Progency = McKinsey × Palantir: The Future of Marketing Execution

Published June 12, 2025

1

Problems

In today’s marketing landscape, a profound disconnect has emerged between the promise of martech platforms and their actual impact on business results. The martech industry aces a crisis of “All Software, No Success” – where sophisticated technology fails to deliver on its transformative potential.

The reality is sobering: marketing technology has evolved into “Software without Service” rather than genuine Software-as-a-Service. Priced on inputs with no link to performance outcomes, these platforms see 60-65% of their features remain unused while marketing teams suffer from constant staff turnover and endless drudgery of repetitive tasks. The end result? Wasted investment, missed opportunities, and sub-par retention that ultimately drains profits.

Over recent months, in conversations with CMOs across industries, I’ve heard the same plea echoed time and again: “We need help.” These marketing leaders face mounting pressure to deliver growth while battling rising customer acquisition costs, diminishing returns from digital channels, and the staggering $500 billion global AdWaste crisis – where 70% of digital marketing budgets are squandered reacquiring customers brands already know.

“What we want is more than just a product,” one CMO told me. “We need you to share best practices from your work with other clients. You understand our industry challenges – can you bring that knowledge to guide our strategy and customer journeys?”

Another lamented: “My CRM team sees constant turnover and cannot leverage the full power of your platform. Can you provide resources to assist?”

The pattern is clear: brands need more than software; they need a genuine partner in growth. As one executive put it: “We don’t want just a tactical relationship. We want you to be our partner in growth, not just a vendor. Will you work on a revenue share basis with skin in the game, where you’re incentivised to deliver upside?”

Though none used the term “Progency,” what they described precisely matches this revolutionary concept – a fusion of product, expertise, AI agents, and continuous improvement that transforms marketing from cost centre to profit engine. Progency represents the convergence of Platform, Experts, AI agents, and Kaizen methodology (PEAK) in a performance-based model aligned directly with client outcomes.

What these marketing leaders seek goes beyond my initial conception of Progency. They want an end-to-end enabler for growth: software combined with deep industry understanding, proven frameworks, established best practices, and a shared commitment to success. This vision prompted me to reimagine Progency as not merely an enhancement to existing martech but as a transformative approach – a McKinsey-plus for Marketing.

The time has come to transform marketing from cost centre to profit engine – by making martech not just powerful, but purposeful.

2

The Model

McKinsey stands as the quintessential management consultancy, renowned for its ability to transform businesses through a distinctive blend of strategic insight, specialised expertise, and methodical execution. Understanding McKinsey’s operating model provides valuable inspiration for what Progency must become in the marketing realm.

The McKinsey Advantage

At its core, McKinsey delivers three fundamental value propositions that create its unparalleled market position:

1. Knowledge Advantage: McKinsey cultivates deep, cross-industry expertise through decades of client engagements, proprietary research, and knowledge management systems that capture insights from thousands of projects. This intellectual capital enables consultants to rapidly identify patterns, apply proven frameworks, and deliver solutions far more sophisticated than clients could develop independently.
2. Talent Advantage: The firm attracts and develops exceptional professionals through rigorous selection, continuous training, and a culture of excellence. These consultants bring both generalist problem-solving abilities and increasingly, specialised domain expertise across industries and functions.
3. Methodology Advantage: McKinsey employs a structured problem-solving approach that breaks complex challenges into manageable components, tests hypotheses with data, and builds irrefutable cases for change. This disciplined methodology ensures consistent quality and enables McKinsey to tackle problems of extraordinary complexity.

What sets McKinsey apart, however, is its transition from pure advisory to implementation support. While traditional consulting ends with recommendations, McKinsey increasingly stays through execution, ensuring ideas translate into measurable outcomes. This shift mirrors precisely what Progency must deliver in marketing.

A Blueprint for Progency

Where McKinsey transformed management consulting, Progency must reinvent marketing execution. Drawing from McKinsey’s model, Progency should deliver:

1. Marketing Knowledge Repository: Just as McKinsey distils insights across industries, Progency must codify marketing best practices, create segment-specific playbooks, and leverage cross-client pattern recognition to identify what truly works. For example, detailed benchmarks on engagement metrics by industry, proven customer journey templates, and a library of high-performing AI-optimised content frameworks that can be rapidly customised.
2. Expert Practitioners & AI Orchestration: Beyond theoretical knowledge, Progency requires practitioners who have successfully navigated complex marketing challenges. These specialists work alongside AI agents that handle execution at scale – creating what McKinsey would call “scaled delivery” but with technological multiplication that even McKinsey cannot match. The AI Agents Collective serves as the operational backbone that enables expertise to scale without proportional resource expansion.
3. Outcome Methodology: While McKinsey’s structured problem-solving produces recommendations, Progency’s PEAK framework (Platform, Experts, AI agents, Kaizen) delivers measurable business outcomes. This methodology isn’t about prescribing what could work but guaranteeing what will work through continuous optimisation and performance-based economics.
4. Implementation That Transcends Advisory McKinsey evolved from recommending strategy to supporting implementation. Progency must begin there – taking full ownership of execution rather than merely advising. When a CMO faces the “Who Will Do It?” problem, Progency doesn’t provide suggestions; it becomes the “Department of One” that handles execution from start to finish.
5. Value Pricing That Redefines Relationships: Like McKinsey’s premium pricing model, Progency must price according to value created rather than resources deployed. However, Progency takes this further through performance-based economics where compensation ties directly to measurable business outcomes – creating even stronger alignment than McKinsey’s engagement model.

The true parallel emerges in how both entities transform entire functions. McKinsey doesn’t simply improve management; it fundamentally rewires how organisations operate. Similarly, Progency doesn’t merely enhance marketing; it reconfigures the entire discipline from cost centre to profit engine through the systematic elimination of AdWaste and activation of underutilised customer relationships.

Where CMOs currently lack a trusted partner capable of both strategy and execution – particularly in the age of AI – Progency fills this void with a unique fusion of knowledge, talent, technology, and outcome guarantees. By combining McKinsey’s strategic rigour with technological enablement that transcends human limitations, Progency creates the new gold standard for marketing execution: not just expertise, but expertise at scale with guaranteed results.

