Beyond Research: How AI Is Transforming Insights into Decision Engines

Hello, hello to everyone joining us. I am Molly. I will be kind of hosting this session. Um so kind of ground rules if you've not been in here yet, as always we are at the mercy of tech, which loves to be uncooperative at all of the best and most convenient moments. Um be respectful. You know, and obviously this is an opportunity to network and interact and meet other people in our industry. For the next hour, we are going to be hearing [snorts] from Dr. Rima Singh. She's going to be presenting on beyond research, how AI is transforming insights into decision engines. I will kind of field Q&A at the end, but I will turn it over to Dr. Rima now and you can take it from here. >> Thank you, Molly. Not used to be called doctor, so it was like at first I was thinking who are you? I'm like who's this Dr. Rima Singh? But thank you, I love that. Thanks for the introduction. Hello everybody. Most of you I cannot see but uh I am the consumer inside lead. I love the fancy word of leader here. And I lead the consumer insights for Cargill's animal nutrition health business. Um and let me start presenting. I'm and I'm very bad at Zoom or we work mostly on the team, so if there's any hiccups, let me just see. Uh let's see. Let's start presenting and I'm hope Molly, yes, sorry. Molly, you're saying you're muted. >> Yes, you should be able you should be able to share now. >> Okay. Uh Now I cannot see anybody and Molly, you also so stop me anytime. All I see is my presentation. So can you see my first slide? >> I cannot. >> You cannot. Okay let's >> Oh, yes, I can. >> Wait, but wait wait wait. Uh It It's >> It's working. I was just looking at the wrong thing. >> Okay, wait. Um can you see now? >> Yes. >> And you see the first big slide, it says beyond research? >> We sure do. >> And not my notes. >> And not your notes. >> [laughter] >> Oh god. Yes, technology, like I said, um AI is advancing everything and we are still stuck in Zoom and Teams, but um sorry for the hiccup. Uh so, yeah, I lead the consumer insights for Cargill's animal nutrition health. Long business title, but um I don't know how many of you know about Cargill. Um we are we are the manufacturers of everything. You name it, we we must be Cargill must be manufacturing that. But I'm in their animal uh health business. We make food and feed for any kind of pet you own. So, if you have a gecko in your house, we might have a or we we have uh food for that gecko. Uh so, go look out for our brand Nutrena. So, I'm not here to sell Cargill's food. So, and the talk is not about AI as well. The before I start talking, you know, this I was in a insights conference um where I met a lot of uh great insights leader. And there was a common theme in the room. We were talking about how AI is embedding into everything that we do. And is insights function becoming replaceable? And there were talks around organizations using AI um to make decisions, which we agree or we may not agree. So, think about it and I was thinking I read the post today only somebody posted on LinkedIn that should AI should in inside function transform into a something else. And that something else is what we need to think about. Um, so I apologize in the advance of it this sounds like teaching or preaching, but it is important because that's what I'm hearing across from all our insights professionals, all my inside teammates that our role what is our role? How is our role evolving? And we are doing amazing work. We are doing using amazing research technologies, but if we don't evolve our role, um, somebody else will take it and that is AI. So, let's So, over the past few years we spend a lot of talk time talking about not past few years like like 6 months per se. AI tools, automation, productivity, but I think there's something bigger's happening. AI isn't changing how we do research, it's changing what inside function is expected to deliver. But we are so bound and I keep hearing and keep talking, oh, we are using AI to do this, we are using AI to do that. But what are you doing? For most of our careers we were valued for generating the insights, research, but now we have to help organizations to make better decision. And that's where we need to embed ourselves and transform ourselves from the research researchers to decision engines. So, let's explore today. And I would like your feedback. I would like to hear the frustrations because this captures a lot of frustrations, a lot of, um, my understanding of what's going on around. So, you can you can challenge me, pause, tell me if you're doing this and if not, if you take one thing from this, I want us and want our inside team to transform into a decision engine. And I'll talk a little bit a detail about decision engine and I'll give you an example that I am implementing in my role um and I'm there a lot to do as well. Look at this a traditional inside journey, right? This looks familiar to all of us. We started with a business question. We were asked there was a business question. Uh we did research. We generated insights. The insight. The product came out of the insight. We created the report. Um some of us shared with the stakeholders. Hopefully all of us did. And then something interesting happens. The decision about the products you gave them uh five key takeaways and it happens with so so many of us. We gave them the insight. We said, "Okay, this is working. This is what the percentage and all that." And decision is made somewhere else. I don't know how many of us were actually