Nico Neumann is deep in the weeds on digital marketing attribution, market mix modelling [MMM] and incrementality testing – likewise the dangers of narrow audience targeting and junk user data - the latter a $20bn market in the US alone. The Melbourne Business School Associate Professor in 2019 published research proving that closing your eyes and randomly selecting male or female audience targets was more accurate than the data brokers and DSPs many advertisers buy from. Neumann claims a senior data broker admitted to him privately that they knew their data was crap, “but who cares? Marketers are buying this”.

(Like Arielle Garcia, UM’s former US privacy lead who last year told Mi3 she had accessed her data profile from multiple third party brokers with laughable results, Neumann has downloaded his own, “and it’s hilarious”. You should do the same - we’ve got a URL in our Mi3 feature to test your profile segments).

Neumann batted away claims his B2C audience studies were too broad and challenged widely held assumptions that niche segments and B2B were where precision targeting of online users actually works. Last year he ran tests with IT giant HP - a paper is coming - that sharply contests most B2B marketing plans and particularly so for tech sector practitioners. “No matter what we used, it was either equal to random targeting, or even worse,” says Neumann.

First party data is better, per Neumann, but there are caveats, particularly around clean rooms and matches that can be bogus. He advises marketers to upload made up email addresses and see what they get back - hashed user 'match rates’ may not be what they seem. His advice: stick with the first and second party data you can trust, but even then, don’t assume targeting will deliver better bang for buck.

“I would even take a step back and ask, do you need to target that narrowly? There are very few cases where it makes sense … Why do you even need to exclude people and increase the cost, instead of just letting the content or message do that?”

Neumann sees the explosion of market mix modelling and measurement approaches as “a good thing”. But there are market rumblings that the big platforms pushing MMMs risk skewing towards inherent model biases. Either way, Neumann’s working on a project to compare how all the main MMMs hitting the market actually perform.

He urges marketers to question all models – and his advice for those emerging from business schools is the same as for seasoned CMOs: Hone fundamentals that will last a lifetime; don’t overspecialise in trends and fads. “Ask hard questions – and just test stuff yourself.”

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