Salesforce, Inc. (CRM) Earnings Call Transcript & Summary

All right. Good morning, afternoon and evening, everyone. I'm Aditi Patel, Associates Technical Product Marketing Manager for CRM Analytics here at Salesforce. Welcome to the CRM Analytics 2023 webinar. If you're watching the webinar on demand, thank you for joining. We hope you will learn a lot from this webinar. Today, we are joined by incredible product leaders to talk about some of the innovations such as single-click navigation, enhancements to input widgets and collections, Slack sharing, estimated story run time and more. We will also cover a new feature of Revenue Intelligence for Sales Cloud, Account Discovery. Leaders will be doing a live demo for most of the features. So you can launch new features in action. So sit back and enjoy as we have amazing innovations to cover today. And your time is valuable to us. So we hope to make the most of it. Before we begin, just a little bit of housekeeping items that I would like to share. First, this webinar will be available on demand after the session. So if you want to watch it again or share it with the world, it's going to be right here in the same URL. The slides will automatically advance as we go through our presentation. So don't worry if you miss anything, you can always come back and watch the webinar recording again. To enlarge the slides, click on the enlarge button in the upper right-hand corner of the presentation. We really encourage you to submit questions any time through today's presentation using the Ask Question widget at the bottom of your console. We will also be doing a Q&A at the end of the presentation. So have your questions ready for us. If you need any technical assistance in today's presentation, click the help widget located at the bottom left corner of your console. We have also added the release notes for the Spring '23, which are available in the resource level window at the right-hand side of your screen, or you can go to this URL that I have it here. I highly recommend checking out the release notes as it will help you to learn more about all the innovations we are introducing in this release. So with that, let's begin with our favorites slide, Forward-Looking Statements. Please be sure to make any purchasing decisions on any product or feature based on what is currently available today. And of course, thank you, all of our customers, partners and trailblazers for your business with us. You all are truly amazing and none of what we do would be possible without you pushing us to innovate and inspiring us with your success. If you are not yet a member of our customer base but would like to learn something new today, we hope you find some useful takeaways that will inspire your team to keep growing your business. Next, public service announcement. We have 3 annual releases, spring, summer and winter. Today's webinar is for Spring '23 and the upcoming release will be the Summer '23 followed by Winter '24. So mark your calendars as we will keep on bringing amazing new capabilities to make your data work for you. And now I would like to introduce the product leaders who will be talking about some exciting new features. So we have Radha Vivek, Product Management Senior Director; Ankita Dutta, Product Management Director; and Bobby Brill, Product Management Senior Director. Today's agenda. Radha will walk us through a new feature account discovery from revenue intelligence in a bit. And then Ankita and Bobby will walk through the new accelerations from experience and intelligence. And now without any further ado, I will pass it over to Radha.

