Thinks 1614

Marc Lore: “Let’s say a founder is shooting for a $1 billion outcome. That’s usually a pretty good sweet spot. You can see a path to building something of that size; it works for early-stage venture capitalists, and it’s not so crazy ambitious that it’s going to require a ton of capital. (If you’re aiming lower than that – say, $100 million – it’s hard to raise capital from traditional VCs.) Next, you need to determine how much capital you’ll need to achieve that outcome. I use what I call “The Rule of 250” to guide my thinking about this. Take whatever sized outcome you’re targeting – $1 billion in our example – and divide it by 250. In our case, we’re targeting $1 billion, so doing the math, we should raise $4 million to get started. If we were shooting for a $10 billion outcome, we should try to raise a lot more to get started and operate at a true startup pace, closer to $40 million. Not everyone can get that kind of capital out of the gate, and you need to have a plan that backs it up. The next step is to operate at true rocketship cadence and raise accordingly. With my companies, my goal has been to double the amount I raise at double the valuation every 18 months.”

Arnold Kling: “The biggest barrier to using chatbots productively is lack of imagination. The only way to become adept at using them is to keep pushing your own envelope by trying to get help from them in novel contexts.”

WSJ: “Companies should start planning for the next stage of artificial intelligence: the orchestration of multiple agents across their businesses. Most companies are still figuring out how to deploy even one AI-powered agent that can perform a task autonomously or in coordination with humans. But developers are creating protocols to harness these agents into teams that handle everything from customer service and coding to supply chain, logistics, finance, marketing and business strategy. Given the pace of innovation and the time it takes for organizations to adapt, companies will do themselves a favor by getting ready now for multiagent systems increasingly available later this year…A true multiagent system, Recchia said, involves agents that dynamically reason, negotiate or collaborate in real time without requiring human-defined workflows, explicit prompts or manual coordination. In other words, the agents take initiative, adapt to new information and interact fluidly with other agents without waiting for human instruction.”

FT: “I ask whether [Sam] Altman finds his brave new world, in which humans are not the most intelligent thing on the planet, threatening — if not for him, then for his son? He is, predictably, too enthralled by his AI creation to feel menace. “Do you think you’re smarter than o3 right now? I don’t . . . and I feel completely unbothered, and I bet you do too,” he says. “I’m hugging my baby, enjoying my tea. I’m gonna go do very exciting work all afternoon . . . I’ll be using o3 to do better work than I was able to do a month ago. I’ll go for a walk tonight. I think it’s great. I’m more capable. He [his son] will be more capable than any of us can imagine.””

EAGLES: The Six Essential Metrics to Revolutionise eCommerce Profitability (Part 4)

Six New Numbers – 1

The current eCommerce analytics landscape is cluttered with dozens of metrics that create data overload without driving strategic action. Most businesses diligently track conversion rates, AOV, traffic sources, and ROAS—yet still struggle with profitability. This disconnect exists because traditional metrics focus on transactions rather than customer relationships, campaign performance rather than structural efficiency, and short-term revenue rather than sustainable profit.

The six numbers I propose represent a paradigm shift. They serve as second-order metrics—measures that derive their power not from tracking isolated activities but from revealing the systemic health and efficiency of your entire business model. Each addresses a critical blind spot in conventional eCommerce analytics. Together, they form a nice acronym: EAGLES.

  • Earned Growth – This metric serves as the perfect starting point since it directly measures the core NeoMarketing principle of building value from existing relationships rather than constant acquisition. It sets the philosophical foundation for everything that follows.
  • AdWaste Percentage – Following Earned Growth, AdWaste creates the natural counterpoint – showing what happens when retention fails. This creates the immediate tension and business case for change.
  • Growth & Profit Balance (Rule of 40) – After establishing the retention-acquisition balance, this metric naturally extends the discussion to the broader business balancing act between growth speed and profitability.
  • LTV/CAC Ratio – This metric then provides the economic engine that powers both growth and profitability, connecting the macro business measures to the unit economics.
  • Existing Revenue Ratio – The percentage of revenue from existing customers serves as the operational bridge that connects strategic metrics to day-to-day business reality.
  • Segmentation Balance (BRTN Split) – Finally, the BRTN framework provides the actionable implementation path – the “how” that brings all the previous metrics to life through specific customer strategies.

**

  1. Earned Growth: The Quality of Revenue

Earned Growth measures the percentage of your revenue that comes from existing customers and their referrals rather than paid acquisition. First popularised by Fred Reichheld, this metric distinguishes between “bought” growth (expensive and often unsustainable) and “earned” growth (organic and highly profitable).

From an earlier essay: “Earned Growth is mathematically represented as Net Revenue Retention + Earned New Customers (ENC) – 100. Reichheld, Darnell and Burns discuss this in an article in Harvard Business Review: “Once you have organized revenues by customer, you can determine your NRR. Simply tally this year’s revenues from customers who were with you last year, divide that amount by last year’s total revenues, and express that figure as a percentage. ENC is the percentage of spending from new customers you’ve earned through referrals (as opposed to bought through promotional channels).” They offer an example: “Company A’s revenues grew from $100 in 2020 to $130 during 2021, or 30%. In 2021 customers who were on the books in 2020 accounted for $85 of revenues. Some of them expanded their purchases by a total of $5, but that growth was more than offset by other customers who reduced purchases by a total of $20, resulting in an NRR of 85%. New customers accounted for $45 in revenues—$25 from earned new customers (referrals) and $20 from bought new customers. Adding the NRR (85%) and ENC (25%) and then subtracting 100% results in a 10% earned growth rate.””

The brilliance of Earned Growth as a metric lies in its simplicity and its power as a predictor. Companies with high Earned Growth percentages typically outperform their competitors in profitability and show more resilient growth during economic downturns. It serves as an indirect measure of customer satisfaction—people who return and refer others are demonstrating their approval more meaningfully than any survey response.

For eCommerce businesses, tracking Earned Growth creates a crucial shift in mindset. Rather than celebrating all revenue equally, it distinguishes between revenue that strengthens your economic engine and revenue that merely keeps it running at considerable cost. A business with a high Earned Growth operates fundamentally differently than one with a low Earned Growth, even if their top-line numbers look identical.

  1. AdWaste Percentage: Marketing’s Hidden Profit Leak

AdWaste represents perhaps the largest overlooked opportunity in modern eCommerce. This metric—the percentage of your marketing budget spent reacquiring customers who already exist in your database—typically reveals that 60-70% of acquisition spending is effectively wasted.

This waste occurs through three primary mechanisms:

  • Retargeting existing customers through expensive platforms
  • Paying to acquire “ghost users” who never provide identifiable information
  • Failing to activate newly acquired customers, necessitating reacquisition

What makes AdWaste particularly valuable as a metric is its direct connection to profit improvement. Unlike incremental optimisations of click-through rates or cost-per-click, reducing AdWaste delivers bottom-line impact at scale. Each percentage point of reduction flows directly to increased profitability.

Most importantly, AdWaste identifies a structural inefficiency rather than a campaign-level problem. By highlighting the systemic disconnect between acquisition and retention strategies, it forces organisations to bridge their marketing silos and create more coherent customer journeys.

Thinks 1613

HT: “So what does being “middle-class” in India really mean? I have a saying: If your spouse doesn’t have to work, you’re middle class. If neither of you has to work, you’re rich. If your children don’t either, you’re wealthy. You’re middle-class if you track EMIs and your child’s school fees more closely than the Sensex. If you fly economy, buy your iPhone abroad, and Google “best mid-range air purifiers”. If you obsess over the price of milk but also shop at Zara. The middle-class is aspirational, stretched, always-reaching.”

FT: “In a new section for Security Analysis, the seminal work by Ben Graham and David Dodd that was republished last year, Combs described how he looks for a moat, a competitive advantage that would be hard for rivals to overcome. “Add on characteristics like low capital intensity, pricing power, recurring revenues, staying power, and the likelihood of long-term growth, and you have a great business,” he added.”

