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They realized this can be possible using the analyses of the various customer interactions we have via the Chatbot "Billie", live chat, phone and email.
For these analyses, they introduced techniques from the Data Science and Machine Learning domain. Natural Language Processing is needed as well as Comprehender Techniques. As a team, they investigated models available in the open source community. In the podcast, we talk about how we adapted them for this purpose followed by the training of these models.
We talk about the four steps to get from idea to the usable information for the product specialists. The product specialist can use the information provided to enhance the product details and descriptions to a level where a minimum of questions from customers is needed.
One of the first deliverables was the introduction of so-called unhappy products report. Products which cause relatively much customer interactions. It presents these products but even more important, possible causes.
Daniël Heres; software engineer
Emiel Ubink; product owner
Design sprints as part of the way of working to increase the speed and shorten the feedback loop:design sprint
Determination of the important words in the text is done by the use of TF/IDF which stands for term frequency-inverse document frequency. It's part of the Natural Language Processing and to determine the Smart Word Clouds. TF/IDF
BigQuery is used to store the data to be analysed later on.
Predictive Models are being used to improve the shop which should result in a better customer journey with a lower number of customer questions.
Unsupervised Learning is discussed as the way the models are being trained and verified
Clustering Topic Modelling; finding out the latent thems (topics)
Update 4th June 2021:
We received a mail from one of our listeners providing us with a link to more up to date info on unsupervised learning
In 2014 it was introduced for the first time and reached a steady but not so big user group. Recently, along with the move to the cloud, we experience a second wave of adoption. When you listen to the stories our two enthusiastic guests share in this episode, you might even believe Go is the future.
Rogier Lommers
Tom Stoepker
The starting point for Go
Learn go by diving into the library of examples
The github library with the opensource code from uber referred to
How to learn Go? Take the tour and you are ready to go
The funny project mentioned to improve the images
Harmen Prins
Erick Webbe
Some very enthusiastic, some a bit more skeptical and eventually they found each other. A new community in bol.com was born, the Kotlin community. In this episode we talk about this journey, the use cases, the adaption and the impact on frameworks that we use in our feature teams.
Anastasija Efremovska
Mykola Gurov
The survey mentioned is the Stack Overflow Survey 2019, In particular this section: https://insights.stackoverflow.com/survey/2019#technology
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