Launch HN: Corrily (YC W21) – Price Optimization for SaaS

Hi HN! We are Abel [abelr] and Andrej [neophocion] of Corrily (https://www.corrily.com/ https://www.corrily.com/). We’re building a price optimization service for subscription and usage-based companies. By wrapping our API around the prices you display on your frontend and integrating with Stripe, we allow you to experiment with your pricing and find optimal prices, and localize them to the user’s country. When we met, we were both quants, and Abel was focusing on price-formation and market dynamics for a hedge fund (this was before WSB became the authority for price-formation). We got along well, did weird side projects such as parsing 17th Century newspapers and throwing them at NLP models, and decided to launch a startup. Before getting accepted into YC we were working on a slack bot of all things. We probably got into YC, in part at least, thanks to this HN post (https://news.ycombinator.com/item?id=24886936 https://news.ycombinator.com/item?id=24886936) which made it to the top page a day before our interview and allowed us to get around 60 leads. But we had trouble pricing our service. So we took a deep dive into how businesses do it currently, and were… underwhelmed. Surveys! Whether Van Westendorp [1], Conjoint analysis [2], or Gabor-Granger [3], pricing generally involves sending out surveys to people asking how much they would pay for a service. It’s expensive (a Simon-Kucher engagement will cost you in the high 6-figures), time-consuming (~9 months), and based on ex-ante perception rather than empirical evidence. We strongly believe in charging a fair price, inclusive of the purchasing power of each country. And it also makes economic sense. But because it is so hard to do price-experimentation, only large companies can adapt their pricing per country. Right now, a slack plus seat costs $12 in the US and $6 in India. Netflix’s monthly subscription will range from $3.75 in Argentina to $19.12 in Switzerland. So we dropped the chatbot and built Corrily! Subscription pricing has an interesting psychological dimension which makes the prices you display linked to each other. The price of your first tier will influence the conversion rate of your second tier, and the annual discount you give will influence the conversion rate of your monthly subscription. Our favourite example of the psychological dimension of pricing is described in Predictably Irrational by Dan Ariely. The Economist for a while had 3 subscriptions: an online subscription for $59, a print subscription for $125, and an online + print subscription for $125. The reason to add this odd print subscription was because it reframes the difficult question in the purchaser’s mind from “how much am I willing to pay for The Economist?” to the much easier question “Which of these offers is the best bang for my buck?”. The way we solve this is by testing all prices at once using Bayesian Optimization. We continuously measure the RPV and LTV of users based on the set of pricing they are shown. We briefly experimented with multi-armed bandits to decide when to show a user an experiment, and found that quant finance techniques used for trading ETFs at VWAP perform much better. Corrily is easy to use and integrate with. Here’s our developer portal with some easy-to-use docs to get you going https://doc.corrily.com https://doc.corrily.com . Let us know if you’re interested in a test API key. We’ve seen early signs of companies growing their conversion rates by >30% as a result of integrating Corrily. It’s not something we’re prepared to shout from the mountaintops just yet, but it is a sign that there are many mispriced prices out there. We’re here to try and fix that. We’re long time fans of HN and have grown up reading it. Any and all feedback or criticism is greatly appreciated. Thanks ~ Abel and Andrej [1] https://en.wikipedia.org/wiki/Van_Westendorp%27s_Price_Sensitivity_Meter https://en.wikipedia.org/wiki/Van_Westendorp%27s_Price_Sensi... [2] https://en.wikipedia.org/wiki/Conjoint_analysis https://en.wikipedia.org/wiki/Conjoint_analysis [3] https://en.wikipedia.org/wiki/Gabor%E2%80%93Granger_method https://en.wikipedia.org/wiki/Gabor%E2%80%93Granger_method