Surfacing Key Highlights, Themes, and Trends
Once customer-research platforms have accurately transcribed video and voice feedback, they can apply NLP/NLU tools to identify important highlights, themes, and trends across thousands of customer responses. Let’s look at some of these NLP/NLU tools.
Topic detection APIs identify and classify topics, according to the IAB Taxonomy, a four-level classification system, listing about 700 commonly identifiable topics. Topic detection APIs help customer-research platforms to identify important, commonly recurring topics across transcripts.
Similar to topic detection, entity detection APIs identify and classify the entities in a transcription text. For example, New York City is an entity that would be classified as a location. Entity detection APIs help customer-research platforms identify important, commonly recurring entities across transcripts.
Sentiment analysis APIs label the speech segments in a transcription text as positive, negative, or neutral. For example, the speech segment I enjoy going to the movies would be labeled as positive. Sentiment analysis helps customer-research platforms to analyze the feedback they’ve collected to identify commonly recurring sentiments about services and products.
Text summarization APIs provide informative summaries of lengthy transcriptions, making them more digestible and useful. Typically, text summarization provides a short headline for each section in which the conversation naturally changes subject or topic, along with a multi-sentence summary of the discussion under each headline.
Together, these intelligent NLP/NLU APIs capitalize on the latest AI and machine-learning research to surface true insights into respondents’ attitudes, behaviors, and actions.
Creating Smart Tags to Categorize and Search
Finally, customer-research platforms can use the APIs I’ve described to categorize the research data and survey responses. These categories can then feed into a smart-tag system, enabling companies and users to search the tags to find the data and responses they need—similar to the use of hashtags on Twitter.
Platform users can then aggregate this tag data by adding sophisticated analysis tags and even generate reports based on these specified tags. Therefore, smart tags can be an extremely sophisticated analysis tool.
The Symbiotic Relationship of AI and Research
AI and machine learning have made massive strides in the past few years, thanks to cutting-edge new models and research methods. These advances have also had significant downstream effects on research areas such as automatic speech recognition, natural-language processing, and natural-language understanding.
Industries are now taking notice, incorporating some of these intelligent applications into their platforms and software—thus, creating some pretty powerful tools for their users.
Customer feedback and research is one of these industries. By incorporating ASR and NLP/NLU applications such as accurate speech transcription, topic detection, entity detection, sentiment analysis, text summarization, and smart tags, customer-research platforms can create competitive offerings that directly impact customer satisfaction, product success, and companies’ earnings.