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Consumer Intelligence

Index Score

Index Score

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Definition:

An index score is a standardized metric used in consumer intelligence and market research to express how a particular group, brand, or response compares to a reference point, typically set at 100. A score above 100 indicates above-average performance or affinity; a score below 100 signals the opposite. Index scores are widely used in brand tracking, audience profiling, concept testing, and segmentation studies because they normalize raw data, making cross-market and cross-segment comparisons meaningful. For qualitative and mixed-method research teams, index scores provide a quantitative anchor that helps stakeholders interpret directional findings with greater confidence and consistency across studies.

How Conveo Does It

Conveo surfaces index scores as part of its multimodal analysis layer, drawing on real voice and video interviews with verified participants, not synthetic respondents or AI avatars. Teams can launch a study in under 30 minutes and receive indexed findings across segments, markets, or time periods within days. The AI Research Assistant allows researchers to interrogate index score patterns directly, asking questions like which audience segments over-index on a specific attitude, and trace every finding back to verbatim quotes and video evidence.

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Frequently asked questions.

An index score is a normalized metric that compares a specific data point to a baseline, usually set at 100. In consumer research, it shows whether a particular audience, brand, or concept performs above or below average on a given measure. A score of 120 means 20 percent above the baseline; a score of 80 means 20 percent below. This standardization makes it easier to compare results across different segments, geographies, or time periods without being misled by raw volume differences.
Index scores matter because raw numbers rarely tell the full story. A brand might have high awareness in one market simply because the market is larger, not because the brand is stronger there. By indexing against a baseline, research teams can identify genuine over-performance or under-performance. This is especially valuable in brand tracking, audience segmentation, and concept testing, where the goal is to understand relative strength across groups rather than absolute counts that can be skewed by sample size or market size differences.
A raw score is the actual observed value from a dataset, such as the percentage of respondents who recall a brand. An index score recalculates that value relative to a defined baseline, making it comparable across groups of different sizes or compositions. Raw scores are useful for understanding absolute levels; index scores are useful for understanding relative performance. In practice, research teams use both together: raw scores to report what was observed, and index scores to interpret whether that observation is meaningfully above or below what would be expected.
AI is making index scores more dynamic and actionable. Traditionally, calculating and interpreting index scores required manual data processing and analyst time, which slowed down how quickly teams could act on findings. AI-powered platforms can now compute index scores automatically across large interview datasets, flag statistically meaningful deviations in real time, and connect indexed findings to the underlying participant language that explains them. This means research teams spend less time building tables and more time understanding what the numbers actually mean for the business.
Enterprise teams use index scores most commonly in brand tracking to monitor how brand perceptions shift over time or vary across markets, in audience profiling to identify which segments over-index on specific attitudes or behaviors, and in concept testing to compare how different consumer groups respond to new ideas relative to a norm. Index scores also appear in ad testing and packaging research, where teams need to evaluate whether a creative or design performs above or below category benchmarks before committing to a full launch.

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