 Hi everyone. I am Ranjan. Thanks a lot for warm introduction. I run a company called as Entropic which is primarily an AI company. Unlike Shubham I heard it was a beautiful presentation about the whole architecture and design but I come from a different lens so I am an AI guy who is trying to build solution into user research as a space and then as user research has evolved and it has been contributing into the mainstream of the whole UX arena, user experience arena. What is the role of AI and how it has come together over a period of time and what is the latest out there and then how it is disrupting this whole way of understanding consumer and as I think Shubham said the role of a user researcher is actually to look beyond what seems very obvious so I will just take a continuity from there. So coming to how the user research as a process has evolved right historically it started with back in the days where user research as means is a the core focus of user research is to get the user perspective and bring them at the center of all things that you are doing as part of your design or user experience design or any sort of journey mapping. Early days it was more about anecdotal research so I will get in people last question take manual notes understand what they are saying that was the means to understand consumer and then came the internet era where you have emails and all that form factors where you can write surveys you can roll out what you call as quantitative interviews essentially to get the user feedback and then from there it further evolved to something like social media evolution that happened at the start of this millennial wherein basically the focus was using more and more form factors to collect data right that's where you have had chatbots you have got centralized feedback tools some of this stuff and something that has taken over in last a decade or so right AI powered user feedback that's what we call it and then I'll come to the sense of it as in how it is creating value lot more than how it has been in past so there are two parts to it one is data collection as in how I can be more efficient fast agile about understanding and gathering data about the consumer than the traditional way of doing things and making it efficient and the second one is how you can assimilate all this source of information to make sense for you right so AI is supposed to enable you in ways where you can look at things which were not so obvious earlier right and that's where all the all the good technologies of AI comes in and and only in the decade there was lot of chatbot virtual assistant automated user comments those are the things that started getting used and now is the era where there's a lot of mixed method research that is coming in where it's not about the boundary conditions of whether you are doing task-based journey mapping unmoderated research moderated research but it's about answering a central theme of a consumer what really his experience is like and and looking looking at a consumer and this is a very interesting thing half of the industry calls consumer a user and half of the industry calls it a consumer so if you go to a PNG you will hear always consumer if you go to a consumer internet company you will always hear a user and there's a difference between a user and a consumer consumer has a lot of life in it so I always call consumers so mixed method research is primarily looking at a user and its behavior for any particular experience from a lens which comprise of things like emotion things like behavior things like beyond what they are saying it's not about you do moderated research testing you do user interviews you ask questions it's not about what people are saying it's also about how they are saying right I can if you give me a pack of lays I can say in two ways whether I like it or not I can say hey I kind of like it and I can say I really love this particular product right and these are very two different statements when you're looking at large set of user interviews massive collection of kind of interview database and try to mine insights at scale I think the problems become complex so that's where AI is bringing in lot more mixed method research to the industry and what essentially does is that so let's say you have one of the core role of AI is analyze unstructured data and I'll not bore you with the text but in simple terms you have user interviews you have thousands of things that a customer is saying essentially this is unstructured data where there's a lot of is are or right what is really meaningful juice out of it that you want to have as a takeaway and can you have it evangelized within the organization is where AI comes and plays a role the second one is categorization the good old quantitative and qualitative in other words moderated and unmoderated and task-based journey today if you ask a researcher I have done a screen recording map the whole user journey on a website or a mobile app and I have known what kind of timestamp is there what is a clickstream behavior and all that stuff and there is a user interview that he does separately which is moderated now are you these two data points talking is it coming together to create a comprehensive story half of the time what you are seeing as a user action when they are not being watched versus when they are asked a question and probe the answers are very different how as a user researcher you can come up with a UX strategy if you don't have a comprehensive view of what really a user is saying and a lot of science and art to both of it so that's where another domain where AI plays that how you can summarize and comprehend in it way which makes sense for you and at scale again at scale is where AI becomes really powerful and the last component of this whole AI breakthrough into user research is about can you go past the language barriers right various culture various religions various regions to express things in various different ways the same way in which in south part of the country we express things with whole nod is not the how we express it in the north right so things are very different do we understand those language barriers do we understand the emotion evoked at the time of whatever we are articulating about our experience as a consumer essentially so that is again another play field where AI is playing a pivotal role these are the three things major analyzing unstructured data bringing the world of quantitative qualitative unmoderated moderated task-based journey user captures screen recording vast set of information that you have trying to make sense out of it is another area where AI is playing big time and last is a new kind of data point right which is behavioral and cognitive science and all these things taken together is where the magic is happening in terms of looking at customer like a onion and you are peeling surface after surface and can you try to understand the non-obvious subconscious behavior of a user I ask a user have you ever seen a survey where somebody has given 6.75 as a rating it never happens I ask a question how do you feel about a product everyone see writes 8 or 9 and net seaside score always looks like 8.75 any organization any product feedback that you take what does it give you as a researcher it gives you probably a directional a b decision can you look beyond that to say that hey this is precisely a point where I was feeling so frustrated feeling in this ugly form where I'm feeling 20 fields half of which I have filled in the previous page simple examples and I'm not a user researcher I'm a sales guy so I would give that up a front warning to you so taking some simple examples on where UX sort of plays out this is a quick example of how some of the cognitive behavior mapping sort of works where in things like eye tracking right simple you have always click tracking time spend all those technologies in play where you know what a user acted on right I