ZeroBase: A New Business Model for the Agentic Marketing Era (Part 7)

Perfect Timing

ZeroBase isn’t merely a pricing innovation—it’s the economic foundation that makes Agentic Marketing possible. As we transition from human-driven campaigns to AI-native marketing systems, ZeroBase provides the perfect alignment of incentives, measurement capabilities, and scalability requirements that this technological revolution demands.

Autonomous Attribution at Scale

The greatest challenge in traditional outcome-based pricing has always been attribution complexity. Human-driven marketing campaigns involve countless variables, making it nearly impossible to isolate the impact of specific interventions. Agentic Marketing fundamentally solves this problem through autonomous AI systems that track, measure, and optimise every customer interaction in real-time.

AI agents operate with perfect memory and continuous measurement capabilities that humans simply cannot match. Every touchpoint, every micro-moment of engagement, and every conversion event feeds into sophisticated attribution models that can accurately isolate the impact of specific interventions. This granular measurement makes ZeroBase’s performance-based compensation not just viable, but inevitable.

Where traditional marketing required debates about causation versus correlation, AI agents create deterministic measurement frameworks that eliminate attribution disputes. The technology finally matches the economic model’s requirements.

Self-Optimising Revenue Generation

ZeroBase’s true power emerges when combined with AI agents’ capacity for continuous improvement. Traditional marketing campaigns require human intervention to optimise performance, creating inherent limitations on scaling and improvement velocity. AI agents operate 24/7, testing thousands of micro-variations and optimising for specific revenue outcomes without human oversight.

This creates a compounding effect where ZeroBase compensation improves over time as AI agents become more effective. Unlike traditional consulting models where expertise remains static, or SaaS platforms where features develop slowly, AI agents’ performance curves accelerate exponentially. ZeroBase providers can confidently offer performance guarantees because their AI systems continuously improve their ability to deliver results.

The economic beauty lies in perfect alignment: as AI agents become more sophisticated at generating incremental revenue, both provider and client benefit proportionally. Success literally funds further innovation and optimization.

Infinite Scalability Without Linear Costs

Traditional marketing services face a fundamental scalability problem: adding more clients typically requires proportional increases in human resources. ZeroBase combined with Agentic Marketing breaks this constraint entirely. AI agents can serve thousands of customer segments simultaneously, personalising experiences at individual levels without linear cost increases.

This scalability transformation makes ZeroBase economically superior to traditional models. A single AI Agents Collective can manage millions of customer interactions across multiple clients, continuously optimising for the specific revenue outcomes that trigger ZeroBase compensation. The marginal cost of serving additional customers approaches zero whilst the marginal revenue from improved performance continues growing.

For the first time in marketing history, providers can offer genuinely unlimited upside potential to clients whilst maintaining profitable unit economics at scale.

Measurement Precision Meets Economic Alignment

Agentic Marketing’s measurement capabilities create unprecedented precision in isolating the impact of specific interventions. AI agents don’t just track broad campaign performance—they monitor individual customer journeys, micro-segment behaviours, and granular conversion attribution. This precision makes ZeroBase’s performance-based compensation scientifically defensible rather than directionally approximate.

Customer twins—individual AI representations that predict next best actions—enable measurement at the individual level. Rather than debating whether a campaign drove overall lift, AI agents can demonstrate exactly which customers were influenced, when, and by what interventions. This granular attribution makes ZeroBase calculations as precise as financial market returns.

The combination eliminates the measurement ambiguity that has historically plagued performance-based marketing models. Both parties can trust the data because AI systems provide transparency and precision that human-driven campaigns never could.

Network Effects and Compound Advantages

ZeroBase’s alignment with Agentic Marketing creates powerful network effects that strengthen over time. As AI agents process more customer data across ZeroBase engagements, their predictive capabilities improve exponentially. Each successful engagement teaches the system patterns that benefit all subsequent clients.

This creates a compound advantage cycle: better AI performance leads to stronger ZeroBase results, which attracts more clients, which generates more data, which improves AI capabilities further. The economic model incentivises continuous innovation whilst the technology platform enables unlimited scaling.

Unlike traditional agencies that compete for finite human talent, or SaaS platforms that develop features slowly, ZeroBase providers with Agentic Marketing capabilities can simultaneously serve unlimited clients whilst continuously improving performance for all.