Morgan Housel: “Dunkirk was a miracle. More than 330,000 Allied soldiers, pinned down by Nazi attacks, were successfully evacuated from the beaches of France back to England, ferried by hundreds of small civilian boats. London broke out in celebration when the mission was completed. Few were more relieved than Winston Churchill, who feared the imminent destruction of his army. But Edmund Ironside, commander of British Home Forces, pointed out that if the Allies could quickly ferry a third of a million troops from France to England while avoiding aerial attack, the Germans probably could, too. Churchill had been holding onto hope that Germany couldn’t cross the Channel with an invasion force; such a daring mission seemed impossible. But then his own army proved it was quite possible. Dunkirk was both a success and a foreboding. Your competitors can probably innovate and execute as well as you can. So every time you uncover a new talent you’re proud of, temper your thrill with the acceptance that other people who want to win as badly as you probably aren’t far behind.”

Christoph Schweizer: “Historically, many companies closing a merger or an acquisition have given lip service to growth. Instead, they should provide the same level of detail in their plans for growth as they do for savings. And then deliver on them. Over my 27 years at BCG, I’ve supported a dozen large-scale PMIs. The most successful integrations were growth-oriented.” [via BCG newsletter]

EAGLES: The Six Essential Metrics to Revolutionise eCommerce Profitability (Part 3)

Previous Writings

PxL: Transforming eCommerce P&Ls

I had reproduced this funnel view from Smart Insights and tracking these numbers is a good idea. Of these, the key is how many sessions convert to a transaction.

I had added: “The ProfitXL (PxL) framework focuses on three categories of customers: the top 20% Best customers, the 80% Rest customers, and then the Next acquisitions. This is a categorization which can work across eCommerce companies. Key to this is segmenting customers by their lifetime value… The one-word missions for the three teams: Maximise (Best), Energise (Rest), Minimise (Next). Done right, there is no reason why eCommerce companies should not be able to drive an additional 10% upside on revenue (and gross margin), and a halving of spending on new customer acquisition costs. This will address the twin problems of low conversions and rising CAC, thus having a multiplier impact on EBIDTA, supersizing profits, and transforming the brand’s P&L.”

The Profipoly Quest: Maya’s Story

I had discussed the BRTLNG framework:

  • Best and Rest (for existing customers), with Best being the top 20%, and Rest being the other 40-50% who account for a significant chunk of revenues.
  • Test and Left (for exiting customers), who have become dormant (the bottom 30-40%) or churned. These are customers who eventually get retargeted via Adtech, resulting in a AdWaste because there is an existing communication channel available to them.
  • Next and Guest (for entering customers), who are the new customers. “Next” customers are prospects yet to engage with the brand and will probably be acquired via Adtech, while “Guest” customers are anonymous visitors to the brand’s platforms​​ who come organically.

I also wrote about the EnCoRe triad – Engagement, Conversion, Retention. Linked to the funnel, it as a combination of three key metrics:

  • Engagement: the percentage of contacted consumers who engaged in the last three months
  • Conversion: the percentage of all visitors who made a transaction in the past three months
  • Retention: the percentage of customers who made more than one transaction, serving as a crucial health check for martech​​

Profipoly Score: The North Star Metric

I had designed a framework for the Profipoly Score: “The Profipoly Score is a tracker for measuring the progress in the transformational journey towards exponential forever profitable growth. It is a leading indicator for the health of the business, a metric which will indicate the capacity of a business to maximise the profits pool in an industry. The Profipoly Score needs to become as important as earnings per share and NPS in the management lexicon.”

How to Boost eComm Profit Margins by 1000 Basis Points

I had outlined a 5-step roadmap for marketers to boost profit margins:

  1. Optimise Customer Segmentation: Utilise the BRTLNG framework (Best, Rest, Test, Left, Next, Guest) to understand customer behaviours and value.
  2. Reactivation for Revenues: Prioritise re-engagement of ‘Test’ and ‘Left’ customers to reduce ad spend waste, employing Email 2.0 Reactivation Progency to revive dormant and churned relationships and convert them into profitable ones.
  3. Enhance Customer Experience: Implement Velvet Rope Marketing to provide ‘Best’ customers with VIP treatment and use the Profipoly Stack to predict and influence ‘Rest’ customers’ behaviours.
  4. Refine Acquisition Strategy: For ‘Next’ customers, adopt a near-zero acquisition cost approach. Leverage the Best Customer Genome for better targeting, encourage referrals, and maximise identification of anonymous ‘Guest’ visitors.
  5. Performance-Based Profit Uplift: Capture a 1250 basis point increase in profit margins by sharing a portion of the gains with martech partners. By doing so, brands can keep a substantial 1000 basis point rise, thereby significantly improving profitability and paving the way for reinvestment in innovation and growth.

Only Once in Action

I had written: “Marketers are paying for four distinct audience segments, but only one represents genuine new customer acquisition. The other three segments – especially known non-buyers and existing customers – represent pure AdWaste.”

I listed critical questions every marketer needs to answer to understand their AdWaste:

  1. Reacquisition AdWaste
  • What percentage of “new” users acquired through paid channels already exist in the CRM database?
  • How many are known non-buyers and existing customers? (These should never have been reacquired; they should have been retained.)
  • What is the total spend on reaching these already-known users? This is Reacquisition AdWaste.
  1. New Acquisition AdWaste
  • Of your genuine first-time users, what percentage provided a digital handle (email/mobile)?
  • The rest are essentially “ghost users” you can never reach again.
  • What is the cost of these unidentifiable acquisitions? This is New Acquisition AdWaste.
  1. True CAC Calculation
  • What is the actual customer acquisition cost (CAC) when measured against truly new identifiable customers?
  • True CAC = Total digital ad spend ÷ New email IDs or mobile numbers added
  • This reveals the true cost of acquiring actionable customer relationships

To summarise: Total AdWaste = Reacquisition AdWaste + New Acquisition AdWaste, where

  • Reacquisition AdWaste = Cost of reaching known users
  • New Acquisition AdWaste = Cost of unidentifiable acquisitions

**

Many of the numbers and metrics I have discussed are not being tracked by marketers — and that’s precisely the opportunity. To unlock sustainable growth and true profitability, we need to shift focus from vanity metrics to value metrics. This is where EAGLES comes in.

Thinks 1612

WaPo: “Just a half-hour of weight training can make you stronger.”

FT: “Is it time for us to redefine what we mean by career success? Rutger Bregman thinks so, and his new book, Moral Ambition, makes a brisk and persuasive case for ditching “mind-numbing, pointless, or just plain harmful jobs” and doing something more meaningful instead. He’s aiming this book squarely at the “idealistic and ambitious” person of any age who works in consulting, law, finance and other well-paid sectors.”

Bloomberg on multi-level marketing: “Let’s say you have a product you want to sell. It could be anything: make-up, clothes, jewelry, cleaning products, meal kits, weight-loss plans, maybe vitamins. Maybe you have a background in science and nutrition and have carefully crafted something to help people meet a known nutritional deficiency. Or maybe you just want to make a buck. There are two ways to persuade people to buy your vitamins. You can sell them in a store and hope customers come to you, or you can take your vitamins to potential customers, either by going door-to-door or, as is more likely these days, selling them online. This is known as “direct selling.” Both of these methods are labor intensive and require significant upfront investment. If you opt for the store, you’ll have to pay rent, stock the shelves, and hire a clerk or two, with no guarantee that you’ll attract enough customers to break even. If you opt for direct selling, you’ll spend your days ringing doorbells or posting TikToks, usually with nothing to show for it. But there is a third way. You could convince other people to do the work for you.”

