46 added, 11 removed. Audit A to A.
---
name: app-download-and-usage-data-for-consumer-companies
description: Quantitative alternative data engine for analyzing app engagement metrics
(DAU, MAU, Downloads) to generate predictive signals for consumer companies.
domain: quant-research-alt-data
subdomain: digital-footprint
tags:
- alt-data
- dau-mau
- consumer-tech
- signal-generation
- churn-prediction
brokers_frameworks:
- generic
- version: "1.1.0"
+ version: "1.2.0"
author: System
license: MIT
---
## When to Use
- Use this skill when processing mobile application alternative data (often sourced from vendors like Apptopia or SensorTower). While raw app downloads are often reported as "vanity metrics" by companies, the actual Daily Active Users (DAU) and Monthly Active Users (MAU) dictate long-term revenue viability. This engine calculates the "Stickiness Ratio" (DAU/MAU) and flags dangerous divergence between high marketing-driven downloads and failing user retention (the "Leaky Bucket" syndrome).
+ Use this skill when processing mobile application alternative data (sourced from vendors such as Sensor Tower, data.ai, Apptopia, or Similarweb) to build fundamental engagement signals for consumer-facing public companies. While raw app downloads are often reported as "vanity metrics" by companies, the actual Daily Active Users (DAU) and Monthly Active Users (MAU) dictate long-term revenue viability. This engine calculates the "Stickiness Ratio" (DAU/MAU) and flags dangerous divergence between high marketing-driven downloads and failing user retention (the "Leaky Bucket" syndrome).
+ Applicable scenarios:
+ - Forecasting revenue durability for consumer-tech, gaming, ride-share, food-delivery, and streaming issuers ahead of earnings.
+ - Constructing long/short alt-data baskets (overweight `is_world_class`, underweight `churn_risk_warning`).
+ - Validating management commentary on "record downloads" against underlying engagement.
+
+ ## When NOT to Use
+
+ Do **not** use this skill when:
+ - You have not yet performed vendor diligence and MNPI/MAR compliance review on the data source. App Annie Inc. was the subject of the SEC's first securities-fraud enforcement against an alternative data provider (Release No. 34-92975, Sept. 14, 2021) for misrepresenting how its estimates were derived and falsely claiming MNPI controls. See `insider-trading-controls-for-alternative-data-usage`.
+ - The issuer's revenue is not materially driven by app engagement (e.g., pure B2B, hardware-only, or pre-product companies with negligible MAU).
+ - You need raw point-in-time alignment. This engine consumes already-PIT-aligned `AppUsageDataPoint`s; perform the publication-lag shift upstream via `alternative-data-feature-integration` first.
+ - You need daily panel spend / transaction signals — use `credit-card-transaction-data-signal-construction` instead.
+
## Prerequisites
- - Python 3.9+
- - Time-series data containing `downloads`, `dau` (Daily Active Users), and `mau` (Monthly Active Users).
+ - Python 3.9+.
+ - A vendor feed providing per-ticker, per-date `downloads`, `dau`, and `mau` estimates.
+ - Completed vendor diligence: panel methodology documentation, data-licensing terms permitting investment use, and a documented MNPI/MAR compliance sign-off (see `alternative-data-vendor-due-diligence-checklist`).
+ - Point-in-time alignment of the feed (event date shifted by the vendor's publication lag, typically 1-7 days).
## Workflow
- 1. **Ingest Metrics**: Load the daily/monthly active user data into `AppUsageDataPoint` objects.
- 2. **Calculate Stickiness**: The engine derives the DAU/MAU ratio, measuring how habitual the app usage is.
- 3. **Analyze Divergence**: The engine compares download velocity against stickiness. If downloads are surging but the stickiness ratio is collapsing, the engine triggers a `Churn Risk Warning`.
- 4. **Signal Generation**: Output a clean `AppUsageSignal` indicating whether the consumer company's user base is growing sustainably or bleeding capital on ineffective marketing.
+ 1. **Vendor Ingestion & Diligence**: Acquire daily panel data (Downloads, DAU, MAU) per ticker. Confirm the vendor's panel composition, extrapolation model, and licensing terms permit investment use. Document the publication lag.
+ 2. **Point-In-Time Alignment**: Shift event dates forward by the vendor's publication lag via `alternative-data-feature-integration` so signals are only usable on the date the data became available (eliminates look-ahead bias).