**

The Inevitable Convergence

ZeroBase represents the natural economic evolution for marketing in an AI-native world. As AI agents become capable of autonomous operation, guaranteed outcome delivery, and continuous optimisation, traditional input-based pricing becomes obsolete. Why pay for hours, messages, or features when you can pay for guaranteed revenue growth?

The convergence of ZeroBase pricing with Agentic Marketing capabilities creates the foundation for Profipoly—sustainable competitive advantages through superior customer relationship technology. Companies adopting this combination don’t just outperform competitors; they operate in entirely different economic categories where marketing transforms from cost centre to measurable profit engine.

The impossible has become inevitable. ZeroBase provides the economic framework, Agentic Marketing provides the technological capability, and together they make outcome-based marketing finally achievable at scale.

Thinks 1711

WSJ: “There’s long been an unwritten covenant between companies and new graduates: Entry-level employees, young and hungry, are willing to work hard for lower pay. Employers, in turn, provide training and experience to give young professionals a foothold in the job market, seeding the workforce of tomorrow.  A yearslong white-collar hiring slump and recession worries have weakened that contract. Artificial intelligence now threatens to break it completely.  That is ominous for college graduates looking for starter jobs, but also potentially a fundamental realignment in how the workforce is structured. As companies hire and train fewer young people, they may also be shrinking the pool of workers that will be ready to take on more responsibility in five or 10 years. Companies say they are already rethinking how to develop the next generation of talent. AI is accelerating trends that were already under way.”

James Marriott: “Reading obviously should be pleasure, but I find it more pleasurable to give myself a little bit of something to aim for, even if it’s just 20 pages, and to just really say, I’ll do 20 pages without looking at my phone.,,that if you really want to get your head around a subject, read two books on it. And it’s amazing how much goes in from reading two books.”

NYTimes: “In Austin, Texas, where the titans of technology have moved their companies and built mansions, some of their children are also subjects of a new innovation: schooling through artificial intelligence. And with ambitious expansion plans in the works, a pricey private A.I. school in Austin, called Alpha School, will be replicating itself across the country this fall. Supporters of Alpha School believe an A.I.-forward approach helps tailor an education to a student’s skills and interests. MacKenzie Price, a podcaster and influencer who co-founded Alpha, has called classrooms “the next global battlefield.””

SaaStr: “A Well Trained AI SDR Simply Beats an “Entry Level” Human SDRs.  And 95%+ of SDRs Are Entry Level Roles…The problem is that being a great SDR requires a combination of skills that’s extremely rare in humans but trivial for AI.”

ZeroBase: A New Business Model for the Agentic Marketing Era (Part 6)

Risks and Mitigation

ZeroBase represents a fundamental departure from traditional pricing models, and like any transformative approach, it carries inherent risks that must be acknowledged and systematically addressed. Understanding these challenges—and the strategies to mitigate them—is essential for successful implementation.

The Attribution Challenge

The greatest risk in ZeroBase lies in measuring Marketing Alpha above baseline performance. Unlike financial markets where alpha calculations are straightforward, marketing attribution involves multiple variables: seasonal effects, external market conditions, competitive actions, and the natural evolution of customer behaviour. When results fall short of expectations, disputes inevitably arise about what constitutes legitimate uplift versus statistical noise.

Mitigation Strategy: Establish rigorous measurement frameworks before engagement begins. This includes creating control groups, implementing clean A/B testing methodologies, and using conservative baseline calculations that account for seasonal variations and market trends. Starting with customer segments that have near-zero baseline performance—like dormant customers—provides the cleanest attribution story and builds trust for later expansion.

Cash Flow Timing Mismatch

ZeroBase creates an immediate financial challenge: providers must invest upfront in technology, talent, and implementation whilst payment occurs only after results materialise. This timing mismatch can strain cash flow, particularly for growing companies or when scaling across multiple clients simultaneously. The risk compounds when dealing with longer sales cycles or seasonal businesses where results may take months to materialise.

Mitigation Strategy: Implement hybrid models during transition periods. Fixed baseline fees can cover infrastructure costs whilst performance bonuses reward results. Additionally, negotiating milestone payments based on leading indicators—like engagement improvements or reactivation rates—can provide interim cash flow whilst maintaining outcome alignment.

Scope Creep and Expectation Management

Outcome-based pricing can inadvertently create unrealistic client expectations. When traditional vendors fail to deliver, clients may expect ZeroBase providers to solve every marketing challenge within the same economic framework. This scope creep can quickly erode profitability and create unsustainable service delivery burdens.

