Online Review Index (ORI)
of the Germany Retail Industry
Take a look at 2021/20 data and insights on how German Retailers perform from a genuine perspective of millions of customers
Why should you read this Whitepaper?
customer reviews as data foundation
Different German retail companies
topics to build ORI on latest NLP methods
Unbiased Customer Insights
We analyze unstructured free-text google reviews. These biases are context sensitive – as people a nudged via push notification, while still being at the store premises. These reviews are not biased also not incentived, e.g. via price competition or coupons.
Innovative Topic Model
We analyze and determin topics based on importance for customers of Aldi, Lidl, Rewe, Edeka, DM, Rossmann, Obi, Bauhaus and many more. Learn why 1) friendliness of staff, 2) price-value, 3) product selection, 4) cleanliness are most important to customers in Germany
Sentiments of Customers
What topics are usually seen critical by your customers, which are considered positive. Understand how “service at checkout” are rated differently between different brands. Learn in which areas Discounters beat supermarket chains such as Edeka and Rewe.
Who are the authors?
Dr. Sarah Maihaus
Scientific Advisor, Predict42
Sarah advises and contributes on retail research projects at Predict42.
Sarah started her career at McKinsey, working primarily on marketing projects in retail, eCommerce and consumer goods space. During her time as Vice President at HRS, she built up the analytics function of the global sourcing organization.
She holds an MBA from WHU Koblenz and published her PHD (RWTH Aachen) on the impact of product harm crisis on the perception of customers and financial markets.
Dr. Johannes Fuhr
Johannes leads the consulting team at Predict42.
Prior to founding Predict42 in 2018, Johannes was running the b2c ecommerce portals HRS.de and Hotel.de. Prior to joining HRS Group, Johannes worked multiple years at Deutsche Lufthansa AG.
Johannes holds an MBA from ESB Reutlingen/ Portland State University and a PHD in economics from TU Berlin. As senior lecturer, he teaches “Data Science for Business” at the executive MBA Program at Mainz University.
Dr. Thomas Görtz
Thomas leads the data science and IT engineering team at Predict42. He also oversees product development of the MIGO Retail Analytics Suite.
Thomas holds a Master in Mathematics and PHD in Computer Science (Johannes Gutenberg University Mainz). As a senior lecturer, he continues to teach decision support theory and data science.
Prior to founding Predict42, Thomas worked as a data scientist at HRS Group and head of data science at Deutsche Bahn AG.