How Digital Scan Works

Digital Scan is an AI-powered tool that predicts behavioral traits without requiring a traditional questionnaire. Built on millions of data points, it provides fast, reliable insights for marketing, communication, and alignment purposes.

Overview

Digital Scan is an advanced AI-driven tool that analyzes behavioral traits from minimal data inputs—such as a person’s name, job title, activities, and company—to generate predictive behavioral insights. This technology enables businesses to personalize interactions, optimize talent management, and enhance client engagement without requiring individuals to complete a traditional psychometric discovery process.

The Digital Scan system is powered by sophisticated machine learning (ML) and artificial intelligence (AI) algorithms, trained to forecast the results that would typically be produced if a person were to complete the full-scan discovery process.

To understand how Digital Scan achieves this, it's essential to first explore how traditional psychometric discoveries work and how they provide insight into human behavior.

How Do Psychometric Discoveries Work?

Psychometric discoveries are structured tools designed to evaluate personality traits, decision-making styles, and interpersonal behaviors. These methods rely on structured response formats and statistical analysis to derive accurate behavioral insights.

The Full-Scan Discovery Process

Before 2025, the Full-Scan Discovery was referred to as the Natural DNA Behavior Discovery. It is an independently validated process, confirmed by multiple university research groups and PhDs. The validation methodology and scientific rigor behind this discovery process can be reviewed here.

Participants engage in a structured behavioral discovery where they complete a series of carefully designed questions.

Each question consists of three behavioral statements, and the participant selects:

✔ One statement that describes them the most
✔ One statement that describes them the least
✔ One statement left blank

This forced-choice method prevents bias and helps uncover an individual’s natural behavioral tendencies.

Example Question:
A participant completing the full-scan discovery would see this question as #2 of 46:

If the participant selects "Expresses optimism" as "Most" and "Lively imagination" as "Least," we can infer several behavioral traits:

• They are naturally optimistic, eager, and positive in outlook—sometimes even overly enthusiastic.
• They are not highly imaginative or creative.
• Their degree of self-reliance is moderate since they did not select "Self-reliant" as either Most or Least.

A person answering 46 questions like this builds a highly detailed behavioral profile. Each selection contributes to a broader dataset that helps identify patterns in their decision-making and interactions.

At the end of the discovery, responses are synthesized and scored against a global dataset of previously completed profiles. The individual receives a report detailing their behavioral strengths, preferences, and potential blind spots.

Example Profile Based on the Above Responses

A person who is naturally optimistic, not highly creative, and moderately self-reliant might thrive in a sales role—but not just any sales role. They would likely excel in an industry where they are selling a proven, established product or service rather than something innovative or disruptive.

For example, they could be a sales representative in a well-known insurance company or a commercial real estate agent selling traditional office spaces. Their optimism would help them engage clients, while their lack of high creativity suggests they are more comfortable working within structured guidelines rather than developing new solutions. Additionally, since they are moderately self-reliant, they would likely prefer working as part of a team rather than in a highly independent, commission-only role.

Now, consider that this profile was built based on just one question from the discovery. Each response provides a deeper layer of insight into how a person thinks, behaves, and makes decisions. If we multiply this process across 46 different questions, we gain a remarkably detailed and highly accurate behavioral profile—helping us understand a person's natural tendencies with scientific precision.

How Digital Scan Works

Now that we have covered how Psychometric Discoveries work, let’s explore the extensive research and development efforts undertaken by DNA Behavior since 2017 to build a scientifically validated model capable of predicting and forecasting behavioral insights using Artificial Intelligence (AI) and Machine Learning (ML).

Data Collection & Research (2017–2024)

Since 2017, DNA Behavior has conducted extensive research, collecting over 3.25 million full-scan responses. This dataset includes answers to all 46 psychometric questions (138 individual behavioral data points per person).

To enhance predictive accuracy, DNA Behavior also collected additional voluntary demographic and contextual data, including:

✔ Job titles, activities, and industries
✔ Company size and type
✔ Education background
✔ Geographic location
✔ Writing style and sentiment
✔ Career advancements
✔ Political and religious preferences
✔ Consumer preferences (cars, lifestyle choices)

Using this enriched dataset, DNA Behavior applied machine learning to analyze patterns and correlations between behavioral discovery results and demographic attributes.

