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[Music] I'm here all the way from Stockholm this is me and my wife a couple of weeks ago walking the dog by a race of hand who's been to Sweden alright quite a few of you I hope you were there on summer I've heard is the best day of the year so anyway this is this is interesting because Sweden is not only a very cold place to live but for the purpose of this presentation is actually an innovation hub so it's ing to Bloomberg it's the second most innovated country in the world so a lot of unicorns are based in Stockholm it's a thriving environment for technology for innovation and you don't have to worry because the US is almost in the list you get there so what's all the possible we believe that we've heard in the speeches Noland yesterday and previous speakers today about how technology and restaurants those are two words that up till now have been kind of two separate verticals but we project that those will merge there will not be one without the other I'm sure you're all familiar with these slides this is what's been happening but it's gonna be continued happening across over the next few years so the restaurant industry will need to rely significantly on technology to be able to continue thriving so I'm part of a company called livid will one of the world's largest restaurant design company where in 43 countries by now and that allows us to get a lot of insights I personally take over 300 planes a year travel the world we are exposed to innovation in a number of markets we see what other brands are doing because what we designed today is actually in to be relevant five years from now ten years from now so having the ability of looking into the future is a very big part of what we do and we worked with most of the brands out there I'm glad to see some familiar faces in the audience we've been doing this for almost 20 years and we've designed over 13,000 restaurants so we should have learned a thing or two by now and what I'm trying to do is I'll be looking to explain what's under the surface so a lot of our customers come to us for the restaurant design and the design and the guest experience is actually just the top of the iceberg really what is more relevant and interesting is what's under the surface you know we have over the years developed strategic consulting we know operations we have our own kitchen design teams in-house so developing everything that's under the surface is really what's gonna impact the top but today I'm gonna talk to you about our tech vertical we're a strange animal you know we're a design company but we have 22 coders on staff permanently developing innovative tech solutions for the restaurant industry so over the years we came up with these cool ideas and we went to our clients I said hey we've had this cool idea that you should implement in your restaurant say oh that's really novel you know who's done it before and with no one's done it before that's what's innovation I'm not sure I would like someone else to try it first so 16 months ago we decided okay let's be disruptive and let's put a couple of million dollars on the table and let's create our own restaurant where we can actually test anything and everything we have learned over the last 20 years so we set a brief and let's say okay what is the ideal throughput we want to restaurant where we could have a transaction every 30 seconds we want to have a restaurant where we can have a capacity of a thousand orders per day we also to want to have short ticket times four minutes and under from the moment you order your food to the moment you get your food but we also wanted to have a culinary edge so we reached out to three different Michelin star chefs to do the recipes and by the way it's pizza we also focused a lot of experience that's what we do that's we design how can we create a lifestyle brand and an immersive experience within this category so we said let's take these operational efficiency and put that into a fine-dining environment no one's done that before and see what happens at the same time we have this Michelin star food in a fine dining environment we wanted to be able to serve it for 10 bucks our average guest check is 16 and a half dollars but we also thought about people is there a way we can design equipment and operations and the line to allow us to hire only people with no restaurant background or experience and then the last point was the fit out we wanted to make sure that it was consistent so we set up a task that we should be able to build the restaurant in another country put it in a container and mount it on site in under 20 days so what kind of a Santa Claus wish list or the ideal and you know yes Santa he lives in Sweden so it was kind of close anyway we made it we opened 10 months ago and this is what it looks like these are actually pictures they're not vendors so we used high-end materials marble tables velvet furniture very high-end lighting we brought in some graffiti artists they just under Miami Dolphin Stadium so it felt a fine-dining restaurant but at the same time we wanted to head all the other objectives that we had so we created a line as fast as efficient we are doing a transaction every 30 seconds we're actually with ticket times under 4 minutes when the three-and-a-half minute ticket time from the moment you order your food - you have already paid and get your food with Michelin star chefs backing it we developed our own oven so it's completely automated no one needs to manage it so any one of us could be deployed here and do the food on day one so we were hitting a lot of our objectives in the exercise but that guest experience part was only the tip of the iceberg this is what our consumers