Progency: The Growth Alpha Engine

Published May 28, 2025

1

The Idea

In today’s marketing landscape, brands face an existential crisis that threatens their profitability and sustainability. Over the past few months, I’ve been exploring five interconnected themes that offer a revolutionary solution to this challenge:

• NeoMarketing: Marketing’s third great era that inverts established priorities—retention before acquisition, relationships before transactions, and individuals before segments.
• AI Agents Collective: A sophisticated ecosystem of AI specialists that handle complex marketing operations at scale, orchestrated by an AI Co-Marketer for true N=1 (1:1) personalisation.
• Progency: A fusion of platform, expertise, AI agents, and kaizen (continuous improvement) with performance-based economics tied directly to measurable business outcomes.
• NeoMails: Interactive AMP-powered email experiences that transform static communications into engaging hotlines with 4-10X higher engagement rates.
• NeoN: An authenticated identity advertising network enabling brand-to-brand collaboration that cuts reacquisition costs by 30-50% compared to traditional platforms.

Among these ideas, Progency has emerged as the linchpin—a revolutionary approach combining proprietary platform capabilities with specialist expertise and AI orchestration in a performance-based model. The more I’ve discussed this concept with customer-facing colleagues at Netcore and clients themselves, the clearer it becomes that Progency represents an essential solution for powering business growth and profitability in an era increasingly defined by Agentic AI and the crushing burden of AdWaste.

Yet as discussions around Progency deepen, numerous questions emerge. What business model will make it sustainable? How will we define and measure key performance metrics? Will clients truly embrace outcome-based compensation with uncapped upside potential? Can we deliver consistent growth whilst avoiding implementations that become increasingly bespoke and difficult to scale? And perhaps most critically, will organisations trust an external Progency more than their in-house marketing teams who are deeply embedded in the brand’s culture and operations?

Recently, a colleague suggested a powerful analogy from the investment world: Alpha. In finance, alpha represents the excess return of an investment relative to a benchmark index—essentially, the value a fund manager creates beyond what the market naturally delivers. Funds and managers are routinely evaluated on their ability to generate this alpha consistently over time, with compensation structures often tied directly to this performance.

Could this be the perfect framework for conceptualising Progency? Rather than selling time, features, or vague promises, a Progency would position itself as an “Alpha Generator” for marketing—delivering measurable, superior outcomes compared to industry benchmarks or the brand’s historical performance. Compensation would be tied directly to this demonstrable outperformance, creating perfect alignment between the Progency’s success and the client’s business results.

This alpha-based approach addresses many of the fundamental questions surrounding the Progency model. It provides a clear metric for success, establishes a compelling value proposition, and creates a sustainable business model with unlimited upside potential for both parties. Most importantly, it moves beyond the traditional agency or platform model to create a true partnership focused exclusively on generating measurable growth beyond what in-house teams or conventional approaches can deliver.

In this series, I’ll explore how the Progency model can systematically generate marketing alpha, transforming customer relationships whilst eliminating the staggering reacquisition AdWaste that currently plagues digital marketing budgets.

2

History

I asked Claude and ChatGPT for an overview of alpha in finance and other industries.

The concept of alpha originated in the investment world as a measure of outperformance. In its simplest form, alpha represents the excess return an investment generates compared to a market benchmark. First formalised by Michael Jensen in his seminal 1968 paper, alpha became the definitive metric for evaluating investment manager skill—separating true value creation from market-driven returns (beta).

In finance, the calculation is straightforward: if a fund returns 12% when its benchmark index delivers 10%, the manager has generated 2% alpha. This seemingly small difference compounds dramatically over time, explaining why top hedge funds and asset managers can command performance fees of 20% or more on alpha generated. The pursuit of alpha has driven the entire active management industry, with performance-based compensation structures aligning manager incentives directly with client outcomes.

What makes alpha particularly powerful is its clarity—it measures value creation in precise, quantifiable terms that cannot be easily disputed. Either the manager outperforms the benchmark, or they don’t. This transparency has made alpha the gold standard for evaluating investment skill for over five decades. At its core, alpha also represents a system of aligned incentives: rewarding providers not for effort or inputs, but for outcomes that exceed market norms.

