So, hi, everyone. Very nice to meet you. So I'm Caroline Boone. I'm the Chief Strategy Officer at Future Intel. I have 20 years of experience in uh strategic planning and advertising and consumer research across three continents, Asia, Europe, and North America in the past 10 years.

And I've always been like on a quest to actually find more insight to decipher the human psyche and understand people better. So I've been working in tech for the past six years now, I've been helping uh develop our own property methodology called Culture Intel that really leverages both artificial intelligence and human intelligence so we can get an insight that can impact the way we understand people and the world in general.

So for today, I wanted to actually uh get you a little bit into my life and my day to day and live with one of the questions that I live with like on a daily basis, which is how can tech, especially artificial intelligence, big data really help us understand people and really understand how humanity.

So with the adoption of technology, you know, we've given our dependence on smart devices, you know, how lives have become completely like data fight. And so now we have access to this plethora of data points about, you know, um the content that we share, you know, that we like that we retreat. Uh We have as well as those conversation that we share online that become like data points. And then we have a lot of data that comes from our behavior. You know, the number of steps, we take the number of breath, our heartbeats, our calorie intakes, you know, the number of times we picked up our phone, the number of hours we spend in the digital vortex and so on. So artificial intelligence and big data really help us not only to access all of those data points, but they actually like help us to really understand, you know, like and analyze those data points so we can start like looking at patterns. So today I thought I could share with you one of the examples and a concrete question we had when we partner with uh researchers at Harvard Kennedy School that were asking us, you know, can you argo with them, help us to understand the barriers women funders face when they are raising capital?

And uh we were definitely excited and we decided to, you know, partner with them to see how we could bring a lot more understanding to that specific question that they had. And let me show you a little bit of the context for that um specific question they had if you look at it and especially in the past two years. So across like 2020 2021 in the US, women entrepreneurs have grown by 48%. That's nearly twice more, a little bit more than twice more, the pace of men entrepreneurs and their growth. So they are definitely outpacing and driving the growth of entrepreneurship in the US all of that in the middle of a pandemic. And what is really interesting is there has been a lot of narrative around, you know, the C session, you know, that was this recession that was affecting, especially like women, we have talked a lot about women, you know, exiting the workforce because they had to actually take care of their Children because childcare was solely their responsibility or most of the time their own responsibilities.

But nobody has been talking really a lot about, you know, those women that actually then started a side gig, a side hustle, you know, from home and then sometimes it became like just like a full on, you know, like business that they carried through. And what was very interesting is the fact that it's 18 to 25 years old women as much as it is as well. Those silver entrepreneurs of the CC five plus. Now the data point that would make everybody cringe, especially in this conference um that we all know, but that is still persisting is the fact that VC funding that was allocated to women entrepreneurs was only 2.2% and that was done 3% back in 2019. So that was really the burning question of, you know, the research of the have a school that was like, why is that, that these women that are driving, you know, this growth of entrepreneurship in the US are receiving like such little funding from V CS. So they ask us, can al your algorithm help us? And so we knew that the algorithm could help, but it was not going to be just down to the algorithm. And I'm going to explain to you why because as much as the algorithm is interesting in terms of how many data points we are going to be able to mind.

There is kind of a pitfall to any kind of like tech devices like this, uh which is the idea that our lives and our collective experience can be reduced to an aggregation of data bonds. And so once we are back in that we are like just giving you a dashboard of data point is not going to help you. And at future Intel, many times during the day, you can ask my team, I'm always about who needs another dashboard. You know, as much as dashboards are amazing about giving you data points. You know, it's like, do they really equip us uh the best to understand people? And so where we stand at culture Intel is the fact that we believe it's not just down to a dashboard of a lot of data points that is important, but it's as well like we need people, people with hearts and minds with, you know, the ability of empathy as as well as human intelligence to actually look at the data points from the algorithm, string them together, connect the dots, go further, find another data point so that we can really reveal the human stories behind, you know, like uh those data points that we have because at the end of the day, all of those aggregated data points cannot, if we just let them cannot reveal, you know, that experience of those women founders.

So this is something that is very important, you know, like uh for us to realize and I wanted to share with you like a very interesting example that's going to take us in the output for a second. But he talking exactly about that about this idea of do aggregated data points, really help us understand better. And so today I wanted to talk to you about two artists uh that were born in the former Soviet Union. And that actually, you know, like uh emigrated to the United States back in the seventies. And they are called Vitaly Komar and Alexander, you know, Milam. And so they had a big question in mind that was, could we actually paint the most wanted painted in the United States? You know, if we were to figure out what are the different criteria that people like the most. So they actually partnered with the research agency and back in December, 1993 they looked at surveying those 1001 Americans across 11 days and they asked them a bunch of questions such as, what's your favorite color? Do you prefer painting with shop angles of soft curves? Do you like smooth canvasses of thick brush strokes? Would you rather look at a painting with figures that are nude or fully clothed? Should people in that painting be walking or be at leisure? Uh or should they be indoors or outside?

