Feedback from 2 Mio.+ retail customers
– unlocking insights from online reviews with AI
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
Large scale Customer Feedback
We have analyzed over 2+ Mio. Google reviews from 12 German retailers (submitted 03/2020 till 02/2021). Lidl, Aldi, Edeka, Rewe, dm, obi and a few others are included.
Innovative Topic Model
We use Natural Language Processing (NLP) to determine the most important topics for retail customers in Germany. Learn how topics such as friendliness of staff, price-value, product selection, cleanliness and others rank in terms of importance.
Sentiments of Customers
Which of these topics are considered negative? Which are seen positive? Understand how, e.g."service at checkout", is perceived between different brands. Take a look at our study of Rewe vs. best-in-segment results.
What are some of our key findings?
Which brands outperform in the online review index (ORI)?
Explore our newly designed quality indicator (ORI) to compare customer perception per brand.
What matters most to customers?
Friendliness / Competence of staff is by far the most important contributor to drive customer satisfaction.
How positive / negative do customers feel towards certain topics?
“Service at checkout” and “availability of products ” are key challenges and are considered critical by customers.
Deep Dive "service at checkout"
We take a closer look at how brands perform in the category “service at checkout”. Findout what drives customer perception on this important topic in our whitepaper.
Interested? Download the full whitepaper!
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.
Explore our MIGO applications and hear from customers
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