Pricing Model Development

Saas pricing is evolving. Many companies try figure out their pricing by copying competitor pricing or debating between conventional options. This misses a couple of key points. Your pricing needs to reflect two critical factors: F͟r͟e͟q͟u͟e͟n͟c͟y͟ Daily active users aren't the same as monthly active users. Why would you price them the same? Accounting software used by your CFO every 2 weeks vs design software used by a junior designer every day meets a different need. B͟u͟s͟i͟n͟e͟s͟s͟ ͟I͟m͟p͟a͟c͟t͟ A bug in your CFO's accounting software is potentially catastrophic. A glitch in your designer's tool? More of an inconvenience. Your pricing should reflect this risk and impact difference, not your opinion of "value". With AI and no-code tools making it easier to build interfaces (I whipped up an API integration in 20 mins the other day), we're seeing a fundamental shift in pricing models. The data backs this up: • 46% of SaaS companies now use hybrid models (subscription + usage) • Only 15% are purely usage-based This isn't all roses though: • 66.5% of IT leaders report unexpected charges from AI/usage-based pricing • Companies underestimate their SaaS spend by 304% So there's certainly hiccups in the transition period. So evolution is moving from seat-based to: • API-based pricing (like OpenAI's token model) • Usage-based pricing (like Snowflake's compute credits) • Task completion pricing (like Intercom's resolution-based pricing) Get this wrong and two things happen: • Churn - customers move to solutions that better match their needs Your pricing needs to align with how customers extract value. • Lost revenue - you're leaving money on the table If your pricing doesn't reflect real value delivered, you're missing out. Because it better reflects the varying value different customers get from the platform. Make sure to run qual & quant research to understand your users and the market rather than speculate. The future isn't about seats - it's about value delivered. As AI capabilities expand, expect even more granular and output-driven pricing strategies.

Most pricing models in crypto infra don’t work for payments. Here’s why. When we spoke with PSPs, they told us about infrastructure providers charging basis points on transaction volume. Payment companies operate on very low margins. When wallet providers take a cut of each transaction, they consume most of the profit. It's economically unsustainable and creates unpredictability. If you're moving $1 billion one month and $500 million the next, your costs fluctuate wildly. PSPs need consistency to scale operations and plan resource allocation. That's why we built our pricing differently at Dfns: • Fixed monthly pricing based on features, not transaction volume • Predictable costs regardless of how much money moves through wallets • No percentage-based fees that eat into already thin margins This model works. Customers like Bridge were able to process payments with stable infrastructure costs even as their volumes grew by orders of magnitude. For infrastructure providers, this means leaving money on the table during bull markets; however, it creates sustainable partnerships where our clients can pass on savings to their customers, rather than raising fees to cover our costs. Percentage-based pricing models kill payment companies in crypto. If you're building payment infrastructure on crypto rails, evaluate your vendors' pricing models carefully. The right structure shouldn't penalize you for success or create incentives misaligned with your growth.

When one of our client CFOs tells me they want to switch to a usage-based pricing model, I always ask them one thing. “How critical is your forecasting for the next 4 months?” For most of them, it’s a top priority. So I give it to them straight: if you switch pricing models, you’re not going to be able to get accurate forecasting for the next 3-4 months at minimum. But there’s a catch. That’s in a best-case scenario. The one where you start instrumenting your environment today. If you don’t do that? You’re looking at 8 - 12 months with no accurate, data-informed forecasting capability. That’s not good in any economy, but a lack of predictability is more detrimental to businesses now. Macro uncertainty is the new norm. Which makes internal predictability models even more important. When changes arise, you need to be able to see them — and act on them — very quickly. Losing predictability for 3 or 4 months to make a tactical business decision is fine, so long as you understand what you’re doing. But losing your ability to accurately forecast for 8 months or more? No CFO can afford fly blind for that long. Especially not now. So if you’re thinking about switching to a usage-based pricing model, ask yourself: – What signals tell me a usage-based customer is healthy? What thresholds correlate to churn? To expansion? – Is my new environment instrumented in such a way that it can yield data to measure those thresholds? – What’s my time horizon for a return to predictability? Am I looking at 3 months or 1 year +? – What kind of data will I need in a usage-based model to determine success, and how can I get it? If you can’t answer these questions before you make the switch, take a step back — or you’ll be looking at a year with zero GTM financial forecasting.

