When we begin a new pricing transformation with a client, we often ask: do you want your pricing strategy to focus on monetization or simplicity? The answer we typically hear is: "Both!” While that’s an understandable goal, it’s a hard one to achieve. The truth is, the structural elements of a pricing strategy that drive greater simplicity often pull away from monetization - and vice versa. As you build your pricing model, it’s important to recognize that these two forces sit in tension. And success isn’t about optimizing both equally. It’s about designing the right tradeoff between them. The right pricing strategy requires balancing monetization and simplicity - not equally, but deliberately - based on your goals and your customers. And while the tradeoff between simplicity and monetization can feel abstract, it’s surprisingly easy to see when you’re ordering a burger (we’ll come back to that!) First, let’s look at what actually drives these forces apart. Why Simplicity and Monetization Pull Apart In pricing, simplicity refers to how easy it is for customers to choose what they want and understand what they’ll pay and how straightforward it is for teams to sell, manage, and explain. Monetization, on the other hand, describes the extent to which the pricing strategy captures the value the customer receives. As a pricing strategy becomes simpler, it often captures less value, because there may be fewer monetizable choices, or fewer levers on which to scale price. The reverse is also true: a more granular pricing structure can better capture value, but usually at the cost of additional complexity. Understanding this push-and-pull is critical. Because a pricing strategy designer will need to decide the maximum amount of complexity they are willing to tolerate in order to maximize monetization, or the amount of monetization they are willing to forgo to maximize simplicity. And all of this will be driven by the vendor’s priorities and customer dynamics. The Structural Levers Behind Each Side Certain choices in your pricing architecture naturally favor one end of the spectrum. Levers that improve monetization tend to include: Granular packaging: Breaking your product into more components (e.g., multiple tiers, à la carte add-ons) lets you assign distinct value and price points to each element. Value-aligned price metrics: Using metrics that better align scale with how customers derive value (e.g., volume of data processed, usage frequency), even if they are less familiar, allows tighter alignment between what customers pay and what they get. Multiple metrics and price fences: Incorporating more metrics and categories that influence price allow an even greater degree of control over the price. Sophisticated price curves: Pricing structures that scale non-linearly, like bands and sliding scales, often do a better job of aligning price to value. For example, a usage-based model might charge $0.10 per unit for the first 1,000 units, then $0.08 after that - capturing value while encouraging higher usage. Levers that improve simplicity typically include: Fewer choices: Simple packaging (e.g., “Good / Better / Best”) shortens the sales cycle and reduces decision friction. Acceptable metrics: Relying on familiar, predictable pricing units (like per seat or per project) makes pricing easier to understand, even if it’s less tightly aligned to value. Per-seat pricing, for example, is easy to explain and forecast - but often fails to scale with actual usage or customer growth. Flat or step-based pricing: Simple structures that avoid marginal cost calculations make it easier to sell, forecast, and adopt. Recognizing which levers push in which direction gives you the control to design the tradeoff intentionally, rather than drifting into unnecessary complexity or missed monetization.

Outcome-based pricing (OBP) is one of the hottest topics in AI and SaaS monetization today. Instead of charging customers for access or usage, vendors charge based on measurable results. The idea? Customers only pay when they see real value. It sounds like the ultimate pricing model - perfectly aligned incentives, no wasted spend, and a direct link between cost and benefit. So why don’t more companies use it? Because in reality, OBP is much harder to execute than it looks. It’s been around for decades, but few companies truly succeed with it. That’s because OBP introduces complexity, risk, and friction that can make it more challenging than traditional SaaS models. Here are the five biggest pitfalls of OBP - and what to do about them. 1. Defining the Right Metric is Harder Than It Looks The biggest challenge in OBP is choosing a metric that accurately reflects value - without creating unintended consequences. If the vendor defines success too loosely, customers will feel overcharged. If the metric is too restrictive, vendors won’t get paid fairly. Example: Zendesk’s AI Ticket Resolution Pricing Zendesk introduced AI-powered customer service pricing based on resolved tickets. But customers pushed back - because Zendesk’s definition of a "resolution" didn’t always match what customers considered a real resolution. The lesson? A pricing metric must be: Meaningful to the customer (aligned with their definition of success). Tied to the vendor’s real value-add (not just surface-level activity). Difficult to game or manipulate (or customers will optimize against it). 