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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false},"responsivegrid":{"columnCount":12,"columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 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They have seen a notable decline in computer profitability over the last two to three months, and they need to fix it fast.\u003C/p\u003E\n","\u003Cp\u003EThat's where you come in.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Will Build\u003C/h3\u003E\n","\u003Cp\u003EIn this lab you will use Sigma Assistant to discover the right data from a plain-language question, enrich your analysis by loading external data directly into Snowflake, apply Snowflake Cortex AI functions to score thousands of unstructured records, use a Sigma Agent to generate price recommendations, and carry those recommendations into a collaborative AI App backed by a Snowpark forecasting model and a governed approval workflow.\u003C/p\u003E\n","\u003Ch3\u003EWhat You’ll Need\u003C/h3\u003E\n","\u003Cp\u003EThis lab uses a shared Snowflake account connected to a public Sigma account. Accessing this account is covered in a following section.\u003C/p\u003E\n","\u003Ch3\u003EWhat You’ll Build\u003C/h3\u003E\n","\u003Cp\u003EYou will build a retail pricing plan for a future quarter. You will leverage a Cortex Agent to start your analysis and then move into building up a financial model where you will configure a Cortex powered Sigma Agent to make recommendations on price changes. Finally you will use forecasting application in Sigma powered by a Snowpark forecasting model to review the recommendations and kick off an approval workflow. All on top of Sigma and Snowflake, without having to write a line of code.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n","\u003Cp\u003EA computer with a current browser. It does not matter which browser you want to use.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partners-snowflake-virtual-2026/sigma-footer.png?v=0684eb21\" alt=\"Footer\"\u003E\u003C/p\u003E\n","\u003Ch2\u003ESigma + Snowflake\u003C/h2\u003E\n","\u003Cp\u003EDuration: 5\u003C/p\u003E\n","\u003Cp\u003EMost Snowflake hands-on labs start by asking you to provision your own account, configure a virtual warehouse, and manage credentials. This lab skips all of that. You are working inside a shared, pre-configured Snowflake environment so you get right into the core of the lab: working with your data.\u003C/p\u003E\n","\u003Cp\u003EBefore we get into the analysis, it is worth taking two minutes to understand the architecture, because everything you do in this session is running on Snowflake.\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sigma-snowflake-architecture.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003ESigma is an agentic analytics platform built directly on top of the Snowflake AI Data Cloud. It does not extract your data or store a copy of it somewhere else. When you interact with Sigma, it generates optimized SQL on the fly, executes it on Snowflake compute, and surfaces the results in an interface that any business user can work in. Your governance policies are inherited automatically and nothing leaves your warehouse.\u003C/p\u003E\n","\u003Cp\u003EThe architecture diagram above shows three integration areas that are directly relevant to what you will experience today.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EData Warehouse.\u003C/strong\u003E Sigma can connect to anything you can write a SELECT against in Snowflake, including standard tables, Iceberg tables, Interactive Tables, and Snowflake Postgres. The dataset powering this lab lives in Snowflake. Sigma reads it live at query time with no intermediate copy and no additional latency layer. As Sigma puts it: if you can write a SELECT against it, Sigma can work with it.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESemantic Layer.\u003C/strong\u003E Sigma surfaces Snowflake Semantic Views and certified metrics directly in the interface. Business users see the concepts and definitions their data team has already built and approved, not raw table structures. This means what your AI returns and what your dashboards show are consistent with each other, which is harder to achieve than it sounds when you are scaling across teams.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAI + Agents.\u003C/strong\u003E This is where the partnership goes deepest. Sigma exposes Snowflake's AI capabilities without requiring code from your business users. In this lab you will use a Cortex Agent via Sigma Assistant to surface the right data from a plain-language question, Snowflake Cortex AI functions to run sentiment analysis across thousands of records in seconds, a Sigma Agent to reason across your performance data and write recommendations, and a Snowpark forecasting model to project the impact of your decisions. All of it runs on Snowflake. All of it happens inside Sigma's governed interface.\u003C/p\u003E\n","\u003Cp\u003EEverything you do today writes back to Snowflake, queries Snowflake, and respects the security model you already have in place. Sigma provides the interface and the intelligence layer. Snowflake provides the compute, the storage, and the governance foundation.\u003C/p\u003E\n","\u003Cp\u003EYour Snowflake investment is only as valuable as what your team can actually do with the data inside it. Most of that potential goes unrealized because the tools sitting on top of Snowflake still require SQL, still require data exports, and still leave business users one step removed from the answers they need. Sigma is built to close that gap, putting the full capability of Snowflake in front of every person who needs it. It is why Sigma has been Snowflake's BI Partner of the Year four years running. And it is why the teams that have made the bet on Sigma consistently report that it is the most effective way to turn their Snowflake platform into a business advantage.\u003C/p\u003E\n","\u003Cp\u003ENow let's put that to work.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partners-snowflake-virtual-2026/sigma-footer.png?v=0684eb21\" alt=\"Footer\"\u003E\u003C/p\u003E\n<!-- END OF SECTION-->\n","\u003Ch2\u003ESetup\u003C/h2\u003E\n","\u003Cp\u003EDuration: 5\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E1:\u003C/strong\u003E Use this link to navigate to \u003Ca href=\"https://app.sigmacomputing.com/snowflake-virtual-hol\"\u003ESigma\u003C/a\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E2:\u003C/strong\u003E Create an account and then enter your email address:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sfs-2026-01.png" width="700"/>\u003C/p\u003E\n","\u003Cp\u003E<aside class="positive">\n<strong>IMPORTANT:</strong><br> Do not sign up for a new Sigma trial for this lab! Use only the URL and instructions provided by your lab facilitator.\n</aside>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E3:\u003C/strong\u003E Check your inbox for a message from \u003Ccode\u003Einfo@send.sigmacomputing.com\u003C/code\u003E and continue the sign-up process by clicking the link in that email.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E4:\u003C/strong\u003E Enter your information as prompted and click \u003Ccode\u003ESubmit\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E5:\u003C/strong\u003E Once you see the homepage, you’re ready to proceed!\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sfv-2026-02.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partners-snowflake-virtual-2026/sigma-footer.png?v=0684eb21\" alt=\"Footer\"\u003E\u003C/p\u003E\n<!-- END OF SECTION-->\n","\u003Ch2\u003ESigma Assistant\u003C/h2\u003E\n","\u003Cp\u003EDuration: 5\u003C/p\u003E\n","\u003Cp\u003ETo understand profitability for this year’s product lineup, we need insight into current performance. As new users at Big Buys, we don’t know the data’s location. Let’s use Sigma Assistant to get started.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E1:\u003C/strong\u003E Open \u003Ccode\u003ESigma Assistant\u003C/code\u003E from the left sidebar to ask a question and get a starting point for analysis:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sfv-2026-03.