Published September 2-9, 2025
1
Pricing Options
The evolution towards AI agents and agentic marketing is creating fundamental shifts in how businesses operate and monetise. As artificial intelligence becomes increasingly capable of autonomous decision-making and execution, traditional business models are being disrupted across industries. The companies that will thrive in this new landscape are those that can adapt their revenue strategies to harness the unique capabilities of AI agents.
Subscription-Based Agent Services: The New Foundation
Subscription-Based Agent Services are becoming dominant because they align perfectly with the continuous, always-on nature of AI agents. Unlike traditional software that requires human intervention, AI agents work around the clock, constantly learning and optimising their performance. Rather than one-time purchases, businesses are moving toward recurring revenue models where customers pay monthly or annually for agent capabilities that improve over time.
This model creates predictable revenue streams while ensuring customers benefit from continuous improvements. As AI agents learn from more data and interactions, their value proposition strengthens, justifying ongoing subscription fees and reducing churn rates.
Paying for Results, Not Tools
Outcome-Based Pricing is gaining significant traction, fundamentally changing the risk equation in business relationships. Instead of paying for a marketing automation platform with uncertain returns, companies now pay based on qualified leads generated or revenue directly attributed to AI-driven campaigns. This model shifts risk to the service provider but enables them to command premium pricing when they deliver exceptional results.
The beauty of outcome-based pricing lies in its alignment of interests. Service providers are incentivised to maximise client success rather than just feature adoption. This creates stronger, more collaborative partnerships and often leads to higher customer lifetime values despite lower upfront costs.
The Platform Economy Meets AI
Platform Ecosystems are emerging as another powerful model, where companies create marketplaces for specialised AI agents. Major players like Salesforce and HubSpot are evolving beyond traditional CRM providers to become platforms where third-party AI agents can plug in seamlessly. These ecosystems operate on revenue-sharing models between platform owners and agent developers, creating network effects that benefit all participants.
The platform approach allows for rapid innovation without requiring the platform owner to develop every capability in-house. Specialised AI agents can address niche use cases while contributing to the overall ecosystem’s value proposition.
Data as the New Oil
Data Monetisation Models are becoming increasingly sophisticated as companies recognise that high-quality, proprietary data is essential for training and improving AI agents. Organisations sitting on valuable datasets are discovering new revenue streams by licencing this information to AI developers. This is particularly valuable in B2B contexts where industry-specific data provides crucial competitive advantages.
The key is ensuring data quality and relevance while maintaining privacy and compliance standards. Companies that can consistently provide clean, actionable data will find themselves in high demand as AI adoption accelerates.
The Transition Strategy
Hybrid Human-AI Services represent a crucial transitional model for businesses and customers not ready for full automation. These services combine AI efficiency with human oversight and creativity, allowing companies to charge premium prices while building trust during the adoption phase. This approach helps customers gradually become comfortable with AI capabilities while maintaining the human touch they value.
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The Value Creation Imperative
The most successful companies in this new era will be those that can demonstrate clear ROI through measurable improvements in efficiency, personalisation, or customer outcomes. The key differentiator isn’t just cost reduction—though AI agents excel at that—but value creation. AI agents that can generate entirely new revenue streams or dramatically improve customer experiences will command the highest premiums and create the most sustainable competitive advantages.
The future belongs to businesses that can prove they’re not just cheaper alternatives, but genuinely better solutions that deliver measurable value in ways that weren’t previously possible.
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Madhavan Ramanujan
In Monetizing Innovation: How Smart Companies Design the Product Around the Price by Madhavan Ramanujam and Eddie Hartman, the authors write:
Price is more than just a dollar figure; it is an indication of what the customer wants—and how much they want it. It is the single most critical factor in determining whether a product makes money, yet it is an afterthought, a last-minute consideration made after a product is developed. It is so much of an afterthought that companies frequently call us and say, “We built a product—oops, now we need your help in pricing it.”
To boil it down, these companies conduct product development this way: They design, then build, then market, then price. What we will teach you in this book is to flip that process on its head: Market and price, then design, then build. In other words, design the product around the price.
…The most successful product innovators we know start by determining what the customer values and what they are willing to pay, and then they design the products around these inputs and have a clear monetization strategy that they follow through with.
Scaling Innovation: How Smart Companies Architect Profitable Growth is a recently published sequel. In a recent podcast with Lenny Rachitsky, Madhavan Ramanujan discusses how to build enduring businesses by dominating both market share and wallet share. A post on X by Lenny summarises the essence of pricing:
From the post:
The bottom left, that is the quadrant where your attribution is low, and your autonomy is low. In that situation, the best pricing archetype is seat-based or a subscription model, because you’re not able to attribute a lot of value to what you bring.