Good afternoon, good evening, everyone. My name is Radha Vivek. I'm a Senior Director of Product Management in Salesforce. In Spring '23, we have 4 net new feature enhancements that are generally available in Revenue Intelligence that I'd like to share with all of you. One of that features is Account Discovery. And the second 1 is going to be 3 predictive models that we are shipping out of box with sales performance metrics, and that includes explainability of those relative insights as well. And number three, you will be able to see multiple forecast types within that forecast chart that you see today inside that Revenue Intelligence. And the last feature that I want to show is historical forecast trending. So let us look at the most exciting feature that I have been talking about, Account Discovery. We are delivering this Account Discovery to help account managers and reps and any leaders of that matter to manage their accounts to grow their business and find upsell opportunities. How do we do that? We are leveraging our discovery predictive models based on big set of signals that are curated for you by applying rich setup, smart transformation such as clustering, aggregations and whatnot. These transformation logic and the signals are available for you in our recipes -- the prebuilt recipes. You can customize these signals to meet your business needs or you can spin up this app and see what your data tells and customize, add more signals, build and train and operationalize AI. Now let's look at how do you set it up, right? So I am here in the setup screen, this is a net new app that you'll need to spin from here. We recognize that you may have already have data sets from Customer Insights App that was available in your earlier releases. So you can use that app as long as it is version 1.7 or above. I mean you can see that I think it's 1.5 not 1.7, giving you that ability to keep your data sets consistent and method definitions consistent across all the apps. And this is where you are going to set up the app. Once you install the app, it is going to give you this button to navigate to the Account Discovery screen right from the list here. Once you click on that, it is going to take you to this Account Discovery app, where you can see smart account management list where a rep or a key account manager can identify all their accounts. How is that account held? What is that account upside, with all the signals included in this. Just know that not all the signals are included in the detailed tab. You can still go ahead and drag and drop or adjust the signals that you do want to see while going into recipes and then adjust these visualizations. You can see that these predictive scores are already here, and let me show you how you can get the explainability part of this head stuff. So you can see that we included the Einstein tab. Once you click on the customer, it is going to show you what is driving the predictive source that we are delivering with you. And the cool thing about this is this show account inspector that I showed you, you can not only view it from here by zooming in on by each of these accounts here. You can take this component and embed this in your account page as well. So that even if you are not navigating to the Smart Account management list, you can still consume the similar insights inside, contextualize it to your account page as well. So like I said before, we are shipping 2 predictive models along with this. And you can see that we are also shipping a bunch of components and dashboards for your use. Same thing with the recipes as well, you can see them in the recipes. So the next 2 items that I also want to show you that I talked about is the ability to see multiple forecast types in the same chart. Up until winter release last year, you were required to install the app for each of the forecast type that you have active in your org. Now you are able to switch back and forth between multiple forecast types on the same chart. The predictive insights that I was talking about is these things. In this org you don't see the inside but if you have enough data in your organization -- in your Salesforce instance, you would be able to see and to close win site -- win rate and average deal size creditor scores with the explainability included. And the last feature that I want to show is the historical forecast trending. This is, again, allows you to make any of your decisions objectively based on the historical trends that are happening in your forecast view. So with that, my demo is done, I will pass it on to Ankita.

Thanks, Radha. That was a great demo. Hi, everyone. Can you hear me?

Yes, we can.

Awesome. All right. Hi, everyone. This is Ankita Dutta. I am 1 of the PM on the CRM Analytics team at Salesforce, and I am super excited today to talk to you about the Experience Cloud, and everything that's ongoing over here. So because we have so much going on, let's talk about it in form of swimlanes, so number 1 is our interact swimlane, right? So what is going on over here? Our formula Editor got a big upgrade for added support for SQL-based formulas. So now you can add your own formulas for any query on Genie, Snowflake or Google BigQuery without the need to edit the underlying SQL query. You can edit a query with bindings now, and you can double-click to modify the visualization, add actions, drop back into the custom query editor and more. When you directly query Salesforce objects, faceting can now operate across object relationships as well. In the visualize tab, we are allowing customization on the amount precision on short in numbers and charts. I know this was a big customer ask. Pivot table features such as totals and sorting are now going GA as well. For our engagement swimlane, we've added a [indiscernible] from the Genie page. We are constantly making enhancements to accessibility. So that's always top of mind for us. And we've added a button to the header for high-contrast mode for color contracts on our dashboard. We've made enhancements to our embedded dashboards, and now they're way faster. So how do we do this, right? We've done this by upgrading our dashboard-embedded component by LWC functionality and replacing the eye frame. Now dashboards load faster and modal windows, fly outs, tool tips, they can occupy the entire screen real estate, making the visualization way better for you. However, if you don't like this, your admins can opt out of this feature in case if there are any issues. We've upgraded collections to be faster to add items. They have a new card layout where you can see high-quality fitness and also add line reports and dashboards to your collections. So for my OA users, this is like a great enhancement as well that you can start using collections as well. We've added new features to personalize your own collections, like you can hide irrelevant collection, and you can also pin them for easy access to the ones you refer to the most. So Home is a one-stop shop for all CRM assets and lightning reports and dashboards. And now we're making Tableau join the party as well. So we're making our published Tableau to Salesforce feature beta with the spring release. And we are also working on the ability to publish from Tableau Server in the 2023.1 release. Bunch of enhancements on Slack. With a new CRM Slack app, you can get more features with this release. You can unfile your link directly from a lighting report or a CRM dashboard directly into Slack now. You can get an up to the minute snapped image, along with the notification. And also, you can connect to all of your Salesforce boards within the same Slack's board space. So it's huge because you can query data from multiple or from within the same workspace. Last release, we shipped our new input widgets, so you can build what-if style applications. So you can learn more about your data. This release, we're making it easier to wire up these types of apps. You can add a column to any input widget without any binding. And once added, you can reference these columns in formulas like any other columns. We're adding more dynamic styling options with more conditional formatting, so you can now add conditional formatting rules for the border, number, text widgets. We've also added conditional formatting inside repeater widgets, and we have a lot of out-of-the-box image options as well. If you don't like the out-of-the-box image options, you can upload your own images as well. Variety of enhancements on the tooltip capabilities, be sure to check those out. And lastly, select and navigate. So link selection events to transition to a new page on a dashboard or a page on a component or even another length or dashboard now. And this release, we're adding select interactions to repeater and table widgets, and click interactions to text and chart widgets. So let me now show you a demo to learn more about all of these features. So I will begin by sharing my screen, give me a second. All right. I am hoping all of you can see my screen.

