Progency: An Implementation Playbook

Published June 5-11, 2025

1

Overview

I have written multiple essays about Progency in the past few months. In this series, I will discuss how it can be implemented in a business. But first, let’s begin with summarising the key ideas and rationale of Progency.

1. The AdWaste Challenge: Modern marketing faces a critical inefficiency—approximately 70% of digital marketing budgets (roughly $500 billion globally) is wasted on reacquiring customers who already exist in brands’ databases. This “AdWaste” represents the single greatest destroyer of marketing profitability and business sustainability.
2. The Root Problems: This waste stems from two fundamental marketing failures: the “Not for Me” problem (lack of true personalisation) and the “No Hotline” problem (absence of reliable engagement channels). These failures lead to attention recession, where customers mentally unsubscribe long before formally opting out.
3. Progency’s Solution: Progency is not merely software or an agency—it’s a revolutionary fusion of platform, expertise, AI agents, and Kaizen (continuous improvement methodology) – the PEAK framework. It combines proprietary technology with specialists and AI orchestration in a performance-based model that reimagines how martech delivers value.
4. Performance-Based Economics: Unlike traditional agencies billing for time or martech vendors charging subscription fees, Progency ties compensation directly to measurable business outcomes. This creates perfect alignment: Progency succeeds only when clients succeed, transforming marketing from a cost centre into a measurable profit engine.
5. BRTN Customer Focus: Progency operates within the Best-Rest-Test-Next framework, focusing primarily on converting “Rest” customers (the middle 40-50% showing declining engagement) into “Best” customers (the top 20% who generate 60-80% of revenue). This systematic approach ensures resources are deployed where they create maximum impact.
6. Department of One: Through AI agent orchestration, Progency enables true 1:1 personalisation at scale—treating each customer as a unique individual rather than a segment member. This “Department of One for a Segment of One” capability allows small marketing teams to manage millions of individualised relationships without proportional staffing increases.
7. The Execution Gap Bridge: Most brands utilise only 30-40% of their martech platform capabilities due to knowledge gaps and resource constraints. Progency bridges this execution gap through expert platform mastery, vertical industry specialists, and AI agents that unlock the full potential of existing technology investments.
8. Growth Alpha Generation: Just as investment funds create Alpha (outperformance above market returns), Progency functions as a Growth Alpha Engine—delivering measurable revenue gains above established baselines through systematic optimisation of customer relationships.
9. Integration & Implementation: Rather than requiring wholesale replacement of existing systems, Progency works alongside current platforms through seamless API integration. Implementation typically begins with a focused pilot targeting a specific segment of Rest customers, with results measured against control groups for transparent performance evaluation.
10. Triple Impact: The ultimate goal of Progency is to transform marketing economics through three measurable outcomes: doubling the Best customer base, halving marketing waste, and tripling profitability. This comprehensive impact repositions marketing from a necessary expense to the primary engine driving sustainable business growth.

As we explore implementation in the coming sections, we’ll examine exactly how organisations can harness Progency to achieve these transformative outcomes whilst minimising disruption to existing operations. In this discussion, we will focus on eCommerce companies.

2

Customer-base Audit

The starting point for a Progency implementation is by building a data foundation. Within data, the first step is to have the past transaction data available to be able to do the Best-Rest-Test segmentation based on forward-looking LTV to be able to plan out the Progency implementation.

Peter Fader, et al call this a “customer-base audit.” Explains Michael Ross:

The first one would be the customer cohort chart: the C3. That really is, I would say, the number-one analysis. It highlights and breaks down how your revenue in each year builds up from customers acquired in every previous period. It gives you a very clear view of the extent to which your growth is being driven by your existing base versus new customer acquisition.

Number two for me is looking at the distribution of customer value. When you look at a distribution of customer value, you will often see that a high-value customer—what we’d call a top-decile customer—can be worth 40 or 50 times a low-value customer. The notion of an average customer may be mathematically true, but it doesn’t really exist in practice or is extremely unrepresentative of a customer base.

