We know marketing works, under specific constraints. Sophisticated marketing does A/B testing to confirm the efficacy. Data collection is part of it. This artic

We know marketing works, under specific constraints. Sophisticated marketing does A/B testing to confirm the efficacy. Data collection is part of it. This article is making sweeping inaccurate statements, muddling the long-standing debates about marketing methodology. > Personalisation assumes that marketers have perfect data on every individual customer. No individual believes this, so it's hard to believe an extrapolation into organizational commitment. > Marketers infer who customers are based on their browsing behavior. Not in isolation. This is mischaracterization. > So, how accurate is gender targeting? It’s accurate 42.3% of the time. Probably not. There is no access to the datasets (https://pubsonline.informs.org/doi/epdf/10.1287/mksc.2019.1188 https://pubsonline.informs.org/doi/epdf/10.1287/mksc.2019.11...). "Gender accuracy ranges from25.7% to 62.7% with an overall average of 42.3%" without explaining how "no data" is handled. Having the actual data 42% of the time and not having it 58% of the time isn't the same as measuring how accurate it is. This looks like using bad statistics paired with confirmation bias. On and on and on. This is an article built on a quick summary of a questionable paper to push some anti-marketing prattle. Not to say that DSPs are reliable or that everyone is handling legitimate data, but bad data is filtered out pretty quickly in AdTech where there are means-tested costs...although it sometimes takes time to present.