Alright. Let’s get to it. So today’s session is on predictive sales forecast in ThoughtSpot, and it takes on the question every sales and rev ops leader asks. Which deals in my pipeline are going to close and when? We’ll show you how to answer this in three moves, running predictive analytics directly in Spotter so a business user can get a fast read on what’s driving wins without writing any code, building a repeatable model in analyst studio that scores every open deal and rescores on a schedule, and putting those scores on a live board with Spotter on top to explain them in plain language to executives, managers, and reps. Anyone who needs to access this information, run through it without any code. You’ll see a live demo of each step. We’ll close with a special no cost offer to get your own data ready for this. Go ahead and drop questions in the chat as we go, and we’ll take them at the end. Let’s get started. I’ll introduce myself. I’m Heather Anne Christensen, go to market leader for the data platform practice at Atrium. We’re a data and AI consultancy and a go to ThoughtSpot partner specializing in democratizing data across analytics and AI and ensuring a sound data foundation to drive the enterprise and, ultimately, business outcomes. That means we can help you create a clean data foundation all the way to scores your sales team can actually use. I’m gonna pass it over to Madison to introduce herself. Hello. I’m Madison, a lead analytics consultant at Atrium. I’ve been at the company for over four and a half years now specializing in analytics across multiple platforms, including ThoughtSpot. Now I’ll hand it over to Victoria for a quick introduction. Hi, everyone. I’m Victoria. I’m a senior consultant in the data science here at Atrium, and I’ve got lots of experience with different data platforms, including ThoughtSpot and statistics and data analytics. Alright. Hey, everyone. My name is Ryan. I lead partnerships here at ThoughtSpot. Been here for a few years and have been working closely with Atrium, one of our most trusted partners, and we’re excited to dive into this today. I’m gonna kick us off with just a high level overview of ThoughtSpot for those of you that aren’t familiar with us or haven’t kept up with us lately. We’ve been around for a long time, and we were actually the the pioneers of the space for natural language search for your data. Our founder came out of Google search, he said, why can’t I have a search engine for data the way that I do for everything else that… That’s connected? And so that’s what we’ve set out to build, something that makes it easy for nontechnical people, folks that don’t know how to spell SQL, to be able to dig into their data and get, you know, deep insights and be able to take action on their data. Because we have that search architecture, we’re well prepared for this new world of AI. And so you can kind of see we call this the fan. This is an overview of our complete AgenTic analytics platform. You can see at the bottom, we connect to almost any data source, whether it’s your data warehouses like Snowflake and Clickhouse and Redshift and BigQuery and all of those to be able to send down SQL queries or via MCP to almost anything else from Teams and Slack to Jira to Asana. We can get your information anywhere. All of that is governed and given context and semantics in our semantic layer that makes sure that everything is accurate, that we’re speaking the same language, that AI understands how the business talks and thinks, not just how data is labeled in column headers. And then from there, we have so many different modes of interacting with the data from conversational AI to AI powered dashboards. We’re gonna look a little bit at those today. But then you can take that experience and white label it and start to build your own embedded applications and deliver AI and agents to your end customers. And then, you know, some of our latest products being able to build your own internal personalized agents and workflows and data applications to be able to publish. Today, we’re gonna focus on kind of that left hand side, the foundational piece of AI dashboards and conversational analytics. But because we’ve got such a big platform, I just always have to brag a little bit that Gartner has named us for the third year in a row as a leader in their Magic Quadrant for analytics. And this year, we were the only leader in that upper right hand quadrant that moved both up and to the right. So we’re really proud of that, and I’m excited to show you kind of why they put us in that space this year. But I’m gonna hand it back to Atrium to kinda set up our scenario for today. Absolutely. Thanks, Ryan. And I I feel like you deserve those bragging rights. That is such a huge accomplishment, and the recognition. And, obviously, we’re really excited to dive a little bit more into today on how ThoughtSpot is solving some key business problems. So let’s start with that. This is a problem that I think most sales leaders and rev op leaders know to their core. We’ve got a great view where we can see pipeline data, every opportunity, stage, amount, and close date. But really, the hard part is answering the most critical question when it comes to sales forecasting, which deals will close and when? As a revenue leader myself, this one hits home, being able to provide back to the business