involved in the decision making. So many insights team create value up to this insights um stage. But we're not involved in the priorities prioritization execution or measurement. And if you are, great. If you're not, stop start thinking about it. And it's not easy. Every organization works different. Um every dynamic is different. But and but you have to start. These are small steps and if you don't take it, think if you become irreplaceable. So the question becomes, are we simply producing insights or we are helping shape the decision? Are we part of the decision? And that's where we are going to drive impact. Then we shape what happens next. You have to And it like I said, every organization some some of you might be thinking it's easier for you than us. But it's not. It's easier for you as well. So let's talk about the challenge and how that has shifted. For decades, the biggest challenge was the information scarcity. You know, the data is expensive, especially in my world. We deal with all kind of pets. We have goats, sheep. The sample cost is so much. So, comes the AI, it is creating synthetic data. I'm creating AI twins. I have The data was limited. Finding the inside will limit it, but think about now. The insights We have an opposite challenge. The information is abundant. We have surveys, CRM data, social listening, reviews, behavioral data. You name it, you're surrounded with signals every day. Now, this is where it is becoming interesting. The organizations come and say, "Oh, we're asking AI for recommendations and we're going with it." No. The decision confidence is becoming so scarce, but you This is what you need to come in and help organization make the right decision based on everything that you bring into the table. You understand your consumer, your market, your customer better than anybody else in the organization. And that's where the future of insight lies. It's not about producing research. It's not about creating more surveys. It's not about creating It's about helping organization make the better decision. Uh There's so many tools all of us are using, and as I'm amazed with what AI and what tools we're doing. Um AI moderation, I hear synthetic data, AI twin. I am bombarded with so many AI tools. And if I even bring those two all that's together, what is that I'm going to do with that? If the decision confidence is now the bottleneck, then our role must evolve. The future of insights isn't producing more reports. It's helping organization make better decisions. That means you create a clarity. That means you drive alignment. That means you help team prioritize and ultimately improve business results. If you're not doing all this, you're not doing you're not catching up the right bus and you'll be left behind. The question now becomes what did we from what did we learn to what should we do? There's a lot of learning happening across us. Now you need to start changing your hats, wear a different hat and start asking what should we do. And this is this is the this is what is happening. Um, you know, um, I'm I'm going to So, when I was creating this presentation, I these were all my thoughts. And I used AI to create these beautiful slides for me because that's not the my capability I had. Right? But I'm using AI to transform the inside to the experience I bring to my stakeholders. And this is just a small example. I bring in all my thoughts together because I know my business, I know my challenges more than anybody else and AI is just making it beautiful, accessible that is in front of you. And this is just one example, right? So, if you think about the forces that is transforming insights and experience today is the first is the digital experience. Consumers are generating signals. Mark this is signals, not the data. Every interaction is creating a signal that is we are catching as an insight professional. There's a data explosion. We have access to more data than we ever had. The customer feedback, analytics. I'm not saying we did not have access to those. But we have access to that at once now. You don't have to wait for CRM data. Somebody has to send you the Excel. You were analyzing that cell and you're thinking, okay, by the time something it moved on. You are getting at once all the data you're bombarded with it. Um so there's signals in that data, there's signals everywhere. So you're there consumer interactions are creating signals, your data is creating signals, and on top of it AI is accelerating everything. So all the insights that you used to get in weeks and weeks, you're getting in hours. Your AI is I have someone in one day four screens open, right? Four tabs open. I'm listening to social conversation. The CRM data is being analyzed by There was a survey and I used to do hours and hours of decision modeling. My AI is doing So you see in my on my laptop, which is poor Dell, is always out of capacity, must be thinking what am I doing? I have five screens that doing all of that in hours. And in fifth hour, I have a beautiful presentation presented created, which I can send it to the team. All happening within the 8 hours of your time, which used to take me days. What it is happening? There's a There's They are AI is creating patterns, it's summarizing, it's generating recommendation. AI does give you recommendation. Boss, stop there. That's where you come in. That is where the challenging is deciding what matters. You are an expert. When you do all this and you are creating this deck and you have recommendation from AI, that's where you know your business more than anybody else. So