The Problem: Legacy Analytics Tools Aren't Built for CS and Product Teams
Customer Success (CS) and Product teams are responsible for keeping users engaged, helping them get value, and driving revenue growth. Yet, many still use dashboard-based tools like Amplitude, Mixpanel, or Gainsight that don’t align with how they work.
These tools often focus on static reporting rather than helping teams make decisions. For example, Amplitude and Mixpanel track user actions but lack features to engage users or provide clear next steps. Gainsight works well for managing customer data but can be complicated for smaller teams, making it harder to figure out what to do with the data.
Why Traditional Dashboards Fall Short
- Steep Learning Curves: Tools like Amplitude require technical knowledge to set up custom events or dashboards.
- Static Reports: Dashboards show past data but don’t flag churn risks or opportunities to help users in real-time.
- Fragmented Data: Many tools keep data in silos, making it hard to get the full picture of a customer’s experience.
- No Engagement Tools: These tools track activity but don’t let teams act directly within the platform.
The result? Teams spend more time figuring out the tools than helping their customers.
The Root Cause: Outdated Thinking About Data
The Legacy Model of Data Consumption
Traditional analytics tools were designed for executives who needed high-level summaries. But CS and Product teams work differently—they need data that connects directly to customer actions.
For example: - A CS manager trying to reduce churn needs to know which customers are at risk and why. - A Product Lead deciding what features to improve needs feedback from users who are struggling.
AI's Role in Rethinking Data
AI is changing how teams handle and act on data: - Predictive Tools: AI can spot churn risks or show patterns in how users interact with features. - Proactive Suggestions: Instead of relying on dashboards, AI can suggest actions (e.g., “Reach out to Customer X; their usage dropped 40%”). - Unified Data Views: AI can bring together disconnected datasets into a single picture, though this often requires some setup and extra tools.
The Roadblocks Holding SaaS Teams Back
Data Silos Create Blind Spots
One of the biggest challenges for SaaS teams is fragmented data: - CRM systems store sales interactions. - Analytics platforms track product usage. - Support tools log tickets.
Without integration, these silos keep teams from seeing the full picture.
Over-Reliance on Manual Analysis
Many SaaS companies still rely on manual processes to pull meaning from data: - Exporting data from multiple tools into spreadsheets. - Manually connecting trends across datasets. - Spending hours building reports for stakeholders.
Lack of Clear Actions
Even when teams have access to analytics tools, they often don’t know how to use the data effectively: - Dashboards show what happened but rarely explain why. - Teams are left guessing how to fix issues like low adoption rates.
Why Amplitude, Mixpanel, and Gainsight Miss the Mark
Amplitude: Great for Analysts, Not Operators
Amplitude tracks user activity but has limitations: - Steep Learning Curve: Requires technical expertise for setup. - No Action Tools: Doesn’t include features to act directly on user data. - Manual Event Tracking: Doesn't automatically track all user actions.
Mixpanel: Powerful but Fragmented
Mixpanel offers strong segmentation but has gaps: - Engineering Dependency: Needs developer support for advanced use. - Limited Revenue Connections: Hard to link product usage to revenue. - No Feedback Integration: Doesn't include customer feedback from support.
Gainsight: Strong CRM Features, Weak Analytics
Gainsight focuses on CS workflows but lacks depth: - Static Reports: Shows past metrics instead of real-time updates. - Siloed Data: Doesn’t integrate easily with other platforms. - High Complexity: Can overwhelm smaller teams.
A Better Way Forward: Rethinking Analytics for CS and Product Teams
Principles for Modern Analytics
To truly empower CS and Product teams, analytics tools should follow these rules: - Be Proactive, Not Reactive: Offer real-time alerts and suggestions. - Give a Full Picture: Combine all relevant data sources into one view. - Help Teams Take Action: Go beyond “what happened” to show “what to do next.” - Be Easy to Use: Avoid tools that need heavy technical expertise.
Real-World Example: Embedded Analytics in Action
A SaaS company offering a customer success platform: - Before using embedded analytics, their team spent hours building custom dashboards for clients tracking KPIs like churn rate or NPS scores. - After switching to embedded analytics, teams saved time on dashboard requests, saw happier clients, and focused more on helping users[5].
This allowed their CS team to spend less time on reports and more time helping customers succeed.
Actionable Steps for Decision-Makers
Review Your Current Tools: - Look for gaps in your existing tools (e.g., missing integrations or limited forecasting features).
Invest in Tools That Work for You: - Choose platforms that make decisions easier and faster.
Connect Your Data: - Pick tools that integrate with your CRM, support systems, and analytics.
Train Teams to Use Data Better: - Help CS and Product teams understand and apply data in practical ways.
Track Results Over Time: - Measure how new tools impact churn rates, feature adoption, and onboarding.
Conclusion
Dashboard-based analytics tools belong to the past. Today’s CS and Product teams need tools that help them take action, not just look at numbers. By moving from static dashboards to smarter solutions, SaaS companies can work more effectively, grow faster, and give their customers a better experience.