DeepSights: Health &
Life Sciences Research Tech Showcase

Hi, everyone. I’m Natalya, and I lead our partnerships here at Market Logic. I work with health care organizations, research agencies, technology partners to help bring market intelligence together into one trusted ecosystem. For more than twenty years, Market Logic has been helping organizations make better decisions by making insights easier to find, easier to trust, and easier to act on. So today, I’d like to show you DeepSights. This is our active intelligence platform. And rather than walking you through feature one by one, I’ll show you how myself as an insights manager in health care might use it throughout a typical day. But first, let’s look at the challenge that most likely we all are going through today and what we’re trying to solve. Healthcare organizations have more information available than ever before. There’s patient patient research. There’s HCP insights publications, advisory boards, syndicated data, and competitive intelligence. But turning all of that information into timely decisions is still really difficult. So valuable insights are remain remain scattered across teams, systems, maybe even regions, and so it’s making it very difficult to discover, and even harder to reuse. So the result is that there’s duplicated research, there’s most likely missed opportunities, and many decisions that are often really are still being made without the full picture. To solve that problem, I’m sure many of us, are already in there, but we are all of us are really turning to AI. And AI is really making it much faster for us, but the challenge is that generic AI doesn’t really understand your organization’s research. Potentially, it doesn’t understand the evidence base or your governance requirements. It gives us answers, but it doesn’t always provide the trusted answers. And especially in health care where every recommendation needs to be evidence based and fully traceable, this is where DeepSights comes in. And so DeepSights has been built purposely for the insights teams. It sits on top of your trusted intelligence ecosystem, so it connects your internal knowledge with trusted external sources. Behind the scenes, we have insight agents that understand the context behind that information, while our trust hierarchy ensures that every response is grounded in the most credible evidence based available. So the results of that is it’s explainable, it’s evidence based intelligence, and teams can confidently use that to make decisions. So what does that look like? I’ll give you a little bit overview of our, different solutions, and then I’ll take you through what a real life example looks like if I was an insights manager in a health care organization. So everything starts with your intelligence ecosystem. We connect your trusted internal, proprietary information. Your agencies can come in there and upload different reports and datasets that they provide for you and also your external market intelligence. So from there, you can have all your patient HCP research, you have syndicated sources, and your competitive intelligence. On top of all of that information now sits the five connected capabilities that support this entire insights workflow. So explore, our first, area, it really helps you connect and discover by instantly synthesizing trusted intelligence to answer your questions. So, really, it’s like your twenty four seven, insights intern that can power through all of that information that you have to get you all of that, the right answers. Now if you don’t know what you’re looking for, RADAR now starts to continuously monitor all of that intelligence and it’s trying to find those emerging trends and early warning signals. When you find those early warning signals, you might want to pressure test those with personas. So personas are now using, your organization’s proprietary consumer data or consumer research. And before you invest in primary research, you can pressure test them with the synthetic synthetic personas. The innovate side is gonna help teams identify more white space opportunities and accelerate concept development. And research management is our entire research site life cycle. So it’s creating a searchable, governed knowledge base for your organization. And so the important thing here is that these aren’t five separate products. They’re really five connected capabilities built on the same trusted intelligence that you have provided. So you can start anywhere. You can expand over time, and every capability benefits from the same market context, the same governance, and the same trusted layer. So now I’m gonna take you into the world of an insights manager and really show you what that day looks like. I’ll take you through. Perfect. So it’s a video demo just so then you don’t have to watch me type all of that. But imagine it’s Monday morning. I have not even gotten my coffee, and I receive an email from DeepSights that highlights an emerging signal around telehealth adoption. So this is now built on how I use the platform in the past. And me as my name is Marisol in this, and I’m an insights manager. I now get a snippet of, of a of a text that it found, and it actually gives me a question that I couldn’t already ask. So without needing to think what do I ask, I can click exploring DeepSights, and I can already get the question started. You can see why this insight or signal has come in with the different supporting evidences, and you can make the decision of how you want to use it. So I go into my DeepSights. I have this insight, the signal, and my home page really show shows to me all of the solutions that I