Retail and Consumer Goods Tableau User Group 14 Aug 2026

0:01 Blake Feiza: The Retail and Consumer Goods Tableau User Group.

0:04 Uh, this is our August 2026 meetup. So happy to have you all here if you're attending in person, and if you're watching the recording, welcome as well.

0:16 A quick peek at the agenda for today. Uh, it's pretty similar to our usual agenda. We're gonna start off with a welcome to everybody and some introductions, uh, and then we're going to jump into our special presentation today, uh, by Hue.

0:30 And then last but not least, we'll close things out with a quick peek at the Tableau Community Slack channel, and actually a new partnership we have with the industry retail channel in that space.

0:43 So without further ado, my name is Blake Feiza. I'm one of the three co-leaders of the Retail TUG.

0:50 Uh, I work my day job over at Apple, and I am a Tableau ambassador based in Austin, Texas.

0:57 Uh, Matt, I will pass the mic over to you if you wanna introduce yourself.

1:01 Matt Huff: Yeah. Matt Huff. Um, a lot of the same things as Blake, except I don't work at Apple, I work at H-E-B, um, which is a grocery retailer out of Texas, if you're not familiar. So that's, that's kind of my background. I lead a, a team of, um, retail media data scientists, so…

1:20 And then Ojo?

1:22 Ojo Basu: Hi, I'm Ojo. I'm a two-time Tableau ambassador. The reason why I'm here is because I dream to become a three-time Tableau ambassador.

1:30 This part is inspired by one of my heroes in Hollywood.

1:34 Uh, there's nothing as, uh, fulfilling as being part of the community. It's a very giving space.

1:41 Retail, I'm in retail banking. That's as close as I can be to retail. Um, I've had several experiences with Walmart, Target, Sears. I've been in retail all life. I understand enough retail. Happy to be here with Blake and Matt, and I, um, and I appeal to everyone to talk about the retail channel, the industry retail channel that we have on Slack. It's something that's net new, uh, where we can have real conversations, and we try to bring amazing talent to these discussions to talk about everything retail and Tableau.

2:16 Over to you, Blake.

2:19 Blake Feiza: Perfect. Thanks for that, Ojo.

2:21 And without further ado, I would like to hand the mic over to Hue in just a moment. Ojo, do you wanna elaborate a little bit on the introduction here? I'll pass it right back.

2:31 Ojo Basu: Awesome. Yeah. Uh, Hue is part of the Next Level Tableau initiative that Andy Kriebel runs. It's a community that talks about Tableau three times a week.

2:42 I came across the work that she, Hue, has been doing as part of that Next Level Tableau, and she has started speaking at the user group events. The one that she ran, and today's session is going to be all interesting. I don't want to steal the thunder, but it is the visualizing the retail journey piece, and we have some real amazing visualizations from a data center.

3:07 I don't wanna drop any spoilers. Back to Hue.

3:09 Uh, take it away. Thank you.

3:15 Hue Vuong: Okay. So, uh, let me continue with my presentation.

3:18 So, uh, the topic I'd like to present today is, uh, visualizing the retail journey to see how the Tableau can help the manager to have a better data visualization for the retail journey from the warehouse to the store. And, uh, before jumping to this topic, I would like to give you a brief introduction about myself.

3:40 And, uh, I'm a data analyst who loves turning the messy data into, uh, meaningful stories.

3:47 I'm also a member of the Tableau Buddy program, the Next Level Tableau, like Ojo just sharing, and the AI Officer Institute.

3:56 I have a background in the supply chains, e-commerce, and healthcare, working in the multi- multinational companies before.

4:05 And, uh, outside of work, I also enjoy playing the piano.

4:10 I feel like it's another way for me to working with the patterns and the flow.

4:15 And the reason why I mention about this hobby is because the way I learned the Tableau is also the similar way I learned the piano.

4:25 Watching the YouTube tutorial, mimicking the exercises, and practice over and over again.

4:33 And I figure out that until we master the design principle, we can develop our creativity in the data visualization in our own way.

4:45 So I hope that you guys can enjoy learning the Tableau in a similar way.