Yes, we can.

Fabulous. Okay. So let's start our demo by talking about Select and Navigate. So I am logged into my Salesforce, or I have already pre-created a dashboard over here, as you can see. And here, I'm looking at my map widget as my starting point, right? And I can select a country to further deep dive into the data and see what's happening over here. You can also use this framework to navigate to component pages as well and see a detailed breakdown for the segment and product analysis. Now in the spring release, we've added more widgets and 1 more interaction event. So now what you can do is use repeater widgets and tables to set interactions. So as you can see over here, I can see all of my details as well. So how do I set all of this up, right? This was actually super easy to set up. So over here, I have my dashboard open in the edit mode. And what I'm going to do is click over here. And if I go to interaction and I click on select or click, and then I can choose my actions over here. So as I was saying, you can go to a saves lens, dashboard, URL, page in component or layout, and you can navigate to all of those. You can also choose to go back on the selection, which we already saw. Now let's check out some of the other repeated widget features. So if I click on my Details tab and go to my repeater widget. I now have the option to conditionally format this. So as I was talking about, if I click over here, I can choose any of the symbols that are available to you out of the box. You can also upload your own. And you can add all of this formatting that's available to you over here. You can also put on tooltips and ad visualizations for advance tooltips using this. Finally, if I go to my dashboard and click on high contrast mode, I can view all of this in high contrast mode. So this looks great. All right. Now let's talk about What ifs. So this is, again, another pre-created dashboard using the What-if input widget. And what I can do over here is change my inputs and set expectations towards future events. CRM can show me all of this data, along with the banded confidence line to see the degree of confidence the system has. Now I'm on my Analytics home tab, and I can see all of my CRM and OA content using one space. If I go to browse, I can further filter all of these data by looking at specific things, I can search for all my assets. I can also add more filters based on all of these like Created By, Last Modified By, et cetera. And for each of my assets, I can share, open an Analytics tab, I can hide, I can add to collections, and I can favorite them. Regarding collection management, if you click this cogwheel over here, you can either choose to hide your collections, if you don't use it or you can pin them to your home page, if you use them all the time and you want to access it. You can also add more to collections or create your own collection and now you can add OA reports and dashboards to your collections as well along with CRM assets. So that is everything for my demo. If I did cover something or if you have any questions, please reach out, and we are happy to answer them. With that, I will pass it on to Bobby.

Thank you, Ankita. That was an amazing demo. I hope everyone can hear me. Can someone give me a thumbs up that this is working or I should refresh my page.

Yes, we can hear you.