how do I see our role evolving? I know I've been talking a lot. It sounds like I'm preaching to a crowd. So, I it comes with an um um history of teaching to the university kids. Um but I feel that is important. I feel that we're missing when I talk to my inside few some of us are still stuck in the researchers mode and some of us are stuck in that wiser mode. And uh it pains me to think that this is where the challenge is coming, right? There's a shift in our role is subtle. When you apply for a job, they still want you to be researchers and advisors. For you to succeed in a job, you need to be a strategic partner decision engines. Let's talk a little bit more about that. For many years, we were researchers. Right? We were asking we were looking at the question what happened, we were creating report, and check the research completed with presented the stakeholders. As the organization started evolving, and if you're not an advisor, you're not doing the job right right now. We became advisors. So, we started talking about what does this mean. And in my career as an inside and I I was leading insights for for Ericsson, I saw that evolving happening as well, right? What does that mean? Um the primary output was um recommendation. Now, researchers are becoming a strategic partner. The next evolution was becoming the strategic partners. Where inside professionals were invited to planning. And if you're not invited to planning, and that's where you need to do more. You need to be part of the planning. And the if you don't ask them give them the answers of what should we do, you would not be invited to the planning. Right? So, your primary output is the business guidance. You need to move away from recommendation to give business guidance. And today we're entering this new phase. This highest the highest performing inside team, you take it anywhere, are becoming the decision engines. And that's what this talk is all about. This role isn't simply to provide information. Our role is to help organizations make better decision by combining the research, combining your advisory role, combine the analytics, combine the business context, and you get the stakeholder alignment. So, the five tabs I was talking in my on during and just now, or this is what I'm doing. There's a researcher tab of me who's doing the service creating. There's an advisor tab on me which would say, "What does this mean?" There's a strategic partner tab open which is telling me, "Okay, now that I have all that, what should we do?" Then there's a decision engine tab which is telling, "What is that? How can this create an impact?" Out of the results that I've got, how is this going to create an impact because I know my business better than anybody else. So, the shift is subtle, but important. We're moving from measuring the results to become to measuring the business outcomes. Um and then we I talked about this again and again, but I think the bigger question as we're moving and as you're thinking about yourself, reflecting on it, the question for me that is very important, and I think about this every day, and I want you to think about this as well. What does the operating model looks like for us? Everything is an operating model. Everything has It has to have an operating model. If not, then you're not you're not doing your role right. Right? What is your operating model? Your operating model is not being a researcher, doing the surveys, and finding what the AI tools you're going to implement. Your operating model is this. Your operating model is to connect everything. You're the one sitting in the center. So, I as I was thinking about this, what is my operating model and how do I work? The five tab that I talked about, and I just put it in and then AI gave me this beautiful visual. And then it is my operating model. Isn't ours yours, too? Right? AI is helping us get all those signals. You sit in the center. AI dramatically changes what's possible in those stages. It can analyze thousand of comments, summarize reports, identify patterns, surface signals, but the real value is you. The most important stages are to prioritize, activate, measure. AI can suggest option, it can give you recommendations, estimate the fully understand your business, your stakeholders dynamic, which is very important. There's no one stakeholder. You're dealing with four, five, six stakeholders, depending on your company. And that's your responsibility to manage the stakeholders expectations using all this app. So, your role is not a researcher. Your role is to be part of all these decisions. Um AI accelerates every stage of the process, but the human judgment, our insight judgment remains the center of every meaningful decision. So, I'm I'm if I haven't said it enough, I've not say spoken it loudly on this. And that's where I need we need to do. We as a community, insights community needs to do. There's a lot on the slide and I think it before I move to the examples, I think it's important that I I talk about this in detail. It's not a fight, it's not AI versus us. I don't think the future is AI versus insights. I hear a lot AI versus insight, AI versus insight. That's not correct. It is a partnership partnership across the value creation journey. We have to take in the AI contribution and you layer it with the insights contribution. It is not only the AI contribution. AI is exceptionally good at finding patterns, scanning large database, generating scenarios, monitoring outcomes. You are exceptionally good in bringing the context. You have the domain knowledge. Exceptional