4:50 Okay. Uh, without further ado, let's jump into our topic.

4:55 So starting with a quick questions.

4:58 Just imagine you are responsible for 100 retail stores, and every morning you have only five minutes to answer three questions: Which stores need the stock today?

5:13 Which warehouse is overloaded?

5:16 Where am I losing sales?

5:19 And here is when a good design of the dashboard will solve a problem of the managers and help them to make a better and faster decisions.

5:32 So usually when we talk about the sale analytics, we will think about the retail analytics and jump directly into the sales performance dashboard.

5:42 However, behind and before the, a product reach to a customer hands, there is a behind story come from the warehouse operations, inventory managements, operations, and then the, uh, the store by itself.

6:02 So with that, uh, saying, I'd like to walk you through the whole process with the agenda come from operational dashboard, the sales dashboard, the design principle that make the dashboard impactful to the managers, and some references for your reading at the end.

6:23 Okay.

6:24 So, first thing first, the operational dashboard, where our retail journey begins by using the warehouse mapping story.

6:35 So why does it matter?

6:39 So just imagine when you're talking about the map, people usually think a geographical map.

6:46 However, Tableau can do way more than that by visualizing the actual warehouse layout for the manager to better visualize their stock management.

6:59 And, uh, what is the case study, the business context at that time? A customer come to us and ask for the support.

7:08 Their warehouse team was struggling with the inefficiency picking routines. They missed the KPI. They have a poor layout because they don't have the visibility of the stock or the real-time operations.

7:23 And the, the SKU, the items were scattered all across the warehouse and causing the operator need to go the zigzag routines in the warehouse, leading to a wasting time, wasting the labor effort, and overall reduce the productivity.

7:43 So with that saying, what was our solution?

7:49 Our solutions was using Tableau to visualize the warehouse.

7:55 So as you can see here, this is a demo of our solution at that time.

8:00 The color ranging from the yellow to the red, which represent- representing the number of a visit that an operator, they need to come to a specific location to pick up an item for an order.

8:16 So with that saying, the darker red, the busier location is.

8:21 So with that visualization, the manager can have more understanding the busier operations, and they can have better solutions by moving the high-frequency picking item close together or relocate those item to a more accessible locations, such as move them from the high rack to the floor. And apart of… on that, they also have the, uh, line chart, as you can see at the bottom.

8:52 They can use that to track the, uh, picking, pick time of the operations, and then they can have better labor planning in advance.

9:02 So next, I will show you how did we design this map.

9:09 The metology, method- methodology is quite simple.

9:13 Three step.

9:15 First one is the warehouse layout.

9:17 The second, the layout mapping.

9:20 And the third step is the data integration.

9:26 So go to the step number one.

9:28 We get the actual warehouse layout. It could be a 3D drawing map or any map that you have to visualize your warehouse.

9:40 And then we use this map and define the coordinates X, Y for the map.

9:47 And by using the X and Y coordinates, we can define the coordinate for each location for the warehouse. For example, here we have the rack number six O one A, have the X equal fifty and the Y equal ten. So we map the information into the Excel file and create the master file for all the locations on the racks in this warehouse.

10:17 And the next step is data integrations.

10:20 This is will come from the operational report from the customer. It can includes the SKU number, the timestamp, the quantity, the, uh, order date, movement ID, et cetera. Depends on the requirements from the customer.

10:40 And then last but not least is the mapping the data to the Tableau dashboard. So here I have two map. The first map we call is empty warehouse by using the data from the master file that we create in the step number one. And then we drag X to the column, Y to the, to the rows, and then we have the full maps layout, which is, uh, like a empty warehouse.

11:08 And then for the map number two, we use the operational report from the step number three, data integration report, and do in a similar way, drag X to the column and Y to the rows, and then using the color to diverging. And we have the heat map to see where and which location is have- having the busier operation activities.

11:33 And last but not least is overlay the map number two on the top of map number one.

11:39 Then voila, we have the final warehouse layout to show the operational efficiency monitoring for managers.

11:49 So why does this layout values and make the impact to the business? The first thing is that this warehouse layout help the managers to optimize the stock placements with the demand-driven insights. As I shared before, they can reallocate the stock more efficient, moving the high-frequency pick item close together and to a more accessible locations.