Jaspreet Bindra: “With AI agents increasingly handling the technical “how-to” of tasks, the human edge will lie in the “why” and the “what next”. The humble subjects of humanities like language, philosophy, grammar and the arts are the ones that provide us the critical frameworks for understanding context, ethics, human motivation, creativity and critical judgment —skills that are inherently difficult for AI to replicate meaningfully. Logic and grammar teach us the principles of clear thinking and communication; arithmetic becomes less about rote calculation and more about understanding quantitative reasoning and data interpretation. Language gives us the superhuman ability to mould words to express the right thoughts. Thus, do not be surprised to see our children preferring humanities to the inevitable computer science or engineering education, and parents rethinking their child’s future education.”

EAGLES: The Six Essential Metrics to Revolutionise eCommerce Profitability (Part 2)

Most Important

I then asked Claude to list out the top 5 metrics and the most important North Star Metric.

Based on their impact on business sustainability and growth, these are the five most critical metrics for B2C/D2C eCommerce companies:

Rank Metric Why It’s Critical
1 Customer Lifetime Value (CLV/LTV) Represents the total revenue potential of your customer base and serves as the foundation for sustainable growth. A rising LTV indicates improving product-market fit, customer satisfaction, and business health.
2 Customer Acquisition Cost (CAC) Determines the viability of your growth strategy. Alongside LTV, it forms the core ratio that defines whether your business model is fundamentally sound.
3 Conversion Rate The most direct lever for revenue growth that doesn’t require additional marketing spend. Improvements cascade throughout your business model.
4 Retention Rate / Repeat Purchase Rate The primary driver of LTV and a key indicator of product satisfaction. Acquiring customers is meaningless if they don’t stay and purchase again.
5 Contribution Margin Provides the clearest view of unit economics by accounting for all variable costs associated with each sale. Essential for understanding true profitability.

Customer Lifetime Value (CLV/LTV) should be your North Star Metric because:

  1. It comprehensively captures the health of your entire business model in a single number
  2. It reflects both acquisition efficiency and customer retention success
  3. It accounts for critical factors like repeat purchase rate, AOV, and margins
  4. It inherently measures product satisfaction and customer experience
  5. Improvements in LTV directly correlate with business sustainability and valuation
  6. It aligns all departments (product, marketing, operations) toward the same goal: creating more value for customers

While many companies choose metrics like GMV or revenue growth as their North Star, these can be misleading if they’re driven by unsustainable acquisition costs or poor retention. LTV provides a more holistic view that encourages building lasting customer relationships rather than short-term growth tactics.

My next question was on how to calculate CLV/LTV.

There are several methods to calculate Customer Lifetime Value, ranging from simple to complex. Here are the most common approaches:

  1. Basic Historical Method
Formula CLV = Average Order Value × Average Number of Orders per Year × Average Customer Lifespan (in years)

Example:

  • Average Order Value: $75
  • Average Orders per Year: 3.2
  • Average Customer Lifespan: 2.5 years
  • CLV = $75 × 3.2 × 2.5 = $600

Pros: Quick to calculate, requires minimal data
Cons: Doesn’t account for profitability, acquisition costs, or time value of money

  1. Gross Margin Method
Formula CLV = Average Order Value × Average Orders per Year × Average Customer Lifespan × Gross Margin

Example:

  • Using data above + 40% gross margin
  • CLV = $75 × 3.2 × 2.5 × 0.4 = $240

Pros: Incorporates profitability
Cons: Still doesn’t account for time value of money or varying retention rates

  1. Predictive Method (Retention-Based)
Formula CLV = Margin × (Retention Rate ÷ [1 + Discount Rate – Retention Rate])

Where:

  • Margin = Average profit per customer per period
  • Retention Rate = Percentage of customers who return (e.g., 0.7 for 70%)
  • Discount Rate = Cost of capital (e.g., 0.1 for 10%)

Example:

  • Margin per year: $100
  • Annual retention rate: 60%
  • Discount rate: 10%
  • CLV = $100 × (0.6 ÷ [1 + 0.1 – 0.6]) = $100 × (0.6 ÷ 0.5) = $100 × 1.2 = $120

Pros: Accounts for time value of money and actual retention behaviour
Cons: More complex to calculate, requires more data

  1. Cohort Analysis Method

This involves tracking specific customer cohorts (grouped by acquisition date) over time to measure their actual value.

Steps:

  1. Group customers by acquisition month/quarter
  2. Track each cohort’s purchases over time (12-24+ months)
  3. Calculate the cumulative revenue or profit per customer for each cohort
  4. Project future value based on observed retention and purchase patterns

Pros: Most accurate, based on actual customer behaviour
Cons: Requires significant historical data and analytical resources

  1. Advanced Predictive Methods

For more mature businesses, advanced methods incorporate:

  • Discount rates to account for the time value of money
  • Varying retention rates across customer lifecycle
  • Different segment-level calculations (e.g., by acquisition channel or customer type)
  • Monte Carlo simulations to account for uncertainty

Practical Implementation Tips

  1. Start simple: Begin with the basic historical method if you’re just starting out
  2. Segment your calculation: Calculate separate LTVs for different:
    • Acquisition channels
    • Product categories
    • Customer demographics
    • First order value brackets
  3. Validate with cohort analysis: Compare predictive models with actual cohort performance
  4. Update regularly: Recalculate quarterly to track changes
  5. Set forward-looking targets: Use LTV projections at different time horizons (30-day LTV, 90-day LTV, 1-year LTV)

Common Pitfalls to Avoid

  1. Using too short a timeframe: LTV calculations become more accurate with 12+ months of data
  2. Not accounting for returns/refunds: Always use net revenue, not gross sales
  3. Ignoring customer acquisition cost: LTV should always be viewed alongside CAC
  4. Overestimating customer lifespan: Be conservative in lifespan assumptions
  5. Not including all variable costs: Marketing, shipping, and customer service costs should be factored in for true contribution margin

Remember that LTV is both a backward-looking metric (measuring what has happened) and a forward-looking projection. The most sophisticated eCommerce businesses use both historical values and predictive models to inform their decisions.

**

While these are undoubtedly important metrics, I believe eCommerce businesses should also adopt a new set of metrics that offer deeper insights into the true health and sustainability of the business.

Thinks 1611

Robert Waldinger on happiness: ““The clearest message that we get from this 75-year study is this: Good relationships keep us happier and healthier. Period.” More: “That strong marriages and family relationships make people happier — yes, that’s intuitive, Lyubomirsky acknowledges. What she found more surprising was just how effective even having smaller points of connection throughout the day could be for happiness — and how achievable that is, if people could only overcome their own hesitation. “If someone were to ask me what’s the one thing you could do tomorrow to be happier, that’s my answer: having a conversation with someone — or a deeper conversation than you normally do,” she says.”

WSJ: “Nvidia CEO Jensen Huang said all American companies will eventually need or become artificial-intelligence factories—or entities that produce both goods and AI—and will create skilled U.S. jobs in the process.  “Just as we make physical cars today, or anything physical in the future, there’ll be a digital version of it,” Huang [said]…“So you need an AI factory to create the AI model that runs in the car.” An AI factory—which can also be described as a sort of one-stop shop of chips, software, design and networking infrastructure designed for AI—is a concept that Nvidia has promoted at past events as a facility that takes in data and churns out intelligence.”

FT: “In the early 2000s, a start-up called Yelp came up with a novel and friendly feature for the emergent world wide web. Ordinary users could post reviews of restaurants that everyone could read. But there was a problem: very few people were interested in writing things on the internet. Yelp’s engineers needed to give them a reason to. The story of how they incentivised this user-generated content is the jumping off point for Like, an upbeat take on internet history by two veterans of business and technology. Yelp reckoned people might be compelled to post reviews if they received compliments from others. Ultimately, alongside similar experiments at other tech start-ups, this led to the ubiquitous expression of instantaneous recognition that is the like button, which, Bob Goodson and Martin Reeves write, is now clicked some 160bn times a day.”

