Deep Analysis Report

Original Question

Why are users not using Feature X after launch?

Detailed Research Questions

  1. How does Feature X usage vary across different user segments (Power User vs others) and plan types (Enterprise, Pro, etc.), and which combinations show the lowest adoption rates?

  2. Is there a correlation between onboarding completion status and Feature X usage scores, suggesting that incomplete onboarding may be hindering feature discovery or understanding?

  3. How does Feature X usage differ between users on the Old UI versus New UI, and could interface design be impacting feature accessibility or visibility?

  4. What is the relationship between days_since_launch and feature_x_usage_score - are users who joined closer to launch using the feature more or less than those who joined later?

  5. What constitutes the feature_x_usage_score scale, and what percentage of users fall into low usage categories (e.g., scores below 3 or 5) that would indicate non-adoption versus light usage?

Analysis & Insights

Feature X Usage Analysis: Understanding Low Adoption After Launch

Executive Summary

Our analysis investigated why users are not adopting Feature X after its launch. The data reveals a concerning pattern: 68.9% of users have low usage (below score 3) and 50% have zero usage, indicating significant adoption challenges that require immediate attention.

Analysis Approach

We conducted a comprehensive multi-dimensional analysis examining user segmentation, onboarding effectiveness, UI design impact, temporal patterns, and overall usage distribution. The analysis processed 1,000 user records across 7 key variables, using statistical correlation analysis, comparative testing, and distribution analysis to identify root causes.

Key Findings

1. Critical User Segments with Poor Adoption

Problem Identified: Specific user combinations show extremely low feature adoption:
- Free Plan + New Users: Average usage score of only 0.08 (165 users)
- Free Plan + Casual Users: Average usage score of 0.24 (172 users)
- Pro Plan + New Users: Average usage score of 0.38 (133 users)

Insight: Free tier users, particularly new users, are almost completely ignoring Feature X, suggesting either lack of awareness, perceived value, or accessibility barriers.

2. Onboarding Process Impact

Strong Correlation Discovered: Users who completed onboarding show significantly higher feature usage:
- Completed Onboarding: Average usage score of 2.98
- Incomplete Onboarding: Average usage score of 0.75
- Statistical Significance: Correlation of 0.329 (p < 0.001)

Insight: The onboarding process is critical for feature adoption, but many users aren't completing it or it's not effectively introducing Feature X.

3. UI Version Performance Gap

Significant Design Impact: The UI version dramatically affects feature usage:
- Old UI Users: Average usage score of 3.15 (408 users)
- New UI Users: Average usage score of 1.64 (592 users)
- Statistical Significance: Highly significant difference (p < 0.001)

Critical Finding: The new UI design is actually hindering feature adoption compared to the old interface, suggesting usability or discoverability issues.

4. Launch Timing Effects

Declining Adoption Over Time: Analysis reveals a negative correlation (-0.228) between days since launch and feature usage, indicating that:
- Early adopters used the feature more
- Later users are increasingly less likely to adopt it
- The launch momentum has not been sustained

5. Overall Usage Distribution Crisis

Adoption Reality Check:
- 56.9% of users score below 1 (minimal usage)
- 68.9% of users score below 3 (low usage threshold)
- 80.7% of users score below 5 (light usage threshold)
- 50% of users have zero usage

Root Cause Analysis

The convergence of evidence points to four primary factors:

  1. Onboarding Failure: Incomplete or ineffective onboarding is the strongest predictor of non-adoption
  2. UI Regression: The new UI has made Feature X less discoverable or usable than the previous version
  3. Segment-Specific Barriers: Free tier and new users face particular challenges in feature adoption
  4. Declining Launch Momentum: Post-launch engagement strategies have been insufficient

Recommendations

Immediate Actions (0-30 days)

  1. UI Emergency Review: Conduct usability testing comparing old vs. new UI for Feature X accessibility
  2. Onboarding Audit: Review and redesign the onboarding flow to better highlight Feature X
  3. Free Tier Strategy: Develop targeted campaigns for Free Plan users, especially new users

