Ep. 2024 The Top 150 Italian Wines in American Restaurants | wine2wine Business Forum 2023

Who wants to be the next Italian wine Ambassador? Join an exclusive network of four hundred Italian wine ambassadors across forty eight countries. Vineetly International Academy is coming to Chicago on October nineteenth is twenty first. And Walmatikazakhstan from November sixteenth to eighteenth. Don't miss out. Register now at Vineetri dot com. Official media partner, the Italian One podcast, is delighted to present a series of interviews and highlights from the twenty twenty three one to one business form, featuring Italian wine producers and bringing together some of the most influential voices in the sector to discuss the hottest topics facing the industry today. Don't forget to tune in every Thursday at three PM. Or visit the Italian wine podcast dot com for more information. Okay. So some of you, I think, are aware that we did a small sneak preview with Jeremy Hot, who is the founder of founder. Right? Co founder? Co founder. Who's who's the other half? Who is he? Finance. So he's in former So he gives you the money, basically. No. No. He's a finance guy. He's a coder. Okay. So he's incredibly boy. Yeah. And you're the front man? Yeah. I'm a sales guy. Okay. Alrighty. Alright. And so we published, like, a small preview of the hundred and fifty top Italian wine list in American restaurants. We're going to expand on that a little bit. I wanted Jeremy to explain what Psalm AI is all about and how he's able to extract all these data. That can be useful for the Italian Mind producers. Okay. So he has a he has a small presentation. If you have a question, raise your hand, accept Gino. Okay. Alright. Take it away, Jeremy. Alright. Perfect. Alright. So Samai, we launched in, twenty twenty one. We commercialized. Our first two clients were Paul Hobbs and Kisler. And so when we first launched, we had sixty eight hundred targets. Just by targets, I mean, accounts in the US. Today, we have forty six thousand. So to kind of give you a scope, of where we're currently at, we don't just target the US. All the things we'll look at later are gonna be US statistics, okay, and data. But just to give you a little quick view of what we also do as well per country, at the very top, you see the USA, you see the total number of placements that we target. We go across all the menus every two weeks. So we track all changes at an account level. The magic to our system is that we have everything that's already pre tagged. And so we know anytime that the AI identifies a SKU on a wine list, We know that that SKU, it's country, region, subregional, appalachian, and predominantly great bridal. And so from those things are, those are sortable features, we also know that that SKU, if it's part of a brand, we have it tagged to that. That brand is part of a wine group or importer, we tag it to that as well. And so that now those are sortable features. And as best we can, we tag to the wholesaler at account level. So between all of these things, there's a million different things the platform does that two major pieces are gonna be benchmarking, understanding where you're at in the whole hierarchy, in any where you wanna look, whether you wanna look across the country, into a state, into a city, multiple cities, into a zip code, however you wanna look at information you're able to do that. Okay? Okay. So I see you have some in France. Nothing in Italy? We haven't targeted Italy yet. Okay. We'll we'll definitely want to. We will soon. Yeah. Alrighty. Yeah? Alright. So if this is a breakdown, I use Germany as an example. We're gonna look at American data for all the wine stuff, but this is a breakdown of how you can see our coverage per city, and this is in Germany. I wanted to use the UI for the slides show you how easy our platform is. Our UI is incredibly slick. Okay. So the way that this reads when you see this is that here you have Citi. Right now, we're looking at Germany. Okay. So you can see by Citi, the number of accounts that we have accounts or anything with a wine list. It could be a restaurant, a wine bar, if they have a wine list, that's what we target. Okay? And we grab everything off the internet. So we grab whether someone takes a picture and it's on the internet, we grab we focus on their websites, one of the things we started doing here recently, if someone's taking a picture of a cocktail and of the menus to the side, if we wanna grab that, we grab that as well, and we can process it. And we know all the changes they that they happen and then the date in which they happen because it saved in the metadata. Okay? Okay, for the non gigs. Can you just explain to us again? We know when the changes happen. Okay. Okay. Sorry. Alright. So the next thing that you see here is placement. So placements is a wineless placement. You can call it a mention, however you wanna look at it. That's what we're looking at. So, like, for example, in Munich, we target