Here is a table which highlights the similarities and differences:

Dimension	McKinsey	Progency (McKinsey+)
Strategic Approach	Problem diagnosis and strategic recommendations based on industry expertise	Strategy plus guaranteed execution through AI-powered implementation
Knowledge Base	Cross-industry insights and proprietary frameworks	Marketing-specific knowledge repository plus real-time pattern recognition across client base
Talent Model	Elite consultants with generalist and specialist capabilities	Vertical experts plus AI Agents Collective that executes at scale without human bottlenecks
Economic Model	Premium fees based on project scope and perceived value	Performance-based compensation tied directly to measurable outcomes (revenue uplift, engagement metrics)
Execution Capacity	Limited by human bandwidth; requires client teams for implementation	Unlimited scale through “Department of One” AI agents that handle millions of micro-segments simultaneously
Value Proposition	“We’ll tell you what to do and help you do it”	“We’ll do it for you and only succeed when you succeed”

3

Palantir

I came across the following in the Economist on Palantir: “Its success is emblematic of two trends. The first is generative artificial intelligence (AI); Palantir helps firms adopt it. Second is the growing demand for intelligence and defence technology in a world of border closures and superpower rivalry, where Palantir (more controversially) excels. In both fields, its edge comes from a unique approach to profits, growth and culture. For about 20 years after it was founded in 2003, Palantir did not make a profit. It had the strange business model of embedding lots of highly paid engineers with its customers, hand-crafting software to solve bespoke problems. Mr Karp conceived of these so-called forward deployed engineers (FDEs) as being like bossy French waiters in high-class restaurants who know what customers want better than they do. They had to be pushy and persuasive. But as their on-the-ground knowledge was incorporated into software products, the number of FDEs per customer shrank and the business began to scale profitably. In the past two years, Palantir’s revenues have surged while its employees, who number 4,000, have barely increased. That has enabled the firm to maintain gross margins of 80%-plus. Its new AI platform, AIP, which maps and organises its customers’ data to help them run large language models, is following a similar path.”

I asked Perplexity for a brief on Palantir: “Palantir Technologies Inc. is a leading American software company specializing in big data analytics platforms designed to empower human-driven analysis and decision-making across various sectors. Founded in 2003 by Peter Thiel, Stephen Cohen, Joe Lonsdale, and Alex Karp, Palantir initially focused on supporting U.S. intelligence and defense agencies with tools to combat terrorism while preserving civil liberties. Palantir’s core products include Palantir Gotham, used by intelligence and defense organizations for data integration and analysis; Palantir Foundry, which enables commercial clients to manage and activate data for operational efficiency; Palantir Apollo, a platform for continuous software deployment; and Palantir AIP, which facilitates secure use of advanced AI technologies on private networks. The company’s software has been instrumental in diverse applications, from counter-terrorism and public health (including COVID-19 vaccine distribution) to financial crime investigations and industrial operations. Palantir is known for its mission-driven engineering culture, emphasizing privacy protection and responsible data use. It partners with government, commercial, and non-profit organizations worldwide to solve complex data challenges and deliver rapid, actionable insights.”

The idea of embedding experts in client organisations offers a transformative blueprint for Progency’s evolution. What Palantir accomplished with their Forward Deployed Engineers (FDEs) wasn’t merely customer success management—it was a systematic knowledge extraction mechanism that simultaneously delivered immediate value while building institutional intelligence that could be codified into software. Progency could deploy its own “Marketing Growth Engineers” who work within client ecosystems to solve immediate challenges while gathering domain knowledge that continually enhances the AI Agents Collective. These embedded specialists would cultivate deep client understanding that typical SaaS providers never achieve, enabling Progency to transition from service-heavy engagements to AI-powered automation as patterns emerge and agents evolve—maintaining white-glove outcomes while dramatically improving profit margins and scalability. The result? A virtuous cycle where human expertise guides AI development, which in turn amplifies human capability, creating an unmatched competitive advantage in marketing execution that competitors simply cannot replicate.

4

FDEs

I decided to dig deeper into Palantir’s FDEs, and asked Perplexity.

At Palantir, Forward Deployed Engineers (FDEs)—often called Forward Deployed Software Engineers (FDSEs) or “Deltas”—are highly skilled software engineers who work directly with customers to configure and deploy Palantir’s platforms (such as Foundry and Gotham) to solve complex, real-world problems. Unlike traditional software engineers who focus on building reusable features for many clients, FDEs concentrate on delivering tailored solutions for specific customers, often embedding on-site or working closely with users in the field

Key Responsibilities and Distinctions

• Customer-Centric Problem Solving: FDEs engage deeply with clients to understand their unique challenges, scope out requirements, and design custom workflows or applications using Palantir’s tools
• Technical and Business Bridge: They serve as the connective tissue between Palantir’s product development teams and end users, translating business needs into technical solutions and feeding field insights back to headquarters
• Rapid, Impactful Delivery: FDEs leverage Palantir’s out-of-the-box capabilities to deliver solutions quickly, focusing on architecture and integration rather than building from scratch
• Ownership and Accountability: The role demands taking full ownership of project outcomes, from initial scoping to deployment and user enablement
• Rigorous Engineering: Despite their proximity to customers, FDEs adhere to high software engineering standards, including code reviews, system monitoring, and production maintenance

Why is the Role Unique?

Palantir’s FDE model is inspired by the idea that the best outcomes arise when technical experts are empowered to say “no” to suboptimal solutions and guide customers toward what will truly work, much like a top chef’s waitstaff ensures the best dining experience. This approach breaks down traditional silos between sales, implementation, and engineering, resulting in faster, more effective deployments and greater customer satisfaction.

Broader Impact

While Palantir popularized the FDE concept, the model is now seen as a blueprint for bridging the gap between powerful, generic software platforms and the specific, high-stakes needs of enterprise customers in an AI-driven world. FDEs are valued for their blend of technical excellence, business acumen, and customer obsession-skills increasingly vital as technology becomes more complex and customizable.

In summary, Palantir’s FDEs are pivotal in ensuring the company’s software delivers real-world impact, setting a high standard for customer-focused engineering in the enterprise tech industry.

