The challenge of high-stakes customer interactions
Customer service in financial and insurance organizations operates in high-stakes territory. Because products are tied to people’s long-term security and well-being, interactions are often emotionally charged. Serving both individuals and agents adds complexity, making data-driven insight essential for consistent, high-quality service.
A large financial and insurance services company had invested in a scalable Databricks-based enterprise data warehouse (EDW), with data spanning digital and call center channels. They engaged Resultant to help operationalize the platform, strengthen its customer experience analytics strategy, and scale insights across the business.
Gaining visibility into customer service outcomes
Using a proactive customer engagement process, feedback was received immediately after each interaction, while details were still fresh. Along with Net Promoter Scores (NPS), which offer a clear read on customer loyalty, this timely input captured real stories: what customers needed, how representatives responded, and whether issues were fully resolved. The team understood that while NPS scores are valuable, they don’t provide enough context to drive meaningful change.
To close that gap, Resultant developed a more nuanced way to understand customer experience. Instead of relying on a single score, how could the team uncover the details behind each interaction?
Reducing complex, emotional exchanges to one number made it easy to miss these lessons. The new approach would bring them into focus.
Defining service quality that fits this business
Resultant consulted on how to define and calculate more granular metrics that highlighted the different aspects of customer service important to the business’s operations.
We began with the RATER framework, a classic diagnostic tool for customer service that attempts to break down service quality into five dimensions:
Since every organization is unique (and a framework is only as good as its implementation and the data that feeds it), we took the time to create customized definitions for each of these service quality dimensions and how they related to this firm’s specific business units, products, and data.
Using Databricks’ unified data intelligence platform, Resultant integrated advanced AI capabilities directly into the client’s existing environment, eliminating delays and complexities of disparate tools. Databricks’ built-in AI query capabilities enabled us to embed sophisticated AI models directly into everyday SQL and Python workflows, accelerating the path from raw feedback to usable insight.
These AI models analyzed customer responses across each service dimension, generating sentiment scores with supporting excerpts and citations. Resultant worked closely with the client to test, validate, and refine these outputs through multiple review cycles. This hands-on, iterative process ensured the insights were both accurate and meaningful in day-to-day operations.
Putting AI-powered customer service analytics to work
By combining NPS with customized RATER sentiment scores and operational performance data, the client gained a clear, practical, and timely view of how customer experiences unfold. Leaders can see what customers felt, what drove those perceptions, and how systems and behaviors influenced outcomes.
With this deeper visibility, the organization can optimize how customer needs are matched to representative expertise. Across a wide range of products and services, the new insights reveal where empathy, efficiency, or technical skill matter most. Teams can now align the right representatives to the right interactions, improving both service quality and customer confidence.
When a representative receives an undesired NPS score, it can be difficult to determine appropriately targeted measures without additional context. The new analytical layer reshapes common approaches to both training and recognition. Managers can move beyond surface-level scores with insights showing where representatives excel and where they need support. Backed by real customer feedback, coaching becomes more focused, achievements more visible, and peer learning more effective.
Accelerating innovation with Databricks
Databricks’ robust architecture and AI functionalities dramatically shortened the data project lifecycle, enabling Resultant to deliver proof-of-value with agility and our client to quickly move from project ideation to production.
By strategically building upon the Databricks-powered EDW with Resultant’s expertise in AI-driven analytics, the financial and insurance services firm is now exceptionally well-positioned to accelerate their journey toward unparalleled customer excellence.
Implementing analytical strategies like these drives positive outcomes such as improved first contact resolution rates, reduced staff attrition, better ability to keep operational costs in check, and customer service representatives feeling more empowered to do their jobs well. This foundational work by Resultant, leveraging the full power of Databricks, ensures continuous innovation and a competitive edge in delivering five-star customer service.
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