meta-cohort-analysis

name

meta-cohort-analysis

description

Builds time-based and behaviour-based user cohorts in GA4, tracks retention and lifetime value by cohort, and translates cohort data into client-language statements that demonstrate campaign durability — not just reach. Invoke when a client asks which campaigns produce repeat customers, when aggregate metrics are masking poor retention, or when preparing a report that must show long-term campaign value.

Cohort Analysis for Client Reporting

Source: Raaz (c.2023) Web Analytics Blueprint

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Use when

• Builds time-based and behaviour-based user cohorts in GA4, tracks retention and lifetime value by cohort, and translates cohort data into client-language statements that demonstrate campaign durability — not just reach. Invoke when a client asks which campaigns produce repeat customers, when aggregate metrics are masking poor retention, or when preparing a report that must show long-term campaign value.
• Use this skill when it is the closest match to the requested deliverable or workflow.

Do not use when

• Do not use this skill for graphic design, video production, software development, or legal advice beyond the repository's stated scope.
• Do not use it when another skill in this repository is clearly more specific to the requested deliverable.

Workflow

1. Collect the required inputs or source material before drafting, unless this skill explicitly generates the intake itself.
2. Follow the section order and decision rules in this SKILL.md; do not skip mandatory steps or required fields.
3. Review the draft against the quality criteria, then deliver the final output in markdown unless the skill specifies another format.

Anti-Patterns

• Do not invent client facts, performance data, budgets, or approvals that were not provided or clearly inferred from evidence.
• Do not skip required inputs, mandatory sections, or quality checks just to make the output shorter.
• Do not drift into out-of-scope work such as code implementation, design production, or unsupported legal conclusions.

Outputs

• A structured audit, report, model, or analytical framework in markdown, with decisions and recommendations tied to evidence.

References

• Use the inline instructions in this skill now. If a references/ directory is added later, treat its files as the deeper source material and keep this SKILL.md execution-focused.

Required Inputs

Ask for the following before generating any deliverable:

1. Client business name
2. Industry (e-commerce, services, B2B SaaS, hospitality, etc.)
3. Country / city (defaults to Uganda / East Africa)
4. Primary goal (e.g. demonstrate campaign ROI, identify best acquisition channel, justify budget reallocation)
5. GA4 access level (Admin / Editor / Viewer — determines which steps are available)
6. Reporting period (weekly or monthly cohorts; 12-week or 12-month window)
7. Acquisition channels in use (organic search, paid social, direct, referral, email, WhatsApp, etc.)

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What a Cohort Is

A cohort is a group of users who share a defining characteristic within a defined time period. Common cohort definitions:

• All users whose first session occurred in a given week or month (acquisition cohort)
• All users who completed a specific action — made a purchase, downloaded a lead magnet, subscribed to an email list — in a given period (behaviour cohort)

Aggregate metrics (total sessions, total revenue) hide the difference between campaigns that bring one-time buyers and campaigns that build loyal, repeat customers. Cohort analysis reveals which acquisition channels produce high-LTV customers versus one-transaction visitors.

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Two Cohort Types

Acquisition Cohorts

Users grouped by when they first arrived (Week 1, Week 2, etc.).

Track: What percentage of Week 1 users returned in Week 2, Week 3, Week 4?

Use for: retention analysis, identifying which channels produce loyal audiences.

Behaviour Cohorts

Users grouped by an action they took (first purchase, webinar attendance, lead magnet download).

Track: What percentage converted to the next funnel stage?

Use for: funnel optimisation, identifying where drop-off occurs after a specific action.

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Building Cohorts in GA4

1. In GA4: Explore → Cohort Exploration
2. Set cohort type:
   • Acquisition date — groups by first session date
   • Event-based — groups by a named event (e.g. purchase, sign_up, generate_lead)
3. Set cohort granularity: weekly (for fast-moving campaigns) or monthly (for longer sales cycles)
4. Set metric: active users, revenue, conversions, or goal completions
5. Set time window: 12-week or 12-month
6. Apply channel filter: segment by Session default channel group to compare organic vs. paid vs. referral vs. WhatsApp cohorts

Permission note: Cohort Exploration requires at minimum Viewer access to GA4. To create custom segments by channel, Editor access is required.

