Hi team, is there any public case study that talks about how an organization with consumption-based pricing model moved from one value metric to another one? What are the operational steps they followed?

Atlantic interview with an economist digging into inflationary causes especially in the grocery industry. Lots of great references to academic research in supply chain, and behavior concepts like price wars, prisoners delimma, rocket/feather pricing... Whitelable/store brands get a focus near the end. (It's entirely US centric discussion, FYI for this broader global audience.) https://www.theatlantic.com/podcasts/archive/2024/10/greedflation-inflation-grocery-prices-corporate-greed/680432/?utm_campaign=the-atlantic&utm_content=true-anthem&utm_medium=social&utm_source=linkedin

I'm writing a guest chapter on AI pricing for another authors upcoming book. So I'm thinking about this a lot lately, and wanted to give you a sneak-preview of a linkedin post that will come out later next week. - I've done maybe 10 pricing projects involving significant AI functionality this year. Here is how I think about it. AI should be considered a 2-layer stack: 1️⃣ The AI compute 'fuel' (i.e. token pricing at OpenAI) 2️⃣ The AI solution (i.e. the value you add on top of AI) The dilemma with AI pricing is that currently: ◾ Fuel is expensive - at least way more so than traditional SaaS. ◾ Solutions are immature and early stage, not yet adding a lot of value. So any AI pricing model needs to both work today AND tomorrow. AI PRICING TODAY: 🔹 Charges usage-based on fuel consumption to ensure costs are covered, as usage patterns of customers is often unpredictable. 🔹 Is mostly focused on low barriers to entry to get users onboarded in order to develop the solution layer and get data on behaviour and cost patterns. 👉 This is unsustainable as fuel costs will drop and 2025 customer will refuse to pay a price-per-token (or token equivalent) that is based on 2024 token costs. This is especially true for enterprise. AI PRICING TOMORROW: 🔹 Charges based on the outcome created by the solution layer and just factors in fuel costs in the use case. 🔹 Protects against cost-downside of over-usage with 'fair usage limits'. HOW I SOLVE FOR THE TODAY-TOMORROW PROBLEM IN AI PRICING 🔸 Focus on speed: just get usage and adoption as fast as possible. You likely have a core non-AI product that monetizes just fine. Consider AI a 'development budget' and focus on profitability later. 🔸 Tell customers you are BOTH charging for fuel and for a solution outcome. Educate them. But keep it 90% fuel and 10% solution early on. 🔸 Over time: shift $$ from fuel to solution pricing. Cut Fuel pricing aggressively, even anticipating future cost reductions. Consider solution pricing separately and from a value perspective.

Hi guys Find below a 38min video teardown of Aumico.io - a financial statement / consolidation solution for accountants. I've done 3 such projects in the past, including quite a bit of competitor research in this market. My advice to Aumico (the short version): 1) Consider selling to banks - they have a far more valuable use case for your solution. 2) Consider a Freemium over your current trial. 3) Don't split out new functionality in additional modules - keep it in and raise prices. 4) Add services to the mix - both one-off onboarding and ongoing year-end support. You can see the full email from Chris /Aumico below - and thank you to that team for playing along! Let me know what you think in the comments - did I get it right? Something you would have done differently? Do you have any advice for Chris on how to grow this? /Ulrik ----------------------- A) You will find our pricing here: https://aumico.io/en/accountant/ For example, you are an accountant with 35 clients. You will buy a license for each client. ( 35 x 65 CHF = CHF 2'275) licenses will be renewed automatically. B) 1) We simplify year-end closing for accountants. Our main target group is accounting companies in the DACH region. We currently have one Modiul but soon launch the next one. 1. Modul 1:  financial statements engine (existing) The pricing above relates to this service 2. Modul 2: financial closing: documentation of financial statements (will be released on October 15th, 2024 (The pricing is not determined yet. Idea is to duplicate the existing 3. How we do sales: Outbound mailings, push into the trial period and convert (own sales org.) 2) Metrics 1.  ARR= CHF 282'000 2. 140 customers 3. Churn rate: 5% 4. Annual growth rate: not sure how to calculate correctly. 5. 2021: CHF 14'000, 2022: CHF 122'000, 2023: 226'000) 3) Goals 1. Increase revenue 2. Increase average deal value 3. make pricing simpler for the client and us

Event with some speakers who are knowledgeable: https://www.benchmarkit.ai/saas-metrics-palooza-24