All sweet. okay. I just want to make sure we're all good here. We're living in the age, everything is virtual, and we still can't even get the webinars right. [indiscernible] showed. Starting with even Radha demo of Account Discovery, some of you might be wondering why it's called the Account Discovery. Well, we threw Einstein Discovery in there. And really the goal of Einstein Discovery is to add a little bit of a machine learning to analytics. This has been our -- been saying this since back in the days of Einstein Analytics, but adding a little bit of ML to the mix in order to help everyone make better decisions. Everything you saw within Ankita's demo, why we're doing these things, why are we making dashboards easier to navigate and all these things. Well, it's all about decision-making. That's what we want to empower our users to do. So with Einstein Discovery this release, we have a lot of cool enhancements. A few that I'm going to go into in a live demo and a couple that I'm going to talk to about right here. First off, we want you to be able to build your models. There's many teams working in the same space, everyone wants to build their models. And 1 of the limitations we had up until now was you can only have 2 models running at a time. That third model would just fail to say, hey, there's 2 running. You can only have 2 running. And when that experience happened to me, I said, well, that's silly. Why can't we just queue it. And there were some technical reasons to release. So now that third model will just queue up. And everything will just be ready for you to -- the resources ready, it will just work similar to how recipes work today. The way that we handle large sums of data before the spring release, we just looked at all the data. So when we trained a model, we looked at every single row that you gave us and some of you that have machine learning background said, well, there's techniques. We sample data. We like to create this representative sample. We hear a lot of feedback from customers about wanting a representative sample. We don't necessarily need all the data. We just need the right data in order to train the model. So with the spring release, we brought our smart sampling feature into the product. It's just going to happen behind the scenes at least for now. But smart sampling will automatically run if you choose an XGBoost model, and there's more than 2 million rows of data. We will smart sample that down to 2 million. If you choose model tournament, model tournament in the past has run really slow in large data volume cases to smart sampling will help that process go a lot better. One of the biggest changes we made is not necessarily a feature. It's the way that we call our assets. The stories are no longer stories. They're now just called models. This whole time with Einstein Discovery, we've been telling our customers to build predictive models, but we told you to start with the story. I'll show you what all that means when I go into the live demo, but I'm really excited to finally call the asset what it is, a predictive model, not a story. We're giving you more finer-grained metrics so we can let you drill down in where you will be able to see how well the model is going to perform in the segments. We have this awesome decision optimization pilot that is live in the spring release. Some of you may have saw Dreamforce. We talked about decision optimization. We had it in our keynote. So the pilot is finally available, and I'm really, really excited for the next stage of how Einstein Discovery is going to help make decisioning a lot better. Produced live predictions with Snowflake data. Well, those of you that know how that works, you create a virtual data set pointing to Snowflake, it just shows up like any other data set. Well, Google BigQuery had that capability, too. So we wanted to make sure that you can get live predictions as well. So anything that can be a data set or representative of a data set in term analytics now supports the ability to get live predictions back in the data. So now I'm just going to go into a couple of slides before I go into my live data. So what does finer grain model metrics mean? So let's say you built a model and you're looking in this case, what are we doing? We're predicting quantity, so sales in various stores for various products. And maybe 1 model is good enough to predict quantity across all your stores. However, maybe there's a particular segment of your data where things just work a little bit different. The signal is different. Many businesses work this way, right? You might have the way that you sell certain products is going to be different in different areas. The way that your business process runs for large enterprise versus small different -- small business, it's going to be different. So how your predictive models work are going to be dependent on some of those signals. So when we train a model, we're going to give you today or in the previous release, you may have seen some metrics like if it's a classification problem, it's called Area Under The Curve or in a regression problem, it's called R-Squared. And that's going to give you sort of the signal and how good is this model, how well is it going to be able to predict versus kind of a random coin flip. And a lot of times, the metrics are going to say, "Hey, this is a great model. But what you really don't see until we gave you the finer-grain metrics is maybe there's a particular segment where the model is not going to work really well. So now you'll be able to see for every variable that you have, all of the individual kind of