that tight, accurate forecasting to make critical business decisions, understand cash flow. This is a complex problem because it’s both an art and a science. And having that predictive component to it, to have a validation check as you think about the data list that you have, is such a critical part of any sales leader’s wish list, and getting that right is not an easy task. I think any analyst or IT professional that can solve this would be a hero in my mind. And ThoughtSpot really helps close this gap to not only create the predictive analytics in a place that folks can actually use it, in a method that is conversational, but more than anything, it is accurate. And I think that is one of the pieces that is so important. So here we have on the left for rev ops leaders. You own the number. We’ve got a list that’s not an answer. So your pipeline report showing stage, amount, and close date, as we talked about, likelihood for each deal. But sometimes, that score is just a black box. So then you’re not building trust with the business and with, even your account executives on what that score can actually look like. Nobody can explain what’s driving that number, and it’s not gonna be used for forecasting. The next issue we continue to see is creating tickets around every question. So rather than having this be democratized or a place that people can self serve this type of pipeline scoring, It has to be run by hand. And that just creates bottlenecks and, again, degrades the trust that we spoke about. On the right, we have sales leaders who really own the outcome. So first question is really simple. Is my pipeline healthy? Is it stalled? As we continue to see a lot more data volume, this gets harder and harder to understand. More metrics are thrown at folks, more different variables when we think about answering that question. And getting to the root cause is so critical to, again, build that trust on actionable insights for sales leaders. SAG reports don’t help. They’re not going to give a manager the ability to coach a rep and understand what’s actually happening within their pipeline. And then another big piece on all of this is investments have to show up as revenue. So as we think about win rates and cycle times, making sure that what we’re building out from a predictive forecast methodology is actually gonna drive the business, that it’s why we’re here. So creating both the the data to support and then really an outcome is so critical for this piece. I think as we went through the intro, I said, we’re not just gonna tell you about all of these great pieces. We’re going to show you how this works in practice. So I’m gonna pass it over to Ryan to dive in. Thanks, Heather. Before I was working in partnerships, I was in sales ops. So this is something that is near and dear to my heart. So for today, I’m gonna put that hat back on and pretend like I am a brand new sales ops analyst at this company. And, my boss, bless him, decided to go on vacation on my second week and is throwing me into the deep end on, work… Getting on a call with my VP to help him prep for the forecast call. And I’m a little freaked out because at my old job, we had some Power BI reports that would show some aggregates of pipeline numbers, but they were pretty limited. It wasn’t live data. It was extracted and, you know, I had to tell them what I wanted on my drill paths before they built it. So I ended up spending a lot of time building Salesforce reports and then exporting them to Excel and building my own pivot tables. But then I’d have to jump around between Slack and Salesforce opportunities to look at notes and and try to, like, understand what’s happening. And it was so much time jumping around between all of these different data sources and trying to understand when the extract was taken and and what’s live. And and it was a lot… Was a headache, and and I would spend almost an entire week prepping for this call. But my boss said, hey, we have ThoughtSpot. Don’t worry about it. Just just dig in and and you’ll be fine. So I’m gonna take him at his words, and and I’m gonna open up ThoughtSpot. And I see here… Alright. Cool. I got my my sales and RevOps dashboard, so let’s dive into that. And right off the bat, I see, oh, wow. Somebody has built this awesome org chart of our entire sales organization that shows what our team quotas are. So I can get a a a cool big picture of what what kind of challenge we’re facing, what the expectations are from the CEO and the board. Thankfully, I am only working for Don Draper and working on North America. So I can kind of zoom in to what his business looks like. Alright. So I can see he’s got he’s got four frontline managers, and they’ve got their teams kinda split out between SMB all the way up to strategic reps. So I wanna understand a little bit of what the sales snapshot looks like today. Alright. Cool. Somebody has put together some some great KPIs on year to date metrics and current quarter metrics, and I can kinda look down here at some of the trend lines. I know in Power BI, I used to be able to, like, I had a couple drill paths I could go into. So I wonder if I can do that here. Oh, wow. There’s… Looks like I can I can look at almost any column and and dig into, dig into this data however I want? I I didn’t have to define what I wanted. So I’m just gonna drill in on on the quarter trends