Medium-term Initiatives (1-3 months)

  1. Segmented Re-engagement: Launch targeted campaigns for the 569 users with minimal usage
  2. UI Optimization: Implement design changes to improve Feature X discoverability in the new interface
  3. Onboarding Completion Drive: Implement strategies to increase onboarding completion rates

Long-term Strategy (3+ months)

  1. Feature Value Communication: Develop clearer value propositions for different user segments
  2. Continuous Monitoring: Establish ongoing metrics tracking for feature adoption by segment
  3. User Feedback Integration: Implement systematic feedback collection to understand adoption barriers

Success Metrics to Track

  • Increase onboarding completion rate from current levels
  • Reduce zero-usage rate from 50% to below 25%
  • Improve Free-New User adoption from 0.08 to above 1.0
  • Achieve UI parity between old and new interface usage rates

The data clearly shows that Feature X's low adoption is not due to a single factor but rather a combination of onboarding, UI design, and user segment-specific challenges that require coordinated intervention across multiple product areas.

Generated Code

View Generated Code (Click to expand)
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots
import statsmodels.api as sm
from scipy import stats

# Initialize list to store all Plotly figures
plotly_figs = []

# ===== DATA PREPROCESSING =====
print("=== DATA PREPROCESSING ===")

# Create a copy of the original DataFrame
cleaned_df = df.copy()

# Remove the unnamed index column as it's redundant
if 'Unnamed: 0' in cleaned_df.columns:
    cleaned_df = cleaned_df.drop('Unnamed: 0', axis=1)

# Separate column types for targeted missing value handling
numeric_cols = cleaned_df.select_dtypes(include=['int64', 'float64']).columns
categorical_cols = cleaned_df.select_dtypes(include=['object']).columns
boolean_cols = cleaned_df.select_dtypes(include=['bool']).columns

# Handle missing values for numeric columns (use median)
for col in numeric_cols:
    if cleaned_df[col].isnull().any():
        cleaned_df[col] = cleaned_df[col].fillna(cleaned_df[col].median())

# Handle missing values for categorical columns (use mode or 'Unknown')
for col in categorical_cols:
    if cleaned_df[col].isnull().any():
        mode_value = cleaned_df[col].mode()
        fill_value = mode_value[0] if not mode_value.empty else 'Unknown'
        cleaned_df[col] = cleaned_df[col].fillna(fill_value)

# Handle missing values for boolean columns (use mode or False as default)
for col in boolean_cols:
    if cleaned_df[col].isnull().any():
        mode_value = cleaned_df[col].mode()
        fill_value = mode_value[0] if not mode_value.empty else False
        cleaned_df[col] = cleaned_df[col].fillna(fill_value)

# Ensure proper data types
categorical_columns_to_convert = ['plan', 'segment', 'ui_version']
for col in categorical_columns_to_convert:
    if col in cleaned_df.columns:
        cleaned_df[col] = cleaned_df[col].astype('category')

# Ensure numeric columns are properly typed
if 'days_since_launch' in cleaned_df.columns:
    cleaned_df['days_since_launch'] = cleaned_df['days_since_launch'].astype('int64')

if 'feature_x_usage_score' in cleaned_df.columns:
    cleaned_df['feature_x_usage_score'] = cleaned_df['feature_x_usage_score'].astype('float64')

# Ensure boolean column is properly typed
if 'onboarding_completed' in cleaned_df.columns:
    cleaned_df['onboarding_completed'] = cleaned_df['onboarding_completed'].astype('bool')

print(f"Cleaned dataset shape: {cleaned_df.shape}")
print(f"Missing values per column:\n{cleaned_df.isnull().sum()}")
print(f"Data types:\n{cleaned_df.dtypes}")

# ===== STATISTICAL ANALYSIS =====
print("\n=== STATISTICAL ANALYSIS ===")

# 1. Segmentation Statistics - Feature usage by plan and segment combinations
def analyze_segmentation(df):
    segmentation_stats = {}
    
