 Thanks for coming into the talk here today. As I was alluding to as we got started, there is a fairly large topic because there was a lot of content that we wanted to cover. And then there is equally long description of this talk. Oh, man, I should have described this before. OK, equally long title for our talk and long description because we would like to be able to talk about various different things as we go along this talk. I will divide this talk into two halves, if you will, where first I'll talk more about consumer preferences. What do we mean? There is a lot of understanding around personalization, around consumer demand, needs, determining, buying behavior. What does all of that mean? I am in no part an expert in the consumer preferences and consumer research part of it. So I'll touch upon some of these things and happy to talk and engage more because we as material do a lot of work around consumer research. Then what we are going to dive into is a case study around where we'll talk about digital transformation and the technical innovation piece in context of a toy manufacturer that we'll talk about. But during this, what I would like to do is draw parallels to why we think Drupal is a great platform, where the industry is going, where all the other Drupal agencies and partners can go out to market and find a much larger Drupal opportunity that we see, which is not being handled today or which is not being addressed by a Drupal-based solution. Anyway, just a quick introduction about me. My name is Ashish Goel. I also go by AG or whatever is convenient for you. There is an online question queue as well. So if you feel comfortable asking questions, I would love for you to chime in, ask questions, get further details, because there's only so much we can fit on the slides. But happy to go in any direction that you would want this talk to go in. And if it is useful for you to go deep down, I can stay over here after the conversation as well and go a little bit deeper as well. Just have a background in digital transformation, digital initiatives. I've done work on the enterprise side of the world. So I understand enterprise integrations, things like SAPs, Oracles of the world, and moved into the open source arena about seven, eight years ago, and have been working in the open source world since then, in the Drupal world there. So as I said, we're going to divide this talk into two parts. First, the consumer preference and the consumer research part of it. And that's intentional of how we came up with this topic of saying what, how, and why of consumer preferences. What does it mean to have consumer preferences captured? And then how do brands translate that into digital transformation initiatives? So 101, I'm sure everybody knows this around consumer preferences, but wanted to set a baseline on what do we mean when we say consumer preferences. Because these words get so loaded, and there is so much interpretation around these words as well. So what we are talking about is the products that are built by business want to align to consumer preference that will result in a more satisfied customer after purchase. And why do we care about that? Because if we can satisfy a customer after purchase, what we can do is expect more purchases to happen. As more purchases happen, we develop brand loyalty. And people talk about our brand. Apple is a great example of that. Or Nike is a great example of that. Coca-Cola is a great example of that, where they've built very large companies, very strong brands, and built consumer preferences, where you would ask for a specific brand rather than a drink. Or you would look for a specific product rather than features. Generally, how do we find out about consumer preferences? How do we cater to that? One of the easiest things that we all know is marketing and advertising. But there's a whole, as they say, there is a whole piece of other items that happen within the ecosystem of market and market research, where marketing and advertising is just one piece. One of my animation. So marketing and advertising is just one part of a very large ecosystem around how brands cohesively work around setting up product direction, setting up product features, setting up product roadmap, and how all of that relates to a brand. I'm just trying to guide this from a customer preference standpoint into how do we then capture these things? This was an area where, at least being in technology for the last 20, 25 years, I was completely elusive to this whole area of market research that exists. By itself, it's over $80 billion software market that just does market research. And there are two prime sides to it. Think of it almost like the first party data and third party data, if you will, in our world of tech, where there is direct demand generation of consumer preferences by asking questions to consumers. That generally is referred to as market research, but there are a lot of different ways how that is captured. What I'm going to focus on today is predominantly this side of the world, which is what we built for one of our customers using, of course, Drupal as a platform, which we believe is a fairly very extendable, very flexible, rapid application development platform rather than only a content management system. I'm going to focus on that, but also know that I'm focusing on only one part of the consumer preference ecosystem that exists out there in the world. And what I mean when I say market research, again, I'm just trying to define some of these things so that, and we'll have all of these slides out as well shared post the talk, so that when we are talking about some of these terms, market research, consumer sentiment or customer satisfaction, sometimes these words get so loaded that they end stop meaning anything. So that's why I've tried to put some official definitions in there, but mostly what a market research is trying to determine is sort of the middle of the bullet is a function where you're trying to find consumer, customers and public on how brands will connect to your consumers and connect through information and data. So it's the world of market research. Market research is automated or manual. Market research is driven through surveys. It is driven through automated pieces. I'm sure everybody's gotten a survey from an airlines. You took a plane, you took a flight here. Everybody got that. And that's actually part of your quantitative survey method where what every customer is trying to determine is how do I do quantitative research and find out the actual numbers, if you will, of satisfaction