Pricing Strategies For Online Products

"Is $20/month too much for our product?" Instead of guessing, we used the Van Westendorp method to find our pricing sweet spot. 4 questions revealed exactly what users would pay (and we haven't touched our pricing since). Here's the framework any founder can steal: 1. Send a survey to actual users, not prospects We surveyed people already using Gamma. They understood the real value of our product, not hypothetical value. Too many founders survey their waitlist or randomly select people who have never used their product. That's like asking someone who's never driven about car prices. 2. Ask these 4 specific questions - At what price would this be too expensive for you to consider it? - At what price is it expensive but still delivering value? - At what price does it feel like a bargain? - At what price is it so cheap you'd question if it's reliable? These create bookends for perceived value. You're mapping the entire spectrum of price psychology, not just asking "what would you pay?" 3. Plot the responses and find where the lines intersect Graph responses from lots of users. Where "too expensive" and "too cheap" lines cross: that's your acceptable range. Where "expensive but fair" meets "bargain": this is your optimal price point. 4. Test within the range, don't just pick the middle The intersection gives you a range, not a number. We ran pricing experiments within that range to see actual conversion rates. A survey shows willingness to pay; testing reveals actual behavior. 5. Lean towards generous (especially for product-led growth) We chose to be more generous with AI usage than our "optimal" price suggested. Word-of-mouth growth matters more than maximizing initial revenue. Not everything shows up in the numbers. 6. Lock it in and stop tinkering Once you find the sweet spot through data, stick with it. We haven't changed pricing in 2 years. Every month debating pricing is a month not improving product. Remember: pricing is a signal, not just a number (Image: First Principles)

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

Salesforce just fired the starting gun on a seismic shift in how we pay for software. At Salesforce #Agentforce, they announced they’re moving away from the traditional per-seat SaaS model to a consumption-based pricing for their AI agents. This is huge. Why? Because it signals the end of paying just to have access to technology. Instead, we’re moving toward paying for outcomes—the actual value delivered. Think about it. In a world where AI agents can perform the job functions of entire departments, does it make sense to charge per seat? Probably not. Here’s what’s changing: - From access to outcomes: Companies will pay for what the AI actually accomplishes. - From subscriptions to value: Pricing adjusts based on usage and results. - From Software-as-a-Service to Agent-as-a-Service: Technology that collaborates with you as a partner This isn’t just a tweak in pricing—it’s a radical upending of commercial models for large SaaS companies. What does this mean for businesses? - Budgeting will evolve: Costs align directly with value received. - ROI becomes clearer: Easier to measure the direct impact of technology investments. - Greater flexibility: Scale usage up or down based on needs without worrying about seat counts. It’s an exciting time, but also a challenging one. Is every SaaS company ready to embrace a model where companies pay directly for the value they receive? At Uniti AI, we’ve been thinking along these lines. We price our AI agents based on the amount of work they do, not on how many seats a company has. I believe this is the future. What do you think? Is the per-seat model on its way out?

Google just rolled out one of the biggest shifts I’ve seen in local search… Those new buttons showing up in search results? “Online estimates” “Have AI check prices” Here’s what most contractors don’t realize yet: Google’s AI is now calling businesses directly, on behalf of the customer, to compare pricing, availability, and service details… and then sending the customer a ranked summary. That means: - No website visit. - No form fill. - No sales call. - Google’s AI becomes the shopper. And your business is being compared side-by-side whether you like it or not. But here’s the real future punchline: The companies that win in the next 12–24 months will be the ones with transparent, accessible pricing — everywhere customers (and AI agents) look. Because if Google can’t find your pricing? It will find your competitor’s. What this means for home-service businesses: - Pricing pages on your website are no longer optional. - Price-range FAQs aren’t “nice to have” — they’re AI-fuel. - “Cost of ___ in [Your City]” articles will drive traffic and help Google AI extract accurate pricing signals. - Internal links from those articles back to your service pages increase relevance and trust. - Your CSRs must have confident price ranges ready — because they’re no longer just talking to customers… they’re talking to Google’s AI agents too. - Inconsistent pricing across your site, GBP, or your CSRs? Google’s AI will see that as uncertainty — and uncertainty means you won’t be surfaced as the best option. This isn’t just an update. It’s a new buying model. We’re entering the era of AI-assisted consumer decisions, where Google becomes the middleman that filters, compares, and routes the customer to whoever is the clearest, fastest, and most transparent. If your business doesn’t adapt, Google will adapt for you… and you probably won’t like the version they create. I’m already helping clients align their: - GBP listing optimization - Pricing content pages - Schema markup & transparent pricing in FAQs (because these can easily become featured snippets) - Service pages - CSR scripting - Cost-based articles …so they’re the obvious choice before AI even compares them. If you want to stay ahead of this shift — instead of getting squeezed out by it — let’s connect. AI calling your competitors for your customers isn’t the future. It’s happening right now.