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E2:\u003C/strong\u003E In dropdown select the \u003Ccode\u003EBIG_BUYS_OFFICIAL\u003C/code\u003E Cortex Agent. This will route our question directly to the agent instead of Sigma's default Assistant (which is still powered by Cortex)\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sfv-2026-04a.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E3:\u003C/strong\u003E In the text box, type: \u003Ccode\u003EHow are computer products performing across regions?\u003C/code\u003E and click the black arrow:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sfv-2026-04b.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003E<aside class="negative">\n<strong>NOTE:</strong><br> The response comes from a Cortex Agent, so your output may differ slightly from this guide.\n</aside>\u003C/p\u003E\n","\u003Cp\u003ENotice that the Cortex Agent is suggesting we use the \u003Ccode\u003EBIG_BUYS_SALES_ANALYST\u003C/code\u003E Cortex Analyst, and that it walks through its reasoning.\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sfv-2026-05.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003EAs the agents responds it provides both top level analysis and a breakdown of how it built the output.\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sfv-2026-06.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E4:\u003C/strong\u003E With your cursor over the table, click the \u003Ccode\u003EExplore\u003C/code\u003E icon in the top-right corner to explore the data in a spreadsheet interface and begin analysis:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sfv-2026-07.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partners-snowflake-virtual-2026/sigma-footer.png?v=0684eb21\" alt=\"Footer\"\u003E\u003C/p\u003E\n<!-- END OF SECTION-->\n","\u003Ch2\u003EFind Underperforming Brands\u003C/h2\u003E\n","\u003Cp\u003EDuration: 5\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E1:\u003C/strong\u003E To ensure that we are at the same spot, make sure that your table has the same columns in the grouping the one pictured here. If there are any missing ones, drag them into the calculations under the store region grouping. Lets also rename the columns to: \u003Ccode\u003ETotal Revenue\u003C/code\u003E, \u003Ccode\u003ETotal Profit\u003C/code\u003E, \u003Ccode\u003ETotal COGS\u003C/code\u003E, and \u003Ccode\u003EProfit Margin\u003C/code\u003E. We should also use the formatting bar to set three columns to use \u003Ccode\u003ECurrency\u003C/code\u003E and \u003Ccode\u003EProfit Margin\u003C/code\u003E to \u003Ccode\u003E%\u003C/code\u003E:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sfv-2026-08.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003E<aside class="negative">\n<strong>NOTE:</strong><br> In this case the agent decided to multiply the profit margin by 100 so that the default output would not show as a percent. We need to modify the formula to remove the unneeded step in the calculation.\n</aside>\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sfv-2026-08b.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E2:\u003C/strong\u003E The first thing that we noticed is the \u003Ccode\u003ESouthwest\u003C/code\u003E is the lowest performing region out of the whole Big Buys network of stores.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E3:\u003C/strong\u003E Let's right click and keep only the \u003Ccode\u003ESouthwest\u003C/code\u003E region:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sf2025-9.png" width="500"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E4:\u003C/strong\u003E Next, let's drag in the \u003Ccode\u003EBrand\u003C/code\u003E column to the grouping level on the right hand side:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sf2025-10.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E5:\u003C/strong\u003E Now let's sort the \u003Ccode\u003EProfit margin\u003C/code\u003E column descending so that our highest margin brands in the \u003Ccode\u003ESouthwest\u003C/code\u003E region are at the top and the lowest ones are at the bottom:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sf2025-11.png" width="550"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E6:\u003C/strong\u003E We should add in some conditional formatting to make this easier to look at. Let's go ahead and right click \u003Ccode\u003EConditional formatting\u003C/code\u003E on the column and add in data bars from the configuration pane on the right side:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sf2025-12.png" width="600"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E7:\u003C/strong\u003E With a little bit of color, we can easily see there are certain brands that are losing us money and others are performing great. To improve overall profitability, we could drop some brands but before we do that, let's inform our decision with some additional outside data:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sf2025-13.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E8:\u003C/strong\u003E Luckily we have a CSV of product reviews that we are able to load into Sigma and directly leverage in our analysis.\u003C/p\u003E\n","\u003Cp\u003ESince we are using Sigma on top of Snowflake, we don't even have to wait for our data team to load the data in. We can do it right here right now.\u003C/p\u003E\n","\u003Cp\u003E<button>\u003Ca href=\"https://sigma-quickstarts-main.s3.us-west-1.amazonaws.com/csv/Product_Reviews_Big_Buys.csv\"\u003EDownload the Product Reviews CSV\u003C/a\u003E</button>\u003C/p\u003E\n","\u003Cp\u003ELet's click on the table button on the left side of the \u003Ccode\u003EElement bar\u003C/code\u003E:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sf2025-15.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003EThen select the CSV option:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sf2025-16.png" width="400"/>\u003C/p\u003E\n","\u003Cp\u003EFrom there we can drag and drop or browse to find the CSV file we downloaded:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sf2025-17.png" width="300"/>\u003C/p\u003E\n","\u003Cp\u003ESigma reads the file so we can review it and then click \u003Ccode\u003ESave\u003C/code\u003E:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sf2025-18.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E9:\u003C/strong\u003E With these 5000+ product reviews, we need to create a quantitative score to leverage in our analysis. So, we could either go one by one and write our own score or let's just leverage Snowflake Cortex to go through and provide us a sentiment analysis on each review.\u003C/p\u003E\n","\u003Cp\u003ERight click the \u003Ccode\u003EReview\u003C/code\u003E column and select \u003Ccode\u003EAdd column\u003C/code\u003E:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sf2025-19.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E10:\u003C/strong\u003E With the new column selected go to the formula bar and type in:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-copy-code\"\u003Esentiment(Review)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou may want to reduce the decimal places shown too:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sf2025-20.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E11:\u003C/strong\u003E Now we need to tie this to our \u003Ccode\u003EBIG_BUYS_POS\u003C/code\u003E table. In Excel, you can do this with a Vlookup. Luckily Sigma has the same paradigm. On the \u003Ccode\u003EBIG_BUYS_POS\u003C/code\u003E table right click \u003Ccode\u003EBrand\u003C/code\u003E and select \u003Ccode\u003EAdd column via lookup\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sf2025-21.png" width="550"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E12:\u003C/strong\u003E In the modal, select the \u003Ccode\u003ESentiment\u003C/code\u003E column as the column to add, set the aggregate to \u003Ccode\u003EAverage\u003C/code\u003E, You will notice that brand has automatically been matched to \u003Ccode\u003EBrand\u003C/code\u003E for us.\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sf2025-22.png" width="400"/>\u003C/p\u003E\n","\u003Cp\u003EOnce you click \u003Ccode\u003EDone\u003C/code\u003E, you'll notice that the sentiment scores have now been averaged and tied to their respective brands.