ARA — Methodology

Tier Definitions

83–100

Awesome

Category authority. Owns its emotional and functional narrative with distinct, highly legible machine presence.

63–82

Strong

Highly recommended and understood by AI, with strong contextual surfacing but lacking absolute category dominance.

39–62

Decent

Exists in machine memory but relies on functional defaults or historical inertia rather than unique, ownable identity.

19–38

Confused

Machine memory contradicts brand intent, or brand is defined entirely by competitors.

0–18

Invisible

Does not surface in relevant queries. No distinct machine identity or emotional residue.

The Five Dimensions

Code	Dimension	Max Score	What It Measures	Key Signals
A1 · STR	Structural Readiness	20 pts	The technical and data infrastructure that enables machine systems to find, parse, and recommend the brand with confidence. Includes schema markup, structured data, first-party data generation, and digital channel coherence.	Schema.org markup depth First-party data infrastructure E-commerce and app data continuity Structured product and service data
A2 · SEM	Semantic Legibility	20 pts	How clearly and consistently the brand communicates what it is, who it serves, and why it matters — in language machines can parse and assign with confidence. Measures vocabulary ownership, positioning clarity, and semantic consistency across channels.	Owned vocabulary and positioning language Ingredient / attribute specificity Consistency across digital touchpoints Occasion and use-case clarity
A3 · SYN	Synthetic Customer Test	20 pts	Direct measurement of how AI recommendation systems respond to category queries — who wins, who loses, and with what confidence. Simulates a purchasing or research decision made by a synthetic customer using AI assistance.	AI recommendation win rate by query type Recommendation confidence score Breadth of occasions won Consistency across AI platforms
A4 · EMO	Emotional Residue	20 pts	The depth and quality of emotional associations, community advocacy, and cultural weight captured in AI training data. High emotional residue means machines associate the brand with specific feelings, communities, or life contexts — not just product attributes.	Community-generated content volume and quality Sentiment intensity and consistency Cultural association depth Brand advocacy patterns in training data
A5 · VOI	Brand Voice and Personality	20 pts	How distinctively and consistently the brand's voice, tone, and personality come through across machine-indexed content. Brands with high VOI scores have a recognisable machine-readable character — AI systems can attribute content and personality to the brand reliably.	Voice distinctiveness and consistency Phonetic clarity of brand and product names Tone consistency across indexed content Voice assistant surfacing quality

A1 · Structural Readiness Rubric

Can machines find and parse the brand? / 20

17–20

Awesome

Proprietary ingredient data, SKUs, revenue figures, and certifications are machine-parseable. Structured data schema is comprehensive and current. Brand generates rich first-party data continuously.

13–16

Strong

Brand category, parent company, founding year, and key campaigns are correctly structured and indexed. Schema markup covers core entities. Data infrastructure is present and mostly coherent.

8–12

Decent

Brand exists in structured data and is correctly categorised, but proprietary or differentiating attributes are absent. Machines can find the brand but lack the specifics needed for confident recommendation.

4–7

Confused

Structured data conflates the brand with competitors or related entities. Machine parsing produces ambiguous or inaccurate results. Category assignment may be incorrect or contested.

0–3

Invisible

Machine systems cannot differentiate the brand from its category. No distinct structured data presence. Brand is effectively invisible to recommendation infrastructure.

A2 · Semantic Legibility Rubric

Do machines understand you as you intend? / 20

17–20

Awesome

Machine systems use the brand's own vocabulary, archetype, and positioning language when describing it unprompted. The brand owns a distinct semantic territory that competitors do not encroach on.

13–16

Strong

Machine description is accurate and clearly positions the brand distinct from competitors. Owned vocabulary is reflected in AI outputs. Positioning is legible and consistently applied across platforms.

8–12

Decent

Machine description is relational — the brand is defined in comparison to a competitor rather than on its own terms. Positioning exists but is not owned. Semantic territory is contested.

4–7

Confused

Machine systems define the brand primarily as "an alternative to X." The brand has no independent semantic identity — it exists only in relation to a stronger competitor in machine memory.

0–3

Invisible

Machine description contradicts brand intent, or the brand is absent from relevant semantic associations. AI systems attribute incorrect positioning or personality to the brand.

