As someone who works in advertising, that is partially true, but also not the complete story…
Data brokers want you to believe that the more data you have the more likely your ads are to be successful, but in reality it’s not about the amount of data but the quality of the data. If you have someone who has looked at reviews of gym shoes/different models on different stores, then that data is pretty valuable as you can focus on getting them to buy from your store or try and advertise models at the top of their budget, which will likely lead to a higher ROI than just advertising on fitness forums (note it is super hard to get the balance between tipping people over the line to buy and advertising them something they were already going to buy/had already decided against - Google particularly are absolutely terrible at this, but also do evaluation in house, so they’ll misrepresent to advertisers that your ad which showed up one link above your non-sponsored link made 100% of the difference in getting the purchase). Similarly, if you have data that someone is active on a car audio forum and recently bought a specific model of car, you can advertise kits/speakers specifically to that car, which is better than just advertising “hey, we make audio upgrade kits for [specific car/cars in general] on a forum/related site”.
This also makes advertising one of the few situations where using ML actually makes sense - there’s huge amounts of data (way more than a person can consider) to come in, and patterns which lead to good results (someone purchasing something) or bad results (someone not purchasing something). It’s not worth a human targeting every single microcategory, but if an ML model can pick up that advertising to (eg) people who have recently purchased cameras who are interested in triathlons and often visit areas with with high rainfall makes them more likely to buy your specific aftermarket lens hood, then it makes buying the ads so much more worth it and also lets you extrapolate onto other microcategories which may also have similar results, and if they don’t then that updates the model.
Generally data is less useful for awareness campaigns (ie “next time you’re in the supermarket/in the business for x, buy our brand” type of campaign), especially if it’s already on a relevant site, but it’s still somewhat useful if someone is reading on a (trustworthy) news site or watching an ad-supported streaming service, however purchase data & activity data is still useful for showing more relevant ads, as while 90%+ of people on a fitness forum are going to be into fitness, I don’t think 90%+ of general site visitors or tv show viewers are going to be into anything specific enough to make it worth it to advertise it.
As someone who works in advertising, that is partially true, but also not the complete story…
Data brokers want you to believe that the more data you have the more likely your ads are to be successful, but in reality it’s not about the amount of data but the quality of the data. If you have someone who has looked at reviews of gym shoes/different models on different stores, then that data is pretty valuable as you can focus on getting them to buy from your store or try and advertise models at the top of their budget, which will likely lead to a higher ROI than just advertising on fitness forums (note it is super hard to get the balance between tipping people over the line to buy and advertising them something they were already going to buy/had already decided against - Google particularly are absolutely terrible at this, but also do evaluation in house, so they’ll misrepresent to advertisers that your ad which showed up one link above your non-sponsored link made 100% of the difference in getting the purchase). Similarly, if you have data that someone is active on a car audio forum and recently bought a specific model of car, you can advertise kits/speakers specifically to that car, which is better than just advertising “hey, we make audio upgrade kits for [specific car/cars in general] on a forum/related site”.
This also makes advertising one of the few situations where using ML actually makes sense - there’s huge amounts of data (way more than a person can consider) to come in, and patterns which lead to good results (someone purchasing something) or bad results (someone not purchasing something). It’s not worth a human targeting every single microcategory, but if an ML model can pick up that advertising to (eg) people who have recently purchased cameras who are interested in triathlons and often visit areas with with high rainfall makes them more likely to buy your specific aftermarket lens hood, then it makes buying the ads so much more worth it and also lets you extrapolate onto other microcategories which may also have similar results, and if they don’t then that updates the model.
Generally data is less useful for awareness campaigns (ie “next time you’re in the supermarket/in the business for x, buy our brand” type of campaign), especially if it’s already on a relevant site, but it’s still somewhat useful if someone is reading on a (trustworthy) news site or watching an ad-supported streaming service, however purchase data & activity data is still useful for showing more relevant ads, as while 90%+ of people on a fitness forum are going to be into fitness, I don’t think 90%+ of general site visitors or tv show viewers are going to be into anything specific enough to make it worth it to advertise it.