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E13:\u003C/strong\u003E With our new looked up column selected, rename it to \u003Ccode\u003EAvg Sentiment\u003C/code\u003E and go to the top bar and hit the decreased decimal point button twice so that we have a simpler number.\u003C/p\u003E\n","\u003Cp\u003EYou may have also noticed that Sigma lets you see the formula that the \u003Ccode\u003EAdd column via lookup\u003C/code\u003E created for us:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sf2025-23.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003EThen right click the \u003Ccode\u003ESentiment\u003C/code\u003E column and select \u003Ccode\u003EConditional formatting\u003C/code\u003E. This time let's add a color scale:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sf2025-24.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E14:\u003C/strong\u003E Now, just a few minutes later, we have a strong understanding of which brands are our least profitable ones, and which brands are preferred by our customer base.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partners-snowflake-virtual-2026/sigma-footer.png?v=0684eb21\" alt=\"Footer\"\u003E\u003C/p\u003E\n<!-- END OF SECTION-->\n","\u003Ch2\u003EMock Up Price Changes\u003C/h2\u003E\n","\u003Cp\u003EDuration: 5\u003C/p\u003E\n","\u003Cp\u003EThe next step is to set up the Price Changes linked input table that our Sigma Agent will populate with recommendations.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E1:\u003C/strong\u003E From the \u003Ccode\u003EProduct_Reviews_Big_Buys.csv\u003C/code\u003E table, select the create child element icon and the select \u003Ccode\u003ELinked input table\u003C/code\u003E:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sfv-2026-25.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E2:\u003C/strong\u003E From there, click the \u003Ccode\u003EChoose column(s) to make unique row identifiers\u003C/code\u003E drop down, and select \u003Ccode\u003EBrand\u003C/code\u003E. Finally click the \u003Ccode\u003ECreate input table\u003C/code\u003E button.\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sfv-2026-26.png" width="400"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E3:\u003C/strong\u003E Rename the input table to \u003Ccode\u003EPrice Changes\u003C/code\u003E, and the default \u003Ccode\u003EText\u003C/code\u003E column to comments by double clicking on the respective names:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sfv-2026-27.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E4:\u003C/strong\u003E Right click the \u003Ccode\u003EBrand Column\u003C/code\u003E and select \u003Ccode\u003EAdd new column\u003C/code\u003E then click on \u003Ccode\u003ENumber\u003C/code\u003E. Rename this new column \u003Ccode\u003EPrice Change\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sfv-2026-28.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E5:\u003C/strong\u003E Now on the \u003Ccode\u003EBrand\u003C/code\u003E column on the \u003Ccode\u003EBIG_BUYS_POS\u003C/code\u003E table, right click and select \u003Ccode\u003EAdd column via lookup\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EChoose the \u003Ccode\u003EPrice Changes\u003C/code\u003E table that we just created and set the \u003Ccode\u003EColumn to add\u003C/code\u003E to  \u003Ccode\u003EPrice Change\u003C/code\u003E with \u003Ccode\u003EBrand\u003C/code\u003E set as the matching key:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sf2025-31.png" width="400"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E6:\u003C/strong\u003E Rename the new column to \u003Ccode\u003EPrice Change\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EThese values should show up as \u003Ccode\u003ENull\u003C/code\u003E right now since we havent entered any changes, but now we're ready to build our model:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sfv-2026-29.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E7:\u003C/strong\u003E In some cases the AI does something unexpected and we don't catch it right away. In our case we just noticed it did not group the \u003Ccode\u003ECOGS\u003C/code\u003E column to create \u003Ccode\u003ETotal COGS\u003C/code\u003E as might be expected, but that is easy to correct.\u003C/p\u003E\n","\u003Cp\u003EWe can drag \u003Ccode\u003ECOGS\u003C/code\u003E into the grouping after the \u003Ccode\u003ETotal Profit\u003C/code\u003E column and rename it to \u003Ccode\u003ETotal COGS\u003C/code\u003E:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sf2025-35.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003E<aside class="negative">\n<strong>NOTE:</strong><br> If "Total COGS" is present in your table, ignore the instructions to add it manually.\n</aside>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E8:\u003C/strong\u003E We want to compare the original gross margin to the price change.\u003C/p\u003E\n","\u003Cp\u003EAdd a new column to the right of the \u003Ccode\u003EPrice change\u003C/code\u003E column and rename it \u003Ccode\u003EAdjusted profit margin\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003ESet its formula to:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-copy-code\"\u003E(([Total Revenue]*(1+[Price Change])) -[Total COGS])/((1+[Price Change])*[Total Revenue])\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EChange the column format to \u003Ccode\u003EPercentage\u003C/code\u003E. Note this column will still be Null as we have not entered any price changes yet:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sfv-2026-30.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003EWe are ready to make a visualization.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E9:\u003C/strong\u003E Click the \u003Ccode\u003EAdd child element\u003C/code\u003E button in the top right of the table and select \u003Ccode\u003EChart\u003C/code\u003E:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sf2025-36.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E10:\u003C/strong\u003E Drag \u003Ccode\u003EBrand\u003C/code\u003E to the \u003Ccode\u003EX axis\u003C/code\u003E and the \u003Ccode\u003EProfit Margin\u003C/code\u003E to the \u003Ccode\u003EY-Axis\u003C/code\u003E. Then change the chart to be vertically aligned by swapping the axes:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sf2025-37.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E11:\u003C/strong\u003E Add the \u003Ccode\u003EAdjusted profit margin\u003C/code\u003E column to the \u003Ccode\u003EX axis\u003C/code\u003E and select \u003Ccode\u003EUnstacked\u003C/code\u003E for the bar chart style, but nothing should new show up since we still have not added price changes.\u003C/p\u003E\n","\u003Cp\u003ENow we’re ready to generate our price recommendations using a Sigma Agent.\u003C/p\u003E\n","\u003Ch2\u003ESigma Agent Pricing Recommendations\u003C/h2\u003E\n","\u003Cp\u003EDuration: 10\u003C/p\u003E\n","\u003Cp\u003ERather than manually entering price adjustments, we’ll use a Sigma Agent to analyze our brand performance data and generate recommendations based on both the profit margin and sentiment data we’ve gathered.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E1:\u003C/strong\u003E In the element tray at the bottom of the screen select \u003Ccode\u003EUI\u003C/code\u003E then click \u003Ccode\u003EChat\u003C/code\u003E. This will create a chat element where we can showcase our agent.\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sfv-2026-31.png" width="600"/>\u003C/p\u003E\n","\u003Cp\u003EThen select \u003Ccode\u003ECreate New Agent\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sfv-2026-32.png" width="400"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E2:\u003C/strong\u003E The Configure Agents window should now be open. This is where we can provide Sigma instructions and tools for the agent to do work for us. First we need to give the agent access to two data sources. In the \u003Ccode\u003EData Sources\u003C/code\u003E section click add and select \u003Ccode\u003EBIG_BUYS_POS\u003C/code\u003E and then \u003Ccode\u003EPrice Changes\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sfv-2026-33.