Alpha Beyond Finance

Over the past two decades, the alpha concept has gradually transcended financial markets to influence other industries where outperformance can be measured against established benchmarks:

• Management Consulting: Firms like McKinsey and BCG increasingly structure engagements around measurable performance improvement rather than time-based billing. “Impact fees” tied to specific business outcomes (cost reduction, revenue growth, margin improvement) mirror the alpha model, with consultancies earning premiums for delivering results that exceed industry norms.
• Legal Services: Alternative fee arrangements have emerged where law firms share risk and reward by tying compensation to case outcomes rather than billable hours. Some litigation firms now operate entirely on contingency models, effectively betting on their ability to generate “legal alpha” through superior outcomes.
• Private Equity: In private equity, ‘operational alpha’ describes the incremental value created through improving acquired companies’ operations—capturing gains beyond financial leverage or market growth.
• Venture Capital: Alpha in VC stems from access: sourcing proprietary deal flow, identifying outlier founders early, or nurturing startups before the market catches on.
• Sports Management: Professional teams employ data-driven performance analysts to identify statistical advantages that translate to competitive edge—essentially seeking “alpha” in athletic outcomes. This approach has revolutionised talent evaluation in baseball, basketball, and football.
• Technology Solutions: Software-as-a-Service (SaaS) providers are increasingly exploring value-based pricing, where fees scale with measurable business impact rather than seat counts or feature sets. This represents an early attempt to tie technology costs directly to the “alpha” generated for customers.

Across industries, alpha signals proprietary advantage—an edge rooted in insight, access, execution, or innovation that cannot be easily replicated or commoditised by markets. Yet notably, while alpha-based models have reshaped industries from finance to consulting, marketing has remained largely tethered to input-based pricing and platform-driven benchmarks—paying for impressions, clicks, or reach without clear linkage to incremental business outcomes.

If alpha has transformed finance, consulting, and even sports, could marketing—long anchored to platform norms—finally build its own path to alpha?

3

Marketing’s Opportunity

The marketing industry stands uniquely positioned to adopt an alpha-based framework, yet has largely remained tied to input-based compensation models (billable hours, media commissions, platform subscriptions). This disconnect creates a significant opportunity for disruption through a Progency model that redefines value creation.

Marketing alpha can be conceptualised as measurable outperformance against established baselines—whether industry benchmarks, historical performance, or control group outcomes. For example, converting “Rest” customers to “Best” status at rates exceeding historical averages, driving retention metrics beyond industry standards, increasing engagement frequencies, or expanding average order values amongst existing customers.

Surprisingly, marketing never fully embraced Alpha in its vocabulary or metrics. Despite substantial budgets, sophisticated tools, and extensive jargon, marketing remained largely tethered to “Beta-priced” outcomes—accepting industry benchmarks as “good enough” (2% CTR, 5% open rates, 3x ROAS) and focusing primarily on acquisition rather than systematically extracting greater value from existing customer relationships.

Traditional marketing lacked the mechanisms to build proprietary, unreplicable advantages because it outsourced customer engagement to generic platforms, spoke in segments rather than to individuals, and failed to truly personalise experiences at scale. Without sophisticated systems for maximising customer lifetime value, marketing teams defaulted to acquisition-based growth rather than depth-based monetisation.

However, marketing can now generate its own Alpha—not through incremental tactical improvements, but by creating proprietary advantages that dramatically increase effectiveness beyond market norms. Just as investing alpha came from information asymmetry and proprietary models, marketing’s growth alpha can emerge from AI-powered hyper-personalisation, interactive engagement mechanisms (NeoMails), sophisticated propensity modelling, and retention-first strategies focused on existing customers.

Alpha in marketing isn’t about more spend—it’s about smarter engagement. It’s about architecting systems where revenue from existing customers compounds through deeper relationships, higher purchase frequency, and increased average order values. In this framing, marketing’s “growth Alpha” (focused on martech and existing customers) can be defined as: Actual Growth Uplift – Baseline Growth Expected from Standard Martech Investments.

The Progency model capitalises on this opportunity by establishing clear baselines, implementing sophisticated AI-driven engagement systems, and tying compensation directly to the measurable growth outperformance delivered. This creates the perfect alignment that has made alpha so powerful in finance—the Progency succeeds only when clients achieve exceptional revenue expansion from their existing customer base.

In a landscape where marketing teams struggle with maximising customer lifetime value and systematically monetising their “Rest” customers, Progency acts as an Alpha Engine—turning underutilised platform features, dormant customer segments, and execution gaps into measurable revenue gains. It creates a private playground of advantages that competitors cannot easily replicate, allowing brands to escape one-size-fits-all marketing approaches and unlock the full revenue potential of their existing customer relationships.

4

Internal Baseline or Industry Benchmark?

A central question emerges as we operationalise the Progency model: against what should Progency’s Growth Alpha be measured? Should we assess performance relative to external industry benchmarks, or base it on the client’s own historical performance?

At first glance, benchmarking against external industry standards might appear attractive. Marketing publications routinely publish average engagement metrics by sector, seemingly providing an objective yardstick for success. However, this approach faces significant limitations that make it impractical for a true Alpha model.

Unlike financial markets with universally accepted indices like the S&P 500, marketing lacks standardised benchmarks with genuine comparability. Even within narrow categories such as ecommerce apparel, performance metrics vary dramatically based on geography, brand positioning, price points, and customer demographics. A premium fashion retailer with a curated customer base naturally exhibits different engagement patterns from a mass-market discount brand, making comparisons to generic industry averages fundamentally flawed.