Mitigation Strategy: Define clear success metrics and scope boundaries upfront. ZeroBase works best when focused on specific, measurable outcomes like dormant customer reactivation or retention improvements rather than broad increase all revenue mandates. Establishing phase-based engagements allows for controlled expansion based on proven results.

The Execution Complexity Risk

Performance-based models often fail not due to economic issues but because defining success becomes contentious when results don’t meet expectations. Marketing environments are inherently complex, and external factors—economic downturns, competitive pressures, supply chain disruptions—can impact results regardless of provider performance.

Mitigation Strategy: Build contractual provisions for external factor adjustments and establish clear escalation procedures for resolving disputes. Regular client communication and transparent reporting help maintain trust during challenging periods. Most importantly, start with smaller, lower-risk engagements that demonstrate competence before tackling larger, more complex challenges.

Scale Economics Questions

ZeroBase’s viability depends on achieving efficient scale across client portfolios. High-touch service delivery may limit profitability, particularly in mid-market segments that most need this model but have smaller revenue opportunities. If each engagement requires extensive customisation, the model becomes unsustainable.

Mitigation Strategy: Invest heavily in productising solutions and building reusable frameworks. AI agent orchestration should reduce manual intervention over time, whilst cross-client pattern recognition creates intellectual property that accelerates new implementations. The goal is transitioning from high-touch service to scalable technology delivery.

Client Dependency and Revenue Concentration

Success with ZeroBase can create dangerous revenue concentration where a few large clients represent the majority of income. If these relationships sour or market conditions change, the business becomes extremely vulnerable. Traditional SaaS models provide more predictable revenue streams that buffer against client loss.

Mitigation Strategy: Diversify the client portfolio actively and avoid over-dependence on any single relationship. Build strong intellectual property and methodologies that can transfer across clients. Consider offering traditional pricing options alongside ZeroBase to provide revenue stability during growth phases.

**

The Trust Building Imperative

Ultimately, ZeroBase success depends on maintaining client trust through transparent measurement, conservative promises, and consistent delivery. The model’s power lies in its alignment of incentives, but this only works when both parties believe in the measurement systems and attribution methodologies being used.

Building this trust requires patience, starting with lower-risk engagements, delivering consistent results, and maintaining open communication about both successes and challenges. ZeroBase isn’t just a pricing model—it’s a partnership philosophy that demands the highest standards of execution and measurement.

Thinks 1710

Arnold Kling: “I would bet on the companies that: (a) have moats now; and (b) employ a large percentage of workers that can be replaced by AI. The interesting game will be between those companies and the AI model firms. The latter will be trying to create lock-in, and the former will be trying to avoid lock-in. For the models, I expect the competition to shift from being “best model on the benchmarks” to being the “best model for ___.” If you’re a law firm, and there is a model that stands out for handling low-level and mid-level legal work, then that means a lot more to you than how the model does in solving advanced math problems.”

FT: “The US has quietly become one of the world’s most shock‑resistant economies. That resilience not only supports equities, it also lets investors lock in once‑in‑a‑generation income on high‑grade bonds that serve as a ballast alongside riskier positions. Services now generate 81 per cent of US GDP and 69 per cent of consumer spending, up from 38 per cent in the 1940s. Real services consumption has contracted only twice year on year — in 2009 and 2020; the higher the services share, the smoother the cycle. Digitised supply chains and flexible labour markets blunt default risk and reduce earnings shocks.”

WSJ reviews “The Wealth Ladder”: “Those on the first rung have almost no wealth; the threshold is less than $10,000. Each subsequent rung represents an upper threshold of wealth 10 times as large as the previous level. Mr. Maggiulli’s scale is designed to express the declining utility of money as it accumulates. He also denotes each rung by the “freedom” such wealth generates. Level 2, $10,000 to $100,000, offers “Grocery freedom” because “you can buy what you want at the grocery store without worrying about your finances.” Level 3, $100,000 to $1 million, provides “Restaurant freedom” because you can order what you want when you dine out. The fourth rung, $1 million to $10 million, means you can travel wherever you want; the fifth, $10 million to $100 million, means you can afford the home of your dreams. Mr. Maggiulli’s sixth and highest level, anything above $100 million, gives you the ability “to have a profound impact on the lives of others” through business and philanthropy.”

