Join us for DataConnect Conference Oct 2-3 in Columbus, OH!
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How can customer reviews inform product design? Corporations typically use surveys and conjoint analysis to gauge customer interest in certain products and services. We see value in applying AI/LLMs/ and text analytics to readily available customer reviews to gain insights into the preferred aspects of successful products. We will explore in our talk NLP and LLM methods to extract these insights by turning the reviews into structured data. We will also explore how to harness the relationship between text and numeric data from reviews and ratings in order to map customer preferences. Not only can we understand these relationships, we can also learn the impacts on customer segmentation.
How can customer reviews inform product design? Corporations typically use surveys and conjoint analysis to gauge customer interest in certain products and services. We see value in applying AI/LLMs/ and text analytics to readily available customer reviews to gain insights into the preferred aspects of successful products. We will explore in our talk NLP and LLM methods to extract these insights by turning the reviews into structured data. We will also explore how to harness the relationship between text and numeric data from reviews and ratings in order to map customer preferences. Not only can we understand these relationships, we can also learn the impacts on customer segmentation.
What will I learn from this session?
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Jessica Pearson is curious about how machine learning can help analysts increase their effectiveness, learn what is hiding in unstructured data, and draw insights from text analysis to elevate the role of qualitative data in the workplace survey tool she uses as part of her consulting work as a Strategy Lead at Gensler. Gensler is a global design firm with over 6,000 professionals in over 50 offices worldwide.
In May 2016, Jessica completed her MBA at the Darden School of Business at the University of Virginia to expand her effectiveness as a partner with clients where she fell in love with Analytics. She has been coding in R and Python since then through courses like Decision Analysis, Marketing Analytics, and electives exploring CEO compensation, operations research and an independent study on project management.
In April of 2022, Jessica was awarded a research grant from the Gensler Research Institute where she set out to use machine learning to analyze open-ended survey comments from a survey that had collected thousands of comments from thousands of clients in the Gensler Workplace Performance Index (WPIx) survey.
Jessica is a licensed Architect, a member of AIA and her career has included time in architecture, construction, municipal planning, and consulting. She is a Strategy lead at Gensler, the world’s largest design firm in their Raleigh office.