went and make a decision of purchase now pay now but what triggered to it is not known enough so click is like the eye movement is followed by click right so as a user experience researcher if you're looking at things you need to know what preceded the click or action and and that's where something like eye tracking comes in beautifully what you see on the right side is fair amount of let's say you're going through a user journey on a digital interaction I've taken simple example for convenience but if you're going through that journey is it known to you what is the second by second attention engagement emotion happy sad excited relaxed bored as a user is going through the journey what if you go through experience and second by second I'm able to map that fetch that and that's what we bring in some technologies like facial coding where we analyze the face expression of the user as you go through the journey comes into picture how about you're narrating your experience as part of user interview and I understand your voice tonality and the confidence with you with which you are making claims I like this product is a different claim than I kind of like this product right so can we read into those layers to really know what is real feedback versus what is kind of feedback so those are the new age technology of the third block cognitive science where it is coming out and bringing a lot of value in terms of how user research has been done historically overall what what it brings on the table as AI brings complexities probably a lot of complexities it brings a lot of advantages at scale also we all know bunch of stuff greater accuracy ability to look into biases right we all have our biases and preconceived notion as user as in what we believe in and what we want to believe in likewise as a UX guy user experience designer I have a lot of biases of what I think is right and wrong can as an artist be I hold back my perspective and really keep consumer at the center that requires tools like this where you can look at not just what customer is stating but what is the subconscious response of it we call it system one and system two system two is the stated response I really like something surveys articulated response and system one is something like my face behavior my expression am I happy excited relaxed board sat am I zoned out am I attentive to what I'm seeing as a experience and and this this and then eye tracking you cannot beat the eye tracking I cannot control a user to focus and respond in a certain way on a given experience every user behaves very differently but then that emerges a pattern across 20 of your personas that this is the clear trend in which people look and navigate through your stuff right and your UX strategies can be very smart if you get into those kind of depths of understanding a user so bringing in new tools with richer deeper granular insights is where some of the AI role AI plays there real-time insights obviously making things fast you did a consumer interview if you're getting insights like 20 days later in a PPT for my agency your life is done right like you cannot make decisions that delayed also you cannot come together to convince your bosses that I'm gonna make a decision like 20 days later when our agency is gonna throw me a PPT so the need is how you can get all these things real time so that you can be a lot more agile and iterative time and cost efficiency bottom line top line whatever we want to look at support for mixed research again layman question I want to answer and understand the consumer at the center method is all we as intellects have defined quant call moderated task based all that is good but at the end you want a clear consumer understanding or user understanding and hence the ability to comprehend it together is gonna be massive so that's one of the major advantages that AI brings today particularly with some of the gen AI stuff that's happening around where you have now large language models that can mine through your conversation like last five year 5000 interviews one of the one of the customers that we work with has about 6000 interviews over last five years consumer interviews that is sitting in silos and nobody has ever looked at every next year they do the same consumer research that they have done two years back every three point of version is the same as one point of version was so point is that do you have a historical friend of being able to look at it wisely and be able to understand whether you are going this way or you are going flat or you're going down right so the ability to collect the trend out of this interview what if I tell you out of 6000 interview every time somebody talked about a checkout page experience I will they I will go and summarize all the high moments of those and those aha moments of those and give you into a stitched one-minute kind of a real you play it and you know everything that has happened for last five years about users expectation of a checkout page right simple example but very powerful very very powerful in terms of how it impacts UX strategies in terms of conversion in terms of whatever example you want you might want to take it brings in complexities as well right there is questions on combining data from various sources is it a science is it art I had user journeys which said something I have user interview which is saying something else I have eye tracking data which is saying something all together different how do I blend this together to create understanding is very capable these days on doing that and obviously it is a enabler it is no way going to give a recommendation that essentially it's a enabler for you to look at things in ways which makes sense and then for you to make decision user trust and acceptance I think with all the data source the prove based in citing something that is coming more and more gone are the days that from a user interview agency will go and pick a small verbat and bases that come and present you a final insights people want to look things lot more holistically nuanced feedback is something that it can interpret and these are some of the complexity that is getting answered by AI context is something with LLM's that's coming in together quick one summarization of some Jaganis tech that is out there content analysis NLP national language processing that is at effect big-time task that is big-time tech that is helping out user research as a process AB testing and predictive analytics how many times you have you would have thought that hey I have researched so much of prototypes over last years is there a way a system that throws me hey this is the best possible prototype for your particular context that can be there so tech is AI is pretty much there it can throw that kind of insight to you it can be within a beautiful way a very really powerful tool to go and ask questions and get a lot more insights gone are the days that you conduct research a people would come you would read you will present you want to ask question like you ask Google now and you want to get answered now that's what it is about some key difference mixed method UX research 60 million panel across 120 countries facial coding eye tracking voice here we hold about 17 patents on that 58 language transcription translation all that stuff and the last is semantic search the difference between past and now in terms of natural language capabilities earlier you ask a question it gives you links as results now you ask a question it gives you contextual semantic summary like a human is answering and that's where things that's a massive big through what large language model or LLM or generative AI has brought to the table in terms of how easy and intuitive it can be for you to summarize the whole user journey user research is a process so that's what broadly AI is bringing to the table in the world of user research and thanks a lot for being a patient audience thanks a lot for your time.