The bottom right quadrant, those are companies that can prove more attribution, and show what they actually bring to the table. But they’re still not in a fully autonomous mode, there are still humans in the loop. A hybrid pricing model is the best option for them.
If you look at the top left quadrant, those are products that are very autonomous, but are not strong on attribution. These tend to be mostly backend or infrastructure kind of products. In that situation, you need to be on a pay-for-what-you-consume model.
The quadrant that you really want to be in is the top right one. That’s the outcome-based pricing model. This is where you have great autonomy and great attribution. You are not only charging for work delivered, but you’re charging for work delivered that was delivered by AI with no humans in the loop.
From the podcast: “[Outcome-based pricing] means you’re not only charging for work delivered but you’re charging for work delivered that was delivered by AI without no humans in the loop… In AI, you can actually charge 25 to 50% of the value you bring to the table… because it is autonomous, you’re doing it with the AI, no humans in the loop… With AI finally founders can solve the attribution problem… you get a lot of pricing power.”
To summarise the key ideas: Outcome-based pricing is the most powerful model for AI products, enabling companies to charge based on measurable, attributable value delivered without human involvement. This approach is ideal when AI systems operate autonomously and impact key business metrics. Currently, only a small percentage of companies use this model, but adoption is expected to grow significantly. Transitioning to outcome-based pricing requires building attribution mechanisms, such as ROI models and value audits. While hybrid pricing models dominate today, founders should aim to evolve toward outcome-based pricing by co-creating business cases with customers and aligning pricing with real-world outcomes rather than inputs or access.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Marketer Story
Maya Nair, CMO of Botanica Beauty, a £25M D2C skincare brand, sat across from Alex, Progency’s VP of Growth Partnerships, in her London office. The quarterly board meeting was just two weeks away, and Maya’s biggest challenge remained unsolved: how to grow revenue whilst improving profit margins in an increasingly competitive market.
Alex: Maya, I’ve reviewed Botanica’s customer data, and I can see why you’re frustrated. You’ve got 40,000 customers in what we call the ‘Rest’ segment—they’ve purchased before but their engagement has declined significantly. That’s roughly 40% of your database generating just 30% of your revenue. What if I told you we could double their contribution without any upfront cost to you?
Maya: I’ve heard this before, Alex. Every martech vendor promises better engagement and higher returns. What makes Progency different? And what’s this ‘ZeroBase’ model you had mentioned in your email?
Alex: ZeroBase means exactly what it sounds like—you start at zero cost. No monthly fees, no implementation charges, no platform subscriptions. We only get paid when we generate measurable revenue increase from your Rest customers above their current baseline. If we don’t deliver results, we don’t earn anything.
Maya: (leaning back) That sounds too good to be true. How do you stay in business if you’re working for free?
Alex: Because we’re not working for free—we’re betting on our ability to deliver results. We use AI agents that operate 24/7, personalising experiences for each customer based on their purchase history, browsing behaviour, and engagement patterns. When these customers increase their spending, we earn a percentage of that incremental revenue. Last quarter, our average client saw 70% uplift in Rest customer revenue.
Maya: 70%? Come on, Alex. I’ve been in marketing for fifteen years. Those numbers don’t add up. What’s the catch?
Alex: No catch, but let me be transparent about the challenges. First, we need deep integration with your customer data and purchase systems. Second, it takes 90-120 days to see meaningful results—this isn’t a quick fix. Third, we focus specifically on Rest customers, not your entire base. And fourth, our success depends on having quality data about customer behaviour.
Maya: Okay, so you’re not promising overnight miracles. But how do I know you won’t just spam my customers and damage our brand reputation?
Alex: That’s exactly why ZeroBase works in your favour. If we damage relationships, those customers spend less, not more, and we don’t get paid. Our AI agents are trained on your brand voice and values. Every communication feels authentically Botanica. We’re not sending more emails—we’re sending smarter emails. Quality over quantity.
Maya: (pulling up spreadsheet on laptop) Let me show you our problem. Our Rest customers spent an average of £85 last quarter, down from £120 the previous year. We’ve tried reactivation campaigns, discount offers, new product launches—nothing moves the needle meaningfully. How would your approach be different?
