Signal Synthesiser Data P

signal-synthesiser-data-p

Agent: Data Analyst

L2 data analyst (Nx) responsible for data modelling, instrumentation implementation, metrics dashboards, funnel analysis, and signal synthesis.

Department ethos: ideal-data-growth.md

Skill Description

The post-launch signal synthesiser aggregates quantitative signals from the first 7-30 days after a feature release — adoption curves, funnel conversion, error rates, support ticket spikes — and distils them into a prioritized list of iteration candidates for the product backlog.

When to Use

• When a feature has been live for 7-30 days and the team needs a data-backed assessment of its performance.
• When a post-launch review meeting is scheduled and requires prepared data synthesis.
• When the product team is deciding whether to iterate, scale, or sunset a recently launched feature.
• When support ticket volume spikes after a release and the team needs to correlate with behavioural data.

Workflow

1. Collect signals: Pull adoption metrics, funnel conversion rates, error logs, latency data, support ticket counts, and NPS/CSAT scores for the launch period.
2. Compare to targets: Measure each signal against the pre-defined success criteria from the goal framework. Flag metrics that miss targets by more than 20%.
3. Segment analysis: Break key metrics by user cohort, platform, and acquisition channel. Identify segments where the feature performs well vs. poorly.
4. Correlate with qualitative data: Cross-reference quantitative signals with support tickets, user feedback, and session recordings to hypothesize root causes for underperformance.
5. Prioritize iterations: Rank potential improvements by expected metric impact and implementation effort. Produce a prioritized iteration backlog with data justification for each item.
6. Deliver synthesis: Present a one-page signal synthesis with key findings, metric performance vs. targets, root cause hypotheses, and the prioritized iteration list.

Anti-Patterns

• Premature synthesis: Drawing conclusions from 48 hours of post-launch data before cohorts mature. Why: early adopters behave differently from mainstream users; patterns stabilize after 7-14 days.
• Metrics without context: Presenting numbers without qualitative context (user quotes, support themes) makes it hard to identify actionable improvements. Why: data tells you what happened; qualitative signals tell you why.
• Ignoring null results: Omitting metrics that met targets and reporting only failures biases the iteration backlog toward fixes instead of scaling what works. Why: doubling down on successful elements can be higher-leverage than fixing marginal ones.

Output

Success:

• A post-launch signal synthesis containing metric performance vs. targets, cohort breakdowns, root cause hypotheses, and a prioritized iteration backlog with data justification per item.

Failure:

• Key post-launch metrics cannot be computed because instrumentation was incomplete. Report the gaps and recommend interim proxy metrics while instrumentation is remediated.

Related Skills

• signal-synthesiser-data -- the general signal synthesiser provides ongoing product health views; this skill focuses specifically on post-launch windows.
• funnel-analyser -- funnel analysis is a key input to post-launch synthesis.
• adoption-tracker-data -- adoption curves are a primary post-launch signal.
• signal-synthesiser -- the product ops signal synthesiser incorporates operational signals alongside the data signals this skill produces.