are seeing this is what they feel this is their experience but what we really was were interested in is about the technologies that lying behind so we said we're gonna measure everything so we packed the restaurant with Wi-Fi access points we have Bluetooth beacons throughout we have geofencing around we do heat mapping we understand how our guests move around the restaurant we know how their sit we have RFID tags on our product we have temperature sensors in our cooling and freezing chambers we have light sensors both in the restaurant and outside of the restaurant we control scent intensity we even developed our own point-of-sale system we developed our own kts system so all these things allow us to understand customer behavior what customer sees nice restaurant affordable price fast efficient in a nice environment but we keep gathering data so we call this business intelligence by AI and it's all about what are the things we input so having the data from point-of-sale we actually also measure every 15 seconds we measure how many guests we have in store and we do that through measuring telephones you don't have to connect to our Wi-Fi you don't have to have bluetooth on we know your phone is there it's a bit scary sorry for that we know the average party size we have weather data in real time their location we have calendar events and calendar events is not only every Friday is a Friday or every pay day is a pay day there's also a non-repeating event there's a cruise ship coming into town with American tourists there is a Justin Timberlake concert in town we have guests recognition we have traffic patterns we know our takeout orders we understand geofencing we get all the data from third party delivery I think it's a challenge many of you have also we scrap that data so we know every time our orders come in where does it come in what are the order from where we match that with credit card data and we plug it into our CRM so basically it's a long list of data in place and I'm sure quite a few of you already are gathering a lot of data but that's not enough it's really about what do we do with it and hopefully I'll be able to share some actionable insights of the learnings we've had with this experiment we've done 1.8 million transactions and over 2100 customer surveys we do surveys every day at least once we test everything we do to see what works and what doesn't work but more than that we really want to understand what are the triggers that change consumer behavior and when we talk about consumer behavior it's literally about two different buckets so we could either impact top-line sales what are the things that drive traffic what are the things that drive emotional connections what are the thing that improved the experience of the guests or the second bucket what are the things that improve operations that reduce cost of Labor that reduced cost a product that may the operations more efficient I'm going to go through different actionable insights we have tons of them we've learned so much and those are all the things that we can then apply to our clients without them wanting to be guinea pigs we're doing an in-house knowing what works knowing what doesn't work and then we can apply that to our designs so a few of the insights we worked with a small burger company their logo as a golden arch I know you're familiar with them we took a few of the restaurants and then another set of benchmark restaurants and we played different behavior based playlists anyone is familiar with Spotify raise of hands okay most of you so Spotify is our partner through their b2b solution called soundtrack your brand if you ever come to Sweden and visit Spotify our restaurant is their showroom where they test the future of music and restaurants so we played in across this burger locations we tested three different music music types actually four so first to the left we played non-brand fit music and that's not bad music it's just music we took Spotify 1000 so music that you listen on the radio in the car music that people listens to but that doesn't have any fit with the brand then we did know music what happens to your sales when you have no music can you imagine this eight stores without music it wasn't easy to convince them but then we did some interesting things we took 50% of the spotify top 1000 and then we mix it with 50% music that is brand fit that feels relevant to the brand's personality and to the brand's values and play that but it was still familiar music to the right what we did is the same exercise but we took only music that was non familiar so for us that have gray hair and have experienced singles think of a sides versus B sides of the same artist and what we were able to measure is that the increase in sales between having just regular popular music and having brand fed music was 9.1% extremely interesting and not only that when we started digging deeper into this we found out that what really changed the consumer behavior was the music when it's not familiar you stay longer in the restaurant and when you stay longer in the restaurant you purchase high margin products so the Sundays of the menu were 15% up so then we've been able to test this into more location and add another layer and it's dynamic volume control so most of you if you have a music system that is programmable you say okay for lunch I'm gonna play this music at this volume because that's the kind of energy I want for dinner I want to play a 38 decibels this kind of music but of course lunch starts at 11:00 a.m. when you open your doors with one guest and you're still playing the same energy and the same volume for the restaurant with one guest and the restaurant one is full with 200 people so what we're now doing