A Brainstorm on Pricing in the Digital Age

I posted a thought a bit ago on Facebook and Google+, which didn’t at the time feel worthy of a blog post. I’ll reproduce it here, though, since the discussion around it has led in some interesting directions:

I think I’ve put my finger on the core error of thinking when people say it’s not reasonable to price digital media affordably. All this stuff about “blood, sweat, and tears,” “what about my sunk costs,” etc. frames the question of price in terms of what the product is worth to the seller. Of course it’s going to command top dollar in the eyes of the people whose hard work brought the art into the world. But the market doesn’t give a damn what it’s worth to you: it’s the value to the buyer that governs the optimal price. You look at your Great American Novel, and it stings to imagine someone buying it for $2.99 (the top-grossing ebook price point according to the data we have), because all those hours of writing and editing add up to so much more than that for you. And that blinds you to the clear fact that it’s better to make 10,000 sales at $2.99 than it is to make 1000 sales at $10.

Folks responded with a few objections, one of which struck me as particularly apt: the fact that it’s really difficult to gauge what the value to the buyer will be, sight unseen. Wherever you set your price, you’re always going to be left wondering, how many of my paying customers would have been willing to give more, had it been asked of them? And how many customers am I missing out on because the price is higher than they’re willing to pay? We can only set an optimal price if we’re armed with that sort of information, and such counterfactual data is extraordinarily hard to come by.

What if we structured a marketplace with the aim of getting that information to the sellers?

Pay-what-you-want pricing can succeed in the right circumstances. Humble Indie Bundles are a spectacular example, with their clever cocktail of buyer-set prices, bundled product, bonus content for greater contribution, and transparency. Viewed from a certain perspective, sporadic sales and markdowns for products on a platform like Steam end up with a pay-what-you-want feel too. A user puts a desired product on their Wishlist, and when its turn comes up to be marked down 10% or 25% or 75%, the user gets a notification–hey, look at the price now! Is now the right time, has it come down enough for you to buy?

I wonder if we could smash these ideas together somehow. Suppose we had a marketplace where for any item that goes up for sale, users can add it to a wish list, along with their suggestion of a price for it. You could start the process pre-release, even: announce that a product will be available for sale on such and so a date, encourage users to queue it up on their wish lists and say what they’d be willing to pay when it comes out. And all that data feeds back to the seller, with nice graphs: 100 out of 800 interested users say $1.99, 250 say $5, and so forth. The system could algorithmically suggest a sweet spot, which the seller could take, or choose their own price based on their own interpretation of the data.

As the service matures, more and more can be done with it. You’d get the Steam-like notifications of sales, with the additional nudge, well known to sellers of used cars, of “you said you’d be willing to buy at this price, and guess what, it’s now $1 below that!” (I’m sure it wouldn’t be phrased like that. Suffice to say I’m not in sales or marketing myself.) You’d accumulate data on the discrepancies between what people say they’ll pay vs. what they actually spend. You could even aggregate Netflix- or Amazon-like recommendation data back to sellers: “Users who bought similar products tended to pay $4.50 for them.”

I’m sure there are holes in the idea. As postulated, it’s maybe a little too much in the buyer’s best interest to game the system by lowballing, for instance; we’d need to monitor and correct for that. But with work, maybe something like this could help purveyors of digital goods narrow the gap between best-guess and optimal pricing. Hell, maybe this sort of calculation is already going on amid the gears and cogs of Steam…

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