From Persuasion to Computation: Reconceptualizing Marketing in Agent-Mediated Markets
Paul F. Accornero
Affiliations
Founder, The AI Praxis
ORCID ID: https://orcid.org/0009-0009-2567-5155
SSRN Working Paper Series: 5713742
Date: 2025
Contact: paul.accornero@gmail.com
WORKING PAPER - NOT FOR CITATION
This is a pre-print version undergoing peer review.
Abstract
For over 125 years, the marketing funnel—rooted in the AIDA model (Attention, Interest, Desire, Action)—has served as the dominant paradigm for understanding consumer purchase behavior. This conceptual framework, developed during the era of mass advertising, assumes a human consumer progressing through sequential psychological stages from product awareness to final purchase. We argue that the emergence of agentic commerce—economic transactions mediated by autonomous AI agents acting on behalf of consumers—fundamentally invalidates this paradigm. AI agents possess no psychological journey; they cannot be made “aware” through advertising, develop “interest” via storytelling, or feel “desire” through emotional branding. Instead, these algorithmic intermediaries execute logical, deterministic processes that bear no resemblance to human decision-making.
This paper introduces an alternative theoretical framework: the API Call Model. Rather than psychological persuasion, agent-mediated commerce operates through three computational phases: Query (structured data requests to multiple sources), Analysis (systematic attribute comparison against objective functions), and Execution (automated transaction completion). We develop seven propositions examining how this architectural shift transforms marketing from a persuasion discipline centered on human psychology to an engineering discipline centered on machine-readable data infrastructure. Our framework challenges fundamental assumptions in marketing theory regarding the nature of consumer decision-making, the role of brands, and the mechanisms of competitive advantage in increasingly algorithm-mediated markets.
Keywords: agentic commerce, AI agents, marketing funnel, AIDA model, consumer behavior, algorithmic intermediation, agent-mediated markets, marketing theory
1. Introduction
In 1898, advertising pioneer E. St. Elmo Lewis articulated principles that would come to define modern marketing: successful advertisements must attract attention, maintain interest, and create conviction (Barry & Howard, 1990). This formulation, later expanded into the AIDA model (Attention, Interest, Desire, Action) and visualized as a “funnel” by Townsend (1924), has remained remarkably durable across technological revolutions. From newspaper advertisements to radio spots, from television commercials to digital banner ads, the fundamental premise has endured: marketing succeeds by guiding human consumers through sequential psychological stages culminating in purchase behavior.
The emergence of agentic commerce threatens to render this century-old paradigm obsolete. We define agentic commerce as economic transactions where artificial intelligence systems possess delegated authority to make or substantially influence purchasing decisions with minimal real-time human oversight. These AI agents range from sophisticated recommendation systems (e.g., Amazon’s algorithms) to autonomous purchasing agents (e.g., smart home devices automatically reordering consumables) to enterprise procurement platforms algorithmically scoring vendors. Early manifestations are already visible: consumers asking ChatGPT for product recommendations, enterprises deploying AI-powered procurement systems, and predictive shopping technologies anticipating consumer needs (Davenport et al., 2020).
The theoretical challenge posed by agentic commerce is fundamental. The marketing funnel assumes a persuadable human audience susceptible to emotional appeals, brand narratives, and psychological manipulation. An AI agent, however, possesses no emotions to manipulate, no status aspirations to exploit, no cognitive biases to leverage. The entire apparatus of modern marketing—from creative advertising to brand management to persuasive copywriting—is predicated on human psychological vulnerabilities absent in algorithmic systems.
This paper makes three primary contributions. First, we provide a systematic critique of the AIDA framework’s applicability to agent-mediated commerce, demonstrating its theoretical incompatibility with algorithmic decision-making processes. Second, we introduce the API Call Model as an alternative theoretical framework, grounded in information systems theory rather than consumer psychology. Third, we develop seven propositions examining the strategic and competitive implications of this paradigm shift for marketing practice and theory.
Our argument proceeds as follows. Section 2 reviews the relevant literature on the marketing funnel, consumer search and decision-making, and the emerging research on algorithmic intermediation. Section 3 provides our theoretical critique of the funnel’s applicability to agentic commerce. Section 4 introduces the API Call Model and its three computational phases. Section 5 develops testable propositions. Section 6 discusses implications for marketing theory and practice. Section 7 outlines a research agenda. Section 8 concludes.
2. Literature Review and Theoretical Background
2.1 The Marketing Funnel: Origins and Evolution
The marketing funnel concept traces its lineage to E. St. Elmo Lewis’s 1898 formulation of advertising principles, later systematized as the AIDA model (Barry & Howard, 1990; Strong, 1925). The model’s core premise—that consumers progress through hierarchical stages from ignorance to purchase—reflects its roots in faculty psychology and associationist learning theory prevalent in late 19th-century thought (Preston, 1982).
The funnel metaphor, formally introduced by Townsend (1924), captured an empirical reality: at each stage of the customer journey, some proportion of potential buyers drops out. A large population becomes aware of a product through advertising; a smaller subset develops sufficient interest to seek more information; fewer still form purchase intentions; and ultimately only a fraction complete transactions. This attrition creates the characteristic funnel shape—wide at the top (awareness) and narrow at the bottom (action).