    # Group by plan and segment combinations
    segment_groups = df.groupby(['plan', 'segment'])['feature_x_usage_score']
    
    # Calculate statistics for each combination
    combinations = []
    for (plan, segment), group in segment_groups:
        stats_dict = {
            'plan': plan,
            'segment': segment,
            'count': len(group),
            'mean': group.mean(),
            'median': group.median(),
            'std': group.std(),
            'min': group.min(),
            'max': group.max()
        }
        combinations.append(stats_dict)
    
    # Convert to DataFrame for easier analysis
    combo_df = pd.DataFrame(combinations)
    
    # Identify lowest adoption rates
    lowest_adoption = combo_df.nsmallest(3, 'mean')[['plan', 'segment', 'mean', 'count']]
    
    segmentation_stats['combination_statistics'] = combinations
    segmentation_stats['lowest_adoption_combinations'] = lowest_adoption.to_dict('records')
    segmentation_stats['overall_plan_stats'] = df.groupby('plan')['feature_x_usage_score'].agg(['count', 'mean', 'median', 'std']).to_dict()
    segmentation_stats['overall_segment_stats'] = df.groupby('segment')['feature_x_usage_score'].agg(['count', 'mean', 'median', 'std']).to_dict()
    
    return segmentation_stats

# 2. Correlation Analysis
def analyze_correlations(df):
    correlation_results = {}
    
    # Convert boolean to numeric for correlation
    df_corr = df.copy()
    df_corr['onboarding_completed_numeric'] = df_corr['onboarding_completed'].astype(int)
    
    # Correlation between onboarding completion and feature usage
    onboarding_corr = df_corr['onboarding_completed_numeric'].corr(df_corr['feature_x_usage_score'])
    
    # Correlation between days since launch and feature usage
    days_usage_corr = df_corr['days_since_launch'].corr(df_corr['feature_x_usage_score'])
    
    # Statistical significance tests
    onboarding_stat, onboarding_p = stats.pearsonr(df_corr['onboarding_completed_numeric'], df_corr['feature_x_usage_score'])
    days_stat, days_p = stats.pearsonr(df_corr['days_since_launch'], df_corr['feature_x_usage_score'])
    
    # Group comparison for onboarding
    completed_usage = df[df['onboarding_completed'] == True]['feature_x_usage_score']
    not_completed_usage = df[df['onboarding_completed'] == False]['feature_x_usage_score']
    
    # T-test for onboarding groups
    if len(not_completed_usage) > 0:
        onboarding_ttest_stat, onboarding_ttest_p = stats.ttest_ind(completed_usage, not_completed_usage)
    else:
        onboarding_ttest_stat, onboarding_ttest_p = None, None
    
    correlation_results['onboarding_correlation'] = {
        'correlation_coefficient': onboarding_corr,
        'p_value': onboarding_p,
        'completed_mean': completed_usage.mean(),
        'completed_std': completed_usage.std(),
        'not_completed_mean': not_completed_usage.mean() if len(not_completed_usage) > 0 else None,
        'not_completed_std': not_completed_usage.std() if len(not_completed_usage) > 0 else None,
        'ttest_statistic': onboarding_ttest_stat,
        'ttest_p_value': onboarding_ttest_p
    }
    
    correlation_results['days_launch_correlation'] = {
        'correlation_coefficient': days_usage_corr,
        'p_value': days_p
    }
    
    return correlation_results

# 3. UI Comparison Statistics
def analyze_ui_comparison(df):
    ui_comparison_stats = {}
    
    # Separate groups
    old_ui_usage = df[df['ui_version'] == 'Old UI']['feature_x_usage_score']
    new_ui_usage = df[df['ui_version'] == 'New UI']['feature_x_usage_score']
    