2. Attribution is a Nightmare (Even with AI) Choosing the right metric is only part of the battle - there’s still another problem: Can you prove that YOUR product drove the result? In many cases, multiple factors contribute to an outcome. If revenue grows, was it because of the AI-powered sales tool, better sales reps, or an overall market uptick? Example: IBM Watson & Salesforce Einstein Both were positioned as transformational AI platforms, but customers struggled to isolate the AI’s impact. They could see business improvements, but couldn’t confidently say, “Watson/Einstein was responsible for X% of that success.” Notably, neither IBM nor Salesforce uses OBP for these products. Why? Attribution is too difficult. If vendors can’t prove they caused the outcome, customers won’t want to pay for it. A better approach: Control more of the process (the more your product influences the outcome, the easier it is to claim credit). Use proxy metrics (if direct attribution is hard, find leading indicators that correlate with success). Offer hybrid pricing (mix base fees with OBP so revenue isn’t fully dependent on attribution). 3. Baselining Gets Messy, Fast Even if a vendor picks the right metric AND can prove attribution, there’s yet another challenge: How do you measure improvement? The problem: Many OBP models assume a static baseline - but in reality, customer environments change over time. Example: Fraud Prevention in Financial Services Some AI vendors charge based on the reduction in fraudulent transactions. But this raises tough questions: What’s the starting fraud rate? (Pre-existing fraud levels may fluctuate.) Should the baseline reset each year? (If the vendor permanently reduces fraud, do they still get paid for maintaining it?) The lesson? Customers won’t want to pay for improvements they believe they would have achieved anyway. And vendors need a way to continuously justify their impact. A better approach: Define clear baseline periods (e.g. compare against the 6 months before implementation). Adjust pricing over time (the vendor’s impact might be front-loaded, requiring a different model in later years). Use tiered pricing (higher fees early, lower fees as impact normalizes). 4. Revenue Delays Can Kill a Vendor Even if everything else works - the metric is solid, attribution is clear, and baselining is fair - there’s still one big problem: Vendors often don’t get paid until months (or even years) after delivering value. This creates massive cash flow risks. Many SaaS companies depend on predictable, upfront revenue to fund operations. But OBP means revenue recognition is delayed, making forecasting difficult. Example: Riskified’s Outcome-Based Model Riskified, a fraud prevention platform, only gets paid when transactions are successfully approved without fraud. This aligns incentives - but it also means their revenue is inherently unpredictable. The lesson? While this approach works for Riskified, not every vendor can afford to wait for long-term verification before getting paid. (Note: Investors may not love it either - Riskified trades at just 1.89x EV/Revenue, a very low multiple for a SaaS company.) A better approach: Charge a mix of fixed fees + OBP to ensure steady cash flow. Offer performance tiers (higher base fees for lower-risk customers, full OBP for riskier bets). Use milestone-based payments - instead of waiting for full verification, charge in phases. 5. Customers Prefer Predictability - Even Over Potential Savings Even if an OBP model delivers better value, many customers still choose predictable pricing over variable costs. Why? Most businesses prefer stable, budgetable expenses over a fluctuating fee - even if the predictable price is technically more expensive. Example: Conversational AI in Customer Support A vendor offering an AI chatbot asked customers to choose between: Payment based on how many conversations the AI fully handled (OBP model). A flat subscription fee. Most customers chose the flat subscription. The lesson? Even if OBP is theoretically the best model, buyers often prefer predictability. The existence of an OBP option, however, can signal vendor confidence and reinforce the value of a fixed-price plan. A better approach: Give customers a choice (some will prefer OBP, but many want predictability). Use OBP as an anchor (show the OBP price, but steer customers toward a fixed option). Cap OBP costs to reduce buyer anxiety. Final Thoughts: OBP Works - But It’s Not for Everyone Outcome-based pricing sounds great in theory, but it’s tough to get right. When structured poorly, it leads to: Customer friction (over unclear metrics or unfair pricing). Revenue instability (due to attribution and baseline issues). Delayed payments (which can crush cash flow). The best OBP models: Pick the right metric - aligned to value and hard to manipulate. Solve the attribution problem - proving the vendor’s role in success. Balance cash flow - with a mix of fixed fees and variable components. OBP isn’t broken - but it’s not a magic bullet. Companies that embrace it need to go in with open eyes and a clear strategy. What’s your take? Have you seen OBP succeed or fail? Let’s discuss.

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The following is an edited excerpt from Capturing Value: The Definitive Guide to Transforming SaaS Pricing and Unshackling Growth, the new book by Monevate founder James D. Wilton.