WSJ: “Apps and online platforms can give us a written record of our video meetings, phone calls or just about any voice recording. AI can even read the transcripts for us, summarizing the key nuggets and action items, so we don’t have to wade through all the ums, ahs and digressions. The promise is that we can have meetings where nothing gets lost; meetings where we can give the conversation our full focus instead of worrying about taking notes; and where people with hearing challenges, attention differences or a noisy work environment can still follow and participate in the conversation. But a full accounting of every conversation could have an unexpected downside: When people in a meeting know they are being recorded, they don’t talk the way they usually do.”

EAGLES: The Six Essential Metrics to Revolutionise eCommerce Profitability (Part 1)

Key Metrics

I asked Claude and ChatGPT to list out key eCommerce metrics.

Category Metric What to Track Why It Matters
Growth & Revenue Gross Merchandise Value (GMV) Total value of all merchandise sold before returns, discounts, etc. Provides high-level view of business scale and growth trajectory; essential for investors and industry benchmarking.
Net Revenue GMV minus discounts, returns, and cancellations. Shows actual realized revenue and more accurately represents financial health than GMV.
Revenue Growth Rate Month-over-month or year-over-year percentage increase in revenue. Indicates momentum and scalability; declining rates may signal market saturation or competitive threats.
Monthly Recurring Revenue (MRR) Predictable monthly revenue from subscription customers. Provides predictability for cash flow forecasting and demonstrates revenue stability.
Customer Metrics Customer Acquisition Cost (CAC) Total marketing and sales expenses divided by number of new customers acquired. Determines if acquisition strategy is sustainable and helps optimize marketing spend across channels.
Customer Lifetime Value (CLV/LTV) Projected revenue a customer will generate throughout their relationship with your business. Helps determine appropriate acquisition spend and guides pricing and loyalty strategies.
LTV

Ratio

Ratio between lifetime value and acquisition cost. Key indicator of business sustainability; healthy ratio is 3:1 or higher.
Active Customers Number of unique customers who made a purchase in a given period. Reflects actual size of customer base and helps detect early warning signs of disengagement.
Churn Rate Percentage of customers who don’t return within a specific timeframe. High churn indicates product-market fit issues or poor customer experience.
Retention Rate / Repeat Purchase Rate Percentage of customers who return to make additional purchases. Demonstrates product satisfaction and brand loyalty; repeat customers typically have higher LTV.
Unit Economics & Profitability Gross Margin (Revenue – COGS) / Revenue, expressed as a percentage. Shows how much revenue remains to cover operating expenses and generate profit.
Contribution Margin Revenue minus COGS minus variable costs (marketing, shipping, etc.). Provides clearer picture of profitability per unit by including all variable costs.
Average Order Value (AOV) Total revenue divided by number of orders. Increasing AOV can significantly improve profitability since many costs are fixed per order.
Orders per Customer Total number of orders divided by number of unique customers. Measures depth of customer relationship and success of cross-selling efforts.
Return Rate Percentage of sold items that are returned. High return rates impact profitability through reverse logistics costs; can indicate product quality issues.
First Order Profitability Profit or loss on a customer’s first purchase. Determines if you can afford aggressive first-order promotions while maintaining sustainable economics.
Marketing & Funnel Traffic Sources Breakdown Percentage of visitors from different channels. Helps diversify acquisition channels and identify which to prioritize.
Conversion Rate Percentage of visitors who complete a purchase, segmented by channel and device. Direct indicator of website effectiveness; small improvements can dramatically increase revenue.
Cart Abandonment Rate Percentage of users who add items to cart but don’t complete checkout. Represents “low-hanging fruit” revenue opportunity; may indicate UX issues or payment friction.
Email Metrics Open/click/conversion rates for email campaigns. Email often delivers highest ROI of any marketing channel when properly segmented.
ROAS (Return on Ad Spend) Revenue generated per dollar spent on advertising. Measures advertising efficiency and helps optimize campaign spending across channels.
Blended CAC vs. Paid CAC Overall CAC across all channels versus CAC for paid channels only. Helps understand true efficiency of marketing mix when scaling.
Operations & Fulfillment Order Fulfillment Rate Percentage of orders delivered successfully on first attempt. Directly impacts customer satisfaction and operational costs.
Average Delivery Time Time from order placement to customer receipt. Speed of delivery is a key competitive differentiator.
Logistics Cost per Order Total shipping, handling, and fulfillment costs divided by number of orders. Often a substantial portion of total costs; optimization can significantly improve margins.
Inventory Turnover Ratio Cost of goods sold divided by average inventory value. Measures inventory management efficiency; low turnover ties up capital.
Out-of-Stock Rate Percentage of SKUs unavailable when customers attempt to purchase. Results in immediate revenue loss and potential customer defection to competitors.
Cancellation Rate Percentage of orders cancelled before dispatch. Indicates issues with inventory management, payment processing, or customer commitment.
Customer Experience & Brand Health Net Promoter Score (NPS) Likelihood of customers to recommend your brand (promoters minus detractors). Predictive of future growth; promoters drive referrals and have higher retention rates.
Customer Satisfaction (CSAT) Rating of customer satisfaction with specific interactions or overall experience. Identifies pain points in the customer journey that need immediate attention.
Product Rating & Reviews Average score and sentiment analysis of product reviews. Directly influences conversion rates for new customers.
Customer Support Metrics Resolution time, satisfaction, ticket volume relative to order volume. Support interactions are critical moments that determine retention.
Time on Site & Pages per Session How long visitors engage with your site and how many pages they view. Indicates content engagement; longer sessions typically correlate with higher conversion.
Bounce Rate Percentage of visitors who leave after viewing only one page. High bounce rates suggest landing page issues, poor targeting, or site performance problems.

 

Thinks 1610

Mint: “Microdramas [are] a new digital storytelling sensation that has spawned a multi-billion dollar industry. These are addictive, professionally-produced, vertically-shot series designed for mobile viewing, with each title comprising of 50-100 episodes of around a minute each—roughly the length of a feature film. The format first emerged in China in 2018 and has grown increasingly popular over the last few years in other global markets. One report by Bloomberg states that China now churns out more than 5,000 microdrama series a year. The rapidly expanding industry earned close to $7 billion last year, surpassing China’s theatrical box office. Individual shows can generate millions of dollars through a mix of advertising and a pay-per-view model on dedicated microdrama apps such as WeTV, MangoTV, Douyin and Youko.”

WSJ: “What if there were a way for the world to break its dependence on China’s rare earths, which the country has repeatedly used as a way to retaliate during trade wars? That’s the promise of new kinds of electric motors—the main place those rare earths are needed—that don’t require them, at all. A new startup called Conifer, which has unveiled a cheap, easily manufactured motor using magnets made from everyday iron is particularly interesting…What they’re doing is risky—the technology they’re playing with hasn’t been used in applications like this before, because historically it’s been hard to manufacture. Iron magnets, meanwhile, haven’t delivered enough power. But if they get the combination of cost and manufacturability right, their motor has the potential to become a go-to low-cost power plant for countless applications.”

Quanta: In 2019, Quanta reported on a then-groundbreaking NLP system called BERT without once using the phrase “large language model.” A mere five and a half years later, LLMs are everywhere, igniting discovery, disruption and debate in whatever scientific community they touch. But the one they touched first — for better, worse and everything in between — was natural language processing. What did that impact feel like to the people experiencing it firsthand? Quanta interviewed 19 current and former NLP researchers to tell that story. From experts to students, tenured academics to startup founders, they describe a series of moments — dawning realizations, elated encounters and at least one “existential crisis” — that changed their world. And ours.”