Subsequent decades witnessed various refinements and extensions. Lavidge and Steiner (1961) expanded AIDA into a six-stage hierarchy of effects model. Rogers (1962) developed diffusion of innovation theory, describing adoption stages from knowledge to confirmation. More recently, scholars have proposed non-linear alternatives acknowledging the complexity of modern customer journeys, including consideration set models (Hauser & Wernerfelt, 1990), decision journey frameworks (Court et al., 2009), and omnichannel touchpoint models (Lemon & Verhoef, 2016).
Despite these modifications, the fundamental assumption persists: marketing succeeds by influencing human psychological states. Whether conceived as hierarchical stages or complex journeys, these models presume a human decision-maker whose awareness, attitudes, preferences, and intentions can be shaped through strategic communication. This psychological foundation becomes problematic when the decision-maker is an algorithm.
2.2 Consumer Search and Information Processing
The information processing tradition in consumer research provides theoretical grounding for understanding how buyers make decisions. Alba and Hutchinson (1987) model consumer expertise as developing through experience with product categories, influencing search behavior and decision strategies. Bettman (1979) proposes that consumers employ various decision strategies depending on task complexity, time pressure, and cognitive effort requirements. This work assumes human cognitive architectures with limited processing capacity, selective attention, and reliance on heuristics to manage information overload (Payne et al., 1993).
Search theory in economics similarly assumes human decision-makers facing search costs and uncertainty (Stigler, 1961; Nelson, 1970). Consumers engage in pre-purchase search to reduce uncertainty about product quality and prices, balancing search costs against expected benefits of additional information. The optimal search strategy depends on the consumer’s prior beliefs, the distribution of prices and quality in the market, and the marginal cost of additional search.
AI agents fundamentally differ from human searchers along multiple dimensions. Agents face effectively zero marginal search costs, enabling exhaustive rather than satisficing search (Simon, 1955). They possess unlimited information processing capacity, eliminating the cognitive constraints that drive humans toward simplified heuristics. They maintain perfect memory, avoiding the recency and availability biases that affect human judgment (Kahneman & Tversky, 1974). These architectural differences suggest agent search behavior may bear little resemblance to human patterns.
2.3 Algorithmic Intermediation and Agent-Mediated Markets
Recent scholarship has begun examining markets where algorithms serve as intermediaries between buyers and sellers. Lambrecht and Tucker (2019) analyze how algorithmic pricing affects market competition. Hagiu and Wright (2020) examine two-sided platforms where algorithmic ranking influences buyer-seller matching. Mussa and Rosen (1978) provide foundational work on quality-based product differentiation, though their model assumes human consumers with heterogeneous preferences rather than algorithmic agents with defined objective functions.
The emerging literature on AI agents in commerce remains fragmented. Computer science research examines agent architectures and negotiation protocols (Jennings et al., 2001) but typically lacks grounding in marketing theory or consumer behavior. Management scholars have studied AI adoption in organizations (Davenport & Ronanki, 2018) but focus more on internal operations than market-facing applications. Marketing researchers have examined recommender systems and personalization algorithms (Ansari et al., 2000; Lambrecht & Tucker, 2013) but these typically augment rather than replace human decision-making.
Two recent working papers directly address agentic commerce. Accornero (2025a) develops the Agent Intent Optimization framework, examining how organizations must optimize data infrastructure for algorithmic evaluation. Accornero (2025b) extends dynamic capabilities theory to explain organizational readiness for agent-mediated competition. However, these contributions focus primarily on strategic implications for firms rather than challenging foundational assumptions in marketing theory regarding the nature of consumer behavior itself.
What remains missing is a systematic theoretical examination of whether—and how—established marketing frameworks apply when the “consumer” is an algorithm rather than a human. This paper addresses that gap by focusing specifically on the marketing funnel’s theoretical validity in agent-mediated markets.
2.4 Theoretical Gap
The literature reviewed above reveals a fundamental disconnect. Marketing theory, including the dominant funnel paradigm, assumes human decision-makers with psychological characteristics—emotions, biases, limited cognition, persuadability. The emerging reality of agentic commerce involves algorithmic decision-makers with completely different characteristics—emotionless, logical, unlimited processing capacity, impervious to persuasion. Existing frameworks do not accommodate this architectural difference.
This gap creates both theoretical and practical challenges. Theoretically, we lack models explaining how markets function when intermediaries are algorithms rather than humans. Practically, organizations continue applying human-centric marketing strategies (emotional branding, persuasive advertising, psychological pricing) to algorithmic audiences that cannot be influenced by these tactics. This paper develops a new theoretical framework specifically designed for agent-mediated commerce, grounded in information systems theory rather than consumer psychology.
3. Theoretical Critique: Why the Funnel Fails in Agentic Commerce
3.1 The Psychological Premise
The marketing funnel’s theoretical foundation rests on a psychological premise: human minds can be guided through sequential mental states from ignorance to commitment. This premise, rooted in 19th-century faculty psychology, assumes discrete cognitive and affective stages that marketing activities can influence (Preston, 1982). The model’s structure—Attention → Interest → Desire → Action—maps directly onto assumed psychological processes.
Attention assumes a human audience with limited perceptual capacity and selective focus. Marketing must “break through” competitive noise to capture scarce human attention. This stage leverages understanding of visual salience, novelty detection, and attention capture mechanisms in human cognition (Kahneman, 1973).
Interest assumes the capacity for curiosity, engagement, and sustained attention. Effective marketing creates interest by connecting products to human needs, aspirations, or identities. This stage exploits psychological mechanisms including self-relevance, hedonic appeal, and narrative transportation (Escalas, 2004).