good in interpreting the meaning what these pattern means. You are the expert on the trade-offs. Sorry, didn't mean that. >> [snorts] >> Um and you're the you're the expect and that's, you know, pause a minute. Think. When did you last was part of this activate part? We are either stopping at two, understand, or three, but have you moved to the activate side? You Where is the alignment of the insights to decision value, culture, stakeholder is comes in and that's where you need to start thinking. And then the measure Measure is the learning cycle. AI you launch a product, the product will launch, what happened? How did that perform? Consumer said 90% purchase intent, but the actual behavior was there's a gap between intent and behavior and we have seen it throughout the history of a consumer behavior that there's a gap between intent and behavior. What is happening? Are they behaving as they said? If not, who's replacing you, what the challenges are and that's where your role comes in again. And then the cycle [clears throat] goes back. You see where we did not I did not stop and I would don't want you to stop. So, let's take an example. I know I've been talking a lot, it's a lot of pre- sounds like a preaching, but uh how a decision engine works. And I'm going to take a hypothetical example of a pet nutrition not revealing what I do, but this example is based on similar to what I do in my real life. Right? So, because I work in pet nutrition, I'll take a pet nutrition example. First, you're thinking about a new insight. You're thinking about identifying emerging consumer needs and market signals. You have a lot of AI tools for social listening, and you're listening for those signals. Now, you will ask me, how do I know what is a signal? If you see a a issue or a pain point or a conversation dropping down in couple of days or sometime not the day hypothetically say days but then it is a noise. You as an inside professional know what's a signal and what's not a noise, and that's what I keep saying that you hold that you own this. Right? Um so, first stage is you're observing. I observe a lot of social listening. I take a lot of you know, what's happening. I keep a tab on this. I get these social listening. I have a partner in AI partner where we do a lot of social listening. We have other tools that Nutrena Cargill has it. I get a lot of data on social listening. And say for example, one of the one of the signal that I keep hearing one of the pain point that I keep hearing is, I don't know if grain free is actually healthier. Oh, my dog has a digestive issue. So, I hear it grain free. I'm hearing um digestive issue. Now, this is where I bring in and then customer feedbacks coming, chats, reviews and all that. Now, AI comes in. It is analyzing these thousands of conversations, and I'm using AI to uh use the theme, sentiment, emerging tools. And you have If you don't have like very sophisticated AI tool, ask use GPT. ChatGPT is very amazing. Um Anthropic, I think, is doing good job. That's what I heard. I'm not an expert. I'm not a big fan of Microsoft Copilot, sorry, Microsoft, if you're listening to this, but not at all a big fan. Um and um so I And you have different So the AI partner you have, they can do this job for you as well. Uh it depends that you'd rely on them so much or you want to do it by yourself. Uh then I What I do is that I've got a pattern. I've got a theme and I I get a recurring theme. I get a recurring theme and then this theme consistently stays for a while. So for me it is important is it a theme or a noise? Uh sorry, signal or a noise? And then I create a chart using, "Okay, this is a signal. This is the noise." And so look how I'm monitoring everything. Now Now my second step is to understand and that is where your my major role comes in. That's where I combine my AI and my my researcher hat. Now, we have tools and you might have some tools where you're creating these AI personas. You're fed You're getting these conversations. You're creating personas based on the conversation, the real data, customer feedback, purchase behavior. And AI has generated generates a persona for me. For example, say the persona name is health-conscious pet parents. The need for this pet parent is that I want the best nutrition and care so my pet can live a live a long and healthy life. And the goal for this persona that AI created based on the conversation was a longer pet life span and better nutrition. And I also found out the jobs to be done, the segmentation of this persona, pain point because the struggle is that there are a lot of nutritional claims. They the ingredient trust, they don't trust them. And that's where I come in. I've already done a research, did an extensive interview for my pet consumers. So your role, you have to continue doing that. So I've done jobs to be done, I've done segmentation study, and I have created these segments outside the online world. I have a static segmentation. So I bring in the segment that I have, and I take in the segment that was created by this persona, I overlay. And you see that where does this health-conscious pet parents sits in the segments that you have created? Where is the overlap? Which behavior of the segment matches the behavior that you identified? So I did the traditional route. I'm not saying don't do it. I did go in consumers' home. I did ethnographies. I did online ethnographies. I did quantitative survey that was a 6-months long segmentation study. I did that already. I did a jobs to be done mapping. I did a need-based mapping. I need