Analyst, Gtm Customer Intelligence

Apollo.io Apollo.io · Enterprise · United States · Revenue

This role focuses on building the post-sales intelligence layer for Apollo's Customer Success organization, using AI tools to enhance analytics, reporting, and decision-making related to customer health, retention, and expansion. The analyst will be responsible for building and maintaining analytics infrastructure, developing reporting, and collaborating with various teams to translate complex customer lifecycle questions into actionable insights.

What you'd actually do

  1. Build and maintain the analytics infrastructure behind Apollo's customer health scoring model.
  2. Develop renewal pipeline reporting that gives CS and Finance visibility into renewal timing, risk, and expected outcomes.
  3. Instrument and analyze data from billing systems, support ticket volumes, and other post-sales touchpoints to identify patterns that predict churn or expansion.
  4. Build reporting around upsell and cross-sell motion—including expansion pipeline, seat growth, product adoption metrics, and net revenue retention.
  5. Build, maintain, and iterate on dashboards that serve the full CS org—from individual GTME book-of-business views to VP-level QBR decks.

Skills

Required

  • 3+ years of experience in an analytics, Customer Success Operations, or Revenue Operations role
  • Direct exposure to post-sales data in a B2B SaaS environment
  • Familiarity with CS data domains—health scoring, NRR, churn, renewal pipelines, support metrics, and customer lifecycle stages
  • Strong SQL skills
  • Experience working with BI tools (e.g., Looker, Tableau, or similar)
  • Experience with Salesforce or a CS platform (Gainsight, Vitally, ChurnZero, or similar)
  • Ability to translate business questions into structured reporting requirements
  • Comfortable operating across functions

Nice to have

  • Leveraging LLMs as an active part of your analytics workflow
  • Structuring and exposing data for AI interpretation

What the JD emphasized

  • post-sales data
  • customer health scoring model
  • renewal pipeline reporting
  • predict churn or expansion
  • upsell and cross-sell motion
  • customer lifecycle