FT on Disco Corp: “Can you run a company as a perfect free market?…For over a decade, a $20bn manufacturer has been conducting a radical experiment. No one has a boss or takes orders. Their decisions are guided by one thing, an internal currency system called Will…Within this state of perfect freedom, most of their decisions will be guided by Will, as Disco’s internal currency is known. Employees earn Will by doing tasks. They barter and compete at auction with their colleagues for the right to do those tasks. They are fined Will for actions that might cost the company, or compromise their productivity. Their Will balance determines the size of their bonus paid every three months.”

Russ Roberts in conversation with Dwarkesh Patel on past and future of AI. Dwarkesh: “If you look at what changed between primates and humans, it was our ability to–Joseph Hendrick has this wonderful book about this–it wasn’t just intelligence. It wasn’t even mainly intelligence. It was our ability to coordinate with each other, to share knowledge, to accumulate knowledge over generations. Now, when we think about AI, instead of thinking of a single AI, super-intelligent AI, what if we thought about billions of beings who are thinking at superhuman speeds who are able to communicate with each other in a way that a human simply cannot. They can literally share their mind-states with each other. They can distill their insights. They can literally merge; they can copy themselves for arbitrary amounts of time. So if you have a super-skilled worker or who has some tacit knowledge, you can just make arbitrary copies of them for pennies per copy. Fundamentally, you have this much larger population size. And if you believe–a lot of theories of economic growth say that more people means more ideas; and that feeds back on itself.”

NeoN: The Beta Efficiency Engine

Published May 29, 2025

1

The Idea

In the previous essay, I discussed how Progency can be thought of as the Growth Alpha engine. In this series, I will explain how NeoN can be considered as delivering Beta at a lower cost. Both Progency and NeoN are the two primary anchors of NeoMarketing, a new era in marketing which focuses on retention over reacquisition, trust over transactions, and individuals over segments. NeoMarketing helps maximise LTV and minimise CAC to deliver sustainable profitable growth.

As I explained in NeoMarketing: Double the Best, Halve the Waste: “NeoN reimagines advertising through authenticated identity, creating a direct marketplace that connects brands without expensive intermediaries. Unlike cookie-based targeting, NeoN leverages first-party data with deterministic, precise matching to enable brand-to-brand collaboration. Through this approach, one brand’s inactive “Test” customers can be precisely targeted through another brand’s engaged “Best” customer channels. Interactive ActionAds within partner emails enable complete transactions without leaving the inbox, dramatically increasing conversion rates. This creates a powerful dual advantage: publishers “print money” by monetising their engaged audience while advertisers “save money” through dramatically more efficient customer acquisition—cutting costs by 30-50% compared to traditional platforms. As third-party cookies disappear, NeoN’s authenticated approach represents not just another advertising channel but a fundamental reimagining of how brands reach both dormant customers and new prospects.”

NeoN’s revolutionary approach is powered by the PACE framework—a comprehensive system designed to eliminate the inefficiencies of traditional advertising while creating a more effective, ethical ecosystem:

  • PII Matching Engine: Unlike cookie-based targeting that relies on probabilistic matching, NeoN’s core innovation lies in its authenticated identity engine. This precise system enables brands to reach their dormant customers through email channels where those individuals are actively engaged with other brands. This deterministic matching ensures messages reach exactly the right people, eliminating the waste inherent in traditional adtech.
  • ActionAds: Traditional digital advertising suffers from a devastating “click-through penalty” where 80-90% of potential conversions are lost when customers must navigate to external sites. NeoN’s ActionAds, powered by AMP technology, create interactive mini-applications within emails that enable browsing, form completion, and purchasing without leaving the inbox—dramatically improving conversion rates whilst reducing friction.
  • Cooperative Structure: Rather than enriching platform intermediaries, NeoN creates a direct marketplace where brands simultaneously play dual roles—as publishers monetising their engaged audiences and as advertisers efficiently reacquiring their dormant customers. This cooperative approach creates powerful network effects where each participating brand strengthens the overall ecosystem.
  • Ecosystem Services: Beyond core matching capabilities, NeoN develops complementary utilities that expand inventory opportunities while enhancing data capabilities, including inbox intelligence, AI-powered newsletters, an Atomic Rewards system for micro-actions, and sophisticated data management functionality for high-potential audience identification.

NeoN focuses on Test and Next customers. It provides an alternative to current ad networks like Google and Meta, using the authenticated identity (PII) available via martech channels and touchpoints like email, notifications, and search to slash acquisition and reacquisition costs, and therefore AdWaste. By creating a more efficient distribution system for reaching target audiences, NeoN delivers the same customer access as traditional platforms (Beta) but at significantly lower costs—effectively reducing the “Beta tax” brands have been forced to pay through auction-based advertising platforms.

The implications are transformative: brands can maintain or even expand their marketing reach while dramatically reducing dependency on expensive intermediaries. This shift represents not just cost optimisation but a fundamental restructuring of the marketing ecosystem—where brands collaborate directly, customers receive more relevant communications, and the massive inefficiencies of AdWaste are systematically eliminated.

2

History

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

The concept of Beta originates in the world of finance, specifically in the Capital Asset Pricing Model (CAPM) developed in the 1960s by Jack Treynor, William Sharpe, John Lintner, and Jan Mossin. Beta measures an investment’s sensitivity to market movements—essentially, its correlation with the overall market. A Beta of 1 indicates the investment moves in lockstep with the market, greater than 1 means amplified movements, and less than 1 implies dampened volatility.

Beta served as the benchmark return investors could expect simply by taking market risk without any special insights or skill. It represented the “cost of being in the game”—the return earned by passively riding the market’s ups and downs.

In contrast, Alpha emerged as the excess return generated beyond Beta. In essence, Beta represented the tide; Alpha measured the ability to swim faster than the tide. Investors paid managers for Alpha, not Beta—because Beta could be obtained cheaply through index funds.

Beta’s Expansion Beyond Finance

While its roots lie in capital markets, the concept of Beta has spread to other industries over the past few decades:

  • Consulting and Professional Services: Clients expect baseline improvements (“Beta outcomes”) from hiring top consultancies—e.g., a standard uplift in productivity or cost savings that any credible firm can deliver. But consultancies earn their premium fees by generating Alpha outcomes—transformative improvements beyond the baseline.
  • Legal Industry: Big law firms price routine legal work (filings, documentation) at Beta rates. But complex litigation or landmark deals—where the outcome can swing by billions—command Alpha fees tied to exceptional results.
  • Technology and SaaS: Traditional software offers baseline automation (Beta) that every competitor can also deploy. But platforms that unlock proprietary data insights, operational efficiencies, or network effects create Alpha—advantages competitors cannot easily replicate.
  • Marketing (implicitly): For decades, marketing operated in a Beta paradigm—benchmarked to industry-standard metrics like clickthrough rates, conversion rates, or ROAS. Agencies promised “average” returns based on historical norms. But they rarely offered Alpha—measurable outperformance that exceeded market norms or the brand’s historical baseline.

The Beta-Alpha Link: Why Both Matter

Beta is foundational—it provides the cost of participation and a minimum benchmark. Without Beta, there’s no basis for measuring Alpha. But Beta is inherently capped—it tracks systemic market returns or industry norms. Only Alpha creates upside beyond the market or category average.

In finance, this distinction became critical as index funds commoditised Beta, driving investors to seek scarce Alpha elsewhere. The same dynamic is now emerging in industries like marketing: platform-driven advertising delivers Beta returns accessible to every brand, leading to diminishing differentiation and rising costs.

This is why the framing of Progency as the “Growth Alpha Engine” and NeoN as the “Beta Efficiency Engine” is so powerful. NeoN delivers the essential customer acquisition reach every brand needs but with dramatically improved efficiency—ensuring they don’t overpay to reach customers they already know. Meanwhile, Progency generates true outperformance by maximizing customer lifetime value (LTV) through proprietary, AI-powered engagement strategies that exceed baseline expectations.

