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Exploratory Data Analysis (EDA) in FirstHive Insights

The Exploratory Data Analysis (EDA) feature in FirstHive Insights enables you to perform comprehensive data exploration and analysis on your customer data. This powerful tool allows you to examine relationships between different customer attributes, identify patterns, and gain valuable insights from your unified customer profiles.

Key Features

Available Data Dimensions

The EDA functionality provides access to multiple customer data dimensions:

  • AGE - Customer age demographics
  • PLACE OF BIRTH - Geographic birth location data
  • COUNTRY OF BIRTH - Country-level birth information
  • GENDER CODE - Gender classification
  • PAN ID GROUP - Tax identification groupings
  • TAX STATUS - Tax filing status categories
  • OCCUPATION GROUP - Professional/employment categories
  • INCOME RANGE - Income bracket classifications

Analysis Options

Single Dimension Analysis

  • Select one dimension from the “Select Options” dropdown
  • View detailed breakdowns and distributions
  • Analyze user counts across different categories

Multi-Dimensional Analysis

  • Compare relationships between two variables
  • Create cross-tabulation views
  • Generate comparative visualizations

Accessing EDA

Perform the following steps to access exploratory data analysis:

  1. Log into the FirstHive platform and navigate to Analytics section on the left menu
  2. Choose Exploratory Data Analysis from the Select Type dropdown. This enables access to all available data dimensions.
  3. From the Select Options dropdown, choose at least two options.
  4. Select the dimensions you want to analyze. Available options are displayed in the dropdown menu.
  5. Click Apply to execute your analysis. The system processes your selections and generates the result.

The results are displayed in multiple formats:

Tabular View

  • S/N: Sequential row numbers
  • Dimension columns: Show the selected attributes (e.g., AGE, OCCUPATION GROUP)
  • User Count: Number of customers matching each combination

Visual Analysis

  • Interactive charts and graphs
  • Bar charts showing comparative distributions
  • Color-coded visualizations for easy interpretation

Understanding EDA through Use Cases

The EDA feature lets you uncover insights, spot patterns, and understand relationships within your customer data. Use these practical approaches to make the most of your analysis.

Age vs Occupation Analysis

Discover how age groups relate to different professions:

  • Identify which age groups are most represented in specific occupations
  • Understand employment patterns across age demographics
  • Pinpoint target audiences for campaigns

Income vs Demographics

Examine income patterns to guide strategic decisions:

  • Analyze income by age to assess earning potential at different life stages
  • Identify high-value regions through geographic income distribution
  • Validate income assumptions across occupations

Multi-Variable Segmentation

Combine multiple attributes to create precise customer segments:

  • Age + Income Range + Occupation
  • Geographic + Income + Tax Status
  • Any other combination of available dimensions

Data Interpretation

Understanding User Counts

  • User Count represents the number of unique customer profiles matching your specified criteria
  • Higher counts indicate larger customer segments
  • Use counts to size target audiences and market opportunities

Pattern Recognition

  • Look for unexpected combinations or gaps in data
  • Identify over-represented or under-represented segments
  • Detect trends across different demographic combinations

Best Practices

Selecting Dimensions

  • Start Simple: Use 2–3 dimensions initially to keep results manageable
  • Business Relevance: Choose dimensions that align with your marketing or business objectives
  • Data Quality: Ensure coverage in your selected dimensions

Analysis Approach

  • Hypothesis-Driven: Begin with clear questions you want to answer
  • Iterative: Refine your analysis based on initial findings
  • Comparative: Use multiple dimension combinations to validate insights

Result Validation

  1. Cross-Reference: Compare findings with known business metrics
  2. Sample Size: Ensure adequate user counts for statistical significance
  3. Time Relevance: Consider data freshness and seasonal variations

Technical Considerations

Data Requirements

  • Select at least 2 options for meaningful analysis
  • Ensure dimensions have sufficient customer records
  • Use unified customer profiles for accurate cross-dimensional analysis

Performance

  • Complex analyses may take longer to process
  • Large datasets may require pagination for display
  • Real-time updates depend on your CDP configuration

Troubleshooting

Common Issues

  • No Results: Check if selected dimensions have overlapping data
  • Low User Counts: Consider broader dimension categories or different combinations
  • Loading Issues: Verify data synchronization and try refreshing the analysis

Note: For additional assistance with EDA functionality, contact FirstHive support or consult your customer success manager.