The burden of fake data: how it can destroy a good data analysis and marketing strategy

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When the role that adolescents - who came from the k-popper environment or from TikTok - was discovered not long ago in the Donald Trump rally fiasco, the first analyzes focused on the power of adolescents and, above all, how they had managed to organize everything without CPA Email List anyone beyond their circle noticing. Trump's circle expected a packed stadium and even more people than could fit. However, he ended up facing empty holes and news coverage in which Generation Z was the protagonist.


What those first analyzes took into account, although some voices were already beginning 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 next US elections. The Trump campaign, as a thread that became quite popular then pointed out, uses the data it collects from its potential voters to profile messages, target campaigns and choose what to launch 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? The 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 teenagers taught Trump a lesson is also one of what happens when marketing is based on data, but that data is not the "good news."

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

They knew little or nothing about Smith, other than that he used Gmail addresses, that he logged in from Google's Silicon Valley headquarters, and that he filled carts just as quickly as he left them. The story of the John Smith mystery has just been revealed by The Wall Street Journal . The end result is very strange: Smith is a Google bot that checks prices to confirm that the information in Google Shopping is correct.

However, for the ecommerces he has passed through, Smith has been a headache, because he created a data trail that was worthless and distorted trends and analysis with his abandoned carts (beyond, of course, that he did waste a few retargeting emails).

The burden of bad 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 strategy of the brands and in their actions.

Big data is the guide that helps shape markets, make decisions and bet on those elements that will become more efficient and effective. The fact 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 furthermore, the burden of false data is increasingly important, because consumers are increasingly aware of why companies want their data and what they do not 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 information to companies, the specialists pointed out, but also did things that contaminated the collection of information, such as sharing accounts.