AI
AGENTS
AI Agents: Simulated representations of real individuals
The AI agents are built on a neural network framework designed to mimic key aspects of human decision-making influenced by experiences with products or services. Each AI agent possesses unique characteristics that shape their to interactions with products or services.
SIMULATION
ENVIRONMENT
The simulation environment allows AI agents to engage with products or services in a controlled setting where we can:
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Test Investment Effectiveness
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Deploy different improvements in customer experience simultaneously or sequentially
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Observe immediate response trends and infer market share growth
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Monitor long-term behavioral changes
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Measure Complex Interactions
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Cumulative exposure effects
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Competitive experience interaction
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Identify unexpected ripple effects
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PROBLEMS
AI AGENTS SOLVE
"The most effective experience" challenge
We can analyze how various product or service experiences influence purchasing behavior. Each AI agent, acting as a simulated customer, possesses unique preferences, needs, and decision-making patterns. When testing enhancements in customer experiences, we can:
- Present different qualities of specific experiences to AI agents.
- The AI agents then "respond" to these experiences based on their training, revealing: - Which experiences most significantly affect purchase decisions.
- How different customer segments react to various experiences. - Whether the level of investment or improvement should be substantial or minimal.
- If combinations of attributes yield better results than individual ones.
"Ripple effects" problem
In the simulation we can observe the indirect or secondary consequences of a customer experience that extend beyond its primary intended impact. For example:
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A customer experience targeting young professionals might unexpectedly influence their parents' purchasing decisions
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A customer experience that successfully attracts new customers might simultaneously cause existing customers to question their loyalty
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A competitive response to your customer experience 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 the different levels of customer experience improvements 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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Experiences might be more effective when spaced out over time
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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 SubconsciousBiological 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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