Ines Daniel

Image Clustering for Marketing Purposes

Clustering algorithms are standard tools for marketing purposes. For example, in market segmentation, they are applied to derive homogeneous customer groups. However, recently, the available resources for this purpose have extended. So, e.g., in social networks potential customers provide images – and other information as e.g. profiles, contact lists, music or videos – which reflect their activities, interests, and opinions. Also, consumers are getting more and more accustomed to select or upload personal images during an online dialogue. In this paper we discuss, how the application of clustering algorithms to such uploaded image collections can be used for deriving market segments. Software prototypes are discussed and applied.

Image data analysis and classification in marketing

Nowadays, the diffusion of smartphones, tablet computers, and other multipurpose equipment with high-speed Internet access makes new data types available for data analysis and classification in marketing. So, e.g., it is now possible to collect images/snaps, music, or videos instead of ratings. With appropriate algorithms and software at hand, a marketing researcher could simply group or classify respondents according to the content of uploaded images/snaps, music, or videos. However, appropriate algorithms and software are sparsely known in marketing research up to now. The paper tries to close this gap. Algorithms and software from computer science are presented, adapted and applied to data analysis and classification in marketing. The new SPSS-like software package IMADAC is introduced.

Lifestyle Segmentation Based on Contents of Uploaded Images Versus Ratings of Items

Clustering algorithms are standard tools for marketing purposes. So, e.g., in market segmentation, they are applied to derive homogeneous customer groups. However, recently, the available resources for this purpose have extended. So, e.g., in social networks potential customers provide images which reflect their activities, interests, and opinions. To compare whether contents of uploaded images lead to similar lifestyle segmentations as ratings of items, a comparison study was conducted among 478 people. In this paper we discuss the results of this study that suggests that similar lifestyle segmentations can be found. We discuss advantages and disadvantages of the new approach to lifestyle segmentation.

Typologisieren und Segmentieren von Verbrauchern auf Basis digitaler Bilder: Verfahren und Einsatzmöglichkeiten

Wahrend Typologien und Segmentierungen von Verbrauchern bisher vor allem auf Basis von aufwandigen Befragungen von Verbraucherstichproben ermittelt wurden, stehen inzwischen im Internet eine Vielzahl alternativer Segmentierungsgrundlagen bereit, z. B. hochgeladene Bilder, Videos, Likes/Dislikes, Clickstreams, und warten auf eine entsprechende Nutzung. In diesem Beitrag wird untersucht, ob Verbraucher auch auf Basis hochgeladener Bilder automatisch typologisiert und segmentiert werden konnen. Die Untersuchung fuhrt zu interessanten Ergebnissen: Gruppierungen von Verbrauchern auf Basis einer Bildahnlichkeitsanalyse weisen grose Ubereinstimmungen mit Gruppierungen auf Basis traditioneller Befragungen auf.