That’s a very, very powerful analysis because it motivates a business to think about the decisions and actions that they’re currently taking based on the idea of an average customer, whether those are marketing decisions, operational decisions, service decisions, etcetera. How do you reorient decisions that are better aligned to customer value?

The third most important analysis for me, and this is a hard one, is the time between first and second purchase. Understanding the time between first and second purchase is incredibly helpful. It is a very good discipline to understand, “Why do customers only buy once? How can we get them to come back? How can we get them to come back faster?” That drives a lot of very good behaviors.

… The data set we started off with is amazingly simple. It’s a list of transactions with a date, an amount, and a product ID.

From a related HBR article:

The starting point is a list of transactions for each customer (date, time, products purchased, total spend, etc.). This will reside somewhere in your company’s operational IT system.

Traditional reports will summarize performance by product. Think of an Excel worksheet where the rows correspond to individual products and the columns correspond to time (e.g., quarter).

Now, imagine an alternative summary table — again, think of an Excel worksheet — where the rows now correspond to individual customers and the columns correspond to time (e.g., quarter). The entries in the table report each customer’s total spend with the firm in that particular time period. Another table tells us how many transactions each customer made with the firm. (For most firms, these tables will contain lots of zeros.) If you’re lucky, you’ll also have an equivalent table that summarizes the profit associated with each customer in each period.

How do we approach the task of gaining insight from such a customer-level summary? As we reflect on the various questions that are asked when leaders seriously engage with the idea of understanding the performance and health of their business using the customer as the atomic unit of revenue and profitability, five broad themes appear, which we call the five lenses of a customer-base audit.

With the audit, it becomes possible to identify the Best, Rest, and Test customers – and thus plan the Progency interventions.

3

Interventions

The implementation of Progency begins with a crucial strategic decision: determining the optimal point of intervention within the customer base. This choice sets the foundation for success, defining both the scope of the initial engagement and the expectations for measurable outcomes.

Which Segment?

1. Best Customer Enhancement: Focusing on the top 20% of customers who already generate 60-80% of revenue and 200% of profits. This approach aims to further maximise the value of already high-performing customers.
2. Rest Customer Activation: Targeting the middle 40-50% showing declining engagement. This segment represents customers at a critical inflection point—either on their way to becoming Best customers or sliding toward Test status.
3. Cross-Segment Approach: Implementing across the entire customer base, addressing all segments simultaneously with varying strategies.
4. Test Customer Recovery: Focusing specifically on dormant customers (inactive 90+ days) through reactivation campaigns rather than expensive reacquisition.
5. Vertical Segment Focus: Targeting customers across segments but within a specific product category, purchase behaviour, or demographic vertical.

Comparative Analysis

Best Customer Approach

• Pros: Immediate revenue impact; builds on existing engagement; easier to measure success; lower risk of failure.
• Cons: Potential cannibalisation of internal team’s focus area; limited growth ceiling; may create internal resistance; highest-value customers already receive attention.

Rest Customer Approach

• Pros: Addresses the largest untapped opportunity; clear differentiation from internal team focus; measurable conversion paths from Rest to Best; significant revenue upside potential.
• Cons: Longer time to demonstrate results; requires deeper platform integration; success metrics may be less straightforward.

Cross-Segment Approach

• Pros: Comprehensive impact; holistic customer strategy; potential for systems-level transformation.
• Cons: Diluted focus; more complex implementation; higher resource requirements; challenging performance measurement; potential organisational resistance.

Test Customer Recovery

• Pros: Clear ROI versus reacquisition costs; highly measurable outcomes; addresses AdWaste directly.
• Cons: More challenging to reactivate truly dormant customers; may overlap with NeoN targeting; smaller immediate revenue opportunity.

Vertical Segment Focus

• Pros: Allows for specialised industry expertise; clearer testing boundaries; easier implementation.
• Cons: May miss cross-category opportunities; creates artificial customer divisions; potential data integration challenges.