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E3:\u003C/strong\u003E Next we have to give the agent a tool to use to update our prices changes in the input table. Click the add tool icon and then select \u003Ccode\u003EActions\u003C/code\u003E:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sfv-2026-34.png" width="400"/>\u003C/p\u003E\n","\u003Cp\u003EThen in the \u003Ccode\u003EAction\u003C/code\u003E type dropdown select \u003Ccode\u003EUpdate row(s)\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sfv-2026-35.png" width="600"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E4:\u003C/strong\u003E Configure the Update row(s) action to work on the \u003Ccode\u003EPrice Changes\u003C/code\u003E linked input table as a \u003Ccode\u003ESingle row\u003C/code\u003E update. Ensure all columns have been added by clicking the \u003Ccode\u003EAdd column\u003C/code\u003E button at the bottom of the modal and configure all columns to be set by \u003Ccode\u003EAgent input\u003C/code\u003E with a value of the column's name.\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sfv-2026-36.png" width="400"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E5:\u003C/strong\u003E Rename the newly created tool to \u003Ccode\u003EUpdate Price Change\u003C/code\u003E by clicking on the pencil icon in the top middle of the modal.\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sfv-2026-37.png" width="600"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E6:\u003C/strong\u003E Finally copy the instructions below into the \u003Ccode\u003EInstructions\u003C/code\u003E panel.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-copy-code\"\u003ERole\nYou are a pricing analyst assistant. Your job is to review brand-level performance data and write price change recommendations into the Price Changes linked input table, one brand at a time.\n\nData Available\nYou have access to two tables in this workbook:\n\nThe BIG_BUYS_POS table, which contains Brand, Profit Margin, and Avg Sentiment for each brand in the Southwest region.\n\nThe Price Changes linked input table, which has a Brand column, a Price Change % column (expressed as a decimal, e.g. 0.07 for 7%), and a Comment column.\n\nStep 1: Read and decide\nRead all rows from the BIG_BUYS_POS table in a single pass. For every brand, determine its Price Change % value and Comment using the logic below. Record your complete decision list internally before making any writes. Do not call the Update Price Change tool until every brand has a recommendation.\n\nStep 2: Determine a recommendation for each brand\nUse Profit Margin and Avg Sentiment as your two primary signals. Weight margin more heavily for brands in distress (near zero or negative). Weight sentiment more heavily when deciding between holding and a modest increase for healthy brands. Treat the two signals together rather than applying fixed thresholds. A brand at 27% margin with 0.29 sentiment should be treated differently than one at 27% margin with -0.73 sentiment, even if both fall below a 0.3 cutoff. Recommend the price change a thoughtful analyst would defend to their manager.\n\nStep 3: Write all rows\nOnce your full decision list is complete, work through it and call the Update Price Change tool for each brand back to back without pausing to re-read or re-reason. For each update write:\n\nThe Price Change % value as a decimal rounded to two decimal places.\n\nA Comment of one to two sentences that references the specific Profit Margin and Avg Sentiment values and explains the recommendation. Example: "Profit margin of 4.2% is below threshold and avg sentiment of 0.68 indicates strong customer preference. Recommending a 7% price increase to improve profitability while demand remains favorable."\n\nOne tool call per brand, in order, until all brands are complete. Do not skip any.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003E7:\u003C/strong\u003E Let's reorganize the page so that the \u003Ccode\u003EPrice Changes\u003C/code\u003E input table is side-by-side with the agent, and the graph we made earlier is directly above them. Note you can move elements by clicking on the 6 dots icon in the top right of the element and resize them by hovering over the edge and having your cursor turn into arrows.\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sfv-2026-38.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E8:\u003C/strong\u003E Lets ask the agent to \u003Ccode\u003EReview product performance and recommend price changes\u003C/code\u003E by putting that in the chat box and clicking the arrow icon.\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sfv-2026-39.png" width="600"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E9:\u003C/strong\u003E The agent completes its full analysis first, then executes all writes in quick succession. You should start to see pricing recommendations populating, and the data points showing up in the lookup columns in the \u003Ccode\u003EBIG_BUYS_POS\u003C/code\u003E table and the graph.\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sfv-2026-40.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003EOnce the agent has finished, review its recommendations. Notice how it has balanced margin recovery with customer sentiment to arrive at each suggested price change. You can adjust any values you disagree with before moving on.\u003C/p\u003E\n","\u003Cp\u003EWhen you’re ready, click \u003Ccode\u003ESave As\u003C/code\u003E and save the workbook as \u003Ccode\u003EBig Buys - {first name_last name}\u003C/code\u003E. This naming format helps us identify your work if others need to review it.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partners-snowflake-virtual-2026/sigma_footer.png?v=0684eb21\" alt=\"Footer\"\u003E\u003C/p\u003E\n<!-- END OF SECTION-->\n","\u003Ch2\u003EUse Big Buys’ corporate profit planning application\u003C/h2\u003E\n","\u003Cp\u003EDuration: 5\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E1:\u003C/strong\u003E Let's navigate to the home page and open up the \u003Ccode\u003EBig Buys Profit Planning Tool\u003C/code\u003E, from the home page.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E2:\u003C/strong\u003E This application was built by Big Buys' central IT team as a Sigma AI App. It allows team members to submit and approve distinct pricing scenarios collaboratively.\u003C/p\u003E\n","\u003Cp\u003EWe’re going to create our own pricing scenario based on what we just learned. Let's click the \u003Ccode\u003ECreate New Scenario\u003C/code\u003E button in the top left:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sf2025-26.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E3:\u003C/strong\u003E Provide a name for your new scenario and type in a quick description. Then click \u003Ccode\u003ECreate\u003C/code\u003E and let's go plug in some numbers:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sf2025-27.png" width="600"/>\u003C/p\u003E\n","\u003Cp\u003ESince this profit planning tool is shared with all category managers at Big Buys, it’s not filtered by default to any specific product type.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E4:\u003C/strong\u003E Let's go ahead and filter it to \u003Ccode\u003EComputers\u003C/code\u003E and then set the \u003Ccode\u003EStore Region\u003C/code\u003E to \u003Ccode\u003ESouthwest\u003C/code\u003E:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sf2025-28.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E5:\u003C/strong\u003E After applying the filters, you'll see the profit margin over time for that subset, along with a Snowpark forecasting model:\n<img src="assets/sf2025-29.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003EThis is our third example of leveraging Snowflake Cortex AI functions directly inside of Sigma to open up new possibilities for real-time analytics.\u003C/p\u003E\n","\u003Cp\u003EWe are now ready to hop in and apply some of our price adjustments from our scratch pad.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E6:\u003C/strong\u003E Return to your \u003Ccode\u003EBig Buys - {your name}\u003C/code\u003E workbook. Sort the \u003Ccode\u003EPrice Changes\u003C/code\u003E input table ascending by the \u003Ccode\u003EBrand\u003C/code\u003E column, and copy all values from the \u003Ccode\u003EPrice Change\u003C/code\u003E column:\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sf2025-39.png" width="500"/>\u003C/p\u003E\n","\u003Cp\u003EGo back to the \u003Ccode\u003EBig Buys Profit Planning Tool\u003C/code\u003E > \u003Ccode\u003EPrice Adjustments\u003C/code\u003E table, click \u003Ccode\u003EEdit Data\u003C/code\u003E, sort the table ascending on the \u003Ccode\u003EBrand\u003C/code\u003E column and paste the values into the \u003Ccode\u003EPrice Change (%)\u003C/code\u003E column.