AI pricing is broken, and everyone knows it. Orb just analyzed 66 AI companies and found something interesting: 𝟗𝟐% 𝐡𝐚𝐯𝐞 𝐚𝐥𝐫𝐞𝐚𝐝𝐲 𝐝𝐢𝐭𝐜𝐡𝐞𝐝 𝐬𝐢𝐧𝐠𝐥𝐞-𝐦𝐨𝐝𝐞𝐥 𝐩𝐫𝐢𝐜𝐢𝐧𝐠. Quietly, completely, across the board. Why? Because usage is unpredictable, infra costs are high, and old SaaS pricing just doesn’t cut it anymore. We’re not pricing features anymore. We’re pricing intelligence. Some insights from the report: ◾𝐇𝐲𝐛𝐫𝐢𝐝 𝐩𝐫𝐢𝐜𝐢𝐧𝐠 𝐢𝐬 𝐭𝐡𝐞 𝐧𝐞𝐰 𝐬𝐭𝐚𝐧𝐝𝐚𝐫𝐝 – 92% blend subscription, usage, freemium, and tiers in one structure ◾𝐓𝐡𝐞 𝐦𝐨𝐬𝐭 𝐜𝐨𝐦𝐦𝐨𝐧 𝐜𝐨𝐦𝐛𝐨? Subscription + usage + freemium + tiered plans ◾𝐏𝐞𝐫-𝐬𝐞𝐚𝐭 𝐢𝐬𝐧’𝐭 𝐝𝐞𝐚𝐝, 𝐛𝐮𝐭 𝐢𝐭’𝐬 𝐧𝐞𝐯𝐞𝐫 𝐚𝐥𝐨𝐧𝐞 – 85% of companies using SaaS pricing now pair it with usage-based pricing ◾𝟏𝟐% 𝐫𝐮𝐧 𝐦𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐦𝐨𝐝𝐞𝐥𝐬 𝐢𝐧 𝐩𝐚𝐫𝐚𝐥𝐥𝐞𝐥 – often segmenting between business and individual users … This shift is more than cosmetic. It reflects a deeper reality: AI products don’t fit cleanly into legacy monetization models. They need pricing systems that scale with usage, support experimentation, and reflect actual value delivered. If you’re building in AI, your pricing strategy isn’t just a detail, it’s a growth lever. 📊Full report https://lnkd.in/g-R3_cwU It’ll reshape how you think about monetizing AI.

Pricing shouldn’t feel like a fight. It should feel like a fair conversation between adults who both want the relationship to last. When costs keep rising and margins start to feel thin, the worst thing we can do is spring a surprise increase and hope customers accept it. The better path is to make small, evidence-based adjustments that people can understand, and to do it with enough notice that trust grows rather than erodes. Here’s how I guide teams through it... We set a simple rule first: price reviews happen on a predictable cadence, anchored to a sensible index, and capped so there are no surprises. Then we give customers a choice. A clear Good / Better / Best set of tiers lets people pick the value that fits, and it means we stop discounting just to “make it work.” For loyal customers, we start with a grace period and then move in small, scheduled steps. It’s respectful, and it smooths cash flow for everyone. We also swap blanket discounts for an early-pay credit that protects the list price while bringing cash forward. We add a few fair boundaries so small, urgent, or high-touch work is priced to match the effort. Where costs have increased in one part of the service, we re-bundle so value is obvious and buyers are never misled. And when it’s time to talk, we keep the message short and human: here’s what changed in our input costs, here’s the adjustment we’re making, and here’s what stays the same in terms of quality and scope. If you track a few signals for 30 days, you’ll see better results like: most eligible accounts receive the scheduled uplift, the overall discount rate falls, more invoices are paid early, average revenue per customer increases, and churn and NPS hold steady. The goal is pricing that is predictable, and defensible. Think caliper, not hammer, with measured moves that protect margin and maintain customer goodwill. How do you explain price changes to customers without losing trust? ------- ➕ Follow Jonathan Maharaj FCPA for finance‑leadership clarity. 🔄 Share this insight with a decision‑maker. 📰 Get deeper breakdowns in Financial Freedom, my free newsletter: https://lnkd.in/gYHdNYzj 📆 Ready to work together? Book your Clarity Session: https://lnkd.in/gyiqCWV2