four hundred and seventy accounts. Okay? The amount of placements that we target is almost fifty one thousand. The average price point in Munich for bottle wine on a wine list is sixty eight dollars. Okay? The last and you can see here we're doing it by net ads. So the last year, year to date or last three sixty five, it's up twelve, which is nominal. It's nothing. Right? As far as for I'm sorry. That's priced by the glass. I'm sorry. It's cut off right here. Net ads right here is negative two zero eight. Okay? Alright. Let's let's bounce to the next one. This is Canada. This is just another another example showing how we list it by city. You can see accounts placements, so on and so forth. Alright. What we do, benchmarking lead generation, we're gonna bounce back between that a bunch of times today, okay, and different examples. Okay? What we do hasn't been done prior? Everyone has always focused on retail. In the US, you build brands on premise. You build them in restaurants. You scale them in retail. Okay? There's been a lot of, As far as retail data, it's been around for twenty years. They follow it over anything that goes over conveyor belt, you skew it. There's so many xgroceries that are major grocery chains. It's very easy to do when you talk about data. Okay? If you're talking about forty six thousand wine list and having every SKU on them already pre tagged and sortable, that wasn't possible until now with machine learning. Okay? Alright. So that's basically showing what we do. We do it in real time. We update every two weeks. Account level data. You can see the actual account and their historical records. Okay, data on competitors. In the past, all the data you're able to buy was only for your own products. Now you can see where everyone's at. Okay? Next piece on here, we track every SKU. So it's not like half the wine list, just the buy the glass list, If there's five thousand selections, we track five thousand selections. Okay? Next piece on here is that we're broad. We're the biggest that there is. There's never been anything even close to this. Okay? Alright. This is just showing our growth over time. We started with with six to eight hundred we're not forty six thousand. We have about sixty major clients, really, really big clients. Well, Jackson family is about to enter the third year contract. Waldman, Tal, Turlado, Wilson Daniels, major players, Frrescabaldi, Laurent Perrier, Silver Oak, tons. So, like, a lot of notable names were a trusted resource for this were proven. Okay? Alright. So now we're gonna get into how this works. With node filters on, when there's a filter, you'll see a tile. Okay? With node filters on, we're gonna search by country. This is how quick this works. I can see what's going on across the country, by country, of origin this quick. Okay? I can see out over forty six thousand US, there's forty four thousand five hundred accounts that buy that. Okay? They have a US product on their wine list. Next, you can see France. You can see Italy. You can see the number of placements. It's number three. Average price point is sixty four dollars on a wine list. It's up eleven thousand placements, which is a two percent increase the last year. Okay. Now I'm gonna sort for Italy. I put on the filter for Italy, and now we're gonna look by by region. Okay? So now I can start seeing top regions. I don't have this sorted. I just pulled it up just to look at something really quick. Tuscany's at the very top. That's interesting. Let's go look at Tuscany. I'm gonna put on a filter for Tuscany, but now I'm looking at it by appylation. Okay? And now I want to see what's doing best by appylation of the US for Tuscany. Okay. So as I look at this, one thing pops off immediately. Rosa, the multichino is incredibly down. They're down fifteen percent right now. That's wild. Right? Is there other tuscan appalachians that are down right now? I can now sort this to descending to look at all appalachians and then sort them by what's declining the most. It's at the very top. Okay. I'm gonna look a little further. Which producers are hurting the most right now? I can see which producers and then their declines here. Okay? We'll get back into this later as far as we're targeting other things that you can do with this kind of information. Next piece over here, now I'm putting it, which Appalachian and Tuscany are doing the best. Right here, and you can see which ones are, are up the most. By sixteen hundred and seven seventy seven placements, IgT's Supertuscans are up. Okay? If I wanna know who's doing best for Supertuscans, click here, Antonori. They're up. Three point seven percent, a hundred and sixty one placements last year. Okay? So this is how quickly you can get to all this information. Right? Next up, we're gonna go look at Petemont. This is another really powerful piece to the, the data that has never been able to be view be viewed before, especially with things like, Italy where were things talked about in