The Technical University Munich (TU Munich), ETH Zurich and b.telligent haven been working on the PROSET research project for three years from February 2011 to February 2014. The project centered on questions which cannot usually be pursued in everyday working life due to lack of time. In this context, questions which are not only relevant for practice, but also bring to light new findings for research are raised. In the PROSET project, we have concentrated on the question of increasing productivity by means of service experience management.
Table of Contents
Linking Research to Practice - the Cooperation with o2
In order to be able to research and answer interesting questions for science and industry, it is first and foremost necessary to analyze "real data". This real data relevant for the project PROSET was provided by the telecommunications business o2, which we were able to attract successfully as industry partner. As consideration for the anonymized customer- and transaction-related data provided by o2, the industry partner received valuable insights into its customer experience management as well as a unique, objective insight into customer relationship management processes and the interaction of contact channel and customer value in the business model.
One Research Project, Two Final Papers and Many Interesting Topics
The cooperation with Telefónica Germany GmbH started in February 2013. After a pre-analysis phase of approximately two months, research on the research questions commenced.
The following topics have been researched:
Success factors in cross- and up-selling in incoming call center calls
Effects of cross-selling strategies on the success of call centers
Customer experience management used for customer loyalty and win back
Two final papers at the TU Munich were prepared within the scope of the project.
After one year of successful cooperation, the sub-project was successfully completed in February 2014. The results of the research project are expected to be published in one volume with the support of our media partner Call Center Association (Call Center Verband) in July 2014.
In Short
What is PROSET:
High-class research thanks to the cooperation with Munich TU and ETH Zurich
Professional handling of the analyses thanks to b.telligent's long-standing expertise
Topics of practical relevance thanks to the cooperation with o2
The project was supported by the Federal Ministry of Education and Research, the project Manager within the German Aeronautics and Space Research Center (DLR) and the Strategic Partnership "Productivity of Services".
At this point, b.telligent would like to thank Professor Dr. Florian von Wangenheim with ETH Zurich and Professor Dr. Rainer Kolisch with TU Munich as well as the many helpful contact persons with o2 for the joint implementation of the project!
Who is b.telligent?
Do you want to replace the IoT core with a multi-cloud solution and utilise the benefits of other IoT services from Azure or Amazon Web Services? Then get in touch with us and we will support you in the implementation with our expertise and the b.telligent partner network.
Neural Networks for Tabular Data: Ensemble Learning Without Trees
Neural networks are applied to just about any kind of data (images, audio, text, video, graphs, ...). Only with tabular data, tree-based ensembles like random forests and gradient boosted trees are still much more popular. If you want to replace these successful classics with neural networks, ensemble learning may still be a key idea. This blog post tells you why. It is complemented by a notebook in which you can follow the practical details.
Azure AI Search, Microsoft’s top serverless option for the retrieval part of RAG, has unique sizing, scaling, and pricing logic. While it conceals many complexities of server based solutions, it demands specific knowledge of its configurations.
Polars, the Pandas challenger written in Rust, is much faster, not only in executing the code, but also in development. Pandas has always suffered from an API that "grew historically" in many places. Polars is completely different: it ensures significantly faster development, since its API is designed to be logically consistent from the outset, carefully maintaining stringency with every release (sometimes at the expense of backwards compatibility). Polars can often easily replace Pandas: for example, in Ibis Analytics projects and, of course, for all kinds of daily data preparation tasks. Polars’ superior performance is also helpful in interactive environments like Power BI.