    # Descriptive statistics
    ui_comparison_stats['old_ui_stats'] = {
        'count': len(old_ui_usage),
        'mean': old_ui_usage.mean(),
        'median': old_ui_usage.median(),
        'std': old_ui_usage.std(),
        'min': old_ui_usage.min(),
        'max': old_ui_usage.max()
    }
    
    ui_comparison_stats['new_ui_stats'] = {
        'count': len(new_ui_usage),
        'mean': new_ui_usage.mean(),
        'median': new_ui_usage.median(),
        'std': new_ui_usage.std(),
        'min': new_ui_usage.min(),
        'max': new_ui_usage.max()
    }
    
    # Statistical tests
    # T-test (assuming normal distribution)
    ttest_stat, ttest_p = stats.ttest_ind(old_ui_usage, new_ui_usage)
    
    # Mann-Whitney U test (non-parametric alternative)
    mannwhitney_stat, mannwhitney_p = stats.mannwhitneyu(old_ui_usage, new_ui_usage, alternative='two-sided')
    
    ui_comparison_stats['statistical_tests'] = {
        'ttest_statistic': ttest_stat,
        'ttest_p_value': ttest_p,
        'mannwhitney_statistic': mannwhitney_stat,
        'mannwhitney_p_value': mannwhitney_p
    }
    
    return ui_comparison_stats

# 4. Usage Distribution Analysis
def analyze_usage_distribution(df):
    usage_distribution = {}
    
    usage_scores = df['feature_x_usage_score']
    
    # Basic distribution statistics
    usage_distribution['basic_stats'] = {
        'count': len(usage_scores),
        'mean': usage_scores.mean(),
        'median': usage_scores.median(),
        'std': usage_scores.std(),
        'min': usage_scores.min(),
        'max': usage_scores.max(),
        'skewness': stats.skew(usage_scores),
        'kurtosis': stats.kurtosis(usage_scores)
    }
    
    # Percentiles
    percentiles = [10, 25, 50, 75, 90, 95, 99]
    usage_distribution['percentiles'] = {f'{p}th_percentile': np.percentile(usage_scores, p) for p in percentiles}
    
    # Low usage categories
    total_users = len(usage_scores)
    
    usage_distribution['low_usage_categories'] = {
        'below_1': {
            'count': (usage_scores < 1).sum(),
            'percentage': (usage_scores < 1).mean() * 100
        },
        'below_3': {
            'count': (usage_scores < 3).sum(),
            'percentage': (usage_scores < 3).mean() * 100
        },
        'below_5': {
            'count': (usage_scores < 5).sum(),
            'percentage': (usage_scores < 5).mean() * 100
        },
        'zero_usage': {
            'count': (usage_scores == 0).sum(),
            'percentage': (usage_scores == 0).mean() * 100
        }
    }
    
    # Usage score scale analysis
    usage_distribution['scale_analysis'] = {
        'unique_values': sorted(usage_scores.unique()),
        'value_counts': usage_scores.value_counts().sort_index().to_dict()
    }
    
    return usage_distribution

# Execute all analyses
segmentation_stats = analyze_segmentation(cleaned_df)
correlation_results = analyze_correlations(cleaned_df)
ui_comparison_stats = analyze_ui_comparison(cleaned_df)
usage_distribution = analyze_usage_distribution(cleaned_df)

# ===== COMPREHENSIVE VISUALIZATIONS =====
print("\n=== CREATING VISUALIZATIONS ===")

# Performance optimization - sample if dataset is too large
viz_df = cleaned_df.copy()
if len(viz_df) > 50000:
    viz_df = viz_df.sample(5000, random_state=42)

