Behind the scenes: a guide to a successful research project

to the Acceler research virtual insights conference. Super excited for you to join us today and very much so looking forward to our speakers. My name is Christa Reid. I am the VP of operations at Accelerant. And I'm happy to introduce our first speaker, Lucy. Um before I do so and tell you a little preview about what she'll be covering today, just wanted to encourage everyone to use this opportunity to network and engage with one another and to also remember that we are at the mercy of tech. This is a virtual conference. So things may happen. There may be a glitch, a dog may bark, a mailman may have a delivery. Um if that happens please stay with us and we will be sure to work through that technology and also at the end of our session we will have a question and answer forum. So if you have any questions, comments and thoughts on Lucy's presentation or our next speaker feel free to leave those comments in the chat and I will be sure to share those with Lucy and we'll cover them at the end of her presentation. We have two speakers today. First, Lucy is going to cover behind the scenes, a guide to successful research project. And Lucy is a senior market analyst at company CAM. And our next speaker will cover how logic fails us with confirmation bias. And that is with uh our director of sweet snacking insights and analytics at Kelanova, Tara. So with that, I will stop sharing my screen, hand it over to Lucy. Please stay tuned for our speaker at two o'clock today as well. And Lucy, thanks so much for your time. It's great to introduce you and we look forward to your topic. >> Thank you, Christa. Let me go ahead and share my screen and please let me know if you can see it. >> Everything looks great. >> Okay, great. So, thanks again, Christa. Um, hi everybody. As Christa said, my name is Lucy. I'm a senior market analyst at company cam. I have over 15 years of experience in market research, analytics, economics, finance, and customer service. I specialize in both uh qual and quant methods. Um, I hold an MS in applied economics and finance, NABS in business and administration management. Um throughout my career I worked in different industries including SAS where I'm at now um retail, hospitality, entertainment, banking, investments, IT, food and auto. So, I called my presentation behind the scenes a guide uh to a successful research project because I believe that when most people see uh the final report, they don't quite understand um what goes behind that report and how much work we do in order to um achieve the final product. So, that's why uh today we'll cover the following topics. So, we'll start with uh defining um objectives and goals. Then we'll go over how to interview your stakeholders. Um I'll walk you through choosing the right uh research approach and then um how to write um your research brief. Um how to analyze data efficiently as well as to build an actionable report and finally uh we'll close out with presenting to executives and uh non-technical audience. So stage one um define clear objectives and business goals. Um before you touch a survey or talk to a single respondent, it's important to know exactly what decision this research needs to inform. Um so normally I follow a three layer objective framework where I start with a business goal. Uh and the question to ask yourself is what decision will this research enable? Um then I narrow it down to our research objectives. Uh what do we need to learn to make that decision? And finally we need to define our success criteria for um our research project. Uh how will we measure our success? How will we know that our um research delivered? Uh so an example in practice would be let's say our business goal is to decide uh whether or not to expand into Mexico by 2027. Then we might uh need to ask ourselves uh the following questions like is there um even white space in the in Mexico? How does the market look like? Uh do consumers resonate with our product? Uh what is their willingness to pay? So in this case your success criteria would be um a solid SWAT analysis uh clear KPIs um and recommendations uh the team can present to the leadership. Some of the pitfalls to avoid are um let's just run a survey and then see what comes up. Um not a good idea. Um in in this case uh you're just risking to have uh meaningless reports and no recommendations uh for the stakeholders. If there's no link between research output and business action, um it's also something to avoid. Uh if your objectives defined by the researcher, not the business. Um in this situation, um usually researcher is the one who guides um business um leaders to define uh research objectives. Uh but the researcher shouldn't be the one who completely defines those objectives. Um uh normally that needs to be um a team collaboration effort where both the business and the researcher decide uh what objectives are. Uh also vague KPIs uh that can be measured or acted upon. Uh so next uh once you know your business goals and objectives um uh you need to interview your stakeholders. So the questions you ask before the research begins are just as important as the research itself and um I usually follow uh this stakeholder interview guide. Uh so I start with a purpose