Hi there. I'm Austin White, an Analytics Consultant with InterWorks. Today, I'm gonna be demoing a pipeline forecasting app that I built in Sigma. This is designed for teams that are using CRMs like Salesforce and wanna leverage that data to support revenue forecasting. I'm excited to show you what I've built, so let's dive in. Let's unpack the problem we're trying to solve a bit further. This tool combines the governance of CRM data with the flexibility to make quick on the fly tweaks, giving users the best of both worlds. The use case here is a b to b sales team that wants to leverage their CRM data for forecasting but also needs flexibility under tight deadlines. With this app, we get Excel like flexibility powered by real CRM data, maintaining both control and agility in the forecasting process. It starts with applying some basic CRM rules for which opportunities to include, and then allows users to make updates directly in Sigma before submitting their forecast. The result is that everybody wins. Executives get instant access to forecast, sales teams can stay out of Excel, and analysts can avoid messy spreadsheet work and hours of cleaning up bad data. Finally, forecasts can be versioned and written back to your data warehouse for instant access across the organization. In today's demo, we're gonna be operating from the vantage point of late twenty twenty five, forecasting for q one twenty twenty six. Alright. Let's get started. When we first land in the app, we get a dashboard showing us our q one forecast against our revenue target. And for many BI tools, this is kinda where the work ends. We'd just be viewing static data. But in a Sigma app, we're inviting the user to actually make updates in real time and re visualize those results. By popping over to this create versions tab, you can start to review which opportunities are being forecasted in purple and those that are not in gray and making adjustments right here in the app. Rather than going back to the CRM under a tight deadline to make these changes, this is the kind of flexibility that would allow a user to manually include an opportunity and, similarly, manually exclude an opportunity. I could copy and paste these options for quicker selections like Excel and continue reviewing opportunities to adjust them. Let's go ahead and lock those in. We can see that we've made a net adjustment of two hundred and fifteen thousand, and we can re visualize what that does to our revised forecast right here. Then we can pop over and make batch adjustments. These are adjustments that we wanna make with less detail than an opportunity. So the user is asked to select a month, a geography, and a product. I'm gonna leave that product field blank just to show you that if we don't enter all the fields correctly, you know, this is the type of thing you can set up where the action sequence won't go through, and the user will be redirected to try again. So despite giving the user a lot of flexibility, we also have the guardrails in place to make sure they're doing things correctly. So let's add a product. Okay. So we've added a positive. Let's go ahead and now use these axes to set those fields a little bit faster, and I'm going to set a negative adjustment. Add some notes. Now we have a net decline of eighty thousand, but I wanna make an optimistic case for this demo. So let's do some put a large adjustment in custom services, I think is what I selected. Great. So now we've made some adjustments both at an opportunity level and at a batch level. We can see that we're now over our target, which is great. And if we want some additional visualization, we can see that in real time. Right? We can pop over and assess the impact to some KPIs from where we started with just the CRM data to this updated version and what changes we've made. We can even pop over back to the overview and re visualize that data from the changes that we've made. So, again, it's not just viewing static data. It's actually interacting with and editing that to re visualize in real time. Let's imagine that's just one version we wanna make, and we wanna be able to do some scenario analysis. So let's go ahead and set medium case. And again, another example of governance where I'm making sure that users aren't creating duplicate version names. And here, we can see the names already used, so it would not let them add that version if they try. Me put demo test instead just because easier one I know is not used, and we can see that name's available. Perfect. Let's go ahead and add this. So at this point, Sigma is working behind the scenes to take all of the work we did in that demo test version, move it into a new table, and allow for us to start over and do some scenario analysis. This is one of the things I've really enjoyed being a relatively new developer to Sigma is the buttons can queue multiple actions in a sequence that also follow strict rules and prevent the one to one button to action limitations that many other BI tools have that create more clunky user experiences. So dozens of actions just went through. It looks like successfully, and now we're back to our home screen with our demo test selected. So we could see all the different versions that had been, you know, already created basically, and we can continue on that process. But at some point, we're ready to do some additional scenario comparison and maybe even submit. So we can pop over here and get, at a glance, you know, three different versions we wanna mix and match to see how various KPIs look between them. So let's go ahead and we can see that medium case does in fact exist. That's why we got blocked before. And let's pick CRM baseline, which was a version I made that basically had no edits from the raw data coming out of CRM. We pop over to the review and submit tab. This is where all the versions that we've made can be seen, so there's five in total. And by editing this data, what I can do is decide whether or not submit a version. So we have two already selected. And if we add this third one, that is going to populate this table, which is our final output of a consolidated forecast. You can see our demo test is in here. Group to the month, geography, and product just as finance wants it. And what's super cool about this table is we can see this little green check mark here tells me that this data has already been written back to our data warehouse. So if I pop over to Snowflake, run a quick query, we can see the three versions we've selected to submit are here, and, of course, all of the detail exists for the whole organization to access in real time. So I think that's what makes this type of application a real game changer is that we're we're editing a lot within Sigma, but we're also creating something that's actually shareable throughout the organization outside platform itself. So it's really creating something more than just analyzing potential scenarios within a a limited scope app. I hope I've gotten you as excited about this as I am. This was super fun for me to put together, and I think there's many different forecasting type applications that could be created for many different business cases. Thank you so much for your time, and that's all I've got.