Definition
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How it relates to marketing
How to calculate
BDI =
( (Brand Sales in Segment / Total Brand Sales) ÷ (Population in Segment / Total Population) ) × 100
Example
- Brand sales in Region A: $12 million
- Total brand sales: $100 million
- Population of Region A: 5 million
- Total market population: 50 million
BDI =
( (12 / 100) ÷ (5 / 50) ) × 100 = 120
A score of 120 indicates that Region A over-indexes for the brand.
How to utilize BDI
- Media planning – Shift advertising weight toward high-BDI markets to preserve share and toward low-BDI markets if the objective is expansion.
- Distribution strategy – Strengthen shelf presence and local partnerships where BDI is high; audit availability where it lags.
- Creative adaptation – Review messaging in weak markets to ensure relevance to local preferences.
- Budget justification – Use BDI data to support spending requests or defend cuts.
Comparison to similar measures
Metric | What it measures | Primary use | Key difference from BDI |
---|---|---|---|
Category Development Index (CDI) | Category sales strength within a segment | Determines category potential | Looks at category, not brand |
Market Penetration Rate | Share of segment households that buy the brand | Tracks brand reach | Ignores purchase intensity |
Share of Market | Brand’s percent share of total sales | Competitive benchmarking | Does not isolate segment variation |
Best practices
- Pair BDI with CDI to see whether brand weakness is due to low category interest or unique brand issues.
- Use rolling periods (e.g., trailing 12 months) to smooth seasonality.
- Validate with qualitative insights—store audits, panel data, and social listening—to uncover the “why” behind a number.
- Set thresholds (e.g., ±15 points) to avoid reacting to statistical noise.
Future trends
- Finer granularity: Transaction-level data and geofencing allow BDI calculation at zip-code or even store level.
- Real-time dashboards: Cloud data pipelines shorten the gap between sales activity and BDI reporting, enabling quicker course corrections.
- Predictive layers: Machine-learning models are beginning to forecast BDI shifts based on leading indicators such as local economic signals or media sentiment.