12:15 Also, they boost the labor efficiency to hit the fulfillment KPIs from a hundred to a hundred and twenty picks per hour, and reducing the walking distance from a, uh, operator come to a location to pick an item.

12:32 And also, uh, this solution help the manager to improve the inventory accuracy and visibility from seventy-four percent to ninety-eight percent of accuracy.

12:44 So after this case, what's next?

12:48 We see this solution is quite useful and can be scalable, so we start to apply to other customer requirement as well.

12:58 And here we use it for the inventory and the capacity planning.

13:03 So by using visualizations, we can understand and help the customer to understand how much capacity has been occupied, how much is still empty, and the utilization rates.

13:17 So by understanding these numbers, the managers can make the decisions if they can continue to optimize the space in the warehouse, or they can go to a further investment in a new facility.

13:34 So that is the warehouse operations. And now the next one, we go to the sales operate performance dashboard, where we can turn the data into a better business decisions.

13:51 So here is an example of my, um, dashboard to show the online sales.

13:56 This data is, uh, created by, um… It's a mock data set and, uh, it build a structure come from the, uh, customer requirements and then the insight and the recommendations.

14:10 So as you can see here on the top, we have the KPI Kanban cards, and it show the key KPIs, the key metrics to measure the success of a, uh, sales performance, including the total sales, profit, shipping cost, order, and the quantity.

14:29 And then to the second line, the data segmented by the country, the number of the reviews, the shipping, and the payments. This gives a second layer for the m-managers to understand why this prevent the number in the KPI, why the sale decreasing, why the profit is decreasing, and give more insight for the measures.

14:54 And the third layer is, uh, using the, um, tree map and the heat map to give more this data under insight for the manager to, uh, have a better segmentations by the category and, uh, to see where and which category they have better sales and, uh, where they can make better improvement based on the review stars.

15:19 So with that saying, this dashboard can help the manager to answer the question who, what, where, when, and, uh, understand more about their current sales performance.

15:33 So this is the executive summary.

15:37 For the operational view, the team would need more details of the sales performance. So I design a list of the detail which include and, uh, segmentations by the, uh, product level.

15:54 And here the operations, they can see the sales, the orders, the merchants, the profit by the every single product that they want to check.

16:07 And on the right side, you can see the merchants percentage is marked in red or blue, which can help them quickly identify which product is having positive or negative profit or margin to the, the store, and then they can adjust the stock inventory or boost the sales accordingly.

16:29 So that is the sales dashboard.

16:31 Now I will go through the design principle, uh, to s-understand what makes this dashboard effective and impactful to the stakeholders.

16:44 Uh, first thing is the context.

16:47 So, uh, in the dashboard, you can see the KPI card.

16:52 So example, we see the total sales, uh, one point six millions in December of twenty-twenty three.

16:59 However, with that number, do we know is that number is good or bad?

17:05 So to make it better, I im-improve my KPI with a context like, uh, compared to the last month or the last quarter, and adding the trend line to give the stakeholder more understanding about the big number in the KPI card.

17:27 And usually it spend me a day or more to design a KPI card.

17:32 So, um, now with the development of the AI, the Cloud Card, and, uh, I get inspiration from many, uh, Tableau expert on the community to design a auto KPI card by using Cloud Card.

17:48 And, uh, by using this way, it drafts out only one minute to design a auto KPI card. So as you can see here, I just need to drop the order date and the sum of the sale, then, uh, I can have the KPI card.

18:04 And also you can have the, uh, card setting to change the, uh, formatting, the colors.

18:11 So this is the final, the KPI card by using the auto KPI Cloud Card viz extensions.

18:19 Just, uh, quicker than the, um, traditional creations before.

18:25 I hope that tips can, uh, help you a little bit.

18:27 And, uh, the next one design principle I use in the dashboard is, uh, ink ratio.

18:34 The, uh, ink ratio mentions the details in the chart that we use to design the dashboard, the chart.

18:41 So, um, the more details in the charts, the more time that the stakeholder need to spend on the dashboard, and causing the reluctance to make the decisions.

