The Ballast of False Data: How They Can End a Good Marketing and Data Analysis Strategy

When the role teenagers – who came from the k-popper or TikTok environment – had in the fiasco of the Donald Trump rally was discovered not long ago, the first analyzes focused on the power of adolescents and, above all, how they had managed to organize everything without anyone beyond their circle realizing it. Trump’s circle expected a full stadium and even more people than could enter. However, it ended up facing empty holes and an informative coverage in which Generation Z was the protagonist.

What those first analyzes took into account, although some voices were already starting to point it out on Twitter, was what the action of k-poppers and tiktokers would impact on the data strategy of the Republican candidate in the upcoming US elections. The Trump campaign, as already pointed out at a thread that became quite popular, uses the data it collects from its potential voters to profile messages, target campaigns and choose what it launches on social networks. Your data, in a highly orchestrated campaign based on what happens on social networks and viral messages, are key.

But what happens when your database is full of false data and information that is not really worth anything? Teens who had sunk attendance data had done so by giving data that was not what their marketing team needed.

And so, curiously, the story of how a very large group of teens gave Trump a lesson is also one of what happens when marketing is based on data, but that data is not ‘the good’.

Closer to the known is another story, that of a compulsive ‘buyer’ who fills carts in online stores and then abandons them. John Smith has been driving data analysts at several American ecommerce companies insane, who ended up wondering among themselves who they thought he was and whether they were also suffering from his ‘attacks’.

Little or nothing was known about Smith, other than that he used Gmail addresses, that he connected from Google headquarters in Silicon Valley and filled carts as quickly as he left them. The John Smith mystery story has just been unveiled by The Wall Street Journal. The end result is very unusual: Smith is a Google bot that checks prices to confirm that the information from Google Shopping is correct.

However, for the ecommerces he has gone through, Smith has been a headache, because he created a data trail that was not worth anything and distorted trends and analysis with his abandoned carts (beyond, of course, he did they wasted a few retargeting emails).

The burden of incorrect data

What these stories have in common is in the data. Information is the key element to connect with audiences, or at least that is what has been established in the brands strategy and in their actions.

Big data is the guide that helps to outline markets, make decisions and bet on those elements that will become more efficient and effective. That the information is incorrect only causes chaos and problems, at least for the marketer in charge of managing that database and making decisions based on it.

And what’s more, the burden of fake data is becoming more and more important, because consumers are increasingly aware of why companies want their data and what they don’t want them to do with it.

A study from the beginning of the year already showed that algorithm hacking was increasingly popular, especially among those more technologically advanced consumers. Teenagers were leading the trend: They already shared Instagram accounts to confuse the algorithm with their behavior patterns. Gartner already considered that, in marketing, algorithm hacking was going to be one of the great challenges of the year.

Consumers not only gave false data to companies, the specialists pointed out, but they also did things that contaminated the collection of information, such as sharing accounts.