\u003C/p\u003E\n","\u003Cp\u003E<aside class="negative">\n<strong>NOTE:</strong><br> Click the first cell in the "Price Change" column, then scroll to the bottom of the table. Hold down the Shift key and click the last cell in the same column.\n</aside>\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sf2025-40.png" width="500"/>\u003C/p\u003E\n","\u003Cp\u003E<aside class="negative">\n<strong>NOTE:</strong><br> To copy, press Ctrl+C on Windows and Command+C on macOS. To paste, press Ctrl+V on Windows and Command+V on macOS. \n</aside>\u003C/p\u003E\n","\u003Cp\u003EClick \u003Ccode\u003ESave\u003C/code\u003E on the input table.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E7:\u003C/strong\u003E After entering the data, you should see a loading bar start. The Snowpark forecasting model will process your changes and show real-time results based on Snowflake’s projections.\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/sf2025-41.png" width="800"/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E8:\u003C/strong\u003E With our scenario now loaded in, we are ready to go ahead submit it for approval because Sigma AI Apps allow for multi step workflows.\u003C/p\u003E\n","\u003Cp\u003EOnce we click approve, this can go into a queue that our manager or compliance officers could go through and review but for the purpose of this lab, we can just approve our own data.\u003C/p\u003E\n","\u003Cp\u003EI mean, we obviously know it's right, so first we'll click \u003Ccode\u003ESubmit for Approval\u003C/code\u003E in the top right and submit our scenario.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E9:\u003C/strong\u003E Finally, click \u003Ccode\u003EReview Submission\u003C/code\u003E, select your scenario, and click \u003Ccode\u003EApprove\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partners-snowflake-virtual-2026/sigma-footer.png?v=0684eb21\" alt=\"Footer\"\u003E\u003C/p\u003E\n<!-- END OF SECTION-->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003EDuration: 5\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n","\u003Cp\u003EIn this QuickStart, you used Sigma and Snowflake to take a business question all the way from initial discovery to a governed, approved decision. You used Sigma Assistant backed by a Cortex Agent to find the right data from a plain-language question, applied Snowflake Cortex AI functions to score thousands of product reviews in seconds, built a Sigma Agent to reason across performance data and generate price recommendations, and modeled the impact using a Snowpark forecasting model before submitting for approval through a Sigma AI App.\u003C/p\u003E\n","\u003Cp\u003EAlong the way, you joined data from multiple sources, loaded external data directly into Snowflake, and wrote recommendations back through governed Input Tables. None of it required SQL.\u003C/p\u003E\n","\u003Cp\u003EThe retail scenario is just the vehicle. The pattern of using Sigma and Snowflake to move from question to governed action applies across every industry and every team that runs on data.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAdditional Resource Links\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://www.sigmacomputing.com/blog/\"\u003EBlog\u003C/a\u003E<br>\n\u003Ca href=\"https://community.sigmacomputing.com/\"\u003ECommunity\u003C/a\u003E<br>\n\u003Ca href=\"https://help.sigmacomputing.com/hc/en-us\"\u003EHelp Center\u003C/a\u003E<br>\n\u003Ca href=\"https://quickstarts.sigmacomputing.com/\"\u003EQuickStarts\u003C/a\u003E<br>\u003C/p\u003E\n","\u003Cp\u003EBe sure to check out all the latest developments at \u003Ca href=\"https://quickstarts.sigmacomputing.com/firstfridayfeatures/\"\u003ESigma's First Friday Feature page!\u003C/a\u003E\n<br>\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://twitter.com/sigmacomputing\"\u003E<img src="./assets/twitter.png" width="75"/>\u003C/a\u003E \n\u003Ca href=\"https://www.linkedin.com/company/sigmacomputing\"\u003E<img src="./assets/linkedin.png" width="75"/>\u003C/a\u003E \n\u003Ca href=\"https://www.facebook.com/sigmacomputing\"\u003E<img src="./assets/facebook.png" width="75"/>\u003C/a\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partners-snowflake-virtual-2026/sigma-footer.png?v=0684eb21\" alt=\"Footer\"\u003E\u003C/p\u003E\n<!-- END OF WHAT WE COVERED -->\n<!-- END OF QUICKSTART -->"],"description":"","title":"From Insight to Action: Agentic Analytics with Sigma and Snowflake",":items":{},":itemsOrder":[],":type":"snowflake-site/components/contentfragment","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"## Overview \nDuration: 5 \n\nIn this lab, you'll step into the role of a business analyst at a retail company investigating a performance problem hiding in your data.\n\nYour new employer, Big Buys Electronics (\"Big Buys\"), is a major electronics retailer. They have seen a notable decline in computer profitability over the last two to three months, and they need to fix it fast.\n\nThat's where you come in.\n\n### What You Will Build\nIn this lab you will use Sigma Assistant to discover the right data from a plain-language question, enrich your analysis by loading external data directly into Snowflake, apply Snowflake Cortex AI functions to score thousands of unstructured records, use a Sigma Agent to generate price recommendations, and carry those recommendations into a collaborative AI App backed by a Snowpark forecasting model and a governed approval workflow.\n\n### What You’ll Need\nThis lab uses a shared Snowflake account connected to a public Sigma account. Accessing this account is covered in a following section.\n\n### What You’ll Build\nYou will build a retail pricing plan for a future quarter. You will leverage a Cortex Agent to start your analysis and then move into building up a financial model where you will configure a Cortex powered Sigma Agent to make recommendations on price changes. Finally you will use forecasting application in Sigma powered by a Snowpark forecasting model to review the recommendations and kick off an approval workflow. All on top of Sigma and Snowflake, without having to write a line of code. \n\n### Prerequisites\nA computer with a current browser. It does not matter which browser you want to use.\n\n![Footer](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partners-snowflake-virtual-2026/sigma-footer.png?v=0684eb21)\n\n## Sigma + Snowflake\nDuration: 5\n\nMost Snowflake hands-on labs start by asking you to provision your own account, configure a virtual warehouse, and manage credentials. This lab skips all of that. You are working inside a shared, pre-configured Snowflake environment so you get right into the core of the lab: working with your data.\n\nBefore we get into the analysis, it is worth taking two minutes to understand the architecture, because everything you do in this session is running on Snowflake.\n\n\u003Cimg src=\"assets/sigma-snowflake-architecture.png\" width=\"800\"/\u003E\n\nSigma is an agentic analytics platform built directly on top of the Snowflake AI Data Cloud. It does not extract your data or store a copy of it somewhere else. When you interact with Sigma, it generates optimized SQL on the fly, executes it on Snowflake compute, and surfaces the results in an interface that any business user can work in. Your governance policies are inherited automatically and nothing leaves your warehouse.\n\nThe architecture diagram above shows three integration areas that are directly relevant to what you will experience today.\n\n**Data Warehouse.** Sigma can connect to anything you can write a SELECT against in Snowflake, including standard tables, Iceberg tables, Interactive Tables, and Snowflake Postgres. The dataset powering this lab lives in Snowflake. Sigma reads it live at query time with no intermediate copy and no additional latency layer. As Sigma puts it: if you can write a SELECT against it, Sigma can work with it.\n\n**Semantic Layer.