Document Processing · Enterprise Knowledge Search
Yabble needed to turn large volumes of survey answers and customer feedback into actionable insights faster, because manual coding and analysis could take days or weeks and users were asking increasingly complex questions about their data.
Reported impact
Yabble cut insight generation from days or weeks to minutes, delivered more nuanced answers to customer questions, and turned Query from a helpful feature into an essential part of customers' business strategy workflows.
Yabble layered GPT-3 into its customer-insight platform to automate the hardest part of feedback analysis: converting large, unstructured datasets into themes, subthemes, and usable answers. The platform already collected survey responses and customer feedback forms at scale, but analysts still had to spend days or weeks coding results and assembling insights. With GPT-3, Yabble Query can interpret more nuanced natural-language questions from users and return answers grounded in the relevant dataset, while Yabble Count analyzes thousands of comments, tags sentiment, and organizes the data into themes and subthemes. The workflow starts with survey and feedback data entering Yabble, continues through GPT-3-based classification and question answering, and ends with business users consuming richer insight outputs inside Yabble's existing experience. That architecture lets Yabble spend less time on manual analysis and more time helping customers make business decisions.
Survey answers and customer feedback forms flow into Yabble, where GPT-3 classifies comments into themes and subthemes, powers Yabble Count sentiment analysis, and supports Yabble Query so users can ask natural-language questions and receive insights tied to the underlying dataset.
Beginning in 2017, Yabble created a platform where organizations could easily analyze thousands of customer data points
Query went from helpful to our customers to absolutely essential to their business strategy
Using GPT-3, data sets that would usually take Yabble teams days to code and develop insights for, were now being translated into meaningful themes in minutes
Yabble Query... uses AI-powered algorithms to provide relevant insights
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
The Organization JSON-LD lists addressLocality 'Auckland', addressCountry 'New Zealand', and numberOfEmployees value 28; fewer than 250 employees maps to SMB.
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