Measuring Alpha against the client’s own historical baseline offers a substantially cleaner, fairer, and more defensible approach. This model defines Progency’s success by its ability to deliver measurable outperformance relative to what the brand previously achieved with its in-house marketing team. When a brand’s retention rate rises from 40% to 55%, or average order value increases from $50 to $60, or the number of active “Best” customers doubles over 12 months, these improvements are unambiguously quantifiable and contextually relevant.

This internal-baseline approach creates perfect incentive alignment. Progency is rewarded specifically for helping the brand outperform itself, not for beating arbitrary industry averages that may bear little relevance to the brand’s specific circumstances. It focuses the engagement on continuous improvement and growth from within, rather than chasing external comparisons that may not apply to the brand’s unique context or strategic objectives.

Crucially, measuring Alpha against internal baselines positions Progency as a collaborative partner augmenting existing capabilities rather than creating an adversarial dynamic that might arise if performance were compared to industry standards suggesting the internal team was “below par.” It establishes a foundation of trust and collaboration essential for long-term partnership.

With compensation tied directly to growth uplift above the brand’s existing trajectory, Progency’s value creation becomes unambiguously demonstrable through actual business outcomes rather than theoretical benchmarks or market averages. This internal-benchmark Alpha creates the clearest path to aligning incentives, building trust, and proving Progency’s worth—one measurable outperformance at a time.

5

Lessons from Finance

This section written with inputs from ChatGPT and Claude.

If Alpha represents outperformance, the natural question is: how does one consistently generate Alpha in investing? Over decades, the world’s top fund managers have converged on several core strategies that separate them from the passive Beta crowd. These winning approaches share an underlying DNA: seeking proprietary advantage, exploiting market inefficiencies, and compounding small edges over time.

One foundational strategy is information asymmetry. Elite investors gain access to data, insights, or signals before others, allowing them to position ahead of market movements. This isn’t merely about having more data—it’s about having better, faster, more relevant information that others haven’t yet priced in. Renaissance Technologies built its legendary Medallion Fund partly on detecting subtle market patterns invisible to conventional analysis.

A second strategy is access advantage. Venture capitalists generate Alpha by securing entry into deals that never reach the open market. Private equity firms create proprietary deal flow through relationships that unlock off-market acquisitions. Even in public markets, activist investors use boardroom influence to drive outcomes inaccessible to ordinary shareholders. Access becomes an Alpha engine because not everyone plays the same game.

Third is operational Alpha—improving an asset’s intrinsic value beyond market expectations. In private equity, this involves overhauling management, optimising operations, or unlocking new revenue streams within portfolio companies. Firms like KKR and Blackstone have mastered this approach, engineering value from within rather than relying on market timing.

Time arbitrage represents another powerful strategy. Many investors operate on compressed timeframes—quarterly earnings, monthly reports, daily liquidity demands. Investors with patient capital can exploit the tendency of markets to underprice long-term compounding opportunities. Warren Buffett’s extraordinary track record stems partly from his willingness to hold undervalued but fundamentally strong assets while others chase faster results.

Finally, there’s structural inefficiency exploitation. Some market segments remain less covered, less liquid, or less competitive—creating fertile ground for Alpha. Micro-cap stocks, frontier markets, complex credit instruments—investors who specialise in overlooked niches often find excess returns by fishing where others refuse to venture.

A few additional alpha-creating strategies:

• Factor-based investing: Many successful funds now use systematic approaches that target specific “factors” (value, momentum, quality) to generate alpha. This could reinforce how systematic, data-driven approaches outperform intuition-based decisions.
• Contrarian positioning: Some of the greatest alpha is generated by investors willing to take opposite positions from the crowd.
• Compound returns: The extraordinary power of compounding small advantages over time could be emphasized more explicitly, as it’s fundamental to investment alpha.

All these Alpha strategies share a common theme: they avoid competing for Beta-priced assets in open, efficient, transparent markets. Instead, they deliberately create, discover, or access proprietary advantages unavailable to the masses.

For Progency, this analogy proves instructive. If traditional marketing plays the Beta game—buying attention at market-clearing prices from Google or Meta—then Alpha in marketing must follow similar logic: finding proprietary edges that deliver superior returns without paying platform tax.

6

Winning in Martech

If finance has its playbook for Alpha, what would the equivalent be for Progency in marketing? How can Progency create excess, proprietary growth for a brand—outperforming both historical baselines and platform-priced market norms?

The answer lies in two critical customer segments: Best and Rest. The strategy: Profit from Best, Growth from Rest.

For Best customers—the top 20% who deliver 60-80% of revenue and up to 200% of profits—the Alpha strategy is extreme retention and expansion. Just as investors maximise returns by doubling down on high-performing assets, Progency’s mission becomes deepening loyalty, increasing share of wallet, and extending lifetime value among this elite cohort.