Desire assumes emotional and motivational systems that can be activated through marketing stimuli. Advertising creates desire by associating products with positive emotional states, social status, or identity expression. This stage leverages extensive research on emotional conditioning, social comparison, and self-concept (Belk, 1988; Richins, 1991).
Action assumes decision-making processes affected by factors like temporal discounting, loss aversion, and commitment. Marketing facilitates action through urgency cues, limited-time offers, and friction reduction. This stage applies behavioral economics insights about human irrationality and present bias (Thaler & Sunstein, 2008).
Each stage presupposes a human decision architecture susceptible to strategic influence. The entire model collapses when applied to an algorithmic decision-maker that lacks these psychological characteristics.
3.2 Architectural Incompatibilities
AI agents exhibit fundamental architectural differences from human consumers across five dimensions:
Dimension 1: Information Processing Capacity
Humans: Limited working memory (Miller, 1956), selective attention, serial processing constraints. Marketing must simplify messages and reduce cognitive load.
Agents: Effectively unlimited parallel processing capability. Can simultaneously evaluate thousands of product attributes across millions of alternatives. Marketing simplification is counterproductive—agents prefer comprehensive structured data.
Dimension 2: Emotional Systems
Humans: Products evoke emotions (happiness, pride, anxiety) that influence decisions. Emotional appeals constitute the foundation of modern branding.
Agents: No emotional systems. Cannot feel happiness from aspirational imagery, pride from status goods, or anxiety from scarcity cues. Emotional branding is invisible to algorithmic evaluation.
Dimension 3: Cognitive Biases
Humans: Systematic biases including anchoring, framing effects, availability heuristics, and loss aversion (Kahneman & Tversky, 1974). Marketing exploits these biases.
Agents: Designed to avoid biases through systematic evaluation against objective functions. Immune to anchoring (evaluates absolute values), framing (parses structured data), and loss aversion (weighs gains and losses equally per programmed utilities).
Dimension 4: Social Influences
Humans: Status seeking, social comparison, conformity, and identity signaling heavily influence consumption (Veblen, 1899; Berger & Heath, 2007). Brands serve as social symbols.
Agents: No social identity, no status aspirations, no peer groups. Cannot be influenced by celebrity endorsements, social proof, or aspirational lifestyles. Evaluates products on user-specified criteria unaffected by social considerations.
Dimension 5: Temporal Discounting
Humans: Present-biased, overweight immediate gratification, respond to scarcity and urgency (Frederick et al., 2002). “Limited time offers” exploit hyperbolic discounting.
Agents: Time-consistent preferences as programmed. Unaffected by artificial scarcity or urgency cues unless explicitly instructed to prioritize speed. “Act now!” messaging is irrelevant to algorithmic evaluation.
These architectural differences are not superficial. They represent fundamental incompatibilities between the psychological mechanisms the funnel assumes and the computational processes agents actually employ.
3.3 Boundary Conditions and Scope
Before proceeding to our alternative framework, we must clarify the scope and boundary conditions of our argument. The API Call Model applies specifically to agent-mediated commerce where AI systems possess substantial autonomous decision-making authority. Several important boundary conditions limit its applicability:
Human-in-the-Loop Decisions.
When humans retain final decision authority, traditional funnel logic may still apply. The model is most relevant when agents have substantial autonomous authority. Hybrid scenarios where agents recommend but humans decide represent an important middle ground requiring integrated frameworks that consider both psychological and computational processes—a limitation we acknowledge for future research. Real markets will likely feature both human and agent buyers simultaneously for extended periods, requiring firms to optimize for mixed audiences.
Emotional vs. Functional Products.
Products purchased primarily for emotional or social reasons (luxury goods, self-expression products, experience goods) may resist agent mediation. Consumers may prefer personal decision-making when symbolic or hedonic value dominates functional value.
Information Availability.
The model assumes relevant product attributes are measurable and codifiable. For products where quality is subjective or experiential (art, entertainment, services), data infrastructure provides incomplete information, limiting agent effectiveness.
Technological Maturity and Agent Evolution.
Current agent capabilities remain limited. Full realization of the model awaits more sophisticated AI systems, ubiquitous data standards, and mature verification infrastructure. As AI agents evolve and potentially develop more human-like characteristics—including learned preferences, synthetic emotional responses, or acquired biases—the boundary between psychological and computational models may blur. Whether future agents with emotional intelligence would respond to traditional marketing remains an open question requiring continued theoretical development.
These boundary conditions suggest our framework applies most directly to: (1) functional products with objectively measurable attributes, (2) autonomous agent decision-making scenarios, (3) markets with mature data infrastructure, and (4) current-generation AI systems operating on logical evaluation principles. The framework’s applicability may evolve as technology advances.
3.4 The Logical Alternative
If agents cannot be guided through psychological stages, how do they make purchasing decisions? The answer lies in understanding agent architecture. At its core, an AI agent is an optimization system: given an objective function and constraints, it identifies the option that maximizes utility. This process is computational, not psychological.
The agent’s “decision journey” consists of: 1. Receiving instructions: The human principal specifies goals and constraints (e.g., “Find the highest-rated coffee maker under $200 with at least 4.5 stars”) 2. Querying data sources: The agent sends structured requests to databases, APIs, and information systems 3. Parsing responses: The agent extracts and normalizes data into comparable formats 4. Applying logic: The agent evaluates alternatives against the objective function using Boolean operations and mathematical comparisons 5. Selecting optimal solution: The agent identifies the alternative that best satisfies all constraints and maximizes the objective function 6. Executing transaction: The agent completes the purchase through payment APIs
This computational sequence bears no resemblance to the psychological journey the marketing funnel describes. The agent never experiences “awareness” (it queries databases, not mental representations), “interest” (it filters data, not emotional engagement), or “desire” (it optimizes functions, not wants). The mismatch is categorical.
