Pricestack vs. Leaflet

Let's see how Leaflet stacks up against Pricestack!

Sell more faster
using science

Retailers use Pricestack’s platform to confidently find fair prices and craft more effective promotions, all while improving customer lifetime value.

Pricestack official logo with trademark
Price Suggestions
the secret sauce
✓  100% Powered by AI
✕ guess and check/AI tests
Time to ROI
move fast to compete
✓ Instant – analyze historical data
✕ wait weeks or months for results
Cross-Product Demand
prevent product cannibalization
✓ Instant – analyze historical data
✕ You'll know it's happening, but can't do anything about it
Odds of Human Error Occuring
human error = bad prices
✓ Our AI prevents human errors
✕ human errors are common due to very complicated manual inputs
Sales & Discounts Analytics
one of the keys to good price is running good sales and discounts
✓ Analytics to find the best sales opportunities
✕ what are discounts?
ROI Guarantee
if you're confident in your tool why not guarantee it?
✓ 10x ROI Guarantee
✕ no guarantee


Leaflet is a robust price testing tool for newish merchants. Their platform helps ecommerce merchants test prices manually or have their AI recommend price tests. Tests can be run manually or they can be set on "autopilot" and run autonomously. Leaflet allows merchants to test prices on as many or as few products as they like.

Sell more faster
using science

Retailers use Pricestack’s platform to confidently find fair prices and craft more effective promotions, all while improving customer lifetime value.

Pricestack vs Leaflet: Who is each tool built for?

The two platforms are for SMB merchants, although Pricestack is for more established brands who have at least $500k in historical sales – ideally more. Leaflet tends to support smaller businesses that find time to analyze data. Both platforms are easy to install with no coding required.

Pricestack offers advanced features that simplify and speed up the creation of data-driven pricing strategies. For example, Pricestack’s platform looks at what you sold in the past, not just what you’re selling tomorrow. Leaflet does not take into account past sales, so your understanding of price ignores past holidays or discounts.

Those aren’t all the differences. At scale, price testing can be extremely dangerous. For example, when a customer sees a price one day that changes the next and then changes again the day after that, you could potentially be creating a UX disaster. That is why Pricestack discourages a guess-and-check strategy for a vast majority of ecommerce brands.

With that being said, Leaflet's price testing capabilities do seem useful for small brands that have just launched. When you're a relatively new brand, you can’t afford to risk losing a few sales due to your prices fluctuating frequently in exchange for arriving at "optimal prices”.

The sales data threshold that Pricestack requires is the exact reason why our AI does not need to engage in testing, but instead can use the historical data alone to arrive at optimal prices and to suggest price suggestions on an ongoing, as-needed basis. Leaflet does not look at what you’ve sold in the past.

Pricestack vs Leaflet: Whose model is better?

Leaflet's AI is not optimizing for cross-product demand. For example, if you had two products that were frequently cross shopped (product A and B) and you "tested" the price of product A, Leaflet is not optimizing for the impact that the price of product A had on the demand for product B. Leaflet does offer "cannibalization" analytics, however, this is nowhere near the same as Pricestack's AI preventing merchants to price products at price levels that would harm the potential sales of other products. 

The Bottom Line

Pricestack does not believe in price testing. Testing prices creates more problems than it solves and can cause a potential UX disaster when customers see different prices within a short period of time. Second, Pricestack's model is built to optimize for cross -product effects, meaning that our AI will never suggest an optimal price that could harm the sales of a closely correlated product. The bottom line is that testing prices introduces human error in  the price optimization equation that can cause customers to become angry, compromise data sets, and harm the sales of products tested on improperly.

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