Aaron Conant  0:18  

Happy Wednesday, everybody. My name is Aaron Conant. I'm the co founder and chief digital strategist here at BWG. Connect where a giant networking knowledge sharing group 1000s of brands, I spend my time just doing strategy sessions with brands of, you know, all sizes startup, the fortune 100 up every vertical, just to serve as a focal point for Hey, what are the newest strategies, trends, pain points that are shaping the digital landscape as a whole. Same topics come up over and over again, we go out to our top recommended partners, the people who you and the network are saying are doing the best things around. And we invite them on to kind of do an educational session. And so this Amazon data strategy, you know, Shane and I are just talking a little bit around the fact that like, we're finally getting to that point where we need to have a robust way to look handle and analyze, and get actionable insights out of all the available data on Amazon. And so Sreenath it over here has been a great friend, partner supporter, the network for years now. And I'll kick it over to you if you want to jump in and do a brief intro on yourself. And then Intentwise, that'd be awesome. Yeah, and we can kind of jump into conversation. So I'm good, my friend.

Sreenath Reddy  1:30  

Yes, sir. So and thank thank you for having me. And just have to say your team has been great, and just getting all this setup. So thank you. My name is Sreenath Reddy, based in San Diego. So if anyone is nearby, and if you still like me, at the end of this conversation, I'd love to meet up. But so I'm the founder and CEO of Intentwise. Just way quickly on Intentwise, we are an analytics platform with three specific solutions. One is called internalize analytics cloud, think of it as a industrial strength, data automation solution for all your Amazon data in NVR, both ISO and SOC two compliant in that regard. And then the second one is designed around Amazon Marketing Cloud, which is the shiny new object these days. And we'll talk more about that. But extracting data from AMC can be difficult. So we have built an extremely robust data pipeline around Amazon Marketing Cloud. And the third solution, which is really about retail media optimization is built on that analytics Foundation. And really is aimed to drive retail aware ad optimization for all your media spent across a number of retailers. So that's, that's her company. And what I've been doing well before internalize also is that like I've been dealing with and handling data in large scale enterprise environments, you know, in public companies. So the point of saying all that is I have lots of Battle Scars learn a lot from that process. And what we have built here at Intentwise has been guided by that. And some of those learnings I'm going to share in today's session as well. So that's my intro. And now and I'm happy to dive into today's content. Yeah, I

Aaron Conant  3:15  

mean, that'd be awesome. And then I just say, Hey, if you have questions along the way, dropping in the chat or the q&a, and we'll try to crunch through those real time. But no, I'm, I'm excited. This is a topic I get asked about all the time is how do I handle all everything that's within Amazon? And how do I report it out?

Sreenath Reddy  3:34  

So yep, awesome. All right, let's let's dive in quick, not an agenda. I'm going to talk about a couple of fundamental shifts that are happening around data in our space. We'll quickly review some of the new datasets that Amazon has started to make available, especially over the last 18 months. The challenges with that data. How do you formulate an executed data strategy, which often is a dense, complex topic, I want to leave you with simple frameworks that you can action upon. And then of course, no conversation about AI is data is complete without talking about AI and its implications. So we'll do that too. I wear lots of slides, I'm accelerate through some of them. I don't I'm assuming this will all get shared out. And like you said, we may have multiple sessions too. But that's my agenda. Awesome. Yeah. So with that, let's just dive in to fundamental shifts, one, Amazon, and even the likes of Walmart, are sharing more and more data than ever before. And I'll unpack that in the next set of slides. So that's one trend. And of course, the other trend. I don't think anyone here has not heard of Chad's GPT at this point. But, you know, but chargeability is one of many solutions where AI for the first time ever is becoming exceedingly and increasingly accessible to all of us. You know, even your Microsoft Word is next Sales have open AI starting to get embedded in them by Microsoft. But the point here is, these are the two fundamental shifts happening in the space. Are you on a path to success in this context, right, which is where data strategy becomes important. The let's talk about Amazon and Amazon sharing lots and lots of data. If you dial back two years, you know, the left two boxes on the advertising side, which was sponsored ads, and DSP. And then on the measurement side, you had access to Seller Central, your access to vendor Central, that was the universe, right? Look at what has happened. Now, on the right hand side, I'm going to call out these four pieces of new sets of information, Amazon Marketing Cloud, which is probably, you know, something that we should all be very aware of. We'll talk about that Amazon Marketing stream, which is our only advertising data selling Partner API, which is Amazon invested a lot of time in opening up data through API's, especially for vendors, where I think all the one P folks here can relate to the pain of not having any automated access to vendor central data. It's not perfect, but they started to make this accessible middle of last year through API's, but selling Partner API advancements. And then lastly, Amazon attribution. And I'll I'll double click on each of these in a moment, but this is what the footprint looks like. And we firmly believe marketing cloud, Amazon Marketing Cloud is the center of Amazon's measurement universe going forward. I just have to laugh just