is that we add a layer of dynamic control so we're combining data from how many phones we have in the store with the data from our music playlist and now we're able to play and have the exact same energy level regardless of if you have one guest in your store or 300 guests in store the third part that we did is when we were able to understand behaviors based on familiarity of music we can drive behavior so what we are now doing is on peak hours we play a higher rpm higher volume and higher energy and ink therefore increased throughput on your lunch that's what you want you want three table turns so we're using this data to increase traffic but on non peak hours we completely change it around because we want our guests to stay we want the restaurant to be full we want them to linger grab a coffee have another dessert so using data to have actionable insights it's what it's all about the next thing we played where this cent how many have artificial scent in your restaurants you're kidding me no one all right across the world most restaurants are installing scent because even if you are a wood fire grill the scent you get from your wood fire grill is not what you want to have in your restaurant it's greasy it smells so now we are able to develop artificial sense and not only develop them I hope international we had maple syrup blowing out of the mouths of Middle East for example drove sales what we're doing here is that we're trying to understand how does that impact the consumer behavior what we did as we play with two different scents one was premium and luxury guests love that and it was warm and it was cozy it brought we sell salads and pizzas and we did doesn't that in purpose so that increases our pizza sells but what we do is what would happen if we change that warm scent to fresh basil our salad sales went up 13 points overnight so what we're now able to do is we changed consumers behaviors with scent to drive sales and P mix so on Mondays and Tuesdays and Wednesdays when we know that consumers are not into pizza because you've had a lot on barbecue on this Sunday and the Super Bowl we can drive salad sales and then turn that so on Thursday Friday Saturday and Sunday we have more pizza sells the next thing we do is real time light seem control so I'm sure you're familiar with the feeling of you know it's sunny outside you walk into the restaurant it's dark it takes a second or the country it's really dark outside and you walk in it's really bright so what we're actually doing is that we're now monitoring and censoring the daylight outside of the restaurant and then running through an algorithm with how many guests we have in the store and then control the like levels within the story in real-time so you always get the ideal scenario for your guests talk about experience we could also do I mentioned the example with the cruise ships that is a real example so Stockholm is Harbor where twice a week to cruise ships of 5,000 guests coming to their Harbor mostly Americans to go and see the fuels and they have a specific pattern we're understanding their patterns what they eat when they eat at what times and that allows us to automatically our AI module creates automatic marketing systems so we could target those customers where they are at the time we want regardless it's a true ship it's in concert whatever that is we can not only do that for event based but also location based so knowing where our customers come from where they order when they order were able to identify pockets of different promotions so we're getting closer and closer to individually individual marketing for the individual consumer of course we have seamless payments you can walk into a restaurant you don't have to pick up your phone we know who you are what's your favorites we know your name we can greet you you go to the cashier you don't have to pick up your phone we ask if you want to pay by your your account you do that no phones up completely frictionless and seamless we could also have automated table management and this is a very interesting insight that we have over the years that really drives performance and restaurants so often when we analyze restaurants and we do heat mapping and we've done this with a couple of casual dining very big chains here in the US you might have a line of the door you may have a weight but still you have twenty to twenty four types with only two parties of two n so you're leaving money out of the door because you're not optimized for your setup so what we did in our test restaurant you can see here there's a number of two tops within the layout every afternoon and every morning the general manager and his phone gets a layout of the restaurant set up by the AI and machine learning platform on how the restaurant should be optimized for performance so he can easily set up for lunch 40% to tops 60% for tops whatever that is same happens for dinner so we maximize throughput making sure that in a high capacity restaurant in a peak hour you get the most bang out of your real estate we also have predictive staffing and scheduling once again improving the operation in the ROI so what we do is we assign to each team member a set of skills so I could be really good at the oven but I'm a disaster at the point-of-sale on peak hour you want to have me on peak hour you want aces in their places everyone should be the best in the best position so the AI module does predictive staffing knowing that tomorrow it's Friday it's raining it was three days since payday and there's a concert in town these are gonna be your sales therefore this is how you should stuff but on the country are non peak hours what you want is flexible labor force you want something that it's not a on the oven