How Quince Used AI to Turn Customer Support Data into Predictive CX Insights |
A real-world look at how Quince leveraged AI and a custom GPT to democratize customer insights, accelerate decision-making, and enable anticipatory CX across the organization.
by Sagarika Prusty, Director – Analytics, Quince
Customer support has always been a gold mine of insight. Every interaction tells a story about friction in the customer journey, gaps in product design, operational breakdowns, or emerging risks. Yet for most organizations, that story is difficult to tell clearly. Support data is often buried in dashboards, trapped in unstructured text, or accessible only to analysts who can translate it into reports and insights.
We set out to solve a familiar CX challenge: How do you move from raw customer data to actionable insights quickly, consistently, and at scale without making insight generation dependent on people or processes? The answer was an end-to-end, AI-powered Voice of the Customer (VoC) platform designed to democratize customer support data across the entire organization.
The Foundation: Moving from Tags to Intelligence
The first hurdle in democratizing data is its quality. Calls, chats, and emails contain valuable signals, but reading through thousands or millions of comments is not scalable. Legacy “disposition codes” or manual tagging are notoriously unreliable, often dependent on an agent’s interpretation.
Our first step was building a robust AI Intent Model that analyzes 100% of our contacts. Instead of relying on a human to categorize a ticket, our model automatically enriches every interaction with:
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Intent & Summarization: What actually happened? (Item has been delayed beyond the promised date.) |
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Root Cause (No movement in tracking.) |
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Sentiment & Churn Risk: How frustrated is the customer, and what is the likelihood of churn? |
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Operational Metrics: Automatically calculating First Contact Resolution (FCR) and Effort Scores based on the actual dialogue. |
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Any other safety flags or privacy concerns |
Instead of treating support interactions as qualitative noise, we transformed them into structured, consistent data points. Every contact became immediately understandable and comparable, allowing trends to surface without manual review.
This alone significantly reduced analysis time, but it was only the beginning.
Augmentation: Enriching the Voice of the Customer with Business Context
Customer experience doesn’t exist in isolation. To generate meaningful insight, VoC data must be connected to the broader business.
We enriched each summarized interaction with order and product level data, such as SKU, size, fit, fulfillment details, and purchase history. This allowed us to link customer sentiment and intent directly to tangible business drivers.
Suddenly, CX teams could move beyond “what customers are contacting us about” to “why this is happening now.” Product issues, operational gaps, and merchandising challenges became visible through the lens of real customer conversations.
Support data evolved from a reactive reporting tool into a proactive decision-making asset.
Example: Many customers in a particular zip code and carrier are complaining about delays in receiving items, indicating an issue with a particular sort code.
The Breakthrough: A Custom GPT for Self-Service Insight Generation
Despite better data, one challenge remained: accessibility. We could have added it all into a dashboard. But dashboards still required interpretation, filters, and time. Insight generation was faster, but still needed effort.
To solve this, we built a custom GPT on top of our enriched VoC dataset.
Instead of navigating dashboards, stakeholders can now ask questions in natural language:
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“How many customer contacts did we receive today, and how does that compare to last week? |
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“Are there any new topics trending over the past few days?” |
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“Which contact reasons saw the biggest week-over-week increase?” |
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“Are we seeing more size or fit issues for specific products?” |
Within seconds, the system synthesizes data, identifies trends, and delivers clear answers. What once required manual analysis now happens in minutes or less.
This marked a critical shift: insight generation became self-service, consistent, and scalable.

Steps from raw unstructured data to self-serve insights
How Different Teams Use the Platform Today
The impact of democratized VoC insights is visible across the organization.
CX Leadership
CX leaders use the platform daily to monitor contact volume, track trends, and identify emerging risks. Instead of relying on lagging indicators or static weekly reports, leadership now has visibility into customer experience and the ability to act faster.
Merchandising and Product Teams
Merchandising teams use the same system to understand which products are driving increased contact rates, particularly around size, fit, or quality issues. These insights directly inform assortment decisions, product improvements, and vendor conversations.
Product teams are using it to identify friction in the products, like issues with creating return labels or placing orders in the checkout process. They use it to identify friction points quickly and add them to the product roadmap.
The Broader Organization
Perhaps most important, the platform is accessible to everyone. There is no gatekeeper, no specialized training required, and no dependency on a single analyst or team. Anyone can explore customer feedback and generate insights relevant to their role



The democratization of data has fundamentally changed our organizational culture. We have moved from a “person-dependent” model to a “self-service” model.
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IMPACT AREA |
BEFORE (LEGACY REPORTING) |
AFTER (CUSTOM GPT PLATFORM) |
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Accessibility |
Limited to Analysts/Power Users |
Accessible to all departments |
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Speed |
Weekly/Monthly |
Instant |
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Depth |
Quantitative (Charts only) |
Qualitative & Quantitative (Thematic summaries) |
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Effort |
High (Tedious manual reading) |
Low (Natural language queries) |
Conclusion: The Future of CX is Conversational
Democratizing the Voice of the Customer is no longer a technical challenge; it is a leadership opportunity. By leveraging AI to summarize every contact and a Custom GPT to make that data searchable, we have removed the friction between the customer’s problem and the company’s solution.
