Amplitude (AMPL) investor relations material

Strategic progress and business transformation

• Focused on expanding capabilities around core analytics and increasing enterprise sales, resulting in enterprise ARR rising from 60% to 69% and ARR growth accelerating from 6% to 22% over two years.

• Integrated acquired and internally developed modules into a unified platform, streamlining workflows and data taxonomy, and enabling a consolidation strategy for customers.

• Implemented significant changes in go-to-market approach, pricing, and packaging, including a shift to event-based pricing and volume-based discounts, which improved customer adoption and reduced friction.

• Adjusted sales incentives to balance new logo acquisition and expansion, moving toward a 50/50 split and incentivizing broader enterprise penetration.

• Broadened customer base beyond tech, with notable wins in non-software verticals, though tech cohort growth remains slightly faster.

AI initiatives and product innovation

• Prioritized AI-driven adoption over immediate monetization, launching agentic capabilities like Global Chat and MCP to lower adoption barriers and broaden use cases.

• Introduced new AI products such as AI Feedback, Agentic Analytics, and Wave, which will be monetized incrementally, opening new revenue streams.

• AI features drive increased data ingestion and platform usage, with agentic modules exemplifying cross-platform capabilities and supporting upsell opportunities.

• Maintained focus on simplifying customer experience, allowing module trials and transparent pricing, which supports AI adoption and multi-product expansion.

Financial outlook and operational efficiency

• RPO has grown over 30% for six consecutive quarters, supporting confidence in 21% revenue growth guidance.

• Margins impacted by rapid data ingestion growth and strategic decisions to drive adoption, with expectations for future monetization as usage matures.

• Cost structure optimized through renegotiated AWS contracts and ongoing efforts to improve economics via contract and model optimizations.

• Marginal cost for additional modules is low, with primary costs tied to initial data ingestion; expansion into new modules increases profitability.