# 1. Heatmap: Feature usage by plan-segment combinations
print("Creating Plan-Segment Heatmap...")
plan_segment_pivot = viz_df.groupby(['plan', 'segment'])['feature_x_usage_score'].mean().unstack(fill_value=0)

fig1 = go.Figure(data=go.Heatmap(
    z=plan_segment_pivot.values,
    x=plan_segment_pivot.columns,
    y=plan_segment_pivot.index,
    colorscale='RdYlBu_r',
    text=np.round(plan_segment_pivot.values, 2),
    texttemplate="%{text}",
    textfont={"size": 12},
    colorbar=dict(title="Average Feature X Usage Score")
))

fig1.update_layout(
    title="Feature X Usage by Plan Type and User Segment
Darker red indicates lower adoption rates", xaxis_title="User Segment", yaxis_title="Plan Type", height=400, font=dict(size=12) ) # Add annotations for lowest adoption areas min_usage = plan_segment_pivot.min().min() min_locations = np.where(plan_segment_pivot.values == min_usage) for i, j in zip(min_locations[0], min_locations[1]): fig1.add_annotation( x=j, y=i, text=" Lowest", showarrow=True, arrowhead=2, arrowcolor="red", font=dict(color="red", size=10) ) plotly_figs.append(fig1) fig1.show() # 2. Scatter plot: Onboarding completion vs Feature usage print("Creating Onboarding Correlation Plot...") onboarding_numeric = viz_df['onboarding_completed'].astype(int) fig2 = px.scatter( viz_df, x=onboarding_numeric, y='feature_x_usage_score', color='onboarding_completed', title="Feature X Usage vs Onboarding Completion Status", labels={ 'x': 'Onboarding Completed (0=No, 1=Yes)', 'feature_x_usage_score': 'Feature X Usage Score' }, opacity=0.6 ) # Add trendline z = np.polyfit(onboarding_numeric, viz_df['feature_x_usage_score'], 1) p = np.poly1d(z) fig2.add_traces(go.Scatter( x=[0, 1], y=p([0, 1]), mode='lines', name='Trendline', line=dict(color='red', width=2, dash='dash') )) # Add correlation annotation correlation_coeff = correlation_results['onboarding_correlation']['correlation_coefficient'] fig2.add_annotation( x=0.5, y=viz_df['feature_x_usage_score'].max() * 0.9, text=f"Correlation: {correlation_coeff:.3f}", showarrow=False, bgcolor="white", bordercolor="black", borderwidth=1 ) fig2.update_layout(height=400) plotly_figs.append(fig2) fig2.show() # 3. Box plots: UI version comparison print("Creating UI Version Comparison...") fig3 = px.box( viz_df, x='ui_version', y='feature_x_usage_score', color='ui_version', title="Feature X Usage Distribution by UI Version", labels={'feature_x_usage_score': 'Feature X Usage Score'} ) # Add statistical significance annotation old_ui_scores = viz_df[viz_df['ui_version'] == 'Old UI']['feature_x_usage_score'] new_ui_scores = viz_df[viz_df['ui_version'] == 'New UI']['feature_x_usage_score'] t_stat, p_value = stats.ttest_ind(old_ui_scores, new_ui_scores) fig3.add_annotation( x=0.5, y=viz_df['feature_x_usage_score'].max() * 0.95, text=f"T-test p-value: {p_value:.4f}
{'Significant' if p_value < 0.05 else 'Not significant'} difference", showarrow=False, bgcolor="lightyellow", bordercolor="orange", borderwidth=1, xref="paper" ) fig3.update_layout(height=400) plotly_figs.append(fig3) fig3.show() # 4. Scatter plot: Days since launch vs Feature usage print("Creating Launch Timing Analysis...") fig4 = px.scatter( viz_df, x='days_since_launch', y='feature_x_usage_score', title="Feature X Usage vs Days Since Launch", labels={ 'days_since_launch': 'Days Since Launch', 'feature_x_usage_score': 'Feature X Usage Score' }, opacity=0.6, trendline="ols" ) # Add correlation annotation launch_correlation = correlation_results['days_launch_correlation']['correlation_coefficient'] fig4.add_annotation( x=viz_df['days_since_launch'].max() * 0.8, y=viz_df['feature_x_usage_score'].max() * 0.9, text=f"Correlation: {launch_correlation:.3f}", showarrow=False, bgcolor="white", bordercolor="black", borderwidth=1 ) fig4.update_layout(height=400) plotly_figs.append(fig4) fig4.show() # 5. Usage distribution histogram and cumulative distribution print("Creating Usage Distribution Analysis...") fig5 = make_subplots( rows=2, cols=1, subplot_titles=('Usage Score Distribution', 'Cumulative Distribution'), vertical_spacing=0.12 ) # Histogram fig5.add_trace( go.Histogram( x=viz_df['feature_x_usage_score'], nbinsx=20, name='Usage Distribution', opacity=0.7 ), row=1, col=1 ) # Add threshold lines for low usage (scores below 3 and 5) low_usage_3 = usage_distribution['low_usage_categories']['below_3']['percentage'] low_usage_5 = usage_distribution['low_usage_categories']['below_5']['percentage'] fig5.add_vline(x=3, line_dash="dash", line_color="red", row=1, col=1) fig5.add_vline(x=5, line_dash="dash", line_color="orange", row=1, col=1) # Cumulative distribution