CMO Comfort Factors

Most CMOs will naturally gravitate toward the Rest customer approach as the optimal starting point for several reasons:

1. Complementary Focus: It doesn’t compete with their team’s likely concentration on Best customers, avoiding territorial conflicts.
2. Clear Measurement: The Rest-to-Best conversion path provides unambiguous metrics for success—counting how many customers migrate upward and tracking their increased spending.
3. Risk Mitigation: Starting with a subset of Rest customers enables a controlled pilot that can scale once proven, reducing implementation risk.
4. Strategic Alignment: Rest customers represent the greatest revenue upside with minimal investment—perfectly matching the “more with less” mandate most CMOs operate under.
5. Organisational Acceptance: Teams typically welcome support for overlooked segments rather than perceiving intervention as criticism of their current focus areas.

The optimal implementation approach typically begins with a specific Rest segment pilot (perhaps 50,000-100,000 customers) with clear control groups for performance comparison. Starting with this focused approach allows for measurable results within 90 days while building organisational confidence before expanding to broader segments or more complex interventions.

By targeting Rest customers first, Progency establishes a clear value proposition: transforming overlooked, declining relationships into high-value Best customers without disrupting existing marketing operations—exactly the balance most CMOs seek when implementing revolutionary new approaches.

4

Alpha Measurement

The promise of Progency is to deliver a “Growth Alpha” over what in-house marketing teams can provide. Let’s assume that the focus is on a section of the Rest customers. How can this Alpha be measured and tracked? As I wrote: “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.”

Establishing Rigorous Measurement Frameworks

For Progency’s Growth Alpha to be credible and compensatable, measurement must be transparent, defensible, and directly tied to business outcomes. This requires implementing a comprehensive measurement methodology that isolates Progency’s specific contribution from market trends, seasonality, and other marketing initiatives.

Control Group Methodology

The gold standard for measuring Progency’s Alpha is a rigorous control-test design:

1. Random Assignment: Randomly divide the target Rest segment into matched control and test groups, ensuring statistical equivalence across key metrics (purchase history, engagement patterns, demographic characteristics).
2. Isolation: The control group continues receiving standard marketing treatments (or none at all), while the test group experiences Progency’s enhanced engagement strategies.
3. Continuous Measurement: Track performance metrics for both groups over identical timeframes, with measurements at 30, 60, and 90-day intervals.
4. Alpha Calculation: The Growth Alpha equals the difference in performance metrics between test and control groups, expressed as either percentage improvement or incremental revenue.

Key Performance Indicators

While the specific metrics may vary by industry and business model, core KPIs for measuring Progency’s Growth Alpha typically include:

• Migration Rate: Percentage of Rest customers who move to Best status
• Average Order Value (AOV): Increase in spending per transaction
• Purchase Frequency: Reduction in days between purchases
• Customer Retention Rate: Improvement in customer continuity
• Share of Wallet: Increase in category spending captured
• Incremental Revenue: Additional revenue generated beyond baseline
• Conversion Rate: Improvement in response to marketing initiatives

Perhaps the simplest and best measure is simply the revenue generated.

Alpha in Action: A Real-World Example

Consider a fashion retailer with 250,000 Rest customers showing declining engagement. Their standard marketing approach typically converts 3% of these customers to Best status each quarter, with an average revenue increase of Rs 3000 per migrated customer.

The retailer engages Progency to focus on 50,000 of these Rest customers (20% of the Rest segment), of which 5,000 (10% of the chosen intervention base) are set aside as a control group.

After 90 Days

	Control Group	Progency Group	Difference (Alpha)
Customer Base	5,000	45,000	–
Migration to Best Status	3% (150)	9% (4,050)	+6% points
Revenue per Migrated Customer	Rs 3,000	Rs 4,200	+Rs 1,200
Total Incremental Revenue	Rs 450,000	Rs 17,010,000	Rs 16,560,000
Normalised Comparison (per 5,000 customers)	Rs 450,000	Rs 1,890,000	+Rs 1,440,000

The Growth Alpha Calculation

• Alpha per 5,000 customers = Rs 1,890,000 – Rs 450,000 = Rs 1,440,000 in incremental revenue
• This represents a 320% outperformance above baseline expectations
• Total Alpha across the entire 45,000 customer Progency group = Rs 12,960,000

Additionally, among customers who didn’t migrate to Best status, Progency will have demonstrated secondary benefits: reduction in churn risk, increase in email engagement, and higher app retention.

