Enter Survey and Brand Inputs
Use 0 to 100 values for each brand metric. Adjust weights to match your strategy and market priorities.
Example Data Table
| Brand | Awareness | Consideration | Satisfaction | Advocacy | Differentiation | Final Score |
|---|---|---|---|---|---|---|
| Brand A | 82% | 76% | 84% | 72% | 68% | 74.90 |
| Brand B | 75% | 69% | 80% | 61% | 58% | 67.85 |
| Brand C | 66% | 57% | 73% | 49% | 46% | 56.40 |
Formula Used
The calculator builds a weighted raw score first, then adjusts it for consistency, sample reliability, data recency, and campaign lift.
1. Weighted Raw Score
Raw Score = Sum(Metric × Weight) ÷ Sum(Weights)
2. Consistency Factor
Consistency Factor = 1 − (Standard Deviation ÷ 250)
This lightly reduces the score when survey signals are highly uneven.
3. Sample Adjustment
Sample Factor = sqrt(Sample Size ÷ 400)
Sample Adjustment = 0.85 + (0.15 × Sample Factor)
4. Recency Factor
Recency Factor = 1 − (Data Age in Months × 0.01)
5. Campaign Factor
Campaign Factor = 1 + ((Campaign Lift × 0.10) ÷ 100)
6. Final Score
Final Score = Raw Score × Consistency Factor × Sample Adjustment × Recency Factor × Campaign Factor
7. Relative Preference Index
Relative Preference Index = Final Score ÷ Benchmark Score × 100
How to Use This Calculator
- Enter percentage scores for each brand metric from your survey or tracking study.
- Adjust metric weights so they match your business model, category, and campaign goals.
- Add sample size, benchmark, target, competitor score, campaign lift, and data age.
- Click Calculate Score to show the result above the form.
- Review the weighted breakdown, driver priorities, and chart for interpretation.
- Use the CSV and PDF buttons to export the current analysis.
FAQs
1. What does the brand preference score measure?
It estimates how strongly customers favor your brand by combining awareness, consideration, loyalty, satisfaction, advocacy, value perception, differentiation, and purchase intent into one practical score.
2. Why are weights included?
Weights let you reflect category reality. For example, repeat purchase may matter more in subscription brands, while awareness may matter more in new-market launches.
3. Why is the final score different from the raw score?
The final score adjusts the raw weighted average for uneven signals, small samples, older data, and campaign lift. That creates a more decision-ready number.
4. What is a good score?
Scores above 75 usually indicate strong preference. Scores above 85 suggest elite market strength. The best threshold still depends on your category benchmark and competitive context.
5. How large should the sample size be?
Larger samples improve reliability. Around 200 responses is often useful for directional tracking, while 400 or more usually supports stronger confidence in the result.
6. Should benchmark and competitor score be identical?
Not always. A benchmark can represent category norms, while competitor score can represent one rival. Using both gives broader context and sharper decision support.
7. Can I use this quarterly?
Yes. It works well for ongoing brand tracking. Keep your survey method consistent and compare score movement, drivers, and gaps over time.
8. Which metrics usually improve preference fastest?
It depends on the category, but differentiation, perceived value, and advocacy often create meaningful lift because they influence both conversion and long-term loyalty.