**

This Palantir post from 2020 has a conversation with Brian, an FTE. “A Forward Deployed Software Engineer (FDSE), or “Delta,” is a software engineer who embeds directly with our customers to configure Palantir’s existing software platforms to solve their toughest problems. While a traditional software engineer, or “Dev,” focuses on creating a single capability that can be used for many customers, FDSEs focus on enabling many capabilities for a single customer. We are deployed across many industries and problem domains, so the breadth of projects we tackle is large and always evolving. Since joining Palantir, I’ve had the chance to work across cyber, healthcare, and defense. Iterating closely with customers across these various industries requires a unique and broad skillset, ranging from software development to data engineering to customer engagement and creative problem-solving. I need to address questions like: “What products are we deploying for this use-case? Why are we deploying them? How will we spin-up workflows that utilize these products to address the customer’s specific needs?” As FDSEs, not only do we need to be able to answer those questions, but we actually implement the solution in collaboration with end-users.”

**

So, what would a marketing growth engineer’s role look like?

5

Marketing Growth Engineer

Drawing inspiration from McKinsey’s elite problem-solvers and Palantir’s Forward Deployed Engineers, the Marketing Growth Engineer (MGE) represents Progency’s front-line catalyst for transformation—combining deep technical expertise with strategic marketing acumen to deliver measurable business impact.

Core Responsibilities and Competencies

Strategic Needs Translation: Like McKinsey consultants who translate business challenges into structured problems, MGEs decode marketing ambitions into actionable technical requirements. They bridge the gap between CMO vision and platform capabilities, ensuring technical solutions address true business priorities rather than superficial symptoms.

Cross-Client Pattern Recognition MGEs work across multiple clients within specific verticals, developing rare pattern-recognition abilities that individual brand marketers cannot cultivate. This enables them to rapidly identify what works and what doesn’t—transferring insights from one client to benefit others while maintaining strict data confidentiality.

Platform Configuration and Deployment: Similar to Palantir’s FDEs who configure existing platforms for specific use cases, MGEs tailor Progency’s martech capabilities to each client’s unique requirements. Rather than building custom code from scratch, they leverage the platform’s modular components, composing solutions through configuration rather than custom development.

AI Agent Supervision: MGEs serve as the human directors of the AI Agents Collective, establishing guardrails, training parameters, and feedback mechanisms that ensure autonomous marketing operations align with brand guidelines and business objectives. This hybrid human-AI approach creates what neither could achieve alone.

Knowledge Codification: Perhaps the most crucial responsibility is systematically documenting solutions, techniques, and client-specific insights that feed back into Progency’s knowledge repository and AI training. This creates the virtuous cycle where individual client engagements enhance the collective intelligence of the platform.

Working Model

Embedded but Independent: MGEs operate with a unique posture—embedded within client teams for deep understanding but maintaining sufficient independence to challenge conventional thinking. Like McKinsey’s “outsider perspective with insider knowledge” and Palantir’s “bossy French waiters,” MGEs must diplomatically push clients toward optimal solutions even when this means contradicting internal politics or legacy approaches.

Outcome Obsession: Unlike traditional customer success managers focused on platform usage, MGEs obsess over business metrics—revenue lift, engagement rates, conversion improvements—and continuously adjust their implementations until targets are achieved. Their compensation ties directly to these outcomes, creating perfect alignment with client success.

Phase-Based Engagement: The MGE role evolves through distinct phases:

1. Immersion: Deep understanding of client business, data architecture, and marketing challenges
2. Configuration: Rapid implementation of platform capabilities and AI agent deployment
3. Optimisation: Continuous refinement based on performance data and user feedback
4. Transfer: Gradual transition from human-led to AI-augmented execution
5. Advisory: Eventual shift to periodic strategic oversight as AI agents handle day-to-day operations

This model enables Progency to follow Palantir’s proven path—starting with high-touch human expertise that gradually transitions to scalable technology as patterns emerge and systems mature.

Key Differentiators

Technical + Marketing Fluency: Unlike traditional martech implementation specialists who understand platforms but lack marketing strategy, or marketing strategists who lack technical depth, MGEs possess the rare combination of both skill sets—understanding customer psychology, conversion architecture, and brand strategy while simultaneously mastering data structures, integration patterns, and AI orchestration.

Vertical Specialisation: MGEs develop deep expertise in specific industries—retail, financial services, travel, healthcare—understanding the unique customer journeys, regulatory considerations, and competitive dynamics that shape marketing success in each vertical.

Measurement Mastery: MGEs excel at establishing rigorous measurement frameworks that isolate the impact of Progency’s interventions from market noise. This includes creating control groups, designing clean A/B experiments, and implementing attribution models that prove causality rather than correlation.

Culture Bridging: Perhaps the most subtle yet valuable skill is the ability to navigate between technical and creative mindsets, translating between data scientists, brand managers, creative teams, and executive stakeholders.

The Progency Advantage

What makes the Marketing Growth Engineer fundamentally different from traditional roles is the combination of:

1. Performance-based compensation that aligns incentives with client outcomes
2. AI force multiplication that extends human expertise beyond traditional limitations
3. Knowledge sharing across a client portfolio that accelerates learning cycles
4. The transition path from high-touch service to autonomous execution

This creates a model where clients receive immediate value from human expertise while simultaneously benefiting from the scale and consistency of AI-powered execution—the perfect synthesis of McKinsey’s strategic brilliance and Palantir’s technical implementation excellence.

Through Marketing Growth Engineers, Progency delivers what neither traditional consultancies nor conventional martech platforms can offer: a partner that combines strategic insight with guaranteed execution, deep technical capabilities with marketing intuition, and immediate impact with long-term scalability.

6

CSMs to MGEs

Progency does not necessarily need to go out and hire new talent to build out Marketing Growth Engineers. Retraining Customer Success Managers (CSMs) is the best way to build the field force of MGEs. This approach leverages existing client relationships, institutional knowledge, and industry expertise—transforming a cost centre into a revenue engine while creating natural career progression for talented team members.

The CSM-to-MGE Evolution

Current CSMs already possess critical foundations for the MGE role: deep platform knowledge, established client relationships, and a service-oriented mindset. What they typically lack is the strategic consultative approach, vertical specialisation depth, and technical configuration expertise that defines truly transformative partners.

