Build data to outcome apps, using clicks, not code

BFSI use case

In this demo, we will showcase how to build an end-to-end ML-powered ‘data to outcome’ application for data at scale, using a drag and drop approach.

We will demonstrate this using a financial industry use case; but the challenges, solution, and benefits are applicable to all domains.

The customer, a leading bank in this case, needed to improve its credit card approval process. Though, the approvals were highly automated, in certain complex scenarios, a lot of manual analysis was required. This was proving to be risky; also, the process had opportunity costs, as high-quality leads were likely getting pushed to competitors due to approval delays.

In this data to outcome demo, we’ll show how to:

  • COLLECT: Ingest batch & real-time data, from various sources such as CRM, call center, campaigns, and more
  • TRANSFORM: Create pipeline to integrate, transform and prepare the data
  • PREDICT: Infuse ML into the pipeline to predict which customers are most likely to take action
  • RECOMMEND: Build supercharged dash, with real-time actionable insights and ML-powered recommendations
  • ACT: Take actions, including calling the prospects, from the same dash
  • MONITOR: Use the same integrated dash, to monitor campaign results in real-time
  • AUTOMATE: Automate time-consuming credit card approval processes and workflows

And, accomplish everything, from a unified platform, without having to write a single line of code.

Gathr’s capabilities on display in this demo

  • Unified UX
  • Drag and drop UI
  • 300+ pre-built connectors
  • 300+ pre-built transformations
  • ML at scale
  • Predictive analytics
  • Insights delivery
  • Process Automation

See you on the other side!

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