Track feature impact with outcome loops

Situation

You shipped a feature three weeks ago. Someone in the board meeting asks: "Did it move the number?" You don't know. The experiment data is in PostHog, the customer feedback is in Slack, and the OKR is in a Google Sheet nobody updates. Specky closes this loop automatically.

What you need

• The feature linked to an OKR or opportunity in Specky
• PostHog (or another analytics source) connected
• The feature already shipped

Steps

1. Create the experiment before you ship (ideal path)

In Strategy → Experiments, click New Experiment:

• Hypothesis — "Adding bulk CSV export will increase activation rate for operations teams by 15%"
• Success metric — "activation_rate for operations cohort" (pulled from PostHog)
• Linked opportunity — select from your graph
• Linked OKR key result — select from your OKRs

2. If you've already shipped, link retroactively

Open AI Chat:

I shipped [feature] on [date].
Link it to the [opportunity name] in my Product Graph and create an experiment
to track whether it improved [metric].
Set the baseline from PostHog data before [date].

Specky creates the experiment, pulls the baseline metric from PostHog, and sets up monitoring.

3. Let Specky track the outcome

Specky checks your connected analytics source daily. When the experiment reaches statistical significance (or after your defined time window), it:

1. Writes the outcome to the experiment node: validated, refuted, inconclusive, or partial
2. Creates an Insight in your Product Graph summarising the result
3. Notifies you in PM Inbox with the evidence

4. Use the outcome for next quarter's bets

In AI Chat:

Summarise all experiments from Q1 2026.
Which hypotheses were validated, which were refuted, and what patterns
do you see in what's working vs. what's not?

Specky compares your outcome history to show which kinds of bets are working, which are not, and what patterns are worth carrying into the next planning cycle.

Output

• A closed-loop record: opportunity → experiment → outcome → insight
• Your validation rate over time (validated / total experiments)
• Pattern analysis across outcomes to inform next quarter's strategy

Variations

No PostHog? Connect your analytics via the Specky API and push metric snapshots directly. The experiment tracking works with any numeric metric you define.

Leading indicators only? Set the success metric to a leading indicator (e.g., "feature used in first 3 days") rather than the lagging metric (retention). You get faster signal, even if it's not the final answer.