um which is direct tied directly to your um business goals. So what are we trying to achieve with this study? Uh is it um is it going to be tied directly to our goto market strategy? Is it something to refine the way um our business operates? Uh is it maybe anything around um customer satisfaction that we need to learn? Uh then uh it it goes down to uh what specific things we need to learn which is essentially your uh study objectives uh that I covered in the previous slide. So that defines uh the scope of your research. Uh next will be the action items. So how will the findings be used? Um and usually I tie this to uh my uh target audience. So who your stakeholders are. Um is it just one executive team? Uh will the report be presented to multiple teams? For instance, um if you run a customer satisfaction uh survey, will that be presented to the executive team as well as the sales team? Um in this case uh you probably will need to create separate presentations for those teams uh to cater to your audience. Uh next is the strategy. How would results impact um our goto market strategy? Um for instance uh if it's something like launching a new product or if it's a pricing study then most likely it will impact your GTM strategy directly. Um if it's things like churn analysis or maybe a brand tracker or again customer satisfaction survey, uh those things um essentially will impact your go-to market strategy but most likely indirectly. Um you you usually run those studies to um detect any red flags uh to act upon or to improve things like your operations or uh you know refine your existing products. Um next uh what budget is available? Uh that's usually a very important question because uh the amount of money we have on hand uh will define our approach and methodology. Um in case if we are like unlimited in funds, you know, we have more freedom to choose between methods. Uh we can potentially uh field the study externally. uh we can purchase um maybe an expensive uh third-party data and so on. So yeah, very important question uh to ask your stakeholders timeline. Um how fast do they need the results? Uh that reveals tradeoffs. Uh for example, if you think an ideal situation would require 4 to 8 weeks to finish the study, but then your stakeholders tell you that they will need the results within the next two weeks. uh then uh you need to move on to the next question like what matters more to them uh speed or quality. Um for example, if the speed was more of a nice to have and your recommendation will be uh to get more time uh to finish the study u appropriately uh then they might uh go back and say like okay we're actually fine with two to four weeks. If they say that speed is the um absolute high priority and they're willing to sacrifice quality uh then it will impact uh the methodology u and the study approach that you pick. So sometimes what happens is uh when you interview your stakeholders um and in general people I worked with u were very curious so they wanted to add like more and more questions they wanted to learn more uh but in reality that's not the ideal case. The ideal case is to always ask uh like what questions are we looking to answer with this particular study and then link it back to your original uh business goals and research objectives. We can't just simply throw every question in one study and then uh expect proper and um good results from it. So it's always important to narrow it down and um connect it to your business goals. So stage three we are at uh choosing the right research approach. So once you collected all that information, you know what you need to learn, uh you know what questions you need to answer with the study, it's time to decide uh whether you'll conduct your research internally or externally. Will it be a qual primary or secondary data? Uh the answer is um it it usually depends. And um here is how to decide. So um on on this slide um I put um a decision tree that will help you um work through that question as to what approach to choose. So let's start with internal versus external. Um to answer this question like the majority of it will depend on your team's budget and timing. As I mentioned before, if we're unlimited in budget uh and if you know um that your um business goal will require a complex study such as uh maybe segmentation or uh need state mapping. Um some of the pricing studies can be complex things like brand health trackers. And if your time permits then it's a good idea to recruit um an um outside agency and let them handle the work. Uh however on the opposite side uh the the the plus side of in of conducting research internally is it's usually cost effective. Um it's uh generally faster and it's uh reach in context. Uh so it's best when you have the expertise, the software and the data um available to you internally and oftentimes we'll pick a hybrid approach where let's say internal teams design a survey and then um an external agency would field it and collect the data and analyze it for them. I've seen those uh situations happening. Uh next um do we go with a quall or quant again? Um if we are unlimited in uh time and budget um a rule of thumb is we can potentially use both uh when our qual study would inform the quant design. Uh so in general um our qualitative study um