by country. Let’s see. Wow. That’s that’s cool. It gives me a whole map, a heat map of where my sales are. Alright. I’m getting distracted. I wanna understand a little bit more about what my snapshot of of sales looks like. I know right off the bat, I always like to focus on what’s what’s in the funnel and number of opportunities and what are we winning. So I see hovering over here, I can… There’s a little alert icon. I’m gonna set an alert here for number of opportunities. I wanna be able to look at this every single day and know whether my whether my number has gone up or down. Are people filling the pipeline, or is it emptying out? So I’m gonna go ahead and set this alert for myself. And I’m also going to add these to my watch list on that front page because I wanna be able to see that as soon as I open ThoughtSpot. But I… I’m I’m… I’ve never seen this kind of a button on a dashboard before. Let’s let’s look at what analyze change looks like. So it looks like ThoughtSpot is is digging into my data here and and maybe looking at some trends. Okay. So it is looking at all of the different attributes of that data that are continue… That are contributing to that drop in my pipeline. So it’s it’s giving me what the… I can look at it by status. I can look at it by opportunity stage. So it looks like my m ones are down. I know that that is a a new business meeting, so we’re not we’re not filling the very top of the funnel as much. Not quite as much of a drop in my mid stage S1. Something for me to dig into, but my boss I know likes to get deep into the details. So I’m gonna jump over to my forecast board and try to understand what what’s… What kind of weeds are we gonna get into this week with with my VP. Alright. So I have I have a view here of what my targets are, what’s in forecast, what’s been committed versus open pipeline, and what’s been booked so far. Now I can… I see… I can kind of pivot here on all of my different layers of the sales org. I only care about North America. So I’m just gonna click over here on the North America number. It’s gonna hide all of the other markets, and I can dig into my VP Don’s business. So I can see all of these roll ups of what’s in forecast and commit for each of his frontline managers, and I can dig into each of their businesses. I can see that Talia’s business is not not looking great, but Jasper’s is even worse. I’m gonna look a little bit deeper into this. So he has several reps that haven’t booked anything, and there’s nothing in their forecast. Maybe this is a new team. I’m… I don’t know well enough. I’m gonna have to go ask my ask my VP to help me understand what’s going on with that team, but I know it’s something I’m gonna have to keep an eye on. But if I zoom back out, Michelle’s team seems to be the big contributors. They’ve got, the most booked ACV, and they’ve got the highest target. So looks like they’re carrying the load for us, this this year. So I can go in and and look at who the top performers are and and who’s struggling a little bit on that team. It gives me a little bit of context as I scroll down here to my deal list. I I I know we’re gonna get deep into every single deal. This helps give… Helps me understand kinda where people fit in the overall org. But, wow, this this is incredible. All all of the information I used to have to go back and forth between Gong and Salesforce and my own pivot table is all right here. I can see what the deal is, who’s running it, what stage, all of that basic information. But it’s even pulling in MedPic info, and I can hover over and see the notes in there and click in to give me a full picture of all of that. That’s pretty powerful. I can even look at notes that the opportunity leader has put in. This is gonna be super helpful, but it’s a lot of information. And I don’t know where to start, but I have lots of questions. And then I remember that my boss said, spotter is your friend. And I see up here this little button spotter, and and and he told me that this was the AI analyst that’ll help me ask questions and get answers of what’s in my dashboards. So I’m gonna open that up, just to save us a little loading time. I’m gonna jump over. But, opening up Spotter, it it pulls in all of that context of that dashboard that I was looking at, this whole deal list. And so I’m not starting from a blank page. It knows what I was looking at and what what inspired my questions, and it’s gonna start with that context. And I went ahead and asked, who are the top five reps in North America with the most deals in pipeline this quarter? Because I just wanna know who’s who’s my top performers, who do I need to make sure I support, that that they are… They’re they’re gonna gonna be the ones that get us over the line. So it pulled up this report. It ranked my my top five by deal volume. That’s super helpful. And I noticed here that in line of the conversation, it’s it’s giving me all of the the filters and formulas that it built this on. So don’t have to know SQL to to understand how this chart was built. And I’m realizing that pipeline here means something very specific. It’s a very… It’s a forecast core… Category, not just a broad statement. So I actually wanna be able to see what is, what’s not just in pipeline, but what’s in best case and commits too. So in line of my conversation, I can go ahead and change that, and it’s gonna update