Progency: Fusing Martech, AI Agents, and Experts to Eliminate $500 billion AdWaste

Published April 8, 2025

1

The Missing Link

In today’s digital marketing landscape, a critical disconnect exists between what marketers need and what they’re getting. Marketers aren’t looking for more software—they’re seeking revenue growth solutions. Adtech has mastered this value proposition with elegant simplicity: “Give us your budget, and we’ll deliver clicks to your website and app.” This direct link between investment and outcome explains why approximately 90% of digital marketing budgets flow to acquisition through ad platforms.

Meanwhile, martech has positioned itself merely as a toolkit—offering better ways to convert those clicks into conversions. But this fundamental misalignment in value proposition has created a massive efficiency problem: an estimated 70% of paid acquisition spending goes toward reaching existing or former customers. This translates to a staggering $500 billion in annual AdWaste—money that should rightfully remain on brands’ P&L statements.

For martech to truly challenge adtech’s dominance, it must evolve from selling tools to delivering outcomes. Marketers don’t want the operational burden of staffing teams to handle customer segmentation, journey orchestration, campaign creation, and micro-analytics—all daily tasks that drain resources and focus. They want what adtech provides: clear business outcomes without the operational complexity.

This is precisely where the concept of “Progency” enters—a powerful fusion of product and agency designed to take ownership of business outcomes rather than just providing software. As a foundational pillar of NeoMarketing, Progency bridges the execution gap between martech’s powerful capabilities and the tangible business results brands demand, making it the essential implementation engine for the future of profitable customer relationships. While martech platforms offer sophisticated capabilities, the critical “service” element is missing entirely from the traditional SaaS (Software-as-a-Service) model. Martech companies have shown little interest in adding even a thin layer of outcome-driven services to complement their software.

I’ve previously identified three perennial marketing problems: the “Not for Me” problem (lack of personalisation), the “No Hotline” problem (ineffective communication channels), and the “No Alternative” problem (dependency on expensive ad platforms). Solutions like N=1 personalisation, NeoMails, and NeoN can address these issues. But a fourth challenge looms larger than all: “Who Will Do It?”

Agentic AI represents the breakthrough technology that could finally bridge this gap. By deploying specialised AI agents that coordinate with each other to complete complex marketing tasks, we can create a “mirror world” that predicts customer actions, reduces operational burdens on marketing departments, and maximises lifetime value—ultimately reducing the dependency on wasteful reacquisition.

This transformation offers martech companies a profound opportunity. By embracing the Progency model powered by Agentic AI fusing product, agents, and agency, they can evolve beyond selling software to delivering the outcomes marketers truly seek. In this series, we’ll explore how every martech company should consider creating a Progency division to offer marketing teams a compelling alternative to mere technology stacks—one that addresses not just what should be done but solves the critical question of who will do it.

2

Past Ideas

I have written about Progency in multiple essays over the past few years:

I asked Claude to provide a summary of the key ideas.

Definition: A Progency is a product-led agency that melds martech platforms with a specialised layer of agency services, operating on a performance-driven pricing model tied directly to outcomes.

Origin of Need: Marketing departments are overwhelmed with daily tasks yet underutilise martech platforms (typically only 30-40% of features). Simultaneously, they waste approximately 70% of their budgets on reacquiring existing customers through expensive adtech platforms.

Key Differentiator: Unlike traditional agencies that use third-party tools, a Progency is built by martech platform creators themselves, ensuring deep platform expertise and creating a sustainable competitive advantage.

Business Model: Performance-based compensation similar to adtech, where the Progency only gets paid for delivered outcomes (like revenue growth, reactivation rates, or customer lifetime value improvement).

Operational Focus: Primarily targets overlooked customer segments—the “Rest” and “Test” customers (dormant and at-risk segments) that marketing departments typically ignore while focusing on acquisition and “Best” customers.

Technological Edge:

  • Operates on a proprietary “profipoly/NeoMarketing stack” that works in parallel with existing systems
  • Creates a seamless data flow between brand operations and Progency initiatives
  • Enables real-time insights and adaptation not possible with traditional agency models

Indian Advantage: For Indian SaaS companies, a “Bundled Kaizen Services” approach through Progency offers competitive pricing advantage against US competitors, allowing continuous improvement services to be bundled into product pricing.

Primary Use Cases:

  • Reactivation of dormant customers (an alternative to costly reacquisition)
  • Maximising revenue from “Rest” customers to convert them into “Best” customers
  • Driving referrals from existing customer bases
  • Using Best Customer Genome for targeted new acquisition

Market Opportunity: The $500 billion currently wasted on inefficient reacquisition represents the potential market for Progency services—approximately 10x larger than the traditional martech software market.

Metrics Framework: Uses the EnCoRe Triad (Engagement, Conversion, Retention) as key performance indicators, with “Earned Growth” as the North Star metric.

Value Proposition:

  • For brands: Outsource outcomes, not just tasks; focus internal resources on high-value activities
  • For martech companies: Transform from feature competition to a profit-sharing business model with unlimited upside (like adtech)
  • For customers: Receive more relevant, personalised experiences based on their actual needs

Implementation Model: Typically starts with quick wins like reactivation campaigns, then progressively adds more sophisticated services (personalisation, referral programs, etc.)

Transformation Potential: Can deliver up to 500-700 basis points of profit improvement for brands through combined savings on acquisition costs and increased revenue from existing customers.

Future Evolution: With Agentic AI, Progency can leverage specialised agents to automate complex tasks, creating a scalable service layer that maintains human-like quality without proportional staff increases.

3

Agentic Power

Agentic AI represents the next significant evolution in artificial intelligence, building upon the foundations laid by predictive and generative AI systems. While predictive AI excels at forecasting outcomes based on historical data and generative AI creates content in response to prompts, agentic AI adds a crucial capability: autonomous action. These AI systems can understand goals, plan steps toward achieving them, execute actions across different environments, and learn from outcomes—all with minimal human oversight.

