3 ms·

> Monthly: $49 / $99 / $249 So on the topic of ${X}0 vs ${X - 1}9 (ie. "50 vs 49", "100 vs 99", "250 vs 249"), what's the current "best practice"? I feel like

by senko 6y ago

> Monthly: $49 / $99 / $249 So on the topic of ${X}0 vs ${X - 1}9 (ie. "50 vs 49", "100 vs 99", "250 vs 249"), what's the current "best practice"? I feel like we might be like those 5 monkeys with a banana: someone figured out long ago in some context that X9 might convert better, now everyone's doing it and the reason might not still be valid. I'd love to hear about some recent studies or comparative experiences related to this. For me, subjectively, "49" has the "sleazy car salesman" vibe, compared to "50" being more "we're not ashamed to tell it like it is". On the other hand, I've always felt like this so I was an outlier before.

dangrossman 6y ago
I split tested $49/$149/$299 per month vs $50/$150/$300 and the 9's converted better.
- wcarss 6y ago
  I would love to hear more about the results of that test -- e.g. was it higher conversion for all categories, how much, did one category do particularly better with the 9's, was the distribution of category conversions similar on both sides, etc.
slim 6y ago
it's actually a mind trick. 49 feels small compared to 50 because the 9 behind the 4 makes it look tiny. perception is full of bugs
zackbrown 6y ago
The best practice is to test. Measure how a set of prices performs against your driving metric, e.g. customer growth or revenue growth. Iterate on pricing, measure again. Repeat until you have enough data to model a demand curve[1] between price and your key metric, then choose your pricing as the point of inflection on that curve. Fine-tune with another round of experiments for charm pricing (e.g. 50 vs 49). Repeat and localize currencies and prices (e.g. via PPP) for each of your top five geographic markets if you really want to optimize.[2] Second-best practice? Research what your competitors are charging and charge roughly that. [1] https://en.wikipedia.org/wiki/Demand_curve https://en.wikipedia.org/wiki/Demand_curve [2] I wrote this article: https://crow.app/blog/price-localization-with-stripe https://crow.app/blog/price-localization-with-stripe
calmworm 6y ago
There is a half-decent book that covers this in part ... I enjoyed it. “Priceless: The Myth of Fair Value (and How to Take Advantage of It)”

by senko 6y ago

dangrossman 6y ago

I split tested $49/$149/$299 per month vs $50/$150/$300 and the 9's converted better.

wcarss 6y ago
I would love to hear more about the results of that test -- e.g. was it higher conversion for all categories, how much, did one category do particularly better with the 9's, was the distribution of category conversions similar on both sides, etc.

slim 6y ago

it's actually a mind trick. 49 feels small compared to 50 because the 9 behind the 4 makes it look tiny. perception is full of bugs

zackbrown 6y ago

The best practice is to test. Measure how a set of prices performs against your driving metric, e.g. customer growth or revenue growth. Iterate on pricing, measure again. Repeat until you have enough data to model a demand curve[1] between price and your key metric, then choose your pricing as the point of inflection on that curve. Fine-tune with another round of experiments for charm pricing (e.g. 50 vs 49). Repeat and localize currencies and prices (e.g. via PPP) for each of your top five geographic markets if you really want to optimize.[2] Second-best practice? Research what your competitors are charging and charge roughly that. [1] https://en.wikipedia.org/wiki/Demand_curve https://en.wikipedia.org/wiki/Demand_curve [2] I wrote this article: https://crow.app/blog/price-localization-with-stripe https://crow.app/blog/price-localization-with-stripe

calmworm 6y ago

There is a half-decent book that covers this in part ... I enjoyed it. “Priceless: The Myth of Fair Value (and How to Take Advantage of It)”