will uncover uh the the wise. So in other words it gives us an initial read. uh those are good in the situations where we explore new territories uh complex behaviors. Uh if we don't have like any idea on uh customer perception uh then generally we would start with a qual and then the quant to follow will tells us like how many people um actually uh agree with our ideas. Uh in other words it will validate our qualitative findings. Uh so things like uh online service trackers would be best uh for validation, privatization and sizing. And then finally uh what type of data do we use for our research primary or secondary? Uh the question is again we might want to use uh both. Uh sometimes I follow um an approach where um I start with the secondary data. So I look at all the existing reports that are available to us. Um any internal data potentially sales data, finance data, inventory data. Do we have access to any third party syndicated data such as Nielson, Spectra? Um some public data sources are also very useful such as census and FRED. Uh so when we start with the secondary data first it sets the initial context and then the primary data will fill in the gaps. Uh sometimes I work in a reverse order where I start with the primary data first. For instance, um like if I run a brand health tracker and I get the results, then I go back to my internal data uh and then I first like cross check the findings and second I look for any additional insight. So for example, if I saw spikes in our uh brand funnel metrics, um I look at our revenue and see uh if it helped our revenue growth. I might also want to look at any potential um marketing campaigns that we ran around this time. Um in a situation uh like if you're in a CPG uh sector, uh it might be important to check if there were any problems with distribution. That's in case um if your brand funnel metrics dropped for instance. So the key message here is like uh you can start with the secondary data layer in primary where the gaps exist and then a rule of thumb is if your time and money permits go qual before quant uh whenever the problem is uh poorly understood and on the next slide um I'm presenting um an example of how it actually works in practice. So from decision to practice. Um so let's imagine we have a scenario where our marketing team needs to reposition the brand with $8,000 available to fund the work. Then most likely will um run the study internally. Uh that mostly due to the available budget. uh so the research approach would be a sequence of uh three internal methods shown above uh to validate the position. So that will be a good approach in case if we have like no ideas how to reposition our brand like we we don't have any uh positioning statements or narratives or any uh stimuli available at hand uh to run uh quant directly. So that's when we would probably start with uh with um either IDI or focus groups to get an initial read on customer perception of our product and of our brand. Um so if you have $8,000 available, uh you might want to utilize your uh current customers um um as well as uh recruit external panel uh with about like you know$100 to $200 per um interview. um you should still have enough uh to run a quantitative survey uh which will help you validate what you found in the qual section. And then finally uh we'll you would do an an internal feedback check. So after you collected the results from the quant um it's a good idea to go back to any um internal customer feedback data and uh to see how new findings uh correlate with um with with what your current customers are saying. So it just you know gives you a piece of mind um and um makes your findings validated. So once we have all that uh we know all our business questions uh research objectives we interviewed our stakeholders uh we came up with methodology now it's a good time to re uh to write a research brief um so uh basically your research brief is a contract between you and the the business I believe that clarity up front prevents uh scope creep misalignment and wasted budget uh before they all occur. And uh on this slide uh there are eight elements um of a research brief which I normally follow but of course uh this is always flexible and it depends on your methodology uh and the um research approach that you picked. Uh so um I like starting with the background and context um and um answer the following questions like why are we doing this research now? What triggered this project? uh what potential problems business was facing that actually uh brought us to this research. Uh then we already covered business objectives and research objectives. So that's a good place to outline those. Uh next your target audience. Um who are we speaking to? Uh so you'll list your screening criteria, your segments, geographies, um any other you know details you might want to include here. Uh of course this is more applicable in the case um if we're looking to collect primary data but also sometimes would be applicable um if you have enough like secondary data available and if you want to narrow down to your target audience. Uh number five uh methodology and approach. So what method did we choose? Um are we running a conjooint test? Is it a turf analysis? Is it a max diff? Is it a traditional survey? Is it a monatic