Cross-Dimensional Insights

business analytics

slug: topic-map-business-analytics-cross-dimensional-insights

Vocabulary:

• dimension_interaction: When effect of one dimension depends on level of another
• main_effect: Impact of a dimension considered alone
• interaction_effect: Combined impact beyond sum of main effects
• synergy: When dimensions together produce more than expected from individual effects
• antagonism: When dimensions together produce less than expected
• conditional_relationship: Relationship that changes based on third variable
• dimension_dominance: One dimension explains much more variance than others
• dimension_redundancy: Two dimensions capture similar information
• orthogonality: Dimensions that are independent (uncorrelated)
• collinearity: Dimensions that are highly correlated with each other
• simpson_paradox: When trend appears in groups but reverses when groups combined
• moderator_variable: Dimension that affects strength of relationship between others

Concepts:

• multi_dimensional_thinking: Analyzing beyond single dimensions
• dimension_hierarchy: Some dimensions naturally nest within others
• dimension_independence: Whether dimensions vary independently or together
• interaction_strength: How much interaction explains beyond main effects
• dimension_complementarity: Dimensions that work together to explain more
• dimension_substitutability: Dimensions that can replace each other
• emergent_patterns: Insights only visible when crossing dimensions
• slice_and_dice: Examining data from multiple dimensional perspectives

Concepts_advanced:

• three_way_interactions: Effects involving three dimensions simultaneously
• moderated_mediation: Complex causal chains across dimensions
• dimension_factorization: Reducing many dimensions to key underlying factors
• tensor_decomposition: Higher-order patterns across multiple dimensions
• dimension_causality: Understanding causal direction between dimensions

Procedures:

• calculate_main_effects: Variance explained by each dimension independently
• calculate_interaction_effects: Variance explained by dimension combinations
• compare_effect_sizes: Rank dimensions by explanatory power
• detect_synergies: Find dimension pairs with positive interaction
• detect_antagonisms: Find dimension pairs with negative interaction
• test_independence: Correlation or chi-square between dimensions
• identify_dominant_dimension: Which dimension matters most
• find_redundant_dimensions: Which dimensions are interchangeable
• analyze_conditional_relationships: How dimension A effect varies by dimension B

Procedures_detailed:

• two_way_anova: Partition variance into dim1, dim2, and dim1×dim2 interaction
• interaction_plot: Line plot showing how one dimension effect varies by another
• correlation_matrix: Pairwise correlations between all dimensions
• chi_square_test: Test independence between categorical dimensions
• relative_importance_analysis: Decompose R² contribution of each dimension
• dominance_analysis: Which dimension is dominant across all model subsets

Topics:

• segment_interaction_analysis
• dimension_prioritization
• targeted_intervention_design
• portfolio_optimization
• dimension_reduction
• causal_pathway_mapping
• synergy_identification
• redundancy_elimination
• conditional_strategy_design
• multi_factor_optimization

Categories:

• multi_dimensional_analysis
• interaction_detection
• dimensional_structure
• explanatory_modeling
• strategic_dimensioning

Themes:

• holistic_understanding: See beyond single-dimension views
• interaction_awareness: Recognize when dimensions combine non-additively
• dimensional_efficiency: Focus on dimensions that matter, eliminate redundant
• targeted_action: Design interventions for specific dimension combinations

Trends:

• automated_interaction_detection: ML finds important interactions
• dimension_network_analysis: Graph-based view of dimension relationships
• causal_discovery: Algorithms infer causal structure between dimensions
• dimension_embedding: Representing dimensions in lower-dimensional space
• interactive_exploration: UI for dynamically slicing across dimensions

Use_cases:

• retail: "Store location and product category interact - urban stores excel in electronics (not clothing)"
• marketing: "Channel and customer segment interact - email works for B2B (not B2C)"
• saas: "Pricing tier and industry interact - enterprise tier strong in finance (weak in education)"
• manufacturing: "Shift and product line interact - night shift has quality issues only on complex products"
• healthcare: "Treatment and demographic interact - medication X effective for elderly (not young patients)"
• finance: "Product and region interact - credit cards popular in urban (not rural) areas"
• ecommerce: "Device and time-of-day interact - mobile dominates evening (desktop dominates workday)"
• hospitality: "Room type and day-of-week interact - suites fill on weekends (not weekdays)"