The Psychology of Pricing: Understanding Value Perception Understanding the intersection of perceived worth and willingness to pay is essential. I’m exploring this through the lens of SaaS, as different verticals have their own unique issues. More than a number, pricing is the narrative that influences how your clients and customers perceive value. This value can come in many forms: efficiency, risk mitigation, innovation, time savings, scalability or peace of mind. As leaders, we must navigate the delicate balance between growth, differentiation and customer satisfaction. But what truly drives value perception? I have seen some recurring challenges from my engagements: 1️⃣ Misaligned Pricing Strategies: No market research often leads to over or underestimating your product’s worth. Pricing tied directly to customer outcomes tends to beat other strategies. 2️⃣ Following the Herd: Mimicking a competitors’ pricing model ignores your unique differentiators. Stay clear on your MOAT and position your solution as indispensable to the customer’s success. 3️⃣ Inconsistent Messaging: Pricing is part science and art; it can’t just make sense—it must feel right. Align pricing communication with the overall brand to build trust and loyalty. 💡 A critical takeaway: successful pricing is not achieved in a silo, it impacts every department and function. Create a cross-functional pricing ethos so that every team is aligned and invested. Actionable Insights: -Don’t ‘Set it and forget it’: Delaying pricing decisions can result in lost revenue, a decline in customer trust and loss of market share. -Stay Agile and incorporate foresight: More than ever, customer needs shift quickly. At a minimum define clear re-evaluation triggers based on usage data, retention patterns and competitive actions. -Leverage Data-Driven Insights: You can’t influence what you don’t measure! Use tools and analytics to monitor customer trends. These insights can help refine pricing tiers, identify expansion or upselling opportunities and guide retention. -Communicate Value Effectively: Articulate the tangible and intangible benefits your pricing reflects, showcase how your solution solves problems and delivers outcomes. Pricing is not just as a financial lever or revenue driver – it is a strategic tool to deliver value, build lasting customer loyalty and deliver profit. 💭 What frameworks or practices have been most effective for you in maintaining pricing agility and relevance? 📸 ThinkFiscally

Despite pricing being the most powerful business lever for growing Operating Profits, many mid-market companies still rely on static, cost-plus formulas to generate prices, missing key opportunities to drive higher profits on both ends (leaving money on the table and missed sales opportunities). Price optimization is built on advanced analytics, including AI and machine learning, to set prices that maximize profitability while aligning with broader business objectives (i.e., balance revenues with gross profit $). It leverages transactional and market data to deeply understand customer behavior and adapt to changing inputs (i.e., competitor prices, inventory levels, seasonality, etc.). Whether you’re in manufacturing, distribution, or retail, some form of an insights-driven, dynamic, and automated pricing strategy is essential for profitable growth. In the below article (see comments), we explore foundational pricing methodologies such as dynamic pricing, value-based pricing, and competitor-based pricing: 1. Dynamic Pricing: Adjust prices in real-time (or near real-time) based on competitor actions, inventory levels, market trends, and financial goals. Amazon’s dynamic model exemplifies how real-time adjustments can balance a low-price reputation with margin optimization. 2. Value-Based Pricing: Set prices on perceived customer value rather than costs or competitors. This ensures your pricing reflects the unique differential value you provide. A simple approach is assigning a competitive price index premium based on detailed customer research. 3. Competitor-Based Pricing: Position products strategically by considering competitors’ real-time prices. Techniques like premium pricing, price matching, and loss leader pricing help assign the right comp-pricing strategy to each customer or product segment. Successful price optimization requires avoiding pitfalls. Overcomplicating pricing models can lead to inefficiencies and erode trust among commercial teams—we’ve seen this too often. Relying on opaque “black-box” AI systems can also cause a loss of control and transparency. The key is balancing sophistication with simplicity, ensuring strategies are effective and embraced by the sales team. Building or insourcing your price optimization capabilities offers significant advantages. It aligns your pricing with business goals, provides greater decision control, and strengthens long-term pricing acumen. You can create a robust, customized pricing engine tailored to your unique needs by fostering collaboration across teams and continuously refining your models. Mid-market companies have a unique opportunity to elevate price optimization from a tertiary (or non-existent) concern to a core business function. Achieving this requires a deliberate, thoughtful approach that leverages advanced analytics, your internal/external data assets, and a collaborative approach with your Finance/Pricing and Commercial teams. #revenue_growth_analytics

