4leds Meetup - October 2016

Agenda:

19:00 - 19:10: Welcome

19:10 - 19:40: Large-scale and continuous customer feedback analysis at Avira (Manuel Eugster, Avira)

19:40 - 20:20: Mining the Voice of the Customer (Stefan Debortoli, MineMyText)

20:20 - 22:00: Drinks, Food and Networking

Talks and speakers:

# Large-scale and continuous customer feedback analysis at Avira

At Avira, the Customer Insights Research group takes responsibility to make the user’s voice heard within the company. With a holistic data-driven view of the customer lifecycle – based on internal and external human- and machine-generated data – the challenge is to transform these massive amounts of (real-time) data into descriptive knowledge about our customers and products.

This talk will present the pipeline for Avira's continuous customer feedback system. In every product, customers are able to give structured and unstructured feedback on different time points during their user experience. The data is processed and analyzed in near real-time, and insights are distributed to relevant stakeholders within the company. For example, an automatic key driver analysis based on linear models continuously monitors the positive and negative customer drivers. Behind the scenes, R is used as analysis tool as well as ETL tool operating on different internal services and data bases.

Manuel J. A. Eugster is a data scientist at the Customer Insights Research group at Avira. There he develops data analysis solutions that transform massive amounts of (real-time) data from various sources into descriptive knowledge about customers and products. Prior to Avira, he was a scientific researcher at the Probabilistic Machine Learning group at HIIT, Aalto University, Finland and the Department of Statistics at LMU Munich. He worked, among other things, on next generation information retrieval systems using brain-computer interfaces. He holds a PhD in Statistics from LMU Munich, a MSc in Computational Intelligence and a BSc in Software and Information Engineering from TU Vienna. ( http://mjae.net )

# Mining the Voice of the Customer

It is estimated that more than 80% of today’s data is stored in unstructured form (e.g., text, audio, video) and much of its content is expressed in rich and ambiguous natural language. Traditionally, the analysis of natural language has prompted the use of qualitative data analysis approaches, such as, manual coding. Yet, the size of textual datasets available from online social networks like Twitter or rating platforms (e.g., Tripadvisor, Yelp, Amazon) exceeds the information processing capacities of human analysts. One approach to overcome these limitations is the use of text mining techniques to (semi-)automatically extract implicit, previously unknown, and potentially useful knowledge from large amounts of unstructured textual data. Although text mining and related natural language processing techniques only scratch the surface of the meaning of natural language, they have proven to be reliable tools when fed with sufficiently large datasets.

The goal of this talk is to demonstrate how to reduce the efforts needed for analyzing unstructured and textual customer feedback with topic modeling. Topic models are unsupervised machine learning algorithms for inductively discovering latent topics running through large collections of documents. Topic modeling algorithms identify topics in a purely data-driven way—neither necessitating any prior labelling of documents, the existence of predefined categorization schemes, or human input. The talk will include a live demo with real data.

Stefan Debortoli is the CEO and co-founder of MineMyText.com and CTO of the influencer marketing platform Reachbird. Previously, he was a scientific researcher at the University of Liechtenstein, focusing on applying big data analytics as a new strategy of inquiry in Information Systems Research. In particular, Stefan investigated the use of text-mining techniques for research purposes. He holds a PhD in Business Economics and a BSc and MSc in Information Systems from the University of Liechtenstein.