Winning Alpha with Best customers requires hyper-personalisation: crafting bespoke offers, exclusive access, curated experiences, and preferential treatment that strengthens both emotional and transactional bonds. It means implementing Velvet Rope Marketing—ensuring every interaction reinforces the customer’s sense of privilege, belonging, and preference for the brand. The AI Agents Collective enables this at scale by orchestrating thousands of micro-personalised segments, campaigns, and touchpoints that human teams simply cannot manage.

Additionally, Best customers function as powerful referral engines. Like investors reinvesting dividends, Progency leverages satisfied Best customers to drive organic acquisition—bringing in new high-value customers at zero or negative CAC. This referral flywheel becomes a compounding Alpha source: existing profits fuelling new growth outside expensive paid channels, creating a structural cost advantage over competitors still dependent on platform acquisition.

Meanwhile, Rest customers—the middle 40-50% showing declining interaction—represent the largest untapped Alpha opportunity. Like neglected assets in a portfolio, they’re undervalued, under-managed, and at risk of decay. Yet, with proper intervention, many can graduate into the Best cohort.

Here, Progency’s Alpha strategy focuses on converting Rest to Best through timely, tailored engagement. AI-driven segmentation identifies micro-patterns of interest and drop-off. Interactive NeoMails deliver both daily engagement and relevant nudges at precisely the right moments. Personalisation engines test offers, messaging, and timing across millions of individualised journeys. The goal: prevent churn before it happens, rekindle lapsed interest, and systematically surface hidden champions from within the middle segment.

Importantly, Rest-to-Best Alpha isn’t about mass campaigns—it’s about orchestrating individualised recovery and activation paths at unprecedented scale. This is where Progency’s AI architecture delivers transformative advantage: deploying autonomous agents that never tire, continuously experiment, and optimise engagement daily without human bottlenecks or operational constraints.

Together, these two strategies form Progency’s core Alpha playbook:

• For Best: maximise retention, deepen loyalty, unlock referrals, increase purchase frequency and average order value
• For Rest: systematically prevent dormancy, reignite engagement, and migrate high-potential customers into the Best tier

Like an investor rebalancing a portfolio, Progency continuously shifts customers up the value ladder—transforming the client’s customer base into a higher-yielding, compounding growth engine. By focusing Alpha efforts inward—on owned relationships rather than rented reach—Progency builds proprietary advantage that competitors cannot easily replicate.

This represents Alpha not from buying more traffic, but from extracting substantially more value from existing relationships. In doing so, Progency elevates marketing from cost centre to profit engine driven by retention, referral, and relationship compounding—delivering measurable Growth Alpha without dependence on external, auction-based acquisition.

7

The Choice – 1

In the world of investing, individual investors routinely underperform professional fund managers, ETFs, and structured investment vehicles. The reasons are well-documented: lack of time, expertise, tools, discipline, access, and cognitive biases that lead to suboptimal decisions. Similarly, in marketing, in-house teams often struggle to achieve what a specialised, AI-powered Progency model can deliver.

Just as funds are designed to systematically extract Alpha—excess returns above the market—Progency is engineered to systematically generate Growth Alpha from a brand’s existing customer base. It doesn’t depend on heroic effort or sporadic campaigns, but on a repeatable, scalable system integrating software, AI agents, expert guidance, and continuous improvement.

Progency’s structural advantage stems from six fundamental factors, each paralleling investment management whilst addressing the realities of martech today:

1. From Software Without Service → Software + Strategy + Success

Most brands purchase martech platforms but struggle to extract value, with 60-65% of features remaining unused. The platform exists, but not the process. Staff turnover, skill gaps, and operational silos leave capabilities idle—akin to an individual investor buying a Bloomberg terminal but using it only to check basic prices.

Progency solves this by providing “Done-for-You” execution, embedding experts who know the platform intimately, and deploying pre-configured best practices. The platform transforms from a tool needing an operator into an engine running with a dedicated pit crew, with every underutilised feature becoming an unlocked lever for incremental Alpha.

2. From Priced on Inputs → Priced on Outcomes

In-house marketing teams function as cost centres: salaries, tools, overheads, campaign budgets. Their compensation and evaluation rarely link directly to revenue outcomes. Progency flips this model by aligning compensation directly to performance—outcome-based pricing rather than input-based fees. This incentive alignment mirrors hedge fund “performance fees” tied to Alpha: we win only when the client wins.

3. The Agency of AI Agents vs Human Bottlenecks

In-house teams rely on human workflows: analysts building segments, content creators producing copy, campaign managers setting up journeys, data teams crunching reports. The result? Endless drudgery and slow iteration. As one CMO confided: “Segments, Journeys, Content, Analytics. Repeat.” It’s exhausting, error-prone, and linear.