approach concept test? Again, good uh place and time to outline all those. Um your sample size uh it will depend your uh research methodology as well as budget, data collection approach, um tools you're using. So um specifically any survey programming tools uh what you will be using for data analysis and so on um deliverables and timeline. So if you u recall in the beginning of my session uh we had um a time to quality trade-off question. So if you had to sacrifice quality for time that would be a good place to outline that um just to have all your bases covered. So for instance, if um in an ideal world, you recommended one approach to the stakeholders, but then they told you they needed the results faster. Uh so you had to agree on their terms and then um switch methodology to something like less complex that will be a good way uh to show what happened uh so that there's no misalignment going forward. So you can put something like um a recommended approach was XYZ. However, due to business needs, uh we had to cut down the time to let's say 10 days. That's why uh we pivoted to XYZ. That will just like you know help you um in the long run. And then budget um usually um boring part but important to outline it here in the research brief um as well. And finally, uh stakeholders uh sign off. Uh doesn't have to be an official signature. Uh something just like a quick message approved or aligned would be sufficient. Uh so the way um I usually do it, I send the research brief to all the stakeholders and I give them some time to review and ask any potential questions. Uh that would be a good time to make refinements. Uh but um as soon as all the parties um um agree on the um research approach and methodology and next steps uh we can start running our project and then again research brief uh protects you from any misalignments uh either during the research um or after um the research is done. Okay, we're at stage five now um analyzing data um efficiently. So we had our research brief. Uh we either fielded um our survey or conducted our focus groups or ideas. Uh we uh gathered all the secondary data. Now it's time to analyze the data. Um so we all know that data without a story um is a noise um is noise. So our job as a researcher is to transform numbers and quotes into insights that drive action. uh not to report everything uh we found. Uh so um throughout my career like I defined uh two main challenges while like analyzing the data. Uh so one of them is like uh how to look at the big picture instead of um just looking at individual data points and then the second one is when the data contradicts itself. So let's look at um each of them um individually and we'll start with uh seeing the big picture. Uh so I usually I zoom out before I zoom in. Uh meaning like instead of looking at um individual charts and graphs and individual like slides and data points um I start from the forest like not the trees. I look at the big picture and see what the data story tells me. Uh so uh a rule of thumb is like con contextualize first. So before interpreting any finding ask is this actually consistent with what uh the world and the market is doing right now? Uh does my data make sense? Um, and if it doesn't, um, you know, then it's it's a good idea to go back, um, and actually like check if you're analyzing it correctly. Uh, or maybe, um, if there are any outliers in the data, maybe it requires some like data cleaning or refinement. Uh, sometimes things like, uh, setting up like weights, um, helps as well. Second, um, good idea to look at the story arc. So do the findings connect to a single narrative? Uh the best reports have one uh central tension uh that all the findings support. Uh third uh check proportionality. Um is this finding actually a signal or just noise? I already mentioned like outliers. Uh so weight findings by frequency, intensity and business impact. uh don't just throw like everything there assuming that every data point uh has an equal weight and equal importance. Again, look at the big picture first. Uh and it's important to revisit your objectives. Uh so yeah, go back to your research objectives and your business goals and ask yourself um am I actually answering uh the question that was asked by the business or am I just throwing um all the data um tables um on my report? Uh then next what to do when data contradicts uh itself? Uh sometimes like it happens quite a bit but I don't see contradictions um as a problem. Usually it's a sign that the business was asking the right questions. Uh because like in the world where everything was easy uh you know we would not need the research. So it's actually sometimes a good sign when you see contradictions. Uh so number one step is uh find the source. Uh was it potentially a difference in methods? So if we let's say if we had a consumer survey and it held it had some open-ended uh qualitative questions versus like quantitative questions. Uh did the contradiction come from qual versus quant? If let's say it comes from a qual like how big is that segment that's different um from the other um insight that you found. Uh so each of them uh will need a different response. So for instance, is it just a few people who provided like negative feedback in an open-ended uh response or was it the entire