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Enriching Qualitative Analysis with Next Generation AI

Written by Scott Barkley

Published August 29, 2023


Qualitative studies have emerged as the clearest opportunity for enhancement. With next-gen AI and large language models, you can expect human-quality outcomes at scale.


This article was first published in CX Scoop on August 24th, 2023.

From hackathons to corporate board meetings, artificial intelligence (AI) is the hottest topic in applied technology today. Yet with any hype cycle, it’s important to sift through the noise and determine the actual value it can offer. AI in and of itself is not the game changer for business. Rather, it’s the application of AI as a tool upon a business process that can deliver amazing outcomes. AI enhances what you do, but in and of itself is not sufficient. It’s like seasoning in cooking. Alone it offers little value, but when added to a recipe it enhances the overall sensory experience.

At Alida, we obsess over helping our clients rapidly design and improve upon their products and customer experiences to drive top line growth. And delivering the right products and experiences with agility requires incorporating the voice of the customer. So, as we assess the utility of AI for our clients, we have naturally focused on how AI can enhance community driven voice of the customer engagements.

Qualitative studies (customer verbatims) stand out as a clear opportunity for enhancement with next-gen AI. The value of qualitative feedback from customers is growing in importance as it often provides a deeper understanding of customer feedback and even uncovers unexpected insights not considered when creating quantitative studies.

However, unlike quantitative studies which naturally lend themselves to standard segmentation, summarization and analysis at scale, qualitative studies traditionally require human synthesis of large numbers of individual responses to glean intent, sentiment, classification and pattern recognition. Humans are great at this, but the time required to do so at scale can make rapid decision making difficult at best. 

AI has been tackling this problem for years with mixed results. However, with the advent of new generative AI systems and large language models, qualitative studies can now achieve human quality outcomes at scale.

A real life example helps illustrate this evolution. We were approached by a prospective retail client who captured customer verbatims as part of their customer experience program. They receive tens of thousands of these per month and were using early generation AI to help automate the analysis. Before they could even see their first dashboard, they spent extensive resources training a model and designing a taxonomy for classifying verbatims. When they went live, the disillusionment began. The model was highlighting topics like “things” (not joking) because it was frequently expressed. Given this example, it’s not surprising that auto-classification returned over 125 key topics. AI was supposed to make their lives easier. Instead they had to spend time retagging verbatims and spend more money retraining their models and rebuilding their taxonomies.

Alida ran the same sample data set on our new generative AI Text Analytics service. The results amazed the retailer. The auto-classification reduced key topics from 125 to 28. “Things” no longer appeared as a topic. Instead, relevant topics such as “response time,” “technical issues,” and “mobile app” were highlighted. Furthermore, the analysis accurately gleaned the discrete sentiment of unique thoughts within verbatims; e.g “service was great [positive], prices are too high [negative].” And the value of this new generative AI was on full display in Alida's Analytics dashboard where the retailer could filter by sentiment, topic or drill down to individual responses. All of this was achieved in hours, not months. We are proud to call this retailer a client now.

This is just one example of Alida’s use of generative AI to help our customers enhance their community-driven, qualitative voice of customer programs. Other areas include using AI to prompt customers to improve the quality and richness of their verbatim responses. And we are just getting started. Generative AI and large language models also hold great promise in automating translations of survey questions and verbatim responses, so non-multilingual practitioners can quickly engage customers and analyze insights globally. 

At Alida, we don’t use AI because it’s a hot topic. We use AI to enrich our client’s programs to deliver great products and experiences.

Note: this post was written by a human, not AI.

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