values of that variable will have an individual performance metric. So for classification, it's essentially the R-Squared and for the regression it's going to be -- sorry, classification is going to be AUC, Area Under the Curve and regression, it's going to be that R-Squared at that particular sector. So we get just finer-grain control. This is going to help you understand, do I need to segment my model? Should I create something specific to a segment? Or should I -- orders this model -- this sort of global model good enough for all. And then we've got estimated story run time, which actually should just be called estimated model run time because we're no longer calling it stories. We're calling in models. This was a pilot feature. So sometimes naming at Salesforce, we love to change names of everything. It's sort of our favorite thing to do. So that's another part of why we choose that story to model. But what you can see here is, as you're creating your model, we're going to give you this estimated time that it's going to take. And it's actually -- we're using a predictive model behind the scene. We've already learned from how long things take. So we're now able to give you some guidance on how long that's going to be. I'm super excited about this feature. It's so simple, but as you're making changes, you can see how long is this going to take? When should I come back? So it's a much better experience. So with that, I'm going to go into a live demo in order to show you all of the amazing stuff that we have done. All right. So -- here we are in my Spring '23 org. And the first thing I want to show you is it's no longer called the story, it's called the model. So if I go to Browse, the first thing that you might notice is there is no more story tab. It is now just models. All of the stories that I had in my winter release will show up here, but they will be called models. So I still have this kind of collection to kind of go in, not a collection, that's the wrong word. But I had this list view in order to see my assets, and I can go manage, it's just called the model. When I click create, it's no longer says a story. It's says a model, but let me show you what this flow looks like. So when I create a model, I still have the option to start from pre-built template. As you saw from Account Discovery, we've got Einstein Discovery models embedded within that. So we want to give you as much sort of out-of-the-box experience as possible, but we have this platform that you can go do your own thing. So I'm going to create from a data set, and I'm just going to start from this churn data set. So in this churn data set, I have all my account data information in the -- I've labeled the column that says whether they turn to not. So I want to predict. And I want to minimize when churn is true. So I want that model to predict a high number if the churn is going to be true, that way, I can build the business process around that. You might notice there's only 2 steps here. Prior to this, there were 3 steps. Everything is a model, so there's no more being able to create an insights-only option. It's always going to be a model. And now we've got either automated or manual. You can see here that we have the estimated model run time built in to this page. I can see based on this data size, based on the settings that automated is going to use, it can take about 10 minutes. If I go into manual, what you'll see here is the same kind of look and feel that you've seen before. We've got the estimated model run time up here. And as I make changes, we try to reevaluate the model run time. For instance, if I want to change my algorithm from GLM to, let's say, model tournament, you're going to see -- it only added about a 5 extra minutes. So this is a great way to understand as you're doing the settings, what -- how that's going to impact the amount of time it's going to take to turn. And then when you click train model, what you're going to see is a new page. Actually, technically, you've seen this in the winter release, the way that we -- the way that the back end works. This page actually seems to have gone out sooner than with the release. So you may have already seen some of these features, which is just kind of amazing that we're able to do that. But now you can see this bar, how much time is remaining and as the bar progresses at time goes down. So it's a much better experience. But when the model is complete, you get a new landing page. I don't need this. You get a new landing page. So we also built in this tooltip that set you for -- I think it shows the first few times that you opened this. I think it's per [indiscernible], not per user, still trying to figure out what goes there. But your users are introduced with, hey, there's a new UI here. it's actually not really a new UI. We've just reorganized how all of the other content works. But stories are now models. I can click through to understand where everything is. All of my settings that used to live up here performance, all my model metrics that used to be a tab up here can be found under performance. If I want to edit things, it's now moved on the left-hand side. So there's going to be a little bit getting used to, but it should be a much better flow. I can edit stories. And then all of those insights -- so all of the whole reason why we called it Einstein Discovery and did sort of this automatic data discovery, it's all built into this data insights app. So it's all there. You're not losing any content. It's just reorganized for a little bit than your use. So within performance, I can do all the same things, I can