so that I can aggregate those numbers across various wide audience and be able to figure out what my customers really want and where the satisfaction level is. But where the companies want to do more and a lot of research goes in qualitative methods which try to uncover emotions of why would somebody want to prefer one product versus another? Why will, so emotional and buying intent is all determined here. The customer satisfaction pieces and how customer satisfaction will work is all in the qualitative. So quick view of where qualitative and quantitative research comes in from techniques as well as from application. Okay, I know I'm just running through some of these slides because this is more background around where the demand is coming from and I'll tie that to what it means for us in the Drupal ecosystem and technology. I was referring to part of this earlier where I said it's an $80 billion software market that I think we as Drupal community can play in because there are very large companies like Qualtrics and Medallia which have surveys that are generated. I live in the US so every time I take a flight on Delta or United, you'll get a survey from those folks and any time you look at the URL it'll either be Medallia or Qualtrics, surveys that those people send out. And these surveys are fairly simple to do in Drupal and that was the question that led us to the engagement that I'm gonna talk about in this sort of teams. Before I move on to from this slide, I just wanted to ask if there is any interest and any questions I can talk a little bit more about how the customer satisfaction market is shaping up and how we feel Drupal can play a good part in that ecosystem. Is that something that is of interest or would you rather move into the case study where we've actually built a manual survey and took that into a Drupal platform? Would that be more useful? Good, okay. So moving on to the digital transformation and the tech part of customer preferences. So what we talked about was there is a whole industry and market which wants to determine buyer intent, it wants to determine preferences for products, it wants to determine satisfaction with my brand. How customers are doing that today mostly is through manual surveys, focus groups, getting people into a room and talking through a lot of different options of products. Similar to that, we worked with one of the toy manufacturers who's one of the top five toy manufacturers in the world and that, I'm trying to see if I can switch off my wifi so that the teams doesn't keep popping up anyway. So these toy manufacturers which was a revelation to me as we got engaged in this project is that they set up these surveys two years before they release a product to see if this product or this toy would be something that my users will like and when they say users, they're looking at two users. One is actually a child who's gonna play with this toy and second is a parent who's actually gonna buy or gonna fund this toy. So they wanna understand the purchase intent, it could be great that there is this great $69 toy that I've made but the parents feel that it is too expensive, they don't wanna buy it, they don't wanna build products like that. Versus if parents think that it is great for $15, you're giving me a great piece of puzzle set but kids don't find it engaging, then again, it is a failure for a product for them. Then there are a lot of intellectual property interactions that are going on within the media and entertainment space. So think about any large toy manufacturer, they have tie-ups with Disney for Marvel Studios, they'll have DC, they'll have Batman toys or Superman toys or they'll have Batmobile or they'll have Harry Potter toys. These are all intellectual property transactions or agreements that are happening. So two years from now, toy manufacturers know what movie is going to be released, what characters are going to come out in that movie so that they can start building the toys today to be released in two years. So which means that any data that they work on is very secretive and they cannot have that data be released to the market because there is things about movie, there are things about characters, right? Iron Man is dead, what happens is that Iron Man's daughter becomes the next Iron Man. What happens there? We don't know, that's the cliffhanger where all of the movies want you to come in but now a Lego world or a Metal world or a Hasbro would come out with toys that they would sell in that piece of equipment, right? So that's all of the context of where we are talking about this digital transformation piece and how their process ran currently. All the details may not be important but the important part here is the process took almost six months for them to set up these surveys and to be able to build, come up to a point where they can actually go out to the market and actually take the assessment. Why did it take six months? Because they want to first, they have to come up with what are the types of things that they want to build. They want to look at all the data of previous years, how is my product selling? What sells in China is very different than what sells in the US. What sells in US is very different than what sells in Europe. So there is regional component to it. Then there is a local market component that they need to pay in that market. There is a local component of China's preferences in that market. So all of this data is one, the input. The second part after that is how do you go and render those sets which is the first thing that you see? The skews are fine, right? So you understand what is selling, what types of sets are selling? Is it a set of 100 piece Lego, let's say, or three dollar set versus is it a smaller set of two? That's the first piece, which takes them a couple of months just to do that piece and understand it. Then you have to render the future piece of how the product would look like. And those are artist drawings, those are drawings that are created by places. A lot of times these are models that are created in 3D and then there are various pictures taken. There are different colors done for those models. Just so that you can figure out is this toy better in pink or orange? Is this toy better in blue or yellow? They're trying to determine all of those types of things up there. Then those images are sent today in the current process. These images were sent out in a catalog. And you would think in 2023 there would be better ways to do this. But these are completely printed in a 60 page PDF catalog. That is printed