This enables them to answer a very valuable question – what the users think about their products.
Automated analysis of customer feedback
Therefore, GN Audio started looking at how they could automate the process so that they could process more data while saving resources.
This is where the partnership with Kapacity began. After a series of initial PoCs (Proof of Concepts), where different solutions were tested, Kapacity in collaboration with GN Audio came up with a machine learning model that with high probability could determine whether a text was positive or negative from the predefined categories.
The model was put into production in Microsoft Azure and has since 2019 analyzed customer feedback for GN Audio.
"It’s actually exactly the same exercise we do today that we did when we manually handled customer feedback. Now it’s just an algorithm that does the work for us. And the big advantage of that is that it can handle significantly more data so we can expand the number of products we can process. At the same time, we have saved a full-time position that previously processed this data."
Søren Christensen, Head of Data and Analytics in GN Group
Technically speaking: This is how the solution works
GN extracts feedback from Amazon and Best Buy on a daily basis. The feedback is analyzed sentence by sentence in a machine learning model that has learned the patterns in data with the purpose of sorting the text into 16 different performance categories and assessing whether the text is positive, neutral or negative.
The machine learning model is built in Python and put into production in Microsoft’s Azure cloud environment with Kapacity’s best practice framework for deployment of batch ML models..
Azure DevOps is used to set up CI/CD pipelines and infrastructure-as-code with the aim of making it easy to update the application code, while the deployment framework keeps track of the deployment pipeline and ensures code testing and release for production.
As GN Audio manually validated more reviews, more and more data was created that the machine learning model could learn from. Therefore, a retraining pipeline was created which made GN able to train the model again and again on new data.
Last year, GN built on the model and decided to switch to Google’s new open source BERT algorithm, which performed better in tests. The basic architecture of the model is the same and data is still in Microsoft Azure.
Do you want to do something similar in your business?
Here is Søren Christensen’s advice
Søren Christensen has been very satisfied with the process of introducing machine learning in his department. He believes this is largely due to the fact that GN Audio had a clear idea of what the end result should look like and what they needed to do to get there.
Søren Christensen has been happy to get Kapacity on board to implement the project.
“Throughout the process with Kapacity, there was a good dialogue, and Kapacity’s Data Scientist was very aware of the possible pitfalls and challenge. We felt that the focus was always on creating a good and functional end product rather than pushing a specific solution through,” Søren Christensen concludes.
Asked what he thinks is important for companies considering a similar machine learning project, he mentions three primary things:
Know what you are trying to achieve business-wise and what the output might look like
Make sure you have a good data foundation for your machine learning mode
If you do not already have good data governance, be sure to focus on this first
GN is a historic company founded by the great Danish industrial mogul C.F. Tietgen in 1869. Today, there are more than 6,000 employees worldwide, and their core business is divided into GN Hearing, which produces hearing aids, and GN Audio, which produces headsets for workplaces and consumers.