Lets say an average Android user is worth 100 value units, and you can get lots of uniquely identifying information about them, say 90% of the ideal amount of info, so their effective value to you is 90 value units each (90% of 100).
Uniquely identifiable Apple users info is potentially much more valuable, say 200 value units. However you can only get a very small amount of actionable information about them because they are so hard to track, due to Apple's security and privacy systems. So maybe you can only get 10% of the information you would ideally like to have about them. That makes their actionable value to you only 20 value units each (10% of 200).
This is simply bullshit. So-called “targeted” advertising is a giant scam pulled on non-technical marketers who don’t understand it at all and use “measurements” provided by the ad networks themselves.
Targeted ads don’t actually work in any meaningful sense: https://thecorrespondent.com/100/the-new-dot-com-bubble-is-h...
(Disclosure: I work on ads at Google)
A 20-second googling produces one example: https://www.forbes.com/sites/toddhixon/2014/04/10/what-kind-...
Google around for more.
Let's say someone is trying to sell snowboards using ads. Previously they could target say "young male users who have previously browsed snow sports." Let's say the ad had a 5% conversion rate and they were willing to spend $100 per conversion (sale). This means they are willing to pay $5 per ad displayed. Now they can only target "all safari users" and their conversion rate is now 0.5% since not everyone likes snowboarding. They are still only willing to pay $100 per conversion, but now they are only willing to pay $0.50 per ad displayed since they need to display 10x as many ads per conversion. If the auction rate is >$0.50, they stop buying ads completely which decreases aggregate demand for ad slots.
The actual story is a little more nuanced: when you target an ad, you are usually bidding for users in an auction. You're generally willing to pay more for more specific filters because you're more confident of a higher response rate.
In ecosystems with a lot of data on every user, specific filters actually work, so you end up with higher bids on fewer users, which should drive the average spend per user up without necessarily driving total spend up. With less data, you have to use less specific filters, so you end up with lower bids on more users, which drives average spend per user down without necessarily driving total spend down.
* User X loves cola
Pepsi and Coke both bid more to serve their cola subscription services or whatever. Ad price goes up. Now add more data:
* User is a Coca Cola fanatic.
Even though this is the same user, the ad price goes down, because Pepsi doesn't want to bid.
Now add another Pepsi fanatic for symmetry, and you can see how "more data about users for better targetting" can in some cases reduce auction prices (while of course still increasing advertiser value.)
Your scenario just seems to single out one particular example where more information can reduce spending on one group, while increasing it on another.
Humans create audience segments, and it's in the interest of dataset producers to have each user be a part of the maximum number of members they can be to maximize monetisation.
Eventually there will be machine learning models involved (maybe there are already? I don't know ads.) Those models might move closer to "perfect targeting" where advertisers could compete less over certain users.
And no matter the incentives of the data providers, "increase customer value" is probably always a Nash equilibrium. If better targeting leads to less revenue, eventually you'll probably do it anyway.
If we can't tell which user is which, we know 50% of our views will lead to a conversion, so you and I will both spend $0.50 per view. The orange seller beats us and the price is $0.75 per user (assuming an old fashioned auction).
If we can tell which user's which, I can spend my $1 on the apple eater, and you can spend yours on the banana eater. Both of us will spend more than the orange seller, who is still only willing to spend $0.75 per view. The price rises to $1 per user.
Dollar per conversion is the constant, and the pile of converting users doesn't change. With less targeting, there's just more hay in the haystack. Dollars per viewer goes up as the number of viewers goes down.
The cost to the advertiser has stayed the same (or in reality, has probably gone down slightly, as it's likely fewer people are bidding on the impression) while the revenue per impression has overall gone up.
So a shoe company may pay a lot for a "male/18-35/basketball-fan/shoe-blog-reader". But Safari does not easily allow for that richness of data to be collected, so the price is low.
But with Safari's recent changes you can no longer target users on Safari where "non-empty cart"=true, because that relied on cookies and tracking which are no longer available.
On a per-impression basis advertisers are willing to pay (way, way) more if they know they are reaching someone who has already visited their site and been engaged enough to add-to-cart. There are plenty of other examples but abandoned-cart-retargeting is one of the most obvious ways advertisers can see good ROI on expensive ad impressions.
Maybe its because the data that can be collected about safari users isn't worth as much.
Exposure increases this value just by playing a numbers game; therefore, the larger the ad network, the more they can charge.
Targeting increases it more, by allowing companies to choose demographics more likely to be interested in their product, so again, they can charge more.
An interesting aside: While working as a mobile developer, I learned that many apps do not have an in-app-purchase to eliminate ads because it tanks the value of the in-app advertising. Why? Because the people most likely to drop $5 or $10 to not look at ads? They're the ones with the most expendable income, and therefore, users more likely to impulse purchase products advertised.
In other words: By allowing people willing to spend money to support the app, even if there is NO OTHER EVIDENCE they'd be interested in a product, decreases the value of your advertising.
This is a great metric for privacy.