** Sigma surfaces Snowflake Semantic Views and certified metrics directly in the interface. Business users see the concepts and definitions their data team has already built and approved, not raw table structures. This means what your AI returns and what your dashboards show are consistent with each other, which is harder to achieve than it sounds when you are scaling across teams.\n\n**AI + Agents.** This is where the partnership goes deepest. Sigma exposes Snowflake's AI capabilities without requiring code from your business users. In this lab you will use a Cortex Agent via Sigma Assistant to surface the right data from a plain-language question, Snowflake Cortex AI functions to run sentiment analysis across thousands of records in seconds, a Sigma Agent to reason across your performance data and write recommendations, and a Snowpark forecasting model to project the impact of your decisions. All of it runs on Snowflake. All of it happens inside Sigma's governed interface.\n\nEverything you do today writes back to Snowflake, queries Snowflake, and respects the security model you already have in place. Sigma provides the interface and the intelligence layer. Snowflake provides the compute, the storage, and the governance foundation.\n\nYour Snowflake investment is only as valuable as what your team can actually do with the data inside it. Most of that potential goes unrealized because the tools sitting on top of Snowflake still require SQL, still require data exports, and still leave business users one step removed from the answers they need. Sigma is built to close that gap, putting the full capability of Snowflake in front of every person who needs it. It is why Sigma has been Snowflake's BI Partner of the Year four years running. And it is why the teams that have made the bet on Sigma consistently report that it is the most effective way to turn their Snowflake platform into a business advantage.\n\nNow let's put that to work.\n\n![Footer](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partners-snowflake-virtual-2026/sigma-footer.png?v=0684eb21)\n\u003C!-- END OF SECTION--\u003E\n\n## Setup\nDuration: 5\n\n**1:** Use this link to navigate to [Sigma](https://app.sigmacomputing.com/snowflake-virtual-hol)\n\n**2:** Create an account and then enter your email address:\n\n\u003Cimg src=\"assets/sfs-2026-01.png\" width=\"700\"/\u003E\n\n\u003Caside class=\"positive\"\u003E\n\u003Cstrong\u003EIMPORTANT:\u003C/strong\u003E\u003Cbr\u003E Do not sign up for a new Sigma trial for this lab! Use only the URL and instructions provided by your lab facilitator.\n\u003C/aside\u003E\n\n**3:** Check your inbox for a message from `info@send.sigmacomputing.com` and continue the sign-up process by clicking the link in that email.\n\n**4:** Enter your information as prompted and click `Submit`.\n\n**5:** Once you see the homepage, you’re ready to proceed!\n\n\u003Cimg src=\"assets/sfv-2026-02.png\" width=\"800\"/\u003E\n\n![Footer](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partners-snowflake-virtual-2026/sigma-footer.png?v=0684eb21)\n\u003C!-- END OF SECTION--\u003E\n\n## Sigma Assistant\nDuration: 5\n\nTo understand profitability for this year’s product lineup, we need insight into current performance. As new users at Big Buys, we don’t know the data’s location. Let’s use Sigma Assistant to get started. \n\n**1:** Open `Sigma Assistant` from the left sidebar to ask a question and get a starting point for analysis:\n\n\u003Cimg src=\"assets/sfv-2026-03.png\" width=\"800\"/\u003E\n\n**2:** In dropdown select the `BIG_BUYS_OFFICIAL` Cortex Agent. This will route our question directly to the agent instead of Sigma's default Assistant (which is still powered by Cortex)\n\n\u003Cimg src=\"assets/sfv-2026-04a.png\" width=\"800\"/\u003E\n\n**3:** In the text box, type: `How are computer products performing across regions?` and click the black arrow:\n\n\u003Cimg src=\"assets/sfv-2026-04b.png\" width=\"800\"/\u003E\n\n\u003Caside class=\"negative\"\u003E\n\u003Cstrong\u003ENOTE:\u003C/strong\u003E\u003Cbr\u003E The response comes from a Cortex Agent, so your output may differ slightly from this guide.\n\u003C/aside\u003E\n\nNotice that the Cortex Agent is suggesting we use the `BIG_BUYS_SALES_ANALYST` Cortex Analyst, and that it walks through its reasoning.\n\n\u003Cimg src=\"assets/sfv-2026-05.png\" width=\"800\"/\u003E\n\nAs the agents responds it provides both top level analysis and a breakdown of how it built the output.\n\n\u003Cimg src=\"assets/sfv-2026-06.png\" width=\"800\"/\u003E\n\n\n**4:** With your cursor over the table, click the `Explore` icon in the top-right corner to explore the data in a spreadsheet interface and begin analysis:\n\n\u003Cimg src=\"assets/sfv-2026-07.png\" width=\"800\"/\u003E\n\n![Footer](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partners-snowflake-virtual-2026/sigma-footer.png?v=0684eb21)\n\u003C!-- END OF SECTION--\u003E\n\n## Find Underperforming Brands\nDuration: 5\n\n**1:** To ensure that we are at the same spot, make sure that your table has the same columns in the grouping the one pictured here. If there are any missing ones, drag them into the calculations under the store region grouping. Lets also rename the columns to: `Total Revenue`, `Total Profit`, `Total COGS`, and `Profit Margin`. We should also use the formatting bar to set three columns to use `Currency` and `Profit Margin` to `%`:\n\n\u003Cimg src=\"assets/sfv-2026-08.png\" width=\"800\"/\u003E\n\n\u003Caside class=\"negative\"\u003E\n\u003Cstrong\u003ENOTE:\u003C/strong\u003E\u003Cbr\u003E In this case the agent decided to multiply the profit margin by 100 so that the default output would not show as a percent. We need to modify the formula to remove the unneeded step in the calculation.\n\u003C/aside\u003E\n\n\u003Cimg src=\"assets/sfv-2026-08b.png\" width=\"800\"/\u003E\n\n**2:** The first thing that we noticed is the `Southwest` is the lowest performing region out of the whole Big Buys network of stores.\n\n**3:** Let's right click and keep only the `Southwest` region:\n\n\u003Cimg src=\"assets/sf2025-9.png\" width=\"500\"/\u003E\n\n**4:** Next, let's drag in the `Brand` column to the grouping level on the right hand side:\n\n\u003Cimg src=\"assets/sf2025-10.png\" width=\"800\"/\u003E\n\n**5:** Now let's sort the `Profit margin` column descending so that our highest margin brands in the `Southwest` region are at the top and the lowest ones are at the bottom:\n\n\u003Cimg src=\"assets/sf2025-11.png\" width=\"550\"/\u003E\n\n**6:** We should add in some conditional formatting to make this easier to look at. Let's go ahead and right click `Conditional formatting` on the column and add in data bars from the configuration pane on the right side:\n\n\u003Cimg src=\"assets/sf2025-12.png\" width=\"600\"/\u003E\n\n**7:** With a little bit of color, we can easily see there are certain brands that are losing us money and others are performing great. To improve overall profitability, we could drop some brands but before we do that, let's inform our decision with some additional outside data:\n\n\u003Cimg src=\"assets/sf2025-13.png\" width=\"800\"/\u003E\n\n**8:** Luckily we have a CSV of product reviews that we are able to load into Sigma and directly leverage in our analysis. \n\nSince we are using Sigma on top of Snowflake, we don't even have to wait for our data team to load the data in. We can do it right here right now. \n\n\u003Cbutton\u003E[Download the Product Reviews CSV](https://sigma-quickstarts-main.s3.us-west-1.amazonaws.com/csv/Product_Reviews_Big_Buys.csv)\u003C/button\u003E\n\nLet's click on the table button on the left side of the `Element bar`:\n\n\u003Cimg src=\"assets/sf2025-15.png\" width=\"800\"/\u003E\n\nThen select the CSV option:\n\n\u003Cimg src=\"assets/sf2025-16.png\" width=\"400\"/\u003E\n\nFrom there we can drag and drop or browse to find the CSV file we downloaded:\n\n\u003Cimg src=\"assets/sf2025-17.png\" width=\"300\"/\u003E\n\nSigma reads the file so we can review it and then click `Save`:\n\n\u003Cimg src=\"assets/sf2025-18.png\" width=\"800\"/\u003E\n\n**9:** With these 5000+ product reviews, we need to create a quantitative score to leverage in our analysis. So, we could either go one by one and write our own score or let's just leverage Snowflake Cortex to go through and provide us a sentiment analysis on each review.  \n\nRight click the `Review` column and select `Add column`:\n\n\u003Cimg src=\"assets/sf2025-19.png\" width=\"800\"/\u003E\n\n**10:** With the new column selected go to the formula bar and type in:\n```copy-code\nsentiment(Review)\n```\n\nYou may want to reduce the decimal places shown too:\n\n\u003Cimg src=\"assets/sf2025-20.png\" width=\"800\"/\u003E\n\n**11:** Now we need to tie this to our `BIG_BUYS_POS` table. In Excel, you can do this with a Vlookup. Luckily Sigma has the same paradigm. On the `BIG_BUYS_POS` table right click `Brand` and select `Add column via lookup`.\n\n\u003Cimg src=\"assets/sf2025-21.png\" width=\"550\"/\u003E\n\n**12:** In the modal, select the `Sentiment` column as the column to add, set the aggregate to `Average`, You will notice that brand has automatically been matched to `Brand` for us. \n\n\u003Cimg src=\"assets/sf2025-22.png\" width=\"400\"/\u003E\n\nOnce you click `Done`, you'll notice that the sentiment scores have now been averaged and tied to their respective brands.\n\n**13:** With our new looked up column selected, rename it to `Avg Sentiment` and go to the top bar and hit the decreased decimal point button twice so that we have a simpler number.\n\nYou may have also noticed that Sigma lets you see the formula that the `Add column via lookup` created for us:\n\n\u003Cimg src=\"assets/sf2025-23.png\" width=\"800\"/\u003E\n\nThen right click the `Sentiment` column and select `Conditional formatting`. This time let's add a color scale:\n\n\u003Cimg src=\"assets/sf2025-24.png\" width=\"800\"/\u003E\n\n**14:** Now, just a few minutes later, we have a strong understanding of which brands are our least profitable ones, and which brands are preferred by our customer base.\n\n![Footer](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partners-snowflake-virtual-2026/sigma-footer.png?v=0684eb21)\n\u003C!-- END OF SECTION--\u003E\n\n\n## Mock Up Price Changes\nDuration: 5\n\nThe next step is to set up the Price Changes linked input table that our Sigma Agent will populate with recommendations.\n\n**1:** From the `Product_Reviews_Big_Buys.csv` table, select the create child element icon and the select `Linked input table`:\n\n\u003Cimg src=\"assets/sfv-2026-25.png\" width=\"800\"/\u003E\n\n**2:** From there, click the `Choose column(s) to make unique row identifiers` drop down, and select `Brand`. Finally click the `Create input table` button.\n\n\u003Cimg src=\"assets/sfv-2026-26.png\" width=\"400\"/\u003E\n\n**3:** Rename the input table to `Price Changes`, and the default `Text` column to comments by double clicking on the respective names:\n\n\u003Cimg src=\"assets/sfv-2026-27.png\" width=\"800\"/\u003E\n\n**4:** Right click the `Brand Column` and select `Add new column` then click on `Number`. Rename this new column `Price Change`.\n\n\u003Cimg src=\"assets/sfv-2026-28.png\" width=\"800\"/\u003E\n\n**5:** Now on the `Brand` column on the `BIG_BUYS_POS` table, right click and select `Add column via lookup`.\n\nChoose the `Price Changes` table that we just created and set the `Column to add` to  `Price Change` with `Brand` set as the matching key:\n\n\u003Cimg src=\"assets/sf2025-31.png\" width=\"400\"/\u003E\n\n**6:** Rename the new column to `Price Change`.\n\nThese values should show up as `Null` right now since we havent entered any changes, but now we're ready to build our model:\n\n\u003Cimg src=\"assets/sfv-2026-29.png\" width=\"800\"/\u003E\n\n**7:** In some cases the AI does something unexpected and we don't catch it right away. In our case we just noticed it did not group the `COGS` column to create `Total COGS` as might be expected, but that is easy to correct.\n\nWe can drag `COGS` into the grouping after the `Total Profit` column and rename it to `Total COGS`:\n\n\u003Cimg src=\"assets/sf2025-35.png\" width=\"800\"/\u003E\n\n\u003Caside class=\"negative\"\u003E\n\u003Cstrong\u003ENOTE:\u003C/strong\u003E\u003Cbr\u003E If \"Total COGS\" is present in your table, ignore the instructions to add it manually.\n\u003C/aside\u003E\n\n**8:** We want to compare the original gross margin to the price change. \n\nAdd a new column to the right of the `Price change` column and rename it `Adjusted profit margin`.\n\nSet its formula to:\n```copy-code\n(([Total Revenue]*(1+[Price Change])) -[Total COGS])/((1+[Price Change])*[Total Revenue])\n```\n\nChange the column format to `Percentage`. Note this column will still be Null as we have not entered any price changes yet:\n\n\u003Cimg src=\"assets/sfv-2026-30.png\" width=\"800\"/\u003E\n\nWe are ready to make a visualization.\n\n**9:** Click the `Add child element` button in the top right of the table and select `Chart`:\n\n\u003Cimg src=\"assets/sf2025-36.png\" width=\"800\"/\u003E\n\n**10:** Drag `Brand` to the `X axis` and the `Profit Margin` to the `Y-Axis`. Then change the chart to be vertically aligned by swapping the axes:\n\n\u003Cimg src=\"assets/sf2025-37.png\" width=\"800\"/\u003E\n\n**11:** Add the `Adjusted profit margin` column to the `X axis` and select `Unstacked` for the bar chart style, but nothing should new show up since we still have not added price changes.\n\nNow we’re ready to generate our price recommendations using a Sigma Agent.\n\n## Sigma Agent Pricing Recommendations\nDuration: 10\n\nRather than manually entering price adjustments, we’ll use a Sigma Agent to analyze our brand performance data and generate recommendations based on both the profit margin and sentiment data we’ve gathered.\n\n**1:** In the element tray at the bottom of the screen select `UI` then click `Chat`. This will create a chat element where we can showcase our agent. \n\n\u003Cimg src=\"assets/sfv-2026-31.png\" width=\"600\"/\u003E\n\nThen select `Create New Agent`.\n\n\u003Cimg src=\"assets/sfv-2026-32.png\" width=\"400\"/\u003E\n\n**2:** The Configure Agents window should now be open. This is where we can provide Sigma instructions and tools for the agent to do work for us. First we need to give the agent access to two data sources. In the `Data Sources` section click add and select `BIG_BUYS_POS` and then `Price Changes`.\n\n\u003Cimg src=\"assets/sfv-2026-33.png\" width=\"800\"/\u003E\n\n**3:** Next we have to give the agent a tool to use to update our prices changes in the input table. Click the add tool icon and then select `Actions`:\n\n\u003Cimg src=\"assets/sfv-2026-34.png\" width=\"400\"/\u003E\n\nThen in the `Action` type dropdown select `Update row(s)`\n\n\u003Cimg src=\"assets/sfv-2026-35.png\" width=\"600\"/\u003E\n\n**4:** Configure the Update row(s) action to work on the `Price Changes` linked input table as a `Single row` update. Ensure all columns have been added by clicking the `Add column` button at the bottom of the modal and configure all columns to be set by `Agent input` with a value of the column's name.\n\n\u003Cimg src=\"assets/sfv-2026-36.png\" width=\"400\"/\u003E\n\n**5:** Rename the newly created tool to `Update Price Change` by clicking on the pencil icon in the top middle of the modal.\n\n\u003Cimg src=\"assets/sfv-2026-37.png\" width=\"600\"/\u003E\n\n**6:** Finally copy the instructions below into the `Instructions` panel.\n\n```copy-code\nRole\nYou are a pricing analyst assistant. Your job is to review brand-level performance data and write price change recommendations into the Price Changes linked input table, one brand at a time.\n\nData Available\nYou have access to two tables in this workbook:\n\nThe BIG_BUYS_POS table, which contains Brand, Profit Margin, and Avg Sentiment for each brand in the Southwest region.\n\nThe Price Changes linked input table, which has a Brand column, a Price Change % column (expressed as a decimal, e.g. 0.07 for 7%), and a Comment column.\n\nStep 1: Read and decide\nRead all rows from the BIG_BUYS_POS table in a single pass. For every brand, determine its Price Change % value and Comment using the logic below. Record your complete decision list internally before making any writes. Do not call the Update Price Change tool until every brand has a recommendation.\n\nStep 2: Determine a recommendation for each brand\nUse Profit Margin and Avg Sentiment as your two primary signals. Weight margin more heavily for brands in distress (near zero or negative). Weight sentiment more heavily when deciding between holding and a modest increase for healthy brands. Treat the two signals together rather than applying fixed thresholds. A brand at 27% margin with 0.29 sentiment should be treated differently than one at 27% margin with -0.73 sentiment, even if both fall below a 0.3 cutoff. Recommend the price change a thoughtful analyst would defend to their manager.