Under the performance-based pricing model, Progency might receive 20% of the incremental revenue (Rs 12,960,000 × 20% = Rs 2,592,000), creating a clear 5:1 ROI for the client while ensuring Progency’s compensation directly reflects value delivered.

Progency Upside

Under normal circumstances, the martech vendor may have received Rs 100,000 a month (Rs 300,000 for the 3 months) for the platform. With Progency’s performance-based model generating Rs 2,592,000 in compensation, this represents an 8.6X increase in revenue for the martech vendor. This dramatic revenue multiplication demonstrates why Progency isn’t merely a service enhancement but a fundamental business model transformation—creating substantially higher value for both the client and the vendor while eliminating the traditional disconnect between martech fees and business outcomes.

Beyond Direct Revenue

While revenue Alpha forms the core measurement, sophisticated implementations also track second-order benefits:

• Earned Growth Acceleration: Increase in referrals from activated customers
• Channel Preference Development: Higher engagement with owned channels versus paid platforms
• Data Enrichment: Improved zero-party data collection enabling better personalisation
• Operational Efficiency: Reduction in marketing team workload through AI automation

By establishing these comprehensive measurement frameworks, Progency transforms marketing from a nebulous cost centre into a precisely quantifiable profit engine, with every rupee of compensation directly linked to measurable revenue uplift—the truest form of marketing accountability.

5

The How

Most brands face what I’ve previously termed the “execution gap”—the vast difference between martech’s theoretical capabilities and its practical implementation. Typically, marketing teams utilise only 30-40% of their platform’s features, leaving powerful personalisation, journey orchestration, and analytics tools largely idle. This isn’t due to lack of ambition but to fundamental human constraints: limited bandwidth, knowledge gaps, competing priorities, and the daily drudgery of routine marketing operations.

Progency’s PEAK framework systematically eliminates this gap through four interconnected pillars that create an exponential rather than incremental improvement:

Platform Mastery

Unlike in-house teams struggling to keep pace with evolving functionality, Progency brings complete platform expertise—unlocking advanced features that often remain undiscovered or underutilised. This mastery extends beyond surface-level application to deep technical integration, sophisticated automation, and complex journey mapping that internal teams rarely achieve.

As I noted in “Progency’s Problem-Solving Prowess,” this expertise allows “unlocking the full potential of your current stack through expert orchestration and AI agents, protecting your investment while delivering superior returns.”

Expert Specialisation

Progency deploys vertical industry specialists who understand specific sectors at a granular level—from buying cycles and decision triggers to competitive dynamics and customer expectations. These experts bring contextual intelligence that general marketers cannot match, identifying revenue opportunities and optimisation levers that internal teams often miss.

This specialisation enables what I’ve previously called “pattern recognition across clients”—the ability to identify what works in similar contexts and apply these insights to new challenges, creating a knowledge transfer advantage that siloed in-house teams never access.

AI Agents Collective

The revolutionary power of Progency comes from its sophisticated multi-agent ecosystem—where specialised AI agents work in concert to execute marketing operations at unprecedented scale and precision. Where human teams typically manage 8-10 segments due to bandwidth limitations, the AI Agents Collective can create and manage thousands of micro-segments with individualised journeys.

This isn’t mere automation but a “Department of One” capable of:

• Generating hyper-personalised content for each segment
• Orchestrating complex, multi-channel journeys
• Continuously optimising campaigns through real-time learning
• Executing thousands of simultaneous tests to identify winning approaches

As I explored in “AI-Native Martech,” this creates “true N=1 personalisation at scale—treating each customer as a unique individual rather than a segment member”—fundamentally transforming the customer experience while eliminating the operational bottlenecks that constrain traditional marketing.

Kaizen Continuous Improvement

While in-house teams typically operate in campaign cycles with periodic reviews, Progency embeds continuous improvement into its DNA. Every interaction generates data that feeds learning algorithms, creating daily rather than quarterly optimisation cycles. This compounds performance over time—each day’s results become slightly better than the previous, creating exponential rather than linear improvement.