Through structured upskilling, CSMs can develop these missing capabilities:

1. Strategic Consulting Skills: Training in problem framing, hypothesis testing, and outcome-focused solution design—borrowed directly from McKinsey’s approach
2. Technical Configuration Expertise: Deeper training in platform customisation, data architecture, and integration capabilities—mirroring Palantir’s FDE technical depth
3. Business Outcome Focus: Shifting metrics from platform utilisation to revenue impact, LTV enhancement, and engagement improvements that directly affect the bottom line
4. AI Orchestration Capabilities: Developing proficiency in supervising, training, and refining AI agents—a skill set that will increasingly define marketing technology leadership

This transformation doesn’t happen overnight but follows a progressive career ladder that aligns evolution in capabilities with increasing revenue responsibility and compensation potential.

CSM vs MGE: Key Differences

Dimension	Traditional CSM	Marketing Growth Engineer
Primary Objective	Platform adoption and usage	Measurable business outcomes and revenue uplift
Compensation Model	Fixed salary with modest variable	Significant performance-based component tied to client results
Skill Focus	Product knowledge and relationship management	Strategic problem-solving and technical configuration
Success Metrics	Usage statistics, renewal rates, client satisfaction	Revenue impact, uplift percentages, engagement improvements
Technical Depth	Navigation and basic configuration	Advanced platform customisation and AI agent orchestration
Meeting Participants	Marketing operations and platform administrators	CMOs, revenue officers, and senior stakeholders
Time Allocation	Reactive issue resolution and standardised onboarding	Proactive strategic initiatives and custom solution development
Development Path	Product-focused continuing education	Cross-discipline learning spanning marketing, data science, and strategy
Relationship Nature	Vendor representative focused on adoption	Strategic partner invested in client business performance
Value Creation	Enhanced platform utilisation	Transformative business impact through performance guarantees

 The Progency Career Ladder: From CSM to Revenue Partner

Here’s an enhanced career progression that creates a clear path from traditional CSM to revenue-generating MGE:

Level 1: Foundation CSM

• Handles routine platform support for smaller accounts
• Fixed compensation structure focused on adoption metrics
• Primarily reactive and platform-focused engagement
• Responsible for onboarding, basic training, and troubleshooting
• Success measured by client satisfaction and retention

Level 2: Enhanced CSM

• Manages mid-tier accounts with more strategic involvement
• Hybrid compensation with small performance incentives
• Begins identifying growth opportunities beyond platform usage
• Develops basic vertical knowledge in client industries
• Success measured by expanded platform utilisation and incremental revenue

Level 3: Strategic CSM

• Handles strategic accounts with revenue enhancement responsibilities
• Significant variable compensation tied to client growth
• Regularly participates in client business planning
• Develops specialised vertical expertise and cross-client pattern recognition
• Success measured by demonstrable revenue impact and client business outcomes

Level 4: Associate MGE

• Manages portfolio of accounts with defined revenue targets
• Compensation structure emphasises performance-based outcomes (40-50%)
• Designs and implements custom solutions addressing specific client challenges
• Works directly with client leadership and influences strategic direction
• Success measured by quantifiable business transformation metrics

Level 5: Full MGE

• Serves as trusted advisor for enterprise clients with substantial revenue potential
• Primarily performance-based compensation with unlimited upside
• Creates and deploys AI-augmented solutions that scale impact beyond human capacity
• Recognised as industry and vertical specialist with thought leadership credibility
• Success measured by transformational impact on client business models

Level 6: Principal MGE

• Oversees strategic client portfolios with enterprise-wide impact
• Fully variable, uncapped compensation based on client outcomes
• Pioneers new solution frameworks that become templates for broader deployment
• Contributes to Progency’s knowledge repository and platform evolution
• Success measured by portfolio performance and Progency’s overall market position

Implementation Strategy

Transforming the existing CSM function into a dynamic MGE force requires systematic execution:

1. Assessment: Evaluate current CSMs against MGE capability requirements to identify high-potential candidates and development needs
2. Curriculum Development: Create a tailored learning journey combining:
   • McKinsey-inspired strategic problem-solving methodologies
   • Palantir-style technical implementation approaches
   • Industry-specific marketing best practices
   • AI orchestration and supervision techniques
3. Shadowing Program: Pair developing MGEs with external experts from strategy consulting and technical implementation backgrounds
4. Guided Client Pilots: Launch controlled MGE engagements with receptive clients, providing intensive coaching and support
5. Knowledge Capture: Document successes, challenges, and replicable patterns to accelerate capability development across the organisation
6. Compensation Realignment: Gradually shift compensation structures to reward business impact rather than activity metrics

This transformation creates multiple benefits: it provides exciting career advancement for top performers, delivers enhanced value to clients, and enables Progency to scale its impact without proportional hiring—following Palantir’s proven path from service-heavy beginnings to technology-leveraged scalability.

The evolution from traditional CSM to revenue-generating MGE represents more than a role change—it embodies Progency’s core philosophy that martech should deliver measurable business outcomes, not just software functionality. By investing in this transformation, Progency doesn’t just create a new type of role; it fundamentally redefines what clients should expect from their marketing technology partners.

7

The 10X Opportunity

We have discussed how Progency can be a tremendous asset for B2C/D2C companies as they pursue sustainable profitable growth against the challenging backdrop of falling organic traffic, rising CAC, economic uncertainty, and the crippling AdWaste crisis. By growing their Best customers and halving wasteful spending, these brands can potentially triple their profits through NeoMarketing’s transformative approach.

Yet there exists an equally compelling—but often overlooked—opportunity: Progency offers martech companies themselves a powerful growth lever in an increasingly commoditised landscape. While most martech providers find themselves trapped in a red ocean of feature-based competition and declining differentiation, Progency’s innovative fusion of platform, AI agents, and agency services creates a genuine blue ocean strategy—a competitive space where rules are rewritten and margins expanded rather than compressed.

The fundamental limitation of traditional martech lies in its consumption-based business model. Whether charging by Monthly Active Users (MAU), emails sent, or API calls processed, these pricing structures inherently cap revenue potential. Even as client businesses might grow at 25-30% annually, same-customer revenue expansion for martech providers typically lags significantly behind—often plateauing at 10-15%. This growth ceiling creates a dependency on new customer acquisition that becomes increasingly expensive in saturated markets.