This evolution follows a natural progression. First-generation AI systems could analyse past customer behaviours to predict future actions (predictive). Second-generation systems could create personalised content at scale (generative). Now, agentic AI can independently orchestrate entire marketing workflows, coordinating multiple tasks while adapting strategies based on real-time feedback.

In marketing, agentic AI manifests in two primary forms:

  1. Operational Agents
  • AI Co-Marketer: Functions as a marketing executive’s virtual partner, overseeing strategy and orchestrating specialised sub-agents. This “manager agent” translates high-level objectives (like “increase Best customer revenue by 20%”) into tactical plans and assigns tasks to functional specialists.
  • Functional Agents: Specialised AI systems that handle specific marketing tasks:
    • Content agents that create and optimise campaign materials
    • Analytics agents that process customer signals and identify patterns
    • Campaign agents that manage execution across channels
    • Testing agents that continuously refine approaches
    • Budget agents that reallocate resources in real-time based on performance

These operational agents work in concert, communicating with each other and coordinating complex, multi-step marketing initiatives without requiring constant human direction.

  1. Customer-Representative Agents
  • Segment Twins: AI systems that model the collective behaviours and preferences of customer segments (for example, Best, Rest, and Test customer groups). These analyse segment-level data to identify trends and opportunities.
  • Singular Twins: The ultimate evolution—personalized AI companions representing individual customers. These digital twins understand specific preferences, historical behaviours, and likely future actions, enabling true N=1 personalisation at scale.

Customer-representative agents continuously learn from interactions, adapting to changing behaviours and preferences. By simulating customer responses to potential marketing initiatives before deployment, they dramatically increase effectiveness while reducing waste.

**

The power of this approach lies in the interaction between these agent types. When operational agents work alongside customer-representative agents, they create a self-organising and self-improving “emergent” system that can:

  • Predict which offerings will resonate with specific customers
  • Pre-emptively address friction points in the customer journey
  • Allocate resources dynamically to maximise lifetime value
  • Continuously adapt strategies based on real-time performance data

While marketing departments would ideally deploy these AI agents directly, implementation challenges remain significant. Most teams lack the technical expertise, data infrastructure, and operational frameworks needed to fully leverage agentic AI’s potential.

This creates a compelling alternative pathway: the Progency model. A Progency combines proprietary martech platforms with specialised human expertise, using AI agents internally to deliver outcomes rather than just technology. This approach shields marketers from implementation complexity while providing the benefits of agentic AI through a performance-based partnership focused on results rather than software adoption.

4

Workings

For martech companies looking to evolve beyond software provision, operationalising a Progency represents both a significant opportunity and a complex transformation. This transition requires rethinking organisational structures, talent acquisition, pricing models, and technology deployment. Here’s how martech firms can bring the Progency model to life:

Organisational Framework

Creating a Progency division necessitates a distinct organisational unit with its own leadership, KPIs, and operating model:

  • Independent Business Unit: Establish a separate P&L unit with dedicated leadership, staffing, and resources to prevent the Progency being subsumed by the core product business
  • Cross-Functional Expertise: Build teams that blend martech platform specialists with performance marketing strategists, data scientists, customer experience designers, and industry vertical experts
  • Performance Culture: Cultivate a results-oriented environment where compensation and recognition tie directly to client outcomes rather than software adoption or utilisation

Technology Integration

The technology backbone of a successful Progency combines the core martech platform with AI agent architecture:

  • Agent Infrastructure: Deploy the AI Co-Marketer alongside functional specialists (content, analytics, campaign agents) and AI Twins in a coordinated system
  • Client Integration Layer: Develop lightweight APIs and connectors to access client data while minimising IT involvement from the client side
  • Parallel Operations: Create systems that can run alongside existing martech implementations without disrupting current operations
  • Dashboard Ecosystem: Build transparent reporting tools showing both real-time activities and business impact metrics

Service Delivery Model

Rather than traditional agency account management, a Progency operates through a blend of human expertise and AI execution:

  • Scoped Outcome Agreements: Replace software contracts with clearly defined performance commitments focused on specific revenue or customer behaviour metrics
  • Tiered Engagement Options: Offer entry points ranging from targeted use cases (like reactivation campaigns) to comprehensive marketing operations management
  • Continuous Optimisation Practice: Implement methodical approaches to incremental improvement, similar to the “Bundled Kaizen Services” model
  • Defined Handoffs: Establish clear boundaries between client responsibilities and Progency activities to prevent scope creep

Economics and Pricing

The financial model represents perhaps the most critical shift from traditional SaaS:

  • Performance-Based Pricing: Structure compensation primarily around achieved outcomes rather than platform access, with baseline fees covering operational costs
  • Revenue Sharing Mechanisms: Implement transparent calculations for sharing incremental revenue or cost savings, especially for reactivation and cross-sell initiatives
  • Long-Term Partnerships: Design multi-year agreements that align incentives for sustained customer relationship development rather than short-term gains
  • Investment Model: Recognise that initial implementation may require upfront investment before revenue sharing becomes profitable

Implementation Pathway

A measured approach to building Progency capabilities helps manage risk while validating the model:

  1. Pilot with Strategic Clients: Select 2-3 existing customers with strong relationships for initial Progency engagements
  2. Focus on Proven Use Cases: Begin with reactivation of dormant customers, where ROI is most clearly demonstrable (and AdWaste is the highest)
  3. Build Agent Capabilities Incrementally: Start with core operational agents before advancing to more sophisticated customer twins
  4. Document Value Creation: Rigorously track both inputs and outcomes to refine the economic model and build case studies

For martech companies, operationalising a Progency represents more than adding a services layer—it fundamentally transforms the business relationship with clients from vendor to partner. While this evolution requires significant investment and organisational change, it addresses the core “Who Will Do It?” problem that currently limits martech’s impact and opens access to the $500 billion opportunity currently lost to AdWaste.