Aaron Conant  6:35  

a little bit, because for the longest time, and even when I was, you know, build an Amazon business, in corporate America, like there's always this, we're always complained, because there wasn't enough data provided by Amazon, right to make the right decisions. And then rapidly like you're saying, they've just done a, what seems like a half hazard organized data dump. Right, where here it is you wanted it? And now people are like, I don't know if I want it.

Sreenath Reddy  7:05  

I think I mean, my viewpoint is I mean, there is a clear vision on their side, but it's just so much data that it may look fragmented may look half hazard, you know, but clearly, they are moving down the path of just making a ton of information accessible programmatically, right? That's, that's also key.

Aaron Conant  7:23  

Okay, awesome. I love him.

Sreenath Reddy  7:25  

So let's let's dive into each of these boxes probably spend a bit more time on Mark amser marketing cloud, because I think it's super critical to understand exactly what it is. My assumption is many of you are dabbling with Amazon Marketing Cloud, but I'm just going to walk you through just the basics, right. But what makes Amazon Marketing Cloud unique in terms of the data that is being made available? Imagine you are shopping on Amazon or anywhere for that matter, right. And this is just a hypothetical scenario, where you have seen an impression of a DSP and then you have clicked on a sponsored ad, then you clicked on a sponsored display ad, and then you made a purchase. Without Amazon Marketing Cloud, all this data is last touch attributed the data is just share the head that sponsored display actually drove that purchase. That's all you know, right? But with Amazon Marketing Cloud, they are tracking every one of these events. Down to the shopper, Id unique shopper even have a field called User ID. Now it's a clean room, you cannot see what that value is. But at that extreme granularity, you have that data set so that you can roll up and see shopper patterns, right? What is the impact of my streaming media campaign? On my purchases? Right? So this see, it just opens up a world of possibilities in terms of new things you can ask and answer. You know, just so very quickly, it is AMC is a clean room. With granular event and shopper level data being made available, you have two ways to access that data. You can go into a browser and type SQL, or you have a programmatic API access, we certainly have our domain and vice perspective, we have API access, and then it is available to anyone that has DSP spent today. So couple of messages here. If you don't already have an instance, for AMC, please request that it comes at no cost to you. And it has a tremendous amount of data. And the other thing to note is data starts collecting from seven days before the day the instance is created. So if you haven't already done it, please go ahead and do it either with your agency partner, or if you don't have a pathway, come talk to me by the end. So last point here is that there is datasets where the data is at the user ID level. And here's an example of things you can do. You can put, identify or define cohorts of users based on the paths that they have been exposed to and see what that performance looks like. So lots of lots of possibilities, including figuring out lifetime value of customers to The impact of top of the funnel on the bottom of the funnel. And the latest exciting thing that they have done is they allow you to create custom audiences and use DSP to target them. And that Korea criteria for audience is very can be very specific to you. I want to go target all these people that saw such and such streaming TV campaign in a certain way. But I want to target all these people that I've added my product to a shopping cart and never bought it. Right, you can define those audiences and push those to your DSP instances, since you know, allocate your media spend in a much more optimal way. So besides granular data, you you can also go create custom audiences. Right. So that's AMC. I'm going to in the interest of time, I'm going to cover off on the other three boxes, marketing stream, this was released sometime last year, this is hourly advertising performance data, where you can, you know, by the hour see traffic conversion, so you can understand efficiency, you also can get data when campaigns are running out of budget, in an hour on an hourly basis. So that stream, the waste stream data as different is batch data, you can invoke API API's and get that data. stream data technically is a different architecture, you have to be listening to that data that's been pushed to you. Right? So there's stream there is Amazon attribution. This is a mechanism where you can spend media off of Amazon and understand the impact on Amazon by instrumenting, some Amazon attribution tags, right. And then the last but not least, seller Partner API. Again, one key vendors did not have API access until the middle of 2020. Now when API access is available, it's not perfect. There's a lot of data that they don't share through API's, but they're going that direction is very clear to us. Right? And then the most recent release less than two weeks ago is they are making sales and inventory data available in real time through the real time analytics. Yeah, typically, if you log into vendor central data is 24 to 48 hours late, you can actually get sales in inventory data in real time. And there's value to it, especially as you go into things like Prime Day, where you can see in much more real time what is going on with your overall sales.