Brands lose 55% more when they sell on E-Commerce Almost all the brands are selling on E-Commerce, but they make 40-55% less revenue every time they sell through Amazon or Flipkart But how is that possible? General Trade (GT) - Retailer Margin = ₹13 - Distributor Margin = ₹3.38 - Total Fees: ₹13 + ₹3.38 = ₹16.38 - Final Realization: ₹100 - ₹16.38 = ₹83.62 E-commerce (Amazon/Flipkart - Avg.) - Distributor Margin = ₹3.85 - Referral + Closing + Pick & Pack + Storage: ₹4 + ₹7 + ₹14 = ₹25 (Avg.) - Shipping Fees: ₹40 (Avg.) - Total Fees: ₹3.85 + ₹25 + ₹40 = ₹68.85 - Final Realization: ₹100 - ₹68.85 = ₹31.15 This is 70% lower realisation and even in the best of the case, the realisation of revenue is 40-55% down for all FMCG companies Then why do the brands still sell on these platforms? - Access to pan-India/global markets without needing physical store presence - 24/7 availability—sales happen anytime, anywhere - No dependency on intermediaries (retailers, distributors) - Data-driven insights: Consumer behavior, preferences, repeat purchases - Helps new brands & D2C players establish without massive offline investment - No stock pressure from retailers demanding minimum supply - Real-time pricing changes based on demand, seasonality - Test new SKUs quickly without retail negotiations E-Commerce has started to become big, and it has the power to make or destroy brands Game Theory in E-commerce Expansion 1️⃣ Prisoner’s Dilemma – Brands know e-commerce reduces margins, but if one enters and others don’t, the first mover gains visibility and market share, forcing others to follow 2️⃣ Nash Equilibrium – Even though sticking to GT/MT is more profitable, all brands sell on e-commerce because no one can afford to stay out without losing relevance 3️⃣ First-Mover Advantage & Lock-in – Early adopters dominate search rankings, platform recommendations, and customer habits, making it harder for late entrants to compete Now with Quick Commerce, do you think the situation is better or worse? #brands #marketing #distribution #sales #ecommerce #qcommerce

Stop copying competitor pricing. These 4 questions will tell you exactly what your specific customers will pay. When we first launched Attic salt, we spent n no of weeks trying to figure out a pricing strategy that will work. Attic Salt is democratising the fashion by bringing in value at a sharp price yet we have to maintain fair wages for our artisans and  technicians who bring the garment alive with so much innovation,skill and dedication. Then I found the Van Westendorp Pricing Model, a simple 4 question method helps you understand how customers really see your price. Used by brands like Dropbox, HubSpot, and Mailchimp, the Van Westendorp model was developed by Dutch economist Peter Van Westendorp.    Here's how it works… You ask potential customers four key questions about price: 📍At what price would this product feel too cheap to trust? 📍At what price would it feel like a good deal? 📍At what price would it start to feel expensive but acceptable? 📍At what point would it feel too expensive to buy?     Now plot these answers on a graph. The intersection points reveal your: Indifference Price Point → where people are split between “cheap” and “expensive”Optimal Price Point → where hesitation from both ends is minimal Acceptable Price Range → your sweet spot for maximum traction When we used this model, we realized we were underpricing. Customers thought the product was “too affordable to be good.” We adjusted, and sales went up without changing a single feature. If you’re launching something new or entering an unfamiliar market, don’t guess. Use this model. Gut feelings are great for design. Not for pricing. Are you still trusting yours? #PricingStrategy #ConsumerInsights #D2CBrands #FashionBusiness

Last weekend, someone in the AI Junto—a community of elite AI practitioners I'd put in the top 1% of users globally—asked a question that stopped me cold: "Is the $200/month pro subscription really worth it?" I had to read it twice. Here's someone who's been experimenting with AI for over a year, someone who regularly shares brilliant use cases, someone who should know better—and they're questioning whether frontier AI capabilities are worth $200. That's when it hit me: if our most advanced users are hesitating over $200, we've got a creativity problem, not a cost problem. Here's what's happening: I call it “The Subscription Psychology Trap.” Most people have exactly two categories of recurring expenses. Either we pay $10-20 for Netflix or ChatGPT, or we pay hundreds for a car payment or thousands for rent. There's literally nothing else in that $200 range. So when someone sees that price tag, they immediately anchor to other subscriptions. "Wow, I would never spend $200 on coffee, so I can't do this." But it's not a coffee subscription. It's not even a ChatGPT subscription. It's cognitive augmentation. The real calculation isn't "$200 vs. Netflix." It's "$200 vs. your monthly rate" or "$200 vs. the monthly rate of the next best available human." When you frame it correctly, it becomes absurd NOT to subscribe. Confession: I can relate to the hesitation because I made the same mistake. I actually didn't subscribe to Pro for months because I didn't think it was relevant to me. It was only after hearing a founder I admire talk about the "no-brainer" math that I started thinking in terms of capping my downside. At worst, I thought, I'd lose $200—not nothing, but not an insurmountable loss either. Even if I decided it wasn't worth it, I'd gain invaluable experience with capabilities that weren't available to me before. Here's what happened in my first week: I found myself shoulder-to-shoulder with the CEO of a multi-billion dollar private business, tasked with evaluating international expansion. We needed a comprehensive marketing and competitive analysis strategy briefing for the potential new market entry. What would have taken me three days to compile—even with a paid ChatGPT Plus account—we assembled in 30 minutes with Pro's reasoning capabilities. Thirty minutes. We're talking market sizing, competitive landscape mapping, go-to-market strategy options, risk assessment—the works. When I presented the analysis, the CEO's mind was blown. Not just by the speed, but by the comprehensiveness and strategic depth we'd achieved in half an hour. The boost to that relationship alone paid for the subscription for the year ahead. Which brings me to my new favorite diagnostic: The $200 Creativity Test (more in the link in comments)

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.