5

Enhancements

While internal marketing departments possess deep brand knowledge, they face structural constraints that limit their effectiveness in fully leveraging martech capabilities. A Progency, with its specialised focus and performance-driven model, can deliver powerful enhancements that transform marketing outcomes. Here are the key capabilities that position Progency as superior to internal implementation:

Data & Intelligence Enhancements

  • Holistic Data Integration: Combine traditionally siloed data streams—adtech performance, news cycles, social media trends, weather patterns, and competitive intelligence—into a unified decision framework, creating insights that cross departmental boundaries.
  • Institutional Memory Preservation: Build an “infinite memory” of campaign successes and failures that persists despite staff turnover. Unlike internal marketing teams that suffer knowledge loss when employees move to higher-paying adtech roles, Progency maintains consistent historical insight.
  • Cross-Client Pattern Recognition: Identify success patterns in and across verticals in similar categories, accelerating the discovery of effective strategies while avoiding proven pitfalls—something impossible for an isolated internal team.
  • Competitive Intelligence Amplification: Deploy specialised tools to monitor competitor activities, pricing shifts, promotional calendars, and product launches, providing strategic early-warning capabilities beyond most internal teams’ resources.

Operational Excellence

  • AI Agent Deployment: Overcome internal resistance to new technologies by implementing AI agents externally, sidestepping the natural human hesitancy to embrace automation that might threaten roles or require significant skills retraining.
  • Continuous Improvement Framework: Apply rigorous Kaizen methodology for systematic enhancement of marketing operations, bringing industry-wide learnings and best practices to each client relationship.
  • Resource Flexibility: Scale resources up or down based on seasonal needs, campaign launches, or market opportunities without the constraints of fixed internal headcounts or budget cycles, ensuring optimal resource allocation.
  • Specialised Expertise On-Demand: Access niche specialists (like data scientists, behavioural economists, or vertical industry experts) when needed without maintaining these high-cost resources permanently on staff.

Performance & Accountability

  • Clear ROI Measurement: Implement sophisticated attribution models that accurately connect marketing activities to financial outcomes, overcoming the internal political challenges of cross-department measurement.
  • Zero-Based Budgeting Application: Continuously justify every marketing expenditure based on performance rather than historical budget allocations, eliminating low-value “we’ve always done it this way” spending.
  • Objective Performance Assessment: Evaluate marketing tactics without the internal biases that often protect underperforming legacy initiatives championed by influential stakeholders.
  • True P&L Responsibility: Accept genuine financial accountability for results, creating alignment between marketing investment and business outcomes that departmental structures often inhibit.

Technical & Martech Mastery

  • Platform Utilisation Optimisation: Ensure the full capability spectrum of martech investments is leveraged, in contrast to internal teams that typically use only 30-40% of available features.
  • Rapid Innovation Adoption: Accelerate implementation of emerging technologies without lengthy internal approval processes, bureaucratic IT roadblocks, or change management resistance.
  • Technical Debt Prevention: Maintain clean, future-proof systems and processes rather than accumulating shortcuts and workarounds that commonly plague internal implementations.
  • Integration Expertise: Seamlessly connect martech platforms with adjacent systems (CRM, e-commerce, customer service, inventory management) to create unified customer experiences across touchpoints.

The fundamental advantage of Progency lies in this comprehensive enhancement portfolio combined with its performance-based model. Unlike internal departments that receive fixed budgets regardless of outcomes, Progency succeeds only when clients succeed—creating perfect alignment between service provider and business results. For executives frustrated by the gap between martech’s promise and practical results, Progency offers a compelling alternative to both traditional implementation approaches and the wasteful adtech dependency cycle.

6

The Pitch

An Open Letter from a Progency Team to Forward-Thinking CMOs and CEOs

Dear Marketing Leader,

We understand the challenges you face today: rising acquisition costs, fragmented customer journeys, complex technology stacks, and the constant pressure to deliver measurable growth. Your marketing department works tirelessly, yet the full potential of your martech investments remains frustratingly elusive while your adtech spending continues to climb.

This isn’t a failure of strategy or talent. It’s a structural challenge that affects virtually every marketing organisation. Let me offer a candid assessment of where we might help—and just as importantly, where we won’t.

Where Progency Adds Value

Focus on the Overlooked Majority

Your team excels at new acquisition strategies and nurturing your best customers. What often falls through the cracks are your “Rest” and “Test” customers—those who have purchased but aren’t fully engaged, and those who have gone dormant. These segments represent an enormous untapped revenue opportunity that we can activate without disrupting your core marketing operations.

Elimination of AdWaste

Our analysis typically reveals that 60-70% of digital ad spending goes toward reaching people who are already in your database. This represents millions in potential savings. We don’t ask you to reduce your acquisition budget; instead, we focus on reducing the need for expensive reacquisition by reactivating these customers through your owned channels.

Outcome-Based Economics

Unlike traditional martech vendors who charge regardless of results, or agencies billing by the hour, our compensation aligns directly with measurable outcomes. If we don’t deliver increased revenue from reactivated customers or reduce your AdWaste, we don’t get paid. This transforms marketing from a cost centre into a predictable profit engine.

AI-Powered Scale

Our proprietary AI agent system can process millions of individual customer journeys simultaneously, delivering true N=1 personalisation at scale. This capability simply isn’t feasible for internal teams without significant technology investment and specialised AI expertise.

What We Don’t Replace

Your Brand Stewardship

Your team’s understanding of your brand’s voice, positioning, and heritage is irreplaceable. We complement this expertise rather than attempting to replicate it.