high-designation, north paper, which is another part of this from a sustainability and all of that standpoint, but they generate a lot of this paper because there's no other good way for them to be able to ensure there is secrecy of data when this is going out. So it is printed in a catalog which is kind of growing up, fairly, very easy as well, with this by the somebody. And they don't, this data doesn't need to actually sign out the catalog. That is for the future catalog. And you can only show it in a survey setting under a controlled environment. So no phones are allowed, people come in and look at all of these things. So they were very distant around any digital technology. So they were thinking what happens to our images, what happens to our IP rights because they can't have any infringement of soil. This is too loud for this room. Okay, so they can't have infringement on these types of things. That process happens up there, right? Multiple version of each catalogs are created as well. Again, for security pieces, what they will do is they will have specific images for specific markets with a specific toy to be able to do traceability in case an image leaks on one of the social platforms. They at least can trace back on where that image leaked from so that they can then control even further who they allow the catalog to get out to, right? So this is really, really sensitive piece for them. Then we start getting into all of this work, it's pre-work where the catalog is now generated. Now what they do is there's a whole different process of getting people onboarded of who will take these surveys. So there are children, let's say from three to six bucket, there are six to nine, nine to 13, different, different buckets for different toys and different parts of their business. They will onboard these folks and some surveys are conducted only with kids, some surveys are conducted with kids and parents, some surveys are conducted with parents, but all of that onboarding today happens manually, literally on the phone. They have a catalog of people that they call on year on year to be able to understand if they are willing to come back and give their point of views for the next years or two years now catalog. But running through this process, this is a 90 minute in-person discussion that the customer pays people from anywhere from 200 to $500 depending upon how intense the conversations are. But these are in-person people are taking out time an hour, two hours of their days, sitting into a third party site, taking the surveys. And then of course there is respondents who respond and flip through the catalog. So there's a lot of disconnect between, okay, I wanna flip through the catalog, then I wanna talk about it. So there is a lot of disconnect between how the process is today versus the process that we came up with and I'll translate that into benefits as well as all the components that we use, right? Because what I've been talking about digital images, so there is a dam component to it, there is a DXP component to it and I'll come to that level of detail as well as we talk through some of these use cases. So first piece that we did was a lot around data management, aggregating their data and bringing all of their data together by market, by product, being able to do just data management because instead of an Excel, being able to do that on a data sheet was the first big advantage for them so that they can actually see some analytics that they do all the time on part of, in a quick view in an air table, right? Simple table, nothing too complex, fairly straightforward, but being able to bring those pieces in and then being able to export that data out into Excel because they wanna be able to send that to markets. So not necessarily changing their process but accelerating it from what is used to take them a couple of months to put together to be able to do that in a week, right? So you still have to create 3D models, 3D images but what we've done is you take those images and put it in a dam and then you can create 3D renderings of those plans. One thing that the PDF did not allow you to do is see 3D renderings, engage with the points. Now I'll talk about the implication around analytics and understanding consumer demands of when you convert that physical data into digital data and now that you have all of these details because we all understand you can integrate hotjar and Google analytics with a page and now suddenly you have data around how long somebody spent on that page. What did they click on? How many times did they click on an image? Did they actually interact with it or did they just skip the image? That tells you a whole lot of other insights than just a catalog and what people are checking on, right? So those are some of the advantages I'll talk about as we go along. Anybody has any questions? I know I'm doing okay on time so I can slow down as well. I'm just rushing through this just to make sure that I leave enough time at the end but anybody has any questions around the process so far or what we've talked about? Please. So this is all of the interactions somebody, rather than spending two hours can do. It's down to less than two hours. A lot of times what they want to do is they want to stay on the paper. They have to shift it to be smart. They have to wait on the process. See it and post it in a box because it is, again, you don't want to offend Disney for what they want to do, right? So this is all of the interactions that we're going to talk about. Because it is, again, you don't want to offend Disney for their intellectual rights. But I'm not going to read all of this on because there is a lot of material on here. But these are some of the advantages of saying this which is like what was the current process and what was the future state, right? And how are they realizing benefits? So one is reduce the significant, reduce the significant resources up front. So what they call this catalog is stimulus, which is the actual content that they create. How can we reduce that up front heavy lifting that happens? There is a challenging survey experience and I'll tie all of these to digital and what it means for us. I'm assuming that there are more people who work on Drupal every day of the week. So how does this relate to Drupal, CMS, DXP on the next slide? I'll relate that to products specifically. But I'll leave this here for a second so that you can look through this and we'll go in each of these slides as well. Good? Let's see a couple of people taking pictures so I just wanted to slow down for a little bit. Okay, so