3.5 Implications for Marketing Theory
The funnel’s inapplicability to agent-mediated commerce creates a theoretical crisis. If the dominant framework for understanding customer journeys fails when customers are algorithms, what framework applies? More fundamentally, does the concept of “marketing” retain meaning when the audience cannot be persuaded?
We argue marketing must undergo a fundamental reconceptualization. Rather than a persuasion discipline rooted in psychology, marketing in agent-mediated markets becomes an information architecture discipline rooted in computer science and information systems theory. Success requires not creative emotional appeals but comprehensive, accurate, machine-readable data infrastructure. The competitive battleground shifts from mental real estate (mindshare, brand awareness) to data real estate (API accessibility, structured data completeness, verification systems).
This reconceptualization challenges foundational assumptions across marketing subdisciplines: brand management (brands become data aggregations rather than psychological associations), advertising (messages become data feeds rather than persuasive communications), and consumer research (understanding shifts from psychological methods to computational analysis). The implications are far-reaching and demand new theoretical frameworks specifically designed for algorithmic decision-makers.
The next section develops such a framework.
4. The API Call Model: An Alternative Framework
4.1 Conceptual Foundation
We propose the API Call Model as a theoretical framework for understanding agent-mediated commerce. The model replaces the psychological progression of AIDA with a computational sequence of three phases: Query, Analysis, and Execution. Each phase represents a distinct computational process in agent decision-making, grounded in information systems theory rather than consumer psychology.
The model’s name reflects its technical foundation. An Application Programming Interface (API) is a structured protocol enabling software systems to exchange data. In agent-mediated commerce, product discovery and purchase occur through a series of API calls—structured requests and responses between the agent and various information systems. Understanding this technical reality, rather than imposing psychological metaphors, provides analytical clarity.
4.2 Phase 1: Query
The Query phase begins when the agent receives instructions from its human principal and initiates structured information requests to identify potentially suitable products or services. Unlike human consumers who begin with general awareness and gradually narrow consideration sets, agents start with precise specifications and query databases to retrieve matching alternatives.
Technical Process:
The agent constructs queries based on the objective function provided by the user. These queries take structured formats (SQL-like database queries, HTTP API requests, or semantic search vectors) and are transmitted simultaneously to multiple data sources: vendor databases, product information management systems, review aggregators, certification registries, and pricing APIs.
Example query structure:
SELECT product_name, price, specifications, certifications, vendor
FROM product_database
WHERE category = 'coffee_maker'
AND price <= 200
AND avg_rating >= 4.5
AND in_stock = TRUE
The query phase fundamentally differs from the “Attention” stage of AIDA in several ways. First, it is deterministic rather than probabilistic—the agent systematically queries all available data sources rather than relying on serendipitous exposure. Second, it is bilateral—the agent requests specific information rather than passively receiving marketing messages. Third, it is comprehensive—the agent retrieves data on all viable alternatives rather than forming awareness of a few prominent brands.
Implications for Marketing Strategy:
Organizations must ensure their products are present in queryable databases and APIs. Physical shelf presence and advertising awareness become less important than digital data presence. Products absent from machine-readable databases are functionally invisible regardless of brand strength or advertising spend.
4.3 Phase 2: Analysis
The Analysis phase involves systematic evaluation of the retrieved alternatives against the specified objective function and constraints. The agent parses responses, normalizes data formats, validates information accuracy, and applies logical operations to rank alternatives.
Technical Process:
The agent receives responses in various formats (JSON, XML, structured data). It extracts relevant attributes, converts them into comparable units, and constructs a decision matrix. Each product is scored across multiple dimensions using weights derived from the user’s instructions.
Example analysis logic:
FOR each product IN query_results:
score = 0
IF product.price <= budget THEN
score += price_weight * (1 - product.price/budget)
IF product.rating >= minimum_rating THEN
score += rating_weight * product.rating
IF product.certifications CONTAINS required_cert THEN
score += certification_weight
IF product.delivery_time <= max_days THEN
score += speed_weight * (1 - product.delivery_time/max_days)
product.total_score = score
SORT products BY total_score DESCENDING
SELECT TOP 1 product AS optimal_choice
This computational analysis differs fundamentally from the human “Interest” and “Desire” stages. It involves no emotional engagement, no narrative processing, no identity considerations. The agent applies pure logic: which alternative best satisfies the mathematical optimization problem defined by the user’s instructions?
Verification and Trust:
A critical component of the Analysis phase involves verifying claims through cross-referencing. If a vendor claims “organic certification,” the agent queries the certifying body’s API to confirm the certification is current and valid. This verification capability eliminates the information asymmetry that brands historically exploited—agents can independently verify any factual claim.
Implications for Marketing Strategy:
Competition shifts from psychological differentiation (brand image, emotional positioning) to objective differentiation (verifiable attributes, measurable performance, documented certifications). Marketing claims must be supported by structured data accessible through APIs. Unverifiable assertions are treated as null values—worse than negative, they are simply absent from the decision calculus.
4.4 Phase 3: Execution
The Execution phase involves completing the transaction once the optimal alternative has been identified. This phase includes payment processing, order confirmation, delivery arrangement, and transaction recording.