data set? Uh next, it's very important uh to not smooth it over. In other words, you need to be upfront and honest and transparent with your stakeholders. Um if there is a contradiction, then we need to report it uh clearly and uh not to be afraid of it. Uh because that's the whole point of a research. um we um need to provide valid results and valid recommendations. Um and finally um not finally the next step is um to reframe um as attention. So for instance um we can say customers say price matters but behavior shows um they pay more for convenience. Um usually in uh what what happens here is uh people explicitly say one thing but then in reality they do the other. You know um as human beings um I I've noticed like it happens a lot uh with respondents. So my rule of thumb here trust your implicit findings more uh than the explicit. So, for example, if you had um a survey question where you asked people if they if they think price is important, but then you ran um a correlation analysis in the background and you determined that they're actually paying more for convenience, uh then the second portion, paying more for for convenience, would be more valuable than uh like people being timid to pay more. that will probably tell you a story that people are willing to pay more if they see the value behind your product and service. Uh so then you know your recommendation to the business would be um to position your uh if if you position like your brand as a premium brand uh then you need to explain uh why people uh should pay premium and then at the end of the day um use your best judgment, intuition and decision making abilities to generate conclusions. Um that's your job um as a researcher. So next uh building a meaningful and actionable um presentation. So once we analyzed all the data, it's time to write um a report and um put together a presentation. So a great research report is open months later and it still makes sense. Um so usually I follow the rule um when um I write reports I follow the rule um where I uh make all my reports readable assuming that I will not be present uh to present those reports. Uh for instance I include uh survey questions uh on the bottom of each slide. um if there is any like acronym or um new methodology um I make sure it's all explained in the report uh things like you know statistical significance you might want to outline it at what level it was testing it was tested and so on um so yeah you you you want your reports to last not only during your presentation to the stakeholders but for everybody else who might want to um um access them months later and and still understand what the research means. But um more importantly uh when I write my reports I follow the three dimension synthesis model where um I look at all the findings at uh three different levels. So number one level is the macro level. So like the world level um what global forces shape this topic. Um I look at um any economic shifts like cultural trends, regulations, um technology and so on. Uh the next level is uh your industry level. So your your sector level. So you know we're going down the step. So it's a more narrow level. So how is this playing out in your category? Um were there any competitive moves? Did like new players come to market? Any benchmarks you can um benchmark yourself against? uh sector specific behavior and finally uh your company and your research will be uh the last uh most narrow level. Um so that's when you will look at what your um study actually revealed uh primary data behavioral signals any verbatims. So um when you put all these uh three levels together uh they will uh they will generate uh the convergent insight which will help you um write a meaningful report and presentation. So the power is in the convergence. uh when your micro forces uh industry dynamics and your own findings all point in the same direction then it means you have an insight uh that is nearly impossible to argue with and the way to explain it in action um like to you you you you write your report for from left to right. So you will start with your observation first. So what you found then the implication how it would um impact your business and uh next it will help you generate an insight and then all three all of those um will be transformed into the final recommendation for the business. So now on the left hand side is an example um of what not to do. So when you're just simply stating a data point not an insight. For instance, 68% of respondents said price was their number one consideration when choosing a vendor. Not a good idea. This is just a data point. It describes what happened but gives the reader no direction. Uh the way to do it is displayed on the right hand side where you start with an insight. Uh price dominates decisions not because buyers are cheap but because value is poorly communicated at the consideration stage. So the recommendation would be something like create a specific um valuebased proposition that will actually show uh why your product is worth the money uh that uh we asking for. Okay. So we are at the last stage um presenting to non-technical audiences and executives. Uh so your findings are only as valuable as your audience's ability to act on them. Uh learn to read the room and tailor accordingly. So