How to evaluate segments relative to a benchmark

business analytics

slug: recipe-business-analytics-how-to-evaluate-segments-relative-to-a-benchmark

Recipe: How to evaluate segments relative to a benchmark

category: business analytics

Problem

You want to understand how different segments perform relative to a baseline, so you can identify overperforming or underperforming groups.

Examples:

• Compare customer segments against the average purchase value
• Identify regions performing above or below expected sales targets
• Benchmark product categories relative to overall portfolio performance
• Detect high- or low-performing campaigns for resource allocation

Solution

Compute relative performance metrics using the Index step, optionally aggregating and visualizing results:

• cube step — Aggregate measures across dimensions for the baseline and target segments
• index step — Calculate index values comparing each segment to the baseline
• chart step — Visualize results for easy interpretation

Step Sequence

cube step -> index step -> chart step

Input Datasets

• Aggregated transactional or performance data (e.g., sales, revenue, units)
• Must include segments and measures to compare
• Baseline for benchmarking (overall average, historical period, or reference group)

Output Dataset

• segment_index — dataset with calculated index values for each segment
• Key columns: segment, measure, metric, baseline_value, segment_value, index_value

Step-By-Step Explanation

Step	Purpose	Notes
cube step	Aggregate measures by dimension for baseline and segments	Ensures comparability
index step	Calculate index values	Index = (segment_value / baseline_value) × 100
chart step	Visualize relative performance	Optional bar chart, heatmap, or dashboard integration

Variations & Extensions

• Combine with filter step to focus on specific regions, product categories, or time periods
• Use [step-rank] to rank segments by index for easy identification of top/bottom performers
• Feed into classify step or contribution step for deeper business analysis
• Calculate indexes for multiple measures simultaneously for cross-segment insights

Concepts Demonstrated

• Benchmarking and relative performance analysis
• Identifying over- and under-performing segments
• Sequencing DAZL steps to create actionable segment insights
• Integration into dashboards and reporting

Related Recipes

• Segment customers by behavior (RFM analysis)
• Understand what drives change and what matters most (Contribution + Pareto)
• How to classify customers for targeted campaigns

Notes & Best Practices

• Clearly define the baseline for meaningful index values
• Ensure consistent aggregation methods across segments and baseline
• Use visualizations to highlight deviations from the benchmark
• Combine with Pareto or RFM analysis for a comprehensive view of performance