Your Strategic Direction

You set the business objectives and north star metrics. We align our efforts with these goals, focusing on specific segments and KPIs that support your broader strategy.

Your Core Creative Process

While our AI systems can optimise and personalise content, your creative teams remain essential for developing distinctive brand assets and campaign concepts that resonate with your audience.

A Practical Starting Point

We propose beginning with a focused 90-day pilot targeting a specific inactive customer segment. This allows us to demonstrate our capabilities while you evaluate the model with minimal risk. Typical results include:

  • 20-30% reduction in reacquisition spending
  • 15-25% increase in revenue from previously dormant customers
  • Clear attribution linking our activities to business outcomes

The beauty of our approach is its complementary nature. Your core marketing functions continue unchanged while we work in parallel, focusing exclusively on areas currently receiving insufficient attention. As the model proves itself, we can explore expanding our scope based on measurable results.

What makes this moment particularly opportune is the convergence of AI agent technology with performance-based engagement models. For the first time, it’s possible to deliver agency-level service with software-level scalability and the economic alignment of a true business partnership.

We welcome the opportunity to discuss how a Progency relationship might enhance your marketing ecosystem and contribute to your growth objectives.

Sincerely,
The Progency Team

7

AI Critique and Improvements

I asked Claude, ChatGPT, and DeepSeek for an assessment.

Claude

Strengths of the Concept

The Progency model presents a compelling vision for addressing several critical gaps in today’s marketing ecosystem:

  1. Value Proposition Alignment: The essay brilliantly identifies the core misalignment in the martech industry – selling tools rather than outcomes. By connecting compensation directly to results, Progency creates natural alignment with client objectives.
  2. Focus on Overlooked Segments: The emphasis on “Rest” and “Test” customers represents a genuinely underserved opportunity. Most marketing departments concentrate resources on acquisition and high-value customers, leaving significant revenue potential untapped.
  3. Practical Implementation Path: The suggested 90-day pilot approach with clear metrics creates a low-risk entry point for clients to test the model before broader commitment.
  4. Technological Innovation: The integration of AI agents – both operational and customer-representative – represents a genuine innovation that could deliver capabilities beyond what traditional marketing departments can achieve.
  5. Clear Economic Case: The focus on eliminating AdWaste provides a compelling financial justification, with a massive $500 billion opportunity that makes the business case immediately apparent.

Practical Challenges and Improvement Areas

Despite its promise, several challenges could hinder the Progency model’s practical implementation:

1. Data Integration and Access

Challenge: The model requires deep integration with client systems and comprehensive access to customer data – often a significant hurdle given privacy regulations, data silos, and security concerns.

Solution: Develop a tiered data access framework with clear governance policies. Start with anonymised or aggregated data for initial projects, progressively demonstrating value to earn deeper access. Create secure data exchange protocols that respect privacy regulations while enabling sufficient operational capabilities.

2. Skill Gap and Talent Acquisition

Challenge: Building a Progency requires a unique combination of martech expertise, data science capabilities, industry knowledge, and AI engineering talent – all in short supply.

Solution: Consider a “build-acquire-partner” talent strategy: identify key skills that must be in-house versus those that can be contracted initially. Develop upskilling programmes for existing martech professionals, potentially creating certification paths. Partner with universities to develop specialised curricula that prepare graduates for these hybrid roles.

3. Organisational Resistance

Challenge: The essay acknowledges potential resistance from marketing teams who may view Progency as threatening their roles or authority.

Solution: Position Progency as an enhancement team rather than a replacement, with clear boundaries around responsibilities. Implement collaborative workshops where client marketing teams participate in defining success metrics and strategic direction. Create explicit career development opportunities for client-side marketers to evolve alongside the Progency, perhaps through joint innovation programmes or secondments.

4. Measuring Causality and Attribution

Challenge: Accurately attributing business outcomes specifically to Progency activities will prove challenging, particularly in isolating their impact from other marketing initiatives.

Solution: Implement controlled experiments with holdout groups where possible. Develop sophisticated multi-touch attribution models that account for both Progency and client marketing activities. Establish baseline measurements before engagement and agree on measurement methodologies upfront to prevent disputes about performance-based compensation.

5. Scalability vs Customisation Balance

Challenge: While AI agents promise scalability, each client will require significant customisation of systems, processes, and metrics – potentially undermining economic viability.

Solution: Create a modular approach to Progency implementation with standardised components that can be configured rather than custom-built for each client. Develop industry-specific templates for common use cases (e.g., retail reactivation, B2B engagement) to accelerate deployment. Build a maturity model that gradually increases customisation as client relationships mature.

6. Competitive Response

Challenge: If successful, traditional agencies, consultancies, and martech vendors will rapidly attempt to replicate the model, potentially with deeper resources.

Solution: Focus on building proprietary AI agent architecture that creates sustainable competitive advantage. Secure exclusive client relationships with multi-year contracts that include innovation roadmaps. Consider creating an industry consortium of Progency providers to establish standards and best practices, positioning early movers as thought leaders.

Enhancements to Strengthen the Model

1. Hybrid Team Integration

The current proposal positions Progency as somewhat parallel to the client’s operations. Consider deeper integration models where hybrid teams of client marketers and Progency specialists work together, allowing for knowledge transfer in both directions while maintaining accountability.

2. Capability Development Path

Extend the model to include a clear capability development path for clients, where initial engagements eventually build client capacity rather than creating perpetual dependency. This would position Progency as a transformation partner rather than just a service provider.

3. Cross-Client Insights Network

Expand the value proposition by creating anonymised, aggregated insights networks across clients (with appropriate permissions). This “collective intelligence” could provide competitive advantage impossible for standalone marketing departments.

4. Regulatory-Compliant Data Framework

Develop specific frameworks for managing data in increasingly regulated environments, particularly addressing how AI twins can operate effectively while respecting privacy regulations like GDPR and evolving legislation.