my rankings. So Sienna had been number two in… When I was just looking at the pipeline forecast category. But when I include what’s in commit and best case, Lawrence comes up even with Sienna. So that’s pretty cool. I noticed that I can save this and come back to this data later so I don’t have to go recreate this whole conversation. Alright. So that’s that’s helpful. I can kinda understand when I’m looking at my deal list. I can see, alright, who’s who’s the rep down here and and look for some of those. I realized I haven’t filtered on North America. Let’s go ahead and filter that down because I don’t know who Genevieve is, and I don’t have to… I I don’t know if she’s on my team or not. Alright. Cool. But I know that the… What’s… What really matters is what’s gonna close and what isn’t. That… That’s what that’s what Don needs to be able to answer up to our CRO, and and he needs to be able to answer up to the board. So I need to understand what’s gonna close and what isn’t. Now I’ve I’ve been in sales ops long enough to know, like, alright. All of these blanks are not great, but I don’t see any indicator here. There’s no scoring. There’s no, likelihood of closing here. So I’m gonna go jump over to Spotter and see if Spotter can help me with that. So I’m gonna ask Spotter, again, it’s pulling in that that context, which deals in this quarter for North America are least likely to close on time or to be lost? So it’s gonna go run a deep analysis across all of that data. And in thirty nine seconds here, it pulls together an analysis of all 162 ops in North America and highlighted the ones that are at highest risk for slipping out of this quarter or being lost. And it’s giving me some of the high level detail and and some of the risk factors here. It’s even gonna give me a summary of the the risk patterns that it’s identifying and giving me some recommended actions here. That’s that’s super helpful because I I don’t know if I would know where to start. I don’t know if MEDDPIC is is important to this organization or if they show it but never fill it out. So this is giving me some some good areas to go dig into on my own. But I want to… I wanna I wanna… I want some data. I want something that tells me whether I can forecast this as likely to close or not. And I know that we have an amazing team of data scientists, so I’m gonna go ask them if they can teach me how to use Spotter to run more predictive forecasts or if they’ve built anything themselves to be able to do any deeper analysis and and more consistent prediction on these. So I’m going to hand it over to Madison, who’s gonna show us what that looks like on going into a predictive forecast. Thanks, Ryan. So let’s take this even further and step into the shoes of sales op mid quarter. Leadership comes to you and asks which of these open opportunities are realistically going to land before the quarter end. In that moment, you need a few things to answer that question. First, you need an objective score per deal, not just a rep in our stage name. Second, you need to know what is driving that score so managers can intervene. And third, you need to an… You need the answer today. You don’t have three weeks to wait for a custom modeling request to clear the data backlog. This is where standard reporting fails. A standard report is great at telling you what is currently sitting in the pipeline, but the question you’re being asked is what will close. Answering that question requires predictive modeling. Up until now, getting to predictions meant meant exporting data, firing up Python, or logging a formal ticket. But Spotter can now act as an agentic analyst built directly into the ThoughtSpot chat experience. Because it leverages standard machine learning libraries under the hood, nontechnical business users can ask it to execute full ML workflows using just plain English prompts. There are many types of data science prompts you can ask, like classification to help predict win or loss conversion likelihood based on historical deal traits, regression and forecasting to estimate continuous outcomes, as well as feature importance or guidance to both rank and inspect the features that carry the most predictive weight. Let’s pivot over to ThoughtSpot and see what this looks like when you walk through a a Spotter modeling session. Note that machine learning models can take a minute or two to run live. So rather than making you watch it, I’m just going to have this prerun session that I’ll walk you through. You can see the prompt I gave Spotter right up at the top. Develop a model that identifies opportunities that are most likely to convert using random forest. Leave 20% of opportunities out for testing and then create a likelihood score for each of those opportunities. Notice how natural that phrasing is. You don’t need to write Python code or anything else. Spotter parses that request, identifies historical deals closed, pulls out 20% of the dataset for testing, and trains a 100 tree random forest classifier. You can then look at how SWATER presents the results back to us. It immediately lays out the baseline metrics, reporting holdout accuracy, precision, and recall. This gives you an immediate gut check on model reliability. Precision tells you how often Spotter is right when it flags a win, and recall tells you what percentage of total winning deals is successfully