Frontoni, E., & Zingaretti, P. (2017). Visual and textual sentiment analysis of brand-related social media pictures using deep convolutional neural networks. In S. Battiato , G. Gallo, R. Schettini & F. Stanco (Eds.), Lecture Notes in Computer Science. Presented at the International Conference on Image Analysis and Processing (pp. 402–413). Springer.
- Kaiser, C. (2014). Soziale Medien als Mittel der Produktgestaltung (Co-Creation). In C. König, M. Stahl, & E. Wiegand (Eds.), Soziale Medien (pp. 171–194). Springer VS.
Contact
![[Translate to English:]](/fileadmin/_processed_/4/f/csm_Dr_Kaiser_V2_0ce892ddb9.jpg "[Translate to English:]")