Business Scenario
Our client showcases a variety of technology products in
their retail stores to customers. Product demonstrations highlight product
capabilities to drive sales. Following product demonstrations, the voice of
customer team collects customer feedback. Customer feedback provides a better
understanding of how improved product demonstrations improve sales.
To capture customer feedback, the voice of customer team
reviewed forms, written comments, reviews, ratings, and surveys using a
supervised machine learning model. The team trained the supervised machine
learning model using textual data from other teams they worked with. But the
supervised machine learning model approach struggled whenever the voice of
customer team wanted to add a new dataset. Because the model was based off the
provided training materials, the model could not identify material themes that
did not originate from the training materials. As a result, each time the voice
of customer team worked with a new dataset, the learning model required retraining.
To minimize training costs and improve scalability, we needed to create an
unsupervised machine learning model.
How We Did It: The Technical Implementation
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| Figure 1: Unsupervised machine learning model |
• Theme identification using supervised models
• Term frequency-inverse document frequency (TFIDF) methodologies
• Key phrase extraction
At the beginning of the project, we conducted research to determine how to implement an unsupervised machine
learning approach for theme identification. Prior to the completion of our
project, no such research had ever been conducted. We started our research with supervised models, even
though our solution needed to eliminate manual theme identification. Studying supervised models enabled
our team to better understand how human heuristics guided machine learning. To
better understand how to track the statistical significance of themes, we
studied TFIDF methodologies. By studying supervised model results and TFIDF
methodologies, we eventually discovered the key insight we used to structure
our unsupervised machine learning model: We could extract key phrases in
English using a recurring pattern: (Adj/verb)*(Noun/Proper noun). After
discovering the pattern, we worked on several proofs of concept using the pke
and rake libraries and Microsoft’s Cognitive Services.
The original implementation of the project involved
extracting key phrases from customer reviews and feedback comments. First, we divided
feedback into different sentences using full stops and spaces. Then, we used several
text preprocessing techniques to extract the core message without contractions,
punctuation, stop words, conjugations, or spelling mistakes. We then employed text processing techniques to extract the key phrases. Key phrases
were identified using nouns and adjectives. (In product feedback, comments typically
used the product as the noun and the characteristics of
the product as adjectives.) We then used the Word2Vec model to convert
the key phrases to vectors. The resulting vectors were then passed through an
unsupervised learning engine to cluster the key phrases in order to decipher
feedback themes. Each cluster helped identify feedback themes. Finally, we performed
a T-test to identify the significant themes. Our approach enabled us to
identify newly emerging feedback themes without requiring model
retraining.
We fed data to our machine learning model through Azure Databricks. Azure Databricks offered our client the speed and flexibility they were looking for. Azure Databricks allows users to run robust analytics algorithms and drive real-time business insights. Azure Databricks also offers one-click, autoscaling deployment that ensures enterprise users’ scaling and security requirements are suitably met. Azure Databricks also features optimized connectors, which we used to run Microsoft Cognitive Service APIs. These APIs allowed our team to quickly implement entity recognition and key phrase extraction. Because the Azure Databricks solution was managed from a single notebook, our teams could collaborate easily across office locations.
We fed data to our machine learning model through Azure Databricks. Azure Databricks offered our client the speed and flexibility they were looking for. Azure Databricks allows users to run robust analytics algorithms and drive real-time business insights. Azure Databricks also offers one-click, autoscaling deployment that ensures enterprise users’ scaling and security requirements are suitably met. Azure Databricks also features optimized connectors, which we used to run Microsoft Cognitive Service APIs. These APIs allowed our team to quickly implement entity recognition and key phrase extraction. Because the Azure Databricks solution was managed from a single notebook, our teams could collaborate easily across office locations.
Business Outcomes:
The completed unsupervised machine learning model enabled the
voice of customer team to scale the solution to new datasets immediately. Azure Databricks further enhanced scalability and improved deployment speed. With
the finished solution, our client can identify themes not contained within
training materials and receive significant themes without delay. The completed
solution also reduced the manual effort required to identify and map themes.