Progency deploys an AI Agents Collective: specialised AI agents for segmentation, content, journey design, optimisation, and analytics—each working autonomously but orchestrated by an AI Co-Marketer. Where human teams can manage 8-10 segments, AI agents handle millions of micro-cohorts simultaneously.

The key advantage: AI agents perform best when configured, trained, and orchestrated by the experts who built or mastered the system. In-house teams may use generative AI via prompt engineering, but lack the systemic, multi-agent orchestration expertise that drives transformative results.

4. Continuous Kaizen vs Project-Based Marketing

In-house teams typically operate in campaign mode—launch, measure, report, pause. Optimisation happens quarterly or after failures. Progency embeds Kaizen: continuous improvement loops where every interaction, every open, every click feeds machine learning models and agent-driven tests. The system improves daily, not quarterly.

This generates Alpha via iteration speed. Markets reward compounding over time. So does marketing—if the system improves continuously. In-house teams rarely have the bandwidth, data pipelines, or automation maturity to run hundreds of concurrent micro-experiments that Progency executes as standard practice.

5. From Cost Centre → Profit Engine

In-house marketing operates on budgets. Martech expenses are sunk costs. Metrics focus on activity (campaigns sent, impressions reached, leads generated) rather than direct revenue uplift per cohort or per customer. Progency transforms marketing into a profit centre with compensation tied to incremental revenue beyond baseline. Every action becomes accountable to business outcomes, not vanity metrics.

6. Trust, Culture, and Institutional Knowledge: Addressing In-House Advantages

One natural advantage of in-house teams is their deep knowledge of brand culture, product nuance, and internal politics. They understand legacy systems, stakeholder preferences, and hidden minefields. Progency addresses this by:

• Working with internal teams rather than replacing them—positioning as augmentation, not substitution
• Embedding brand liaison roles to interface daily with client teams
• Co-designing strategy while taking over execution—leaving high-level decisions and sensitive communications within the client
• Customising AI agent rules to reflect brand tone, compliance requirements, and audience sensitivities

In effect, Progency supplements in-house advantages while solving in-house limitations, absorbing operational burden, bringing in missing expertise, and applying technology at a depth most in-house teams cannot match.

8

The Choice – 2

Additional Structural Advantages

Beyond these six core factors, Progency offers several additional structural advantages often overlooked:

• Cross-client pattern recognition: Learning from similar challenges across multiple brands creates invaluable knowledge transfer that siloed in-house teams never access
• Specialised vertical expertise: Developing deep domain knowledge in specific industries enables faster pattern recognition and solution deployment
• Risk diversification: Unlike in-house teams whose careers depend on avoiding failure, Progency’s performance-based model enables bolder experimentation within guardrails
• Tech ecosystem integration: Maintaining seamless connectivity with evolving martech, adtech, and data platforms that in-house teams struggle to keep current

**

Here’s a comparison table (generated by ChatGPT and refined by Claude) summarising the key differences between In-House Marketing Teams vs Progency.

Dimension	In-House Marketing Teams	Progency (AI Agents + Experts + Platform)
Operating Model	Campaign-driven; manual execution; reactive	Continuous; AI agent-driven; always-on optimisation; proactive
Resource Limitation	Bounded by team size and bandwidth; linear scaling	Exponential scaling via AI Agents Collective + automation
Platform Utilisation	30-40% of platform features used; feature bloat	80-90%+ of features unlocked through expert orchestration
Pricing Model	Fixed salaries; cost centre budgeting; input-based	Outcome-based pricing; shared upside on revenue uplift; growth-aligned
Incentive Alignment	Effort/activity-based KPIs; marketing metrics	Directly tied to business outcomes (growth Alpha); revenue metrics
Experimentation Capacity	2-5 A/B tests per month; sequential testing	50-500 concurrent micro-experiments via AI agents; parallel optimisation
Customer Personalisation	8-10 macro-segments; broad-based targeting	1:1 hyper-personalisation at scale (N=1); individualised journeys
AI Usage	Ad hoc; basic prompt engineering; isolated tools	Orchestrated multi-agent ecosystem by AI-native experts; integrated workflows
Iteration Speed	Weekly/monthly campaign cycles; quarterly planning	Daily continuous improvement (Kaizen loops); real-time adaptation
Customer Focus	Acquisition-biased; low activation of existing customers	Retention-first; Best & Rest strategy to maximise customer LTV
Skill Turnover Risk	High (staff churn; skill drain); knowledge gaps	Centralised expertise maintained within Progency; institutional continuity
Institutional Knowledge	Deep brand familiarity; legacy knowledge	Embedded liaison roles + cross-industry pattern recognition
Strategic Role of Marketing	Cost centre; budget-dependent; defensive posture	Measurable profit engine; C-suite aligned; growth driver
Data Integration	Siloed systems; fragmented customer view	Unified customer profiles; cross-channel orchestration
Adaptability to Market Changes	Slow response to trends; lengthy approval cycles	Rapid implementation of emerging tactics; agile deployment
Alpha Creation	Limited; constrained by in-house tools, skills, processes	Structural advantage; designed to extract Growth Alpha at scale

In a world where Growth Alpha is the new currency of marketing success, Progency isn’t competing with internal teams—it’s playing an entirely different game, at a higher level of automation, precision, and incentive alignment. Just as investment funds systematically outperform individual investors through superior tools, teams, and processes, Progency systematically outperforms in-house marketing by combining technology, expertise, and outcome-based economics into an Alpha Engine that transforms marketing from cost centre to profit driver.

