Data Decide

Data-Decide SOP

Boundary

This skill (data-decide) = HOW to interpret data. The data analyst: event taxonomy design, cohort analysis methodology, A/B test statistical rigor, vanity vs actionable metric evaluation, and decision frameworks for acting on data.

Not this skill — use growth-track instead for WHAT to track: AARRR funnel metrics, North Star metric definition, budget management, experiment design templates, and weekly growth reports.

1. Event Taxonomy

Design events before writing code. Use object_action naming convention consistently.

Core events to track (minimum viable analytics):

• Authentication: user_signed_up, user_logged_in, user_logged_out
• Activation: onboarding_started, onboarding_completed, feature_first_used
• Engagement: project_created, item_edited, search_performed
• Monetization: plan_selected, payment_completed, subscription_cancelled
• Retention: session_started, user_returned (after 7+ day absence)

Naming rules (Amplitude taxonomy best practices):

• Always object_action, never action_object — keeps autocomplete useful
• Past tense for completed events (payment_completed), not gerunds
• Properties use snake_case: plan_name, referral_source, time_to_activate_seconds
• Never put PII (email, name) in event properties — use anonymous user IDs
• Document every event in a tracking plan (living doc, reviewed monthly)

Source: Amplitude — Plan Your Taxonomy, PostHog SaaS Guide

2. Vanity vs Actionable Metrics (Eric Ries, Lean Startup)

The test: if the metric doesn't change your next action, stop tracking it.

Three A's of actionable metrics (Ries):

1. Actionable — clear cause and effect; you know what lever moved it
2. Accessible — simple enough that the whole team understands it
3. Auditable — you can trace the number back to raw data

Source: Eric Ries on Tim Ferriss blog, Lean Startup Co.

3. Cohort Analysis

Group users by signup week. Track retention at day 1, day 7, day 30.

How to read cohort curves:

• Flattening curve = users who stay are sticky (good)
• Newer cohorts retaining better than older = product is improving (great)
• All cohorts dropping to zero = you have a leaky bucket (fix retention before growth)

Implementation (minimal):

1. Store signup_date with every user
2. Query: for each signup week, count active users at +1d, +7d, +30d
3. Plot as a retention table or curve chart
4. Compare curves across cohorts — the shape matters more than absolute numbers

4. A/B Test Design

Before running any test:

1. Write the hypothesis: "If [change], then [metric] will [direction] by [amount]"
   • Bad: "The new button will be better"
   • Good: "If we change CTA from 'Start Free' to 'Try It Now', signup rate will increase by 10%"
2. Calculate sample size BEFORE running — use Evan Miller's calculator
   • Inputs: baseline conversion rate, minimum detectable effect (MDE), power (0.80), significance (0.05)
3. Run for full weeks — minimum 14 days to avoid day-of-week bias
4. Do not peek — checking results early inflates false positive rate
5. Statistical significance: p < 0.05 minimum (95% confidence)
6. Practical significance matters too — a statistically significant 0.1% improvement may not be worth implementing

Source: Statsig — Determine Sample Size, GuessTheTest — Sample Size Guide

5. Decision Framework

Data informs, doesn't decide. Use this ladder:

When data and intuition conflict: favor data for incremental decisions, favor vision for strategic bets. Document why you overrode data when you do.

6. Tools (Solo/Small Team)

Next Steps

Report to user: "Analysis complete. Key finding: [one line]. Recommendation: [action]"

Suggested next steps (user decides):

• Product issue found → "Run product-eval"
• Acquisition issue → "Run growth-track"