Strategic Pricing Models

We're moving away from charging for *access* to software and toward a model of charging for the *work delivered* by a combination of software and AI agents. Let’s dive into what’s happening and what it means for you ⤵️ 1. The rise of disruptive AI pricing models Tech companies are realizing they can't solely rely on seat-based subscriptions in an age of AI, automation and APIs where value is disconnected with how many people are logging in. Perhaps Salesforce going all-in on Agentforce (and charging $2 per conversation) was the push the industry needed. Each product category has its own flavor of disruptive pricing. - Legal AI products might charge for a demand package generated by AI or an AI-generated summary. - Creator AI products might charge for the content that gets produced such as a video generation or amount of video created. - GTM products might charge for specific tasks completed or workflows executed by the AI. 2. Selling work, not necessarily success As a customer, I wish I only had to pay for software when it delivered results. But the reality is that true success-based billing won’t work for the vast majority of today’s products. Most products should charge for work output instead. The issue is attribution. You want the customer to get a fantastic outcome — and you want them to recognize that your product powered that outcome. As soon as you start charging for success, the customer begins to rethink the results. 3. Goodbye ARR as we know it? Shifting to these newer value-based pricing models isn't a simple pricing change you can just announce in a press release. It's a business model evolution that looks a lot like the shift from on-prem to SaaS in the first place. These new AI pricing models might mean greater volatility in both usage and spend. Variable margin profiles across products and customers. Seasonal revenue fluctuations. The potential for project-based, non-recurring use cases. Put simply, annual recurring revenue (ARR) continues to get dethroned. — Full post in today’s Growth Unhinged newsletter: https://lnkd.in/ea5eTrVD Things are about to get interesting 🍿 #ai #pricing #saas

If you buy a coffee in Caffè Nero they'll suggest upgrading your beans to something fancier for 30p. They could say a standard flat white is £3.40 and with the fancy beans it's £3.70. But that would generate fewer sales compared to saying standard is £3.40 and for just 30p extra you can upgrade. Positioning the price in this way is called differential price framing. David Hardisty at the University of British Columbia ran some of the earliest experiments into the idea in 2019. He gave participants two subscription options for The New York Times: $9.99/month: web and app $16.99/month: web, app, print, podcast, and crossword The first group saw these prices as listed above. But the second group saw: $9.99/month: web and app +$7/month: web, app, print, podcast, and crossword In the first group, 22% picked the pricier option. In the second group, 47% chose it - a more than 2x uplift. Why did this happen? After all, the prices in both groups were mathematically the same. But that's not how we look at prices. Instead, we fixate on the number in front of us, rather than what it represents. And since the price difference ($7) was numerically smaller than the total price of the premium subscription ($16.99), the same cost looked a lot more affordable. So if you want to get people to pick a premium offering - frame it as a price difference with the standard option. This is one of the biases we covered in this week's episode of Behavioral Science for Brands with MichaelAaron Flicker. We also looked at two other insights around pricing: ⏰ Present Bias - why you should push costs into the future to boost conversion ⚠️ The Discount Danger - why you shouldn’t lower your prices too often, as this signals bad quality    🎧 Links to Spotify etc in the comments or just search for Behavioral Science for Brands. Let me know your thoughts in the comments! 

Replit's gross margins went from 36% to negative 14% in two months. Same product. Same pricing. Same team. The only thing that changed: they launched a more autonomous AI agent that consumed more LLM resources than their pricing covered. Traditional SaaS has 70-80% gross margins because one more subscriber costs almost nothing. AI products pay for compute on every prompt. Your best users are your most expensive users. That single fact breaks every pricing model designed for the SaaS era. I mapped pricing across the top 50 AI startups by valuation with Moe Ali. Six patterns emerged. The scariest finding: in most AI products, the P90 user costs 10-40x more than the P50 user. Both pay the same subscription. You're subsidizing your heaviest users with revenue from your lightest ones. And that subsidy grows as power users discover more ways to use the product. Cursor learned this the hard way. They switched from flat 500 requests/month to a credit pool system. A developer burned the entire monthly allocation in a single day. $7,225 invoice. The CEO published a public apology on July 4th. The plan description quietly changed from "Unlimited" to "Extended" twelve days after launch. Anthropic took a different approach. Their $17/$100/$200 tiers map to genuinely different user personas. A casual user, a power user, and a developer replacing an IDE. Those are different products with different willingness to pay. Then weekly rate limits targeting less than 5% of subscribers to push the heaviest users toward the API, where per-token pricing covers actual compute. The pattern across all 50 companies: pure flat pricing is dying. Nearly half use two or three models simultaneously. Here's the full breakdown: 1. Complete AI pricing guide: https://lnkd.in/gdKaQSMk 2. Replit guide: https://lnkd.in/gmA_c_AG 3. AI product strategy: https://lnkd.in/egemMhMF 4. AI agents guide for PMs: https://lnkd.in/eeey5Cxr If you can't estimate your cost distribution across P10 to P90, you're not ready to set a price.

