I love to go in more details here. So the basic idea is to have (from mythology point of view) the three-step approach. And again, the applications are across the whole buying funnel. Basically, what you can do everywhere is ask your target group a close- ended question.
Essentially, a rating question. Example: How well does this website make your visitors want to learn more or to book a demo or how much does this advertising make you want to book a demo – a rating question, right. With respect to rating, you can also have it in Amazon or whatever. And then, you ask “why” did you give this rating or “why” did you rate this way?
Thus, you collect quantitative and qualitative data and these couldn’t be elaborated. For instance, our survey bot is in real time, understanding what people are saying and then ask another question. So, you can get in very short time “in one minute” very elaborative input of what people really care about.
But the interesting point is, now this is still qualitative research, which has huge disadvantages, because you basically don’t know what it means towards behavior. Therefore, what you need to do next is – to do two more steps. And the first step is, measure with AI, because AI can help to ask another question.
So, you get really in-depth feedback in a very short amount of time. The second step is that, you need to quantify what people are saying. The quality of the feedback needs to be quantified in order to be able to put it into a predictive model. So, we use here deep learning and we will talk about later how this exactly works, but it can be quantified (in a very granular code book) to tell us exactly what the person is speaking about.
And was this on scale? No matter how many people are answering, if it’s 50 or 500 or 5,000 or 5 million within milliseconds, you have an exact information what people are talking about. But now this is still not enough because you want to know which of those 50 things they typically say, are the killer things, which essentially drives behavior.
There is where a so-called causal, artificial intelligence, causal machine learning comes into play. I know this sounds a little bit scientific, but basically it measures how important it is and that’s all what counts. And what you see here is metrics ie.
X axis’ the importance, and on the y-axis is on how often the topics are mentioned and what you will see here is that they’re basically unrelated. The frequency of mentioning, has nothing to do with its importance, which is very interesting because if you ask your visitors or your clients, the question “why”, you would expect them that they will tell you why.
But in reality, they don’t know for most cases and they have no intention to really dig deeper. So, they give a true answer because you just ask an open question and they first answer something. What I mean is, if you ask your wife why did you take this jacket on today, she will tell you something because it’s not so important.
So that’s why there are some topics popping up, which are not so important. And you really need to find out which one are those. Therefore, this structure, well, this sole methodology, or which is basically nearly an automated system, can be applied on many, many different fields. And I would like to go through those fields now.
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