"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)
Pricing Strategies For Online Products
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Massive news that marketers must understand Digital was built on the promise of being "better" than TV. Now, it's being sold increasingly like linear TV. Isn't that hilarious? Social gunned for targeting now it pushes broad reach. There are more unskippable formats for long-form viewing, like YouTube. Prime introducing ads as standard. Now, Meta appears to be moving to principal-based trading. Principal-based trading occurs when a media owner sells a large chunk of inventory to an agency, which the agency then marks up and sells to clients. It can be incredibly profitable for the owner and agency and guarantees steady revenues. This practice (partly) originally divided creative and media, with media agencies splitting off to run their "not messy" numbers-focused businesses. A great quote from the below: this is the practice of being "transparently un-transparent". This cynical view is born from the idea that if an agency buys ยฃ100mil of media inventory, they won't build strategies and plans to deliver incremental effectiveness. Instead, they'll push whatever they've bought. It's hard to argue that this won't be an outcome here. And this is another step to huge hold cos winning clients on the promise of discounts and efficiencies. However, principal-based trading has always been around, and there have still been HUGE jumps in media strategy, effectiveness and planning from brilliant teams. Attention-based incrementality planning, MMM analysis and signalling theory. It feels wrong not to mention the work of EssenceMediacom, Richard Kirk, Jellyfish, Lumen Research, Thinkbox and Amplified Intelligence here. It should be simple for advertisers to unpick this mess. If you want to lower your cost per reach (CPM) and save some cash, go with whoever sells you on discounted bulk media. If you want incremental growth, use a media planning team that can add value and deliver a connections plan specifically for your brand. Sometimes, these two will overlap. The real losers here are brand managers who consistently need to up their media knowledge and deliver growth with an increasingly complex media mix. I share #advertising and #marketing insights daily, follow for more. https://lnkd.in/e-QRuBfs
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AI pricing is broken, and everyone knows it. Orb just analyzed 66 AI companies and found something interesting: ๐๐% ๐ก๐๐ฏ๐ ๐๐ฅ๐ซ๐๐๐๐ฒ ๐๐ข๐ญ๐๐ก๐๐ ๐ฌ๐ข๐ง๐ ๐ฅ๐-๐ฆ๐จ๐๐๐ฅ ๐ฉ๐ซ๐ข๐๐ข๐ง๐ . Quietly, completely, across the board. Why? Because usage is unpredictable, infra costs are high, and old SaaS pricing just doesnโt cut it anymore. Weโre not pricing features anymore. Weโre pricing intelligence. Some insights from the report: โพ๐๐ฒ๐๐ซ๐ข๐ ๐ฉ๐ซ๐ข๐๐ข๐ง๐ ๐ข๐ฌ ๐ญ๐ก๐ ๐ง๐๐ฐ ๐ฌ๐ญ๐๐ง๐๐๐ซ๐ย โ 92% blend subscription, usage, freemium, and tiers in one structure โพ๐๐ก๐ ๐ฆ๐จ๐ฌ๐ญ ๐๐จ๐ฆ๐ฆ๐จ๐ง ๐๐จ๐ฆ๐๐จ? Subscription + usage + freemium + tiered plans โพ๐๐๐ซ-๐ฌ๐๐๐ญ ๐ข๐ฌ๐งโ๐ญ ๐๐๐๐, ๐๐ฎ๐ญ ๐ข๐ญโ๐ฌ ๐ง๐๐ฏ๐๐ซ ๐๐ฅ๐จ๐ง๐ โ 85% of companies using SaaS pricing now pair it with usage-based pricing โพ๐๐% ๐ซ๐ฎ๐ง ๐ฆ๐ฎ๐ฅ๐ญ๐ข๐ฉ๐ฅ๐ ๐ฆ๐จ๐๐๐ฅ๐ฌ ๐ข๐ง ๐ฉ๐๐ซ๐๐ฅ๐ฅ๐๐ฅ โ often segmenting between business and individual users โฆ This shift is more than cosmetic. It reflects a deeper reality: AI products donโt fit cleanly into legacy monetization models. They need pricing systems that scale with usage, support experimentation, and reflect actual value delivered. If youโre building in AI, your pricing strategy isnโt just a detail, itโs a growth lever. ๐Full report https://lnkd.in/g-R3_cwU Itโll reshape how you think about monetizing AI.