**

The Always-On Revolution

Perhaps the most transformative aspect of Progency is its “Always-On” nature. Traditional marketing operates in human time—campaigns planned weeks in advance, optimisations scheduled around team availability, insights delayed by analysis backlogs. The AI Agents Collective never sleeps, enabling:

• Real-time response to customer behaviour patterns
• Midnight optimisations based on performance data
• Weekend campaign adjustments without staffing implications
• Seamless scaling during peak periods without resource constraints

This operational continuity creates a fundamental advantage: while traditional marketing teams can achieve excellence in bursts, Progency delivers excellence consistently, continuously, and at scale—turning marketing from a periodic initiative into a perpetual growth engine.

Through this comprehensive framework, Progency doesn’t merely enhance what in-house teams already do—it fundamentally reimagines what’s possible, creating capabilities that traditional operating models simply cannot match regardless of talent or resources.

6

Expansion

Once Progency demonstrates its Growth Alpha with a sub-segment of Rest customers, CMOs face a transformative opportunity: expanding this revolutionary approach across their entire customer base. The early success creates a compelling case not just for expanding within the Rest segment, but for applying Progency’s PEAK framework to Best customers as well—where even modest percentage improvements create substantial absolute value.

The Strategic Inflection Point

For forward-thinking CMOs, this moment represents a critical decision point. The data speaks clearly: Progency’s AI-orchestrated approach delivers measurable outperformance that traditional marketing operations cannot match. Yet implementing this model at scale challenges conventional thinking about marketing ownership, team structure, and executive identity.

The barrier isn’t technological but psychological. CMOs must recognise that outsourcing retention to Progency is no different conceptually from their established practice of outsourcing acquisition to agencies that leverage Google and Meta. Both involve delegating execution to specialists with superior capabilities while maintaining strategic control and accountability for outcomes.

Elevating the CMO Role

This evolution demands that CMOs transcend their traditional boundaries to become true business leaders. Rather than viewing themselves as marketing specialists making lateral moves every few years, they must reposition as Chief Profit Officers with direct P&L responsibility—and potential CEO candidates.

As I’ve written in “From CMO to C-Suite MVP,” this transformation elevates marketing leadership from tactical execution to strategic business growth—with the measurable metrics to prove this value in the boardroom. The CMO who embraces this future gains unprecedented influence within the organisation while creating a clear path to the corner office.

The Comprehensive Value Proposition

The full implementation of Progency unlocks a triple benefit that transforms marketing economics:

1. Customer Value Maximisation: Converting at-risk and churned customers into high-value relationships with substantially higher lifetime value
2. Referral Acceleration: Creating advocacy that generates zero-cost acquisition, dramatically reducing CAC
3. AdWaste Elimination: Systematically redirecting the approximately 70% of marketing budgets currently wasted on reacquisition toward value-creating initiatives

This isn’t merely optimisation but fundamental business transformation—turning marketing from a cost centre into the primary profit engine driving sustainable growth.

The Measurement Revolution

Implementing this transformation requires a new measurement framework. The EAGLES metrics (Earned Growth, AdWaste Percentage, Growth-Profit Balance, LTV/CAC Ratio, Existing Revenue Ratio, and Segmentation Balance) provide CMOs and CEOs with the comprehensive dashboard needed to track this evolution and prove its impact.

These metrics form the foundation of NeoMarketing—the third great era of marketing that follows Traditional Marketing (1950s-1990s) and Modern Marketing (2000s-2020s). But achieving these metrics at scale requires breaking free from traditional approaches.

Breaking the Low-Equilibrium Trap

The current marketing ecosystem exists in a low-equilibrium trap: martech vendors price on inputs rather than outcomes, agencies bill for time rather than results, and brands churn through endless acquisition cycles without building sustainable value. This model benefits platforms at the expense of brands and consumers alike.

The revolutionary potential of Progency—alongside innovations like NeoN—lies in breaking this destructive cycle. By leveraging multi-agent systems and reimagining business models around performance and outcomes, these approaches create a new virtuous cycle where all participants benefit from delivering genuine customer value.