+ 3. **Ingest Metrics**: Load PIT-aligned data into `AppUsageDataPoint` objects (frozen; `ticker`, `date`, `downloads`, `dau`, `mau`).
+ 4. **Calculate Stickiness**: `AppUsageSignalEngine.process()` derives `stickiness_ratio = DAU / MAU`. If `DAU > MAU` (impossible in a genuine panel), DAU is clamped to MAU *without mutating the input* and the event is logged as a data anomaly.
+ 5. **Analyze Divergence**: The engine compares download velocity against stickiness. If `downloads >= 10% of MAU` while `stickiness < 20%`, the engine emits `churn_risk_warning=True`.
+ 6. **Signal Generation**: Output an `AppUsageSignal` classifying the issuer's user base as world-class engagement, leaky-bucket churn risk, or average.
+ 7. **Decision Points**:
+ - Overweight equities flagged `is_world_class`.
+ - Underweight/short equities flagged `churn_risk_warning`.
+ - Hold equities with average engagement; treat as noise unless combined with other alt-data signals.
+ > Full procedure: see `references/workflows.md`.
+ > Standards reference: see `references/standards.md`.
+ > Printable pre-flight checklist: see `assets/checklist.md`.
+
## Common Pitfalls
- - **Confusing Downloads with Growth**: Equating a spike in app downloads (often driven by expensive ad campaigns) with revenue growth. If DAU/MAU is below 20%, those downloaded users are churning out immediately.
- - **Ignoring Point-in-Time (PIT)**: Ensuring that the alternative data vendor's publication lag is accounted for (see the `alternative-data-feature-integration` skill).
+ - **Confusing Downloads with Growth**: Equating a spike in app downloads (often driven by expensive ad campaigns) with revenue growth. If DAU/MAU is below 20%, newly acquired users are churning out immediately.
+ - **Ignoring Point-in-Time (PIT)**: Backtesting on the event date rather than the vendor publication date introduces look-ahead bias. Always shift by the publication lag upstream (see `alternative-data-feature-integration`).
+ - **Skipping MNPI / Vendor Diligence**: Consuming app-usage estimates without confirming the vendor's derivation methodology and MNPI controls risks repeating the App Annie (SEC 34-92975) failure mode — trading on data whose provenance and compliance posture were misrepresented.
+ - **Trusting DAU > MAU**: DAU can never exceed MAU. A panel reporting otherwise is a data-quality defect; the engine clamps and logs it, but recurring occurrences indicate a vendor panel problem requiring escalation.
+ - **Overreading Cumulative Downloads**: Cumulative downloads have weak correlation with long-term enterprise value on their own; always pair with engagement (stickiness) and acquisition cost context.
+ - **Stale Data**: App-usage panels update with a lag. Monitoring freshness (last received event date per ticker) is required; a stale feed silently degrades signal quality.
## Verification
- Run `python scripts/test_app_download_and_usage_data_for_consumer_companies.py` to confirm that the engine accurately identifies "world-class" stickiness vs "leaky bucket" churn risk.
+ - Run `python -m unittest discover -s skills/app-download-and-usage-data-for-consumer-companies/scripts` and confirm all tests pass (covers world-class stickiness, leaky-bucket churn, threshold boundaries, DAU>MAU clamping without input mutation, invalid inputs, custom config, and batch processing).
+ - Manual check: construct an `AppUsageDataPoint` with `dau=6000000, mau=10000000` (stickiness 60%) and confirm `is_world_class=True`; construct one with `downloads=200000, dau=150000, mau=1000000` (stickiness 15%, high acquisition) and confirm `churn_risk_warning=True` and `"LEAKY BUCKET"` in the summary.
+ - Confirm `process()` does **not** mutate its input when `DAU > MAU` (regression test `test_dau_exceeds_mau_anomaly`).
+ - Confirm a documented MNPI/vendor-diligence sign-off exists before promoting any signal to live trading.
## Related Skills
- `alternative-data-feature-integration`
+ - `insider-trading-controls-for-alternative-data-usage`
+ - `alternative-data-vendor-due-diligence-checklist`
+ - `backtesting-alt-data-strategies-with-realistic-availability-lag`
- `web-scraped-sentiment-data-pipeline`