In this final episode with Ryan Scott from DNA Behavior, we explore the ethical boundaries of AI personalization, data ownership rights, Gene AI's revolutionary hiring capabilities, and the future of democratized executive coaching through artificial intelligence.

Keywords

Ryan Scott, DNA Behavior, Gene AI, AI Ethics, Data Ownership, Executive Coaching, Behavioral AI, Privacy Rights, Microsoft AI Standards, DISC Migration, AI Coaching, Hiring Intelligence, Interview Questions

Key Takeaways

When NOT to Use Personalization

- Avoid personalization where "telephone game" can occur

- Emergency communications (fire drills, safety instructions) should remain consistent

- Large office-wide messages need uniform clarity to prevent confusion and mistrust

- Best for one-on-one sales/marketing interactions, not group communications

Microsoft's AI Ethics Framework

- Fair, reliable, and safe implementation

- Privacy and security protections built-in

- Inclusive and transparent processes

- Company accountability for adherence to standards

- Leading enterprise AI adoption methodology

Revolutionary Data Ownership Model

- Individual owns their behavioral insights, not the company or coach

- Users can opt-out of data sharing with other companies anytime

- Complete control over data access and permissions

- Fundamental shift from traditional B2B2B model ownership

Gene AI Capabilities

- Natural language interface for all DNA Behavior insights

- Automates complex hiring processes previously requiring certification

- Creates hiring benchmarks for specific roles (civil engineer example)

- Generates behavioral interview questions based on candidate strengths

- Identifies outlier behaviors that matter most for coaching focus

Hiring Process Revolution

- Input: 100 job applicants

- Gene AI ranks candidates against rockstar benchmarks

- Shortlist top 5 candidates automatically

- Generate personalized behavioral interview questions

- Focus on strengths AND potential struggles for each candidate

DISC Migration Strategy

- 50% discount for DISC coaches switching to DNA Behavior

- More competitive pricing than traditional DISC

- AI translator converts DNA insights into familiar DISC language

- Reduces switching costs by maintaining familiar terminology

- Additional insights beyond standard DISC offerings

AI Coaching Preparation Notes

- Focuses on outlier behaviors rather than typical patterns

- Provides speaking notes for coaches and consultants

- Speeds up facilitation by highlighting what matters most

- Eliminates manual reading on small screens

- Enables coaches to focus on high-value work

The Future of AI Coaching

- Democratizing executive coaching beyond C-suite access

- $500+ per hour coaching expertise available to middle managers and manufacturing workers

- Real-time, secure AI coaching conversations

- Video AI coaching interfaces

- Scaling behavioral intelligence across entire organizations

The 60% Mismatch Reality

Ryan's key insight: "If you were to be matched with anybody in the world, there's about a 60% likelihood that you two will be a mismatch."

The solution isn't necessarily technology - it's adapting personal communication styles to work better with others.

Big Data Organizational Intelligence

- Advanced number crunching capabilities

- Organizational-level behavioral insights

- Enterprise-wide pattern recognition

- Scaling individual insights to company-wide intelligence

Implementation Resources

- DNA Behavior website: dnabehavior.com/start

- Dedicated page for podcast listeners and event attendees

- Video tutorials and trial access

- Self-guided navigation options

This episode demonstrates how AI can democratize expertise while maintaining ethical standards and individual data rights, transforming both hiring processes and ongoing professional development across organizations.

Links

https://dnabehavior.com/

In this episode, we continue our conversation with Ryan Scott from DNA Behavior, exploring AI development processes, revolutionary conference experiences with behavioral intelligence, and practical marketing personalization strategies using HubSpot's smart content features.

Keywords

Ryan Scott, DNA Behavior, Conference Networking, HubSpot Smart Content, Behavioral Personalization, N8N Development, Digital Scan, Event Technology, Marketing Automation, Content Personalization, Behavioral Intelligence

Key Takeaways

AI Development Process Framework

- Start with Excel to understand calculations and insights

- Focus on one process per worksheet (agentic AI approach)

- Keep data small initially (tested with 100 people vs. 3.5 million)

- Use N8N for R&D and local agentic solutions

- Export code or hand off to developers for custom implementation

The "Onion Model" for Simplification

- Take charge

- Outgoing

- Patient

- Planned

Revolutionary Conference Experience

- Colored lanyards (blue, green, black, gold) based on behavioral personas

- Natural networking through behavioral compatibility

- Currently implemented with Better Business Bureaus and Chambers of Commerce

- Summer enterprise conferences planned

Three-stakeholder event value

- Attendees: Better networking with behaviorally compatible people

- Event organizers: Curate content based on audience personas (data-driven vs. energetic presentations)