jobs under that. I've done all of that. So I know where this health-conscious parent pet parent lies. What does this segment truly care about? And that's where you come in. You're saying, "Okay, this health-conscious pet parent matches my segment. This is the segment need is this is this. This is what they're looking for, that brands they're buying." So you Now what you've done, you're listening about the segment real time using these personas. So what you're delivering here is a clear actionable insights that informs strategy, messaging, and innovation. You're thinking, "Okay, what is driving this behavior? What is the pain point? Okay, this consumer is thinking like that. What is the hidden? It's not just a longer pet life center. There's something behind that. What is the unmet need? So, you're you're validating evidence based on what you have done. And that's what your output is. Okay? It takes a while. I'm not saying it's going to be done in one day, but that's how my role has evolved. I was doing just the inside role before last year. I'm I'm doing this all this together now. Because I don't have to do the foundational research. I've done that. Now, I'm using the AI personas and AI conversation to overlay that and to real-time analyze my products. Real-time analyze what is the need. And how's that shaping? Um then, I have everything that I've done. Okay? So, this is a very important stage. AI can estimate, so your you simulated persona reaction, right? So, you're um how would different personas react to the grain free? So, you create a different personas, um which is mapping to your segments, um personas that you created. Um now, you ask start asking the questions. Remember what you heard in in the starting? You heard that consumer want grain free. They they're they don't know what the nutrition claims are. Um they're looking So, you start thinking about ideas, and you start asking these persona to respond to the grain free formula, personalized nutrition subscription. Um and then, AI can estimate the adoption likelihood, purchase intent, market size. It is doing all that for me. And I'm asking AI to do that. But here's where you come in. Okay? Adoption likelihood of grain free is 72%. What does it mean? What segment does it talk to me? Are there brands that are already doing that? So, what what what extra need that I'm fulfilling? So what? What is the implication? What should we do? What are the best option and trade-off for these consumers and also for your business? Can you Can your business make a grain-free formula? You have to start doing the homework as well, right? Um and what's next? Where should we test, pilot, and prioritize? See, the output is where you've got all this. This is what you're presenting to your team. You're presenting the decision, you're not presenting the option that AI gave you. You said, "Based on all that, I think personal nutrition is the highest priority. Subscription service is the medium priority, but I don't think so we can do it given the constraints of my organization. And grain-free is the low priority because there are other options that exist. We don't have the capability and all that." Now, your decision, your slide, your recommendation is based on what you know, what not what an AI suggested. Because if I go back to AI, it has given me grain-free as a top priority, personal nutrition as the second, and subscription as a medium. But when I translate it to my business, I found that personal nutrition is the highest priority. Hypothetically speaking, if you're recording, we didn't do this in the real life, but this is what I do in real life. Another example. So, this comes to activate part, and this is where I felt that most of my insight um community member dropped off because AI doing a lot messaging, product concept, marketing brief, ad campaign idea, somebody else is doing it. But we're not helping the team make strategic relevant decisions, stakeholder alignment, customer relevancy, business feasibility. Are we doing that? Are we creating turning strategy into real world? We need to activate. We need to show them how to activate. I'm not even stopped for questions. So, if anybody has a question, please stop it or we'll take it in the end. Um I know this is a lot to take, but this is an example that I do um almost um this is how I have evolved um if you ask me. Um and I'm I'm it's just not a preaching. I am involved in the activation as well. Because when I do any AI generates a concept, say for example, the personalized nutrition concept was created. I'm making sure that we're solving a real customer problem. So, AI suggested a personalized feeding recommendation. Now, my insight that supported inside that supported that recommendation was this. And then you refine the concept. This is you're not doing it alone. You have your marketing team, your um your agency doing that, and then you're testing that. So, we're not my contribution was that we're not selling personalization, we're solving for a trust. You see how the personalized feeding recommendation moved to a higher order with the solving for trust. Because it is not about personalized. So, what marketing team did, they're they're taking that solving for trust instead of personalized nutrition recommendation. And that's what it sets you apart from every any other recommendation that AI or um AI would give, right? Now, you do strategic in alignment. You tell them why did you use personalized nutrition? Why um you feel that uh this uh solution is better for the growth priorities, better for the brand. You've