Rama Bijapurkar: “A fragmented, fatigued population can’t fuel economic growth the old way…Perhaps we should change our conceptual frame of a single middle class to a two tiered one – an economic development-driving “genuine” middle class that has the attributes discussed earlier; and a consuming capable class with purchasing power at the moment. The linkage between the growth of the middle class and the contribution to powering economic and social development will be different for different groups, as will their spending and saving choices, and the policy levers and electoral value propositions too. Perhaps this is what Gokarn meant when he said to ensure the virtuous circle, we need to recognise and respond to the changing nature of the class itself.”

ZeroBase: A New Business Model for the Agentic Marketing Era (Part 5)

A New Word

As I have been explaining Progency and its outcome-based pricing to potential customers, I realised I needed a single, simple, and memorable word to describe it. My choice: ZeroBase.

The Power of One Word

ZeroBase instantly communicates the fundamental shift from traditional UpFront pricing models to true outcome-based partnerships. Where conventional martech forces customers to pay first and hope for results later, ZeroBase flips this equation entirely: you start at zero cost and only pay from the measurable growth we create together.

The beauty of ZeroBase lies in its immediate clarity. When a prospect asks What’s your pricing? the answer becomes refreshingly simple: ZeroBase—you start at zero and only pay from the growth we generate. No complex pricing tiers, no confusing usage calculations, no upfront risk. Just pure alignment between vendor success and customer success.

Beyond Risk Mitigation

ZeroBase represents more than just risk-free pricing—it’s a confidence statement. By offering ZeroBase terms, we’re literally putting our money where our mouth is. We’re so certain of our ability to drive measurable revenue uplift that we’ll work without payment until we prove our value. This positioning transforms us from cost centre vendors to genuine growth partners.

The model works across our entire technology stack. For email marketing, instead of charging per message sent regardless of outcomes, we can offer ZeroCPM with revenue sharing from conversions. For customer engagement platforms, rather than monthly active user fees, we  base compensation on revenue uplift from previously dormant customer segments. For product discovery, instead of API call charges, we earn from measurable conversion improvements.

The Mathematics of ZeroBase

The revenue sources fall into three clear categories: direct revenue sharing from sales we facilitate, performance uplift percentages from growth above agreed baselines, and cost savings shares from operational improvements like reduced churn or support costs. The strongest opportunities emerge from dormant customer reactivation—where attribution is cleanest—and combined plays leveraging our integrated technology stack.

Consider a typical implementation: a brand’s 40% Rest customer segment generating minimal revenue. Under ZeroBase, we reactivate these dormant relationships through AI-powered personalisation and earn 15-20% of the incremental revenue above their previous near-zero baseline. The brand gains substantial new revenue at zero upfront cost; we earn compensation directly tied to proven results.

The Category-Defining Moment

ZeroBase positions us ahead of a major industry transformation. Whilst traditional martech remains trapped in volume-based models—charging for emails sent, users contacted, or features accessed—ZeroBase aligns with the broader shift towards service-as-software and outcome-based business models.

The timing couldn’t be better. CFOs increasingly scrutinise marketing ROI, demanding proof that technology investments generate measurable returns rather than just activity metrics. ZeroBase directly addresses this pressure by making every payment self-justifying—if you’re paying us, it’s because we’ve already proven our value through your P&L statement.

From Vendor to Partner

ZeroBase fundamentally changes the conversation. Instead of selling software capabilities or professional services hours, we’re offering guaranteed business outcomes. Instead of asking customers to trust our promises, we’re demonstrating our confidence through our willingness to work without payment until results materialise. Instead of treating martech as a cost centre, ZeroBase transforms it into a profit centre where growth funds our fees. We win when our customers win.

This isn’t merely pricing innovation—it’s the foundation for an entirely new category of marketing partnership that finally eliminates the waste, misalignment, and uncertainty that has plagued the industry for decades.

Thinks 1709

Ruchir Sharma: “The bigger mystery is why the stagflationary impact of tariffs has yet to materialise in the aggregate data. Is the US really enjoying a free lunch, taking in $300bn a year in tariff revenues with none of the expected heartburn? By some estimates, foreign exporters are indeed absorbing 20 per cent of the costs — a much larger share than they did in response to tariffs in Trump’s first term. The remaining 80 per cent, however, is still getting paid in roughly equal shares by US corporations and consumers. The likely answer is that the negative economic effect of tariffs is being countered by other forces, including the mania for artificial intelligence and more government stimulus. Since January, estimates of what the big tech companies will spend this year on building out AI infrastructure have risen $60bn to $350bn. Smaller businesses are scrambling to catch the wave too, further boosting growth. And all this excitement is neutralising the fear that trade policy uncertainty would dampen animal spirits and freeze new capex.”

