As a customer success manager, you likely spend a lot of time preparing reports, decks, and updates meant to reinforce the value of your product. Teams are still expected to share data, but the amount of data available to choose from has significantly increased.

Product analytics, CRMs, health scores, and AI-powered tools now surface more metrics than most teams can reasonably act on. When data lives across systems and follows different definitions, deciding what to put in front of a customer becomes a real challenge.

The best customer conversations are not driven by how much data you share, but by how clearly that data helps a customer understand their progress and decide what to do next. Cutting through the noise requires clean, trusted data and a more automated way to turn it into customer-ready content.

As Customer Success becomes more digital and more scaled, having a repeatable framework matters more than ever. When building customer-facing presentations or reports, it is still worth asking the same core question: which data points do customers actually care about, and which ones will they act on?

Which Data Points Customers Actually Care About

1. Account context and goals

Before getting into metrics, customers want to see themselves reflected in the conversation. That starts with their goals.

In 2026, this goes beyond restating objectives captured during onboarding. Strong CSMs ground their updates in where the customer is today relative to where they expected to be by now. This often shows up as value realization milestones, maturity stages, or progress against a success plan tied to real business outcomes.

Reinforcing this context builds trust and keeps the conversation anchored in what matters to the customer, not what is easiest to report on. It also gives meaning to every metric that follows. Data without context can feel disconnected while data tied back to stated goals feels more intentional.

When this context is consistently pulled from trusted systems and automatically reflected in customer-facing materials, it prevents every update from becoming a manual exercise in reconciling numbers and definitions.

2. Who is using the product and how consistently

Usage metrics are still important, but customers care less about raw counts and more about whether the product is becoming part of how their teams actually work.

Instead of focusing on total logins or short-term spikes, it is more meaningful to highlight patterns that indicate product stickiness. This includes consistency of usage over time, adoption across the right roles or teams, and engagement tied to core workflows.

When shared thoughtfully, usage data opens the door to practical conversations about enablement, change management, or expansion. Automated usage summaries and AI-assisted interpretation help teams focus on what the patterns mean, rather than debating whose dashboard is correct.

3. How the product is being used to drive progress

Feature adoption matters most when it is framed as progress, not completeness.

Rather than listing every feature a customer has or has not touched, effective CSMs focus on which capabilities support the next stage of value. Customers want to know whether they are using the product in a way that aligns with their goals, not whether they have explored every corner of the platform.

Calling out underutilized features only works when it is paired with a clear reason to care. Framing adoption in terms of maturity or outcomes makes those recommendations easier to understand and easier to act on.

4. Value and ROI over time

ROI conversations have also matured. Customers are less interested in one-time success stories and more interested in understanding whether value is continuing to build.

This might include time saved, revenue influenced, risk reduced, or efficiency gained, depending on the product and the customer’s objectives. What matters is showing how value has accumulated over time and what continued usage is likely to unlock next.

When value metrics are consistent and grounded in clean data, they help reinforce renewal and expansion conversations long before a contract is up for review.

5. Benchmarking that provides perspective, not pressure

Benchmarking can be powerful when done carefully.

Generic industry averages often feel abstract or disconnected from a customer’s reality. What resonates more is showing how similar customers progress over time. Anonymized cohort benchmarks grouped by size, segment, or maturity help customers understand what is typical and what is possible without putting them on the defensive.

Used well, benchmarking becomes a way to guide next steps rather than rank performance.

What Drives Meaningful Customer Conversations in Digital CS

Customer Success has always been about helping customers achieve outcomes. What has changed is the environment in which those conversations happen.

As Digital CS scales, the goal is not to share more data, but to remove friction between insight and conversation. Clean data foundations reduce confusion. Automation removes manual overhead. Clear value storytelling keeps the focus on what matters most to the customer.

At the end of the day, customers do not remember every metric you show them. They remember whether the conversation helped them understand their progress and what to do next.

To learn more about how CSMs can standardize metrics, automate customer content, and tell clearer value stories at scale, check out our eBook, How to Create Data-Driven Content for Customers.

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