Building the Analytics Stack for SaaS

The Three Layers of SaaS Analytics

SaaS analytics typically involves three distinct layers, each serving different needs:

Product Analytics tracks what users do inside your product. Which features do they use? Where do they get stuck? What paths lead to activation? Tools like Mixpanel and Amplitude excel here.

Customer Analytics takes a broader view of the customer journey including marketing touchpoints, support interactions, and lifecycle stages. This connects product behavior to business outcomes.

Business Intelligence focuses on financial and operational metrics. Revenue, churn, cohort analysis, and forecasting live here. Tools like ChartMogul or Profitwell serve this layer.

Start with Product Analytics

For most SaaS companies, product analytics is the foundation. Understanding user behavior informs everything from product development to marketing to support.

Mixpanel and Amplitude are the leading dedicated tools. Both offer generous free tiers and scale well. Choose based on your team's preferences; both are excellent.

PostHog provides an open-source alternative with additional features like session recording and feature flags. Self-host for full control or use their cloud version.

Connecting Analytics to Action

Analytics data is most valuable when it drives action. The clearest connection is to email marketing. When analytics identifies specific user behaviors, Sequenzy can trigger appropriate communication.

Examples of analytics-driven email automation:

• Declining login frequency triggers re-engagement sequence
• Feature discovery prompts educational follow-up
• High engagement signals upgrade opportunity
• Onboarding completion triggers next-stage guidance

This closed loop between analytics and communication turns passive data into active improvement.

Building Your Stack

Start simple and add complexity as needed:

Stage 1: Product analytics (Mixpanel or PostHog free tier) plus email automation (Sequenzy). This covers most early-stage needs.

Stage 2: Add business intelligence for financial metrics. ChartMogul connects to Stripe for revenue analytics.

Stage 3: Consider a customer data platform like Segment to unify data flows across tools. This becomes valuable when you have many tools that need to share data.

Do not skip stages. Each layer adds complexity. Make sure you are fully utilizing current tools before adding more.

Common Mistakes

• Tracking everything: More data is not better. Focus on events that drive decisions.
• Ignoring data quality: Inconsistent event naming and missing properties make analysis impossible. Establish standards early.
• Not acting on insights: The goal is decisions, not dashboards. Ensure analytics connects to action through tools like Sequenzy.
• Over-engineering early: Data warehouses and complex pipelines can wait. Start with product analytics and grow from there.