Most startups play defense when discussing pricing with customers. They dance between asking for too little, leaving money on the table, and asking for too much, only to lose the customer’s interest. The very best companies lead their customers in that dance. They use pricing as an offensive tool to reinforce their product’s value and underscore the company’s core marketing message. For many founding teams, pricing is one of the most difficult and complex decisions for the business. Startups operate in newer markets where pricing standards haven’t been set. In addition, these new markets evolve very quickly, and consequently, so must pricing. But throughout this turmoil, startups must adopt a process to craft a good pricing strategy, and re-evaluate prices periodically, at least once per year. The Three Core Pricing Strategies There are only three pricing strategies startups should pursue: Maximization, Penetration and Skimming. They prioritize revenue growth, market share and profit maximization differently. Maximization (Revenue Growth) - maximize revenue growth in the short term. Startups should pursue maximization when there are no clear differences in customer segments’ willingness to pay, and when the optimal short term and long term prices are equal. Many mid-market software companies price with the goal of revenue maximization, negotiating for the highest possible price in each sale. Penetration (Market Share) - price the product at a low price to win dominant market share. A bottoms-up strategy lends itself to penetration pricing. Price low to minimize adoption friction, grow quickly, and then move up-market after developing broad adoption. Penetration pricing leads to land-and-expand sales tactics. Expensify, Netsuite, New Relic, Slack follow this model. Penetration prioritizes market share. Skimming (Profit Maximization) - start with a high price and systematically broaden the product offering to address more of the customer base at lower prices. Skimming is widespread in consumer hardware. Apple sells the latest iPhones at the highest prices, and repackages older models at lower prices to address different customer segments. As Madhavan Ramanujam tells it, Steve Jobs was both a product genius and pricing genius. By pairing the two skills, he led Apple to record-breaking profits quarter after quarter. Skimming is less common in the software world because few startups develop a product at launch that will be accepted by the most sophisticated customers (and those willing to pay prices that generate the greatest margin). There are exceptions: Oracle’s database, Tanium’s security product, Workday’s human capital management software. Read the full post here : https://lnkd.in/g-mxQiV9

Too many companies set prices arbitrarily. But pricing isn’t just about covering costs—it’s a powerful strategy that drives growth. Here are 6 pricing models and how to make them work for you:   𝟏. 𝐂𝐨𝐬𝐭-𝐩𝐥𝐮𝐬 𝐩𝐫𝐢𝐜𝐢𝐧𝐠 The simplest model. Take your costs, add a margin, and set your price.    But it’s short-sighted.    If you don’t consider what customers are willing to pay, you could be leaving serious money on the table.    𝟐. 𝐂𝐨𝐦𝐩𝐞𝐭𝐢𝐭𝐢𝐯𝐞 𝐩𝐫𝐢𝐜𝐢𝐧𝐠 Benchmark against your rivals.    If you’re better, charge more.    If you’re scaling fast, go lower—but only if it’s part of a strategy, not a race to the bottom.    𝟑. 𝐏𝐫𝐢𝐜𝐞 𝐬𝐤𝐢𝐦𝐦𝐢𝐧𝐠 Start high, then reduce over time.    Luxury brands and tech companies use this to create exclusivity early on before broadening their market.    𝟒. 𝐏𝐞𝐧𝐞𝐭𝐫𝐚𝐭𝐢𝐨𝐧 𝐩𝐫𝐢𝐜𝐢𝐧𝐠 The opposite of skimming—start low to grab market share, then increase prices later.    But if you train customers to expect discounts, raising prices can be tough.    𝟓. 𝐕𝐚𝐥𝐮𝐞-𝐛𝐚𝐬𝐞𝐝 𝐩𝐫𝐢𝐜𝐢𝐧𝐠 What are people really paying for?    Netflix does this well—offering an ad-free experience and premium features at a higher price.    If your product has added benefits, customers will pay more.    𝟔. 𝐃𝐲𝐧𝐚𝐦𝐢𝐜 𝐩𝐫𝐢𝐜𝐢𝐧𝐠 Used by Uber, airlines, and hotels—pricing shifts based on demand, competition, and availability.    For some businesses, this is the future.    Pricing can make or break your business. A 1-2% price increase can 𝐝𝐨𝐮𝐛𝐥𝐞 𝐩𝐫𝐨𝐟𝐢𝐭𝐬. So if you’re guessing your prices, it’s time to rethink.