Progency shatters this constraint through its performance-based model. By tying compensation directly to measurable business outcomes—revenue uplift, increased customer lifetime value, engagement improvements—martech companies can create uncapped upside aligned perfectly with client success. This paradigm shift enables exponential rather than linear growth from existing customer relationships.

Consider the maths: The $500 billion global AdWaste pool represents approximately 70% of total marketing spend—a massive prize that currently flows primarily to Google, Meta, and other adtech platforms. By redirecting even a fraction of this wasted spend toward more efficient, outcome-based solutions, Progency-powered martech companies can tap into a revenue stream many times larger than their current addressable market.

The 10X growth potential emerges from several converging factors:

1. Expanded Revenue Pools: Moving beyond software subscriptions to capture a percentage of business impact value
2. Higher Customer Value: Transitioning from relatively modest platform fees to significant revenue shares of improved performance
3. Enhanced Competitive Position: Creating defensible differentiation through outcomes rather than features
4. Reduced Churn: Establishing stickier client relationships based on proven value delivery
5. Marketing-as-a-Profit-Centre Credibility: Elevating conversations from cost justification to revenue generation

For martech providers, this represents an evolution comparable to what happened in enterprise software—moving from license-based pricing to cloud-based subscriptions, and now toward outcome-based partnerships. The companies that pioneer this shift won’t merely grow incrementally; they’ll fundamentally redefine the economics of the industry.

The ultimate prize isn’t merely capturing a larger share of existing marketing technology budgets—it’s redirecting the massive flow of wasted acquisition spending toward more effective, retention-focused approaches. By solving CMOs’ most pressing challenges while simultaneously transforming their own business models, martech companies embracing the Progency approach can achieve the rare combination of helping clients succeed while dramatically improving their own growth trajectory and profitability.

In an industry where marginal improvements and feature parity have become the norm, Progency offers the path to 10X growth—not through incremental enhancement, but through fundamental business model reinvention that aligns technology providers’ success with genuine client outcomes.

8

Tech Analogies

I asked Claude for examples from the tech industry.

The Progency model draws inspiration from several industries that have successfully combined consultative expertise with technical implementation and outcome-based economics. These analogies demonstrate that the synthesis of McKinsey’s strategic approach and Palantir’s embedded expertise is not merely theoretical but has proven transformative across multiple sectors.

Accenture Interactive (now Accenture Song)

Accenture’s transformation from pure management consulting into digital services offers a compelling parallel. By acquiring creative agencies and digital specialists, Accenture built a hybrid model that delivers both strategic direction and implementation expertise. Their “business of experience” approach mirrors Progency’s ambition to combine high-level thinking with execution capability. However, Progency goes further by adding AI agent orchestration and true performance-based economics beyond Accenture’s predominantly time-based billing.

NVIDIA’s Enterprise Solutions

NVIDIA has evolved beyond hardware manufacturing to become a strategic technology partner. Their NVIDIA Enterprise team embeds domain specialists directly with clients, combining deep technical expertise with industry-specific knowledge. These specialists aren’t merely implementing NVIDIA technology; they’re solving business problems through a consultative approach. Like Palantir’s FDEs, they translate complex technical capabilities into business outcomes while feeding customer insights back into product development.

Goldman Sachs’ Strats Division

Goldman Sachs created its “strats” division as a hybrid team combining quantitative analysis with trading expertise. These professionals possess both technical skills (mathematical modelling, algorithmic development) and domain knowledge (market mechanics, trading strategies). They don’t merely support traders; they collaborate to create competitive advantage through technological innovation. This mirrors Progency’s fusion of marketing strategy and technical implementation within a single role.

Epic Systems’ Implementation Teams

In healthcare technology, Epic Systems deploys specialised implementation teams that embed within hospitals during multi-year deployments. These teams combine clinical workflow expertise with technical configuration capabilities—ensuring the software adapts to each organisation’s unique needs while maintaining core functionality. Like Palantir’s FDEs, they customise a powerful platform to solve specific client challenges while maintaining the integrity of the underlying technology.

Snowflake’s Data Cloud Consulting

Snowflake has pioneered the concept of “data cloud consulting,” where their specialists don’t just implement technology but help clients reimagine their entire data strategy. These consultants possess both strategic vision and technical depth—designing data architectures that transform business capabilities while implementing the platforms to support them. This parallels Progency’s ambition to be both strategic advisor and technical implementer.

IBM’s Garage Methodology

IBM’s Garage methodology combines design thinking, agile principles, and enterprise-scale implementation into a unified approach. Cross-functional teams of strategists, designers, and technical specialists co-create solutions with clients in dedicated spaces. This customer-centric, outcome-focused model mirrors Progency’s aim to bridge strategic vision with tangible results through embedded expertise.

Stripe’s Solutions Architecture

Stripe has reimagined traditional customer success through “solutions architecture”—embedding payment experts who possess both business acumen and technical depth within client organisations. These specialists don’t just support implementation; they help clients reimagine their entire payment strategy to drive business growth. Their focus on business outcomes rather than mere technical implementation aligns with Progency’s vision.

What Makes Progency Unique

While these analogies demonstrate the viability of hybrid models, Progency introduces several innovations that make it uniquely powerful:

1. AI Agent Multiplication: Unlike all these examples, Progency uses AI agents to dramatically scale human expertise beyond what’s possible with traditional staffing models.
2. True Performance-Based Economics: While some of these models incorporate elements of value-based pricing, Progency takes this further by tying compensation directly to measurable business outcomes.
3. Marketing Focus: By specialising specifically in marketing effectiveness, Progency develops deeper vertical expertise than generalised consultative models.
4. Evolutionary Path: The clear progression from human-led to AI-augmented execution creates a scalable model where margins improve over time, unlike service-heavy approaches that face linear scaling constraints.

These industry analogies demonstrate that Progency isn’t pursuing an untested concept but rather synthesising proven approaches into a revolutionary model specifically designed for marketing’s unique challenges. By learning from the successes and limitations of these predecessors, Progency can create a truly transformative approach to marketing effectiveness—one that delivers McKinsey-calibre strategic thinking and Palantir-style embedded expertise, enhanced by AI agents and aligned through performance-based economics.

9

Non-tech Analogies

Beyond the technology sector, several traditional industries have pioneered business models that mirror Progency’s fusion of strategic expertise, embedded implementation, and outcome-based economics. These examples demonstrate that the fundamental principles underpinning Progency have proven successful across diverse sectors.