If it's outside, where should that be, et cetera, et cetera. So then they create like all of this abundance of data points and they actually use those data points to create the painting. So let me show you how that look like. So they found out for example, that 44% of Americans say that they prefer the color blue. 49% of those Americans as well say that they favor that outdoor scene, you know, something that depicts maybe a lake or a river or an ocean, maybe that's related to color blue. 41% expressed that preference. It had to be a large painting, you know, especially more like dishwasher size versus like fridge size, you know, type of painting. And 56% of Americans say that they actually wanted historical figures, you know, in the painting rather than modern celebrities.

So those two artists actually took all of those data points and decided we are going to create this painting based on all those aggregated data points of the aggregation of Americans preference. And that should give us the most wanted painting that we call the people's choice.

And I'm pretty sure you want to see what was the result. So let's see how it looked like. Here we go. So the first thing and maybe you are part of those 44% of Americans that actually prefer the color blue. You may be drawn by all of these like blue lens scale that takes, you know, half of the painting already. But if you start like staying a little bit more with this painting, there is something like very odd that is happening and maybe it's like in your guts or even like your mind is trying to wrap its head around all the different elements and actually create a narrative with that.

But it's kind of weird because we are like, why do we have George Washington in the middle and then the deers on the right and then a bunch of people passing by and, and so, and our head cannot even wrap ourselves around the narrative that could be we just, you know, um, laid over like different criteria that had nothing to do with the each other just to create SCS because there is a sense of uh let's say a will to create some kind of harmony.

And so for me, this is an example of what a dashboard can do if we don't have a human intelligence to actually explain the dashboard, or we view the story, we are just going to be confronted to all of those like different criteria, but that not really like strung together, that don't create a certain narrative or coherence.

So this is very important for us when we look at consumer research because that really breaks away, you know, from this idea of data points, especially the aggregation of data point can just tell us, you know how to understand people. And so really the point of departure here, uh when we look at consumer research is that realization that as much as A I and big data tools, for example, are extremely efficient for us to gain more data points. No, they are just tools and as tools, they are as good as the people who use them. And so that's why in terms of consumer research, you know, we really advocate for the merging and leveraging both sides, leveraging the artificial intelligence and the tech because they are going to help us gain a lot of efficiencies. But as much as we lean toward that sense of efficiency, we need to lean as well with what we call H I that kind of like human intelligence, this ability to tap into empathy, the ability to tap into your human intelligence to actually connect the dots. And that's really where we can gain more understanding about people through all those data point that A I and big data are providing us.

So you can think about it just like we talk about, you know, like uh an IQ and an A Q and cognitive intelligence versus emotional intelligence. There is as well a certain, you know, bipolarity but partnership between artificial intelligence and human intelligence that then help us to really understand better people and somehow serve humanity. So you may wonder, you know how we applied this thinking. And this approach, for example, back to the question of Harvard Kennedy school, researcher about women funders. So I'm going to take you through that so you can see our approach in action. So the first thing we did is we actually said, yes, we are going to partner with you. It was a big passion of um project for us and we calibrated the algorithm. So first we uh calibrated the algorithm in a way that we looked at all the conversation that happened online that were peer to peer, you know, like public domain that were about raising capital. And we actually have received thanks to A I and big data tools. 443,000 conversation that happens across 12 months in the US. Among those, we found 100 and 69,000 of people who identified as women entrepreneurs.

So here we had at our fingertips, this plethora of data points, you know, for us and what we did is to actually look at those data points and start seeing the one that really caught our attention. So there was the first one that was very interesting for us. That was the fact that when women entrepreneurs were coming online to talk about raising capital, they were not really discussing the logistic of raising capital. So business plan presentation, development pitch, perfect, et cetera. The different of visit type, even the process of raising capital.

That was the topic, but that was not the topic. They were the most into, they were coming online first to discuss, you know, if anyone had advice or support for them or even to give that advice and support to others. And they were really into sharing their experience and learning from the experience of others. So they were into that intimate experience of raising capital. And that was something like very fundamental because that would tell us that they are looking into joining forces with other women learning from the one that have been there, you know, rather than really learning from a playbook and a theory. So that's what the first inside that we found. The second one is when we look at the uh data point that were coming from the algorithm, we could see what were the emerging mindset. And what we saw is women entrepreneurs were much more uncertain and pessimistic when they were talking about raising capital much more than, you know, men entrepreneurs. And as one of them had said in this conversation, she had said as a woman and woman of color founder, I need to work twice as hard as white men. Pretty much.

