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Machine Learning (ML) > Natural Language Processing (NLP)

Kessel Run's business strategy prioritized our team to serve as the test bed for the integration of a Data Scientist because of our large active user base, of over 12,000 users. This decision aligned with the organization's goals of improving user experience, increasing customer engagement, and driving innovation.


As a Product Manager I recognized the value of having a Data Scientist on the team to leverage their expertise in data analysis and modeling. But I knew we had to develop a strategy to better use the insights we would enventually gather.


Along with the Data Scientist and other team members, I created a roadmap outlining the implementation of analytics in the product. They defined the desired outcomes for each data point we wanted to capture, specifying the metrics, measurements, or insights we aimed to gather from user interactions.


Once the analytics implementation roadmap was established, the team began monitoring user interactions with the product. They deployed tracking mechanisms and data collection processes to gather relevant data points according to the defined roadmap. This included capturing user engagement metrics Click Through Rates, Session Durations, Feature Usage, Bounce Rates along with qualitive feedback and any other relevant information.


As the team collected data, they recognized the potential of natural language processing (NLP) techniques to streamline user data entry. Using NLP to automate the population of fields, reduced manual data entry time for users. It also reduced error rates from humans entering the same information in different formats. The introduction of NLP aligned with the product goals and vision, which aimed to improve user experience and increase efficiency in producting mission reports.




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