Hey, I've been thinking about the common issue with invoices not being paid on time, or not at all. Guidelines on this subject usually converge to the same playbook: reminders, dunning flows, escalation, incentives. It makes sense, but it also gets complex quite fast. Most of it still revolves around chasing people to pay. I'm exploring a much narrower angle: Instead of improving dunning, reduce friction in the payment step itself. Think payment links, QR codes, simple payment page linked to an invoice ... In your experience, do frictionless payments actually help with payment collection?

I've been thinking about pricing in the context of AI-driven quoting which leads to highly dynamic pricing. Let's say we get really good at coming up with prices that maximize conversion (AI + sales). But what happens once the customer commits and we start charging on a regular basis? That's where I see a gap. In most setups I've seen, we lose the context behind the price very early on: - why is this customer paying $X instead of $Y? - was it an AI suggestion, a discount, or a manual override? - what inputs influenced the price point? I'm thinking about this problem from the perspective of plan migrations, where we often don't know how to handle certain (small) cohorts with non-standard billing setup. In your experience, is it a common practice to link pricing decisions (CRM / quoting + context) to the actual billing objects (subscriptions, customer records, etc.) in a structured and automated way?

Curious to hear how other pricing strategists and operators are using AI in their workflows. Are you just getting started, or do you have a full flow built? What tools do you like vs. which tools are causing you the most trouble? I'm especially curious if anyone has developed a way to leverage AI to refine ICPs?? Or if anyone has found any good Claude skill files specific to pricing? I'll kick off! Some context: We're refining our sales-led pricing model at the moment and need to move quickly, so I don't have the support to run formal research. I'm also a team of one at a Series A company that just underwent a massive change in how we deliver our product 😅 I've been using Claude to refine new pricing structures and develop HTML pricing calculators for sales-led deals. I've been funneling context to Claude using our product's MCP server, knowledge files, and more, which have allowed me to develop and pressure-test various pricing strategies that I have then vetted with cross-functional leadership and sales as viable to test. My sales team now has a self-service hub they can rely on to run with these new models for certain deals. The hub provides guidance on choosing a model, the value story for the chosen model, pricing, and ROI calculations. Would love to hear what everyone else is doing!

Hey all, I'm new to the community and interested to hear if anyone else has successfully used Claude Skills to augment/influence sellers during the sales cycle. I've built a few skills that are showing some promise but I haven't asked my team to adopt them yet. I am just running the skills against deals in hubspot to spot check their quality. Curious if anyone has and if it worked well.

I just added this to a comment so sharing for visibility: The SaaS Monetization Tech Stack Billing has Stripe. Analytics has Amplitude. Feature flags have LaunchDarkly. Etc. But the system that decides when to upsell a user, what usage limit to enforce, or which paywall variant to show? That usually lives in a tangle of product code, feature flags, and duct tape... A few things that have came up: - The fragmentation problem is worse than most teams realize. Entitlement logic ends up duplicated across billing, product code, and feature flags. A simple pricing test that should take a day takes weeks because three systems need to stay in sync. My CTO (ex-Atlassian, Dropbox) is really good at explaining the risks/headaches from this "brittleness" to technical folks! - Mobile solved this years ago. RevenueCat and Superwall own the monetization layer for iOS/Android apps. Web SaaS has no equivalent — most teams are still stitching it together manually. The above content is really resonating with an ex-Revenue Cat guy who is now Head of Product at a web SaaS business. - The main benefit of getting it right (in-house or via tooling) is experiment velocity