9

Objection Handling

I asked Claude to list out possible objections from CMOs and other stakeholders that merit thoughtful consideration.

“This sounds expensive. How does it fit into our budget?”

Unlike traditional platforms or agencies requiring upfront investment, Progency’s performance-based model aligns perfectly with financial constraints. There are no platform fees or retainers—payment is tied directly to incremental revenue generated above established baselines. This transforms marketing from a cost centre into a self-funding profit engine, with Progency’s compensation representing a fraction of the additional revenue created.

“We’ve invested heavily in our current martech stack. Does this replace it?”

Progency doesn’t replace your existing investments—it maximises their value. Many organisations utilise only 30-40% of their martech capabilities due to expertise and bandwidth limitations. Progency unlocks the full potential of your current stack through expert orchestration and AI agents, protecting your investment while delivering superior returns. We can operate alongside your current systems or gradually transition components as results warrant.

“How do we handle data privacy and security concerns?”

Data privacy forms the foundation of Progency’s approach. All operations adhere to GDPR, CCPA, and other regional regulations with comprehensive data processing agreements. Customer data remains within your environment or in secure, compliant cloud infrastructure with encryption, access controls, and regular security audits. Unlike cookie-based targeting that raises privacy concerns, our authenticated identity approach operates with explicit consent and transparency.

“Our brand has unique requirements. How do you maintain our voice and standards?”

Progency doesn’t apply one-size-fits-all solutions. The AI Agents Collective operates within brand-specific guardrails—incorporating your voice, compliance requirements, and quality standards. The implementation begins with comprehensive brand immersion, and all content and journeys undergo review processes aligned with your governance requirements. Rather than replacing your brand expertise, Progency amplifies it through AI orchestration.

“How do we know the reported performance improvements are real?”

Transparency drives Progency’s measurement framework. Performance is tracked through rigorous control group methodology, with incrementality clearly isolated from other marketing activities. All reporting includes comprehensive attribution logic, with shared dashboards providing real-time visibility into key metrics. Performance calculations follow mutually agreed methodologies established before implementation, eliminating ambiguity around results.

“My team will feel threatened by this. How do we address the cultural impact?”

Progency positions itself as augmentation, not replacement—freeing your team from operational drudgery to focus on strategic initiatives. Most marketing teams welcome relief from repetitive tasks like segment creation, campaign setup, and performance analysis. By handling the Rest segment, Progency allows your team to concentrate on high-value activities like brand strategy and VIP customer experiences. We’ve found that marketing teams typically become our strongest advocates once they experience how Progency amplifies their impact.

“What happens if you don’t deliver results?”

This concern highlights Progency’s core advantage: our incentives align perfectly with yours. If we don’t deliver incremental revenue, we don’t get paid. Unlike traditional vendors that collect fees regardless of outcomes, Progency bears the risk of underperformance. This creates unprecedented accountability rarely seen in marketing services or technology. We’re betting on our ability to outperform your current baseline—and only succeed when you do.

10

Pitch – 1

I asked Claude and ChatGPT to create a role-play pitch between Maya (CMO) and John (Progency salesperson).

Claude

Scene: Maya’s office at a mid-sized B2C retailer. She’s reviewing marketing performance reports with a furrowed brow when her assistant announces John’s arrival for their scheduled meeting. John represents Progency, a new marketing solution she’s curious about but sceptical of.

Maya: (looking up from her laptop) John, welcome. I’ve got 30 minutes before my next meeting, so let’s get straight to it. I’ve read some of your materials about this “Progency” concept, but I’m still trying to understand how it’s different from the dozen other martech solutions and agencies constantly pitching me.

John: (taking a seat) Thanks for making the time, Maya. Before I dive into Progency, may I ask you a quick question about your current marketing approach?

Maya: (gestures) Go ahead.

John: What percentage of your digital marketing budget would you estimate is spent on reacquiring customers who already know your brand—people who’ve purchased before but gone dormant?

Maya: (pauses, considering) We’ve never broken it down quite that way. Between Google, Meta, and our other platforms… I’d guess maybe 40-45%? Our acquisition costs have risen nearly 20% year-on-year.