I don't think that I have the luxury of showing up to someone's office and saying I have this idea. Can you fund me? So that was very interesting for us because that meant that they were already starting from a disadvantaged perspective, you know, kind of a setback already foreseeing that it's going to not go their way is going to be more difficult than others. And they don't really know if they are going to actually raise that capital. So we went further and we look at those conversations to try to see the sentiment that they expressed. And what was interesting is in this 100 and 69,000 conversation, half of those were negative among women entrepreneurs twice as much as men entrepreneurs. So which was really a big gap. But then we're like, it's, it's interesting. But then why, why are they so negative about it?

And that's when we went back to the algorithm and actually recalibrate one more time to look at what were the emerging theme around those negative conversation. And this is what we found, we found that women entrepreneurs were talking about three different types of barriers they were talking about environmental barriers, which were, you know, the politics, the regulations, you know, the uh competitive threats in the industry that they wanted to start, you know, their um their business, they were talking as well about personal barriers.

So they were mentioning maybe the lack of education, you know, that was not fit for this industry. They were talking about the lack of understanding of the process, the lack of support, the lack of network, you know, so they were and sometimes even the personality that they first maybe was another barrier for them to raise capital. And finally, they talked about social barriers and those social barriers were the barriers that were really related to the interaction that they had with V CS and their relationship with V CS. And they were talking about, you know, the kind of prejudice they could feel when we were talking with disease. They were talking about even the stereotypes that they had to confront where people were kind of like questioning, you know, their motivations, their capabilities, even like their fortitude, you know, in actually getting on this entrepreneurial adventure. And of course, guess what those social barriers I was just mentioning were 42% of all the barriers they were mentioning. And as one of them was saying, as a woman in fit tech pitching a product, I do feel a disadvantage when I walk into a room of angels.

And there are only two women in the room who would understand the pain that I am presenting. So at this point, you know, people may say perception is reality. So that's the perception of women entrepreneurs. But that's maybe, and that's their own reality, but that's maybe not the reality of this is maybe they didn't want them to feel this way or they didn't mean this way. And, you know, we have been served these comments sometimes of like it was just in their heads, you know, as women especially. So what we did is we actually wanted to count that argument and find a little bit more what was happening. So we went back with the algorithm to look at another set of conversation, which was online conversation about entrepreneur abo about entrepreneurs among V CS. And we found 84,000 of those and especially 14,000 about women founders. And there we go, we could see the voice of V CS about women founders. What we found is the fact that they were very negative about women funders. One inch conversation was negative more than for overall funders. And there were two main reasons that were rising to the top that they were discussing. They were discussing the their perception of a certain vulnerability that women founders had. And some comments were like, I have doubts about women's entrepreneurs, ability to brave the dragon's den.

Oh, they were talking about the pers about the personality of those women founders saying women entrepreneurs, personalities, abrasive and demeaning. And those two barriers to women entrepreneurs were much more important than lack of capability, lack of readiness, like all of the more, you know, like objective type of barriers that we could have um fund. And so here we go with that study, we suddenly manage with the use of A I and big data, but as well as human intelligence to actually discover, you know, and see in plain site that kind of value that those women founders, you know, we're facing and that kind of unleash a full stream of work, you know, with um the Harvard Kennedy School by working on creating programs for women entrepreneurs that were not about how to write your resume or how to write the per perfect pitch deck.

But that was much more about how do you manage the room, you know, how are you going to present yourself? How do you use your personality to your advantage creating as well a mentorship program that could really, you know, like address those different issues that we had uncovered with future intent. So what does that mean for us? That means that especially when it comes to consumer research, uh as much as we are going to keep on leaning on artificial intelligence, we really have to keep on leaning as well with that human intelligence as well. So that A I can empower our human intelligence so that we can tap into our empathy, we can tap into as well, you know, uh into that kind of emotional intelligence. So we can string the different data points together, we can connect dots. And at the end of the day, we view those human story, hid them behind those numbers. So we can make an impact, you know, like in the world. So that's really where we stand by, you know, uh at picture in there. And I wanted to thank you all for letting me share a little bit of our philosophy today uh around A I and big data and how they can help us better understand people.

So if you have any question and you can stay, I'm happy to stay a little bit longer. Um Otherwise you can just uh find me on linkedin.