have inside of it and all the recent and latest information that my team has uploaded or any secondary sources that have come in. So what I do first is I want to understand this trend or say I want to just ask what are the biggest barriers to telehealth adoption. Behind the scenes, DeepSights searches across all of this proprietary information that I mentioned earlier. And rather than it returns you the documents that you are looking for, what it’s going to do is create you a natural language response, sharing with you what it found. I can now continue the conversation, and I can say, okay. Great. This is a lot of text. Can you put this into a SWOT analysis? So now I can, you know, continue that real that experience of building an email if I wanted to. And so that really helps me to make those decisions faster, figure out, okay, do I need to bring this to the next strategy meeting? Do I need to bring this to my agency before a campaign comes out or anything around that. Everything is sourced. There’s different color coding of sources based on your internal research, your external research. I can click on that document, and I can click on the page where that information is coming from so I can really validate if it’s worth it and if it’s real, and if it’s it’s reusable. One great feature that all of our health care customers really value is this, DeepSights Watch Out detector. So it really tries to find different concentrations of sources or different, kind of watch outs that you, as a human, should consider. You can see here, now I can also break it out by filter and collection. So what that means is I can go through all of my research, or I can decide which doc documents I want to synthesize so you can have your own experience. So now I started my day. I had a question. I’m kind of exploring that. But saying, my boss has told me and I can share the content here. But now that, the opposite experience is now I don’t know what I don’t know, but I know I need to continuously help the business grow. So if I didn’t know anything, I go into our radar experience where this is an always on agent that sits on top of all of that knowledge, and it continuously monitors all of that intelligence and tries to surface anything that’s changing across patient behavior, health care systems, scientific developments, and even competitive activity. So say for me, I’m working on a health care system shift. I see these library cards of really detailed views of what’s happening in that. And what it does is it goes through all those pages that are inside the platform. So as a as an old assistant brand manager, in my time, I would dig through all of these insights myself, and I would try to find a signal or a a trend that I need to tell my boss to think about. This is now doing it for us. So these are living intelligence, library cards, and they continue to evolve. You can see how many versions there have been. I can really download it. I can create a infographic out of it to make it very simpler, and I can go on my day. So say this topic for me is quite interesting, and now what do I do? I can technically go out and conduct a focus group, but that probably will take time and budget. So what if I could just do it all in one morning? So I’ve already had my first coffee. I’ve asked questions. I’ve now looked at a new signal, and now I want to understand if the signal is worth it. That’s where our personas come in. So you can create personas, really with any data that you have. So these kind of you can be submit commissioning an agency to create you consumer segmentation reports. You can connect usage and attitude studies or your demand space, and all of these personas are built in on your data. So now I’m speaking to a Rachel. She’s a patient, and I’m asking her what is important for her. I can ask her, to continuously, you know, check-in of what’s what’s what’s important to her, what’s not easy for her. I can send her concepts, video, or images. I can add other individuals into the group so I can make a focus group. And so even before my lunchtime, I’ve already now got a bit more visibility of how a persona or individual might react to a signal that I discovered or a question that I had. I can give it specific tasks. So sometimes we have quite a few ranking ideas that we want, so I can list those ideas out, in in very specific ways, and it will Rachel will now have a guided way to rank those for me, and she will kind of work work specifically of how I give her the instructions. Obviously, there’s a big conversation. I can summarize it. I can share this with my agency or with my internal team. And then the next thing I can do is I can compare it with the insights that are inside platform. So Rachel is based on our consumer segmentation data, but we obviously have a lot of research behind the scenes. So what Rachel has told me, you can see the green check mark, it’s really aligned with the research that we have. So now it’s really validating that on top of that to make sure that, hey. There’s actually breadth to it, and I can now think about, do I want to commit more time, and do I wanna commit more budget to it? Now I am confident that the hypothesis is worth exploring, but I want to really go into even further validations. So, again, within my half a day of work, I now go and create a panel concept test that I call Care Hub Home, and I have panel database behind the scenes that are connected to here. So I can connect the health care patient panel that I have have in a raw data file inside my organization. I can decide the therapeutic area. I can decide the gender, the age, and then the sample size is quite small, but