Technical Process:
Having identified the optimal product-vendor combination, the agent initiates the purchase through payment APIs. This typically involves: 1. Sending transaction details to the vendor’s order API 2. Transmitting payment tokens (not raw financial data) to payment processors 3. Receiving order confirmation and tracking information 4. Recording transaction details for future analysis 5. Monitoring delivery status through logistics APIs
Unlike human consumers who may experience doubt, hesitation, or post-purchase cognitive dissonance, agents complete transactions mechanically once the Analysis phase identifies an optimal solution. The “Action” barrier in AIDA—converting desire into purchase—doesn’t exist for agents. If the analysis determines a product is optimal, execution follows automatically unless technical errors occur.
Implications for Marketing Strategy:
The conversion optimization tactics central to human marketing (urgency cues, cart abandonment emails, friction reduction) become irrelevant. Agents don’t abandon carts due to hesitation; they either complete purchases or don’t based purely on whether alternatives satisfy constraints. The focus shifts to ensuring technical infrastructure (APIs, payment systems, inventory management) functions reliably.
4.5 Theoretical Contrast: Funnel vs. API Call Model
Figure 1: Theoretical Contrast – AIDA Funnel vs. API Call Model
Note: This figure illustrates the fundamental difference between the AIDA Funnel (left) and the API Call Model (right). The AIDA Funnel depicts a psychological process where human consumers progressively narrow from broad awareness to final purchase action, with characteristic attrition at each stage creating the funnel shape. Each stage relies on persuasion tactics targeting human emotions, biases, and limited cognition. In contrast, the API Call Model depicts a computational process where AI agents execute parallel operations without attrition. The rectangular (non-funnel) shape reflects that agents do not experience psychological filtering—all viable alternatives meeting query criteria are systematically evaluated. The foundational difference—consumer psychology versus information systems theory—drives distinct predictions about market dynamics, competitive advantage sources, and effective marketing strategies. This is not a refinement but a categorical paradigm shift from persuasion-based to information-based competition in agent-mediated markets.
Table 1 systematizes the theoretical differences:
[Table 1: Theoretical Comparison of AIDA Funnel and API Call Model]
Note: This comparison reveals not a refinement but a categorical difference between psychological and computational approaches to understanding purchase behavior. The frameworks make fundamentally different assumptions about decision-maker architecture and therefore predict different market dynamics.
Key Insight: The API Call Model is not "AIDA for machines"—it represents a paradigm shift from persuasion-based to information-based competition.
5. Propositions and Theoretical Development
Based on the API Call Model, we develop seven propositions examining how agent-mediated commerce transforms marketing dynamics. These propositions are testable through empirical research, which we identify as directions for future work.
Proposition 1: Data Completeness and Competitive Advantage
P1: In agent-mediated markets, product data completeness (the proportion of queryable attributes for which structured data exists) positively predicts market share, controlling for product quality and price.
Theoretical Rationale:
The Query phase of the API Call Model requires products to exist in machine-readable databases. When agents evaluate alternatives, products with incomplete data are systematically disadvantaged. Missing attributes are treated as null values, effectively removing those products from consideration for queries requiring those attributes.
Consider two functionally identical products: Product A provides comprehensive structured data (100 attributes) while Product B provides minimal data (20 attributes). When an agent queries “highest rated product with organic certification under $50,” Product A is evaluated if its data includes organic certification status. Product B is excluded if that attribute is missing, regardless of whether the physical product actually possesses organic certification. Data completeness becomes a prerequisite for consideration.
This creates a new source of competitive advantage unrelated to traditional marketing investments. A product with inferior quality but superior data infrastructure may outperform higher-quality products with poor data presence. This inverts traditional assumptions about the relationship between product quality and market performance.
Proposition 2: Advertising Effectiveness Inversion
P2: In agent-mediated markets, the correlation between advertising expenditure and market share weakens or reverses compared to human-mediated markets.
Theoretical Rationale:
The AIDA funnel assumes advertising creates awareness and shapes preferences. This assumption requires a human audience exposed to and influenced by advertising messages. In agent-mediated markets, advertising plays no direct role. Agents don’t watch television, read magazines, or encounter display ads. They query databases.
Traditional high-advertising brands (Coca-Cola, Nike, Apple) built dominant market positions by creating strong psychological associations in human minds. These associations are invisible to agents. When an agent receives instructions to “find the highest-rated fitness tracker under $300,” it evaluates specifications and reviews—not brand image built through advertising. An unknown brand with superior specifications and higher ratings may be selected over established brands with massive advertising budgets.
This proposition challenges fundamental assumptions in marketing strategy regarding the returns to advertising investment. If agent-mediated transactions grow, organizations may discover that reallocating advertising budgets toward data infrastructure generates superior returns.
Proposition 3: Verifiability as Differentiator
P3: In agent-mediated markets, third-party verified attributes (certifications, test results, audited claims) command price premiums exceeding those of unverified marketing claims.
Theoretical Rationale:
The Analysis phase incorporates verification mechanisms. Agents can cross-reference claims against external databases. A claim of “organic” accompanied by an API-verifiable USDA certification carries decision weight; an unverified claim of “natural” does not.
This creates a new competitive dynamic around verifiability. Organizations must invest in obtaining third-party certifications, submitting to independent testing, and ensuring these verifications are machine-readable. The cost of verification becomes a barrier to entry, but verified attributes command premium prices because agents weight them heavily in optimization.
This proposition suggests a bifurcation: verified premium products and unverified commodity products, with the middle ground (self-proclaimed quality without verification) collapsing as agents dismiss unverifiable claims.