do before within settings. Here's where I can make changes and retrain my model. You're still going to get your model version. So if I make a change here and I go back, like, hey, there's -- I didn't save any change did retrain, so this thing worked before, can access all my versions here. If I want to either change my outcome or sort of clone the story, this is sort of a workaround that we've had. I can always do that here. And if I want to create a new model that's based on the same stuff. So all of that still exists. I can still view my model What-if scenario here. So I can set some things, I can play with how changing various variables impact that model. All of this exists in the old UI, it's just been repurposed. And then data insights, everything is the same, but you might notice that the variable exploration is now moved to the right instead of the left that is probably the biggest change, but all the capabilities really, so it works exactly if you can like before. I can still bookmark, I can access my bookmarks. We didn't want to take any functionality away. So stories are now models. I think it's going to be a lot easier for everyone to understand, hey, there's a predictive capability. How do I get that predictive capability? Well, I'm going to create a model. And then I'm going to apply that model back on my data. As you see in Account Discovery, we use recipes to write the prediction back so that you can visualize those things. So you can see things like an account health. So you can see things like the -- what accounts are likely to buy more. All of that is sort of based on how the model is trained. The recipe is going to give you that ability to build that training set. The last thing I want to show you is something I'm super excited about -- and that is -- well, 2 things actually. We redesigned the Lightning card. All of the functionality, again, exists. The heading is a little bit better. We've moved from the old Einstein head to our new Einstein logo. We've cleaned it up a little bit. Some things are going to look a little bit cleaner, I think. So we're excited to finally make this change. But 1 of the things that I've heard from customers time and time again is they love the top predictors, but the second you're showing this to end users, they don't understand what this means. We had this conversation a few releases ago, where the suggested improvements, we gave the ability to customize that text. Well, finally, we've decided to give you the ability to customize all the text that shows up here. So instead of saying things like case origin is phone and number of owners is 5, what Peter would say to you, you can translate this speak into what an actual service rep might understand, or functional area is SAQL, the service rep probably can make the termination what this needs. But wouldn't it be great if you can put some sort of text template on top of this in order to show the text a little bit better to the end users. Again, we've done this for this suggested improvements, but we've streamlined the process. So within the model manager, I have one without custom, one with custom tech. Here's where I can make my prediction customer. So based on -- if I have any action variable selected and then any top predictor, I can go in here and I can choose which improvements -- improvement variables I want to customize this again, we had [indiscernible]. And then which top predictors I can customize. So I'm going to go into sort of both variable -- both types are customizable the same way. I can do a -- I can customize by which value shows up. So if I have -- if no is a value that it was trained on and I want to hide this as a top predictor, I can go do that. I can say, "Hey, let's not show no. Maybe the value of SAQL. I'm going to change the text to just say SAQL was fun, so you could see what that looks like. I can add another value or I can do all the remaining values. So I can set a general text template if I want to. Like for description, we're pulling out keywords from a description. And those keywords, in fact, when we go back to so you can see what that looks like. Text clustering will allow me to call 3 -- 2 to 3 keywords and give a signal strength to that. And I'm just saying description is dashboard Analytics Einstein. That might not mean anything to anyone. So maybe I want to change it to say for description I want to say, customer mentioned and then I can access what the value is going to be because in the description, that's what the customer is saying. So I can do that. And then I don't want to say the support owner count is 5. I want to say a number of people that touched this have touched this case, so I can choose the text there. So once I save that, and I apply it to a record, it would look like this. So case origin is phone and 5 owners have touched this case. Instead of saying the type is SAQL, I've now said SAQL is fun. And then no longer -- like just the text, the way it shows up to the end user is now fully custom. So I'm really excited about this feature. It's going to make it a lot easier for end users to consume the predictions. Hopefully, you know that this Lightning card is just 1 way to deliver predictions to your end users. There's many other ways that you can do it, like in a recipe, like in flow. So there's many ways that end user could consume predictions. This is just 1 path. Helps me show it off. Some customers actually want it this way, but other customers just want to sort of hide that prediction and make automate decisions based on it. So with that, I'm going to stop presenting and I think we can go into Q&A.