NYTimes: “For some believers in manifestation, vision boards have become a little like movie trailers, as brought to you by A.I.”

WSJ: “Electronic shelf labels are spreading at grocery chains in Europe and the U.S., enabling instant price drops—and raising fears of surge pricing…Shoppers in Norway are used to seeing prices at the grocery store change in front of their eyes—and Americans may one day encounter similar shifting shelf labels. On electronic labels that line the shelves at Reitan’s REMA 1000-branded grocery stores across Norway, the listed price for eggs or milk fades, the screen blinks and a new figure flashes up, all in a matter of seconds. Prices can change up to 100 times a day—and more often during holidays. The idea is to match or beat the competition with the touch of a button, says REMA 1000’s head of pricing, Partap Sandhu. “We lower the prices maybe 10 cents and then our competitors do the same, and it kind of gets to [be] a race to the bottom.””

Business Standard: “Private credit has been able to solve two unique gaps in the market. First, the ability to fund use cases where the banking system is unable to lend due to regulatory restrictions and more stringent lending norms. Acquisition finance and leveraged buyouts being one example. And second, the ability to offer bespoke lending solutions that meet the specific requirements of corporate borrowers.”

ZeroBase: A New Business Model for the Agentic Marketing Era (Part 4)

Progency

Over the past months, I’ve been developing Progency (products + agents + agency)—a revolutionary marketing approach that perfectly embodies outcome-based pricing principles. Through extensive discussions with CMOs and marketing leaders, a clear picture has emerged: they desperately need partners who win only when they win.

I need results. Now. My team’s overwhelmed, my CRM data’s a mess, and retention is tanking. I’m spending a fortune on reacquisition and still losing customers I’ve already paid to acquire. I don’t want a vendor. I want a growth partner who knows what’s working, who’ll do it for me, and who’ll win only if I win. This is a typical quote encapsulates the frustration driving CMOs towards outcome-based models.

Progency addresses this by fundamentally inverting traditional marketing economics. Instead of charging for inputs—hours worked, messages sent, or monthly active users—it operates on pure performance models tied directly to measurable business outcomes. This represents a seismic shift from the $500 billion AdWaste crisis plaguing brands globally, where 70% of marketing spend goes towards reacquiring customers already in their databases.

The Progency Pricing Revolution

There could be multiple distinct outcome-based pricing frameworks for Progency, each addressing different risk profiles and market segments:

The Pure Performance-Based (RevShare) model represents the boldest approach—no fixed fees whatsoever. Progency earns only through a percentage of incremental revenue or lifetime value uplift, benchmarked against historical baselines. For instance, 15% of net incremental revenue, measured quarterly and reconciled monthly. This model particularly appeals to early adopters and challenger brands willing to embrace radical change.

The Tiered Revenue Share model scales compensation with value delivered: 5% share on 0-10% uplift, 10% on 10-20% uplift, and 15% on 20-30% uplift. This creates perfect alignment—the more value Progency generates, the higher its compensation. It’s particularly effective for brands with seasonal volatility or enterprise-level growth ambitions.

For more cautious organisations, the Fixed + Performance Tiered model provides predictability through a base fee covering platform deployment and Marketing Growth Engineers, plus performance bonuses tied to uplift. A typical structure might include $10,000 monthly base fee with 10% of revenue uplift above baseline.

Most compelling is the 30-Day Outcomes-First Pilot—a risk-mitigated entry point requiring no long-term commitment. At $50,000 with clear targets like improving customer reactivation rates, Progency only earns bonuses when it hits those targets. This lets CMOs test results before making bigger commitments.

The Hedge Fund Parallel

Like quantitative hedge funds that generate alpha through systematic market inefficiencies, Progency creates Marketing Alpha—growth uplift beyond historical baselines (Beta). The model mirrors financial services’ most successful structure: management fees covering infrastructure plus performance carry sharing upside. [Think of this as Alpha-Beta-Carry.]

