He sold the product at the Warrior Special Offer section of the Warrior Forum, and was surprised by his sales figures a few weeks later, which were as follows:

$1… 126 sales = $126
$3… 25 sales = $75
$5… 9 sales = $45
$7… 15 sales = $105
$9… 8 sales = $72
$12… 5 sales = $60
$17… 23 sales = $391
$27… 19 sales = $513
$37… 3 sales = $111
$47… 4 sales = $188

While it was not surprising to him that the majority chose to pay the least they could ($1), still about 46% actively chose to pay more – in some cases, substantially more!

In fact, he made the most money at the $27 price point, which earned him $513 from 19 sales. Also doing well was $17, which generated $391 from 23 sales.

Now, he didn’t mind the $1 sales, because he’d created his product initially to be a bonus for an existing product, and also to be a list building tool. So at the end of his campaign, he had 237 new paying customers on his list, and was $1,686 better off!

There are lots of awesome marketing lessons we can take from this little experiment, but the big one is that: when you get the opportunity, you should test your prices!

As I explain and fully demonstrate in my report Pricing For Big Profits, lower prices do not always equate to more sales, and certain not to higher profits. (In that report, I show you all the psychological factors that go into pricing, that very few marketers really understand – as a marketer, you need to know things like the “decoy” and “Picasso” effects, and the “contrast principle” and why you can make more money by employing them.)

In Ross Dalangin’s experiment, $1 was a great price point for generating lots of sales (but only $126 of revenue), while the $27 price point earned him the most immediate money, at $513.

This also highlights another point that is a critical factor in deciding your price points – what do you want to achieve? If Ross wanted to build a big customer list to which he could sell higher priced products at a later date, he could keep selling it at $1 and build that list… but if he wanted the revenue first, $17 or $27 would seem like a “sweet spot”.

In my opinion, the best way of testing your prices is by split testing. And if you don’t want to hand over your conversion data and hence revenue data to the people who also set your pay-per-click prices, then use a split testing program that you install on your own server, like my very own Power Split Tester. (You can test as many price points “side by side” as it were, as long as you have the traffic.)

To sum up, I hope Ross Dalangin’s experiment has given you some thoughts and ideas – in particular, this one… that some people want to pay more!

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