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

Concepts··FinBrain Team

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

alternative-dataapp-ratingsmobileconsumer

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("UBER", as_dataframe=True)


print(df.tail())
#             ios_score  ios_ratingsCount  android_score  android_ratingsCount  android_installCount
# date
# 2024-01-15       4.7            1250000            4.3              32500000             500000000
# 2024-01-14       4.7            1249000            4.3              32480000             500000000


# Calculate rating changes
df["play_change"] = df["android_score"].diff()
df["app_change"] = df["ios_score"].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["android_score"].diff(periods=window)
    df["app_delta"] = df["ios_score"].diff(periods=window)


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


    return declines[["android_score", "ios_score", "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["android_score"],
                "app_score": latest["ios_score"],
                "play_ratings": latest["android_ratingsCount"],
                "avg_score": (latest["android_score"] + latest["ios_score"]) / 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']}")

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["android_ratingsCount"].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["android_score"] >= 4.0
            app_ok = latest["ios_score"] >= 4.0
            scale_ok = latest["android_ratingsCount"] >= 1000000


            # Rating trend (not declining)
            if len(df) >= 30:
                month_ago = df.iloc[-30]["android_score"]
                trend_ok = latest["android_score"] >= 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["android_score"],
                    "app_score": latest["ios_score"],
                    "ratings_millions": latest["android_ratingsCount"] / 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

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

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