caught. As we scroll down, Spotter automatically ranks the top drivers. It groups them and shows you exactly what variables are carrying the predictive weight. This is huge because it strips away the black box problem right away that we discussed earlier. Scroll further and Spotter categorizes the holdout deals into clear likelihood tiers, high, medium, and low. It even generates a complete downloadable scoring table as a CSV so that you can inspect individual opportunity IDs, predicted outcomes, and exact probability percentages, as well as gives us some business recommendations. Now I’m going to scroll further. There we go. Continuing further, Spotter doesn’t just stop at running the model. If I ask it, are there new features we could calculate to improve the model? It gives us a structured list of recommended feature engineering ideas. It suggests time based features, ratio metrics like deal size relative to company revenue and company maturity tiers. So Spotter gives us an incredible first read, but let’s pause and talk about the gap between exploration and production. Spotter answers what drivers matter right now in this chat session, but an exploratory chat response isn’t an enterprise production system. Spotter outputs live in the chat thread or in a local CSV export. It can’t automatically write those likelihood scores back into your governed database, nor can it set up an automated background job to rescore next week’s live pipeline. So to turn these insights into repeatable governed asset that the entire company can rely on every week, I’ll hand it over to Victoria from the data science team. Thanks, Madison. So like she said, I’m going to show you how we can take this model out of the chat and make it repeatable. We’re actually gonna start back in Spotter. Before we create a model, one of the first things we do as a data scientist is exploratory data analysis. This is where we get an idea of the trends in the data, see any outliers, and get a gut check on what the data looks like. In this spotter conversation, I am asking it to do this discovery for me with a mix of tables, summaries, and charts. It first gives me the overall win performance, which for here is 63%. Then we can see the win rate by sector, by region, and by product. And we can see how we can see the different products have different win rates. We can see which ones have the highest win rates and which ones have the lowest. Spotter also gives us a monthly trend, so we can see how our win rates change over time and month to month. At the bottom here, it’ll give us a summary, of what this data looks like. From here, we can start thinking about modeling. But before we do that, we wanna think about the features or variables we wanna include. Madison ended by asking Spotter for recommended features, and so we can do the same thing here. These recommendations I got from Swatter, which are very similar to what Madison got, I am using in today’s demo. I have two of them here, days to close, which is the length of the sales cycle and seasonality effect, which is that whether this is a quarter end. So I’m able to use these or any of these down the line to build my model. Now that we’re ready to build, I’ll actually jump back into the slides since it’s a little bit more technical here. In a notebook in analyst studio, we are able to create explicit defensible logic that can repeat on a schedule and a cadence so that the opportunity scores update on their own. These scores that I’m saying are that likelihood an opportunity will be one. So here’s the workflow from start to finish. First, we engineer the features from our conversation with Spotter so that they’re specific to our business. Next, we can do that same train and test split we did with that eighty twenty so we can see how the model performs on opportunities it hasn’t seen. We can also iterate through other algorithms. Spotter used a random forest. In a notebook, we can use Python to build logistic regression or gradient boosted model as an example. We can try different combinations of features to see which performs the best and which algorithm performs the best. One thing that’s great about a notebook in analyst studio is that we can see the code side by side with the results, which helps us in this next step, which is choosing the champion model. We can pick an accuracy metric and look at the models we iterated through to pick the one with the best overall accuracy. We can then use this champion model to score the opportunities and get the top drivers behind each score. We can also segment these scores into tiers. I’m gonna keep saying green, yellow, and red, so I wanna kind of explain what they mean. So for me, a green is an opportunity with a high probability of winning, something like 70% or more. A red would be something like a low likelihood the opportunity will win with under 40%, and yellow there somewhere in the middle. Finally, the last piece is we can take those information that we got from the model and publish it to a data model, which we can use in, live boards and, again, in Spotter. So we can use this throughout ThoughtSpot. So I’m actually gonna pass it back to Madison to show us those live boards. Thanks, Victoria. Now let’s look at how this changes the day to day experience on a live board. In the past, pipeline dashboards like what Ryan mentioned earlier only showed