5. Environmental and Ethical Considerations

Strengthen the model by explicitly addressing the environmental impact of AI operations and establishing ethical guidelines for AI agent decision-making, particularly regarding personalisation boundaries.

Conclusion

The Progency model represents a genuinely innovative approach to solving critical marketing challenges. Its focus on outcomes rather than inputs, overlooked customer segments, and AI-powered execution creates a compelling value proposition. However, successful implementation will require thoughtful approaches to data integration, talent acquisition, organisational change management, and measurement methodologies.

With appropriate refinements to address these practical challenges, Progency could indeed become the crucial missing link in marketing’s evolution toward truly accountable, customer-centric operations that eliminate wasteful spending while driving sustainable growth.

ChatGPT

1. What Works Well

a) Clear Recognition of Martech’s Gaps

Your essay effectively outlines how traditional martech vendors sell “toolkits” rather than end-to-end solutions. By focusing on outcomes rather than mere software features, Progency repositions martech as a driver of genuine revenue growth and retention. This framing resonates with the industry’s increasing demand for turnkey, results-focused partnerships.

Why This Matters
Many brands remain overwhelmed by the operational requirements of sophisticated martech platforms. Progency’s emphasis on performance-based services—rather than merely licensing software—addresses a real pain point for marketers who lack time, skill, or personnel to exploit 100% of any given platform’s capabilities.

b) Performance-Driven Pricing

Your advocacy for aligning pricing with delivered outcomes is a significant strength. Much of adtech’s success comes from tying budget outlay directly to measurable returns (like clicks or conversions). Applying a similar logic to martech via performance-based pricing (for instance, sharing incremental revenue from reactivation) strongly incentivises the Progency team to solve the “Who Will Do It?” problem.

Why This Matters
Many martech solutions fail to achieve adoption precisely because internal marketing teams do not have the capacity to deploy or optimise them. By adopting a performance model, a Progency effectively assumes both the financial and operational risk, which can be compelling to brands reluctant to invest in more tools without guaranteed payback.

c) AI at the Core: Agentic AI and Co-Marketers

Your essay’s integration of Agentic AI and the concept of an AI-powered “Co-Marketer” is well-articulated. Moving beyond simple automation into agentic autonomy and orchestration suggests Progency’s potential to operate “in parallel” with existing marketing stacks, delivering quick wins like reactivation, then layering more advanced campaigns over time.

Why This Matters
Most AI in martech is still limited to predictive analytics or segment-based personalisation. Your essay’s vision of operational and customer-representative AI agents coordinating complex tasks gives Progency a competitive edge. This helps justify the value proposition: brands are not just buying more software—they’re accessing a self-improving AI-driven service layer.

d) Holistic Approach to “The Three Perennial Problems”

By describing the “Not for Me”, “No Hotline”, and “No Alternative” challenges—and then clarifying the fourth, “Who Will Do It?”—you showcase a thorough grasp of marketing’s structural weaknesses. Progency’s integrated approach (platform + services + performance-based incentives) is a logical remedy and sets it apart from fragmented “agency + third-party martech” models.

Why This Matters
Many solutions address one or two marketing challenges but not all. Progency’s holistic scope—coupled with your emphasis on bridging the execution gap—feels refreshingly comprehensive and could appeal to mid-market and enterprise brands alike.

2. Where It Could Be Stronger

a) Talent and Organisational Readiness

The essay mentions the need for a dedicated business unit within a martech vendor to run Progency. However, it does not deeply explore how the necessary talent and organisational culture will be built.

Potential Issue

  • Managing a performance-driven services arm requires very different skill sets from running a B2B SaaS product team. You will need experts in data science, campaign operations, creative strategy, and account management, all comfortable working under revenue-sharing or outcome-based compensation.

Suggested Improvement

  1. Talent Strategy: Include clear guidelines on acquiring, training, and retaining staff with both martech and agency expertise.
  2. Cultural Bridge: Emphasise how Progency staff will integrate with or remain distinct from core product teams, ensuring that neither group is overshadowed.

b) Detailed Business Modelling for Profit-Sharing

While the concept of “performance-based pricing” is compelling, there is a gap around specific commercial structures. Performance-based deals can be difficult to manage when attributing incremental revenue to Progency activities versus a brand’s other marketing efforts.

Potential Issue

  • Conflicts may arise when multiple vendors or in-house teams contribute to revenue gains. Determining the “incremental uplift” or “counterfactual” scenario can become contentious.

Suggested Improvement

  1. Attribution Framework: Propose an agreed model for measuring the baseline and the incremental revenue or performance lifts that Progency claims.
  2. Tiered Engagements: Define scenarios where a client might pay a small baseline fee to fund overheads, with bonuses only if certain thresholds (revenue or retention lifts) are exceeded.
  3. Case Studies or Pilots: Outline real or hypothetical examples demonstrating how the fee structure might work, thus reducing friction over measurement or “false positives”.

c) Handling Client Data and Systems Integration

The essay states Progency will likely operate “in parallel” with a brand’s existing marketing stack, minimising direct interference. However, in practice, data synchronisation and systems integration can be major bottlenecks. Without robust data flows, the AI cannot deliver the personalisation promised.

Potential Issue

  • Onboarding new clients could be lengthy if they have legacy CRMs or incomplete data pipelines. Progency’s effectiveness depends heavily on timely, high-quality, and appropriately permissioned data.

Suggested Improvement

  1. Integration Blueprint: Provide a step-by-step approach for connecting Progency’s AI layer to a client’s data warehouse, CRM, e-commerce platform, etc.
  2. Parallel Ops Explanation: Offer more detail on how you minimise disruption. For instance, do you deploy your own tracking pixels? Do you create a “mirror data layer” fed from existing systems?

d) Scalability and Profitability Concerns

Performance-driven pricing holds undeniable appeal, but as Progency grows, each engagement might require deep involvement from AI experts, data analysts, and marketing strategists. The essay could better address how to scale these resources.

