All Posts By

Claire Quito

#4 Blog – A Revolutionary Research Method enabled by AI

A Revolutionary Research Method Enabled by AI

A Revolutionary Research Method Enabled by AI


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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#2 Blog – What is AI not doing (today)

What is AI Not Doing (Today)?

What is AI not doing (today)? 


There are areas or things where it’s still not deployed. And that’s basically the whole area of strategic management. For example: what should I do so my people don’t churn? What should I do to refrain from making a bad advertising? What should I change on my website so my visitors convert? These are also abstract and high-level questions that AI and black box systems cannot or do not answer. 


Nowadays, on the other hand, they automate operative decisions. So basically, they look at what people do. For instance, the things they do are the input and the counter-reaction they do (for example, clicking a button) is the output and they can find the relationships in the process. But what they don’t address (simply because they don’t have the data available for that) is what people know, think and feel, when visiting your website


You just basically look on the data you gathered and try to do something from them, which is great. You need to use what you have. But there are limits. Limits of what your customers or your prospects think, feel, want, and know. And if only you could tap on this field, you could improve much better and you could answer those questions I raised earlier much, much better.

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#1 Blog – What is AI in Marketing?

What is AI in Marketing?

What is AI? 

It’s very interesting and also because AI has been around for a long time. In here, this is me, and this is a book that I have published. This book is now over 20 years old to-date.  It’s in German, but let me translate it. You can imagine neural networks in marketing management and the applications described in this book are basically the same, as what you know today, because the main application is to predict and the prediction is basically used to automate processes.

So, if you can predict which person will respond to your targeting therefore, you can basically adjust your happening, or if a Google car can predict that there will be an obstacle when a person jumps on the street therefore, it can then push the brakes. So simply, AI is used for 99% in a black box way that it basically predicts things and we can automate certain decisions. 

So that’s what AI is doing. Nothing magical. It’s simply learning the unknown. Basically, the formulas. Unknown deterministic relationship between input and output from data and that’s what it does. secure our future.

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