Brand Visibility for Agentic Commerce (BVAC)

Table of Contents

  1. Definition
  2. How It Relates to Marketing
  3. How to Build a BVAC Assessment
    1. The Eight Dimensions at a Glance
  4. How to Utilize the BVAC Framework
  5. Comparison to Similar Frameworks
  6. Best Practices
  7. Future Trends
  8. FAQs
  9. Related Terms
  10. Sources

Definition

The Brand Visibility for Agentic Commerce (BVAC) Framework is a diagnostic that scores how well a brand’s product data answers the questions an AI agent resolves before it assembles a purchase shortlist. It evaluates the data surface an agent reads — whether the agent can identify the brand and product, compare them against competitors on equal terms, and find structured grounds to trust them — rather than the human-funnel metrics that no longer capture where the purchase decision forms.

The framework was developed by Greg Kihlström, martech futurist and Principal at The Agile Brand. He introduced the concept in the May 2026 article “How Purchase Decisions Now Form Before the Customer Is Involved,” which set out the core observation the framework addresses: the consideration set is built inside the agent before a customer reviews anything, so a brand can be excluded with no corresponding signal in its own analytics (Kihlström, 2026).

The framework addresses a shift in how discovery and comparison happen. When an agent performs the research, comparison, and purchasing a consumer once did directly, it builds a consideration set from structured product data and returns a small set of purchase-ready options. A brand outside that set is absent for that purchase, with no traffic decline and no campaign to diagnose. Stibo Systems describes this as decision invisibility (Molino Sánchez, 2026). A related effect, the attribution gap, means a brand the agent does recommend often sees the customer arrive later through a direct or organic path, so the influence that drove the decision leaves no trace in the merchant’s analytics (Hanna, 2026).

The scale of the shift is measurable. Adobe Analytics recorded generative-AI-referred traffic to US retail sites growing roughly 690% year over year across the 2025 holiday season (Adobe Analytics, 2026). McKinsey estimates conversion from AI-generated product recommendations at about 4.4 times that of traditional search (McKinsey, as cited in MetaRouter, 2026). Capgemini’s consumer research finds 58% of consumers have already replaced traditional search engines with generative-AI tools for product recommendations (Capgemini Research Institute, 2025).

The framework scores a brand across eight dimensions in two tiers, on a shared five-stage maturity scale, and produces an integrated output: a scorecard, a dependency-ordered remediation roadmap, and a measurement loop that tracks directional signals under the attribution gap.

Back to Top

Back to Top

How It Relates to Marketing

Marketing owns most of the surface BVAC evaluates, and most of the work that earns attention from a human shopper — narrative, design, persuasive merchandising — is invisible to the system that now decides whether the brand reaches that shopper. The framework reframes several marketing functions:

  • Positioning becomes a data problem. Traditional positioning lives in taglines, emotional benefits, and brand promise. Agents parse structured attributes. BVAC separates differentiation that can be expressed as verifiable attributes (durability, warranty terms, materials, certifications) from differentiation that resists structuring (perceived prestige, design heritage) and assigns a remediation path to each.
  • Reputation management becomes part of the positioning stack. In Sabbah and Acar’s testing across 16,000+ simulated purchase decisions, structured ratings were the one signal that consistently moved agent selection across every model and product category. Verified reviews, third-party certifications, and return-policy clarity now qualify a brand for inclusion.
  • Persuasion can become a liability. Advanced reasoning models discount overt persuasion cues as manipulation signals, and the suppression strengthens as models advance. Strike-through pricing and countdown timers showed no stable selection benefit in the Sabbah and Acar study, and bundling reduced selection in at least one case.
  • Measurement shifts off last-click. The attribution gap means a recommended brand may show no upstream signal. BVAC measures directional signals — AI share of voice, citation rate, substitution rate, brand agent transaction performance — from controlled simulation re-runs rather than analytics traffic.
  • The work is cross-functional. Identity, attributes, protocol surface, and governance sit across marketing, product, engineering, legal, and commerce. The assessment requires named representatives from each.