Proposition 4: Brand Equity Transformation
P4: In agent-mediated markets, brand equity shifts from emotional associations (psychological value) to data aggregation efficiency (computational value).
Theoretical Rationale:
Traditional brand equity theory emphasizes emotional associations, perceived quality, and identity expression (Keller, 1993; Aaker, 1991). These psychological constructs influence human purchase decisions. Agents, lacking psychology, cannot be influenced by emotional brand associations.
However, brands retain value in agent-mediated markets through a different mechanism: data aggregation. A recognized brand name serves as a proxy for a bundle of verified attributes. When an agent encounters “Patagonia” as a clothing brand, it can retrieve data about sustainability certifications, labor practices, product durability, and return policies—all aggregated under that brand identifier. The brand functions as an index key in databases, enabling efficient data retrieval.
This transformation preserves brand value while fundamentally changing its nature. Brands become valuable not because humans love them but because they efficiently aggregate machine-readable data. Organizations must shift brand management from psychological positioning to data infrastructure management.
Proposition 5: Price Sensitivity Increase
P5: In agent-mediated markets, consumer price sensitivity increases for products where quality is objectively measurable, as agents eliminate the price-quality heuristic humans employ.
Theoretical Rationale:
Humans often use price as a quality signal (Rao & Monroe, 1989), assuming higher prices indicate higher quality when direct quality assessment is difficult. This heuristic allows brands to command premium prices even when objective quality differences are minimal. Marketing reinforces this through prestige positioning.
Agents don’t employ heuristics—they evaluate quality directly through data. If two products have identical specifications, performance metrics, and reviews, the agent selects the lower-priced option regardless of brand. The price-quality inference collapses when quality is directly measurable.
This proposition suggests commoditization pressure on categories where quality is objectively verifiable. Premium pricing requires either genuinely superior attributes or attributes agents cannot verify. Psychological premium pricing becomes unsustainable.
Proposition 6: Search Cost Irrelevance
P6: In agent-mediated markets, product discovery becomes independent of consumer search costs, eliminating the competitive advantages that historically accrued to high-awareness brands.
Theoretical Rationale:
Human consumers face high search costs (time, effort, cognitive load) leading to satisficing behavior and reliance on familiar brands (Simon, 1955). High brand awareness reduces search costs, creating competitive advantage for heavily advertised brands (Keller, 1993).
Agents face near-zero marginal search costs. Querying 1,000 databases requires no more human effort than querying one. This enables exhaustive search rather than satisficing. An obscure brand with superior attributes is equally discoverable as a famous brand if both exist in queryable databases.
This proposition predicts market share redistribution from high-awareness brands to objectively superior products, independent of marketing investments in awareness building.
Proposition 7: Temporal Purchase Pattern Shift
P7: In agent-mediated markets, purchase cycles shift from episodic (triggered by need recognition) to continuous (automated monitoring and optimization), increasing category purchase frequency.
Theoretical Rationale:
Human purchase behavior follows episodic patterns: need recognition triggers search, evaluation, and purchase (Howard & Sheth, 1969). Between episodes, consumers remain inactive even if superior alternatives emerge.
Agents can monitor continuously. A delegated agent with standing instructions (“maintain home coffee supply at optimal price-quality ratio”) continuously monitors prices, reviews, and alternatives. When a superior option emerges or prices change, the agent automatically repurchases, even if the human wouldn’t have recognized the need or opportunity.
This proposition suggests transformation from episodic purchase occasions to continuous optimization, increasing category purchase frequency and amplifying competitive volatility as market positions become less stable.
6. Implications for Theory and Practice
6.1 Implications for Marketing Theory
The API Call Model and associated propositions challenge several foundational assumptions in marketing theory:
Challenging the Persuasion Paradigm
Marketing theory traditionally conceptualizes the field as a persuasion discipline: firms communicate messages designed to influence consumer attitudes and behaviors (Percy & Elliott, 2016). This paradigm assumes persuadable audiences. Agent-mediated commerce eliminates persuadability, requiring reconceptualization of marketing’s core function from persuasion to information provision.
Redefining Brands
Brand theory emphasizes psychological associations and emotional connections (Keller, 1993; Fournier, 1998). In agent-mediated markets, brands serve computational functions—data aggregation, verification proxies, search cost reduction—rather than emotional functions. This necessitates new theoretical frameworks for understanding brand value rooted in information systems rather than psychology.
Reconceptualizing Competition
Competitive strategy in marketing assumes firms compete for “mindshare” and favorable positioning in consumer consideration sets (Keller, 2001). When consumers are algorithms, competition shifts to “database presence” and favorable positioning in query results. Competitive dynamics require new theoretical frameworks incorporating information infrastructure as a source of advantage.
Expanding Consumer Behavior Scope
Consumer behavior theory focuses on human psychology (Bettman, 1979; Kahneman & Tversky, 1974). As non-human entities (algorithms) increasingly mediate or make consumption decisions, the field must expand to incorporate computational decision-making alongside psychological processes. This represents a paradigm expansion comparable to extending microeconomics to incorporate behavioral insights.
6.2 Implications for Marketing Practice
Strategic Reallocation
Organizations must reallocate resources from traditional awareness-building (advertising, public relations) toward data infrastructure (APIs, structured data, certification systems). The optimal marketing mix shifts dramatically when the audience cannot be persuaded.
