Databricks Reduced SLA Misses by 40% and Increased CSAT

Customer support is critical for Databricks, which services more than 5,000 global organizations. As the company scaled, however, it found it was missing an essential piece of the customer service puzzle: the ability to identify and rectify customers’ most urgent concerns and frustrations as they arose. Databricks knew it needed to leverage an AI-based platform that could identify customers’ sentiments and case trends in real-time in order to optimize customer support.

By partnering with SupportLogic, Databricks was able to implement a more proactive approach to customer support, which ultimately led to increased CSAT scores and a 40% reduction in SLA misses.

About Databricks

Databricks works to simplify and democratize data and AI. Users can unify their data, analytics, and AI on Databricks’ central platform, which allows them to more easily collaborate across the entire data and AI workflow. Recently named one of Forbes’ 50 AI Companies to Watch, Databricks counts Google, Microsoft, Amazon, and Salesforce among its backers.

Databricks serves more than 5,000 global organizations, including Shell, Comcast, CVS Health, HSBC, and T-Mobile. Each year, the company’s five support centers help customers solve over 14,000 support ticket items.

Download Databricks Case Study

Download the full case study to share

Analyze Unstructured and Structured Data

As Databricks grew, so did its support volume. Its customer support centers grappled with over 14,000 cases each year, but the company was still relying on reactive approaches to customer support analytics: namely, survey analyses like CSAT. According to data from a TSIA webinar, 81% of companies have the technology for CX analytics, but 38% of these are only using basic survey tools. For such companies—including Databricks—customer support is thus a backward-looking endeavor.

“By the time you wait for CSAT to be the deterministic factor to understand what the customer experience was like, it’s too late,” says Tanvir Kherada, Senior Director of Technical Solutions at Databricks. “The damage has been done. But if you look at the ticket’s lifecycle…there’s a lot more you can do to salvage the situation and provide the best-desired outcome for the customer by intervening at the right time.”

Databricks resolved to adjust its approach to customer support in order to do just that: intervene at the right time and thus improve customer satisfaction and outcomes by addressing concerns more quickly. The company initially built an in-house sentiment analysis mechanism, which it ultimately abandoned. “It was essentially sub-optimal,” explains Tanvir. “It rendered a lot of false positives.”

Databricks wanted an AI-based tool that could analyze and process both structured and unstructured data—i.e., ticket metadata combined with customer messages, customer comments, and ticket updates—in order to identify customers’ most urgent situations earlier in the support process.

In addition, Databricks needed a more comprehensive understanding of the issues its customers frequently faced. “[We were looking for] something that would identify trends,” says Tanvir. “Are there consistent tickets coming from the last couple of days, where they are having specific issues that may be something we broke within the product? We want to identify that and put together a plan to solve that quickly.”

Databricks also wanted to optimize time zone alignment in order to make its global team of engineers as effective as possible—and solve customer problems in real-time, no matter where they occurred.

Its solution? SupportLogic SX.

Panoramic View of All Customers Insights

SupportLogic SX’s AI-based platform enabled Databricks to activate both structured and unstructured data in analyzing and proactively responding to customer concerns. Databricks implemented the SupportLogic SX platform as well as the Customer Management Module, which offers companies a panoramic view of their customers and provides them with data-driven insights that help improve customer renewal and retention.

Getting started was easy: Databricks simply opened a Salesforce connector, which allowed SupportLogic to begin streaming signals almost immediately.

SupportLogic’s natural language processing (NLP) engine works by parsing unstructured data to glean insights on churn risk, customer sentiment, and impact on customer and product feedback. These insights support internal workflows and delegation. The combination of this unstructured data and actionable customer sentiments has optimized time zone assignments and empowered support agents to intervene on urgent customer problems in a timelier way.

Increased CSAT by 20%

By partnering with SupportLogic, Databricks has located the missing piece in its customer service puzzle. Now, the company can more proactively handle customer support cases and intervene while there’s still time to offer a solution. With SupportLogic SX, Databricks leverages an automated feedback loop that alerts each relevant function in the company to take action prior to a customer escalation.

And customers have noticed. Since implementing SupportLogic SX’s tools, Databricks has realized a 20% increase in direct CSAT, a 9% increase in partner CSAT, and a 40% reduction in SLA misses. What’s more, by partnering with a domain expert rather than relying on a proprietary solution, Databricks has been able to take advantage of SupportLogic’s speed of innovation.

SupportLogic has opened a new world of possibilities for Databricks’ support team. Armed with the right customer insights in real-time, the company can assign cases more intelligently, manage cases proactively, and expand case insights to the engineering team.

The Challenge:

Analyze unstructured and structured data to improve customer satisfaction in real-time

Leverage SupportLogic to identify and resolve customer pain points in real-time

A proactive approach to customer support increased Databricks’ CSAT by 20%



Increase in Direct CSAT



Increase in partner csat



reduction in sla misses

Databricks’ next steps with SupportLogic

The Databricks team plans to expand its use of SupportLogic to proactively manage assigning cases to engineers with the SupportLogic Agent Management module. The company has observed that assigning the engineers with the most relevant skills to a particular problem yields the best customer results. To support this initiative on a global scale, Databricks plans to explore automated case handovers and time-zone realignments and expand case insights to JIRA tickets in order to drive meaningful insights from engineers.

“We use sentiment analysis to make sure support cases are handled correctly, to prevent escalations and to deliver a differentiated service experience to our customers.”

Matt Blair

SVP Support and Customer Success, Databricks

Want to learn more?

Check out our webinar with Databricks and see how the company is predicting customer escalations in advance using AI.

Watch Webinar