Customer Success Manager

Indiafull-timemid

Customer Success ManagerCustomer

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Quick Summary

Overview

About the Role Our Tier 3 customers don't need frequent check-ins, but they do need to know how their virtual assistant is performing and how to make it better. In this role,

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Customer Success ManagerCustomer

About the Role

Our Tier 3 customers don't need frequent check-ins, but they do need to know how their virtual assistant is performing and how to make it better. In this role, you'll build automated reporting that turns conversation transcripts into clear, actionable insights, so customers can improve the experience for their end users without a heavy-touch relationship.

What You'll Do

Build and maintain automated workflows that analyze conversation transcripts at scale, surfacing trends in intents, containment, fallbacks, escalations, drop-offs, and sentiment
Use LLMs and NLP techniques to tag, cluster, and summarize conversations, and validate output quality before it reaches customers
Create reusable reports and dashboards that can be deployed across the Tier 3 portfolio with minimal manual effort
Recommend specific improvements (new intents, prompt and response changes, knowledge gaps, flow fixes) backed by transcript evidence
Flag anomalies and regressions early, such as a spike in fallbacks after a release
Track customer KPIs, business case metrics, and profitability across your book of business
Identify expansion opportunities in the data and partner with customers to pursue them
Present findings in periodic insight reviews and act as a customer educator on conversational AI best practices
Work with product and engineering to log Jira tickets, pilot new analytics features, and feed customer insights into the roadmap

What You'll Bring

4-6+ years in analytics, customer success, or client-facing data roles (conversational AI or CX analytics experience is a plus)
Hands-on experience with Python, SQL, or similar scripting, plus BI or workflow tools such as Tableau, Power BI, Looker, or Zapier
Working knowledge of LLMs and prompt engineering, including evaluating output for accuracy and bias
Experience turning unstructured text or conversation data into clear, business-relevant findings
Strong data storytelling skills and advanced Excel and PowerPoint
Familiarity with conversational AI concepts like intents, NLU, dialog flows, and containment
Excellent communication skills and the ability to build customer relationships in a low-touch model
High attention to detail, strong initiative, and the ability to prioritize across a large portfolio

Nice to Have