but maybe to be on five stations so that's how the system could actually program and staff through predictions with actually actually knowing names of team members any I driven inventory and sourcing we really wanted to get deeper into waste of product inventory management so once again what the system can do is says okay tomorrow it's Friday there's a Super Bowl game you're going to sell 400 cans of this beer but through RFID tags we know exactly how many beers you have on stock and we know that three or four days in advance so the manager does not have to go through the whole inventory and think that I need to remember to put an order to get more beers for Friday the system does this automatically we've done a Pioneer test with cause burg where our system automatically sends the orders for beverages to be delivered don't store based on the predictions so you're optimizing your inventory you're optimizing your storage capacity so this is a store that is 2,900 square feet and we have 145 seats thanks to things like this where we can save on real estate we have geofence order marketing so when you order food through our app it only takes 3 minutes to cook so why should we trigger it you're able to say hey I'm gonna pick up my food in 15 minutes which that's fine but right when you're gonna walk out of your office your boss calls you and there's an urgent call you're going to be 5 minutes late so what we actually do is that we do not put the food into the oven until you're three and a half minutes away from the store so as you walk into the store your food is smoking hot and you can just grab it and leave you don't have to talk to anyone I'm sure some of you are familiar with these images the tablet nightmare one of the first thing we did is alright we studied the impact that this have in your restaurant you'll want to do delivery that's great but we measured that on average on the Russians we studied it this is costing you $400 a month because you need to have one team member aware of the incoming orders the bling-bling ring ring ring that happens and manually put them into your POS system not only that distracts them but it takes time to punch the order into the POS system but it also becomes human error and you don't find out that error until the order has reached the customer and he calls you and I said I ordered three pizzas and there's only two and you need to send a voucher and you need to reach out to them and spend a lot of time so what we did is all five different delivery players and they're all the big guys we funnel them directly into the POS and into the KD s so no one has to think of the orders coming in they come in directly into the KD s ordering system with the data we are actually getting from all the third-party delivery we know where do they order when they order and what the order and that allows us to plan on where should we open the next store we can identify what are the hot spots for our concept and that allows us to have a development strategy based on data so these are just a few of the things we've learned I wanted to share them with you I think they're relevant they're all gonna be part of our day-to-day they are available today so this is not the future this is today we have a ton of things that we're looking into the future I will save that for next conference but we at the same time we also want to make sure that we have a great experience how many of you are familiar with NPS Net Promoter Score all right so for those who you don't know it it's a way of measuring guests satisfaction because what it measures is likelihood to recommend so we run you can see some of your brand's are actually in the list and to the far right we did not beat chick-fil-a but almost so not only do we get the insights do we get the data do we get the intelligence but we are able to have a great experience then a lot of our clients and when I'm out speaking they say yai you know this seems pretty simple right if then what if you have one guess then you put this volume if you have ten guests then you put so most people say what did you do and if then what system and then I try to explain that every input that you have on the left is not directly correlated but it looks like this so not until today where we have the computing power of algorithms and machine learning we're able to mix all these events and understand layering and what really drives performance and guest experience so the big big big question when we've been developing this was always integration anyone knows what this picture is rosetta stone absolutely right so the Rosetta Stone was written 200 years before Christ and it was found in 1799 in Egypt and basically it's the same text written in three different languages and what allowed us is to understand with through Greek how the Egyptian jugglers were and thanks to this stone we can understand another language so what we did is let's create the rosetta stone because when you reach out to micros or Aloha or putting a few names out there and say hey I have this cool innovative idea I have data I want to integrate and I want to make sure that I can talk to you the answer is always the same sorry is not in our roadmap what's in it for us so to solve that what we did is we created a pool API where we can pull all the data regardless of who it is regardless of what POS we're using regardless of what weather data were using regardless of what third party delivery company we're using we are able to understand that language bring it into our data mining platform normalize it structure it then we've created a dashboard where we have our reports where we can have our objectives when we get our business intelligence and we put in our calendar events but at the same