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Why our approach works (simplified)

Some people asked how our approach of simulating advertising effects can work without doing any actual live research.
 
The problem with this question is obvious as soon as you look closer.
A simulation is always based on historical data. Otherwise it would be an observation.
 
Most people think of market research and then they think of observations.
However, to do a really good and valid study, you would need to spend hundreds of thousands of dollars. At least if you do not have a best friend, who is a University professor, who conducts research in this specific field. And I am not speaking of the enormous time, you need to spend (and wait for results).
This is not useful for most advertising activities!!
 
Let us rethink this!
Think about Artificial Intelligence.
There are two main approaches in Machine Learning. One is gathering data through sensors and deriving some results out of it. This is relevant, if a robot needs to follow a line or recognize a person. The other approach is gathering tons of historical data and find patterns in order to predict the future. This works well for all kinds of stuff. Weather is just some old school example. Think of the AI that won Chess, or Go, or even Poker, or predicting sport results! No matter the fact that this happens right now (and I only mentioned things, which the media wrote about), most people do not believe it, because humans tend to think their actions are totally individual and unique. They are not (see our post about Free Will).
 
Anyway, Placedise works the same way.
We analyze historical data and simulate the most possible outcome for a given situation.
This works, because in the end, if you take enough data, humans are totally predictable. 🙂
 
Or, to stick to the weather forecast example: The forecast might be less accurate than looking out the window, but you know it before you get wet!
 
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Jens Kuerschner

Author: Jens Kuerschner

Jens Kuerschner is founder and CEO of Placedise. As an expert in the field of consumer behavior and media enthusiast, he is especially responsible for the product know-how. In our blog, this knowledge and the experience of many years of research and study will be shared with you.

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