Awesome. Thank you, leaders for walking us through amazing innovations that we have for Spring '23 and for doing live demos. Seeing the product in action helps us to fully understand the value and potential it brings and it makes everyone excited about the solution. So thank you for amazing presentation. Okay. Let's jump into the Q&A session. So the first question is for Ankita. So for the What-if analysis, Customers are always eager to learn about some top use cases. Can you talk about a few?

Yes. Yes, sure. Happy to talk about some use cases. So let's talk about some different scenarios, right? One would be if you're a sales manager, then what you can do is as you're monitoring all your reps, you would like to change each of your sales reps' pipeline values and evaluate how that impacts your quarterly revenue. If you are working on rebate management as a dashboard user, you'd like to change the rebate percentage and see that how it will impact your profit to test different scenarios. In supply chain business, you can see how orders and margins could be impacted by a shortage of material. So these are like some of the scenarios I can think about top of my head that you can use this for.

Thank, Ankita for sharing that. Okay. Second question is for Account Discovery, so is for you, Radha. Does Account Discovery need a separate license?

No, account Discovery is available for customers who already have revenue intelligence license.

Great. Thank you for sharing that. The third question is for prediction. So it is for you, Bobby. The question is customizing the text shown in prediction is great, but how does this work for different languages?

That's an excellent question. When we first went down the path of doing customized predictions, I said, this has got to be localizable. But you may know that doing a template is very hard to localize because the way languages work and how things are placed is going to be different. So what we said was, hopefully, 1 day, there's going to be machine learning to help us with that kind of language translation, which I think we're close to that. But for now, each customization is going to be specific to a language. So if you wanted to have multiple languages, you could create multiple model segments each for a specific language, and you can customize that text that way. So it is sort of -- I'm going to say it's a workaround until things like GPT can help us do the actual translations.

Yes. Great. Thanks, Bobby. Okay. One more for you Ankita, related to interaction. So are we working on any other type of interactions in the future?

I'm glad you asked because this gives me a chance to market what's coming up, right? So we are currently working on slow interaction. I'm personally very excited about this because that brings more actionability to our product. So you will now be able to trigger flows based on click or selection events. And then you can either trigger a background flow to do a particular task. Or you can also like think about a scenario where you can pass a bunch of IDs, like user IDs, right? You want to sign users to cases. And now you can directly do that from a dashboard where you can pass -- select a bunch of IDs and then pass that in a flow. So that's sort of what we're working on for the future in terms of other interactions. If you have any other use cases, we would love to hear from you and understand what else would you like to see over there.

Great. Thank you, Ankita. Okay. Let's take the last question for today regarding stories. So it is for you, Bobby. What happens to mine site only stories that don't have models?

I should have said that in the demo. So yes, we had a feature for the last couple of years where you could do what's called an Insights-only story. And as you saw in my flow, you can't do that anymore. And it's -- we had it because it was really slow to build the models. We made the models go a lot faster. So all of those insight-only stories will show up as models. And you will see when you open up the asset that if you're going to update it, then it's going to just require you to do a model. Don't worry about it. It may take a few more minutes, but ultimately, it's a better experience. So they're not gone. You just won't be able to create them anymore.

Perfect. Great. Thank you. Thank you so much, Ankita, Bobby and Radha, for answering all questions and providing great knowledge to all of us, that's truly valuable. So we have reached the end of this presentation. But before we end, I would like to ensure you all have the resources you need to learn more about the Spring '23 release. So you all can access release notes using the link provider here or you can access them via the Resource Library. We also have Trailhead modules that you can check out. So please utilize these resources as its free-of-charge. And with that, I would like to thank all of you for being here today and for joining us. Have a great rest of the day, everyone.