A3 · Synthetic Customer Test Rubric

Who wins when an AI agent shops? / 20

17–20

Awesome

Brand is surfaced unprompted across 5 or more query types. Consistently recommended in top-1 or top-2 position. Strong social signal boosts recommendation confidence. Wins across multiple purchase occasions.

13–16

Strong

Recommended across 3–4 query types. Wins at least one distinct purchase occasion clearly. Recommendation is consistent across AI platforms but not dominant across all contexts.

8–12

Decent

Recommended in 1–2 query types. Appears in secondary tier only — listed but not prioritised. Machines know the brand exists but do not lead with it in competitive contexts.

4–7

Confused

Brand only surfaces when its category is named directly in the query. Not recommended in contextual or occasion-based queries. AI agents do not associate the brand with specific use cases.

0–3

Invisible

Brand is absent from all synthetic query testing. AI agents consistently recommend competitors in every tested occasion and context. Effectively zero AI-driven recommendation presence.

A4 · Emotional Residue Rubric

What did brand building leave in the data? / 20

17–20

Awesome

Brand owns a specific, named emotional territory. Multiple cultural moments are cited by machine systems. The brand has transcended its category — AI associates it with a feeling, community, or life context, not just a product.

13–16

Strong

Clear emotional associations are present and consistent. At least one cultural moment or community touchpoint is cited unprompted by machine systems. Sentiment is positive and differentiated from generic category affect.

8–12

Decent

Machine systems associate the brand with generic emotions such as "fun" or "youthful" but cannot name specific moments, communities, or cultural contexts. Emotional associations exist but are not ownable or distinct.

4–7

Confused

Emotional description is entirely relational — machines describe the brand as "feels different from [competitor]" with no independent emotional identity. Brand's emotional residue exists only in contrast, not in its own right.

0–3

Invisible

No emotional associations present. Machine systems describe the brand in purely functional terms. No evidence of brand-building investment in machine-indexed content. Pure utility, zero affect.

A5 · Brand Voice and Personality Rubric

What happens in a zero-visual world? / 20

17–20

Awesome

Brand identity is entirely verbal and tonal. A specific archetype is named by machine systems unprompted. The brand's personality survives complete visual removal — it remains distinct and attributable in pure text or audio contexts.

13–16

Strong

Brand personality is a mix of verbal and visual assets, but at least one strong verbal or tonal asset is identified by machines. Archetype is discernible and attributable without visual cues in most contexts.

8–12

Decent

Brand archetype is discernible but relies heavily on references to visual platforms or assets. Machine description of personality includes frequent references to what the brand looks like rather than how it sounds or speaks.

4–7

Confused

No clear archetype is present. Brand personality collapses entirely without visual references. Machine systems cannot describe a consistent voice or tone — personality is platform-dependent and non-transferable.

0–3

Invisible

No audio or verbal personality is detectable. Brand is interchangeable with category competitors in any non-visual medium. Machine systems apply generic tonal descriptors with no brand-specific attribution.

Audit Process

01

Category Scoping

Define the competitive set, relevant query universe, and geographic scope. Identify the key purchase occasions and recommendation contexts that matter most for the category.

02

Structural Audit

Technical review of schema markup, data infrastructure, first-party data generation, and digital channel coherence across brand properties. Benchmarked against category peers.

03

Synthetic Query Testing

Systematic prompting of leading AI platforms (ChatGPT, Claude, Gemini, Perplexity) across the query universe. Win rates, confidence levels, and recommendation rationale are captured and scored.

04

Scoring and Recommendations

Dimension scores are assigned and combined into the overall ARA score. Findings, vulnerabilities, and a prioritised recommendation roadmap are developed for each brand in the study.

Important Note on Scoring

ARA scores reflect a brand's algorithmic readiness at a specific point in time. AI recommendation systems update continuously as training data evolves — brands that invest in structured content and data infrastructure will see scores improve over time, while brands that do not risk score compression as competitors build machine presence.

ARA is a proprietary methodology developed by araco.ai. Scoring reflects a combination of automated analysis, synthetic query testing, and proprietary methodology. All brand audits are confidential and intended for the commissioning client only.