time we did the reverse engineered equation so we are then able to talk to whatever music system you have whatever lighting control you have whatever send control you have whatever scheduling my tool you have so really being able to create this without integration I would say was the biggest challenge the intellectual we had we just needed computing power and we needed understanding integration and to me really the best thing is that this all happens invisible to the guests there's no screams there's no kiosk there's no technology between the guests and the team members our team members our general manager they can focus on the guests they can focus on delivering guest experience that is extraordinary thank you that sums up we have time for some for a few questions here excellent presentation so the question is where can we get it one before solely should this this we did this as an experiment to learn really about behavior right we didn't do this to create a product I think that's the different you got a lot of kids in Silicon Valley thinking I'm gonna create this I know I'm a no app for the restaurant industry and they don't know anything about restaurants so I think we had a different approach where operators were designers and then okay what would be the ideal world you know we wrote the list to Santa Claus and then this happened so what we'll have to do eventually with all the resources we put in is that we're just developed the roadmap for it so we're going to create a beta platform we are doing this as a hackathon so every Monday we start coding on something new that we test on Friday and we do that every week and then we read the results in five months from now we would have a ready product out in the market and by the end of the year we want to open a test lab here in the US as well so that's when we go we'll go with the whole suit of what we have today but this keep in mind that this is not you know these are the ten things that we want to test these are the 10 things that we're testing now every day we come up with the next thing and the next thing so the objective is not to create a product the objective is to really learn beyond any other design company what is important what drive sells so a bit of a question and an answer so two questions are just kind of wondering how you created your brand at music and then also the scent strategy how did that come about alright so the music the brand fit music we actually have something we call a sound scan and it's two columns of adjectives and there's no right or wrong you could be call or you can be energetic as a brand so there 15 different layers with a post so you would tell me okay my brand is an a scale from zero to five energetic my brand is rugged my brand is Airy so we are able to understand what's the brand personality and then what we work with Spotify is that we've catalogued all the music in the world into any of those platforms then you could have filters so if you don't want expressive music or F words or yes or not there's another set of filters but it's really about understanding what the brand is about and then that's already plugged into that so these playlists are actually not only available in there I left my phone back there but I could open our app right now and I could listen to the music that is playing in the store in Stockholm now and if I push a button I can download that playlist into my Spotify in my phone so you could take your brand music your brand experience out of the store to the homes of your guests so that was something on the sound part on the scent part there's a lot of research I would say restaurant industry is years behind hotels figure this out years ago most hotels have their own scent and they did it for familiarity they wanted you to feel at home so we work with a number of companies around the world there's a lot of research in terms of cents and what are the things you want to trigger so if you're on an impulse location you want something sweet you want syrups you want waffle cones that those are the things you want if you are a steak house you would would fire and different types of would trigger different behaviors so really understanding once again what's the objective then you can pick and choose from the scent so I guess when you're doing so much data gathering how long do you have to actually gather that because when you look at the US I mean we have so much diversity as far as you know the climates weather I mean demographics so to me it would seem like if you're you know looking at Atlanta you wouldn't just gather information for a few months because of how all those things are changing throughout the year so how long do you have to spend I guess gate gathering all the data before you can even go to the point of analyzing it so much less than we thought we had a similar approach but really if you look at how things have been done in the past it's all historic data so you look backwards what happened last year what happened last month but you don't really know you see yourselves and what why why did you have a peak there because it was a game night or because it was snowy or because something happened there was elections you don't have the access to the data when when you are putting so many inputs the system is smart and it gets just smarter and smarter and we're seeing this every day that passes our machine learning platform it just gets more and more intelligence so what we're able to do is point it into one direction and say that's the objective and then it keeps alpha beta testing constantly until it finds the right the right direction the more locations you have the faster is gonna learn better I want to thank you so much for flying all the way here [Applause]