I know in the beginning of my uh presentation um I already mentioned creating different presentations for uh different audiences within your organization. Uh for instance sales teams, marketing teams, executive teams, potentially a finance team. Um in general they can all be divided into two audiences. Uh it's either executive audience and or non-technical audience which of course sometimes those two um cross uh connect and your executive uh audience might be uh non-technical audience and vice versa. Uh so let's go over some good tips on um how to present to executive audience. So, uh, always lead with the so what. Um, state your number, uh, one findings and recommendations, um, in slide one. Uh, don't make your, um, executives wait until you go through the entire presentation, um, before they actually see the conclusion. Uh, use the pyramid principle. U, so conclusion first, then evidence. Um, executives normally work from, uh, um, top to down. Uh then if possible connect uh your findings to revenue or risk. So every insight needs a business consequence. Um it's either growth, cost or risk. Uh limit your slides to about like 10 maximum. Um I think in the modern world uh you know with social media and everything like um a listener attention span is just so short that most people probably won't be able to see it through. uh 25 30 different uh slides if it's an executive team. Uh that's usually limited on time. Um so again put around 10 slides uh for your seuite readout and then put everything else in the appendix for those who are interested to learn more. And then finally anticipate challenges. So usually when I uh prepare for a presentation um for the executive team um I think ahead about potential questions um that they might ask or any uh challenges that um I might face um along the way. Uh so it's a good idea to foresee those ahead of time and then uh prepare. Um common questions are uh like so what should we do about all this uh or and sometimes people ask uh why should we trust this data so you should be prepared for that and um as far as non-technical audience um I found that it's uh useful uh not to give methodology upfront so um don't overwhelm them in the beginning of your uh presentation uh with things like statistics and sample um move it to the appendix because some people uh might feel a little bit intimidated. So they might not like um listen um as attentively going forward. Um use plain language instead of industry jargon. So for instance, replace statistically significant with things like we're confident that. Um anchor with uh specific stories. Uh so it's a good idea to bring an example from um real life or use some uh personas uh that make sense uh to your specific audience um that that you're presenting to. Uh so for example if I have to explain uh how a conjooint study is designed sometimes I show a screenshot uh of how respondents see it and I say something like a conjooint is a real life scenario where we imitate real market. Uh so imagine a consumer that goes to a grocery store and he or she uh needs to pick an ice cream um at a certain price. Uh so that's uh what a conjooint is. It mimics exactly that. Uh we ask respondents to make um a choice and actually like uh make a decision and buy the product. So that helps your audience um and understand um where you're coming from. Uh next is um try to visualize not uh tabulate. Um I know sometimes it makes sense to put tables instead of charts. Um I personally prefer horizontal bar charts uh over everything else if possible. Uh now like some line graphs um are very useful if you show historical data or progress. So things like NPS, customer satisfaction points, uh maybe revenue, anything that has a point in time uh like a line chart would be good for. Um I generally um avoid using pie charts too much unless it's uh something like very simple um or if you really have to um show a share um in percentages then a pie chart would be a good option. But um at the end of the day just emphasize the insight not the number and statistics behind it. uh and then uh the way you present it uh in in in the form of a table or a chart won't be as important as the actual uh finding and recommendation. And then finally uh building Q&A checkpoints. So um stop after a few key findings. Um I know today I'm not doing it because we're doing um a Q&A session at the end. uh but generally I pause after a few slides when I present and I ask um if there are any questions if something doesn't make sense um just to engage with the audience um and not to present for 45 minutes uninterrupted and this is it um thank you so much um I'll stop sharing my screen and I think I'll pass it on to Christa Thank you so much Lucy for that very detailed and thorough process overview. Your mo road map was extremely helpful. I am not seeing any questions but I am seeing positive feedback and comments. So one participant wanted to say thank you so much so many great reminders and you've given such a clear road map of your process. So, a lot of helpful tips certainly today. Um, greatly appreciate that. One thing that really stood out to me was your discussion on quality versus speed and how sometimes you may have to evaluate if there is a tradeoff and what that means for your methodology, your approach, and