The Problem

In the current market landscape, existing tools exhibit proficiency in summarizing structured feedback, yet the challenge persists in effectively processing unstructured feedback, an area largely underutilized. To address this issue, our team leveraged GenAI, alongside select proprietary in-house products and accelerators, to devise a tailored solution for our client.

Employing Customer Interaction Insights (CII), we meticulously parsed and organized unstructured feedback, thereby establishing hierarchical structures conducive to analysis. Subsequently, both structured and unstructured feedback streams were seamlessly integrated into CRUX, a sophisticated platform capable of generating graphical representations and actionable insights derived from the amalgamated data inputs.

Our approach and features

Step 1

In our initial phase, we leveraged the cutting-edge capabilities of CII (Customer Interaction Intelligence) product to address critical pain points. Through the utilization of CII, our endeavors yielded significant outcomes:


Positive Insights

Thorough analysis facilitated the identification and elucidation of positive attributes pertaining to the client.


Negative Observations

Likewise, negative aspects were pinpointed, providing valuable insights for improvement.


Hierarchical Categorization

Points of interest were systematically categorized into hierarchical structures, enabling comprehensive understanding and actionable strategizing.

Step 2

The above data points were then fed into CRUX. The platform provides reliable insights derived from structured datasets, ensuring accuracy and credibility without the risk of hallucinations. With over 20 connectors, it offers seamless integration with popular data sources, simplifying setup processes. Deployable on major cloud platforms such as AWS, Google Cloud, and Microsoft Azure, the platform ensures flexibility and scalability. Utilizing secure connections to Large Language Models (LLMs) via Azure OpenAI prioritizes data privacy and confidentiality. Moreover, users can interact with their data in a conversational and intuitive manner, posing critical business inquiries and receiving detailed, actionable answers, enhancing decision-making capabilities.

Integrating these data points into CRUX facilitated correlations between the generated categories data, sentiment, NPS scores, and other dimensions. Following the setup’s completion, we were empowered to provide insightful responses to queries such as:


Identifying which specific areas within the project, when improved, would result in a quantifiable percentage increase in NPS scores.


Assessing the areas in which the client excels and areas requiring improvement.


Enhancing analytical depth through a chat experience layered atop the analysis, enabling nuanced exploration of correlation variances between generated categories and other dimensions.


They can use these features and improve NPS scores, as they now know the voice of the customer and the reasons for NPS drop.

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