what does that mean, right? So first piece I already referred to this is the ability for us to store, manage, orchestrate and share safely digital content. So one of the big pieces that we brought in here was a dam offering where they had these images that can be created, we have not. So now I'm gonna talk about what we have and what we are doing as well, right? Just so that we can figure out where we are and where we are going. One of the things that we're doing with them now is taking AI to generate look-alike pictures of their model and actually tag each picture to the market and to the terminal where it will be shown so that you can then trace back in case there is a leak of a picture that happens and Reddit is one of the most popular places where they will see some of these things pop up. And so they monitor these things very, very closely and they'll kill a thread if they see things that have leaked. But now they also have the traceability because earlier if somebody took a picture off of a catalog it was very difficult to see where it came from. We can now, with them, we can actually orchestrate images down to a terminal where people are taking the survey which could be an iPad, which could be a screen. Currently all of this is still going to be in a proctored environment. So it is still not like you can take a survey sitting at your home which is what our original thought was that we can do that. So what we also built for them is if you are taking a survey on the screen and somebody pulls a phone out. So the screen would shut off. So we basically have a webcam on and we have an AI model running in the back which is trying to identify anything like a phone. And so if you have a pencil box up it'll still shut down but it's not as intelligent to detect a phone. But basically anything looking like a phone if it sees that in the preview. Again, it's not fail safe. Somebody can take a picture from the side. All of that is still possible. But these are security features that we are starting to build and close the loop holes of saying how can we create this digital content that is safe, secure, and traceable. That's the first bullet of dam. The next piece was what we call the customer experience platform where we connected the data and feedback from the users from diverse systems into one piece. So when we think about customer experience platform it is basically aggregation of data from various different sources. So this is what I talked about from a data platform standpoint. How do you bring data from various different markets? Sales data that is retail. So Walmart is selling your products and Target is selling your products and Tesco is selling your products. How do you bring that data? Because all of these three companies may have the same product but they call it completely different. They sell it at a different price point. They sell it as a different package. So how do you narrow that down back to the product that we are talking about? Because a product may be sold as a set in Walmart versus it may be sold as an individual unit in Target. So that's where you start getting into complexity of product variance and customer experience platform. Then you go to a digital adoption platform which is something I did not know that existed but apparently it does, DAP as well. So if DXP wasn't enough, there is a DAP as well which accelerates the set of services for adoption. How do you disseminate this information electronically so that people can adopt it faster? Then there is, beloved, we all understand, we are all here in DrupalCon, we all understand where DXP comes in. And the part of DXP which was really interesting for this context was in the consumer survey world, there is very little understanding of what marketing automation can do on a web page which we were very surprised with. Just being able to think about a survey today when you take a survey, it does make a difference about how long did you read the question for? How long did you take to respond? If there was rich text, if there was text or images, did you interact with the image or not? We all do that for our websites. If there is a product catalog, did you click on different views because there is propensity to buy if you were looking at the carousel versus if you just skipped the page. So those are the types of things that is completely missing today in the survey world. So when we brought in that instrumentation that the DXP provides, and these are simple things as I was referring to earlier like a hot jar integration or a Google analytics integration, that was a completely new data set for them whereas they have all of this data set already about what my consumers like and what they have told me, how much they like it. They have told me all of this but what are they actually seeing on the set is a completely new data set. So this would be the first year when they would start capturing this data. We don't have outcomes of how it impacts their product catalog two years from now but this is something that we are studying around. Is there an actual financial difference between what people told me that they will buy versus what my system told me they engaged with the most? What's the delta between the two? That could have a material implication on results and how they conduct these surveys. We're very excited to see as we roll some of these things out and as we measure these data sets, we are interested to see is there an actual difference between financially significant difference between those two. Last two things is analytics, everything as you collect more data, you can understand more and you can get better voice of analysis or voice of customer analytics. And then the last piece there is the sense data. Data became dark. If anybody else noticed it, then it could. So bullet six instead of, so that should say insights using sense data. It is not another acronym with DAA but understand and predict consumer needs like purchase behavior which is what I was talking about that how does the sense data actually impact the consumer preferences going forward. Any questions on this? Because after this it will just get into a little bit more depth on a little bit more detail around these things as well. And we are at noon. I do wanna end in the next five, seven minutes so you can get early for lunch because it runs out very quickly. So I have a lot more slides, a lot more details around, of course, what you see in here is Wyden which was the dam that we resulted and I'll share these slides you can read through each point. So all the six points that I talked about