done the brand new tracker. You you're you're analyzing the brand. You're analyzing the customers. You're analyzing competition. You're on top of everything 360°. And then you choose this to drive premium monetization and differentiation. So you suddenly created the opportunity that aligns with the business goal. Now comes the third part and then it's a tricky part and you're dealing with different stakeholders. So when you're presenting this idea to different stakeholders, the brand managers, the marketing managers, the R&D, you have to now treat them as three different or four different consumers. What are they looking at? What are their pain point? What are they How can you sell help them solve that? How can you help them bring this to life? And here's why it matters to them. So you're not just wearing a strategic alignment hat, you're wearing a stakeholder buying in internal consumers. And then comes the outside So act the campaign ideas. Now you go into fun part. They're doing the campaign ideas. You bring back and you test it. Um AI created message. You tested with the AI personas you created, right? Uh or you tested with your samples, you tested with your survey. So um we create ideas. I tested both AI persona and I test with uh my service because I'm still old school. I still don't I need to hear from my real consumers as I'm hearing from my AI personas. I'm not saying I don't trust them. I just need extra validation. That's me. You can stop at just AI persona or if you're me, you will do both uh validation, two-step validation. So what I did was I elevated my role from just a report generator to to uh presenting to a stakeholder to activation. I'm embedded in the activation part so much. And finally is the measure part. This is My role doesn't stop. If you're launching the personalized nutrition, what happened? How the conversations are shipped? You need to monitor continuously. When you launch it, what was an impact on the brand sentiments, brand awareness, and what the product reviews, the campaign performance. You're using AI to do that monitor that for the product at the same time you're listening for what's next. So your role is to in the scale to determine what's happening. So the final stage is the measurement, right? We didn't we're not involved in this. We stopped it, we started again. But it is a it is a cycle. The decision engines don't stop. AI is continuously monitoring and we are determining what to scale, what is to optimize, what to change. So this learning is how we improve, we fuel the better decision engines and stronger results. So with this AI did a beautiful job in creating this engine. I don't think so I could have ever done that. But if you think about it, this is what it is, right? You measure the decisions, not the projects. Move away from the projects. You create living inside systems. I have created a living inside system which mirrors sits on top of my non-living inside system. I'm using AI to generate signal. I'm getting the signal. You can do it on a monthly weekly. If you have enough time, do it daily, right? And then you embed inside the early planning. You know when the planning hours is a fiscal cycle. I have already embedded and we know what's going I know where the growth, you know, what is there the brand manager has a goal. How can you help the brand manager reach the goal is what you need to start thinking. So I start from there and I work backwards. Um and then you track your business continuously. I think it was enough of me talking. Um and this is where I'm going to leave everybody, all of us. So, tomorrow when you start thinking about what is the future of insight, it is from insight generation to decision market. I don't want us to be inside generation anymore. For decades, that's what we have done. For today, we need to ask ourselves what should we do. And in the future, if you want to be working together with AI, we need to embed ourselves in the decision organization making. And I'm not saying I'm the great, I'm the best. And then if that sounded, that was not my intent. But I realized that talk if I don't do that, somebody else will do it for me. Or I'll be easily replaced by AI because then I'm not I'm just generating the data that AI can easily generate. So, what is Reema bringing to uh Cargill is it's on the second and the last half is helping um um >> [clears throat] >> my brand managers, my species lead, my innovation team make better decisions. With that, I think I'm going to stop sharing and I talked a lot. And if you're still awake, everybody, I've not I've not scared you with my talking, please um come back and let's have the discussion. Um and I would be happy to answer any questions. And you know what? There's no right or right or wrong questions. Um this was not me sharing a capability, it was me sharing what I'm hearing. And today itself and I was um you know, a lot of our insight professionals are going to cons for um and this is like everybody asking this question. Where do we go from here? I mean, this so timely. Bill and I and Molly, you were there that when we were in the and we were all asking the same question. We're like, "Okay, what are we doing?" And so, I I that this was timely, but um it felt um, um, you know, we are all expert, Molly. We are here we're using these different tools and we I didn't want to preach a crowd that is already an expert in insights. So, I wanted to share my opinion on what I'm doing. I still have a job, so I think I'm doing something right. >> [laughter] >> Yeah. I did see Jody asked uh, what AI tool