Alex: Traditional reactivation treats all Rest customers the same—send everyone a 20% discount and hope for the best. Our AI agents create individual customer twins that predict what each person actually wants. Sarah might need a skincare routine refresh, while Jessica is price-sensitive and responds to bundled offers. David might be interested in your new men’s line but doesn’t know it exists. We personalise at the individual level, not segment level.
Maya: Individual level sounds expensive. How do you make the economics work?
Alex: That’s the beauty of our AI Agents Collective—they scale infinitely without proportional cost increases. One agent can simultaneously personalise for thousands of customers. The marginal cost of adding another customer approaches zero, whilst the marginal revenue from better personalisation keeps growing. It’s why we can afford ZeroBase pricing.
Maya: (checking her phone) I’ve got a board meeting in two weeks where I need to show a path to improved margins. Even if your approach works, how quickly can we see results?
Alex: For your board meeting, we can implement tracking and start showing engagement improvements within two weeks. Meaningful revenue impact typically shows in 60-90 days. But here’s what I’d propose for Botanica: let’s start with just 25% of your Rest customers—about 10,000 people. If we can’t show clear results within 90 days, you’ve lost nothing except some data integration time.
Maya: What kind of results are you talking about specifically?
Alex: Based on Botanica’s data, I’m confident we can move your test group from £85 average quarterly spend to £130-150 within 90 days. That’s roughly £450,000 to £650,000 in incremental revenue from just the test group. Our fee would be 15% of the incremental revenue above the £85 baseline.
Maya: (calculating on her laptop) So you’re saying if each customer goes from £85 to £150, that’s £65 incremental per customer. Times 10,000 customers is £650,000 additional revenue. Your 15% would be… £97,500. And if they only improve to £130, you’d earn £67,500.
Alex: Exactly. And if they don’t improve at all, we earn nothing. But Maya, here’s the crucial part—this isn’t a one-time boost. As our AI agents learn more about your customers’ preferences and behaviours, performance compounds over time. Quarter two typically shows even better results than quarter one.
Maya: What about attribution? How do I know the revenue increase comes from your interventions versus natural customer behaviour or our other marketing efforts?
Alex: We create a control group from your Rest customers—similar profiles, purchase histories, and engagement levels. The control group receives your standard marketing whilst the test group gets our AI-powered personalisation. We measure the difference between the two groups to isolate our impact. Clean attribution is crucial for ZeroBase to work.
Maya: (pausing thoughtfully) I have to ask—what’s your success rate? How many clients actually see these results?
Alex: Honestly? About 85% of our clients achieve meaningful uplift within the first 90 days. The 15% who don’t typically have data quality issues or unrealistic baseline expectations. That’s why we’re selective about partnerships and why the pilot approach makes sense.
Maya: And the clients who do succeed—what happens next?
Alex: Most expand the programme. Once you see 10,000 Rest customers generating an extra £650,000 quarterly, the natural question becomes: what about the other 30,000 Rest customers? That’s when ZeroBase really demonstrates its value—scaling proven results across your entire underperforming segment.
Maya: (closing laptop and leaning forward) Alex, I’ll be direct. I’ve been burned by marketing vendors before. Lots of promises, fancy dashboards, but no real business impact. Your ZeroBase model addresses my biggest concern—paying for results, not just activity. But I need to protect Botanica’s brand and customer relationships.
Alex: I completely understand, Maya. That’s why I’d suggest starting with your most disengaged Rest customers—the ones spending the least and engaging the least. If our approach damages those relationships, you’re not losing much. But when it works, you’re reactivating revenue you’d essentially written off.
Maya: (standing up) Alright, Alex. I’m willing to try this with 10,000 of our lowest-engagement Rest customers. But I want weekly reporting, complete transparency on what communications are sent, and the ability to pause the programme if I’m not comfortable with the approach. And we’ll need legal to review the revenue-sharing agreement.
Alex: (shaking hands) Absolutely. Full transparency, weekly check-ins, and your brand guidelines are our operating principles. I’ll have our legal team send over the pilot agreement today. Maya, I’m confident that in 90 days, you’ll be presenting some very positive numbers to your board.
Maya: I hope you’re right, Alex. Because if this works, you might just have helped me solve the growth versus profitability challenge that’s been keeping me awake at night.
Alex: That’s exactly why ZeroBase exists, Maya. We win when our customers win. Let’s make sure you sleep better and your board sees the results they’re looking for.
As Alex gathered his materials, Maya felt something she hadn’t experienced with a vendor in years: genuine optimism about finding a true growth partner rather than just another service provider.