How LinkedIn Saved $60M Annually with AI-Powered Ticket Resolution - GraphRAG
Talvinder Singh
3x Founder & Experienced Product Manager | Backed by Y Combinator, 500 Startups, Harvard, Goodwater, Sequoia | Key Note Speaker | Featured in Forbes, NBC, TechCrunch, Times of India, Hindustan Times, Mint
At 35+, reinventing is tough.
But so is handling long ticket resolution times.
Why does this matter? Because quicker resolutions boost customer satisfaction (CSAT) and cut costs dramatically.
Did you know that companies with fast resolution times see 40% higher customer satisfaction? And every extra hour spent on a ticket could cost up to $15 in additional support costs?
LinkedIn did something incredible. They used Graph RAG to slash their ticket resolution time from 40 hours to just 15.
Here’s how:
?? They created a Knowledge Graph (KG) from customer support tickets, turning them into structured trees. This kept all the relationships intact.
?? They linked tickets based on context, dependencies, and references.
?? This created a comprehensive graph.
?? Each node in this graph was embedded, enabling semantic search and retrieval.
?? Their RAG QA system identified relevant sub-graphs by traversing the KG and searching by semantic similarity.
?? It then generated context-aware answers, improving accuracy and efficiency.
The result?
A 28.6% reduction in median resolution time.
That’s huge! ??
But what does it mean in numbers?
Let’s do the math.
?? Suppose LinkedIn handles 10,000 tickets per month.
?? Reducing the resolution time from 40 hours to 15 saves 25 hours per ticket.
?? That’s a total saving of 250,000 hours per month.
?? If the average cost per hour for support is $20, that’s a saving of $5 million per month.
?? Annually, this translates to $60 million!
What about CSAT?
?? Faster resolutions mean happier customers.
?? Studies show a direct correlation between reduced resolution time and increased CSAT.
?? A reduction of 25 hours can increase CSAT by up to 20%.
?? Higher CSAT means better customer retention, which translates to higher revenue.
Other metrics?
Think about employee productivity. Less time on each ticket means support teams can handle more tickets, improving overall efficiency.
I am sharing the research paper and a python library that implements the research paper, so you can implement in your own company or just nerd out like me.
Research Paper - https://arxiv.org/pdf/2404.17723
Python library - https://github.com/sarthakrastogi/graph-rag/tree/main
Struggling to improve your ticket resolution process? Try implementing Graph RAG in your company. It’s a game-changer for efficiency and customer satisfaction.
Do share what your findings were.
This could be the future of customer service. Faster, smarter, and more efficient.
I nerd out heavily on research papers and will continue sharing all such discoveries. Do share with your team members, and your network, so everyone can learn better.