The data is no longer a “black box” owned by the support department. It is a shared, living resource that informs every part of the business, from the warehouse to the boardroom. In an era where customer expectations are higher than ever, the ability to turn noise into knowledge in minutes isn’t just an advantage; it’s a necessity.
The Rise of Multimodal Customer Experience: Are We Moving Too Fast?Omnichannel was promised as the solution to a fragmented customer journey. While it delivered in many ways a new paradigm is taking shape, one defined by multimodal experiences powered by AI, automation, and real-time context. Customers can now move fluidly between voice, chat, video, and digital channels, often without a visible transition. For some, this represents the ideal journey. For others, it can feel as though the human element of customer care is slipping away. As organizations race to innovate, many are unintentionally creating gaps, not just between channels, but between themselves and key segments of their customer base. With varying levels of digital fluency and generational differences, and varying expectations, a one-size-fits-all approach to CX no longer scales. So, the question becomes: In our pursuit of the future, are we leaving parts of our customer base behind? In this candid and forward-looking discussion, CX leaders will explore:
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CX Livewire: Consumer Voices, Real-Time ReactionsCustomer expectations are constantly evolving, and understanding how consumers perceive service, support channels, and emerging technologies is critical for shaping effective CX strategies. In this fast-paced and interactive session, panelists will explore key insights from Execs In The Know’s latest research findings, capturing the perspectives and expectations of CX leaders and consumers. Throughout the discussion, panelists will react to both the research findings and live polling of the CRS audience, creating a dynamic comparison between what consumers say they want and how organizations are currently approaching service delivery. These real-time insights will allow attendees to benchmark their own thinking against the room, while panelists share practical perspectives from inside their organizations on how they interpret, and respond to, shifting consumer expectations. Expect candid reactions, engaging audience participation, and thought-provoking contrasts between consumer sentiment and operational reality. This high-energy session is designed to spark conversation, challenge assumptions, and highlight where CX leaders may need to adapt in order to meet the evolving demands of their customers. |
Agent-Facing AI for CX: Through the Eyes of the AgentFor decades, contact center agents have been expected to act as human search engines navigating complex knowledge bases, policy documents, and fragmented systems to find the right answer for customers. But the emergence of agent-facing AI is beginning to shift that paradigm. Instead of simply retrieving information, modern AI tools can now interpret context, surface relevant guidance, and recommend next-best actions in real time. This panel will explore how CX leaders are deploying AI to transform the agent role, and what this experience is like from the agent’s perspective. Panelists will discuss how tools such as AI copilots, real-time knowledge synthesis, contextual assistance, automated summarization, and predictive assistance are helping agents navigate complex conversations more effectively while reducing cognitive load. At the same time, organizations must carefully balance automation with human judgment, ensuring agents remain empowered decision-makers. Panelists will also address the operational and cultural challenges of introducing AI into the agent workflow including trust, training, governance, and change management. Attendees will hear practical insights (and hopefully firsthand feedback from agents) on what’s working, what’s not, and how agent-facing AI can simultaneously improve efficiency, enhance employee experience, and deliver better outcomes for customers. |
The Next Gen CX Business Plan: Preparing for the Next 3–5 YearsFor years, organizations have piloted AI-powered support, automation, proactive service models, and intelligent self-service. Now, the industry is reaching an inflection point: what happens when these capabilities mature into the standard operating model? The question for leaders is no longer if these technologies work, but how to architect a business plan that thrives once they are fully integrated. Moving from pilot to scale requires a fundamental shift in how we lead. It demands a roadmap for workforce evolution, a commitment to data integrity, and a new definition of “success” that balances efficiency with the human connection customer still crave. What does workforce strategy look like when AI handles a significant portion of interactions? How do roles evolve? What investments must be made now in data quality, governance, and systems integration to support intelligent, proactive service? How is success measured? How do organizations deliver the trust, clarity, and the confidence that define Customer Assurance? In this discussion, CX leaders will explore:
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Customer Assurance: A Leadership Decision, Not a DepartmentCustomer Assurance is not a department or a checklist. It is the confidence customers feel when they know a company will show up with clarity, competence, and care. It is built through leadership decisions that shape how the organization communicates, operates, and responds when something matters most. In an era defined by automation, AI, and no-reply emails, customers are tired of simply being processed. They are asking deeper questions: Do I feel safe doing business with you? Do I trust this experience? Do I believe this company will take care of me when it counts? True assurance is what turns a transaction into trust. It requires more than strong service design. It takes leadership alignment, clear decision-making, and systems that make confidence possible at every stage of the customer journey. That includes how expectations are set, how issues are owned, how employees are empowered, and how technology is used to support rather than distance the customer relationship. In this discussion, CX leaders will explore:
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