Organizational Restructuring
Marketing and IT functions must integrate. The Chief Marketing Officer and Chief Technology Officer must co-own “digital shelf” management. Organizations need data scientists, API developers, and information architects in marketing departments alongside traditional brand managers and creative directors.
Skill Set Evolution
Marketing professionals need technical skills: data structuring, API design, schema implementation, verification systems. Traditional advertising and creative skills become less central. Marketing education must adapt accordingly.
Measurement Transformation
Marketing metrics must evolve from psychological measures (brand awareness, consideration, preference) to technical measures (API response time, data completeness rates, verification coverage, query success rates). What gets measured must reflect what actually drives agent decisions.
6.4 Ethical Considerations
Agent-mediated commerce raises ethical questions beyond this paper’s scope but warrant acknowledgment:
Principal-Agent Problems
When AI agents make decisions on behalf of humans, whose interests do they serve—the ostensible human principal, the platform operating the agent, or vendors paying for favorable algorithmic treatment? Traditional principal-agent theory (Jensen & Meckling, 1976) must extend to algorithmic contexts.
Manipulation Resistance
While agents resist psychological manipulation, they may be susceptible to data manipulation (false specifications, fake reviews, manipulated APIs). New forms of market manipulation may emerge.
Equity and Access
If agent-mediated commerce advantages products with sophisticated data infrastructure, smaller firms and developing-market producers may face systematic disadvantage, raising distributional equity concerns.
7. Research Agenda
The propositions developed in Section 5 suggest multiple research directions:
Empirical Validation
Researchers should test whether data completeness, verification coverage, and API accessibility predict market performance in categories with significant agent mediation. Natural experiments comparing agent-mediated vs. human-mediated purchase outcomes would provide strong evidence.
Experimental Approaches
Laboratory experiments can isolate specific mechanisms. Comparing agent vs. human purchase decisions while manipulating data completeness, brand familiarity, and price would test specific propositions while controlling for confounds.
Computational Modeling
Agent-based models simulating markets with varying proportions of human vs. algorithmic buyers could explore competitive dynamics, market concentration effects, and welfare implications.
Longitudinal Studies
Tracking how specific categories evolve as agent mediation increases would document real-world transformation patterns, identifying which predictions materialize and which boundary conditions matter most.
Cross-Cultural Research
Cultural values may influence agent delegation and objective function specification (Hofstede, 2001). Comparative research across cultures could identify whether the API Call Model generalizes or requires cultural adaptation.
8. Conclusion
The marketing funnel has served as the dominant paradigm for understanding consumer purchase behavior for over 125 years. Its longevity reflects genuine insight into human psychology and the effectiveness of persuasion-based marketing when targeting human audiences. However, the emergence of agentic commerce—where autonomous AI agents make or substantially influence purchasing decisions—renders this paradigm obsolete.
This paper has demonstrated the categorical mismatch between the funnel’s psychological assumptions and the computational reality of algorithmic decision-making. We introduced the API Call Model as an alternative theoretical framework grounded in information systems theory. The model replaces psychological persuasion with computational processes: Query (structured data requests), Analysis (systematic optimization), and Execution (automated transactions). Seven propositions develop testable implications regarding data completeness, advertising effectiveness, verifiability, brand equity, price sensitivity, search costs, and purchase patterns.
Our argument carries significant implications for both theory and practice. Theoretically, marketing must reconceptualize its core function from persuasion to information provision, brands from emotional associations to data aggregations, and competition from mindshare battles to database presence contests. Practically, organizations must reallocate resources from advertising to data infrastructure, restructure marketing organizations around technical capabilities, and develop entirely new skill sets.
This transformation is already underway. AI-powered search, recommendation systems, and autonomous purchasing platforms are proliferating. Organizations continuing to apply funnel-era strategies to algorithmic audiences will find themselves systematically disadvantaged, regardless of brand strength or marketing budgets. Success in agent-mediated commerce requires not better persuasion but better data—comprehensive, accurate, verified, and machine-readable.
The death of the funnel marks not the death of marketing but its transformation. From art to engineering, from psychology to information science, from persuasion to infrastructure—marketing must evolve or risk obsolescence in an increasingly algorithm-mediated economy. This paper provides a theoretical foundation for that evolution, but substantial empirical work remains to validate, refine, and extend these ideas. The research agenda outlined above offers pathways forward for scholars willing to question long-held assumptions and develop frameworks adequate to our algorithmic age.
References
Aaker, D. A. (1991). Managing Brand Equity. Free Press.
Accornero, P. F. (2025a). Agent Intent Optimization: A conceptual framework for marketing in the age of autonomous AI shoppers. Unpublished manuscript.
Accornero, P. F. (2025b). Competing in the age of algorithmic intermediation: A dynamic capabilities framework for algorithmic readiness. Unpublished manuscript.
Alba, J. W., & Hutchinson, J. W. (1987). Dimensions of consumer expertise. Journal of Consumer Research, 13(4), 411-454. https://doi.org/10.1086/209080
Ansari, A., Essegaier, S., & Kohli, R. (2000). Internet recommendation systems. Journal of Marketing Research, 37(3), 363-375. https://doi.org/10.1509/jmkr.37.3.363.18779
Barry, T. E., & Howard, D. J. (1990). A review and critique of the hierarchy of effects in advertising. International Journal of Advertising, 9(2), 121-135. https://doi.org/10.1080/02650487.1990.11107138
Belk, R. W. (1988). Possessions and the extended self. Journal of Consumer Research, 15(2), 139-168. https://doi.org/10.1086/209154
Berger, J., & Heath, C. (2007). Where consumers diverge from others: Identity signaling and product domains. Journal of Consumer Research, 34(2), 121-134. https://doi.org/10.1086/519142
Bettman, J. R. (1979). An Information Processing Theory of Consumer Choice. Addison-Wesley.