AI & Machine Learning Methodology

The Digital Scan algorithm is built using Python-based ML models and operates in a secure Microsoft Azure cloud environment. The model utilizes a combination of:

✔ Machine Learning (ML): Predictive analytics to match behavioral traits with similar individuals in the dataset
✔ Large Language Models (LLM): Word embeddings and tokenization to standardize job titles, user inputs, and items gathered from the DNA Behavior AI web-miner

When a Digital Scan request is made, the AI system:

1. Processes user input (name, job title, company, and other contextual details).
2. Matches user attributes with millions of prior responses from similar individuals.
3. Uses LLM for web embeddings, tokenization and as a web miner: The model scans the open web and demographic data partner databases for additional behavioral indicators. This allows Digital Scan to enhance accuracy by incorporating external data sources relevant to the participant's attributes.
4. Generates a behavioral profile that mirrors the accuracy of a traditional full-scan discovery.

This method enables DNA Behavior to create a psychometric profile without requiring direct responses, while ensuring Digital Scan records remain compatible with Full-Scan records. This allows firms to compare, analyze, and report insights seamlessly within the DNA Web App.

Frequently Asked Questions:

Accuracy & Validation

How Accurate is Digital Scan?

Currently, Digital Scan has an accuracy rate of 70%+ compared to the full-scan discovery.

While this level of accuracy is exceptionally high for the industry, Digital Scan is not intended for high-stakes decisions due to the depth and precision of the full-scan discovery.

Labels including messages such as “Do not use for high-stakes decisions” appear throughout the application to remind users of the predictive nature of the results. This disclaimer appears whenever a client views results that were generated via Digital Scan rather than through a direct full-scan discovery.

How is Accuracy Measured?

DNA Behavior has implemented two native feedback loops to continuously refine the accuracy of Digital Scan, in addition to robust model improvements.

1. Users of the DNA Web App can provide direct feedback on Digital Scan results by selecting a thumbs up or thumbs down button, indicating how well the insights align with the individual’s actual behavior.
   
2. Digital Scan is intended for marketing, communication, and alignment purposes only—and not for high-stakes decisions—users seeking deeper accuracy are instructed to have individuals complete the Full-Scan Discovery. This enables a test-retest validation process, where the model compares Digital Scan predictions to Full-Scan results, further refining its accuracy over time.

✔ Direct Comparisons: The model's predictions are compared to actual full-scan discovery results for the same individuals.
✔ Model Refinement: Continuous improvements are made using real-world data feedback to increase precision.
✔ Statistical Validation: DNA Behavior uses cross-validation techniques and predictive analytics to ensure robustness.

What machine learning techniques does Digital Scan use?

The model relies on:

• Supervised Machine Learning (trained on labeled psychometric data)
• Natural Language Processing (NLP) (to interpret job titles and free-text inputs)
• Word Embeddings & Tokenization (to standardize data formats)
• Web Mining via LLMs (to enhance behavioral predictions with external data)

Does Digital Scan replace the full discovery?

No—while Digital Scan provides a highly accurate behavioral prediction, organizations that require the most precise insights should still prefer the full-scan discovery.

How secure is the data?

All data is processed in a secure Microsoft Azure environment, and no personally identifiable information (PII) is stored after analysis.

Who benefits from Digital Scan?

✔ Businesses looking to enhance hiring and talent management
✔ Financial advisors seeking deeper client insights
✔ Sales teams aiming to personalize customer interactions
✔ HR professionals optimizing team dynamics

How do I know if someone has been Digitally Scanned or Full-Scanned?

There are several indicators that identify whether a person’s profile was generated through Digital Scan or through active participation in a full-scan discovery:
✔ Digital Scan Indicator on their profile picture
✔ Pop-up message on the screen when viewing their data
✔ Label on every report generated

This transparency ensures users can distinguish between AI-predicted data and directly assessed behavioral insights.

Example of a Digital Scan indicator on profile picture: 

Example of Digital Scan labels on reports: 

Example of Digital Scan labels on web screen: