Discover Europe’s Best Foodie Cities with Einstein Analytics

Hello, and welcome to another X Force data summit, presentation. This one's a real interesting one. It's by Steve Marcel, who's the CEO of Cloud Jungle. And, basically, it's about using Einstein Analytics to develop discover the best food in Europe, which is something we'll all want to do after this quarantine is over.

So here's Steve. Thank you, Leonard. Yep. So great introduction.

Thank you very much. So yeah so if you're listening to this I guess this is a pre recording so something must have gone drastically wrong on the day. But I've just got my Twitter handle here JungleHQ so I'll make sure that I'm around at the time of the recording and so any questions you can just tweet JungleHQ. So a little bit more about me.

So yeah I'm a COMD as we call them in the UK of Cloud Jungle and we're Salesforce Consulting Partners and my journey with Salesforce began in two thousand and six as a customer. Started a business doing Blackberry rollouts if you remember them and I used Salesforce for my sales service at the time and it completely transformed my business. So we were a cloud first business in two thousand and six. It was pretty successful and was sold in twenty fourteen.

At that point I was then unemployed thinking what do I do next and a few of my customers said help us out with Salesforce. So we did and yeah so then I went off and got my certs when I realised I didn't know too much about Salesforce even though I'd used it with my previous business and went into some contracting and believe it or not use Salesforce to generate applications to run power stations and all things. And then before then deciding to set up my consultancy firm, kept with the telecom speciality that I had previously.

And in terms of Salesforce products, we're also developing a product special in Einstein Analytics, hence here today.

I'm a member of the Salesforce Einstein Analytics Champions Programme. I think there's about now one hundred and ten-one hundred and twenty of us globally and yeah we're all advocates for the platform.

And as I said before, any questions you can tweet me JungleHQ.

Promise not too many more slides and we're going to spend more time actually diving into Einstein Analytics rather than looking at PowerPoint.

And so what I've done is, what we're going to do rather is we're going to look at an Excel spreadsheet and we're going to prepare it and we're going to upload that into Salesforce. And that spreadsheet is thirty thousand of the top restaurants in Europe by number of reviews and we're going to analyse that to see where we can spend our next vacation whenever that may be.

So what we're going to do is when we upload that it's going to give us some insights into the data, so again help us find ways to go and also the factors and the variables that influence the success of a restaurant's score on mine's gone blank. What was it we're looking at?

TripAdvisor scores, that's it. So whatever influences the TripAdvisor score, then we can look at the variables that go into that. So we'll also look at how you can actually get your hands on some Einstein analytics with a demo org and then obviously you can plan your next European foodie break as well.

So a little bit about our data set, yeah thirty thousand of the top reviews, okay. So we want to know where to go, okay which city, what our budget is, that's another variable, and what type of food we might like, okay. And our goal is to try and find somewhere with best TripAdvisor scores. So that is our outcome when we look at this data set. So then the variables are city, price bracket, food type and number of reviews. Okay and then we basically give that to Einstein Analytics and they can then be our team of data scientists from there on.

So without further ado, let's jump into the data set. Okay, and what we're going go through now is a little bit of prep of the data so we can see what we get. So this is the raw data downloaded from TripAdvisor and I'll talk to you a little bit about that in a minute. So first of all we've got the name of the restaurants and there's thirty thousand of these.

We don't need those, okay, because we're not going down to specific restaurants. But what we do want to know is the city, Okay, we've got the number of reviews that the restaurants have had. Okay, the cuisine style, so what type of food it's going to be and what I've done with this is rather than just upload this, I've gone and looked at just a little formula to see if that contains American, then true, if not false basically. But what I'm going to do is I'm going also show you the rating.

It's very important, so this is the rating of the restaurant, okay? The ranking over here, so this is like the TripAdvisor ranking in the city for that restaurant. This restaurant was ranked number sixty, okay?

Now, we're actually going to load this ranking data in there. It's going to be false data, but I'm going to show you how analytics actually handles that.

Because obviously if it's ranked very high in the city it's going have a good rating so these two are going be very much correlated.

But we'll have a little look to see what Einstein does with that. Then we've got the price bracket. So again this is a raw download from TripAdvisor. And so what we've done with that is I've changed it around.

Scoot this over, my columns are a bit wide. Okay, I've changed this to high, medium or low because it makes a bit more sense than having the dollars in there. Okay, now I've actually done a little bit of run through this once before so I know that some of this data is not going to give us very significant information. So what I'm going to do is I'm going to prep this data so I'm going to delete other and American and I'm going to delete French, seafood and Italian but I'm going leave vegan in there so it may be a bit of a clue to the outcome. The reviews this is interesting actually I'm going to delete this however in a future version of Einstein Analytics I think later this year, it's also going to look at sentiments of comments and so it will be able to look at reviews and look at some of the language that's being used and generate a sentiment score. That feature is not available yet so I'm going to delete my reviews.

I've got my price ranges high, medium and low rather than this dollar thing.

So we can take out that and I'm going to take out cuisine stock because I just used that to generate the other information. The reason for sort of leaving that in there was to sort of say that this is often an iterative process in terms of analysing your data.

You as business people will have a lot of information and you know your business very well so you should be extrapolating what information you want to give to Einstein because obviously it's only going to be as good as the information you give it and this sort of demonstrates the fact that we've been through a few iterations, we've created a few discovery stories and we found out sort of the things that really, really matter.

Okay, so I'm going to save this now. So we're going to save this file as X Force Eurofood Live, call it. So just to say that this is in real time.

Okay and then continue.

Okay so that's now my data set prepped and saved. So what I now need to do is stick this into Einstein Analytics.

Okay, so this is Analytics. So for those of you that don't know, it's a platform that is different from Salesforce. It was an acquisition of Salesforce a couple of years ago, but it very much is embedded right in the heart of it now. They've really integrated it very very well. And then to get a data set into Salesforce I go to my data manager and basically here I'm just going share on this connect bit here, we've actually connect to our local Salesforce data. We could actually connect to any other external system as well and I think Xpence know all about these various integrations.

And I'm now going to create a data set which I'm going to upload. So I'm going select my file which is the X40 Live file and then start the upload process.

Okay, so during this process it's looking at what columns we want to import and basically I'm just going to accept the defaults and upload that file. So now here we've got Einstein who's basically sucking all the data, the thirty thousand rows and he's going to start doing his analysis in the background. Okay. It will take him about a minute so I'm just going to flick back to my slides and I'm just going talk a little bit more about the model. So if you remember, what we want to do is we want to find the best place to go and eat.

So we need to find our outcome, right? So that is going to be the trip advisor score.

And in order to do that we've got some variables that we need to model, okay?

So it's very much we want to define our outcome and then define our variables. So that's what we've just done when we've been sort of selecting the columns to put in, is saying what are we going to model. So let's flip back to analytics, see if he's done his thing.

And great. So I've now got my data set loaded in, and now I'm going to create my story. Okay, so this is the analysis bit that's coming up. And it's very easy, it's a simple wizard.

So this is the outcome variable that I want to measure and it's not ranking, it is rating. Remember ranking is how that restaurant performs in its various city. So I want to maximise the rating. Am I expecting numbers greater than zero?

Yes I am, hopefully. Give it a name and then select what type of story. We can do a quick dirty story but we're not going do that. We're going do our complete analysis here.

Okay. So but we are going to take the automated version and by the completed analysis it's going to help us sort of down the line we will be able to deploy this model to Salesforce. So if you've got a record, a way at which you've modelled the data to predict an outcome, then it will give you your potential score for that outcome. So let's say for example you're a subscription business and you've got a customer and you want to see what the chance of them cancelling their subscription is or their attrition rate, you could actually do that by modelling customers that have and have not treated previously and look at all those variables that influence that and then that will give you a predictive score. So Einstein's crunching the numbers now. We're going to leave him to it for another minute or two and go back to our slides.

And this is what we hope to see when he finishes work.

So we're going to have a descriptive insight and this is like what has happened. It's basically doing that BI basic analysis of our data, you know, what can we see from that data. And you can pretty much do this with any BI tool out there really.

Then we're going to have a descriptive analysis. What were the variables that influenced that outcome?

What are the positive factors and what were the negative factors for us to come to that conclusion.

Then we could do some diagnostics so we can actually compare things. So for example, we could compare two different cities and see what the differences are between those cities. So in terms of their outcome, their score, is one city better than another, for example.

And then we could do some predictions and this is this predictive bit that we set up whereby we can actually just decide on like yeah I want to go to Athens and I've got a medium budget and I want to go to a vegan restaurant and then that will give us a predictive outcome for a score for going to that restaurant.

So let's spin back now to oh so he's done it, fantastic! So right, so what has happened, so what it does here it's at the very top of the screen, there's a whole bunch of analysis down here, it's picked up the biggest factor going, okay? And it says that the ranking of the restaurant explains the biggest variation. So the highest scoring restaurants are the highest ranked ones. That is not surprising, is it? So what we've done is we've put a whole bunch of data into our system which is pretty meaningless because ranking and racing are very, very similar. So what Einstein should have done is he's making some recommendations for us to look at as well and he said here that well ranking is our strongest predictor and so what we're going to do is we're going ignore ranking and see what else it comes up with.

Unfortunately what we have to do now is we have to run that story again.

Okay, so now we're running the story and he's crunching his numbers again now but removing the ranking, which whilst he's doing his work, can flick back to our slides so I can talk some more. Right, Okay.

I'm missing a slide.

Right. Okay. What I'm going to do is I'm going to talk a little bit about No, I can't find it.

I don't know what was on there. So I'm going to talk about the data sets and where do we find this data set of thirty thousand TripAdvisor reviews. Because what we're going to do is from this is realise how simple it is to analyse data.

So there's plenty of websites where you can download data sets. One of the big ones is called Kaggle and so I went onto Kaggle and you can review all sorts of different data sets whether it be the weather or your football scores or soccer scores in the States over history. You can import these and analyse them. But because I like my food I chose TripAdvisor European restaurants.

Okay, so that's very timely. So now we've got rid of ranking and now Einstein's looking at this and going 'Wow, the thing that has the biggest influence here on our outcome, so our TripAdvisor scores for restaurants in Europe is if it's a vegan or not. So if vegan is true, score is significantly higher than if it wasn't true. Okay?

Now this doesn't mean that it's a vegan restaurant, it was the vegan was in the description. So the restaurant has a vegan offering.

So typically if a restaurant in Europe has got a vegan offering, it's likely to score much better than a restaurant that doesn't have a vegan offering. Okay, so this is the biggest factor. Okay, I can even scroll down now and to look at some other factors and there's also like the number of reviews. So well, if a restaurant's got a lot of reviews then it's significantly and vegan is false does better. Okay, Which is a bit of a strange one, if it's got eight sixty to sixteen thousand reviews, vegan is false does better, so maybe a bit of anomaly.

Scroll down again and actually in Rome vegan false does better as well. So actually if you go to a restaurant in Rome and they haven't got vegan on menu, it doesn't matter. So again these insights are something that we can, we would never normally pick up just by looking at the data itself. Okay so you go to Barcelona, vegan performs very, very strongly.

Okay, so what I could do there is actually let's look at these number of reviews as an example here and I want to analyse that a little bit more. So I can put in a number of reviews there and then we can see actually there's quite a strong correlation with score and number of reviews. So just to explain this here why we've some grey bars. The grey bars are not statistically significant whereas the blue bars are obviously significantly low scores and likewise up here with the high number of reviews.

So what this is telling us is the more reviews a restaurant has got, the better it scores. And obviously if it's got Vega on the menu, that's a good thing as well unless you're going to Rome. Okay but what we really want to know is about where to go don't we? So I can sort of look and see what's happened in my data by looking at city.

Yeah so I'm just going to look at city on its own and here we go And here's all of our cities. Now we can see that our average TripAdvisor score is four.

Okay, so Russell's down here at three point nine, Leon at three point nine statistically lower than the average. The grey bars are kind of average and the blue bars above the orange line are very good.

And this is ordered left to right in terms of the number of reviews as well. So London has had, it's got the number of restaurants rather, the most number of restaurants in this TripAdvisor data set were in London, Paris, Rome, Milan, so all the big European cities up here and then maybe some of the smaller ones down here. But what it does show you is actually there's some, I was going to try and say Ljubljana features pretty well, it's a good place to go and as does Athens. Athens is probably the top one there.

So what I can actually then do is I can even then drill down into Athens a little bit more. It's like okay I want to go to Athens, what does that look like? Okay well vegan is true, that's better, that's not surprising. Okay and actually also we can see that when the price point is low or medium it does better.

So actually if you're on a budget, Athens has not only got your best TripAdvisor scores going but you can do it on a budget. Actually you know, you go to the higher priced restaurants in Athens they kind of not so significantly different from the average.

Okay and then you can explore a little bit more about Athens if the city that you want to go to. So let's just x that now.

The next thing I'm going to do, so this is like what's happened in the data but now we're going to look at the reasons behind everything. Okay, so we can see here vegan is false was described a big chunk of the variance. So you can see here this is our average rating, our average rating rather, not vegan is true, our average over the whole data set. And then all these things here are negative impacts which would drive the score down if vegan is false.

So vegan is false on its own is a big thing, okay. There's a little bit of a positive one here. So what's this one? Yeah, this number of reviews again if it's like between eight sixty and sixteen thousand reviews and Vegan is False, it does a bit better, a bit of an anomaly but hey, it's still statistically significant.

Paris, Vegan is False bad, as is price is low and vegan is false very bad so just go somewhere cheap and they haven't got any vegan on the menu, not good. This is a big one here by looks of it. So the price is medium and vegan is false but okay if you go to a medium price restaurant you might expect some choice in the menu and then there's a whole bunch of small terms that influence the outcome. So overall vegan is false, it's driving down the average right down to three point nine.

Okay so this waterfall chart you can really drill down into the specifics.

It's all very well and good but probably it's a good idea to think about how this may play out in real life, know, we're not just choosing a restaurant. Now the actual reason why I created this data set in the very first place was to pitch into a client a couple of months ago and they are an airport lounge business, a global airport lounge business and they've got the challenge of maintaining customer satisfaction scores. Okay now the busier they get naturally the lower the score their customer satisfaction. If you remember going into an airport business lounge and it's really really busy you might be a little bit miffed that you might pay to go to the lounge or it might be part your you know the expensive business class ticket that you bought and you get into the lounge and it's packed and then you ask to fill out a customer survey and yes you're not very happy because it's busy.

Well so being a busy airport lounge is going to be a negative factor on CSAT scores but there's other things other insights that you're going get which improve the score. It might be that you've got lots of nice scallops on the menu or the or you know different types of food or the magazines are all organised or whatever it may be.

If you're capturing that data you'll be then to be able to look into all these factors and what this airport lounge business is going to do is they get predictions on how busy the airport's going to be so they know then to roll out the real nice things to help their CSAT score know if they're going to be particularly busy. If they're not going to be busy then perhaps they don't need to have those expensive scull ups because generally the customers are going be happy going into a nice relaxed lounge in the first place. So it helps them manage their sort of customer satisfaction.

Obviously this was bad timing for me to go and pitch this to them because their business just pretty much stopped overnight. So all that's on hold at the moment hopefully it'll come back.

Okay, so the next thing is I want to look at what's the difference. Okay, so we're going look at some more of these waterfall charts. So let's just pick a couple of cities. So Budapest, keep with the bees, Bratislava, so some of the sort of smaller cities.

And if you want to compare these two cities, you can look at the average rating here. So where Bratislava outperforms Budapest, so at the top here, this is the average when the city is Bratislava, so it's got a four point one four pretty much score.

Budapest, sorry, here and then Bratislava is down the bottom here, so it's a couple of points below. Now these are all the factors that drive the rating of the difference.

Basically down here we've got Bratislava and the fact is vegan is false is a real bad thing and Bratislava as is vegan is true basically obviously that means that Budapest outperforms Bratislava regardless of whether it's vegan, true or false.

And then we've got some else here drives a rating Oh the rate, not really sure what that one is.

Okay price is medium and vegan is false. So again some various factors from our various outcomes which is driving the score down but we can see there's one big factor green one here which is helping the score in Bratislava and in fact that's just small types, small terms. That's sixty two various other things which are insignificant on their own but they combine to be fairly significant. So actually what that's telling us really is don't go to Bratislava, go to Budapest if you've got a choice.

Okay so that gives you some difference. Now once we've had all that actually you decided '**** no actually I might want to go to I'm not sure where I want to go, I'm going to try some different things'. Einstein Analytics gives you the option to say what could happen, alright. So I can now go, well I know that vegan is true is a good thing to do, so I'm gonna go pick a restaurant where vegan is true.

I can now pick my city, so where do I want to go, so let's say I want to go Lisbon at random.

What's my budget? High, medium, low? Well I've got a low budget.

Am I just going to try and find that secret restaurant without many reviews or find one with lots of reviews? So I'm going to pick one with lots of reviews.

Wow! And it's sending me like, you know, for these things I'm gonna get a really high rating. You'd be likely to get five. You've got lots of reviews, low price in Lisbon. That's really good.

That was completely at random. Let's change the price point to see how that changes.

Yeah, so if we choose a medium one, Einstein's saying, yeah, the probably average rating of that is four and if you go high price in Lisbon, again you've got another four. And then we can look at all the reasons behind that, okay, so we can look at all the influences for our selection. In terms of vegan is true, being a positive and then we can look at some negatives. The fact that the price is high and the city is Lisbon those two things are not a good combination.

And then here we've got the number of views in there as well-being a bad combination there. So just by changing this around and the price points, change the shape of my waterfall graph and actually this selection here for this bin which is a price of lower and a lower number of reviews, that's a really good thing. Yeah, so there's some really good positive things to come out of that. So that's helping me influence my choice and giving me a overall ranking. In fact it's giving me a four point four rather than a four. Think iScientists rounding this up to four, actually the result of this would be a four point four rating. Okay, so that gives you some sort of insight into analytics and hopefully I'll just spin back to my slides here.

I think what we showed you is you don't have to be a data scientist to do that. All I did was I prepped some data and uploaded it into iSite Analytics.

I would have iterated a few times so I could go through this process a few times and as you saw right at the beginning I took out some columns which I knew were not the influence. I took them out to help the upload go a little bit more quickly.

And so yeah, a pretty simple place to go. So in terms of how you get started with Einstein Analytics, there's a really strong Einstein Analytics Salesforce community. So if you're familiar with Salesforce you're almost certainly familiar with the Salesforce community and all of the groups that are involved with that and so search out for the Einstein Analytics community and also Trailhead. Trailhead is Salesforce's learning platform and there are a whole bunch of trails to get you started with Einstein Analytics and this is where you get your free Einstein Analytics trial org.

Okay so by doing these trails you get access to Einstein Analytics. Okay there are limits on the org, it's not like full blown one and I've actually just been using a trial org for this. So it can handle thirty thousand records to analyse so you can still upload a decent amount of information in there but there are sort of certain limits whereby you know you wouldn't use it in your business full time because you would be busting the limits but for an example to play with the data and get some analysis done it's great place to start.

So yeah, so go trailblazing, don't be scared of getting your hands on and it's something you can learn really really quickly without being a data scientist. So that concludes the talk. So I want to thank X Plenty for inviting me along and it's been an absolute pleasure and I look forward to seeing all the other presentations at the summit.

Thanks Steve. I've got a couple questions if you don't mind.

No problem.

The first one was when you were doing this analysis, there was you had the choice between getting insights only and then the full blown Yeah.

Analysis that you showed. What what's the use case or why why would you use one or the other?

So what could happen is the full blown one, yeah, which we uploaded. So you can actually play with the numbers here and it does all this analysis for you. So it's doing your predictions on all the variables. You can actually, rather than look at just what has happened you can then chop and change and pick your own set of variables to get a potential outcome basically.

So putting this into real life, let's say you're in marketing and you're marketing your products the USA, you might have variables such as a city, product, retail outlet, the type of outlet it is and the type of promotion that you're doing, whether you're doing an in store promotion or a coupon or whether you do a sort of direct mail promotion. So you might want to look at all the different effects of their various campaigns. So effectively if you've got a whole bunch of data from previous campaigns, can sit as a marketeer and just play around with this feature here to see what might work best in one city might not be so good in another.

For example, something happening in Florida, might not want to sell hats and scarf in Florida in March but they might go really well in Chicago because it's been colder for example. So there's some obvious data there but to give you sort of the real specific insights, if you've got some stuff which is less obvious like which promotion type is performing better than others then yeah then it's really good insight to have and for the marketers to play around and get a feel for what's going on with the data.

And sorry one more thing and then you can deploy this into Salesforce. So what I was saying before about like customer attrition for subscription businesses. So the one that we're going be looking at very shortly is a telecoms company who are doing broadband prescriptions and they're going to want to see what the likelihood is of the customer stopping quitting their subscription and going to a competitor for example. But we'll know that from a lot of the previous interactions we've had with that customer and how long they've been with us, how much they're paying, when we last renewed their modem for example, could be a key factor. And then so what that then does, it gives your customer support agents when they phone up and say oh yeah we're going to cancel our subscription', your customer support agent will see that customer, that profile right there and it will be able to make some recommend, gonna make some immediate recommendations in terms of what to do to retain that customer.

That kind of brings you to our next question.

What I've generally gleaned from seeing Einstein, presentations and and talking to people who use it is that it's mainly useful if you already have a Salesforce org and you put Einstein Analytics on top of it. It's not quite as good if you just want to analyze whatever data you have. Do you agree with that or do you think there's a standalone use case for Einstein Analytics?

There potentially is a standalone use case.

So it's interesting I was on a call with Salesforce last week about this.

Generally Einstein analytics is very deeply integrated in Salesforce, right? So but I think you can get a skew where you can buy analytics on its own because obviously you can connect to obviously we just pulled in a CSV data set or you can connect to other platforms that you may have to do the analysis. But the key thing about this in terms of the Einstein Discovery is the write back to Salesforce. So that customer support agent for example, you know gets that information from analytics right at the heart of where they need it most about that time when that customer's about to cancel.

They can see what to do to retain that customer.

Or it might be the other way around, they might want to get rid of the customer.

So you might say 'hey let them go because they're costing us a fortune' so that's good that they're going.

But to get back to your question then Salesforce have recently acquired Tableau and so I believe that the way that they are positioning the products is if you do most of your work in Salesforce, then Einstein Analytics is definitely the right solution for you.

If you are somebody that doesn't use Salesforce and you're doing some data analysis but you don't necessarily need all that customer data then you could look at like Tableau might be the answer.

So because that's obviously very good at connecting to lots of different data sets and different platforms etc. So that's the marketing spiel from Salesforce is if you're you're not using Salesforce in your day to day work then look at Tableau and then Salesforce is deployed within business and you're using Salesforce then it's analytics. Which means that they'll then say that's a case whereby you can have Tableau and analytics in a large organisation. Some people using analytics and some people using Tableau but we're great advocates of single source of truth. We like to use Salesforce like from like right at the beginning where you're looking at acquiring customer through to get paid. So really for our clients we would we're more aligned to Einstein Analytics.

And unless Tableau has changed in the last couple of years, the last time I used it it had zero predictive analytics it was just simple analytics.

I think they've got some AI stuff in there now. I but I don't quote me on it because it's a it's a recent acquisition and and because of the way that we're positioned and what we do with our customers, we we're not intending to go down the Tableau route at the moment, but it does look great. It does I mean, I think I think some of the visuals from it look really good.

Yeah you can do some amazing stuff with it.

Yeah and we've not and but we've not looked at any dashboards here. This this is the discovery part of Ice Analytics. I mean it's a whole big piece on the on the dashboard analysis side of things as well And some of the dashboards that we're building at the moment for some of our clients are stunning, you know, whereby we've got you know millions of records and especially for the board meetings now whereby they just got one dashboard and they can drill down and see everything they need to see in the business.

Whereas before they were like, we've all been in those businesses where at the end of the quarter, heavily scrabbling around with Excel spreadsheets trying to get the numbers into their boss so they can present it to their boss, you can present it to their boss and eventually get some to the board meeting and by the time the data gets there it's probably meaningless anyway because everybody's chopped and changed it to make it look good whereas this you know with a really good day is that you can get a single source of truth you really know what's going on in your business.

Yeah. We have a couple other presentations in the conference about Einstein Analytics that show dashboards. One is obvious. It's a guy from Austin who does it has a nice dashboard. One that's kind of hidden is a woman who does part who is a Pardot specialist and showed Pardot dashboards which were really nice.

Yeah. Absolutely. Yeah. You get some really good marketing insights from from your dashboards. Yeah. I think a lot of these now, some of these Salesforce premium products come with Einstein Analytics built in.

And what Salesforce do is let's take like the basic sales Einstein Analytics dashboard. Obviously that's not a built in feature with standard Salesforce license but they've already created some great dashboards and some really good templates because obviously the standard Salesforce functionality of you know leads and accounts opportunities and that sort of thing. They built really good dashboards around that. So out of the box you can get started pretty quickly.

The futures as well of Einstein Analytics looks really rosy. The call I was on, I was saying earlier this week, were talking about before about looking at sentiment as well so taking text fields and looking at sentiment that looks quite exciting. That's really exciting for discovery actually, especially when looking at reviews and things.

And then also the data prep side I think so I guess some of the guys doing the dashboard presentation later may allude to the fact that they're looking at data prep three point zero which is the latest version will be coming out.

I think it goes beta in the summer but general release is probably winter this year, makes the data prep a lot easier. So actually for the building dashboards, bespoke dashboards, you're not using standard templates, they can be a little bit challenging especially if you're not super technically orientated to start with but that's going to get really easy going forward. So it's to make it a lot, lot easier.

Well, that's fascinating.

Well, I'm glad to see that Salesforce is still pouring money into Einstein Analytics even though they acquired Tableau. And this presentation was awesome, fascinating, and hopefully, when we get past this useful. So thanks so much for your time, Steve, and, we really appreciate it.

Thank you.

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