How do you position and price a new AI product when you know users might be skeptical? OpenStore had created OpenDesk - an AI-powered customer support tool designed for small eCommerce brands. But they anticipated challenges: overcoming merchants' natural resistance to AI and making their value proposition immediately clear. So they asked Irrational Labs to help position and price OpenDesk for success. Through our behavioral science approach, we transformed OpenDesk from "just another support tool" into a compelling investment for eCommerce merchants. What behavioral barriers did we need to overcome? ⚠️ AI Aversion: Small business owners hesitated to trust AI with complex customer issues. ⚠️ Mental Accounting: Support tools were viewed as expenses, not investments. ⚠️ Status Quo Bias: Switching from established workflows felt risky. Our 3-step Behavioral Design process helped us address these challenges: 1️⃣ Behavioral diagnosis: We reviewed OpenDesk's prototype, analyzed competitor pricing, and conducted behaviorally-informed interviews with merchants. 2️⃣ Psychological mapping: We identified how to reframe customer support from a cost center to a revenue driver. 3️⃣ Strategic redesign: We created: 📊 A positioning strategy that emphasized customer retention over just solving support tickets 🎨 A landing page design that instantly communicated value 💰 Three transparent pricing models tailored to merchant psychology For the pricing strategy, we explored multiple pricing models and built behaviorally optimized pricing pages to play out how consumers may react and how to mitigate the pain of paying: 💲 Hybrid Pricing Model: A mix of monthly subscription fee and per-ticket charge 🔢 Usage-Based Pricing Model: A simple pay-per-ticket structure 👥 Per-Seat Pricing Model: A flat fee per user per month, offering straightforward costs that made budgeting easier Our recommendations helped OpenDesk successfully launch in a crowded market with clear positioning and a pricing structure that felt fair to merchants. Shoutout to our core team on this project Katie Dove Karl Purcell Pauline Kabitsis Lydia Trupe and also to Gigi Melrose and Eamon Davis at @OpenStore for their partnership 💪 Want to know exactly how we reframed AI tools, which pricing model worked best, and the specific techniques we used to build trust? Check out the full case study in the comments! Want help positioning or pricing your AI product? Hit me up: kristen@irrationallabs.com   #BehavioralDesign #AIStrategy #ProductPricing

AI broke a lot of pricing muscle memory, but the four fundamental questions haven’t changed. How to think through pricing an AI product... Full Post: https://lnkd.in/gjERFnFw First, what’s different with AI : Variable costs & unpredictability: AI introduces meaningful variable costs and the usage from users can be highly variable and unpredictable. Value ≠ inputs: Customers buy outcomes, not tokens. Figuring out how to map price to an external value metric that your customer understands is key. Hybrid > Perfect (in most cases): Market is moving from seat → usage → outcomes; hybrid models are emerging as a bridge. 🧩 The 4-part monetization puzzle that hasn't changed Scale(how price scales): The unit customers associate with value (tickets resolved, docs generated, seats, etc). What (what’s in each plan): Gate in ways that accelerate adoption and expansion. If collaboration drives growth, don’t choke virality by gating seats. Amount (how much): This is your actual price point—$19/month, $99/seat, $0.002/token, etc. The amount must balance value perception, competitive positioning, and unit economics. When (when you charge): Do customers pay upfront, get a free trial, use a freemium tier, or pay based on usage? The timing of payment affects willingness to pay, conversion rates and more. ✅ Three tests every AI pricing model must pass Customer value test: Does perceived value beat perceived price by a clear multiple? Growth model test: Pricing should enable loops (free → team → expansion), not block them. (Classic example: shifting the free limiter from people to usage to unlock virality.) Cost-to-serve test: AI introduces variable COGS—your model must sustain both CAC and compute. The original framework developed by Elena Verna and Dan Hockenmaier as part of the Reforge Monetization Strategy course. We use it in the new AI Growth course to explore different ways to price AI products. Details 👇

One of the least-discussed challenges in AI adoption today is pricing. Everyone talks about model performance, benchmarks, or features. But for enterprises, the real sticking point often shows up when the bill discussion starts. The problem: current pricing models don’t align with how enterprises budget and buy. Usage-based pricing makes perfect sense for vendors, but it feels like a blank cheque for buyers. If adoption succeeds, the bill grows in unpredictable ways. No CFO wants to be surprised by a doubling in costs because usage spiked. Flat subscriptions feel safer for buyers, but they put vendors at risk. The underlying compute costs fluctuate, and a heavy customer can easily push margins underwater. Hybrid models try to balance the two, to put in predictability for buyers’ forecast, and vendors try to to defend and improve profitability. This mismatch slows progress. Solution: a new generation of pricing models. Simple enough to understand, predictable enough to budget for, but still sustainable for vendors. It could also mean having periodic reviews instead of fixed term pricing for multi year deals. That could mean outcome-based contracts, tiered usage bands with hard caps, or bundled services that absorb variability in spikes. Until AI economics are solved, adoption will remain slower than the technology itself.