- Sponsors: Understand how to communicate with leads based on behavioral data

HubSpot Smart Content Integration

- Moved from Salesforce to HubSpot specifically for smart content features

- Uses HubSpot's native rules-based content swapping

- Personalizes emails, web pages, blogs, videos, and infographics

- DNA provides behavioral persona data, HubSpot handles the technology

Content Personalization Strategy

- Outgoing personas: Bright colors, energetic content

- Analytical personas: Facts, background knowledge, detailed science

- Patient personas: Lifestyle-focused infographics

- Take-charge personas: Bulleted lists with direct facts

Implementation Best Practices

- Create four content flavors instead of 4,000 variations

- Always include a fallback option for adaptive clients

- Import contact lists for quick persona analysis

- Don't overcomplicate the personalization process

- Focus on broad reach with personalized touches

HubSpot's 2025 Marketing Recommendation

- Every firm should analyze behavioral personas of their customer base as a key marketing strategy for 2025

Technical Tools Mentioned

- Excel: Initial calculations and workflow mapping

- N8N: R&D and local agentic solution development

- HubSpot Marketing Hub: Smart content and rules-based personalization

- DNA Behavior Digital Scan: Instant behavioral analysis without questionnaires

This episode demonstrates how behavioral intelligence can transform both event experiences and marketing personalization, making AI-powered customization accessible through existing marketing platforms.

Links

https://dnabehavior.com/

In this episode, we explore the intersection of AI and behavioral science with Ryan Scott, Head of Product at DNA Behavior, who has transformed traditional personality testing into an AI-powered behavioral intelligence platform over his 15-year journey with the company.

Keywords

Ryan Scott, DNA Behavior, Behavioral Intelligence, AI Personality Testing, Digital Scan, DISC Alternative, Myers-Briggs, Machine Learning, Behavioral Prediction, Enterprise Psychology, Workplace Analytics, Custom GPTs

Key Takeaways

DNA Behavior's Evolution Journey

- Started with faxed PDF questionnaires requiring manual data entry by interns

- Four major iterations over 15 years: workplace talent → financial insights → combined platform → AI-driven enterprise solution

- Founded in Australia, moved to Atlanta for Georgia Tech research partnerships

- Differentiated by making behavioral insights actionable through dashboards vs. static PDF reports

The Traditional Assessment Problem

Traditional personality tests (DISC, Myers-Briggs, Enneagram) follow a broken model:

- 60-90 minute questionnaires that produce PDF reports

- Reports "die in a dust drawer" and aren't used day-to-day

- No integration with business systems or decision-making processes

- High switching costs for organizations with existing assessment data

Digital Scan AI Innovation

DNA Behavior's breakthrough solution predicts behavioral insights using only:

- Person's name and job title

- Company information and background data

- No questionnaire required

Training data foundation:

- 3.5 million behavioral questionnaire responses

- 3.25 million people across 4,000 behavioral insights

- Backwards compatible with 15 years of historical data

- Machine learning algorithm predicts same insights as traditional assessments

AI Implementation Cost Savings

Ryan's practical tips for reducing LLM costs:

- Clean and standardize data locally before cloud processing

- Use local LLAMA models for initial data processing

- Convert to CSV format before uploading to cloud services

- Use custom ChatGPTs for R&D before paying for APIs

- Structure responses as JSON instead of unstructured text (reduces hallucinations)

- Process only necessary data rather than scanning entire documents

Organizational AI Adoption

- Required making "hard decisions" about team members resistant to change

- Used behavioral insights to identify team members suited for fast-paced innovation

- Some people "weren't really suited for the fast-paced innovation that AI brings"

- Essential to choose adaptable people for AI transformation success

Business Model Innovation

B2B2B structure with coaches/consultants as intermediaries:

- Reduces switching costs by importing existing DISC/Myers-Briggs reports

- AI translator contextualizes insights in familiar assessment languages

- No retraining required for managers familiar with other systems

- Seamless comparison between AI-scanned and traditionally assessed individuals

Market Differentiation Strategy

- Contextualized insights for specific use cases (financial decisions, relationships, management)

- Enterprise-grade platform vs. individual assessment tools

- Big data approach with millions of behavioral data points

- Focus on actionable intelligence rather than static reports

This episode demonstrates how AI can revolutionize traditional industries by solving fundamental usability problems while maintaining compatibility with existing systems and knowledge.

Links

https://dnabehavior.com/

In this episode, we conclude our conversation with Jeremy Haug, founder of Revenx, covering small budget marketing strategies, essential metrics that matter, and the one AI habit that will keep you competitive in the evolving marketing landscape.

