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Empowering sales teams to create meaningful customer engagements


Faster time-to-market of new ML models


Improvement in customer adoption


For sales enablement solution provider Bigtincan, being able to deliver continuous, meaningful insights to their global clientele is crucial to enhancing sales productivity and fueling customer engagements. As they were creating content, accessing sales enablement resources and interacting with customers at various touch points along the buyer journey, Bigtincan was generating copious amounts of siloed data across their suite of solutions which presented challenges in delivering insights to their sales teams. They turned to Databricks to unify business-critical data from across multiple platforms and support their team’s ability to collaborate more effectively on the development of data science solutions. As a result, Bigtincan was able to enhance their product development cycle with AI-fueled automation and faster data processing capabilities, allowing its teams to perform tasks and share data faster to generate meaningful insights for customers.

Siloed sources of data across multiple platforms

As a sales enablement automation platform, Bigtincan enables sales teams to deliver personalized content and insights to their customers before making a purchase. It also provides the latest training materials to help their sales teams keep their skills sharp and stay relevant. With the copious amounts of content generated, it was a challenge having siloed data sources housed and specialized within each platform of their three major AI-enabled platforms.

“Each of these platforms had a different reporting structure specific to that platform. Previously, we were using Pandas notebooks for adhoc analysis. We realized we needed a platform that could process the large amount of data in a unified and consolidated manner that facilitated reporting,” said George Ye, Senior Product Manager, Data Science Reporting, Bigtincan.

In addition, Bigtincan had a young and growing product development team of data scientists and developers located all over the world. There was a need to improve their data science capabilities to its operations to enable teams in Bigtincan to collaborate on advanced data projects regardless of location.

Bigtincan turned to Databricks to unify their data reporting structures and enable its teams to harness richer insights to develop data science solutions and ML-powered content recommendation engines for better and personalized customer experiences.

Enabling consolidated insights with a collaborative data model

“We realized that beyond data scientists, we needed data engineers to centralize the data collected and data analysts to process the data for business analysis and product development. Databricks’ drove the initiative for us to expand our data teams,” shared George.

With the expertise from Databricks’ professional services team, Bigtincan realigned their data team structure and skill sets with industry best practices. The enterprise was able to implement a unified data analytics platform, powered by Databricks on their AWS architecture, that enabled the company to centralize data access and analytics on a single platform. By leveraging Delta Lake, Bigtincan could create an integrated data engineering function, deliver a consistent data pipeline and improve the pace of reporting by over two times.

Data scientists were also able to gain real-time access to data to create consolidated reports and effectively harness ML-powered solutions for the customers they serve.

Databricks’ interactive notebooks further enhanced the collaboration between Bigtincan’s global data team. The team can share projects with peers and fast track the delivery of MVP reporting to create data science products at scale. This was the result of the Databricks platform automating DevOps processes, which allowed a shift in focus to MVP development projects.

Bigtincan leveraged MLflow to streamline its product development lifecycle, allowing its data scientists to automatically track the ML lifecycle of their models, while ensuring that the right models were utilized at the right time. By doing so, Bigtincan has been able to continually improve their sales enablement platform and deliver personalized products for their customers.

Improving the time-to-market of ML-powered solutions by 4x

Databricks helped Bigtincan develop new models at record pace. One of the recommendation engines Bigtincan developed is their ‘Promotion Recommender’, which was developed within a week using Databricks. The ‘Promotion Recommender’ analyzes sales activity to recommend the best content to customers by sending real-time notifications to the sales team on their phone or via email when new materials are available. This ensures the relevancy and accuracy of content when interacting with customers.

‘Bigtincan Genie’, another model built on Databricks, is a virtual sales assistant that uses natural language processing to deliver customized content that caters to their customers instantly. As customers received more relevant recommendations, Bigtincan registered an improved level of adoption, improving adoption rates by as much as 27 percent.

“It’s a major success from a data science standpoint as such projects usually require months of investment. Databricks essentially allowed us to fast track the delivery of data-driven solutions, like the content recommender, in a week to our customers,” said George.

Moving forward, Bigtincan plans to explore additional use cases in the areas of video and audio analytics for its data analysts to conduct media coaching and sales chatbots to help customers automate more aspects of their day-to-day operations. These developments, using Databricks as the core foundation, are set to support Bigtincan’s vision of empowering sellers to deliver sales readiness, just-in-time content, and automated document personalization, to transform their customer engagements into long-term valued relationships.