The question I hear most from founders during Sequoia Capital's Arc program is about #pricing. Pricing is one of the most underutilized levers for startups. Why does it matter so much? It has the most direct impact on revenue, and the moment you establish your pricing, you determine your TAM. Getting the pricing metric right is, by far, the most important one. The key is to imagine the future: when you are a large and successful company, how have you changed the world, and what metric correlates best with your success? Hitch your financial wagon to that metric! If you are Figma, success is all designers using the app; therefore, the pricing metrics is per designer seat. If you are VMware, success is all workloads run in virtual machines; therefore, the right pricing metric would have been a virtual machine. A pricing metric is like the genie in a bottle: once you get it out, it is tough to rein it back or change it. The pricing model is about when and how frequently you charge. Recurrent subscriptions are the predominant model for SaaS apps, and usage-based pricing is the model for infrastructure solutions. Usage-based pricing creates a beautiful alignment of incentives but is less predictable. Upfront credit purchases and commitments are efforts to make usage-based practice more aligned with the rigid corporate budgeting processes. You can be the premium solution or the affordable one. Both are legitimate approaches. But your pricing needs to be consistent with the rest of your strategy: with your product and distribution channels.  You can’t have an affordable solution distributed through an expensive enterprise sales force. In this case, you need to sell either online or through inside sales—the product better be simple and the sales cycle quick. Many technical founders are shy about asking for a lot of money for their product. Don’t be. If customers like the product and it delivers value, they will gladly pay for it. Unless you hear customer complaints that you are expensive, then for sure you are underpricing. Calculate the ROI of your product, and take 20% of that value as your price point. How much it costs you to build the solution should not guide your pricing. But you should do a sanity check that you have a decent gross margin. Most companies start by selling a single package. Over time, they realize that different customer segments have different maturity levels and willingness to pay. To price discriminate between these segments, you need to introduce multiple packages.  Start by creating a customer maturity curve to inform your decisions on how many packages you need. The trick is to have the smallest number of packages to cover the broadest range of customer needs. Your packages will change and evolve quickly as your product matures. 

I have spent years in the highs and lows of the consumer goods industry but never seen a pricing climate quite like this. Manufacturers are getting squeezed from every direction-tariffs, skyrocketing raw material costs, and relentless supply chain disruptions. The old playbook of raising prices to cover costs? That’s dead. Why? Because consumers are feeling the pressure too. A 2024 Nielsen report makes it clear: today’s shoppers are scrutinizing every dollar they spend, and brands that aren’t strategic about pricing risk losing market share fast. Here’s what I’m seeing from top CPG brands that get it: 1️⃣ Walmart is investing heavily in AI-driven pricing models to keep costs competitive-e-commerce now makes up 18% of total revenue. 2️⃣ PepsiCo is doubling down on pack-size innovation, offering smaller, affordable options to maintain volume without excessive discounting. 3️⃣ Luxury brands are using price elasticity models, testing demand thresholds before rolling out increases-avoiding consumer pushback. 4️⃣ Supply chain resilience is non-negotiable. Companies are shifting manufacturing away from China, despite short-term cost spikes, to avoid future geopolitical risks. The smartest brands aren’t just reacting. They’re rethinking. They’re moving toward Revenue Growth Management (RGM) frameworks that help them: ✅ Optimize pricing and promotions (because blanket price hikes are a losing game) ✅ Focus on margin-smart growth, not just revenue ✅ Leverage data analytics to make smarter, faster pricing decisions Brands that don’t evolve risk eroding profitability or pricing themselves out of the market. CPG leaders who master strategic pricing, operational efficiency, and consumer-driven value creation will own the future of this industry. Are you adjusting your strategy, or just reacting to rising costs? Because in 2025, only the most adaptable brands will win. #CPG #FMCG #PricingStrategy #RevenueGrowth #ConsumerGoods

