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Get Started Free →Apply brand equity frameworks (Aaker, 1991; Keller, 1993) to assess and build customer-based brand value. Use this skill when the user needs to audit brand strength, diagnose brand equity components, design brand-building strategies, or when they ask 'how strong is our brand', 'what drives brand value', or 'how do we build brand equity'.
.claude/skills/asgard-ai-platform-grad-brand-equity/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-06 | ✗→✓ | ▲ Improved | 51% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 63% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 46% | 0% |
| case-20 | ✗→✓ | ▲ Improved | 36% | 0% |
| case-07 | ✓→✗ | ▼ Worse | 5% | 0% |
Brand equity is the differential effect that brand knowledge has on consumer response. Aaker (1991) identifies five dimensions: awareness, associations, perceived quality, loyalty, and proprietary assets. Keller (1993) structures Customer-Based Brand Equity (CBBE) as a pyramid: Salience, Meaning, Response, and Resonance.
IRON LAW: Brand equity is measured from the CUSTOMER perspective.
Internal brand investment (spend, campaigns) does NOT equal brand
equity. Only customer perceptions and behaviors count.Key assumptions:
| Dimension | Key Metrics | Data Sources | |-----------|------------|--------------| | Awareness | Top-of-mind, aided/unaided recall | Surveys, search data | | Associations | Brand image, personality, positioning | Perceptual maps, qualitative | | Perceived Quality | Quality leadership, consistency | Customer ratings, reviews | | Loyalty | Repeat purchase, price premium willingness | Transaction data, CLV | | Proprietary Assets | Patents, trademarks, channel relationships | Internal audit |
| Level | Block | Question | |-------|-------|----------| | 1. Identity | Salience | Who are you? (depth and breadth of awareness) | | 2. Meaning | Performance + Imagery | What are you? (functional + abstract associations) | | 3. Response | Judgments + Feelings | What about you? (opinions + emotions) | | 4. Relationships | Resonance | What about you and me? (loyalty, community, engagement) |
Compare current state to desired positioning. Identify which pyramid level or Aaker dimension is the bottleneck.
Target the weakest pyramid level with specific marketing programs. Build from bottom up — salience before meaning, meaning before response.
markdown## Brand Equity Analysis: [Brand] ### Aaker Dimensions Assessment | Dimension | Strength (1-10) | Evidence | Gap | |-----------|-----------------|----------|-----| | Awareness | | | | | Associations | | | | | Perceived Quality | | | | | Loyalty | | | | | Proprietary Assets | | | | ### CBBE Pyramid Status - Salience: ... - Performance / Imagery: ... - Judgments / Feelings: ... - Resonance: ... ### Strategic Recommendations 1. [Priority dimension/level]: [action] 2. ...
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-04 | pass→pass | 22,687 | 16,539 | -27% | 1 | 1 | 0% | 3,553 | 3,749 | +6% | 0 | 0 | — |
case-01 | fail→fail | 62,906 | 49,044 | -22% | 1 | 1 | 0% | 4,431 | 4,037 | -9% | 0 | 0 | — |
case-02 | fail→fail | 25,923 | 50,225 | +94% | 1 | 1 | 0% | 3,990 | 3,752 | -6% | 0 | 0 | — |
case-03 | fail→fail | 68,605 | 20,294 | -70% | 1 | 1 | 0% | 5,483 | 3,939 | -28% | 0 | 0 | — |
case-05 | pass→pass | 16,657 | 14,406 | -14% | 1 | 1 | 0% | 2,324 | 3,266 | +41% | 0 | 0 | — |
case-06 | fail→pass | 22,571 | 23,450 | +4% | 1 | 1 | 0% | 2,827 | 4,274 | +51% | 0 | 0 | — |
case-07 | pass→fail | 22,327 | 19,356 | -13% | 1 | 1 | 0% | 3,393 | 3,574 | +5% | 0 | 0 | — |
case-08 | pass→pass | 17,957 | 14,771 | -18% | 1 | 1 | 0% | 2,908 | 3,346 | +15% | 0 | 0 | — |
case-09 | pass→pass | 12,636 | 10,348 | -18% | 1 | 1 | 0% | 1,954 | 2,565 | +31% | 0 | 0 | — |
case-16 | pass→pass | 14,959 | 16,522 | +10% | 1 | 1 | 0% | 2,470 | 3,364 | +36% | 0 | 0 | — |
case-10 | pass→pass | 17,489 | 14,169 | -19% | 1 | 1 | 0% | 2,666 | 2,867 | +8% | 0 | 0 | — |
case-11 | fail→pass | 16,279 | 17,748 | +9% | 1 | 1 | 0% | 2,322 | 3,789 | +63% | 0 | 0 | — |
case-12 | pass→pass | 11,114 | 13,729 | +24% | 1 | 1 | 0% | 1,689 | 3,046 | +80% | 0 | 0 | — |
case-13 | fail→pass | 17,872 | 20,626 | +15% | 1 | 1 | 0% | 2,574 | 3,746 | +46% | 0 | 0 | — |
case-14 | pass→pass | 16,066 | 16,614 | +3% | 1 | 1 | 0% | 2,465 | 3,474 | +41% | 0 | 0 | — |
case-15 | pass→pass | 19,236 | 15,526 | -19% | 1 | 1 | 0% | 2,629 | 3,360 | +28% | 0 | 0 | — |
case-17 | fail→fail | 10,472 | 13,354 | +28% | 1 | 1 | 0% | 1,713 | 2,655 | +55% | 0 | 0 | — |
case-18 | pass→pass | 14,554 | 15,881 | +9% | 1 | 1 | 0% | 2,175 | 3,242 | +49% | 0 | 0 | — |
case-19 | pass→pass | 18,740 | 27,444 | +46% | 1 | 1 | 0% | 3,086 | 4,723 | +53% | 0 | 0 | — |
case-20 | fail→pass | 14,803 | 17,208 | +16% | 1 | 1 | 0% | 2,440 | 3,327 | +36% | 0 | 0 | — |
case-21 | pass→pass | 15,233 | 17,102 | +12% | 1 | 1 | 0% | 2,474 | 3,825 | +55% | 0 | 0 | — |
case-22 | pass→pass | 19,963 | 17,439 | -13% | 1 | 1 | 0% | 3,101 | 3,839 | +24% | 0 | 0 | — |
case-23 | pass→pass | 24,530 | 20,173 | -18% | 1 | 1 | 0% | 3,179 | 4,105 | +29% | 0 | 0 | — |
case-24 | pass→pass | 22,141 | 21,112 | -5% | 1 | 1 | 0% | 2,774 | 4,169 | +50% | 0 | 0 | — |
case-25 | pass→pass | 17,589 | 14,733 | -16% | 1 | 1 | 0% | 2,387 | 2,940 | +23% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 25 cases were attempted. The headline lift of +12 percentage points is the difference between those two pass rates over the 25 comparable cases. 2 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.