you current statuses. They showed total open pipeline dollars, target attainment metrics, and coverage ratios. That information is, of course, necessary, but it’s incomplete for our purposes. It shows you the volume of data, but not the actual health of the pipeline. Now look at this new live board to see how we elevate our original live board by joining our analyst studio model outputs directly into the dashboard. On the right hand visual, we aren’t just listing open opportunities. We are displaying our largest open deals ranked directly by the analyst studio opportunity score. Think about how this transforms leadership review. Instead of assuming a high dollar deal in stage three is a healthy deal, a VP can instantly see if the data science model has flagged it as a low score. Managers can also filter the entire LiveBoard down to only medium or low score high value opportunities that require immediate intervention this week. And if a manager wants to know why a key account dropped in score, they don’t have to leave this LiveBoard. They can now trigger Spotter directly on top of the LiveBoard data, prompt it to explain the score shift, and receive plain language explanations right on the spot. Furthermore, because row level security is enforced in the data model, regional directors only see scored deals for maybe their territory, and scheduled live board digests can automatically push weekly at risk summaries straight to leadership inboxes. Now I’m gonna hand it back to Victoria to show you what, exploring Elistido results look like in Spotter. Thanks, Madison. So now that we have this data in a data model, we can go back to Spotter to help us understand what these likelihoods mean. This is where we can make the numbers make sense. We can think about Spotter in three different personas. The first one being an executive who might wanna know the overall characteristics of the opportunities with high win rate… High win rates, or we can think about it as a manager, and wanting to understand the opportunities of their sales agents and how we might be able to coach them. So here in Spotter, I am acting as the manager’s summer, and Spotter is able to give me in quite a few different ways some key highlights. So I… To see who has the high average win probabilities across their opportunities and who might have some lower ones and need more attention. There’s a lot of detail here that Spotter provides us, but I think the big thing here is the next piece that I can ask Spotter as a manager, which is getting recommendations. How can I get coaching tips to help my team move their red and yellow opportunities to green? Which is what I ask right here with Spotter. It gives me recommendations around deal duration, deal size, different product mix, and, again, quite a lot of additional information with the final being a summary of a coaching playbook. So I can go to my meeting with my sales agent and provide helpful recommendations. The last persona that I wanna think about is if I am a sales, agent myself and I wanna understand my individual opportunity. I have one here as an example. It’s one in the yellow, and I wanna know what can I do to to improve its likelihood to be a win? The first thing that it notices is it’s a very new opportunity. So… And it’s not just gonna tell me it’s the… It’s a new opportunity and we… And green opportunities typically have a longer sales cycle. It’s gonna give me actions I can actually take to fill that sales cycle time. It’s gonna provide recommendations like discovery calls and technical deep dives. It’s also gonna show me how my product mix compares to green opportunities. In this case, it’s actually telling me that my deal size is just right. I don’t need to upsell or complicate things. I’m on track with where it is at now. And finally, we get, again, that nice summary of this is how we can improve and move the opportunity from the yellow to the green. So now we’ve gotten quite a bit of information. So I’m actually gonna pass it back to Madison to kinda give us an overview of where we started and how we got here to the end. Perfect. Thanks. So this brings us to the most important concept of the entire session, one continuous loop. Look at how all of these pieces fit together. First, analyst studio trains, validates, and scores machine learning models against live warehouse tables. Secondly, the ThoughtSpot data model holds those scores, risk tiers, and driver metrics as governed enterprise wide fields. Then live boards provide a standing automated command center that monitors pipeline health on a schedule rather than on an ad hoc request. And lastly, Spotter sits on top, letting anyone ask about scores and natural language. And here’s the best part. As business users chat with Spotter on top of these scored liveboards, Spotter uncovers new correlations and patterns that weren’t included in the original model. Those new insights become the exact feature requests that are handed back to the data science team. Then we can keep on iterating in the next, version of analyst studio. The system continuously learns from itself. So thank you all so much for your time. I’ll now hand it over to Ryan to cover the connected insights journey in ThoughtSpot. Okay. Thanks, Madison and Victoria. As as that sales ops person… You can go ahead and click through to to fill up the the slide. You know, this