Potential Issue

  • The labour-intensive nature of multi-client management can strain margins if not carefully planned. “Service + platform” is often more lucrative than plain SaaS, but it also has higher labour costs.

Suggested Improvement

  1. Agentic AI Roadmap: Show how, over time, more tasks can be handled by AI agents to keep operational overheads stable even as client numbers grow.
  2. Service Delivery Tiers: Perhaps propose gold/silver/bronze tiers to cater for varying client budgets and complexity, ensuring that not every client demands your top experts.

e) Risk Allocation and Contractual Safeguards

The essay highlights the need for trusting, multi-year partnerships, but performance-based deals often mean forging a new level of contractual risk-sharing.

Potential Issue

  • Clients might expect guaranteed improvements or unlimited campaign iterations. Conversely, Progency might overpromise on performance or become entangled in legal disputes if incremental revenue is disputed.

Suggested Improvement

  1. Contractual Clarity: Add detail on how you will handle performance shortfalls—e.g., do you offer “money back guarantees” or partial refunds if targets are missed?
  2. Shared Success: Explain how you manage circumstances (e.g., brand crises unrelated to marketing) that hamper results. Clear out-clauses or “force majeure” style clauses could be needed.

3. Practical Suggestions for Strengthening Progency

  1. Gradual Roll-Out and Pilots
    • Proof of Concept: Start with 2–3 “strategic clients” willing to try performance-based reactivation campaigns. Document successes (and lessons learned) to refine your attribution methods and fee structure.
    • Use Cases: Reactivation is an excellent initial angle—once trust is built through small-scale wins, expand into bigger, broader marketing transformations.
  1. Adoption of Industry Benchmarks
    • Maturity Models: Provide a “Progency Maturity Model,” so clients see how they progress from basic reactivation or personalisation to advanced AI orchestration.
    • Best Practice Community: Encourage knowledge sharing among Progency clients, fostering a collective sense of innovation and helping cross-vertical learnings accelerate.
  1. Ethical AI Governance
    • Data Security & Privacy: Deepen your coverage of how Progency’s AI upholds privacy legislation (GDPR, CCPA), especially with advanced data usage.
    • Algorithmic Transparency: Offer periodic reports on how AI decisions are made (to the extent possible) to avoid brand mistrust or hidden biases.
  1. Detailed Talent Roadmap
    • AI/Martech Specialists: Clarify the mix of data scientists, marketing strategists, creative storytellers, and client success managers who will deliver Progency services.
    • In-House vs. Outsourced: Specify whether you foresee in-house teams, or if you’ll partner with smaller agencies and freelancers to quickly scale resources.

4. Overall Assessment

The Progency concept holds considerable promise, bringing together technology, services, and outcome-based commercial models at a time when brands are searching for genuine alternatives to both the “toolkit-only” martech approach and the “spray-and-pray” adtech treadmill. By focusing on delivering outcomes rather than mere software functionality, you position Progency as a potent solution to marketing’s perennial question: “Who will actually do the work?”

However, the real-world success of Progency will hinge on its capacity to tackle practical, everyday challenges. Data integration, talent acquisition, measurement and attribution, and the complexities of scaling a services-based business model all deserve deeper exploration. With more explicit roadmaps, sample commercial structures, and clarity on how you mitigate both operational and contractual risks, you can bolster Progency’s credibility and reduce the friction likely to arise when pitching performance-based partnerships to cautious brands.

In summary, your essay deftly captures why Progency matters now—reacquisition burn rates remain shockingly high, and brands are craving simpler, more direct ways of turning martech potential into real revenue and profit. By refining your plans to address practical hurdles and offer robust guidance on implementation, you’ll fortify Progency’s standing as an ambitious yet thoroughly viable new business model for martech and beyond.

DeepSeek

The Progency model presents a compelling vision for bridging the gap between martech capabilities and tangible marketing outcomes. Below is an assessment of its strengths, practical challenges, and suggestions for refinement, using UK English conventions.

Strengths

  1. Addressing AdWaste: Progency’s focus on reactivating dormant customers (the “Rest” and “Test” segments) tackles a well-documented inefficiency: the $500 billion wasted annually on reacquiring existing customers. This aligns with brands’ growing need to maximise customer lifetime value (CLV) over constant acquisition.
  2. Outcome-Driven Model: Shifting from SaaS subscriptions to performance-based pricing ensures alignment with client goals. This model mirrors adtech’s success (e.g., paying for clicks) while offering martech’s precision, making it attractive for ROI-focused marketers.
  3. AI Agent Scalability: The integration of agentic AI (e.g., operational and customer-representative agents) could automate complex workflows, enabling hyper-personalisation and real-time optimisation at scale—tasks impractical for human teams alone.
  4. Complementary Approach: Progency’s emphasis on working alongside internal teams—rather than replacing brand stewardship or creativity—minimises resistance. It positions itself as a force multiplier, not a disruptor.
  5. Market Opportunity: Tapping into the $500 billion AdWaste pool offers a lucrative revenue stream for martech firms, with 10x the potential of traditional software sales.

Practical Challenges and Improvements

1. Technical Implementation Complexity

Issue:

  • Deploying AI agents that integrate seamlessly with clients’ legacy systems requires robust APIs and data infrastructure. Many brands lack the technical maturity for such integrations, risking delays or failures.
    Solution:
  • Pre-built connectors: Develop industry-specific integration templates (e.g., for Shopify, Salesforce) to reduce setup friction.
  • Hybrid human-AI workflows: Use AI for data processing and predictions but retain human oversight for strategic decisions, ensuring adaptability to edge cases.