John: That’s actually better than most. Industry data suggests the average is closer to 70%—what we call the $500 billion AdWaste crisis. Essentially, brands are paying platforms premium prices to reach customers they already own.

Maya: (frowns) That feels… inefficient. But what’s the alternative? Once customers stop opening emails or engaging with our app, we have to find them somewhere.

John: That’s exactly the problem we’re solving. Right now, you’re forced to choose between three bad options: keep emailing dormant customers and risk deliverability issues, stop contacting them and lose the relationship entirely, or pay Google and Meta steep prices to “rediscover” them.

Maya: (nodding) That sounds about right. Our email open rates hover around 12% for active customers, and much lower for the rest. The “rest”—that’s what my team calls those middle-tier customers who aren’t our VIPs but aren’t completely gone either.

John: (perking up) Your team’s onto something there. We call it the BRTN framework—segmenting customers into Best, Rest, Test, and Next. The “Rest” segment is precisely where the biggest opportunity lies. How many customer segments does your team actively manage today?

Maya: (sighs) Realistically? About 8-10 different segments across our campaigns. I know we should do more, but each additional segment means more creative, more management, more analytics.

John: That’s the universal constraint. Even sophisticated marketing teams can only manage a handful of segments because of human bandwidth limitations. But here’s the thing—each of your customers is unique, with specific preferences, behaviours, and needs. They feel it when messages don’t resonate personally.

Maya: (sceptical) So what’s your solution? More consultants? Another platform with AI features we’ll barely use?

John: Neither, actually. Progency is something entirely different. We call it “software with success” rather than “software as a service.” Instead of charging platform fees, we only earn money when we deliver measurable revenue gains above your current baseline.

Maya: (intrigued) Performance-based pricing? That’s refreshing, but how do you deliver that performance?

John: Through what we call the PEAK framework—combining Platform capabilities, Expert strategists, AI agents, and Kaizen continuous improvement methodology. The revolutionary element is our AI Agents Collective—a sophisticated system of specialised AI working in concert to deliver true 1-to-1 personalisation at scale.

Maya: (sceptical) Everyone claims to offer personalisation these days. What makes yours different?

John: Traditional personalisation inserts a name in an email and maybe changes a product image. Our AI agents create genuinely individualised journeys for each customer—targeting micro-moments with precisely the right message, on the right channel, at the right time. Where your team manages 8-10 segments, our system can effectively treat each customer as their own segment—what we call “N=1 personalisation.”

Maya: (considering) But don’t we need to manage all this? My team’s already stretched thin.

John: That’s the beauty of the model. You don’t manage it—we do. Think of it as your “Department of One” handling everything from segmentation to content creation to campaign orchestration. Your team focuses on high-level strategy and your Best customers, while we transform your Rest customers into future Best customers.

Maya: (intrigued but cautious) So you’re saying you could take over our middle segment—these “Rest” customers—and increase their value without my team doing the heavy lifting?

John: Exactly. And we only get paid when we deliver results above your current baseline. It’s similar to how investment alpha works—we generate excess returns from your customer base beyond what you’re already achieving.

Maya: (nodding slowly) And how do you address the dormant customer problem—the ones we’re paying platforms to reacquire?

John: Two solutions there. First, NeoMails—interactive email experiences that boost engagement 4-10X by delivering real value in the inbox. These create reliable daily hotlines that prevent Rest customers from becoming dormant in the first place.

Second, for already-dormant customers, we have NeoN—an authenticated identity network that lets you precisely target these customers through partner brands’ emails at 30-50% lower cost than Google or Meta.

Maya: (thinking) What about my team? I don’t want them feeling threatened or replaced.

John: We’re not replacing your team—we’re augmenting them. Your marketers stay focused on strategy, creative direction, and managing your VIPs. We handle the operational execution for your Rest segment, which frees your team from the daily drudgery of “segments, journeys, content, analytics, repeat.” Most importantly, we create a clear path to double your Best customers while halving your acquisition waste.

Maya: (showing more interest) Okay, assuming I’m intrigued—which I am—what would next steps look like?

John: We’d start with a simple data analysis to establish your BRTN segmentation and identify your baseline performance. Then we’d propose a 90-day pilot focused on a specific segment of your Rest customers—say 50,000 of them. We’d implement our AI Agents Collective to deliver personalised journeys while measuring uplift against your control group. You’d see exactly how much incremental revenue we generate, with complete transparency.

Maya: (nodding) And your compensation would be tied to that uplift?

John: Precisely. We only succeed when you succeed. No platform fees, no hourly billing—just a percentage of the incremental revenue we create above your baseline. We’re betting on our ability to generate what we call “Growth Alpha” for your business.

Maya: (checking her watch) This is definitely more interesting than most pitches I receive. Send me a proposal for that 90-day pilot. I want to see specific KPIs, the segment definition, and exactly how you’ll measure success. I’ll need to get buy-in from my marketing directors and potentially our CFO.

