
AI
AGENTS

Unique Neural Network Architecture
The agents employ a neural network structure specifically designed to mirror the key aspects of human decision-making in response to advertising:
Perception Layer: Processes incoming advertising messages
Context Layer: Integrates personal characteristics and historical purchase behavior
Decision Layer: Generates behavioral responses that mirror real consumer decisions
Behavioral Modeling
Each AI agent is initialized with unique characteristics that influence their response to advertising:
Individual preference vectors
Price sensitivity parameters
Brand loyalty metrics
Historical purchase patterns
Demographic attributes
Psychographic profiles
SIMULATION
ENVIRONMENT

PROBLEMS
AI AGENTS SOLVE
The simulation environment creates a controlled space where we can:
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Test Message Effectiveness
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Deploy different ad messages simultaneously or sequentially
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Track immediate response patterns
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Monitor long-term behavioral changes
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Measure Complex Interactions
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Message-to-message interference
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Cumulative exposure effects
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Competitive message interaction
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Identify unexpected ripple effects
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"The most effective attribute" problem
We can test how different product attributes affect purchasing behavior.
Each AI agent as a simulated customer has their own preferences, needs, and decision-making patterns. When you want to test product attributes, we can:
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Present different product attributes to these AI agents
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The AI agents then "react" to these attributes based on their programmed characteristics, showing:
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Which attributes most strongly influence purchase decisions
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How different customer segments respond to each attribute
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Whether certain attributes matter more to some customers than others
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If combinations of attributes work better than individual ones
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"Message-to-message interference" problem
AI Agents may demonstrate us how different advertising messages can affect (or interfere with) each other's effectiveness when consumers are exposed to multiple messages over time. For example:
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If a consumer sees an ad emphasizing your product's low price, and then later sees another ad highlighting its premium quality, these messages might conflict in the consumer's mind, potentially reducing the effectiveness of both messages.
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When multiple messages from the same brand compete for attention or create cognitive dissonance. For instance, if a car company simultaneously runs ads about environmental friendliness and high-performance capabilities, these messages might interfere with each other and confuse the consumer's perception of the brand.
"Ripple effects" problem
In the simulation we can observe the indirect or secondary consequences of an advertising message that extend beyond its primary intended impact. For example:
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An ad targeting young professionals might unexpectedly influence their parents' purchasing decisions
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A promotion in one product category might affect sales in seemingly unrelated categories
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A message that successfully attracts new customers might simultaneously cause existing customers to question their loyalty
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A competitive response to your advertising might trigger a chain reaction in the market that affects multiple brands and customer segments
"Cumulative exposure" problem
Simulation environment helps us to observe how repeated exposures to advertising messages over time can create a compounded impact on consumer behavior that's different from a single exposure. Here's how it works:
Memory Effects
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Each exposure builds upon previous exposures
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Messages might be more effective when spaced out over time
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Too many exposures too quickly can lead to advertising fatigue
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Too few exposures or exposures too far apart might fail to create lasting impact
THE HUMAN ELEMENT

Every customer is a unique individual with a story that goes beyond their purchasing habits Our goal is not just to monitor customer actions, but to comprehend the reasons behind their decisions. By grasping their motivations instead of just their behaviors, we can better anticipate and respond to their evolving needs over time.
While technology aids in understanding our customers, we must always remember their individuality. The true strength lies in unique traits rather than relying on averages. What some may consider outliers, we see distinct expressions of human character, with each person showcasing their own special qualities.
Conventional customer segmentation often forces complex individuals into simplistic categories, which we reject. Rather than imposing arbitrary divisions, we celebrate the inherent diversity and fluidity of human preferences and behaviors. This mindset allows us to view our customers authentically, rather than through a restrictive lens.
These beliefs underpin Divera's AI technology. Our AI Agents are not mere data sets; they are digital reflections of real people, each possessing their own mindset waiting to be understood. We perceive these profiles not as numbers, but as virtual personas that embody the complexity and richness of humanity.
MIND TO MODEL

The Three Foundations of AI Agent Development
Our AI system relies on three vital data types to craft a genuine virtual persona:
1. Purchase Behavior Analytics
We analyze trends in commonly bought and used products/services to establish baseline behavioral patterns This not only highlights customers' purchasing habits but also illustrates their interactions with products over time and in various contexts.
2. Brand Perception Mapping
We investigate individuals' beliefs about brands across different products and services. This in-depth exploration of brand perceptions allows us to grasp the emotional and rational ties people form with various brands, shedding light on their decision-making processes.
3. Biological Response Data: Unlocking the Subconscious
Biological data paramount as it provides insights into subconscious reactions. These measurements offer unfiltered perspectives on authentic emotional and cognitive responses, circumventing the limitations of self-reported information.
This comprehensive approach merges behavioral patterns, conscious perceptions, and unconscious responses to build a virtual identity that accurately reflects the intricacies of human decision-making.
From Mind to Model
We employ a sophisticated process to transform individual customer experiences and beliefs into actionable digital insights. Here's how we unlock the hidden patterns of consumer loyalty.
Data Capture and Neural Response Analysis
For each individual we virtualize, we capture the residual imprints of their experiences in belief form, converting them into structured digital data. Our proprietary algorithm measures subconscious reactions to specific belief statements, quantifying them as probability metrics for potential opinion shifts. These subconscious responses are captured through precise biological measurements, providing unfiltered insights into genuine consumer reactions.
Vector-Based Analysis
We transform all biological data into probability vectors with defined direction and magnitude. This mathematical approach allows us to precisely map and analyze consumer responses within a multidimensional space of preferences and behaviors.
Creating Individual Mind Models
The end result is a unique mental model that reveals why a specific individual maintains loyalty to particular brands and products. This model goes beyond traditional behavioral analysis to uncover the deep-seated motivations and beliefs driving consumer choices.
THE ENGINE OF
AI AGENTS

Each virtual consumer is powered by a unique artificial neural network that functions as a decision engine. The unconventional use of artificial neural networks enabled the virtual consumers to make authentic decisions.
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