\n\nStep 3: Write all rows\nOnce your full decision list is complete, work through it and call the Update Price Change tool for each brand back to back without pausing to re-read or re-reason. For each update write:\n\nThe Price Change % value as a decimal rounded to two decimal places.\n\nA Comment of one to two sentences that references the specific Profit Margin and Avg Sentiment values and explains the recommendation. Example: \"Profit margin of 4.2% is below threshold and avg sentiment of 0.68 indicates strong customer preference. Recommending a 7% price increase to improve profitability while demand remains favorable.\"\n\nOne tool call per brand, in order, until all brands are complete. Do not skip any.\n```\n\n**7:** Let's reorganize the page so that the `Price Changes` input table is side-by-side with the agent, and the graph we made earlier is directly above them. Note you can move elements by clicking on the 6 dots icon in the top right of the element and resize them by hovering over the edge and having your cursor turn into arrows.\n\n\u003Cimg src=\"assets/sfv-2026-38.png\" width=\"800\"/\u003E\n\n**8:** Lets ask the agent to `Review product performance and recommend price changes` by putting that in the chat box and clicking the arrow icon.   \n\n\u003Cimg src=\"assets/sfv-2026-39.png\" width=\"600\"/\u003E\n\n**9:** The agent completes its full analysis first, then executes all writes in quick succession. You should start to see pricing recommendations populating, and the data points showing up in the lookup columns in the `BIG_BUYS_POS` table and the graph.\n\n\u003Cimg src=\"assets/sfv-2026-40.png\" width=\"800\"/\u003E\n\nOnce the agent has finished, review its recommendations. Notice how it has balanced margin recovery with customer sentiment to arrive at each suggested price change. You can adjust any values you disagree with before moving on.\n\nWhen you’re ready, click `Save As` and save the workbook as `Big Buys - {first name_last name}`. This naming format helps us identify your work if others need to review it.\n\n![Footer](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partners-snowflake-virtual-2026/sigma_footer.png?v=0684eb21)\n\u003C!-- END OF SECTION--\u003E\n\n## Use Big Buys’ corporate profit planning application\nDuration: 5\n\n**1:** Let's navigate to the home page and open up the `Big Buys Profit Planning Tool`, from the home page. \n\n**2:** This application was built by Big Buys' central IT team as a Sigma AI App. It allows team members to submit and approve distinct pricing scenarios collaboratively.\n\nWe’re going to create our own pricing scenario based on what we just learned. Let's click the `Create New Scenario` button in the top left:\n\n\u003Cimg src=\"assets/sf2025-26.png\" width=\"800\"/\u003E\n\n**3:** Provide a name for your new scenario and type in a quick description. Then click `Create` and let's go plug in some numbers:\n\n\u003Cimg src=\"assets/sf2025-27.png\" width=\"600\"/\u003E\n\nSince this profit planning tool is shared with all category managers at Big Buys, it’s not filtered by default to any specific product type. \n\n**4:** Let's go ahead and filter it to `Computers` and then set the `Store Region` to `Southwest`:\n\n\u003Cimg src=\"assets/sf2025-28.png\" width=\"800\"/\u003E\n\n**5:** After applying the filters, you'll see the profit margin over time for that subset, along with a Snowpark forecasting model:\n\u003Cimg src=\"assets/sf2025-29.png\" width=\"800\"/\u003E\n\nThis is our third example of leveraging Snowflake Cortex AI functions directly inside of Sigma to open up new possibilities for real-time analytics. \n\nWe are now ready to hop in and apply some of our price adjustments from our scratch pad.\n\n**6:** Return to your `Big Buys - {your name}` workbook. Sort the `Price Changes` input table ascending by the `Brand` column, and copy all values from the `Price Change` column:\n\n\u003Cimg src=\"assets/sf2025-39.png\" width=\"500\"/\u003E\n\nGo back to the `Big Buys Profit Planning Tool` \u003E `Price Adjustments` table, click `Edit Data`, sort the table ascending on the `Brand` column and paste the values into the `Price Change (%)` column.\n\n\u003Caside class=\"negative\"\u003E\n\u003Cstrong\u003ENOTE:\u003C/strong\u003E\u003Cbr\u003E Click the first cell in the \"Price Change\" column, then scroll to the bottom of the table. Hold down the Shift key and click the last cell in the same column.\n\u003C/aside\u003E\n\n\u003Cimg src=\"assets/sf2025-40.png\" width=\"500\"/\u003E\n\n\u003Caside class=\"negative\"\u003E\n\u003Cstrong\u003ENOTE:\u003C/strong\u003E\u003Cbr\u003E To copy, press Ctrl+C on Windows and Command+C on macOS. To paste, press Ctrl+V on Windows and Command+V on macOS. \n\u003C/aside\u003E\n\nClick `Save` on the input table.\n\n**7:** After entering the data, you should see a loading bar start. The Snowpark forecasting model will process your changes and show real-time results based on Snowflake’s projections.\n\n\u003Cimg src=\"assets/sf2025-41.png\" width=\"800\"/\u003E\n\n**8:** With our scenario now loaded in, we are ready to go ahead submit it for approval because Sigma AI Apps allow for multi step workflows. \n\nOnce we click approve, this can go into a queue that our manager or compliance officers could go through and review but for the purpose of this lab, we can just approve our own data. \n\nI mean, we obviously know it's right, so first we'll click `Submit for Approval` in the top right and submit our scenario.\n\n**9:** Finally, click `Review Submission`, select your scenario, and click `Approve`.\n\n![Footer](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partners-snowflake-virtual-2026/sigma-footer.png?v=0684eb21)\n\u003C!-- END OF SECTION--\u003E\n\n## Conclusion And Resources\nDuration: 5\n\n### What You Learned\nIn this QuickStart, you used Sigma and Snowflake to take a business question all the way from initial discovery to a governed, approved decision. You used Sigma Assistant backed by a Cortex Agent to find the right data from a plain-language question, applied Snowflake Cortex AI functions to score thousands of product reviews in seconds, built a Sigma Agent to reason across performance data and generate price recommendations, and modeled the impact using a Snowpark forecasting model before submitting for approval through a Sigma AI App.\n\nAlong the way, you joined data from multiple sources, loaded external data directly into Snowflake, and wrote recommendations back through governed Input Tables. None of it required SQL.\n\nThe retail scenario is just the vehicle. The pattern of using Sigma and Snowflake to move from question to governed action applies across every industry and every team that runs on data.\n\n**Additional Resource Links**\n\n[Blog](https://www.sigmacomputing.com/blog/)\u003Cbr\u003E\n[Community](https://community.sigmacomputing.com/)\u003Cbr\u003E\n[Help Center](https://help.sigmacomputing.com/hc/en-us)\u003Cbr\u003E\n[QuickStarts](https://quickstarts.sigmacomputing.com/)\u003Cbr\u003E\n\nBe sure to check out all the latest developments at [Sigma's First Friday Feature page!](https://quickstarts.sigmacomputing.com/firstfridayfeatures/)\n\u003Cbr\u003E\n\n[\u003Cimg src=\"./assets/twitter.png\" width=\"75\"/\u003E](https://twitter.com/sigmacomputing) \n[\u003Cimg src=\"./assets/linkedin.png\" width=\"75\"/\u003E](https://www.linkedin.com/company/sigmacomputing) \n[\u003Cimg src=\"./assets/facebook.png\" width=\"75\"/\u003E](https://www.facebook.com/sigmacomputing)\n\n![Footer](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partners-snowflake-virtual-2026/sigma-footer.png?v=0684eb21)\n\u003C!-- END OF WHAT WE COVERED --\u003E\n\u003C!-- END OF QUICKSTART --\u003E\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-baf170e0dc","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-bd065a700e",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-f373490d47","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-07-13",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-ca09718a0a","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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