One thing I push early-stage B2B founders to do (and it’s harder than it sounds) is to really understand — and quantify — the value you deliver to customers. Very few can put a dollar number on it.💡 Try to estimate the value your product creates for a customer in real dollars ($Z) 💰 Once you do that, , you can ask a few important questions to qualify how robust and urgent the value proposition really is: ▪️ Is $Z actually meaningful in the context of the customer’s business? (If it’s a rounding error for them, say <2% of top line, selling will be painful 😬) ▪️ Can you show or prove $Z quickly, or are you asking the customer to take a leap of faith? Quantifying value proposition also helps with 💵 pricing and 📐market size, which many founders struggle with early on. Example 1: cost / time savings ⏱️ - Say you’re selling software that saves a RevOps team ~5 hours per week. - Fully loaded cost is ~$80/hour → ~$20k/year in savings. That’s your $Z. - If you’re saving time or money, customers will often pay ~10–20% of that value. So a ~$2–4k ACV is a reasonable first pricing hypothesis 🎯 Example 2: revenue generation 📈 - Now say your product helps a sales team close 2 extra deals per quarter. - Each deal is worth ~$50k → ~$400k/year in incremental revenue. That’s $Z. - When you’re directly helping customers generate revenue, they’re often willing to pay more — say ~20–30% of the value. That points to an $80–120k ACV range (assuming you can prove the value). More importantly you can use $Z to estimate market size.  📐 Start bottoms up. Market = X customers × $Y ACV = market size Where: ▪️ $Y ≈ 10–20% × $Z (for cost/time savings) ▪️ $Y ≈ 20–30% × $Z (for revenue generation) Finally, pressure-test the assumptions: ▪️ Are we being precise about who “X customers” actually are? Do I need to sell a story where I start with a small #X and then expand? ▪️ Does $Y line up with real budgets and comparable spend? ▪️ Can we acquire customers for less than ~$Y/3? ▪️ Do we need more product to credibly charge $Y? You don’t need perfect answers early but a strawman that allows YOU to understand why you are willing to spend the next 10 years of your life working on something. 🚩

Many founders treat pricing as a revenue optimization problem. Figure out the product first, scale usage, then monetize. That's backwards. Pricing isn't about extracting money. It's about discovering whether you built something people actually value. At Gamma, we used pricing as a proxy for value and kept it pretty much the same for over 2 years. Free usage will lie to you (especially for B2B and prosumer products). Usage spikes feel like PMF. They're not. Usage without payment tests your onboarding, not your value. If you come out with too generous of a free plan, you'll never know what true willingness to pay looks like. Here's how to use pricing as a proxy for value: 1. Pick your value metric Choose the thing customers actually hire you for. Documents generated. API calls. Minutes transcribed. At Gamma, we gated by AI credits as the primary value metric, with business levers like custom branding. 2. Draw a hard boundary between free and paid Let people experience the "aha," then stop them at a generous but bounded gate. We gave users plenty of AI credits up front. Once they hit the limit: upgrade for access to more AI. 3. Research your range, then let behavior decide We used Van Westendorp to find our starting range. Ask users four price points: too cheap to trust, good value, getting expensive, too expensive to consider. Plot where these intersect to bracket your range. Then test a few prices within it. Research shows what people say they'll pay - conversion shows what they actually do. We watched free-to-paid conversion and early churn signals, picked the winner, and moved on. 4. Instrument retention and talk to customers Track whether paid users keep crossing your value threshold each week. Stay close to customers through power-user communities or direct outreach. Ask questions like: "What job were you hiring us for?" and "What would justify a higher price?" 5. Treat pricing changes like product pivots Once you've validated pricing, the only reason to change it is if you've fundamentally changed what you're selling. We haven't changed ours in two years because the value metric (AI usage) hasn't changed. Constantly repricing means you're still searching for product-market fit. Why this matters: Pricing early clarifies who values you, which channels convert, and which segments to double down on. You're better off launching pricing way earlier so you can see who's actually willing to pay for it.