here, the challenges and the future state, how we related those six points into a digital experience platform or digital innovation and the things that we are doing in terms of how we are implementing each of them so that it's just not a story, right? You can see actual details around what we've done. So this is a Drupal screen that we've built. There are a lot of contributed modules that let you do surveys. What they wanted to be able to do is, and why it took 90 minutes, was a person would sit with a child and go through in a catalog, say tell me the top 10 things that you like and now they are going completely based off memory because they've looked at the catalog and now they're doing this assessment. So it could be the last thing that they saw or it could be the first thing that they saw. So right now it is all, we had converted it all as top 10 list. You can sort them, we actually record what did you select as top 10 and then selected it down to top three, things that you ranked up and down. So you may have selected something as three, but then you remembered something else and you put that at three and moved the three to four. We track all of that because all of that is useful data for us to see. You ranked this over that, right? So, and then just from an option standpoint, some catalogs, pieces, and we've just tried to generalize some of the images here on how some of these things are going to work. This is the digital experience platform part of it. You will see a couple of stars on there which are things that are coming, which is what I referred to. Things that we've built today and things that are coming. Some of the interesting things that we're doing with them is actually evaluating and eye tracking technology where what you look at first has and how long you look at something has an impact around how much you like it, right? So there is eye tracking piece in the surveys. There are VR pieces around how you want to interact with a 3D model because we already have 3D models. So these are things that we have not done yet, but these are things that are coming down the pipe to say how much do you want to interact with the system? There are variables, integration. There is natural language processing, so you don't have to type all of what you like and what you did not like about the product. You can talk about it and we are transcribing what you are typing or what you are saying. And all of this data, of course, all of the survey data is anonymized. Personal identity is not stored. We don't record anybody's voice. It is just the transcription that we store, right? So all of the platform is GDPR compliant. The customer is a European toy manufacturer, so they're very, very concerned in to ensure that privacy is met, especially because we are dealing with children in a lot of cases, right? Then there is voice of customer analytics. So these are the types of, I'm hoping that you can see, but there is a radar chart on here which talks about what are the segmentations and what age group is most likely interacting with that data. So what you see as a spike is Marvel. There is a certain age group that they are interested in and the rest of the age groups may not be interested in as much, right? I have some description in slide notes and all of that as well, that detail out all of these analytics if there is interest. Happy to talk a little bit more about this. What you see as a blue graph chart is my general survey question, so one thing that as you enroll people, you ask people about what do you like? What type of toys do you like to play with? Do you like to build things or do you like to race cars or do you like automated pieces versus manual? So I'm sorry, there are no legends because we wanted to remove any customer-specific data, but what you see in blue charts are my preferences as in a survey taker's preferences and the general line is mapping my portfolio to my survey respondents. So how does my portfolio relate to the products that the people are interested in? Some of these analytics are things that they are doing today, but an easier access to mapping something like that was great for them to be able to quickly move to the next level. Insights, I've talked a lot about this already about how the insights would work, and then I think we'll just move on to the next one. So quick summary, we talked a lot about digital asset management, capturing sentiments, capturing true voices, which is what we mean about not only what you thought you like, but actually how did you arrive to that decision? We can actually drill down based on all the data that we are capturing in a survey environment on how we actually can get to that level. We can capture the likes, dislikes, interests, patterns, and themes of what you like. Security is a big piece. Identity, privacy is a huge piece in this, and that's built in from the baseline. There's one source of truth now rather than multiple of these catalogs that are generated over time. There is one dam becomes your source, and DXP becomes your window for all interactions within the system, and then it provides rather than having a paper-based catalog, it provides a modern digital interface which anyway, as kids, are becoming more and more fluent and more comfortable with. And this is my last slide. I will just leave it at this around. What were the key takeaways for us and why we thought it would be an interesting piece for Drupal, or talk about here is one is the technology significantly transforming the accuracy, timeliness, and actionable insights piece. Bringing technology to this general survey and consumer research platform, and as I said, every company is doing this. They are using proprietary tools today to do this, and those proprietary tools does not have the capability that a Drupal-based framework that can result in much better user experience, far better flexibility, as well as much deeper data insights. And so that's one, this brings them into a digital transformation arena, and this gives them platforms to not only run this one survey now, it gives them, which was surveyed to what will be launched in two years, but they do many other surveys around what to sell in retail versus what to sell on funds, what retail price is here. So all of those types of consumer segments and consumer research can be driven out of a platform like this. Good. All right. Any questions? Happy to talk more one-on-one if you wanna go a little bit deeper into any of the things that I've talked about, or any questions I can monitor this queue as well. Okay. Thank you so much. Thanks for coming.