John: (standing) Absolutely. I’ll have that to you by tomorrow. One final thought—every month that passes with your Rest customers not fully monetised is revenue permanently lost. We can stop that leakage while simultaneously reducing your dependency on expensive platforms.

Maya: (smiling slightly) You’ve certainly given me something to think about. I look forward to seeing the proposal.

As John leaves, Maya reopens her marketing dashboard, scrolling to the segment analysis with new interest. The conversation has shifted her thinking—perhaps there is a way to make martech as straightforward as adtech, while delivering superior results.

11

Pitch – 2

ChatGPT

Scene:
Maya, CMO of a B2C brand, is in her office. John, the Progency salesperson, is meeting her to pitch the Progency model. Maya has heard of Progency but isn’t sure how it’s different from her internal marketing team or her existing agency.

John (smiling, slides open on tablet):
“Maya, thanks for making time. I know you’ve got a great team here—but I wanted to show you how Progency can be a multiplier, not a replacement.”

Maya (leaning forward):
“Honestly, John, I’m sceptical. We’ve invested heavily in platforms and people. Why would I need an outside team to do what my marketing department’s already doing?”

John (nodding):
“Completely fair. Let me frame it differently. Think of your in-house team like an individual investor—they know the market, they make picks, they’ve had wins. But even the smartest individual investors underperform the best funds. Why? Not for lack of effort—but because funds have access to tools, teams, systems, and processes that an individual simply can’t replicate.”

Maya (thoughtful):
“So you’re saying Progency is… like a fund manager?”

John:
“Exactly. We’re engineered to systematically generate Growth Alpha—measurable, excess growth above what your existing team, platforms, or agencies are achieving. And just like a fund doesn’t rely on a few lucky trades, we don’t rely on one-off campaigns or talent spikes. We build a machine that extracts incremental gains every day.”

Maya:
“Okay, but my team knows the brand inside out. They’ve built these campaigns, they own the customer voice. How can you possibly replicate that?”

John (smiling):
“That’s one area where we work with them, not against. We embed liaison roles who act as the bridge. Your team stays the guardians of brand tone, compliance, product nuance. We don’t take over that role—we amplify it. But where they’re limited is scaling it.”

(John flips to a slide showing “human bottlenecks” vs “AI Agents Collective.”)

John:
“Right now, your team’s building segments, journeys, content, analysing reports. It’s endless. The best they can manage? Maybe 8–10 macro segments, a few A/B tests a month. Our AI Agents Collective manages millions of micro-cohorts, running hundreds of simultaneous experiments. The system self-optimises daily.”

Maya (raising eyebrows):
“AI’s great, but we already use AI in our team—our marketers use prompt engineering. What’s different?”

John (leans in):
“Great point. But let me ask: are your marketers orchestrating multi-agent systems, or just using isolated AI tools? Are they integrating agents across content, analytics, segmentation, journey optimisation—running in real-time, collaboratively, autonomously?”

(Maya pauses, realising the distinction.)

John (continues):
“That’s the difference between handing someone ChatGPT and giving them a fully orchestrated AI co-pilot managing an army of specialist agents. The technology isn’t just the tool—it’s the system, the architecture, the orchestrators who built it.”

Maya:
“So… you’re saying it’s not about having AI, it’s about unlocking its collective power?”

John (smiling):
“Exactly. AI works best when experts who built the system are running it. Otherwise, it’s like giving a Formula 1 car to an amateur driver—you can drive, but you won’t win races.”

Maya (crossing arms):
“Sounds promising. But what’s in it for you? How are you compensated?”

John (shows slide “from inputs → outcomes”):
“That’s the other key difference. We don’t charge for time, tools, or features. We only get paid when we generate measurable Alpha—when we move the needle beyond your baseline. No uplift? No payment. But if we drive 15% higher retention? Increase average order value? Grow your Best customers? We share in that upside.”

Maya (impressed):
“Interesting. But doesn’t that make it risky for you?”

John (grinning):
“Only if we can’t deliver. But we’re confident because we’ve designed for scale, speed, precision. The model works because the incentives align perfectly. We win only when you win.”

Maya:
“And the internal team? They won’t feel threatened?”

John (reassuringly):
“Not if positioned right. We’re not here to replace—they’re still steering strategy, owning creative, customer intimacy. We’re the engine under the hood—making the car faster, more efficient, without changing the driver.”

(John flips to table comparing in-house vs Progency.)

John:
“Bottom line, Maya: your team’s playing the Beta game—doing what every other brand does, paying platform tax, chasing industry benchmarks. We play the Alpha game—finding proprietary advantages, compounding small gains, unlocking value no one else can replicate.”

Maya (smiling):
“Okay, John. You’ve piqued my interest. Let’s explore what Alpha could look like for us.”

John (closing tablet):
“That’s all I ask. Let’s build your Alpha engine together.”