this gives me so many more tools now to be able to understand what’s happening in my pipeline beyond just what I can see on the surface and to know where I need to dig in and spend time and which reps might need a little bit more coaching on how to progress the deal in in the highest likelihood way to get it closed. So this is gonna help me a ton as I dig into all of those deals and know where to focus on my forecast prep call with my VP. I I think what’s pretty incredible is that I was able to dig in as a brand new person and understand what was happening. And then my data scientists were able to help me understand why it’s happening and what might happen in the future all in one platform. And it’s all governed by the same semantics in our semantic layer. And so from me exploring in a dashboards to getting some of these automated change analyses to asking questions in Spotter to even my, machine learning scientists writing Python code in the same platform, we know that there’s not gonna be gaps in how key metrics are defined, how we look at the data, what different business language means. And then I can I can use all of these different modes of getting insights delivered proactively to me with recommended actions so I know what I should do about it, not just kinda stare blankly at some numbers? So I hope that landed with you all. We we do have a q and a button over there. If if anybody has questions, please feel free to to chime in, and and we’ve got some time after we wrap up here with a couple more things. But wanna make sure we answer any of your questions. And please, you know, schedule some time with us to get into… If you wanna see what it looked like for Victoria to go write that… Write those models and run the testing and training in Analyst Studio, which is integrated into the ThoughtSpot platform, but something me as a business user will never have to interact with. Thank goodness, because Python scares me. But, you know, we we wanna get deeper into all of this with you if if you have any questions or or wanna see how how it was done in a bit more detail. And if you are curious about what… How this might be able to help you, I’m excited to announce a a special promo we have going on right now. So moving BI platforms is a big is a big ask. We know that. You you have contracts, you have workflows, you have the way people are are used to working. And we don’t want to feel our customers to feel like they’re pressured to make… To flip a switch. And so right now, we are offering up to a year free on a multiyear contract if you are migrating from from a legacy platform like Power BI or Qlik or Sisense and you wanna come over to ThoughtSpot, you won’t have to double pay for two platforms at once. You can have both running. You can can take your time to migrate over to build a seamless transition to hold people’s hands through the change of all of that. And you won’t be double paying. You’ll be able to take your time. That comes with our unlimited spotter usage. So no extra AI costs. You get unlimited AI value out of the platform without paying for additional tokens every time you ask a question. And you’ll get our hands on experts, which include our partners like Atrium on how to get started and make that transition happen smoothly. So please reach out if you think this might be something you guys would be interested in, and I’ll hand it back to Heather to talk a little bit about how Atrium can help you navigate this process. Yeah. We can’t let we can’t let Ryan and and ThoughtSpot have all the fun. So I think just the incredible value that you saw all in one platform from activation to modeling to answering questions real time and diving deep. I think we’re just so passionate about the business outcomes that this can drive for revenue leaders across the board. And again, can’t let Thoughts About have all the fun. So as we talked about a lot of the why, we want to help out with the how, so how we can get you ready for this type of transformation, this type of access, and these type of business outcomes with an offer to affix to what ThoughtSpot is so generously providing to webinar guests today with our own four weeks assessment. So this is really thinking about what your data environment looks like, how we can get you ready for a platform like ThoughtSpot, and really optimize that strategy across your data sources. We’re really excited for that today. And as Ryan spoke to, please feel free to reach out to anyone who’s on this webinar or directly to ThoughtSpot or Atrium with any questions you have. Kick the tires. Ask questions. Let’s see where this can be a meaningful driver for your business, and we’re happy to have those discussions real time. Okay. I think we’ve got some offline questions, but we did want to wrap it up. So we’ll make sure to do follow ups with all of you as as we continue. Thank you so much for joining. What a great, piece of content. Thank you, Ryan, Madison, Victoria. This has been so insightful, and we’re excited to connect with all of you, and figure out where ThoughtSpot can support. Yep. Absolutely. Feel free feel free to find me on LinkedIn. My email is is first dot… It’s Ryan.Harter@ThoughtSpot.com. You guys can reach out directly to me, but definitely definitely reach out and connect with the Atrium team, and and they can they can help you along the journey. Thanks so much.