In other words: NeoN optimises the necessary market participation costs (delivering Beta with superior efficiency), while Progency creates exceptional performance beyond industry norms (delivering Alpha that competitors cannot easily replicate). Together, they form the complete NeoMarketing framework—optimizing both sides of the profit equation.

3

The How

The modern digital advertising ecosystem operates through a series of interconnected auction-based platforms that systematically drive up costs while delivering increasingly diminishing returns. At its core, this system hinges on probabilistic targeting through cookies, device IDs, and algorithmic modelling—all designed to approximate audience characteristics rather than definitively identify individuals.

This inherently flawed approach creates multiple inefficiency points. First, the auction-based pricing mechanisms ensure costs consistently trend upward as brands compete for the same attention. Second, platform intermediaries (Google, Meta, etc.) extract significant revenue—often 30-50% of spending—simply for facilitating access to audiences that brands may already have relationships with. Third, the targeting imprecision means approximately 20-40% of impressions reach incorrect audiences despite sophisticated algorithms, creating unavoidable wastage.

Perhaps most troublingly, this system fundamentally lacks transparency. Brands never know exactly whom their adverts reach, cannot verify audience quality directly, and are beholden to black-box attribution models owned by the very platforms selling the inventory. This creates an impossible situation where those selling the service also control the measurement of its effectiveness.

NeoN’s Beta Efficiency Revolution

NeoN fundamentally transforms this broken model by addressing each inefficiency point directly.

Firstly, NeoN targets the largest source of AdWaste: reacquisition spending. By focusing specifically on dormant “Test” customers (inactive 90+ days), NeoN addresses the approximately 70% of digital marketing budgets currently haemorrhaged to repeatedly reach customers brands already know. This represents the greatest efficiency opportunity in the entire marketing landscape.

Secondly, NeoN replaces probabilistic with deterministic targeting through its PII (Personally Identifiable Information) matching engine. Rather than approximating audience characteristics, NeoN creates precise matches between a brand’s dormant customers and the active audiences of non-competing brands. This eliminates the 20-40% targeting waste inherent in cookie-based approaches, ensuring messages reach exactly the intended recipients. If traditional ad platforms are like blindfolded archers shooting arrows and hoping they hit the target, NeoN hands marketers a laser pointer—directing their message precisely to known customers already waiting to hear from them.

Thirdly, just as Singular AI Twins enable true 1:1 personalisation for retention and growth, NeoN’s authenticated identity approach enables perfect precision in reacquisition. When an inactive “Test” customer of Brand A is simultaneously an engaged “Best” customer of Brand B, NeoN creates the ideal targeting environment—reaching people through channels where they’re actively engaged, with permissioned identity ensuring accuracy.

Furthermore, NeoN applies this same authenticated approach to new acquisition through its sophisticated DMP (Data Management Platform). By leveraging cross-brand intelligence and zero-party data, NeoN identifies genuinely high-potential prospects with unprecedented precision, reducing the wastage endemic to traditional lookalike modelling.

Finally, NeoN eliminates the platform intermediary “tax” by creating direct brand-to-brand collaboration. Rather than paying Google or Meta premiums through competitive auctions, brands establish mutually beneficial partnerships—monetising their engaged audiences while efficiently reacquiring their dormant customers at substantially lower costs.

Here is a table summarising NeoN’s fixes versus traditional platforms.

Problem (AdTech) Solution (NeoN)
Auction-based inflation Direct brand-to-brand collaboration
Probabilistic targeting (cookies) Deterministic PII matching
Black-box attribution Transparent identity-based reach
Click-through penalty In-email ActionAds conversion

The result is a comprehensive Beta Efficiency Engine that delivers the same essential customer reach as traditional platforms but with dramatically improved efficiency—cutting reacquisition costs by 30-50% and simultaneously improving targeting precision and conversion rates through interactive ActionAds embedded directly within partner emails. NeoN doesn’t merely patch the inefficiencies of today’s ad ecosystem—it rewires the economics of attention for a world where identity, trust, and efficiency form the new competitive advantage.

4

Learnings from Finance

I asked Claude and ChatGPT for lessons which NeoN can learn from finance.

In the world of finance, few innovations have been as transformative as the index fund revolution pioneered by Vanguard’s John Bogle. By offering market exposure (Beta) at dramatically lower costs than actively managed funds, index funds fundamentally restructured investment economics. NeoN brings this same revolutionary approach to marketing—applying key financial principles to create unprecedented efficiency in customer acquisition.

The Index Fund Parallel: Just as index funds strip away unnecessary intermediary fees to deliver market Beta at minimal cost, NeoN eliminates the platform “tax” that currently inflates reacquisition spending. Traditional adtech platforms operate like high-fee active managers, charging premium rates for access to audiences that brands may already have relationships with. NeoN, by contrast, functions as the marketing equivalent of a low-cost index fund—delivering the same essential customer reach without the exorbitant platform premiums. In other words, NeoN is to customer reacquisition what index funds were to investing: stripping out hidden fees, intermediaries, and inefficiencies—delivering the same reach at a fraction of the cost.

Attention Arbitrage: Financial markets thrive on arbitrage—the practice of exploiting price discrepancies across different markets. NeoN creates a similar “attention arbitrage” by identifying a fundamental pricing inefficiency: the same customer can be reached as an engaged email recipient far more cheaply than through competitive ad auctions. By tapping into partner brands’ active email audiences, NeoN allows brands to reacquire their dormant customers at 30-50% lower costs—a classic arbitrage opportunity that creates immediate value.

Factor-Based Targeting: Modern finance has evolved beyond simple indexing to “Smart Beta” strategies that target specific factors (value, quality, momentum) driving returns. Similarly, NeoN replaces crude probabilistic targeting with deterministic identity factors. Where traditional platforms rely on behavioural proxies and lookalike models, NeoN uses authenticated PII as a proprietary targeting factor—turning known identity into a precision advantage that auction-based platforms cannot replicate.

Optimising the Cost Structure: The investment world clearly distinguishes between low-cost Beta (0.1-0.3% fees for index funds) and premium-priced Alpha strategies (1-2%+ for active management). NeoN brings this same cost discipline to marketing, allowing brands to secure their necessary customer reacquisition at “index fund rates.” This fundamentally transforms budget allocation, freeing resources previously wasted on platform premiums to be redeployed toward true Growth Alpha initiatives.

Disintermediation and Direct Access: Financial markets have steadily moved toward disintermediation—enabling investors to access markets directly without unnecessary middlemen. NeoN creates this same direct access in marketing, connecting brands to customers through authenticated channels rather than platform intermediaries. This isn’t merely cost-cutting—it’s restructuring the entire value chain to eliminate entities that extract disproportionate value without adding commensurate benefits.

Just as Vanguard’s index revolution permanently altered the investment landscape by demonstrating that Beta exposure needn’t come at premium prices, NeoN transforms marketing by proving that customer acquisition doesn’t require platform dependency. By unlocking attention arbitrage, eliminating hidden fees, and replacing probabilistic guessing with deterministic precision, NeoN delivers the marketing equivalent of Bogle’s greatest insight: the most reliable path to superior performance often begins with minimising unnecessary costs.

5

Objection Handling – 1

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

  1. Data Privacy & Consent Concerns

Objection: “NeoN’s PII-based matching seems concerning from a privacy perspective. Are we compliant with GDPR, CCPA, and other regulations? Will customers perceive this approach as invasive or creepy?”

Response: NeoN operates on a privacy-first foundation that exceeds industry standards:

  • Only uses permissioned, first-party data from existing brand relationships—never buying or selling data
  • Functions as a closed-loop matching system without exposing PII across brands
  • Operates like a secure “clean room” where only anonymized identifiers are compared
  • Provides clear opt-out mechanisms for users across all participating brands
  • Represents a privacy-forward alternative to cookie-based surveillance tracking

“NeoN uses the data customers have already shared with you—ethically, transparently, and with their permission. We don’t follow customers around the web; we reconnect you in channels they already trust.”