2. Data Privacy and Security

Issue:

  • Centralising customer data across multiple clients increases exposure to breaches, especially under regulations like GDPR.
    Solution:
  • Zero-trust architecture: Implement encryption, anonymisation, and strict access controls.
  • Transparency protocols: Provide clients with granular audit trails showing how data is used and protected.

3. AI Reliability and Bias

Issue:

  • AI agents may misinterpret context (e.g., cultural nuances) or inherit biases from training data, leading to tone-deaf campaigns.
    Solution:
  • Bias mitigation frameworks: Regularly audit AI outputs using third-party tools and diverse testing cohorts.
  • Client-specific guardrails: Allow brands to set rules (e.g., brand voice guidelines, ethical boundaries) that AI agents must follow.

4. Client Trust in Performance Metrics

Issue:

  • Clients may sceptically view Progency’s attribution models, especially if internal teams dispute credit for outcomes.
    Solution:
  • Third-party validation: Partner with independent analytics firms (e.g., Nielsen) to verify results.
  • Real-time dashboards: Offer clients transparent, interactive reporting tools to track progress against KPIs.

5. Scalability Across Industries

Issue:

  • Marketing needs vary widely between verticals (e.g., B2B vs. B2C). A one-size-fits-all Progency model may struggle to adapt.
    Solution:
  • Modular service tiers: Offer industry-specific packages (e.g., e-commerce reactivation, SaaS upsell campaigns) with tailored AI agents.
  • Vertical expertise hires: Build teams with deep domain knowledge in high-priority sectors (e.g., retail, fintech).

6. Economic Sustainability

Issue:

  • Performance-based pricing could strain cash flow if outcomes take time to materialise, especially in long sales cycles.
    Solution:
  • Hybrid pricing: Combine a reduced upfront fee with outcome-based bonuses to balance risk.
  • Phased milestones: Tie payments to incremental metrics (e.g., 30% reactivation within 90 days).

Conclusion

The Progency model is a visionary response to martech’s execution gap, combining AI scalability with outcome accountability. Its strengths lie in addressing AdWaste, aligning incentives, and enhancing operational efficiency. However, technical complexity, data governance, and client adoption hurdles require strategic mitigation.

Recommendations:

  • Start with niche verticals to refine the model before scaling.
  • Invest in hybrid human-AI workflows to balance automation with human judgment.
  • Prioritise transparency in metrics and data usage to build trust.

If executed thoughtfully, Progency could indeed become the “missing link” in profitable customer engagement—provided its proponents navigate these challenges with agility and client-centricity.

8

Deep Research

I asked OpenAI’s Deep Research for a more comprehensive report on the state of B2C martech and an assessment of the Progency idea.

Below is a comprehensive report on the current state of B2C martech, the opportunities now arising in the age of generative and agentic AI, and how martech providers can create their own “agency” layer. This report also addresses why martech has lagged adtech, how AI will transform retention and engagement, and why a thin services division can be the next strategic frontier for B2C martech companies.

1. Overview of B2C Martech

B2C martech platforms primarily focus on customer engagement and retention: push notifications, email/SMS marketing, in-app messaging, automation workflows, analytics, and personalisation.

Despite continuing innovation, most of these solutions still struggle to match the scope and simplicity of adtech: “Give us your budget, we’ll deliver X new clicks.” As a result, martech’s share of budgets has remained overshadowed by adtech spending, which regularly consumes the majority of marketing dollars.

2. Why Martech Lagged Adtech

2.1 Value Proposition Gap
Adtech excels at tying spend directly to outcomes—views, clicks, or conversions. Brands typically see a direct correlation between money in (ad spend) and leads or traffic out. In contrast, martech is often sold as software with a toolkit of features—data management, segmentation, analytics—that requires in-house teams or agencies to drive real returns. This gap in “service vs. software” partially explains why 90% of digital budgets flow to ad platforms, while retention efforts tend to be underfunded.

2.2 Operational Complexity
Martech implementations demand ongoing staff effort to create segmentations, build workflows, craft messages, and fine-tune personalisation. By contrast, ad platforms largely automate the “hard parts” (e.g., setting up campaigns, recommending bids) via wizard-like interfaces or fully managed options. This difference in day-to-day workload discourages deeper martech adoption.

2.3 Organization and Mindset
Many brands—especially large enterprises—are built around acquisition-focused KPIs. Retention rarely receives the same leadership attention or budget. Large marketing orgs often have entire teams dedicated to media buying and ad operations, but only a skeleton crew for lifecycle messaging and CRM.

3. Key Opportunities in the AI (Generative + Agentic) Age

With AI evolving from predictive models (forecasting) to generative (content creation) and now agentic (autonomous execution), B2C martech is on the verge of a new renaissance:

  1. Hyper-Personalisation at Scale
    • Generative AI can produce custom creative—emails, visuals, push notifications—tailored down to “segments of one.”
    • Agentic AI can orchestrate entire workflows: from building audiences to writing copy, testing variants, and optimizing in real time. Human marketers simply guide the strategic goals.
    • This eliminates a core limitation of martech: the burden of staffing large teams to craft and execute complex messaging journeys.
  1. Reduced Dependence on Adtech
    • Improved retention cuts reacquisition costs—addressing the massive “AdWaste” spent reaching existing customers via external platforms.
    • AI-empowered martech can autonomously reactivate dormant customers, effectively recapturing revenue that would otherwise go to expensive ad auctions.
  1. Revenue-Sharing and Performance Models
    • AI-driven martech can track incremental revenue uplift from retention/loyalty campaigns. This opens the door to performance-based pricing (e.g., a percentage of reactivated revenue).
    • Such arrangements could help martech providers replicate the straightforward “we deliver X leads for Y dollars” promise of adtech—but on the retention side.
  1. Better End-to-End Customer Understanding
    • Agentic AI unifies data from multiple sources—transaction history, real-time behaviors, social signals—and automates reengagement.
    • Over time, the system continuously self-improves through feedback loops, letting martech providers deliver unstoppable personalisation that makes each customer feel individually served.