sorted_scores = np.sort(viz_df['feature_x_usage_score']) cumulative_pct = np.arange(1, len(sorted_scores) + 1) / len(sorted_scores) * 100 fig5.add_trace( go.Scatter( x=sorted_scores, y=cumulative_pct, mode='lines', name='Cumulative %', line=dict(color='blue', width=2) ), row=2, col=1 ) fig5.add_vline(x=3, line_dash="dash", line_color="red", row=2, col=1) fig5.add_vline(x=5, line_dash="dash", line_color="orange", row=2, col=1) # Add annotations for low usage percentages fig5.add_annotation( x=3, y=50, text=f"<3: {low_usage_3:.1f}%", showarrow=True, arrowhead=2, bgcolor="red", font=dict(color="white"), row=1, col=1 ) fig5.add_annotation( x=5, y=70, text=f"<5: {low_usage_5:.1f}%", showarrow=True, arrowhead=2, bgcolor="orange", font=dict(color="white"), row=1, col=1 ) fig5.update_layout( title="Feature X Usage Score Distribution Analysis
Red line: Score < 3 (Non-adoption), Orange line: Score < 5 (Light usage)", height=600, showlegend=False ) fig5.update_xaxes(title_text="Feature X Usage Score", row=2, col=1) fig5.update_yaxes(title_text="Count", row=1, col=1) fig5.update_yaxes(title_text="Cumulative Percentage", row=2, col=1) plotly_figs.append(fig5) fig5.show() # ===== COMPREHENSIVE RESULTS SUMMARY ===== print("\n" + "="*60) print("FEATURE X USAGE ANALYSIS - COMPREHENSIVE RESULTS") print("="*60) print("\n1. SEGMENTATION ANALYSIS:") print("Lowest Adoption Rate Combinations:") for combo in segmentation_stats['lowest_adoption_combinations']: print(f" {combo['plan']} - {combo['segment']}: Mean Usage = {combo['mean']:.2f} (n={combo['count']})") print(f"\n2. CORRELATION ANALYSIS:") print(f"Onboarding Completion vs Feature Usage:") print(f" Correlation: {correlation_results['onboarding_correlation']['correlation_coefficient']:.3f}") print(f" P-value: {correlation_results['onboarding_correlation']['p_value']:.3f}") print(f" Completed Mean: {correlation_results['onboarding_correlation']['completed_mean']:.2f}") if correlation_results['onboarding_correlation']['not_completed_mean'] is not None: print(f" Not Completed Mean: {correlation_results['onboarding_correlation']['not_completed_mean']:.2f}") print(f"\nDays Since Launch vs Feature Usage:") print(f" Correlation: {correlation_results['days_launch_correlation']['correlation_coefficient']:.3f}") print(f" P-value: {correlation_results['days_launch_correlation']['p_value']:.3f}") print(f"\n3. UI VERSION COMPARISON:") print(f" Old UI: Mean = {ui_comparison_stats['old_ui_stats']['mean']:.2f}, n = {ui_comparison_stats['old_ui_stats']['count']}") print(f" New UI: Mean = {ui_comparison_stats['new_ui_stats']['mean']:.2f}, n = {ui_comparison_stats['new_ui_stats']['count']}") print(f" T-test p-value: {ui_comparison_stats['statistical_tests']['ttest_p_value']:.3f}") print(f" Mann-Whitney U p-value: {ui_comparison_stats['statistical_tests']['mannwhitney_p_value']:.3f}") print(f"\n4. USAGE DISTRIBUTION:") print(f" Overall Mean: {usage_distribution['basic_stats']['mean']:.2f}") print(f" Median: {usage_distribution['basic_stats']['median']:.2f}") print(f" Standard Deviation: {usage_distribution['basic_stats']['std']:.2f}") print(f"Low Usage Categories:") for category, stats in usage_distribution['low_usage_categories'].items(): print(f" {category.replace('_', ' ').title()}: {stats['count']} users ({stats['percentage']:.1f}%)") print(f"\n5. KEY INSIGHTS:") lowest_combo = segmentation_stats['lowest_adoption_combinations'][0] print(f" Lowest adoption: {lowest_combo['plan']} - {lowest_combo['segment']} users ({lowest_combo['mean']:.2f} avg score)") print(f" Onboarding impact: {'Strong' if abs(correlation_results['onboarding_correlation']['correlation_coefficient']) > 0.3 else 'Moderate' if abs(correlation_results['onboarding_correlation']['correlation_coefficient']) > 0.1 else 'Weak'} correlation ({correlation_results['onboarding_correlation']['correlation_coefficient']:.3f})") print(f" UI version impact: {'Significant' if ui_comparison_stats['statistical_tests']['ttest_p_value'] < 0.05 else 'Not significant'} difference (p={ui_comparison_stats['statistical_tests']['ttest_p_value']:.4f})") print(f" Launch timing effect: {'Positive' if correlation_results['days_launch_correlation']['correlation_coefficient'] > 0 else 'Negative'} correlation ({correlation_results['days_launch_correlation']['correlation_coefficient']:.3f})") print(f" Non-adoption rate (score <3): {usage_distribution['low_usage_categories']['below_3']['percentage']:.1f}%") print(f" Light usage rate (score <5): {usage_distribution['low_usage_categories']['below_5']['percentage']:.1f}%") print(f"\nTotal Plotly figures created: {len(plotly_figs)}") print("Analysis complete!")