imagine you have a sample size of six hundred, thousand respondents, that I want to test this concept with. So you can upload images, you can do descriptions, and you can really, start to see is it worth it. So here there’s some pre based questions that, the panel can be asked, and I can create my own questions as well. So now it’s going to match me the panel. It’s going to score all those stimuluses or all those concepts that I wanted to provide, and it’s going to generate me if it’s worth to continue this further, further concept testing or further initiative. Now using DeepSights, I can chat with the results. So instead of actually chatting with Rachel or with the healthcare professionals, I will chat to explain this result further. So if I don’t really understand the numbers or if I don’t really understand what I should do next, I can use DeepSights to really walk me through the results, and the information. So within all that, I took you through three, panels within our persona’s offerings. I took you through three of the solutions that really now within one day, you were able to really see from having a question in your email inbox to talking to DeepSights to, taking a look at what’s outside in the market and to ultimately pressure testing it and even going and validating it with a larger panel size. Even further, you can move into the innovation side to start thinking about the two, three, four year pipeline that you’re that you’re focusing on or that the team is focusing on. So in closing, the vision isn’t really five separate products. We really want to build an active intelligence platform that really focus on the insights expertise. And so when I’m discovering insights, when I’m monitoring the changes, when I’m validating ideas, driving innovation, or if I’m even managing research, all of my decisions are powered by the same trusted intelligence foundation. And for us, this intelligence is also available through MCPs and APIs. So now if you have your own tools internally, but you are still looking for your colleagues or for your teams to be grounded in trusted new market context, we can power it, power your broader AI ecosystems with those. And, hopefully, that is a lightning, demo of of what Market Logic is, and I’m happy to take any questions or comments if there are any. Oh, we can’t Savannah, you’re on mute. Thank you, Elliot. Just a reminder to everyone watching, if you have any questions, you can put them in that q and a box. But to start off, something I imagine many people are curious about is what are the benefits of the DeepSights personas versus various other stand alone personas offering offerings out there? Yeah. So stand alone personas, usually fall into four archetypes, I would say. There’s a data bank simulators, statistical augmenters, or full service synthetic agencies, and insight platforms with persona chats bolted on. What DeepSights personas can do, they’re really grounded in your own data and through a lot of different data sources. So we can take your raw usage and attitude studies. We can take your segmentation decks. We can take your transcripts, and we’re not really borrowing respondent pool. So, like, we’re taking a lot of that data that maybe your one agency might use one commission panel study or one transcript or one focus group study. We can take all that and then make those personas grounded in all of that data. And then we have different types of personas that you can create. So you can have persistent personas, which means, say, you have a deck from an agency that has created you five segmentation personas. So you have Nancy, Jackie, Bob, and Jill that as patients that you’re working on, and you want the whole organization globally, regionally, market focused to only speak to those four. Those are persistently always there, and the insights teams or the owners of those personas are consistently monitoring to make sure those are the individuals that we want to speak to as our patients. We also have dynamic personas. So these are can be generated on raw, UNA studies. And so that gives you an opportunity to create these on the fly personas. So now you have your personas, five individual patients that you want everybody to focus on. But then we wanna build out even more personas. So say there’s markets that are a bit smaller and that, you obviously did not have the biggest budget to go out after, you can create these on the fly personas. So say there is Natalia that lives in Germany, twenty five to thirty five years old, that is looking to take care of her fitness more. If that dataset is not a persistent persona, I can also build it out from a raw data persona. So we are doing a lot of kind of different variations of the way you can speak to these synthetic personas and then building on top of that now to compare it with the research you have. So a recent example that was given was there’s a lot of GLP data. There’s a lot of GLP insights, but we might not have segmentation data on those people out right now. So being able to build a persona based on the market research before it’s even a consumer segmentation data, that really is where we can we can support as well. So being able to build it out very differently from a persistent persona to a dynamic to even building it out from raw massive data files and then also adding the panel is how we we, I guess, differentiate ourselves with this solution. Wonderful. Thank you so much. That’s all the time we have for today. Thank you, Natalia, for being here with us, and thank you to our audience for joining us today as well. Hope to see you all next month. Wonderful day, everybody. Thank you. Bye.