4. The Need for a “Thin Services Layer”: Introducing Progency

One of the largest hurdles for brands is “Who Will Do It?”—who will build and run the day-to-day campaigns that fully leverage martech capabilities? Vendors rarely provide ongoing managed services, leaving marketers to figure out everything themselves. The documents refer to a “Progency” model—an agency layer combined with proprietary martech technology that aligns incentives with client outcomes rather than purely licensing software.

4.1 What is Progency?

  • Definition: A product-led agency built on top of a martech platform, offering not just a toolkit, but a service layer that executes strategies on behalf of the brand.
  • Value Proposition: Instead of marketers laboring over complex segmentation, campaign orchestration, and micro-analytics, the Progency unit takes ownership of driving growth and retention.
  • Outcome-Based Pricing: Compensation can be tied to measurable business outcomes (e.g., share of reactivated revenue, cross-sell uplift).

4.2 Business Rationale

  1. Bridging the Skills Gap
    • Many marketing departments lack specialists in data science, lifecycle strategy, or AI-driven personalisation.
    • A Progency team, deeply versed in the martech platform, ensures that advanced features (like multi-agent AI orchestration) are actually used to their fullest.
  1. Reducing Operational Friction
    • Marketers often juggle multiple vendors—email providers, automation suites, design agencies—and rarely have time to execute every feature set.
    • A Progency model eliminates friction by combining platform + agency in one solution, guaranteeing cohesive execution.
  1. Adtech-Like Simplicity
    • By focusing on business outcomes (“we’ll deliver X% reactivation and Y% cross-sell”), a Progency approach gives martech the same clarity that has fueled adtech’s success.
    • Marketers can then compare acquisition vs. retention ROI on a more level playing field.
  1. New Revenue Streams for Martech Providers
    • Traditional martech is capped by subscription-based or usage-based fees.
    • A Progency unit can share in the upside if it boosts customer revenue, replicating adtech’s success in capturing a slice of brand budgets.

5. How a Progency Division Could Work

From a structural standpoint, a martech company adding a service layer might follow this path:

  1. Dedicated Business Unit
    • Operate separately from core product engineering so the services team can focus on client engagements, P&L accountability, and performance metrics.
  1. AI-Driven Execution
    • Deploy AI “Co-Marketers” and specialized sub-agents for segmentation, content generation, campaign orchestration, budget optimization, and analytics.
  1. Performance-Based Pricing
    • Incentivize the Progency division to deliver real revenue impact. For example, if the division reactivates 100,000 dormant users who generate $X million in additional revenue, the Progency earns a share.
  1. Phased Implementation
    • Launch small pilots with strategic clients to prove the model, focusing on quick wins like reactivation or upsell campaigns.
    • Scale up by adding agentic AI, forging multi-year performance partnerships with core enterprise clients.
  1. Talent and Expertise
    • A synergy of martech technologists + creative strategists + data scientists + industry experts.
    • A performance-based culture that differs from a typical software subscription model.

6. The Future of Retention and Engagement in an Agentic AI World

6.1 Autonomous Personalisation
Agentic AI systems can “own” daily marketing tasks such as:

  • Testing endless creative variations to see what resonates
  • Dynamically adjusting segments based on real-time user behavior
  • Triggering orchestrations across email, mobile, SMS, WhatsApp, in-app messages, etc.

6.2 Seamless Customer Lifecycle Journeys
With AI Agents that coordinate across multiple channels and data sources, each user’s journey becomes a fluid conversation rather than a siloed set of campaigns. Dormant customers get reactivation nudges, brand advocates get loyalty boosters, and high-value VIPs receive “white-glove” messaging.

6.3 Reducing “AdWaste”
AI-based martech reengages users without paying third-party tolls repeatedly. Over time, marketing teams can cut reacquisition spend, reinvesting savings into deeper personalisation and brand experiences. In effect, martech becomes a profit engine—less about tool licensing, more about outcome-based marketing transformation.

7. Conclusion and Recommendations

7.1 Closing the Martech vs. Adtech Gap

Martech’s historical lag behind adtech is rooted in complexity, incomplete adoption, and a value proposition that demands too much from resource-constrained marketing teams. By adding a managed service “Progency” division, martech providers can finally offer the clear, outcome-centric proposition that acquisition platforms have long delivered.

7.2 Embrace Agentic AI

Generative and agentic AI are reshaping retention and engagement. Rather than solely delivering features, martech firms can harness AI to autonomously run campaigns, personalisation, and continuous optimisation, reducing the operational burden for brand teams.

7.3 Add a Services Layer to Unlock Revenue

Creating a Progency (product + agency) allows B2C martech vendors to:

  • Remove friction for client marketing teams.
  • Align martech usage with tangible revenue growth.
  • Capture a share of that incremental value.

7.4 Capitalise on Retention’s Hidden ROI

By focusing on reactivation and cross-sell/upsell campaigns, martech providers can unlock massive latent revenue for brands—while building more reliable, high-margin annuity streams for themselves.

Final Thoughts

The next era of B2C martech is poised to revolve around generative and agentic AI, enabling true end-to-end, outcome-focused solutions. This shift can solve martech’s historical disadvantage relative to adtech. Done right, a thin yet potent services layer—integrated with advanced AI—will help martech vendors deliver turnkey results, rather than software complexity.

As more brands realise they are wasting a significant portion of budgets reacquiring known customers, retention efforts will finally receive their due. B2C martech companies that embrace Progency and AI-driven “agentic” capabilities will position themselves at the forefront of this transformation, capturing untapped market potential and redefining what modern marketing can be.

Published by

Rajesh Jain

An Entrepreneur based in Mumbai, India.