We had three contributors: Klaus Miller from Frankfurt gave a hands-on talk / tutorial on how to deal with the empirical analysis of really big data sets. A key ingredient of the recipe that he presented was sparklyr, and I think this is really helpful for other researchers dealing with large data sets. In any case, it was uncharted territory for many in the audience.
The second presentation was by Stefan Mayer from Tübingen, and it dealt with how we can use Deep Learning in marketing applications. Again, this was very, very applied.
The third talk was more of a methodological contribution by Max Pachali, also from Frankfurt, dealing with sign and order constraints on priors in Bayesian analyses that are used for counterfactual predictions.
There were three aspects I particularly liked about this session.
(1) The talks were not typical research papers, but rather small tutorials. You could view it as an attempt for development of skills in these areas in the community. I think, our conferences would benefit from more of these tutorial-style talks.
(2) The room was packed with people, some in the audience had to stand or sit on the floor. Apparently, people found these topics to be valuable, which supports point (1) from above.
(3) All slides and all code that was used and presented in this session was uploaded in a git repository. Clearly, this is extra work, and in some cases this may be too much to ask for from presenters, but I believe this is very helpful in making it stick, to create impact, to allow people from the audience to actually try the stuff directly.
So thanks to the three presenters for providing this service to the community!
P.S.: This is the abstract of the session:
Quantitative marketing research has seen substantial advances in recent years. These advances concern both the way the research process is organized as well as the methods that quantitative marketing researchers use for analyzing data. This session will cover examples of advances in both areas to showcase important recent developments and potentially rewarding applications in quantitative marketing research.
While it is easy to recognize the advantages that are associated with large data sets, it is less straightforward to actually perform successful work with really large data sets. One important challenge that arises is producing reproducible code that handles and analyzes large, complex, and unstructured data sets. Another issue is scaling this code to process these data sets in a cloud computing environment. The first presentation “Managing and Analyzing Big Data” will therefore discuss and showcase several approaches to deal with these issues and provide a hands on introduction to working with large scale data in quantitative marketing research.
Machine learning is one development in the methodological domain that has the potential to substantially impact the way the marketing discipline will analyze data in the future. The specific value, however, of machine learning methods for solving marketing problems is often unclear. The presentation “What is deep learning, and why should I care?” will therefore showcase one particular method (i.e., deep learning) and describe potential applications for the marketing discipline (e.g., the use of transfer learning).
While the focus of machine learning methods is traditionally on making predictions, causal inference that is valid beyond the specific context being analyzed is still the goal of many applications in quantitative marketing. For example, many standard approaches in the literature perform well in predicting consumer choices locally but violate basic economic principles and thus do not extrapolate well in counterfactual simulations (such as optimal pricing or product design). The third talk will therefore discuss the pitfalls of relying on standard unconstrained models and proposes a practical way of specifying more economically faithful hierarchical prior distributions. It will also document that this approach improves counterfactual predictions substantially.
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