Back to Top

Back to Top

How to Build a BVAC Assessment

The BVAC Framework is a structured diagnostic rather than a single numerical calculation. A standard assessment runs in this sequence:

  1. Define scope and inputs. Set the scope (product line, category, or full catalog), the top SKU set (default: top 20 by revenue), the category baseline reference set (top three competitors), the agent interface set (three to five interfaces such as ChatGPT, Gemini, and Perplexity, with model versions recorded), and the function representative roster. Stratify the query set by user mandate type, because the prompt the agent carries shapes selection as much as the model does.
  2. Score the prerequisites. Score Identity Legibility and Attribute Completeness. These two dimensions cap every strategic dimension’s effective score at the lower of the two prerequisite stages.
  3. Check the Trust Signal Density floor. Trust Signal Density sits below the floor when a brand has none of three signals: structured Review entities, certification schema, or sameAs authority anchors. Below the floor, every other strategic dimension caps at Discoverable.
  4. Score the strategic dimensions diagnostically. Run each strategic dimension’s assessment method in full, producing a diagnostic score independent of caps.
  5. Apply the caps. The effective score for each strategic dimension is the diagnostic score capped by the lower prerequisite stage, then additionally capped at Discoverable if Trust Signal Density is below the floor. The binding cap is the lower of the two.
  6. Compute the composite. The composite framework score is the lowest effective score across all dimensions. A brand is only as agent-ready as its weakest dimension allows.
  7. Produce the gap map. Document each gap with a current state, target state, and a recommended remediation horizon (90-day, 6–12 month, 12–24 month).
  8. Run the cross-functional readiness review. Convene function representatives to validate scoring and gap prioritization, and produce a signed-off scorecard.

The five maturity stages function as a shared scale across all eight dimensions: Invisible (structural absence), Discoverable (present and parseable on default attributes), Comparable (attribute parity with the category), Differentiated (unique value encoded in verifiable form), and Agent-native (the brand operates its own agent and participates in agent-to-agent interactions on equal footing).

Back to Top

Back to Top

The Eight Dimensions at a Glance

DimensionTierCore Question
Identity LegibilityPrerequisiteCan an agent reliably resolve which brand and product it is evaluating?
Attribute CompletenessPrerequisiteDo products carry the full set of structured attributes the category requires, including policy attributes?
Differentiation EncodingStrategicDo unique value propositions exist as structured, verifiable attributes rather than only as prose?
Brand-Agent RepresentationStrategicDoes the brand operate a governed agent that can represent it in agent-to-agent interactions?
Trust Signal DensityStrategic (with floor)Is trustworthiness encoded in a form an agent can weight — structured reviews, certifications, authority links?
Protocol ReadinessStrategicDoes the brand expose its commerce stack through standardized agentic protocols across data access, checkout, discovery, and identity?
Latency and Data FreshnessStrategicDoes the brand serve real-time, accurate data fast enough for machine-time decision-making?
Governance MaturityStrategicDoes the brand have the ownership, decision authority, and policy infrastructure to govern agentic commerce activities?

Back to Top

Back to Top

How to Utilize the BVAC Framework

Common use cases include:

  • Agent-readiness diagnosis. Establishing where a brand stands across the eight dimensions and identifying the binding constraint that holds the composite score down.
  • Dependency-ordered remediation. Converting the gap map into a sequenced roadmap. The scoring logic forces an order: prerequisites first, floor crossing second, then strategic dimensions in binding-constraint order. Work performed out of order produces diagnostic improvement with no effective improvement.
  • Quick-win prioritization. Identifying the highest-leverage low-effort actions. Crossing the Trust Signal Density floor is a single 90-day schema action that lifts the Discoverable cap from the entire strategic tier.
  • Directional measurement under the attribution gap. Baselining AI share of voice, citation rate, substitution rate, and brand agent transaction performance before a remediation block, re-measuring after, and treating movement as real only when it exceeds the noise floor of stochastic agent outputs.
  • Recalibration over time. Using monthly measurement as an early-warning system that fires a focused re-assessment when a signal degrades beyond the noise floor, ahead of the scheduled annual cycle.
  • Self-administered or consultant-led delivery. Running the same scoring rubric either internally or through a deeper consultant-led engagement, with the shared rubric ensuring consistency across both paths.
  • Vertical adaptation. Flagging dimensions where vertical-specific weighting applies — the Trust Signal Density floor in regulated categories, authority anchors in B2B — while scoring against the universal core.

Back to Top

Back to Top

Comparison to Similar Frameworks

FrameworkFocusOriginPrimary Use
BVAC FrameworkAgent-readiness of a brand’s product data surfaceGreg Kihlström / The Agile Brand (2026)Diagnosing and sequencing remediation for agent-mediated commerce visibility
SEORanking in human-facing search engine resultsMultiple originsOrganic visibility for human searchers
Generative Engine Optimization (GEO)Brand presence within generative AI answersEmerging practiceVisibility in AI-generated responses
Answer Engine Optimization (AEO)Surfacing as the answer to a queryEmerging practiceVisibility in answer-engine results
Balanced Scorecard (BSC)Multi-perspective strategy executionKaplan & Norton (1992)Translating strategy into balanced objectives and measures
Digital Shelf AnalyticsProduct content quality and availability on retail sitesMultiple vendorsMonitoring product page completeness and compliance

BVAC overlaps with GEO and AEO at the visibility layer but extends past presence in an answer to whether an agent can resolve, compare, and trust a brand well enough to place it in a purchase-ready set, and it carries the prerequisite-and-floor scoring logic, the dependency-ordered roadmap, and the measurement loop that the optimization practices do not specify. It is frequently paired with digital shelf analytics for the underlying content audit and with GEO/AEO measurement as inputs to its directional signals.

Back to Top

Back to Top

Best Practices

  • Fix the prerequisites before anything else. Strategic dimension work above the prerequisite cap delivers diagnostic improvement with no effective improvement. A brand at Comparable prerequisites that moves Differentiation Encoding to Differentiated sees no effective gain until the prerequisite rises.
  • Treat the floor crossing as the single highest-leverage action. Establishing any one of structured reviews, certification schema, or authority anchors is low-effort 90-day work that unlocks the entire strategic tier from the Discoverable cap.
  • Score down on borderline stages. When a dimension sits between two stages, score to the lower stage and note the partial progress in the gap map. Scoring up declares readiness the brand has not earned.
  • Encode differentiation as verifiable attributes. A unique benefit without a structured field and a verifiable source drops out of the agent’s comparison. Premium SKUs fail the comparison test unless the premium is encoded in specifications.
  • Dial back persuasion in the data surface. Advanced models discount overt promotional cues. Clarity outperforms story in machine-mediated comparison.
  • Measure against a noise floor, not against last-click. Agent outputs are stochastic. Establish the noise floor from repeated baseline sampling and treat only movement beyond it as real.
  • Record model versions. Agent behavior shifts across model releases. Index measurement by model version so a release becomes a recalibration event rather than unexplained drift.
  • Run the assessment as a living loop. The full assessment runs at least annually; monthly directional measurement is the early-warning system that catches degradation between cycles.

