Reporting Deliverables & Analytical Artifacts

When your organization commissions an advisory engagement with Monitor PulsePoint, your engineering and product teams receive permanent, version-controlled artifacts rather than disposable presentation decks.

Every deliverable is crafted to be immediately actionable for product managers, reproducible for data engineers, and defensible before board directors.

1. The Multi-Tier Cohort Retention Matrix

The foundational artifact of our retention audits is a comprehensive cohort survival matrix. Unlike default analytics tools that enforce fixed calendar weeks, our matrices calculate rolling 24-hour retention intervals anchored to the exact moment of initial app launch.

What This Matrix Reveals in Practice

In the representative diagnostic sample above, comparing Paid Network A against Organic Search highlights an acute cliff between Day 1 and Day 7. While Day-1 retention on Network A is superficially acceptable (39.1%), the cohort experiences a catastrophic 58% drop by Day 7 (to 16.3%), signaling low-intent ad creative misaligned with initial onboarding expectations.

2. Reproducible SQL Transformation Repository

We provide production-ready SQL scripts formatted for your specific data warehouse stack (Snowflake, BigQuery, ClickHouse, Amazon Redshift, or standard PostgreSQL).

-- Sample Cohort Retention SQL Extract Template (PostgreSQL / Redshift)
WITH user_first_touch AS (
    SELECT
        user_id,
        DATE_TRUNC('week', MIN(installed_at)) AS cohort_week,
        acquisition_channel,
        MIN(installed_at) AS first_event_time
    FROM raw_telemetry.app_install_events
    WHERE installed_at >= '2026-01-01'
    GROUP BY user_id, acquisition_channel
),
user_activity AS (
    SELECT
        e.user_id,
        f.cohort_week,
        f.acquisition_channel,
        FLOOR(EXTRACT(EPOCH FROM (e.event_timestamp - f.first_event_time)) / 86400) AS day_offset
    FROM raw_telemetry.app_session_events e
    INNER JOIN user_first_touch f ON e.user_id = f.user_id
    WHERE e.event_timestamp >= f.first_event_time
)
SELECT
    cohort_week,
    acquisition_channel,
    COUNT(DISTINCT user_id) AS cohort_size,
    COUNT(DISTINCT CASE WHEN day_offset = 1 THEN user_id END) AS retained_day_1,
    COUNT(DISTINCT CASE WHEN day_offset = 7 THEN user_id END) AS retained_day_7,
    COUNT(DISTINCT CASE WHEN day_offset = 30 THEN user_id END) AS retained_day_30
FROM user_activity
GROUP BY cohort_week, acquisition_channel
ORDER BY cohort_week DESC, acquisition_channel;

3. Strict Event Telemetry Schema Dictionary

To prevent future schema corruption, we formulate an authoritative data contract specifying each tracked event, required parameters, and validation constraints.

4. Executive Decision Memorandum

The culmination of the audit is an executive briefing dossier that translates mathematical cohort findings into concrete prioritization guidelines for your product roadmap.

Key components of the dossier include:

1. Executive Summary of Cohort Health: High-level diagnosis of baseline stability.
2. The Five Primary Drop-off Inflections: Prioritized list of friction points ranked by user volume and revenue exposure.
3. Channel Quality Rating: Objective assessment of paid acquisition sources based on Day-30 retained value rather than Day-0 install volume.
4. Actionable Roadmap Recommendations: Concrete interventions for design and engineering sprints over the upcoming 60 to 90 days.