regions or appalachians. Okay? So now if I wanna go in here, and if I want to go look at, bivaridal in Piedmont, How everything stacks out? You can see Nebula is significantly the leader. Look at its average price point on a wine list. That's wild. Look right here on net ads. It's up three point three percent a year that was called a recession. Right? That's crazy. Okay? This is gonna shock you guys. You all ready for this? The two top producers for Nebula from Piedmont are Barbarresco. Did anyone think that? Right? Okay. So now from here, I can see these kinds of things. Look at look at me placements and accounts they have. They lead them both. Right? That's wild. Okay? Look at the average price point of guy on a wine list in American is the most placed in Nebula. Okay. Other things that you can see from here, now that we see Barbarresco's interesting, let's go look at this by appalachian. So by appalachian, I can see that Borillo is definitely the leader. Okay? Seven, basically eight thousand accounts, forty three thousand placements. It's up. I can see all these things. It's up four percent. Okay. Next up. Let's go look back at Tuscany. I just put a filter on for Tuscany. You can see it in the tile. I'm looking at it by producer. Okay? So this is all at Tuscany. It doesn't go to Kiyani or IGT, all of it just by producer. Okay? So as I look at this number, number one is Anthony. Okay. That's great to know. So far, everything that I've looked at has been at national information. I can drill down. So if I wanted to go in here, look at some key states, like, let's say, Illinois, Texas, and New York, I can now sort that information by each, and I can pick what I wanna put for these columns. I'm gonna look at it by the number of accounts, the number of placements it has. I'm gonna rank off the placements, and then I'm gonna add in the average price for each state. So now I can see from here, and I'm gonna put these in order for Texas. I live in Texas. We're gonna talk about Texas. Alright. So right here, number one, I can see the Banthees number one in Texas. If I was Antonori, I'd wanna know what's going on in Texas. Okay? I have a question here, though. You have Castillo Banfi and then Antonio. I know. Right. For those of you can't read very well. And then, and then, what's the difference between Antinori and tenuta Guadal Tasso Antinori. We have them tagged separately. We can put them together. That's Yeah. Important to those people, but we keep them separate. Just my producer, that's the way we have them listed. Okay. If if, like, one producer, we'll we'll show the house that owns it. But essentially, if they're separate as far as, like, their branding, then we'll separate them from there. If people want us to re tag it, we can re tag it and we do that very easily. This category was, super tuscany. Right? No. No. No. We're just looking at tuscany in general. Okay. So all of tuscany. Okay. Alright. Now from here, if I wanna get some more interesting information on Texas and catch a quick glance of what's going on, I can now zero into the major markets of Texas just by going into City. Now I can see it by Houston. I can see it by Dallas, by Austin, same four, columns teed up, accounts, placements, rank, so on and so forth. Okay. Besides just looking at these as far as, like, dots on a map, we can give you account level information. Okay? So now, bro, I wanna understand for this category what's where? Oh, sorry about that. I'll jump in that in just a second, sorry, with a different slide. We're gonna back up really quick. We're gonna do the same search that we were just doing, and said, now we're gonna focus on appalachian. We're gonna do the Bernela de Montecino. Okay? Once again, we're focused on Texas, Bernela de Montecino. We can tell you the total potential for an area. So you might not be in the top twenty. You might not be in the top thirty. You might not be in the top ten. Right? But it's still important to know if you're gonna sell in that market, what's the opportunity? You can see it here in the summary. So even though these might list as right here, you're able to see in Texas, there's five hundred and nineteen accounts in Texas that by Bernelo. The total placements between that's two thousand three hundred. Average price point, you can see there, and you can see if it's a growing category. Okay? You can see Carpazo's number one at the very top. When I click into here, the interesting thing that I see is that Carpazo's making most of their business in Houston. I I live in Houston. The head guy, the vice president of, of vignor brands lives there. So, obviously, this is where he's doing a lot of his business. Right? I used to sell Colombini, and so that's why this is really, really great right here. Alright. Next up, beside just looking at them like this, you can see exactly where the accounts are at. Okay? So in the next piece here, these are the accounts for Bernelo in Texas. And when you see the list of accounts, you