Conclusion

Feature X is experiencing a critical adoption failure driven by three interconnected product issues that require immediate intervention.

Root Cause: Triple Product Failure

The low adoption isn't due to lack of user interest but rather systematic product failures:

  • Onboarding Crisis: Users who complete onboarding use Feature X 4x more, but too many users aren't completing or benefiting from the onboarding process
  • UI Regression: The new UI has made Feature X significantly less discoverable/usable than the old version (usage dropped from 3.15 to 1.64)
  • Segment Abandonment: Free tier users, especially new ones, have been effectively abandoned with near-zero adoption rates

Key Takeaways

  • 50% zero usage rate indicates a fundamental product experience problem, not a feature problem
  • The new UI launch has actively harmed Feature X adoption - this is a regression, not progress
  • Onboarding completion is the strongest predictor of feature success, making it the highest-leverage intervention point
  • Free Plan users represent a massive untapped opportunity (337 users with minimal engagement)

Recommended Next Steps

  • Emergency UI Audit (Week 1): Conduct immediate usability testing to identify what changed between old and new UI that's blocking Feature X access
  • Onboarding Redesign (Weeks 2-4): Rebuild the onboarding flow with Feature X as a core highlight, not an afterthought
  • Free Tier Rescue Campaign (Month 2): Launch targeted re-engagement specifically for the 337 Free Plan users who are currently ignored
  • Success Metrics: Target reducing zero-usage from 50% to under 25% within 90 days through these coordinated interventions

This is a solvable problem - the data shows clear paths to improvement through onboarding, UI fixes, and targeted user segment strategies.