Keywords

Jeremy Haug, Revenx, Small Budget Marketing, Facebook Ads Testing, Marketing Metrics, Customer Acquisition Cost, Recurring Revenue, AI Daily Usage, ChatGPT, Marketing ROI, Lead Generation

Key Takeaways

Small Budget Marketing StrategyThe core problem: You don't know what you don't know - ideal audience, ideal service, or ideal pain point.

Jeremy's approach for under $10,000 budgets:

• Don't do anything big
• Start with $5/day Facebook ads
• Remove friction - use Facebook forms with minimal fields
• Test systematically every 3-5 days

The iterative testing framework:

• Week 1: Can you get clicks? (No clicks = bad ad or targeting)
• Week 2: Getting clicks but no leads? Iterate the offer
• Week 3: Getting leads but no replies? Ask for phone numbers

Marketing ROI ExpectationsIndustry benchmarks:

• Ideal marketing ROI: 10-to-1 return
• E-commerce: typically 3-to-1
• Large B2B relationships: can reach 100-to-1
• Recommended spend: 10% revenue on marketing, 10% on sales

Facebook Marketing RealityPlatform advantages:

• Cheap to test at $5/day
• 70% of US adults use Facebook daily
• 80-90% use it monthly
• Universal audience reach across demographics

Friction reduction principle: Remove reasons why people won't move forward - use pre-populated forms, minimal fields, easy opt-ins.

Sales Process for Financial ServicesJeremy's two-call approach:

• First call: Build relationship, book second appointment
• Second call: Pitch the product
• Never pitch on the first call for their specific audience
• Different for product-specific opt-ins where immediate pitching is appropriate

Referral vs. Paid Lead RealityKey insight: Most small businesses close 80-90% of referrals but struggle with paid leads because "the estimation of effort is completely different."

Recommendation: Perfect your referral closing process before scaling to paid advertising.

Essential Marketing MetricsMetrics to ignore:

• Cost per click (clicks don't count)
• Cost per lead (misleading without context)

Metrics that matter:

• Total marketing spend vs. total revenue (3-month lookback)
• New revenue vs. recurring revenue
• Customer acquisition cost (the real calculation)
• First-time sales vs. repeat sales ratio

Business Valuation MetricsFor business sale preparation:

• 60-70% of revenue should come from second or subsequent sales
• Recurring contracts provide predictable revenue
• Avoid 90% dependence on new client acquisition

Revenue stability: Contracts vs. spikes and crashes - predictable income from existing relationships.

AI Implementation AdviceJeremy's final recommendation: Keep ChatGPT or Gemini open at all times and ask it questions about everything.

The competitive threat: "The next generation are gonna take our lunch money. Some 16-year-old will ask ChatGPT how to build a marketing agency and get better guidance than any of us."

Tools Mentioned

• ChatGPT: Daily question-asking and strategy guidance
• Google Gemini: Free alternative for constant AI access
• Facebook Ads: Primary testing platform for small budgets
• YOLM.ai: Jeremy's family software development platform

Links

https://www.revenx.com/

In this episode, we continue exploring AI marketing limitations and advanced strategies with Jeremy Haug, founder of Revenx, covering retargeting frameworks, multi-channel follow-up systems, and realistic AI implementation for financial services.

Keywords

Jeremy Haug, Revenx, AI Limitations, Retargeting Strategy, SmartWriter.ai, Traffic Awareness Levels, Multi-Channel Follow-up, ChatGPT Marketing, Financial Services Marketing, Lead Nurturing, B2B Marketing

Key Takeaways

AI Reality Check

- Facebook compliance gaps: AI isn't current on platform restrictions in regulated industries

- Development limitations: Simple tools work in 4 commands, complex integrations require real understanding

- Quality control needed: AI makes mistakes - content requires human review

- Security concerns: Chinese AI models like DeepSeek send data to China

Traffic Awareness Framework

Three levels of prospect awareness:

1. Cold traffic - Not aware they have a problem

2. Warm traffic - Aware they have a problem

3. Hot traffic - Actively looking for solutions

Key strategy: Move prospects to next awareness level, don't jump straight to sales

Value-First Retargeting

Instead of "buy now" messages, provide:

- Free calculators and ebooks

- Educational mini-courses

- Social proof content

- Digital versions of paid resources

Multi-Channel Follow-Up System

Essential follow-up checklist:

- Call within 5 minutes

- Try different times of day

- Facebook messages and email sequences

- Value-driven content across platforms

- Multiple touchpoints before giving up

AI-Powered Follow-Up Tools

- SmartWriter.ai: Scrapes LinkedIn profiles for personalized outreach

- ChatGPT/Gemini: Creates 10-part email sequences for specific audiences

- High Level/HubSpot: CRM integration with AI capabilities

- Scale vs. personal touch: Use AI for 100+ clients, handle 5 clients manually

Platform Strategy for Financial Firms

Jeremy's business progression model:

1. Learn core skills (sales, follow-up, closing)

2. Find reliable lead/appointment vendors

3. Build custom branded campaigns

4. Scale to in-house marketing team

Niche focus: Target "teacher guy" vs "Tampa Bay guy" - expertise trumps geography

AI Tools Mentioned

- SmartWriter.ai: LinkedIn profile research for cold outreach

- ChatGPT/Gemini: Email sequence creation

- High Level/HubSpot: AI-integrated CRM systems

- Brand.ai: Social media content (testing phase)

In this episode, we explore AI marketing for financial services with Jeremy Haug, founder of Revenx, who has generated over 44,000 qualified appointments for insurance agents and financial advisors, resulting in tens of millions in revenue.

Keywords

Jeremy Haug, Revenx, B2B Marketing, Financial Services, Insurance Agents, AI Marketing, Call Transcription, Process Optimization, Appointment Setting, ChatGPT Marketing, Lovable.dev, Sales Coaching AI

Key Takeaways

The Revenx Success Story

- 44,000 qualified appointments booked directly onto agents' calendars last year

- 3+ years of sustained client relationships proving effectiveness

- Focus on actual appointments, not just "leads" that don't convert

- Specialized in insurance and financial advisory niches

Jeremy's AI Implementation Framework

1. Define existing processes - Document current workflows

2. Identify optimization opportunities - Find inefficiencies

3. Deploy AI to scale - Use technology to amplify what works

Practical AI Applications

- Call transcription: Built custom tool for 1-2 cents vs. $9/month using Lovable.dev and OpenAI API

- Sales coaching: Feed transcripts to ChatGPT with prompt: "Review these calls, find the five furthest from my script, tell me what to fix"

- Facebook ads analysis: Upload data to ChatGPT for insights that cost agencies thousands

- Email validation: Replace paid tools by building custom solutions in 4-8 hours

Marketing Best Practices

- Avoid over-promising: Challenge agencies making claims without proven track records

- Industry expertise matters: Don't work outside your niche

- Realistic timelines: "Nothing works in 30 days. Give yourself at least 90 days"

- Budget wisely: If you can't afford an agency, learn and build skills first

Financial Services Insights

- Different tiers need different approaches: Basic insurance vs. high-end advisory services

- Higher commissions = bigger budgets: Annuity sales can generate $30K commissions

- Longer relationship cycles: Comprehensive planning requires sustained engagement

AI Tools Mentioned

- Lovable.dev: No-code platform for custom tool development

- OpenAI API: Call transcription and analysis

- ChatGPT: Sales coaching and strategic guidance

This episode provides a masterclass in practical AI implementation for B2B marketing, emphasizing systematic improvement over flashy technology adoption.

Links

https://www.revenx.com/

In this final part of our conversation with Satej Sirur, CEO and co-founder of Rocketium, we explore common AI implementation pitfalls, industry applications beyond retail, and the future of AI content creation. Satej shares candid insights about competitive threats, misconceptions in AI adoption, and offers practical advice for staying grounded amid rapid technological change.

KeywordsAI Implementation, Marketing Pitfalls, Financial Services, AI Misconceptions, Content Creation Future, Competitive Strategy, AI Adoption, Marketing Technology, Creative Destruction, Status Quo Challenge, AI Evolution, Marketing Workflows, Technology Leadership, Business Strategy, AI Innovation

Key Takeaways

Common AI Implementation Pitfalls

• Most mistakes in AI adoption are reversible except staying closed-minded
• AI-generated images initially performed worse than real human imagery for retail clients
• Analytics revealed customer preference for authentic human content over AI-generated
• Corporate AI security committees can create adoption barriers
• Teams often blame AI failures more harshly than human mistakes
• Ego conflicts during adoption can slow implementation
• Data shows patterns but cannot explain causation behind performance differences
• Half-life of AI implementation mistakes is very short

Industry Applications Beyond Retail

• Financial services shows strong potential for AI content creation
• Multiple products, audiences, and lifecycle stages create content complexity
• Personalized messaging essential for different financial product categories
• Credit cards, loans, and investment products require tailored approaches
• Multi-channel touchpoint optimization drives customer funnel progression
• Industries with limited customer relationships less suitable for AI content tools
• Oil and gas example of industry not recommended for current AI content solutions

