Why Customer Success Teams Are Still Struggling With Usage Data and How to Fix It in 2026.
Customer Success should be about helping customers achieve outcomes. In reality, many Customer Success Managers spend most of their time building reports instead of working with customers.
When a QBR or monthly review is scheduled, the process is predictable. CSMs pull data from product analytics, CRM systems, billing tools, and support platforms. Metrics do not match. Definitions change month to month. Meetings turn into explanations of numbers rather than strategic conversations.
This problem is widespread in SaaS companies between 2M and 20M ARR. Teams are large enough to feel the operational strain but not mature enough to have solved it.
Why More Usage Data Creates More Problems
The issue often starts with a simple request. Customer Success asks for usage data. Engineering responds by exposing product events. Clicks, page views, background actions, and system events all become available.
Technically, the request is fulfilled. Practically, it creates confusion.
Customer Success teams gain access to massive volumes of data but still cannot answer basic account level questions. Every QBR becomes a one off effort. Every presentation is rebuilt. No insight carries forward.
Raw event data also pushes teams into unhealthy behavior. When teams focus on daily activity, they start reacting to noise. A user logged in less this week. A feature was not used on Tuesday. Activity dipped yesterday.
Customer Success does not operate effectively at that level of detail.
What matters is trend analysis. Usage movement over 30, 60, or 90 days. Adoption spread across multiple users within an account. Progress toward behaviors that correlate with retention and expansion.
Without clear aggregation and ownership, teams overreact, waste time, and distract customers with unnecessary outreach.
The Real Problem Is Not Technical Skill
Many Customer Success leaders believe the solution is becoming more technical. Learning APIs, schemas, and data warehouses seems like the logical next step.
This approach rarely works.
Requests remain vague. Data teams interpret them as instrumentation rebuilds or requests for every available event. Engineering teams deprioritize the work. RevOps focuses on sales needs. The initiative stalls.
Customer Success leaders do not need to write code. They need to define outcomes clearly.
Data teams respond to precision.
Effective requests sound like this:
We cannot identify churn risk or expansion opportunities without account level usage signals.
We need five metrics including active users, adoption percentage, onboarding completion, feature depth, and time since last value milestone.
We are asking for clean rollups from the warehouse or analytics platform, not raw events.
This level of clarity moves projects forward and establishes credibility.
Where AI Actually Adds Value in Customer Success
AI does not deliver value by summarizing calls or rewriting emails. Its real strength is pattern recognition across time.
When applied correctly, AI monitors usage trends across multiple signals and flags meaningful change. Declining engagement across several metrics. Adoption stalling after onboarding. Expansion behavior appearing earlier than human review cycles would catch.
The goal is not more dashboards. The goal is fewer manual processes and clearer signals tied directly to action.
When this foundation exists, QBR preparation shrinks from days to hours. CSMs stop maintaining spreadsheets and start having better conversations grounded in real customer behavior.
How to Fix Broken Usage Data in 2026
Improving usage data does not require deep technical expertise. It requires structural decisions and accountability.
First, establish a single source of truth. Decide where customer reality lives, whether that is your data warehouse, CRM, or product analytics system.
Second, ensure usage data is processed before it reaches the Customer Success platform. CSPs should orchestrate workflows and playbooks, not store raw data.
Third, deliver clean account level rollups. Focus on five to seven metrics your team can review weekly and act on immediately.
Fourth, assign ownership. Every data pipeline needs a named owner. Ownership cannot be assigned to a department.
Common high impact metrics include onboarding milestones, adoption thresholds, usage frequency and depth, and time to value indicators.
Treat Usage Data as Infrastructure
Customer Success and CX initiatives fail for the same reason year after year. Teams invest in platforms, build playbooks, and launch workflows, only to discover that nothing becomes proactive without reliable usage data tied to accounts.
The result is a reactive system driven by dashboards, meetings, spreadsheets, and intuition.
In 2026, usage data must be treated as infrastructure, not a feature request. When it is, AI becomes leverage, Customer Success becomes proactive, and teams finally spend their time driving outcomes instead of building reports.