In the mobile-first economy, app store metrics have become a leading indicator of company performance. Before a company reports declining engagement, it often shows up in their app ratings. Here’s how to use this data for investment analysis.

Why App Store Data Matters

Mobile apps are the primary interface between many companies and their customers. App store data reveals:

User satisfaction – Star ratings and reviews
Growth trends – Download counts and install velocity
Competitive positioning – Ratings vs competitors
Product issues – Sudden rating drops often signal problems

Leading Indicator Advantage

App store metrics often move before financial results:

Metric Change	Potential Implication
Rising ratings	Improving user experience → retention → revenue
Falling ratings	Product issues → churn → revenue decline
Download surge	User acquisition success → growth
Download decline	Market saturation or competition

Key App Store Metrics

1. Star Ratings

Both Apple App Store and Google Play Store use 5-star rating systems:

Rating	Interpretation
4.5+	Excellent – users love the product
4.0 - 4.5	Good – solid user experience
3.5 - 4.0	Average – room for improvement
Below 3.5	Concerning – potential product issues

2. Ratings Count

The total number of ratings indicates:

App popularity and scale
User engagement (people who bother to rate)
Statistical significance of the score

3. Download/Install Count

Google Play shows install ranges (e.g., “500M+ downloads”). This indicates:

Market penetration
User acquisition success
Total addressable market capture

Accessing App Store Data

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

# Get app ratings for Uber
df = fb.app_ratings.ticker("S&P 500", "UBER", as_dataframe=True)

print(df.tail())
#             playStoreScore  playStoreRatingsCount  appStoreScore  playStoreInstallCount
# date
# 2024-01-15            4.3               32500000            4.7             500000000
# 2024-01-14            4.3               32480000            4.7             500000000

# Calculate rating changes
df["play_change"] = df["playStoreScore"].diff()
df["app_change"] = df["appStoreScore"].diff()

# Detect significant drops
significant_drops = df[(df["play_change"] < -0.1) | (df["app_change"] < -0.1)]
print(f"\nSignificant rating drops: {len(significant_drops)}")

Investment Use Cases

1. Detecting Product Issues Early

A sudden rating drop often precedes negative earnings surprises:

def detect_rating_decline(df, threshold=-0.1, window=7):
    """Detect significant rating declines"""
    df = df.copy()

    # Calculate rolling change
    df["play_delta"] = df["playStoreScore"].diff(periods=window)
    df["app_delta"] = df["appStoreScore"].diff(periods=window)

    # Find declines
    declines = df[
        (df["play_delta"] < threshold) |
        (df["app_delta"] < threshold)
    ]

    return declines[["playStoreScore", "appStoreScore", "play_delta", "app_delta"]]

declines = detect_rating_decline(df)
if not declines.empty:
    print("Warning: Rating decline detected")
    print(declines)

2. Comparing Competitors

Compare ratings across competitors in the same space:

def compare_competitors(tickers, market="S&P 500"):
    """Compare app ratings across competitors"""
    fb = FinBrainClient(api_key="YOUR_API_KEY")
    results = []

    for ticker in tickers:
        try:
            df = fb.app_ratings.ticker(market, ticker, as_dataframe=True)
            if df.empty:
                continue

            latest = df.iloc[-1]
            results.append({
                "ticker": ticker,
                "play_score": latest["playStoreScore"],
                "app_score": latest["appStoreScore"],
                "play_ratings": latest["playStoreRatingsCount"],
                "avg_score": (latest["playStoreScore"] + latest["appStoreScore"]) / 2
            })
        except:
            continue

    return sorted(results, key=lambda x: x["avg_score"], reverse=True)

# Compare rideshare companies
rideshare = compare_competitors(["UBER", "LYFT"])
for r in rideshare:
    print(f"{r['ticker']}: Play {r['play_score']}, iOS {r['app_score']}")

3. Tracking Growth Trends

Monitor download growth over time:

def analyze_growth(df):
    """Analyze app growth metrics"""
    df = df.copy()

    # Ratings velocity (new ratings per day)
    df["new_ratings"] = df["playStoreRatingsCount"].diff()

    # Average new ratings
    avg_new_ratings = df["new_ratings"].mean()

    # Growth trend
    recent = df["new_ratings"].tail(7).mean()
    previous = df["new_ratings"].tail(30).head(23).mean()

    if recent > previous * 1.2:
        trend = "accelerating"
    elif recent < previous * 0.8:
        trend = "decelerating"
    else:
        trend = "stable"

    return {
        "avg_daily_ratings": avg_new_ratings,
        "recent_velocity": recent,
        "trend": trend
    }

growth = analyze_growth(df)
print(f"Growth trend: {growth['trend']}")
print(f"Average daily new ratings: {growth['avg_daily_ratings']:,.0f}")

4. Building Screening Signals

Create a simple screen based on app metrics:

def app_quality_screen(tickers, market="S&P 500"):
    """Screen stocks based on app quality metrics"""
    fb = FinBrainClient(api_key="YOUR_API_KEY")
    passed = []

    for ticker in tickers:
        try:
            df = fb.app_ratings.ticker(market, ticker, as_dataframe=True)
            if df.empty:
                continue

            latest = df.iloc[-1]

            # Quality criteria
            play_ok = latest["playStoreScore"] >= 4.0
            app_ok = latest["appStoreScore"] >= 4.0
            scale_ok = latest["playStoreRatingsCount"] >= 1000000

            # Rating trend (not declining)
            if len(df) >= 30:
                month_ago = df.iloc[-30]["playStoreScore"]
                trend_ok = latest["playStoreScore"] >= month_ago - 0.1
            else:
                trend_ok = True

            if play_ok and app_ok and scale_ok and trend_ok:
                passed.append({
                    "ticker": ticker,
                    "play_score": latest["playStoreScore"],
                    "app_score": latest["appStoreScore"],
                    "ratings_millions": latest["playStoreRatingsCount"] / 1_000_000
                })

        except:
            continue

    return passed

# Screen consumer tech stocks
consumer_tech = ["UBER", "LYFT", "ABNB", "DASH", "SPOT", "NFLX"]
quality = app_quality_screen(consumer_tech)

print("=== Stocks Passing App Quality Screen ===")
for q in quality:
    print(f"{q['ticker']}: {q['play_score']}/5 Play, {q['app_score']}/5 iOS ({q['ratings_millions']:.1f}M ratings)")

Which Companies to Analyze

App store data is most relevant for:

Sector	Examples
Consumer tech	UBER, LYFT, ABNB, DASH
Streaming	NFLX, SPOT, DIS
Social media	META, SNAP, PINS
Fintech	SQ, PYPL, AFRM
Gaming	EA, TTWO, RBLX
E-commerce	AMZN, EBAY, ETSY
Food delivery	DASH, UBER (Eats)

Less relevant for B2B companies (enterprise software, industrial, etc.) where apps aren’t the primary customer interface.

Limitations

Keep these factors in mind:

Limitation	Consideration
Rating manipulation	Some companies incentivize positive reviews
App vs company	App ratings may not reflect overall business health
Platform differences	iOS and Android users may behave differently
Category context	4.0 in gaming is different than 4.0 in banking

Key Takeaways

App ratings can be leading indicators of user satisfaction and company performance
Ratings below 4.0 warrant investigation
Sudden rating drops often precede negative news
Compare competitors within the same category
Most valuable for consumer-facing tech companies

Track app store metrics with the App Ratings Dataset and API Reference.

Using App Store Data for Investing: A Guide to Mobile Metrics