AI Misconceptions and Pet Peeves

• Greatest misconceptions come from people not experiencing operational pain
• Those without real problems have luxury of pontificating about AI capabilities
• Investors often have narrow canonical view of what constitutes AI
• People not building customer solutions dismiss practical AI applications
• Expectation that single prompt can solve all campaign problems unrealistic
• Zero-sum nature of marketing means universal improvement impossible
• Fame-seekers without practical experience spread misleading information
• Customers with real pain points approach AI pragmatically regardless of underlying technology

Future AI Capabilities and Trends

• Multimodal AI inputs and outputs rapidly improving across all media types
• Image and video input processing enabling more sophisticated content analysis
• Exponential improvement pace continues across all AI capabilities
• Cost reduction will drive universal adoption more than capability increases
• No fundamentally new breakthroughs expected, just better execution of existing concepts
• Creative destruction threatens even AI companies as foundational models improve
• Competitive advantage lies in staying ahead of rapidly advancing baseline capabilities

Staying Current and Grounded

• Large teams provide natural intelligence gathering through customer and investor networks
• Daily updates from multiple sources create information abundance rather than scarcity
• Focus on lighthouse principles: customer problems and team wellbeing
• Ignore technological turbulence while maintaining focus on core business metrics
• Hyperbolic claims about daily game changes mostly contain kernels of truth
• Stability comes from unchanging customer needs despite changing solutions
• Team mental and emotional health serves as key performance indicator

Links

• https://rocketium.com/

In this continuation of our conversation with Satej Sirur, CEO and co-founder of Rocketium, we dive deeper into data-driven creative insights and practical AI implementation strategies. Satej shares frameworks for extracting actionable analytics from creative assets and provides guidance on when teams should move beyond off-the-shelf AI tools toward custom solutions.

KeywordsData-Driven Creative, Creative Analytics, Brand Safety, AI Implementation, Content Optimization, Marketing Automation, Performance Analytics, Creative Operations, Brand Guidelines, Marketing Workflows, AI Scalability, Content Performance, Creative Intelligence, Marketing Technology, Visual Analytics

Key Takeaways

Data-Driven Creative Insights

• Introduces "lenses" framework for analyzing creative elements systematically
• Product lens analyzes discount messaging, product placement, and call-to-action effectiveness
• Branding lens examines logo size, positioning, partner logos, and brand consistency
• Messaging lens evaluates copy length, tone, and language complexity
• Layout and style lens assesses visual hierarchy and design effectiveness
• Short copy under 50 characters often outperforms longer messaging
• Discount and offer messaging consistently drives higher engagement
• AI copilot enables conversational analytics for immediate insights

Brand Safety and Compliance

• Automated brand guideline checking during content creation process
• Performance best practices integrated into creative workflow
• Accessibility standards automatically validated before review
• Language complexity analysis ensures audience comprehension
• Color contrast verification for visual accessibility
• Brand voice consistency maintained across campaigns
• Reduces manual review burden by 30-70% through automation
• Balances creative expression with performance requirements

AI Implementation Strategy

• Process audit approach: map entire workflow to identify pain points
• Focus on weakest links in content creation chain first
• AI excels at probabilistic tasks like ideation and creative variations
• Human refinement essential for final 20-40% of creative work
• Start with biggest operational bottlenecks, not flashiest features
• Systematic approach yields better adoption and ROI than tool-first strategies

Scalability Framework

• Solo creators: Use AI for initial content direction and marketing vibes
• Growing teams: Invest in context-aware AI that learns brand specifics
• Enterprise: Implement comprehensive platforms serving as content systems of record
• AI adoption scales more affordably than traditional creative infrastructure
• Custom solutions become valuable when managing multiple teams and channels
• Generic AI output becomes problematic as business complexity increases
• Context-rich AI systems essential for maintaining brand consistency at scale

Performance vs. Creative Balance

• Tension exists between brand expression and performance optimization
• Analytics bridge creative vision with measurable outcomes
• Brand safety ensures guidelines compliance without stifling creativity
• Performance treadmill requires rapid content iteration and optimization
• Long-term brand impact difficult to measure in fast-paced performance marketing
• Creative differentiation essential when all competitors use similar AI tools
• Brand voice becomes competitive advantage in AI-generated content landscape

Technical Implementation

• Multiple AI model integration through abstraction layers
• Real-time analytics enable immediate campaign adjustments
• Automated asset tagging and categorization upon upload
• Bulk content creation through spreadsheet imports with AI enhancements
• Price callout extraction from creative assets enables performance correlation
• Integration with major advertising platforms for comprehensive tracking
• AI assistant provides conversational interface for complex analytics queries

Links

• https://rocketium.com/