Private Equity Operating Partners

Private equity firms like KKR, Blackstone, and TPG have evolved beyond pure financial engineering to deliver operational transformation through dedicated operating partners. These industry veterans embed within portfolio companies to drive value creation through expertise rather than mere capital allocation. Their compensation is directly tied to exit value improvement—creating perfect alignment with outcomes rather than activities. Like Progency’s MGEs, they combine strategic vision with hands-on implementation, bringing cross-industry best practices while delivering measurable performance improvements.

Asset Management “Multi-Manager” Platforms

Investment firms like Millennium, Citadel, and Point72 have revolutionised asset management through their “pod” structure. They provide a centralised infrastructure (technology, operations, compliance) while enabling specialised investment teams to focus exclusively on generating alpha. These teams receive significant performance-based compensation tied directly to their investment results, not assets under management. This parallels Progency’s model of providing a unified platform with specialised vertical expertise, compensated based on measurable performance outcomes.

Medical “Centers of Excellence”

Healthcare systems like Mayo Clinic and Cleveland Clinic deploy multidisciplinary teams of specialists who collaborate on complex patient cases. These centres combine strategic diagnostic expertise with treatment implementation, creating seamless integration between analysis and execution. Their approach of treating the whole patient rather than isolated symptoms mirrors Progency’s holistic approach to marketing effectiveness. Most importantly, many have shifted toward value-based care models where compensation ties directly to patient outcomes rather than procedures performed.

High-End Architecture and Design

Design-build firms like Foster + Partners combine strategic architectural vision with practical implementation expertise. Unlike traditional architects who merely provide plans, these firms oversee projects from concept to completion, ensuring the original vision translates into practical reality. Their integrated approach eliminates the execution gap between design and construction, much as Progency bridges the gap between marketing strategy and implementation. Some have pioneered performance-based contracts where compensation links to building efficiency metrics or occupancy rates.

Management Contracts in Hospitality

Hotel operators like Four Seasons and Marriott operate under performance-based management contracts where their compensation directly ties to property profitability rather than fixed fees. They contribute both strategic brand expertise and operational implementation, transforming physical assets into revenue-generating businesses. Their ability to apply global best practices to local properties mirrors how Progency brings cross-client insights to specific brand challenges. The management contract model also demonstrates how service providers can create significant upside through performance without asset ownership.

Legal Contingency Practices

Elite litigation firms have moved beyond hourly billing to contingency models where compensation depends entirely on case outcomes. These firms combine strategic legal analysis with implementation expertise, shouldering both the risk and potential reward. This approach creates perfect alignment with client objectives, just as Progency’s performance-based model aligns martech compensation with marketing outcomes. The most sophisticated versions incorporate sliding fee structures based on settlement amounts or timeframes, similar to how Progency might scale compensation based on revenue impact magnitudes.

Agricultural Crop Consultants

Modern precision agriculture employs specialist crop consultants who work on performance-based arrangements with farmers. Rather than charging fixed fees, they receive a percentage of yield improvements or cost reductions achieved. These consultants combine strategic agronomic knowledge with implementation expertise across soil management, irrigation, and pest control. Like Progency’s MGEs, they translate data insights into practical actions while assuming accountability for measurable results.

Sports Performance Coaching

Elite sports training has evolved beyond generic instruction to comprehensive performance management. Companies like Exos don’t merely train athletes but implement integrated systems spanning nutrition, recovery, and skill development. Many structure compensation around performance achievements (draft position, competition results) rather than training hours. This holistic approach to improvement mirrors Progency’s comprehensive marketing transformation strategy, while the outcome-based compensation model demonstrates how service providers can confidently tie their success to client results.

What Progency Can Learn from These Examples

These diverse analogies offer valuable insights for Progency’s evolution:

1. Long-Term Relationship Building: The most successful models create enduring partnerships rather than transactional engagements
2. Tiered Compensation Structures: Many combine baseline fees with uncapped performance incentives to balance risk and reward
3. Clear Measurement Frameworks: All establish transparent, mutually agreed metrics to determine success and calculate compensation
4. Vertical Specialisation: The deepest value creation comes from domain-specific expertise rather than generic capabilities
5. Knowledge Codification: Leaders systematically capture insights from each engagement to build proprietary intellectual capital

By studying these proven models from traditional industries, Progency can refine its approach while gaining confidence that its fundamental value proposition—combining McKinsey-style strategic expertise with Palantir-style embedded implementation under performance-based economics—has demonstrated success across diverse sectors for decades. The innovation lies not in creating an entirely new business model, but in applying these proven principles to marketing’s unique challenges while enhancing them through AI agent multiplication that enables unprecedented scale and efficiency.

10

Summary

I asked Claude to summarise the key ideas.

The marketing landscape stands at a pivotal inflection point. Brands face unprecedented challenges—falling organic traffic, skyrocketing customer acquisition costs, vanishing attention spans, and the colossal £375 billion AdWaste crisis where 70% of digital marketing budgets are squandered reacquiring customers brands already know. Traditional solutions—whether agencies peddling creative services or martech platforms selling unused features—have failed to address these fundamental inefficiencies.

Progency offers a revolutionary alternative that transforms marketing from a cost centre into a profit engine through ten defining principles:

1. The PEAK Framework as Foundation: Progency’s integration of Platform, Experts, AI agents, and Kaizen methodology creates an unparalleled value proposition that transcends traditional service or software models. This framework delivers both strategic insight and guaranteed execution—fusing McKinsey’s consultative approach with Palantir’s embedded expertise while adding AI multiplication that neither can match.
2. Outcome-Based Economics as Core Philosophy: By shifting from input-based pricing (time, features, emails) to performance-based compensation tied directly to measurable business outcomes, Progency creates perfect alignment with client success. This revolutionary model transforms martech from a fixed expense into a variable investment with unlimited upside potential for both parties.
3. Marketing Growth Engineers as Value Catalysts: The MGE role—developed by transforming existing CSMs through systematic upskilling—combines technical platform expertise with strategic marketing acumen. These cross-functional specialists serve as the human directors of AI agent systems, delivering immediate value while building institutional intelligence that powers continuous improvement.
4. AI Agents Collective as Execution Engine: The Department of One handles complex marketing operations at unprecedented scale without proportional resource expansion. These specialised AI systems work collaboratively under human oversight to deliver true N=1 personalisation that would be impossible through traditional approaches constrained by human bandwidth.
5. Knowledge Repository as Competitive Moat: Systematic codification of marketing insights, solution patterns, and client learnings creates a proprietary knowledge base that continuously enhances platform capabilities and AI performance. This self-reinforcing system generates compounding advantages that competitors cannot easily replicate.
6. Vertical Specialisation as Differentiation Strategy: Deep expertise in specific industries enables pattern recognition and best practice transfer that generic approaches cannot match. This vertical focus creates higher-value insights while establishing credible thought leadership that elevates client relationships beyond tactical implementation.
7. Growth Path as Evolution Strategy: Following Palantir’s proven trajectory, Progency begins with high-touch human expertise that gradually transitions to AI-powered automation as patterns emerge and systems mature. This creates improving economics over time as marginal costs decrease while maintaining premium outcomes.
8. Measurement Mastery as Trust Foundation: Rigorous approaches to establishing baselines, isolating variables, and proving incremental impact create unambiguous validation of Progency’s value contribution. This scientific approach transforms marketing from perception-driven to evidence-based, elevating its credibility with finance and executive teams.
9. Cross-Client Intelligence as Scale Advantage: Insights gained across multiple clients accelerate learning cycles and solution development beyond what any single organisation could achieve independently. This network effect creates value for all participants while maintaining strict confidentiality around specific client data.
10. 10X Growth Potential for Martech Companies: By redirecting AdWaste toward more effective approaches and capturing a percentage of the value created, Progency enables martech providers to tap into revenue streams many times larger than traditional subscription models. This represents not incremental improvement but fundamental business model reinvention.

Progency isn’t merely another martech innovation—it’s a paradigm shift that reconceptualises the relationship between marketing technology, strategy, implementation, and outcomes. By combining McKinsey’s strategic brilliance and Palantir’s embedded expertise with AI multiplication and performance-based economics, Progency offers both brands and martech providers a revolutionary path forward—one that finally fulfils the promise of transforming marketing from necessary expense into the primary engine of sustainable profitable growth.

11

AI Inputs – 1

I asked Claude and ChatGPT on how the Progency model can be improved.

Claude

To further elevate the Progency framework, here are some additional dimensions to consider that would strengthen both the concept and the essay:

1. Client Co-Investment Model

Introduce a progressive partnership structure where clients contribute to Progency’s development in exchange for preferential economics. Similar to how Palantir sometimes accepted equity in early government projects, this creates deeper alignment and reduces initial adoption barriers. Clients could contribute data, domain expertise, or even engineering resources in exchange for more favorable revenue-sharing terms. This approach transforms clients from customers into true partners, accelerating both adoption and results.

2. Talent Incentive Structure

Detail a revolutionary compensation approach for MGEs that mirrors venture capital carry structures—allowing top performers to participate in client success at portfolio level rather than just account level. This would attract elite talent from both consulting and technology sectors while creating internal entrepreneurship. MGEs would effectively “invest” their expertise in client portfolios, receiving significant upside when those portfolios outperform baseline expectations.

3. Network Effects Marketplace

Expand beyond the core Progency offering to create a “marketing solutions marketplace” where successful patterns, workflows, and AI agents developed for one client sector become productized assets available to others. This creates a flywheel where each client engagement generates intellectual property that benefits the entire ecosystem while creating additional revenue streams for Progency.

4. Proprietary Benchmarking Index

Develop a comprehensive “Marketing Effectiveness Index” drawing from anonymized performance data across the Progency client base. This would become the industry standard for measuring marketing performance, similar to the Net Promoter Score but encompassing engagement, conversion, and retention metrics. Clients would gain unprecedented comparative insights while Progency establishes unrivaled market intelligence.

5. Decentralized Autonomous Marketing Organization (DAMO)

Introduce a visionary future state where AI agents within the Progency ecosystem could function as a self-organizing marketing department—autonomously handling workflows across acquisition, engagement, and retention while adapting to changing conditions without human intervention. This conceptual framework positions Progency at the forefront of the agentic AI revolution, similar to Andreessen Horowitz’s “autonomous corporation” thesis.

6. Risk Transfer Mechanism

Detail a more sophisticated economic model where Progency initially assumes significant performance risk—potentially even guaranteeing minimum outcomes—and is compensated proportionally for exceeding them. This dramatically simplifies the client decision process by effectively transferring implementation risk from client to Progency, similar to how performance insurance works in other industries.

7. Cognitive Diversity Integration

Emphasize how Progency deliberately combines diverse thinking approaches—analytical, creative, strategic, and technical—within both human teams and AI agent collectives. This cognitive diversity creates solutions that transcend traditional marketing silos, much as McKinsey’s frameworks integrate multiple perspectives to solve complex business challenges.

8. Progency Ventures

Establish an innovation arm that identifies promising marketing technology startups and accelerates their growth through preferential access to the Progency client base. This creates an innovation pipeline while giving clients privileged access to emerging technologies, similar to how Salesforce Ventures creates strategic advantages for both Salesforce and its portfolio companies.

9. Closed-Loop Research Institute

Create a dedicated research function that works with academic institutions to develop marketing science breakthroughs, establish rigorous testing methodologies, and publish influential thought leadership. This elevates Progency beyond implementation into genuine knowledge creation—building intellectual credibility comparable to McKinsey Global Institute while creating proprietary insights that inform client strategies.

10. Ethical AI Governance Framework

Develop a comprehensive governance system that ensures all AI agent activities maintain brand integrity, regulatory compliance, and consumer trust. This “Ethics by Design” approach would include explainability mechanisms, bias detection, and continuous human oversight—establishing Progency as the trusted steward of both brand equity and customer relationships.

11. “Earned Media Twins” Concept

Introduce AI-powered simulations of how marketing initiatives will perform across earned media channels before deployment. These predictive models would allow brands to test approaches in a synthetic environment, optimizing both paid and organic strategies in a unified framework rather than treating them as separate disciplines.