see their names. You see maserafs. That's Maastros in Houston. Pappas Steakhouse, that's a grand award restaurant, grand award restaurant, grand award restaurant, Tony's super historical, fifty five years been around, another grand award restaurant. The way that they list is listed by best target. So these are accounts that carry a lot of Bernelo starting from the top going down. Other things you'll see on here is that we also tag them by their price point, and you also tag them by what they are. So you can see like new Americans, seafood cocktail bar. You can also see their estimated wine cells that's proprietary to us, and they can see the last time that they did a menu update. If I ever wanna sort by that kind of information, I can look by who's updated their menu most frequently, or I could look by highest wine cells. Okay. Christopher? Yeah. Is there a a joke like CUona? Yeah. Yeah. She was just hiding out from I can do really well. You you say menu update. So is that purely an internet update of the wine menu? Yes. So it's up to the restaurant to keep that Yes. Current and ready to roll. Otherwise, your data will be skewed. Yes. But we if if ex client wants to pull that information in and have us process that we do that for some clients, we house them separately, but we also do that as well for some of our major clients. Well, but if you're major at one of your clients is, let's say, winery, who's looking for this data, the data that they're getting is not gonna be accurate if the restaurant's not giving you the accurate data on the website. The the big point to note here, if the wine list is still old, that doesn't change the it's still a great target. Right? So even if that wine list is three months old or nine days old, right? And most of the top end restaurants, out of our forty six thousand accounts, we skew towards the high end. Okay? So, like, out of the Michelin Star restaurants in America, our coverage is, like, eighty five percent. At a total coverage in the US, we have about thirty three percent of total coverage of accounts in the US or eighty percent independent accounts. We also have in tagged in five hundred restaurant groups, but that's we we do it very creatively so it doesn't tank the data. Okay? Alright. Other things that you can see on here, right, you can see what they're tagged as. These are also sortable features. If I just wanted to go after steak houses or if I'm a sicilian, winery and all they wanna go target is sicilian restaurants, I can do that as well. Okay? So from here next slide, this is where you can actually see what's on what menu and their price on those menus. Okay? Notice the column here. We also track by awards. So if you only wanna go after Mission Star restaurants, you wanna bump up your Prestige, You wanna go after James Beard restaurants. You wanna go after Grand Awards from my spectator. We can do that as well. Okay? So here you see the name. This is number one, two, three, four, going across top ten. These are their prices on these menus. This was the first listed one. Look how many Bernellas they have just from the top ten SKUs. So on and so forth, you can see where the by the glasses are. You can see the other formats. That's a half bottle, mag three liter. Okay. So you get a complete view of where your competitors are. And where they're at. Okay? I was looking in Texas. Maybe I'm doing a trip to Texas, and I'm selling Bernelo, but I'm only gonna go hit Houston and Dallas. You can geofence to certain cities. And so that's what I did. I threw a lasso around Houston. I threw a la or Houston here and I threw a lasso around Dallas. Okay? Now I can still look at those same information. Other filters I threw in here is that now all I wanna go target when I'm on my market visit to Dallas and Houston, selling Berniello, just wanna go see state steak houses and Italian restaurants that are the most expensive, super high end restaurants. Okay? So these are the forty six accounts I wanna go target. These are the wines that are most placed there, and these are their prices. You can export this. Okay? Alright. Up next. We're going back to Italy. We talked about category of restaurants. We can give you your exact avatar per appolation of where you should be selling your wine best. So, like, maybe in the past, you're like, I have a high acid white, should be focusing on sushi restaurants. That seems to be a very good thing, or seafood restaurants, wherever that might be. We can tell you what does best wear. Okay? And besides just understanding which restaurants to go target and look at, You can go in here as well. And then you can also see their price here, which ones to focus on for your particular products. Okay? Alright. So for the next piece, we're gonna do like a hypothetical. And in this situation, this is a one I used to sell. And I love this producer. I'm gonna be Paula Skavino for a second. Okay? Besides just pulling up a region and benchmarking off of that, I can do it