Time to Value Framework

What it is

A model that breaks the single, often loosely used phrase "time to value" into distinct, measurable sub-metrics, so a team stops treating "how fast do customers get value" as one vague number and starts tracking the specific point that number actually describes. The core distinction, closely associated with Lincoln Murphy's customer success writing at Sixteen Ventures and now converged practice across customer-success and product-led-growth literature (Gainsight, ProfitWell, and others publish comparable definitions), is between Time to First Value (TTFV), the moment a customer first experiences a genuine, specific benefit, and Time to Core Value, the later point at which product usage becomes a sustained pattern that actually predicts renewal, not just a single good first session. A related pair of distinctions sharpens both: Activation rate versus time to value (activation rate measures how many customers reach a value milestone; time to value measures how fast the ones who do get there), and the "aha moment" versus the activation event (the aha moment is the emotional, qualitative instant a customer feels the product's value; the activation event is the specific, measurable, in-product action that stands in as a proxy for that feeling, since a feeling itself cannot be logged in analytics). The Customer Onboarding Maturity Framework already names "time-to-first-value" as a milestone metric to track; this model is the sub-metric breakdown that framework's measurement step assumes but does not itself define.

When to use it

• "Time to value" is used loosely across the team, with sales, product, and customer success each meaning something slightly different by it. This model gives a shared, precise vocabulary before the metric is used in a business case or a board update.
• Activation rate looks healthy but retention still lags, or vice versa. Separating "how many reach value" from "how fast" often reveals the two are moving independently and need different fixes.
• A defined activation event exists in the analytics but retention data suggests it is the wrong proxy for the real aha moment. This is the clearest sign the activation event needs re-validating against actual qualitative customer feedback, not just usage logs.
• Customers report an early "aha" moment in interviews, but usage does not become a sustained pattern afterward. This signals a gap between Time to First Value and Time to Core Value: a real early win exists, but nothing in the product experience carries it forward into habitual use.
• You are setting or defending an onboarding-speed target and need the Customer Onboarding Maturity Framework's milestones to map to a specific, named sub-metric, rather than a single blended "time to value" number that different teams read differently.

Ownership

At a scaled company with a specialised PMM team, the Head of Product Marketing typically partners with Product and Customer Success to define and agree the activation event and the Core Value threshold, since both require product-usage data neither function holds alone; PMM's specific contribution is translating the resulting metrics into onboarding messaging and the milestones a self-serve or sales-assisted funnel is built around. Customer Success or Product Analytics usually owns instrumenting and reporting the actual TTFV and Time to Core Value numbers on an ongoing basis. At a solo or founding-PMM stage, the founding PMM typically defines the activation event directly with the founder or a product lead and tracks the resulting numbers personally, since no dedicated analytics or CS function yet exists to own the reporting.

How to apply it

1. Define the aha moment qualitatively first, through customer interviews, not analytics. Ask recent activated customers what specifically made them think "this is going to work for me," in their own words, before choosing a quantitative proxy for it; skipping this step risks anchoring the activation event on whatever is easiest to log rather than what customers actually describe as the moment of value.
2. Choose the activation event as the closest measurable proxy for that aha moment. The event should be specific and unambiguous in the product's usage data (a report generated, a first successful sync, an invited teammate accepting), not a vague composite like "logged in three times."
3. Measure Time to First Value as the elapsed time from signup to that activation event, segmented by acquisition channel, plan tier, or persona where the sample allows; a self-serve trial signup and a sales-assisted enterprise onboarding will have structurally different TTFV even for the same product.
4. Separately define and measure Time to Core Value, the point at which usage becomes a sustained, repeating pattern, validated against actual retention data rather than assumed. Test candidate definitions (for example, "used the core workflow in 3 of the last 4 weeks") against which customers in that pattern actually renewed, and pick the definition that correlates most strongly with real retention.
5. Track activation rate and time to value as two separate numbers, not one blended metric. A rising activation rate with flat or worsening TTFV means more customers eventually get there but more slowly; a falling activation rate with fast TTFV among those who do activate means the product works well once someone gets going but is losing people before that point. Each diagnosis points to a different fix.
6. Feed the validated activation event and Core Value threshold into onboarding design and messaging. Give the Customer Onboarding Maturity Framework's milestone list a concrete, evidenced Time to First Value target to design interventions against, and use the Core Value threshold to decide when a customer has genuinely "arrived" rather than guessing.
7. Re-validate the activation event and Core Value threshold periodically, not just once. As the product adds features or the customer base shifts, the specific action that best predicts long-term retention can change; an activation event chosen two years ago on old usage patterns can quietly stop correlating with what actually predicts renewal today.