Aaron Conant  12:25  

Your very quick question comes in, see when able to get access to AMS? Or is that for? Is that for DSP users only? Because I know you're saying for AMC,

Sreenath Reddy  12:35  

yeah. If the question if the question is are on AMC, you need to be a DSP spender to have an AMC instance created for you. Right? Because it's still you know, we need to request and somebody on Amazon site is creating an instance for you. So that's a basic criteria. I do know that over the next six months, they are going to introduce versions of AMC into when you say MSI, as you assume your main sponsor ads, because AMS also examines their marketing stream. But if you're referring to sponsored ads, a piece of AMC will become available in sponsored ads also sometime this year. That's what they are marching towards. I hope that answers your question. Awesome.

Aaron Conant  13:19  

So there's the app, if you have questions drop into the chat or the q&a. And we'll Oh, there's one here on the q&a side, too. We'd pop that open right now. Is there a minimum investment level?

Sreenath Reddy  13:30  

I don't believe so. I think if you have a DSP account, what you need is a DSP ID and a sponsor added ID and we can get in AMC instance create. Awesome. Now, so with all this data coming at us, what are the challenges with Amazon? And I think most of you can relate to this like one is data collection and ownership is a pain, right? So free, I'll give you a simple example. Sponsor ads API changed six times in 2020. Right? Sitting there and trying to keep up with all this is a bit of a challenge, right? Data quality. In the data business, there's always quality issues, right? Having the right processes to detect issues and fixing them proactively. non trivial, right? Even when you collect the data, data is fragmented. The use case I talk about is imagine constructing a 360 degree view of a singular product, right, which is advertising, search and DSP, organic sales, inventory, position, ratings, reviews, all of that in a singular view. Those are all disparate data sets. Even when you collect it, you have to sit and organize that data set. Right. And of course, data skills have always been a scarcity. And with more data thrown at us, this is even bigger of a problem. Right? So I think these are all the challenges we are contending with. In the context of which we need to frame our data strategy. But before I get into data strategy, I think it's important. At least you know, the way we think about data sets, we think about them in two broad types. One is what we call shopper centric signals. The other is what we call brand centric data. What do I mean by this? When I say shopper centric signals, I'm talking about the I search for vacuum cleaners, all that stuff I see like the reviews, you know, the images, the videos, right? These are all signals that shoppers are basing their decisions off of. So I call that shopper centric signals. And this data, this data is important, right? And then on the brand centric side, this is your advertising data from, you know, search, DSP, cloud stream attribution, and of course, operational data from on the retail side, right. So that's what I mean by brand centric data. But I think we can all agree that if you want to compete effectively on Amazon going forward, you have to have the ability to corral these datasets organize, and really turn this into actionable insights in a rapid basis, right. So that's our thesis. Not so difficult to get behind. And I think when we think about data strategy, it is in this context that we never will have a frame it. Right. So let's dive into the strategy conversation. Like I said before, in our data strategy considers this complex, dense, painful kind of imagery, right. But the way I like to frame data strategy is it has a singular purpose, which is, is it allowing you to ask and answer super critical questions way quickly, and help you drive growth? No matter what investments are being made in your company? If this answer to this question is not yes, then we don't have an effective data strategy. That's how we think about this. What does it take to actually execute frame and execute one. And it boils down to like a number of things, it boils down to these three components. And I'll peel the layers on each of these. So process tools, and people. Let's talk about process. If you don't take away anything else from today's presentation, the next four slides is what I like for you to declare it is the number one step in executing a data strategy is to frame or to build a road map or what is a road map. It is basically a set of questions that either are causing you anxiety at night, or or take forever for you to answer today. Or if answered, they can give you a fundamental strategic advantage, right. In fact, the way we think about it is operational diagnostic strategic are the three buckets. And I even have examples of what I mean by these questions. So operationally, Hey, what is my year over year performance? Right? How is my E commerce and tacos trending or