  1. “Creepiness” or Perception of Cross-Brand Targeting

Objection: “Won’t customers find it invasive if Brand A seems to know what Brand B knows about them? Is this secretly a data-sharing arrangement consumers didn’t agree to?”

Response:

  • NeoN doesn’t share raw customer data between brands—it acts as a secure matching and delivery system
  • Recipients only see ads from brands within channels they’ve explicitly opted into
  • The approach is less intrusive than traditional web display ads or third-party cookie retargeting
  • All content is contextually relevant, privacy-safe, and easily opt-out-able
  • Research shows consumers prefer relevant offers in trusted channels over random targeting across the web

“NeoN never transfers your customer data. It operates like a postal sorting service: matching addresses behind the scenes, delivering relevant offers inside permissioned inboxes without exposing identities between brands.”

  1. Email Attention Overload / Customer Fatigue

Objection: “Will embedding ActionAds in emails dilute attention or annoy subscribers? Won’t this reduce engagement with our own brand’s content?”

Response:

  • NeoN uses non-intrusive placements (email footers, designated sections) that complement, not compete with, primary brand content
  • Each ActionAd is contextually matched to recipient behaviour—creating relevance, not interruption
  • Brands maintain control over placement density and frequency (e.g., limit to one ActionAd per email)
  • Early implementations show well-designed ActionAds actually increase overall email engagement by providing additional value
  • The approach transforms passive email space into an active revenue stream without overwhelming the audience

“NeoN turns under-utilised email real estate into a profit generator—without distracting from your message or overwhelming your audience. It’s like adding a trusted, relevant partner message that enhances rather than detracts from the recipient experience.”

  1. Deliverability & Spam Risks

Objection: “Will adding third-party ActionAds trigger spam filters or harm our email deliverability score? Will inbox providers penalise us for advertising other brands?”

Response:

  • NeoN ads are native to the email format (AMP/CSS), avoiding external links or attachments that trigger spam rules
  • The system works within inbox platform guidelines (Gmail AMP approval, etc.)
  • Comprehensive A/B testing and deliverability monitoring ensures placements don’t impact sender reputation
  • Brands can limit NeoN ads to specific audience segments or email types (promotions, newsletters)
  • The system follows best practices that maintain deliverability while opening new monetisation streams
  1. Brand Safety & Control Concerns

Objection: “What if my brand’s email ends up showing a competitor’s ad? How do we ensure we don’t promote brands that conflict with our values?”

Response:

  • NeoN includes comprehensive brand-level whitelist/blacklist controls
  • Each brand approves exactly who can advertise in their inventory
  • No direct competitors will be shown without explicit permission
  • Vertical segmentation ensures ads only appear from non-competing categories
  • Complete transparency into which brands will appear in your communications
  • Regular reporting shows exactly which brands are appearing and their performance

“With NeoN, you’re in complete control: you choose exactly who can advertise in your customer communications. No unwanted competitors, no mismatched messages, no surprises.”

6

Objection Handling – 2

  1. ROI Skepticism & Adoption Risk

Objection: “Sounds promising, but will this really save 30-50% on acquisition costs compared to traditional platforms? What’s the evidence this works better than Google/Meta?”

Response:

  • Comprehensive case studies and benchmark data demonstrating improved CAC versus traditional ad platforms
  • Risk-free pilot campaigns or guaranteed performance benchmarks to encourage adoption
  • NeoN positions as an incremental, not replacement solution—doesn’t require dismantling existing ad spend initially
  • Transparent measurement allows direct comparison with other channels
  • Unlike black-box attribution models, NeoN provides clear visibility into exactly which dormant customers were reached and their actions

“NeoN lets you test without risk—plug it into a subset of your dormant customers and compare outcomes side by side with existing channels. The data will speak for itself.”

  1. Implementation Complexity

Objection: “This sounds promising but complex to implement. Will this require retraining my team or overcomplicating workflows? Do we need to overhaul our tech stack?”

Response:

  • NeoN operates as a fully managed service or via existing ESP integrations—no new dashboard overload
  • Connects to existing email service providers and CRM platforms through standard APIs
  • Most brands can be onboarded within 2-4 weeks with minimal technical overhead
  • Dedicated implementation specialists guide the process from start to finish
  • For brands concerned about complexity, managed service options handle all technical details
  • Progressive adoption model allows starting with a small segment before scaling based on proven results

“NeoN doesn’t ask your team to learn new systems. We slot seamlessly into your existing email templates, automations, and processes—like adding a high-performing co-pilot that enhances your current capabilities.”

  1. Reciprocity Requirements

Objection: “For this to work, we’d need to place other brands’ ads in our emails, which we’re hesitant to do.”

Response:

  • NeoN’s cooperative structure offers flexible participation models
  • Brands can start as advertisers only before gradually incorporating publisher capabilities
  • The system is designed to provide value at each stage of adoption
  • Additional revenue stream—often generating 30-40% more than the cost of email sending—creates a compelling business case
  • Complete control over which partner brands can appear in your communications
  1. Measurement and Attribution Skepticism

Objection: “How can we trust NeoN’s measurement when we’ve been burned by opaque attribution models before?”

Response:

  • Unlike black-box adtech platforms, NeoN’s measurement is built on deterministic identity, not probabilistic models
  • All interactions occur within authenticated environments, making attribution direct and transparent
  • No more questionable “view-through” claims or conversion credits
  • Comprehensive reporting shows exactly which dormant customers were reached and which actions they took
  • Creates unprecedented accountability compared to traditional platforms where those selling inventory also control measurement
  1. Consumer Opt-Out or Brand Loyalty Dilution

Objection (from end users): “Why is Brand A showing me ads for Brand B? Is this a sellout? Will I opt out?”

Response:

  • NeoN targets opt-in communications and prioritises non-intrusive, contextually relevant offers
  • Customers see fewer, more relevant ads in channels they already trust—no tracking across web/apps
  • The approach aligns with evolving consumer expectations around personalisation
  • Research shows 71% of consumers expect personalised interactions and 76% get frustrated when this doesn’t happen
  • The transparency and value exchange create clarity rather than confusion
  • In-email ActionAds deliver genuine convenience—allowing customers to take action without leaving their inbox

“We don’t follow you across the internet. We only show you exclusive offers from partners we trust, inside the emails you already opted into.”

The key pattern across all objections is control, transparency, relevance, and trust. NeoN represents “a more transparent, brand-controlled, privacy-safe alternative to third-party cookie retargeting and auction-based platforms—delivering Beta at a fraction of the cost, without sacrificing customer trust or experience.”

7

The 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 with $200M annual revenue. She’s reviewing her digital marketing dashboard, frowning at the steadily rising acquisition costs. John, representing NeoN, enters for their scheduled meeting.

Maya: (looking up) Thanks for making the time, John. I’ve got about 30 minutes before my next meeting. Your email mentioned something about ‘Beta Efficiency’ and reducing customer acquisition costs, which honestly couldn’t come at a better time. Our CAC has increased 22% year-over-year.

John: (taking a seat) I appreciate the time, Maya. Before I dive into NeoN, might I ask what percentage of your digital marketing budget goes toward reacquiring customers you already know? People who’ve purchased before but have gone dormant?

Maya: (sighs) We’ve actually been trying to figure that out. Our best estimate is around 65-70%. I was shocked when our analytics team showed me the numbers. We’re essentially paying Google and Meta to reach people already in our database. It feels… inefficient.

John: You’re not alone. We call it the $500 billion AdWaste crisis – the industry average is indeed about 70%. The question is: why are brands forced to pay these platforms premium prices to reconnect with customers they already have relationships with?

Maya: (nodding) It’s a necessary evil, isn’t it? Once customers stop engaging with our emails or app, we have no choice but to find them elsewhere.