off my own SKU. All the information that we have as far as things are tagged, That's already done. The only information we pull off the wine list is gonna be its price. That's how we can build out instant comp sets. So I can benchmark very specifically to certain items. Okay? So if I wanna go pull up Paulo Scavino, I'm looking at it as skew, look at the tile. Let's look at this one SKU, this one wine from Pollo Scavino. I can see it's in three hundred and twenty six restaurants, and I can see that its price sits usually between seventy eight to one thirty three. Its medium price is ninety eight bucks. I can do a one click comp, one click. It built out my comp set. Piedmont, Naviola seventy eight to one thirty three. One thirty eight. I'm blind. Alright. You can see it right there. Once again, I can see my total summary of my potential for this one SKU in the US. I can see that I have a possible five thousand six hundred targets, restaurants I can go target for this wine, and I can see that there's seventeen thousand placements. This is a very ripe thing to go after. Okay. And then I can also see if it's trending up or down. But I can also see where I sit in the hierarchy of it instantly. Okay? Other things you can do with this is that you can jump over and just look at the top states. So just flip filter to state. Now you can see how all this stuff shakes out. Right? And look right here, this is, it's okay. But right here, you could see, once again, I've jumped to Texas. It's number six in Texas. Okay? I can see the number of, placements it has in its average price point. Okay? And once again, tiles, Piedmont Nebula, seventy eight to one thirty three. Alright. Next piece on here. If I wanna go in here, look at an account level, once again, same thing. I can pop into here. It's looking at here. This is showing me exactly where to go fish. Right? The name of the account is what I'm going up against in each market. Okay? This is a really cool example. Let's say that on Paul scavenino, I wanna raise the price of that wine. It's averaging between seventy to one thirty. Let's go look at one hundred to one fifty. And I wanna know my potential, but more importantly, if I'm gonna raise my price and I'm gonna have a difficult conversation with my wholesaler or importer, I'm gonna give them something that's gonna incentivize them that they're gonna have a plan to execute this. Okay? So what I'm gonna do is that I'm gonna look for Barolo this time. I'm not gonna look at at at Nebula and Pete Monument. I'm gonna zero directly into Barolo. Okay? I'm gonna look at one hundred to one fifty. And what I'm gonna identify or other SKUs that are doing poorly. Okay. I I I'm gonna identify these two right now. Right? Are you guys still with us? Sorry. Yeah. Alright. So what I just did is I identified those two SKUs. Now I stacked them next to each other. Okay? I can see where they're all sitting. As far as my placements, accounts, average price point, I can see that they both are in negative for this price tier. Okay. Next thing I can do, five hundred and eighty six accounts to buy one of these three skews. Okay? And now I can see everywhere I don't have a price, that's my targets. I can run a target list. This is easy and short sorted by state and pass it out to my wholesalers or importer or whoever it is. Okay? The other major piece that we provide our clients is that you can see what's happening to your wines in real time. And so I can just tee up the brand, Paula Scavino, and I can see all of my SKUs and the actions that have happened on the wine list. So I can see when they lose placements, what the account was, where it was, and what more price was on that menu, what the item was. I can see when they go in. I can see all through here. We update every two weeks. We track all changes. Other things I can do is show sweet spots on wine menus. So earlier when we look at this information for a barolo, the average sweet spot was a hundred and seventy dollars on a wine list. If you're only looking at wines between, under two hundred dollars on a wine list for a barolo, the sweet spot is right at, ninety eight point ninety nine. Right where Scavino was at. Okay. And you can see it here, and you can see the representation. Just look for the high tower. That's the sweet spot. Okay. Other things we can also show here, and this is showing you the one seven. This is the average for all burrow replacements, but you can see it by quarter. Is it turning up or down? You can also check for the by the glasses. That's my information. Let's give it up for Jeremy Hart. He's been incredibly generous, to share his data with us. Okay. Listen to the Italian wine podcast wherever you get your podcast. We're on SoundCloud, Apple Podcasts, Spotify, EmailIFM, and more. Don't forget to subscribe and rate the show. If you enjoy listening, please consider donating through Italianline podcast dot com. Any amount helps cover equipment, and publication costs. Until next time.