Back to Top

Back to Top
  • Protocol consolidation and versioning. Data-access, checkout, agent-discovery, and identity protocols continue to emerge and version up. Capability-based scoring rather than single-protocol scoring is built in to absorb this churn.
  • Agent-to-agent commerce as the default interface. As brands deploy their own agents that negotiate with buyer agents, Brand-Agent Representation and Governance Maturity move from advanced dimensions to baseline expectations.
  • Persuasion suppression strengthening. As reasoning models advance, the discount applied to overt promotional cues is expected to deepen, widening the gap between human-facing and agent-facing content strategy.
  • Vertical overlays. Category-specific weighting — heavier Trust Signal Density floors for regulated goods, earlier authority-anchor work for B2B — adjusts priority within the strategic block while the universal scoring logic holds.
  • Closing the attribution gap, partially. Brand agent observability provides first-party transaction data where analytics traffic provides none, shifting measurement from inference toward controlled, comparative signal tracking.
  • Standardized agent-readiness benchmarking. Versioned behavior databases that track how agents select across model releases support model-agnostic, comparable measurement over time.

Back to Top

Back to Top

FAQs

1. Who created the BVAC Framework? Greg Kihlström, martech futurist and Principal at The Agile Brand, developed the framework and introduced the concept in the May 2026 article “How Purchase Decisions Now Form Before the Customer Is Involved.”

2. What does the BVAC Framework measure? It measures the data surface an AI agent reads when assembling a purchase shortlist — whether the agent can identify the brand and product, compare them against competitors on equal terms, and find structured grounds to trust them — across eight dimensions on a five-stage maturity scale.

3. Why is agent visibility different from SEO? SEO optimizes ranking for a human who then reviews the results. An agent assembles the consideration set before a human reviews anything and builds it from structured data, so a brand can be filtered out with no traffic decline and no campaign to diagnose.

4. What are the eight dimensions? Two prerequisites — Identity Legibility and Attribute Completeness — and six strategic dimensions: Differentiation Encoding, Brand-Agent Representation, Trust Signal Density, Protocol Readiness, Latency and Data Freshness, and Governance Maturity.

5. What are the five maturity stages? Invisible, Discoverable, Comparable, Differentiated, and Agent-native. A brand can sit at different stages on different dimensions; the composite is the lowest effective score.

6. What is the prerequisite cap? A strategic dimension’s effective score is capped by the lower of the two prerequisite scores. A Differentiated diagnostic drops to Comparable effective if the lower prerequisite sits at Comparable.

7. What is the Trust Signal Density floor? A brand below the floor — holding none of structured reviews, certification schema, or authority anchors — has every other strategic dimension capped at Discoverable. Establishing any one of the three crosses the floor and lifts that cap.

8. What is decision invisibility? The condition in which an agent filters a brand out before a human is involved, producing no bounce-rate change and no campaign to diagnose. The brand was never in the set.

9. What is the attribution gap? Discovery and comparison occur inside the agent and the customer arrives later through a direct or organic path, so the influence that drove the decision leaves no trace in the merchant’s analytics. BVAC measures directional signals rather than last-click attribution to operate within this gap.

10. How is the framework measured if attribution is broken? Through controlled simulation re-runs against a fixed query and agent interface set, sampled repeatedly, compared against a baseline and competitors, and gated by a noise floor. Signal movement triggers a focused re-assessment rather than rewriting the score directly.

11. Can the framework be self-administered? Yes. It supports both self-administered and consultant-led delivery against a shared scoring rubric, with the consultant-led path adding deeper diagnostic methodology and engagement-specific output.

Back to Top

Back to Top
  1. Agentic Commerce
  2. Agent2Agent (A2A) Protocol
  3. Agent Card
  4. Model Context Protocol (MCP)
  5. Agentic Commerce Protocol (ACP)
  6. Generative Engine Optimization (GEO)
  7. Answer Engine Optimization (AEO)
  8. Business to Agent to Consumer (B2A2C)
  9. Product Information Management (PIM)
  10. Search Engine Optimization (SEO)
  11. Share of Voice (SOV)
  12. Balanced Scorecard (BSC)
  13. Trust Signal Density
  14. Decision Invisibility

Back to Top

Back to Top

Sources

Back to Top

Tags: , ,

Was this helpful?

Previous

Agentic Merchant Protocol (AMP)