diagnostic in my sales are down year over year? Why? Does it take you one week to go answer that question? Does it take you 30 minutes to at least understand where that problem is? Right? And the strategy question, lifetime value? How do I set my efficiency goals based on lifetime value of customers who really is my competition? Right? Today, competition on Amazon is not at a brand level, it's at an individual product level. But this is what I mean, I honestly think if you don't already have a 10 to 15 set of questions on a single white piece of paper, that could be a therapeutic exercise to bring the team together and frame these questions. Because without this, no day strategy will be successful. So I think super important. But let's assume you do have a strategy data strategy in plays. In terms of execution, it kind of boils down to, I would say, four critical pieces, right? One is, are we collecting and owning the data sets, we need to answer that question. On that note, I will say most brands today are not collecting and owning the data they need, right? There is data loss, if you just let it sit on the Amazon portal, it goes away after some time, or you're working with service providers where for whatever reason, if you stop working with them, the data goes with them. So I would go and evaluate your current relationships and make sure you're on a path of owning your data, no matter what happens to your service provider relationships. But anyway, so collecting and owning, or even when you collect and owned, there's still work to be done in terms of enriching. If you're a brand with 5000 item catalog, you group those that catalog into 1015 20 unique groups. Amazon data doesn't know that you know that. So there is a need to enrich that data. Like you could be a brand in multiple geographies and you need to normalize data across currency. So currency conversion data. So point is collecting on Connect and enrich and visualize and analyze is the place where you're building dashboards, you're analyzing data. And then finally, action. Let me make this a bit more real with a simple example as to how these two pieces work together. Let's assume for a moment, your roadmap question was, I don't understand my item level profitability when I've factored in all my media spend and everything. I also don't know if my agency is aligning my media spend with my profitability at the item level? That's your question, right? But the way you can execute on that is okay, what what data sets do I need? Well, I need certain vendors, General reports and sponsored ads and DSP data. What enrichment do you need? Well, I have custom groupings of products. I'm going to maintain this somewhere. And I have margin data by product, I'm going to tie that in, what visuals will be helpful, hey, I want item level profitability view year over year, right? And then how am I going to act on this, I'm going to take this, I'm going to put this in front of my agency and said, This is misaligned. I want this to be much better aligned. Or if it's an internal team running your media spend, they could do that. But this is just an example of how you can go from framing a question and getting that answer. And by the way, by all this, I do not mean 18 month it projects. This should happen very quickly. Whether that means people pull data together from different places into a spreadsheet and answer this, right. And then in parallel, your infrastructure project should be in progress as well. But one track feeds the other. You know what I mean? So So basically, these two artifacts become extremely essential in thinking about data strategy at the starting one. And again, I'll reiterate this, if you do not have a list of 10 to 15 critical questions, I would urge you to frame them immediately. And then everything else will be informed by it. In terms of tools, you know, there's a standard data stacks when it comes to data tools, right? So at the lowest layer is Amazon data connectors, like how do you get the API data, the real time data, the RPA data from the web Amazon website, and you have data warehouses that all this data feeds into, and you've got reporting tools, analytics tools and AI capabilities, right? What I suggest is that, if you're if you're you know, most brands today have made the choices on what data warehouse to go with the reporting tools they use, like the snowflakes, and the tableaus and whatnot. Honestly, from my perspective, you can pick any ecosystem, the Amazon, the Microsoft glue, it doesn't matter, right, they're all fine, just make a choice and stick with it. Of course, there's contractual aspects that I'm sure you will sort out. But I will say that the Amazon Data Connector component, this is the one area where more often than not, it doesn't make sense for you to build because it is low value, it is a pain to keep and maintain. And of course, it's a, you know, a bit of a plug for us. But this is where we are stepping in and taking that pain off your hands. So you go focus on the top four pieces and deliver value to your business stakeholders.