12. Implementation Blueprint

Provide a detailed, phased approach for how existing martech companies can transform toward the Progency model, identifying key milestones, resource requirements, and success metrics. This practical roadmap would make the concept immediately actionable for readers considering this transformation for their own organizations.

By incorporating these additional dimensions, you would strengthen Progency’s positioning as not merely an evolution of existing models but a comprehensive reimagining of what marketing effectiveness means in the AI era. These elements address strategic, operational, economic, and ethical dimensions—creating a more robust, defensible, and visionary framework that would resonate with both marketing leaders and technology innovators.

12

AI Inputs – 2

ChatGPT

1. “The 3-Phase MGE Maturity Model”

The lifecycle of client engagement through MGEs (Marketing Growth Engineers):

• Phase 1: Human-Led Execution
  MGEs work onsite, with hands-on martech configuration and personalised playbooks.
• Phase 2: Human-AI Co-Orchestration
  AI Agents gradually take over execution under MGE supervision.
• Phase 3: Autonomous AI Loops
  AI Agents optimise campaigns in real-time with MGEs offering strategic oversight.

This can tie to how Palantir reduced FDEs per customer over time while boosting margins.

1. “AI-Backed Pattern Libraries”

Progency captures reusable, high-performing patterns (e.g., journeys, nudges, segments) across clients and feeds them back into:

• The AI Agents Collective
• The Strategy Repository
• The Playbook Engine for MGEs

This makes Progency a learning system—growing smarter with every client it serves.

1. Progency Competitive Matrix

Dimension	Traditional Agencies	Martech Vendors	Consulting Firms (e.g., McKinsey)	Progency
Strategic Thinking	Brand- and campaign-focused	Limited; mostly product support	Deep and structured problem-solving	Combines CMO-level strategy with cross-brand playbooks
Execution Capability	Human-led, fragmented, often slow	DIY/self-serve platforms	Light; often advisory only	End-to-end AI-powered execution via MGEs + AI Agents
AI Enablement	Minimal	Bolt-on features	Emerging; limited depth	Core capability: AI Agents Collective + 1:1 personalisation
Performance Orientation	Retainers or hourly billing; no upside tie-in	Usage-based billing (MAU, API, etc.)	Fixed fee based on time/project scope	Outcome-based pricing; aligned incentives with brands
Reusable IP & Learnings	Limited; often proprietary to client	Productised, not contextual	Framework-driven but not operationalised	Continuously learning system with codified playbooks
Scalability	Linear: more clients = more people	Platform scales, but adoption lags	Human bandwidth limited	AI-driven scale; human effort reduces over time
Client Embedding	Account managers, limited on-ground expertise	CSMs for adoption support	Consultants embed briefly, then exit	MGEs embed deeply, translate business needs into execution
Vertical Specialisation	Varies by agency; often generalist creative focus	Feature-focused; verticalisation weak	Strong vertical teams but high cost	Industry-specific MGEs with deep journey and segment knowledge
Value Proposition	Creative excellence and brand storytelling	Technology platform with support	High-level transformation strategies	“Strategy + Execution + Outcomes” in a single partner

 

1. The CMO’s Cry for Help: A Buyer Persona Scenario

“I don’t need another dashboard, another deck, or another demo. I need results. Now. My team’s overwhelmed, my CRM data’s a mess, and retention is tanking. I’m spending a fortune on reacquisition and still losing customers I’ve already paid to acquire. I don’t want a vendor. I want a growth partner who knows what’s working, who’ll do it for me, and who’ll win only if I win.”
— Priya, CMO of a $50M D2C brand in personal care

What Priya Wants — and Why Progency Delivers

What CMOs Want	How Progency Delivers
End-to-end execution, not just strategy	Embedded Marketing Growth Engineers + AI Agents Collective
Confidence in proven, not experimental, ideas	Access to cross-client playbooks and benchmarked journeys
Revenue impact, not vanity metrics	Outcome-based pricing tied to LTV, uplift, and engagement
A partner, not a vendor	Long-term, performance-aligned relationship with shared incentives
Speed without complexity	Plug-and-play modules, vertical templates, and rapid AI-enabled configuration
Internal team augmentation	Progency’s “Department of One” handles the heavy lifting

 

1. Progency Pricing Frameworks: Aligning Success with Outcomes

Progency’s value lies in measurable performance. Unlike traditional input-based pricing (hours, MAUs, CPM), Progency adopts outcome-based models that ensure skin in the game and eliminate upfront resistance.

1. Fixed + Performance Tiered

A predictable base + upside sharing:

• Fixed Fee: Covers platform, MGEs, agent deployment
• Performance Bonus: Percentage of incremental uplift vs baseline
• Example:
  • ₹10L/month base fee
  • 10% of revenue uplift above ₹X baseline

Best for: Mid-to-large clients needing predictability with upside alignment.

2. Pure Performance-Based (RevShare)

No fixed fee. Progency earns only if it delivers:

• % of Incremental Revenue or LTV Uplift
• Benchmarked against historical or control group metrics
• Example:
  • 15% of net incremental revenue
  • Measured quarterly; reconciled monthly

Best for: Confident pilots, challenger brands, or when brand insists on risk-sharing.

3. Tiered Revenue Share

Aligned to value delivered — the more uplift, the higher the share:

• 5% share on 0–10% uplift
• 10% share on 10–20% uplift
• 15% share on 20–30% uplift
• Capped or uncapped depending on brand size

Best for: Brands with seasonal surges, high volatility, or enterprise-level goals.

4. 30-Day “Outcomes-First” Pilot

Lower barrier to entry to showcase results fast:

• No long-term commitment
• ₹5L or $6K pilot with defined KPIs (e.g., Reactivation rate, LTV boost from Rest)
• Performance bonus only if targets met (e.g., +10% revenue from Rest)

This risk-mitigated onboarding allows CMOs to evaluate Progency’s effectiveness before scaling.

Summary Table

Model	Risk Profile	Ideal For	Alignment
Fixed + Performance Bonus	Medium risk	Mid-sized brands, budget-conscious	Balanced
Pure Performance (RevShare)	Zero upfront risk	Early adopters, aggressive growth seekers	High alignment
Tiered Revenue Share	Dynamic, value-tied	Large enterprises, performance-focused	Highly scalable
30-Day Pilot	Low commitment, fast	Cautious brands evaluating partners	“Proof before scale” model