Financing services company TVS Credit optimized campaign costs by 15% and increased feature adoption by 42% with Netcore
2026-03-08 16:28 Diff

15%

Campaign costs optimized via Raman AI’s
preferred channel

42%

Uplift in feature adoption via no code nudges

Company: TVS Credit is an app for availing vehicle loans and other finance needs

Website: https://www.tvscredit.com/

Industry: BFSI

Location: India

Category: Customer Engagement, Product Experience

Solutions Used: Customer Engagement, Product Experience, Raman AI

Objectives:

  • Lead Generation:
  • Invoke the interest of users who get dropped off from website lead pages

  • Omni-Channel marketing:
  • Orchestrate an omni-channel marketing approach for their cross-sell and lead nurturing initiatives

  • Cost Optimization:
  • Craft automation journeys based on user behavior/interaction in an optimized manner to save spends

  • Feature Adoption:
  • Gain more traction from users for new feature releases and hand-hold them to derive value from the Website/App

The Netcore Strategy:

The Netcore AI-powered Customer Engagement and Product Experience platform helped TVS Credit by:

  • Journeys and segmentation:
  • Creating automated journeys to engage with users at various touchpoints across channels..

  • Preferred channel:
  • AI-powered preferred channel helped predict the best channels customers would be likely to engage.

  • Web messages:
  • Web messages were used to display offers at various stages, from viewing to adding to the cart.

  • No-code nudges:
  • All of this resulted in higher engagement on the app and increased engagement.

"At a time when TVS Credit is thriving to become a global leader w.r.t transforming user experience digitally, our partnership with Netcore over the last few years has been a game-changer for our marketing efforts. Netcore’s CE & PX platforms helped us streamline our campaigns, saved time, and delivered results. We’ve observed an impressive uptick of 8% for our qualified leads and optimized our campaign costs by 15% by effectively leveraging multiple features of the stack."

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