did you use to create your beautiful deck that you presented? >> That GPT. >> Good to know. >> This is what I've been doing is like I I have a lot of slide like I type it in the word document, my thoughts. And um, I say, "Okay, now A put in the slide." And they do a beautiful job. >> Yeah. I I also wrote out a couple questions uh, that I'll ask in case anybody else is still typing. Um, you know, I've been in this industry a little over a decade now and and even when I was first starting, there was that conversation of research versus consultants and like strategic adviser. So, I guess what skills do you think researchers really need to focus on to be prepared to be in that decision architect, decision engine role that might be separate than skills that they focused on developing for a pure researcher kind of mindset? >> So, you were so right. Open that. [laughter] >> Yeah. >> Open that. Um, when I sit my days and I'm not like I said, I'm not an expert. Um, but I this is what I really love. It's read outside your domain. Um, you're an expert in research and nobody's doubting that. And I believe all of us in this room are an expert in what we do. Now, do you know the consumer sentiments are down? Do you know they were all time down? What is happening? What are the external factors are going to happen? What are the new brands coming in? How the consumer dynamic changing? Start reading outside your business domain. And I I somebody many years ago when I worked in Kellogg's uh somebody in Kellogg's told me this. It was 2009 and that's all that I am. >> [laughter] >> Um but she told me that read outside the brand. Because we get so focused on the recent methodology. I have a psychology book here. I have a research book. But every day I sit down I read about grocery, what is happening, what are the new brands coming in. Um how's the oil prices changing because the oil prices decide my side of the business a lot. What are the new brands coming in? What's happening with the private label? So, what you need to do for the decision engine is to open this. Because when you're sitting in a room when you're presenting it to the consumer uh sorry, your stakeholders, you're giving them the 360° view and just not the research. The moment it becomes really research heavy is when you've lost them. And so, your I was I was like maybe because I I worked in brands and maybe I've been fortunate enough to work with different people, different countries, different continents. And um but I we need to start reading more. >> Definitely. Um I mean, you're in-house researcher. Um I'm more on the agency side. So, I guess what what advice do we have in terms of deliverables? So, like I often end up in situations where I'm trying to prioritize recommendations from research without maybe fully knowing the like internal capabilities and politics and like so kind of how do we still kind of own that role even though we might not be in house and have the full context. >> You're very close to your client partner. That's always the first thing. I am friends with my my vendor partners. And the friends in the sense that when we talk we talk so the first question we ask is that who's the stakeholder, whose problem are we solving? What is the tension in the room? Because you need to start from the tension in the room. Don't start with the data in the room and what you have. So as for you, you and your client partners, you need to ask them the question Okay, you're presenting this to sales team. What is that the tension you've heard? Like what is the what are the bottlenecks? What are the kind of questions you can get? And who is this presentation for? So there's one doc that we live with me every me and you working, which is a comprehensive deck and everything. One that you're presenting or is your or I am presenting on your behalf, so who's the audience? Now you need to draft and this is what I do with all my stakeholders. Remember when I talked about activating and stakeholder management? This is what I do when I'm talking to the brand new okay, I'm like what are who are you presenting to? What tensions have you heard? What pushback have you heard? And so your story changes based on the audience. If the story is not changing based on the audience, you're not doing your job right and that goes back to what I was saying break opening this up. You know the politics, you know the R&D teams like oh he always come with this new ideas, but you know it's not feasible and viable, but you're You're telling them to make a no. Or you've heard your learning team and they based on that is you you're presenting a recommendation. What I am telling you as our team and everybody in this room is embed yourself in the in the room more often than our in our silos of the researchers. And being in remote, I know there's a challenge that you're not in the same office, right? I'm in California, somebody's in Minnesota, somebody's in Quebec, Canada. Uh but then you're talking to them, you're listening to their problems, you're understanding their challenges, and then you're presenting. So researchers insights is not about results. >> Very helpful. >> [gasps] >> I don't have any more questions. I don't see any more coming in, but thank you so much for for presenting. This was so fun to kind of hear this come together, especially after meeting you last month. Um so I will I guess sign off and share my screen, but uh if you want to stay around everyone, we'll have another speaker at the top of the hour. Thank you again, Rima. We very much appreciate you. >> Thanks. >> Good night.