Leonie Gehrmann, Florian Stahl
Hebrew University (Israel), Economist
We analyze how print advertising content has adapted to changes in speed-of-life, best defined as the “rapidity and density of experiences, meanings, perceptions, and activities” (Werner et al. 1985), over time.
On the one hand, the development of our image mining algorithm promises relevance for a variety of alternative use cases, since it is easily extendable to images beyond the print advertisements in our data set. Similarly, the analysis could be continued for other magazines, as well as social media postings, further refining our algorithm. On the other hand, our research also has direct implications for marketing practice. Increasing advertising clutter is a largely recognized phenomenon and our findings are likely to provide insights into the existing competition for consumer attention.
Chair of Sales & Services Marketing
Gaining insights into predictors of a customer’s purchase behavior and personal traits enables a more flexible and adaptive approach to the sales process. Utilizing data on behavioral patterns, consumer characteristics and self-reported traits, we aim to understand and anticipate customer behavior in an app-based context. This is made possible by the application of machine learning algorithms to the enriched data set and leads to a better understanding of mechanisms underlying consumer decisions and the leveraging of predictive abilities in the optimization of the sales process.
Applications arise in many areas, in which behavioral patterns can be observed and more enriched customer data could possibly be gathered. This data, whilst valuable, remains often unsaved and unexplored. Insights may be valuable, both from a customer and provider perspective, as optimized processes lead to higher customer satisfaction and operational efficiency.