This transformation addresses the fundamental misalignment in marketing: while adtech captures 90% of spend through outcomes-based pricing (cost-per-click, cost-per-acquisition), martech remains trapped in input-based models despite delivering superior long-term value. Progency bridges this gap, applying outcome-based economics to retention-focused marketing.

The result transforms marketing from cost centre to measurable profit engine, creating self-reinforcing cycles where success funds further innovation. Unlike traditional marketing spends that disappear regardless of results, outcome-based models ensure every pound invested generates measurable returns—precisely what modern CMOs desperately need.

Thinks 1708

WSJ: “There are at least a dozen chip startups all battling to sell cloud-computing providers the custom-built inference chips of the future. Then there are the well-funded, multiyear efforts by Google, Amazon and Microsoft to build inference-focused chips to power their own internal AI tools, and to sell to others through their cloud services. The intensity of these efforts, and the scale of the cumulative investment in them, show just how desperate every tech giant—along with many startups—is to provide AI to consumers and businesses without paying the “Nvidia tax.” That’s Nvidia’s approximately 60% gross margin, the price of buying the company’s hardware.”

George Selgin:”I use the term to mean laissez-faire banking—banking without any special government regulations or restrictions. Like free trade, it’s an ideal concept. It doesn’t refer to any specific or actual banking system, although some, like Scotland’s in the early 19th century, came close. My own ideal version of free banking would have no special requirements for note issuance. Private banks would be able to issue their own notes on the same basis as they create demand deposits. They would also be free to open branches and invest in all kinds of securities. Finally, there wouldn’t be any sort of implicit or explicit government guarantees, like deposit insurance.”

FT: “Enter the offices of the future. In the City of London, proposed developments at 50 Fenchurch Street and 63 St Mary Axe will use energy efficient underfloor cooling systems that raise oxygen levels and reduce pathogens, while also employing radical new ideas such as opening the windows. At 63 St Mary Axe, which was approved [recently] by the City of London Corporation, an underfloor “displacement” system will pump in large volumes of cool air through the floor, which will rise as it warms. At night, windows at the top of the building will open automatically to let the hot air escape. A similar method will be used at 50 Fenchurch Street, which Andrew MacKay, associate director at Arup, which is consulting on the development, says will bring about 30 per cent more outside air into the building. That will lead to higher oxygen levels, which studies say increase cognitive ability — and worker productivity.  MacKay predicts that within five years most new buildings will have this system, which he notes is also easier on the eye. “When you walk into the spaces you don’t see cooling systems on the ceiling, pipes or ducts. It’s a visually cleaner space.””

NYTimes: “For the protein-obsessed, the bar still reigns supreme. The category-leading protein bar, Quest, tops out at 21 grams of protein for 180 calories: almost as much protein as a McDonald’s Big Mac, for less than half the calories…The David bar [offers] 28 grams of protein, 150 calories, and zero sugar, basically a protein Scud missile wrapped in gold foil.”

ZeroBase: A New Business Model for the Agentic Marketing Era (Part 3)

In Business

I asked Perplexity for an overview of outcome-based pricing in the business world.

Outcome-based pricing is transforming how businesses think about value creation and client relationships. Rather than charging customers based on features, usages, or hours spent, companies employing outcome-based pricing only get paid for the measurable results they deliver. This approach sharply aligns what clients pay with the business value they receive, creating a win-win dynamic that rewards both performance and partnership.

What Is Outcome-Based Pricing?

Outcome-based pricing means the price of a product or service is directly tied to the results achieved for the customer. For example, a marketing agency might be compensated according to leads generated or sales increases, while a SaaS provider could be paid based on cost savings or efficiency gains clients experience from using their solution. In manufacturing, it may be pegged to uptime or energy savings, and in healthcare, it’s often linked to improved patient outcomes or lower readmission rates.

This model contrasts with traditional methods like flat fees, hourly billing, or usage-based pricing. Instead of paying for inputs or activity, the client only pays when agreed-upon outcomes or KPIs are met.

Key Benefits

  • Perfect alignment of incentives: Both the provider and the customer are invested in achieving success. The provider is motivated to deliver results, while the client is assured payment is tied to real, measurable outcomes.
  • Stronger relationships and trust: Because payments reflect real business value, trust deepens, and clients are less likely to churn. The model typically leads to more collaborative, long-term relationships.
  • Shared risk: This model shifts some risk from the client to the provider, making it easier for customers to say yes—and faster to close deals.