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Key Insights to Extract

For each cohort analysis, extract and report the following:

Insight	How to read it
Week-4 retention rate	What percentage of Week 1 users are still active 4 weeks later? Under 10% is typical for cold traffic; above 30% indicates a loyal audience
Channel comparison	Which acquisition channel produces the highest Week-4 retention rate?
Revenue by cohort	Which cohort contributes the most total revenue over 6 months?
Decay curve shape	Slow decay = loyal audience building. Steep drop after Week 1 = one-time curiosity traffic — review content and offer alignment

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Translating Cohort Data for Clients

Do not present raw cohort tables to clients — they cannot interpret colour-coded retention grids without guidance. Translate every cohort analysis into three plain-language client statements:

Statement 1 — Retention: "Of every 100 people who found you through [channel] in [month], [X] were still engaging with your brand 4 weeks later."

Statement 2 — Channel comparison: "Your [channel A] audience retains twice as well as your [channel B] audience — meaning [channel A] produces more durable customers at the same acquisition cost."

Statement 3 — Cohort revenue: "Your [month] cohort is your most valuable — they have generated [X]% more revenue per customer than the [earlier month] cohort."

Pair each statement with a single clear chart (line chart showing retention decay by channel). See meta-dashboard-design for chart selection and mobile-first design rules.

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Cohort Reporting Output Format

Generate a cohort analysis report structured as follows:

Section 1 — Executive Summary (3 sentences) What the cohort data shows at a glance. Which channel or period is performing best. The single recommended action.

Section 2 — Acquisition Cohort Table Present a simplified cohort table (Week 0 through Week 8 maximum) with the top 3 acquisition channels compared. Highlight the Week-4 retention row.

Section 3 — Behaviour Cohort Funnel If behaviour cohort data is available: show the conversion percentage from acquisition action to next funnel stage for each cohort period.

Section 4 — Channel Comparison Summary A ranked list of channels by Week-4 retention rate. One sentence interpretation per channel.

Section 5 — Recommendations Three SMART actions derived from the cohort data. Format each as:

• Recommendation: [action]
• Rationale: [what the cohort data shows]
• Success metric: [how to measure the outcome]

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EA-Specific Considerations

• WhatsApp as an acquisition channel: GA4 does not automatically track WhatsApp referrals. Advise the client to use UTM parameters on all WhatsApp links (e.g. ?utm_source=whatsapp&utm_medium=social&utm_campaign=[name]). See meta-utm-tracking for UTM setup.
• Mobile-first data: In Uganda/EA, the majority of sessions originate from mobile devices. Cohort analysis should always segment by device type to identify whether mobile vs. desktop users retain differently.
• Short purchase cycles: For EA e-commerce and service businesses, use weekly cohorts rather than monthly — the typical EA purchase decision cycle is shorter than in Western markets, and monthly cohorts lose resolution.

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Quality Criteria

Output meets the standard for this skill if:

• Every cohort insight is translated into a plain-language client statement — no raw data tables presented without interpretation
• The report distinguishes between acquisition cohorts and behaviour cohorts and uses the correct type for the client's stated goal
• Channel comparison is included, with at least two channels compared by retention rate
• At least three SMART recommendations are derived directly from the cohort data — not generic analytics advice
• WhatsApp is addressed as an acquisition channel if the client uses it for customer acquisition
• The Week-4 retention rate is calculated and contextualised against the 10% (cold traffic) and 30% (loyal audience) benchmarks
• All monetary values use the client's local currency (UGX for Uganda; KES for Kenya) unless otherwise specified
• Language is British English throughout; imperative in all instructional sections