Margin vs Markup: What's the Difference? 🤔 Let’s find out! After 10+ years in finance and accounting, I've seen countless professionals mix up these two crucial metrics. Let's break them down once and for all! Whether you're pricing products, analyzing profitability, or making strategic decisions, understanding the distinction between margin and markup is ESSENTIAL. Let's dive in 👇 ➡️ Margin Margin shows the percentage of your selling price that's PROFIT. Formula: (Selling Price - Cost) / Selling Price For example, if a pair of sneakers sells for $200 and costs $150 to buy, the margin is (200 - 150) / 200 = 25% ➡️ Markup Markup is the percentage ADDED TO THE COST price to get the selling price. Formula: (Selling Price - Cost) / Cost Using the same example, if a pair of sneakers costs $150 and is sold for $200, the markup is (200 - 150) / 150 = 33.33% ➡️ Why Should You Care in Accounting? A few reasons... 1️⃣ Pricing Strategy: Markup helps you ensure all costs are covered and desired profit margins are achieved. You don't want to sell shoes at a loss! 2️⃣ Profit Analysis: Understanding margin helps in analyzing profitability and making informed decisions on which product lines to expand or discontinue. 3️⃣ Competitive Analysis: Both metrics help you understand how your pricing compares to competitors. Are you leaving money on the table, or pricing yourself out of the market? ➡️ Pro Tip: Consistency is Key When sharing financial information with your team or stakeholders, be clear about which metric you're using. Mixing them up can lead to confusion and poor decision-making. Always specify whether you're talking about margin or markup to keep everyone on the same page. === That's my breakdown on the crucial difference between margin and markup, with a little help from our local shoe store. How do you use these metrics in your business? Have you ever encountered confusion between the two? Step into the discussion in the comments below 👇

Set and forget is not a pricing strategy ! Price--> Design--> Build We know that's what everyone says, but thats an oversimplification of what the entire process should look like. The assumption your pricing was correct in the pre-design phase and doesn't need change is dangerous, dangerous, dangerous !! I have seen too many physical and software products change drastically between initial design to final delivery. Product owners will typically assume that pricing still holds. You have to change that philosophy. In the real world we need a lot more iteration in price: Step 1: Initial Price: This stage you quantify the value and set an initial target price. This is a combination of internal/external research, some value quantification and pricing knowledge. Step 2: Design: With that price info, the product team designs a product that hits product and profitability targets. This is also where you need to keep track of the product margins. Often product will go design a better product at the expense of higher cost, and margins suffer before launch. Step 3: Reprice: Now that we know the new design constraints that impact the profitability, this stage gives you the opportunity to reprice the product based on the design. If substantial value has been added, price should go up. Do not fall into the 'lets over deliver on value and keep price same' trap. Step 4: Build: Now with that new price info and product roadmap the product goes through the build stage. Step 5: Pre launch reprice : Now significant time may have passed since last price review. The market for the product, the economy etc may have changed. This stage can assist in making last changes before product goes out. Good time to also establish guardrails for price performance, discount strategy, or sales strategy. Step 6: Launch: Goes without saying the product is out in the real world. Great way to capture feedback. Also a stage where performance is measured against the price guardrails. Step 7: Reprice 3: Based on sales feedback, you start charting next steps. Selling too slow, you may need discount or reprice. Selling too fast, it may be overdelivering on price vs value. Pricing metric may need change. Fx may have changed. This is the price adjustment stage, should be annual or semi annual. You can incorporate these steps into new product introduction framework or annual or semi annual pricing strategy process, either ways it will help establish good pricing principles in the org. I know of many products that once designed were never repriced years into its life.. Surely things must have changed all those years... Think of Pricing as a lifecycle !! -------------------------- We are in #Pricingtribe.

Our most underestimated pricing tool? AI. It’s easy to assume that pricing is all about intuition or guesswork, but AI is transforming how businesses approach price optimization. However, AI isn’t a one-size-fits-all solution—it’s a tool that, when used right, can drive smarter, data-backed decisions. Here’s why AI matters for your pricing strategy: → Dynamic Adjustments AI helps businesses adjust pricing in real-time, responding to shifts in demand, market conditions, and competitor activity. It ensures prices are always competitive and aligned with the market. → Data-Driven Insights By analyzing large sets of data—like past sales, customer behavior, and trends—AI helps identify the best price points to maximize profit without alienating customers. → Personalized Pricing AI enables businesses to tailor prices to individual customer segments, increasing both loyalty and conversion rates while optimizing profit margins. → Simulated Scenarios AI allows companies to simulate different pricing strategies and predict their outcomes. This way, businesses can test new approaches without taking unnecessary risks. So, how can you leverage AI in pricing? → Start Small Begin by integrating AI tools that align with your existing pricing strategies, and gradually scale as you learn. → Combine AI with Human Insight AI is a powerful tool, but it needs human judgment to adapt to the nuances of the market and customer sentiment. → Embrace Dynamic Pricing Implement AI-powered dynamic pricing models that adjust in real-time based on factors like demand and competitor actions. AI isn’t just a trend—it’s a game changer for smarter pricing strategies. It’s time to stop guessing and start optimizing. How are you using AI to optimize your pricing strategy? Let’s talk!