John: That’s exactly the problem we’ve solved with NeoN. Think of it this way: in finance, there’s a distinction between Alpha – outperformance through skill – and Beta, which is the baseline market exposure every investor needs. The issue is that brands are paying astronomical prices for what should be basic Beta – simply reaching their own customers.

Maya: (interested) I’m listening.

John: NeoN is what we call a Beta Efficiency Engine. It delivers the same customer reach you need but at 30-50% lower cost. We do this through authenticated identity targeting instead of cookie-based auctions.

Maya: (skeptical) How exactly does that work?

John: Remember when you mentioned customers who’ve gone dormant? We call them “Test” customers – inactive for 90+ days. Here’s the key insight: while they might be dormant for your brand, many of these same individuals are actively engaged with other non-competing brands.

NeoN creates a direct marketplace where brands can reach their dormant customers through the active email channels of these other brands. The magic is in our PII matching engine that makes precise connections between your inactive customers and where they’re actually paying attention.

Maya: So if I understand correctly, you’re saying my dormant customers might be opening emails from Brand X, and I could reach them there instead of through Google or Meta?

John: Exactly. And not just reach them – engage them. Traditional retargeting has another massive flaw: the “click-through penalty.” When customers click an ad, they’re taken to a landing page where 80-90% drop off before converting.

Maya: (nodding vigorously) Tell me about it. Our funnel analytics show exactly that pattern.

John: NeoN solves this with ActionAds – interactive AMP-powered units embedded directly in partner emails. Your customers can browse products, fill forms, even complete purchases without ever leaving their inbox. It’s like eliminating all the friction from the traditional customer journey.

Maya: This sounds promising, but I have concerns. First, what about privacy? Using PII to track customers across brands sounds like it could raise red flags.

John: That’s a great question. NeoN is actually more privacy-forward than traditional adtech. We never share raw customer data between brands. Think of us like a secure postal service – we match addresses behind the scenes but never expose identities. Everything happens with explicit consent, within channels customers have already opted into.

Maya: What about brand safety? I’d be worried about which other brands might appear in our emails, or where our ads might show up.

John: You maintain complete control. Our platform includes comprehensive whitelists and blacklists, ensuring no competitors or misaligned brands ever appear in your communications. You approve exactly who can advertise to your audience.

Maya: (thinking) And implementation? My team is already stretched thin.

John: NeoN slots seamlessly into your existing email service provider through standard APIs. Most brands are onboarded within 2-4 weeks with minimal technical overhead. We offer fully managed services if you prefer a hands-off approach. It’s designed to enhance your current capabilities, not create new workflows.

Maya: (nodding slowly) The cost savings sound impressive, but I’d need to see proof that this actually works better than our current channels.

John: Absolutely. We typically start with a risk-free pilot – you select a subset of your dormant customers, and we target them through NeoN while you continue reaching a control group through traditional platforms. The side-by-side comparison speaks for itself.

Maya: (intrigued) And this is where the “Beta Efficiency” concept comes in?

John: Precisely. Just as index funds revolutionized investing by providing market exposure without the hefty fees of active managers, NeoN delivers customer acquisition Beta without the platform tax. This means you can redirect those savings toward initiatives that actually generate Alpha – like using Progency to maximize customer lifetime value.

Maya: (checking her watch) This is genuinely intriguing, John. Could you send me a proposal for a pilot program? I’d want to test this with maybe 100,000 of our dormant customers – enough for a statistically significant test.

John: Absolutely. I’ll have that to you by tomorrow. One final thought – every dollar you save on reducing AdWaste is essentially pure profit. If NeoN can cut your reacquisition costs by even 30%, what would that mean for your bottom line?

Maya: (calculating mentally) Given our current spending… that would be substantial. Let’s explore this further. Send me that proposal and some case studies if you have them.

John: (standing to leave) Will do. Thank you for your time, Maya. I believe this could be transformative for your marketing economics.

Maya: We’ll see. The data will need to speak for itself. But if you can deliver what you’re promising – the same reach at half the cost – that’s certainly worth exploring.

8

The Pitch – 2

ChatGPT

Scene: Video call between Maya, CMO of a mid-sized D2C beauty brand, and John, NeoN sales executive.

John:
Maya, thanks for taking the time. I know you’re busy, so let me get straight to it. You’ve probably felt it: your acquisition costs rising every year, Google and Meta squeezing more from your budget—and yet, the returns aren’t keeping pace. Does that resonate?

Maya:
Unfortunately, yes. CAC is up 40% in the past two years, and reacquiring churned customers is burning a hole in our budget. But what’s the alternative? We need to reach them somewhere.

John:
Exactly. That’s where NeoN comes in. Think of us as the “Beta Efficiency Engine” for your marketing. Just like an index fund gives investors market exposure at the lowest possible cost, NeoN delivers the same audience reach—your dormant customers, your next customers—but at 30–50% lower acquisition costs than you’re paying through auctions.

Maya (curious):
Wait, how? If Google and Meta own the ad space, how do you get me cheaper reach?

John:
Great question. Right now, you’re paying those platforms to reacquire customers you already know. Customers who’ve bought from you before. You’re paying a toll to rent their attention again.

NeoN cuts out the middlemen. We connect you directly to other brands’ active audiences—using privacy-safe, authenticated identity matching. If one of your dormant customers is actively engaging with Brand B’s emails, NeoN lets you reach them inside those emails, where they’re already paying attention.

Maya (raising an eyebrow):
Hold on—so you’re showing my ads inside another brand’s emails? Won’t that annoy customers—or dilute their attention?

John:
Fair concern. Actually, we place ActionAds in designated, non-intrusive slots—like the email footer or a sponsored section. Research shows they don’t harm engagement. And they’re fully interactive: your customers can browse, even complete purchases, without leaving the inbox.

And here’s the key: it’s permission-based. They’ve opted into Brand B’s emails. We’re just helping you reconnect with them where they’re already present and engaged. Much less intrusive than following them around the web with cookies.

Maya (thinking):
Interesting. But… my team’s already overloaded managing campaigns. Is this going to add more complexity?

John (smiling):
Not at all. NeoN integrates directly into your existing ESP or CRM. We generate the ActionAds, we handle the matching, the reporting. For your team, it’s as simple as pasting a block into your email template—or letting us do it as a managed service.

Maya (cautiously):
I still worry about privacy. Customers are already skittish about data sharing.

John:
That’s why we’re different. NeoN never shares raw data between brands. It’s like a secure postal sorting room: addresses get matched, but no one sees each other’s lists. GDPR, CCPA compliant, opt-out mechanisms built in. And because it’s deterministic identity—not probabilistic cookie tracking—it’s actually more transparent and privacy-safe than traditional retargeting.

Maya:
What about competitive conflicts? I don’t want a rival’s ad in my emails.

John:
You won’t. NeoN gives you full whitelist/blacklist control. You decide exactly which brands can advertise in your inventory, and whose inventory you’re allowed into. No competitors slip through.

Maya (leaning forward):
Okay… let’s talk numbers. You’re claiming 30–50% lower costs. Have you proven this?

John:
We’ve piloted NeoN with brands in fashion, beauty, and health. On average, they’ve cut reacquisition CAC by 35% compared to Meta and Google. I’d be happy to set up a risk-free pilot—let’s test it on a segment of your dormant customers, side by side with your current channels, and let the data speak for itself.

Maya (smiling):
Alright, John. You’ve piqued my interest. Send me the pilot proposal. If you can back up those claims, I’ll gladly divert some budget.

John (grinning):
Maya, I look forward to helping you stop overpaying for what you already own. I’ll send over the next steps today.

As John ends the call, Maya reflects: what if customer reacquisition didn’t have to mean paying platform tolls every time? NeoN offered not just a cheaper route—but a smarter one. Maybe, just maybe, this was the index fund moment her marketing team had been waiting for.