Aaron Conant  23:25  

You know, then, like people like do you just step in and do a demo and say, Hey, this is this is. That's what I'm thinking, I guess is awesome. I've got all this data sitting out there, I see it, I've got access to it. Yeah, I need to visualize it. At the end of the day, data crunch, I need a visual, that gives me an indicator, what actions I need to take,

Sreenath Reddy  23:47  

give me a feel, I don't need to think about where time goes, right. If you do not rely on an out of the box capability to pipe in all your data, you'll see your entire team will keep spending data, just piping data in not visualizing your point. I much rather have brands spend a ton more time on the visualization and analysis and very close to the business, you know, in a way you can action upon things versus just trying to get data pipe data into the into your internal systems. So that's what I mean. Awesome. The people aspect is very critical also for this right, one of the biggest gaps in silos ICS there's a gap between IT teams and business teams. And within business teams, there's a gap between advertising and media teams and operations teams. So as an example, is your media spend aligned with inventory position at a product level? You know, it's not a technically complex problem. It's just the two different teams looking at those two data sets, right? So having stewards that can bridge those silos can be super helpful. I mentioned SQL skills and I say this because if you want agility Have you certainly have a question? And you don't have to wait 3045 days for someone to build a report, the best way to solve it is someone on your team can write SQL and get data out of internal data warehouses and put the pieces together and answer the question quickly. So super important, I think valuable. And also, things like Amazon Marketing Cloud, the web interface, you have to write SQL to get data out. So there's many ways to gain that skill. We had Intentwise have a self serve module on our website, too. But I think it's an important skill. And then the last two pieces, I'll say, is understanding basics of regression analysis. And you can do this in Excel, you can look up LinkedIn courses, if you want. It just sets you up to understand how machine learning and AI starts to work. I think it's an important skill to have on the team. And then lastly, there is no software pitch today doesn't have that doesn't have generative AI on the slides, right? I'm sure you're all bombarded by those. What I suggest is, look, it's you know, you don't have to become an AI expert, but just understand the basics so that you ask the right questions. And I give people two questions to ask every time they are pitched by about AI, right. One is, what inputs go into the model AI model. Two is when does it fail? When does it not work very well. When you get transparent answers, then you have a partner to rely on. Right? So the idea is just get comfortable enough where you can ask the hard questions and just help you make informed choices. And let's just talk quickly about AI. Right. You know, I've got a couple of minutes here. But, you know,

Aaron Conant  26:46  

we're getting right to the end. But I think this is this. I think we need to make this an hour too. But anyways, yeah, I want to jump into this really quick. Yeah,

Sreenath Reddy  26:54  

yeah. So AI has been around us a long time. And the best example is the demand and how good text messaging has gotten, like almost never type. Everything I just picked us auto suggests. That's AI at work. It's been around for quite some time, I think what has shifted is that now AI is become extremely powerful around unstructured data, it is it has been in a reasonable shape on all the structured data meaning like metrics and dimensions, but like text, and images and videos, that's where there has been tremendous progress over the last four years. And the likes of Chad GBT will become increasingly accessible. The real question, though, is, as this becomes accessible, as you as the brand, ready to make use of it, and ready to really extract the most value from it, which is where I want to just call out the second bullet, which is, what are the essential ingredients for any AI to work for you? It boils down to fundamentally three things one is, the larger the volume of data you can supply, the better it gets. The higher the quality of data, the better it gets, the more connected your fragmented datasets are, the better it operates. Right? So when you think about data strategy, I would keep this in mind to be in the best position to leverage all the AI capabilities. I'll come at you in every direction possible.

Aaron Conant  28:17  

It's super interesting. And I know we're at the time here so people have to drop I understand we're going to keep going a little bit longer because I'm super interested in your we need as brands to be collecting that data, that large volume of high quality connectable data, now grab our own because everybody thinks we should say everybody, commonly a lot of people and even myself are thinking about okay, data, I'm going to use chat GBT. And it's going to go out and AI. And it's going to pull from, you know, the web from two years ago. And it's going to give me an answer. And I'm going to have all these generative AI tools that are helping imagery or videos. But if we want it, there's so many tools out there. If we want it to be as powerful for our individual use as possible. Get our data in there as much volume high quality connectable so we can leverage all these new tools. And if we do this through your right, like it's garbage in, garbage out an AI. If you put the best information in there, maybe the tool isn't around till next week, next month, next quarter, there's going to be an AI tool that helps out with all of this. Yep. I mean, you have to get this data organized right now. You can't be you can't be retroactive it's going to be is moving too fast at this point in time for you to get stuck behind. Like