Real-World Examples

  • Technology & SaaS: Intercom’s Fin, an AI customer support solution, charges $0.99 per resolved issue—not by seat or message volume. Customers pay only if the bot solves their problem, making the relationship directly value-based.
  • Manufacturing: Hitachi Rail’s trains-as-a-service lets customers pay based on train punctuality and availability, not ownership—again, tying cost directly to the promised service.
  • Professional Services: Consulting firms and marketing agencies commonly use this model, charging for revenue increases, cost reductions, or campaign performance, so that their success mirrors the client’s success.

Challenges

While intuitive, outcome-based pricing is not without complications:

  • Measuring success: Both sides need to clearly define the outcomes and measure them within a specific timeframe. Ambiguity—such as what exactly counts as a resolved ticket—can cause disputes if not clarified upfront.
  • Attribution: Especially for complex services, proving your solution alone drove the outcome can be tricky.
  • Financial planning: Predicting revenue becomes harder for providers as payment fluctuates with performance. Investors may also require education about this model.

When to Use Outcome-Based Pricing

Outcome-based models work best when:

  • The outcome is easy to define and measure (e.g., sales numbers, cost savings, uptime).
  • The provider and client can align on fair metrics and trust the data.
  • The solution clearly drives the business results.

In today’s competitive and results-oriented business world, outcome-based pricing is gaining ground as a partner-focused, innovation-driving model. By connecting revenue directly to value delivered, companies can build deeper connections, earn premium fees when they over-deliver, and secure client loyalty in the long term.

Thinks 1707

ET: “Is the Indian CMO the next CEO-in-waiting?…In the ever-changing business arena, Indian CMOs are stepping up as influential enterprise leaders, artfully combining the precision of data science with the power of storytelling to propel organizational success. They skillfully navigate a challenging landscape, integrating analytics, innovative thinking, and strategic foresight.”

SaaStr: “AI agents are wrong 30-40% of the time on anything complex. API integrations, edge cases, architectural decisions — they’ll give you confident-sounding answers that are just wrong. What to do: Always ask for multiple approaches. “Give me three ways to build this algorithm” became my default. Push back on suggestions that don’t sound right.  And if it said it’s done something — verify that it actually has.  Don’t move on. Stop treating AI suggestions like they are … right. They’re hypotheses to test.”

WSJ: “Be the reader you want to see in the world…The written word is the most basic building block of all great nations.”

David Brooks: “I applaud ambition, but aspiration sounds a lot more important. It takes courage to build the kind of relationships you’ve never experienced before, to cultivate the kind of virtues you’ve never possessed before. The world doesn’t applaud you as much when you devote yourself to the inner sanctification rather than to outer impressiveness.”

NYMag: “Generative-engine optimization — also known as answer-engine optimization, GEO, or, if you’re feeling limber, LLMGEO — is best understood as an aspirational term, a way for marketers to assure their employers and clients that, in a world where people spend their days chatting with fast-developing AI chatbots that have eaten the entire web and can regurgitate it on demand, there are still ways to get an edge for their brands. Chatbots are trained on the web, continuously scrape the web, and often still link to the web; some have search features built in or call on search engines in the course of conversations with users. In other words, the optimizers still have hope, and early folk wisdom is taking shape, making the rounds, and finding its way into practice. “LLMs, like Google AI mode or ChatGPT, will use what is called a fan-out technique with lots of queries covering every angle,” says Solis. “Then they will match these variations not with whole pages but with passages, or chunks,” she said. In response to a question, in other words, a chatbot will tend to summarize and excerpt, with citations rather than prominent links. If you want to get cited, she says, you should publish content with that in mind. A lot of SEO-driven content “was very wordy,” she said, which doesn’t help with being scraped by AI. Now, she said, publishers should “structure the content in an easier way to be grabbed” — in citable chunks, with clear authorship.”

WSJ: “[Managers are more disengaged now because] they already had a lot on their plates, including meeting senior leadership’s expectations, communicating changes, administrative things like timesheets, motivating high performers, developing star performers and keeping people. And now suddenly you’ve got a workplace where there are all these disruptions happening. You’ve got postpandemic job reshuffling, a hiring boom and then bust, restructuring of teams, changing and sometimes shrinking budgets. The advent of AI and digital transformation. Flexible work expectations, which put more burden on managers to keep track of people. So the combination of an already high-demand job combined with these recent changes is a big reason.”