Bit of pricing advice: businesses making a lot of money don’t mind paying for software. I think the $10 flat fee will come back and bite you in the future. SaaS metrics was so important for us and we were dropping $150 a month on ChartMogul when our MRR was only $3000.
Show HN: Revenue Forecasting for SaaS Apps
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Re: Show HN: Revenue Forecasting for SaaS Apps
#12Re: Show HN: Revenue Forecasting for SaaS Apps
#13Bit of pricing advice: businesses making a lot of money don’t mind paying for software. I think the $10 flat fee will come back and bite you in the future. SaaS metrics was so important for us and we were dropping $150 a month on ChartMogul when our MRR was only $3000.
Interesting. Did it help figuring it out your pricing?
Re: Show HN: Revenue Forecasting for SaaS Apps
#14One feature that I think will help smaller SaaS businesses: Revenue Calendar.
For example, on Jan 1, $500 will be charged, Jan 2, $250 will be charged, etc..
Sure, it's great that in a given month you will process X in MRR, but knowing when that ends up being charged is always a plus for SMBs.
Re: Show HN: Revenue Forecasting for SaaS Apps
#15a bigger player is https://www.zodiacmetrics.com/ , who have many peer-reviewed papers and are thus transparent with their (general) forecasting approach. i would love to see some model details given the maturity of other teams. (disclosure: i took a class with the prof whose research drives zodiac)
Zodiac is definitely worth a look. They did a pretty good job of predicting our long-term user’s future revenues. Also all of the academic papers around it are good reads.
That said, that didn’t solve our problem because what we really wanted was predictions of our new customers. So we could quickly see whether or not their quality was changing. I just wrote something up about where we landed. https://engineering.ezcater.com/early-prediction-of-customer...
Re: Show HN: Revenue Forecasting for SaaS Apps
#16You should consider using a production build of react for your front-end!
Re: Show HN: Revenue Forecasting for SaaS Apps
#17Staring at a blank screen, wondering why.
2.3MB JS on your home page...
Re: Show HN: Revenue Forecasting for SaaS Apps
#18Bit of pricing advice: businesses making a lot of money don’t mind paying for software. I think the $10 flat fee will come back and bite you in the future. SaaS metrics was so important for us and we were dropping $150 a month on ChartMogul when our MRR was only $3000.
Interesting. Did it help figuring it out your pricing?
I wouldn't say we used Chartmogul as a basis for our pricing. I do think a forecasting feature in Chartmogul would have helped us realise much quicker that our current pricing had us on an unsustainable path.
We got Chartmogul to be able to gauge the health of our startup, and for that it's been worth every penny. I can't sing their praises enough.
Re: Show HN: Revenue Forecasting for SaaS Apps
#19Earlier quoted context omitted.
Interesting. Did it help figuring it out your pricing?
I wouldn't say we used Chartmogul as a basis for our pricing. I do think a forecasting feature in Chartmogul would have helped us realise much quicker that our current pricing had us on an unsustainable path. We got Chartmogul to be able to gauge the health of our startup, and for that it's been worth every penny. I can't sing their praises enough.
Interesting. Why it was an unsustainable path?
I am trying to figure out pricing for my own startup and it’s no easy task.
Re: Show HN: Revenue Forecasting for SaaS Apps
#20a bigger player is https://www.zodiacmetrics.com/ , who have many peer-reviewed papers and are thus transparent with their (general) forecasting approach. i would love to see some model details given the maturity of other teams. (disclosure: i took a class with the prof whose research drives zodiac)
I, too, took a class with one of the profs whose research drives zodiac (Peter Fader). He's incredible; probably the best at combining mathematics and practical notions of business value (and, frankly, charisma) that I encountered at Wharton. Made me fall in love with both bayesian stats and calculus all over again.