How to Move from Billing Per Hour to Project-Based Billing

July 21, 2022

Author

James D. Wilton

Managing Partner

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Loved seeing this post on “Saying goodbye to the billable hour” in legal firms from Alice Stephenson. It is certainly true, in my opinion, that the billable hour pricing that lawyers (and many professional services firms, actually) follow is full of friction. And, for clarity, it’s the strict itemization and charging for every unit that causes the friction, rather than the underlying mechanics.


One particular past legal bill I received sticks in my mind. The price felt ok to me at a first glance. But then I opened the invoice and saw 15-minute bills for people I spoke to for maybe 5 minutes, tasks that I felt should have taken an hour that took over 2 etc. etc. In other words, the price structure – the justification for the total price – had made turned me against a price point that I otherwise would have felt ok with!


In professional services, we are in a difficult position where we have high costs, and that costs are driven by labor hours. It makes complete sense, in principle, to align our monetization with that unit of cost. But here’s the rub: customers don’t care about how long your worked – they care about the output.


The answer is to move to more of a project-based billing system, where you charge a fixed fee for a certain output, regardless of how long it takes. There are big benefits here, not least because we avoid the feeling of “nickel and diming” for time. In our experience, there are a few things that you need to do this well:


Understand your costs: Make no mistake – even when you move to project-based billing, you are still monetizing time and resources – just not explicitly. And, above all else, you need to cover your costs, which means need you need to understand them intimately. For all your “levels” of resource, based on their salary, benefits, expenses, and average utilization, figure out the cost of a billable hour and go from there.


Create a workload buildup for each typical “project”: For each typical project, based on the expected duration, hours per week of people from different levels etc., you should understand the number of hours of work required. Plan for the usual case here, not the “best” case. Needless to say, this will need to start with an analysis of previous projects.


Set a markup based on value AND unpredictability: Your underlying rates in this model will likely be higher than your rates in your bill-per-hour model (don’t worry – if you do this right, your customers need never see them). That’s because in addition to marking up your costs based on the value that you add, you also need to include buffer to account for the amount of uncertainty in your workload per project. For example, project A might usually be completable with 30 hours of work, but how often could it run to 35 hours? 40 hours? Your markups should be set so that while across multiple projects on average you might work slightly longer hours than you (implicitly) bill for, you are still covered. And yes – this means you might have different markups for the same resources on different types of projects, as some projects will be more value-added than others, and some will have more predictable workload.


Build T-shirt sizing – while project-based pricing necessitates standardization, standardization doesn’t always mean “one size fits all.” If there is a lot of variation in the number of hours required for a certain project type, and that number of hours scales with characteristics of the project or client, then you can use that to build simple small\medium\large sizes to match these situations.


Set up your Tracking – workload forecast accuracy will translate directly to profits in this model, so you should strengthen this muscle. Monitor every project and refine your estimates of the number of hours required for each project type over time, and your markups based on the change in uncertainty.


Ditch the itemized billing – hourly billing necessitates complete transparency on hours works and hourly rates. The benefit of the project-based model is that it requires neither. You might give a customer an idea of who’s working on the project and the rough hours/days per week, but the overall price should be blended, and never broken out by individual resources. Is this going to be an easy transition? Probably not. You will need to build the discipline around this and have clear messaging. But if you can do it, you will have shifted the conversation from paying for time to paying for value / output, which is a much better place to be.


Change your Mindset – in this model, you really need to move from a mindset where you maintain a certain profit margin on every project and every billed hour of work to one where you are willing to make less (sometimes maybe even make a loss) on individual projects in order to make more overall.



If you’re looking to make such a transition and would appreciate guidance, contact us – we have tools and templates for this type of pricing that can be helpful for the transition.

By James D. Wilton May 28, 2025
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. 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