Sreenath Reddy  29:39  

I related to the hot real estate market. You're always too late buying buying something just like that, you know, and it's not like these are cost prohibitive, right. The mechanisms are put in place to collect all your data. They're not cost prohibitive, like you can do that now. It's just a matter of focus and priority in my opinion. Yeah. Awesome. Have

Aaron Conant  30:01  

you got a summary slide? And we can wrap it up? And then yeah,

Sreenath Reddy  30:03  

I mean, I think, yeah, you know, I'm gonna not gonna read through every bullet, I think there's tons of data coming at us, there's lots of AI, I would absolutely avoid the service provider trap where, you know, you go evaluate your relationships where if that ends, your data doesn't go with them, you need only your data. And then the other thing I'll point out like the, this is a tendency to boil the ocean. And what happens is like, you've got long IT projects, you got nothing coming out of it, everyone's pissed off. And in fact, I have a visual here, right, which is a lot of time see Data Execution been done this way, which is, you've got some kind of a building of data stack that is going on, and it takes months, years. And after that, you may ask some interesting questions. This is really bad for organizational morale. Right? It team unhappy with best teams, business teams unhappy with IT teams, what I highly recommend, is you do that, which is build your data stack. Keep getting your answers now, not not months from now. But now and that may require a hack here and there. But these need to go in parallel, so that everybody can be happy, extracting real actionable value from the investments you're making on the data side. Yeah, I would absolutely avoid the first approach. And by the way, I've seen this many times like, so I highly recommend we take this approach the second one.

Aaron Conant  31:33  

Yeah. Well, this has been, this has been awesome. Like if we have like key takeaways. Let's do those. So we can wrap this up. And then let's definitely I mean, I dropped everybody. I hosted strengthen the podcast, we have the digital deep dive, I dropped that episode there. But we're going to do another one of these webinars in the open format, where you can just drop whatever questions you want in there. But I think this is meaningful enough. And it's something that's coming up over and over again. And I mean, maybe even we work into, you know, what is what is the platform look like as a whole? But, yeah, yeah, if you want to wrap us up here, that'd be great. And then we can get people on with their days.

Sreenath Reddy  32:12  

Yeah, no, I think. So fundamentally, there's so much coming at you that I think that the brands are to stay competitive are the ones are able to do a lot with that data. I will also say that there are steps you can take today that are very palatable, you know, and easy to execute in terms of getting ownership of data. So So yeah, so go on your data, just be ready for so much that is coming at you avoid long projects that don't see the light of day anytime soon. Take incremental approaches, if that means you need to hack in parts, that's okay. And I do think the people component of this is super essential, and having ownership where the silos gets bridged between IT and business and within business between media and ops. I think that's essential as well. Yeah. But, uh, and then

Aaron Conant  33:08  

platform if people want to check it out, like, is it? Is it month to month, is there a free trial, like, people just see what it's like. So the way on

Sreenath Reddy  33:16  

the analytic side in terms of data collection and infrastructure, there is a proof of concept stage where we are at no cost to you, you know, we'll start with a demo a proof of concept. And if you like, what's going on, the data starts piping in into either your data warehouse or ours, we can do month to month, we can do yearly. So we are flexible in terms of contractual contracts with respect. Also, a lot of times brands don't have resources to go build out the visuals they want. And we have a robust services layer on top where we can do that too, right? So if you have, if you want us to go build visuals, in Power BI or Tableau, we can do that. We already have a whole bunch of pre built templates that accelerate that process around very specific use cases. So it's both a combination of we have a tech stack that automates a whole bunch of things that are super time consuming and painful. And we our services stack that can actualize and get you insights a whole lot quickly. Yeah.

Aaron Conant  34:18  

As to Awesome. Well, again, Sreenath, thanks for your time today. Thanks, everybody who dialed in the questions. I encourage everybody health conversation with the team at Intentwise, they come highly recommended from you know, your peers in the network as a whole. I think you can kind of see the importance of what they're doing. And they're helping shape a ton of brands digital strategy on Amazon, how they're approaching it and other marketplaces as well. With that, we're gonna wrap up this episode. Thanks again, treating this for your time today. Everybody. Have a fantastic Wednesday. Take care, stay safe and look forward to having you at a future event. All right. Thanks for Fred. It was awesome.