Court, D., Elzinga, D., Mulder, S., & Vetvik, O. J. (2009). The consumer decision journey. McKinsey Quarterly, June, 96-107.
Davenport, T. H., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24-42. https://doi.org/10.1007/s11747-019-00696-0
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.
Escalas, J. E. (2004). Narrative processing: Building consumer connections to brands. Journal of Consumer Psychology, 14(1-2), 168-180. https://doi.org/10.1207/s15327663jcp1401&2_19
Fournier, S. (1998). Consumers and their brands: Developing relationship theory in consumer research. Journal of Consumer Research, 24(4), 343-373. https://doi.org/10.1086/209515
Frederick, S., Loewenstein, G., & O’Donoghue, T. (2002). Time discounting and time preference: A critical review. Journal of Economic Literature, 40(2), 351-401. https://doi.org/10.1257/002205102320161311
Hagiu, A., & Wright, J. (2020). When data creates competitive advantage. Harvard Business Review, 98(1), 94-101.
Hauser, J. R., & Wernerfelt, B. (1990). An evaluation cost model of consideration sets. Journal of Consumer Research, 16(4), 393-408. https://doi.org/10.1086/209225
Hofstede, G. (2001). Culture’s Consequences: Comparing Values, Behaviors, Institutions and Organizations Across Nations (2nd ed.). Sage.
Howard, J. A., & Sheth, J. N. (1969). The Theory of Buyer Behavior. Wiley.
Jennings, N. R., Faratin, P., Lomuscio, A. R., Parsons, S., Wooldridge, M., & Sierra, C. (2001). Automated negotiation: Prospects, methods and challenges. Group Decision and Negotiation, 10(2), 199-215. https://doi.org/10.1023/A:1008746126376
Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305-360. https://doi.org/10.1016/0304-405X(76)90026-X
Kahneman, D. (1973). Attention and Effort. Prentice-Hall.
Kahneman, D., & Tversky, A. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124-1131. https://doi.org/10.1126/science.185.4157.1124
Keller, K. L. (1993). Conceptualizing, measuring, and managing customer-based brand equity. Journal of Marketing, 57(1), 1-22. https://doi.org/10.2307/1252054
Keller, K. L. (2001). Building customer-based brand equity: A blueprint for creating strong brands. Marketing Management, 10(2), 15-19.
Lambrecht, A., & Tucker, C. (2013). When does retargeting work? Information specificity in online advertising. Journal of Marketing Research, 50(5), 561-576. https://doi.org/10.1509/jmr.11.0503
Lambrecht, A., & Tucker, C. E. (2019). Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads. Management Science, 65(7), 2966-2981. https://doi.org/10.1287/mnsc.2018.3093
Lavidge, R. J., & Steiner, G. A. (1961). A model for predictive measurements of advertising effectiveness. Journal of Marketing, 25(6), 59-62. https://doi.org/10.2307/1248516
Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69-96. https://doi.org/10.1509/jm.15.0420
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81-97. https://doi.org/10.1037/h0043158
Mussa, M., & Rosen, S. (1978). Monopoly and product quality. Journal of Economic Theory, 18(2), 301-317. https://doi.org/10.1016/0022-0531(78)90085-6
Nelson, P. (1970). Information and consumer behavior. Journal of Political Economy, 78(2), 311-329. https://doi.org/10.1086/259630
Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The Adaptive Decision Maker. Cambridge University Press. https://doi.org/10.1017/CBO9781139173933
Percy, L., & Elliott, R. H. (2016). Strategic Advertising Management (5th ed.). Oxford University Press.
Preston, I. L. (1982). The association model of the advertising communication process. Journal of Advertising, 11(2), 3-15. https://doi.org/10.1080/00913367.1982.10672800
Rao, A. R., & Monroe, K. B. (1989). The effect of price, brand name, and store name on buyers’ perceptions of product quality: An integrative review. Journal of Marketing Research, 26(3), 351-357. https://doi.org/10.2307/3172907
Richins, M. L. (1991). Social comparison and the idealized images of advertising. Journal of Consumer Research, 18(1), 71-83. https://doi.org/10.1086/209242
Rogers, E. M. (1962). Diffusion of Innovations. Free Press.
Simon, H. A. (1955). A behavioral model of rational choice. Quarterly Journal of Economics, 69(1), 99-118. https://doi.org/10.2307/1884852
Stigler, G. J. (1961). The economics of information. Journal of Political Economy, 69(3), 213-225. https://doi.org/10.1086/258464
Strong, E. K. (1925). The Psychology of Selling and Advertising. McGraw-Hill.
Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving Decisions about Health, Wealth, and Happiness. Yale University Press.
Townsend, W. W. (1924). Bond Salesmanship. Moody’s Investors Service.
Veblen, T. (1899). The Theory of the Leisure Class. Macmillan. https://doi.org/10.1037/14834-000
Author Note: This working paper establishes a theoretical framework for understanding agentic commerce—an emerging phenomenon with significant implications for marketing theory and practice. By releasing this paper as a working paper, the author seeks to establish theoretical priority on this topic while inviting scholarly dialogue and collaboration. Empirical studies testing the propositions developed herein are planned. The author welcomes comments, suggestions, and potential research collaborations. Please contact paul.accornero@gmail.com.