Time to value (TTV)
How long it takes a new user to get their first real benefit, measured from signup to the moment the product pays off.
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What it is
Time to value is the clock running between 'I signed up' and 'oh, this is useful'. It usually comes in two sizes: time to first value (the first small win, like seeing your own data in a chart) and time to target value (the full outcome you sold, like a team running its weekly review in the tool). Measured properly, it is the gap between the signup event and the activation event.
The levers are all about deleting the empty room: templates instead of a blank canvas, seeded sample data, smart defaults, importers that do the setup for the user, and deferring account details until after the first win. Canva drops you into a template gallery, not a blank page. Figma hands new teams a demo file. Every one of those choices is a TTV cut.
Gotcha: report the median, not the mean. A handful of users who wander back six weeks later will drag an average into nonsense, and the users who never activate at all are missing from the calculation entirely, which flatters the number. Second trap: not all setup is waste. Ripping out the step where someone connects their real data can shorten TTV on the dashboard while gutting retention, because that step was the value.
Ask AI for it
Design a time to value measurement and reduction plan for my product (described below). Step 1: define time to first value and time to target value as the gap between two named tracked events, and specify reporting them as a median with p25/p75, split by signup cohort and by acquisition channel, excluding nobody silently (state how never-activated users are handled). Step 2: map the current path from signup to that event as a numbered step list with an estimated time cost per step. Step 3: propose 5 concrete cuts (templates, seed data, deferred setup, defaults, import) ranked by minutes saved versus build effort, and flag any cut that would remove a step that is actually the value.