how you deliver your insights. Can you share a little bit more about that and how you as a researcher have had to pivot in order to make those adjustments? >> Sure. Yeah, I've had quite a bit um situations like that uh in the past. Uh one example would be uh when I was asked to conduct um I think it was around like u micro trends uh which ideally uh you know would involve um fielding a consumer survey and then support it with internal data and then potentially uh running uh a forecast analysis uh in the background with those two uh but that required um somewhere around like four to 6 weeks. Uh but then I was asked to deliver the results within a week. So instead um I had to completely switch methodology and uh just uh get help um from the um data that was available on the web. So I just gathered what was available internally uh through internal reports and then what was available on the web and then I just put all the information together. Um I used you know AI to analyze it to to help me with that. Uh but yeah that was the most efficient way to deliver on time. Um it was just a critical situation where um the business needed to go to market fast and you know we couldn't wait for four to six weeks. So that yeah that was one example. >> Thanks thanks for sharing. We did receive two new questions. Uh one question is would it not be better to have stage 4 for example a research brief before you start on a research approach? Uh you potentially could go this way, but um I recommend uh writing a research brief after because you would want to outline your uh research u methodology on your research brief so that stakeholders um like um see a clear picture what to expect. Uh cuz the idea of a research brief, it's a contract like between you and the business. Uh so you all want to agree on methodology before you start building uh your project. Um so yeah um research brief in general um I I write it when I already know what method um I will follow so that uh stakeholders can approve. Uh but if you feel you can if if you feel you want to start research brief before the research approach and then maybe brainstorm um together with the stakeholders you could do that too. Um it just usually uh I make that call on the research approach um you know I make that decision so I come up with the recommendation uh to the stakeholders. Uh so it just it's been working uh better for me. part of that bit of being a researcher, iterating and figuring out what works best as you go along further in the process. >> Another question for teams that don't have time to develop multiple versions of a report but need to present to multiple audiences. What advice would you give for optimizing the original report to give yourself flexibility to repackage multiple versions from a single base deck? Yeah, that will depend on how much time you actually have. Uh historically what I've done um I would have uh like the the core section the same for all audiences. So for instance um when I when I worked in one company I had a big customer satisfaction survey then um I would present findings to different um sales teams within different departments. So I would have the main section the same for everybody with all like the highlevel total market findings and then um I would create additional slides that are applicable to this like specific department. So that will save you a lot of time because you can just copy the same report and then uh you know modify the last few slides uh depending on your target audience. If you absolutely like have no time to modify your presentation, um then you might want to think how to change at least the headers uh of some of the slides, you know, to change your conclusions and recommendations to make it more actionable for your target audience. Or if you don't have time to even do that, then you know, think about how you can uh change the way you present like during your presentation. uh maybe you know how you can avoid like industry specific jargon for instance if it's a non-technical audience uh maybe remove some of the slides that are too like overwhelming for certain audiences um yeah I hope it answers your question >> I think that did and I really enjoyed hearing the piece about understanding your audience and knowing how to speak to their priorities and what their business strategy is and incorporating that into how you present and deliver your insights. So again, very helpful reminders and tips for all of us as researchers who are constantly thinking about methodology and processes and how to package our insights and just make them engaging and compelling for whichever audience we're speaking with. So with that, I'm not seeing any other questions or comments in the chat. I've also checked on YouTube. I'm not seeing anything else there that we haven't covered yet. So Lucy, really appreciate your time. Thank you for sharing your knowledge and experiences with us. For all of you who attended, thank you so much for joining Accelerant for another virtual insights conference. Our next speaker will present at 2:00 and our very own Molly Hagen will be introducing our speaker. Uh Lucy, I'll let you say any parting words that you have and thanks again. >> Thank you so much, Christa. I appreciate the opportunity. All right, thank you everyone.