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Customer Growth Analysis Plan

Based on the stakeholder questions in your attached files, here’s a proposed six-week analytics roadmap to evaluate customer acquisition trends and optimize marketing investments across regions. The plan aligns with JAF Analytics’ Q3 growth priorities and integrates both marketing and product data sources for full-funnel visibility.

Define Success Metric

• Primary metric: New paid users per region

• Supporting metrics: conversion rate from trial to paid, CAC by channel, regional activation rate

• Goal: Increase paid conversion by 12% in high-growth markets (EMEA, LATAM)

Data Sources

• CRM: SaleslyPro — lead and deal tracking

• Product telemetry: AppEvent Stream (D-Data export) — activation, retention, and engagement data

• Ad performance: AdScope360 — spend and ROI by campaign and region

• Finance system: LedgerIQ — revenue recognition for attribution validation

Feature Pipeline Generator

Using the provided schema and feature definitions, I generated SQL transformation logic and validation rules to support the rollout of SteamForge AI’s real-time personalization pipeline. The goal is to produce consistent, monitored feature tables for downstream model training and inference.

Transformation SQL

• Created user_activity_features table joining events from user_events and session_metadata

• Applied normalization for activity frequency and duration metrics

• Implemented window functions to calculate rolling 7-day engagement averages

• Stored transformations as versioned .sql scripts under /pipelines/features_v2/

Validation Tests

• Added null checks for critical columns (user_id, timestamp, event_type)

• Configured range tests for numerical fields such as session_length and page_depth

• Defined join integrity tests to ensure all user_ids map correctly to the master profile table

• Integrated automated assertions via DataGuard CI

Model Drift Report – Conversion Prediction v3

Summary

An automated drift analysis detected a significant input distribution shift in the session_duration feature across recent mobile sessions. This report compares post-September event data with the baseline training window used for Nsyte Systems’ Conversion Prediction v3 model.

Drift Findings

• Input drift magnitude: 0.42 (moderate)

• Feature impacted: session_duration — mean increased from 4.2m to 6.7m

• Affected cohort: mobile users (both iOS and Android)

• Impact on model accuracy: –3.2% overall predictive score drop

Root Cause Indicators

• Product update in early September changed in-app timer behavior

• Increased presence of background sessions skewing duration distributions

• Minor logging latency introduced by TelemetrySync v2.4 rollout

Executive KPI Dashboard Draft

Based on your uploaded analyses, I’ve created a unified dashboard storyboard summarizing key revenue and retention metrics for Aurora Insights Group. The storyboard is designed to highlight regional growth opportunities, retention health, and emerging churn risks to inform next-quarter planning discussions.

Consolidated Metrics

• Revenue by region: Aggregated from the Revenue_By_Region.ipynb output, normalized by currency and fiscal week

• Customer retention: Derived from Customer_Retention_Analysis.csv, segmented by acquisition cohort and plan tier

• Churn trends: Computed rolling 3-month churn rate and overlaid with campaign spend data for context

• Added calculated KPIs for Lifetime Value (LTV) and Net Retention Rate (NRR)

Storyboard Outputs

• Auto-generated slides grouped by theme: Growth by Region, Retention Levers, Revenue Stability

• Each section includes summary charts, trend annotations, and talking points for executive review

• Included dynamic callouts for outlier regions and notable retention swings

• Draft exported as a .pptx deck for preview and as JSON schema for dashboard ingestion