18:52 So that's why I just keep the necessary and key insight on the chart. That means remove all the unnecessary item, like the grid line, the axis, the label, which not contribute value to your chart at all.

19:11 The next thing is the chart usage.

19:13 Before I thought that using pie chart in the dashboard is quite a fancy way to make the dashboard look, uh, good and, uh, beautiful. However, the more I work in the dashboard, the more I realize that using the pie chart really somehow hard to make the, differentiate the, the numbers. For example, here the retention fifteen percent and the sale is twenty percent.

19:41 Uh, however, it's, uh, hard to difference the difference between two pieces here.

19:48 So what is the better way to visualizing that?

19:52 I turn them into a bar chart. The original ones, it, um, make the measures and the stakeholder better visualizations of each category and understand which is the higher number and which is less, even though just a slightly tiny difference.

20:13 And the next thing is the data misleading.

20:18 Uh, as you can see here on the left-hand side chart, the USA sale is, uh, four point three millions and the France is, uh, four point zero millions.

20:28 And I draft the zero at the X-axis so we can… Even though it's just a slightly difference, but maybe we can misunderstand that the USA has four times sales gra- larger than France.

20:46 So to make it and reflect the data more correctly and consistently, I come back to the original way to visualize the data, adding the zero, starting the X-axis from the zero.

21:03 Uh, in some case that, uh, people still want to see, to focus on the difference rather than the actual value. So, um, we can use the zoom in, zoom out functions to give the more options for the user.

21:19 The next one is the color usage.

21:22 Use the color purposely.

21:24 What I mean by saying that is, um, for the neutrals items, use the non-highlighted data points. For the key insight, we can use the cold colors and make sure to have the consistent color across the entire dashboard.

21:40 I will show you the more explanation later on.

21:46 And another thing come to the color blinds is around eight percent of males and one percent of the females, they have the visions deficiency.

21:57 So when we use the red and the green to visualize our data, it's very hard for them to, um, differentiate the numbers.

22:06 So even though sometime the managers always request to have the green and red as a traditional approach for increase and decrease data, but we need to be aware of these features.

22:20 And how to solve this problem.

22:25 The first way to solve that is turned using green or red by using orange or the blue. And the second solution is, uh, we can add the symbol or the texture in front of the numbers so the user, the audience, they can have something else to compare the numbers together, not only based on the color.

22:53 And the color consistent, as I mentioned before, use the consistently color across the entire dashboard. So here I use blue for the sales, so whenever you see any charts, you can immediately understand that this vis is talking about the sales number.

23:13 It will save the audience lots of time and can understand, get the insight quickly and make a better decision.

23:23 And for the table, usually the people prefer to use a table with the numbers to easily download in the Excel files.

23:32 However, we can make our table also as a visualizations.

23:37 So here I have the sales ratios using a bar chart, even though having the number, but we also have the way to give the better visualization for the audience.

23:49 And also for the deviations, increase or decrease, I also use the symbol to have the audience easily recognize those de-deviations, the changing.

24:02 And for the percentage of the margins, I use a divergent color, red and blue, to differentiate between the positive margin and the negative margins.

24:12 And the better way to, uh, check if the color is, uh, colorblind friendly or not, uh, I can use the, uh, cooler.com.

24:23 It's very a good tool to, uh, do, uh, the color contrast checking, colorblind checking, and also generate your color palettes.

24:35 So overall, with the operational dashboard, the sales dashboard, and some design principle I just shared, three takeaways for the design of data visualization is starting with the business questions.

24:51 What is their pain points? What questions do they want to answer? What decisions they want to make?

24:59 And then we will go with the chart visualizations, choose the suitable chart, choose the suitable colors to answer those questions.

25:09 And last but not least, you design the dashboard for people, not for a dashboard, which means a beautiful dashboard but not used by anyone is doing not value at all, rather than a simple dashboard that can help the managers to make the decisions, can bring the insight to the stakeholder, it way more useful and better.

25:36 So that's very much about my presentation. Thank you so much for your time and attention. Feel free to connect and continue to exchange the insight and experience.

25:48 Blake Feiza: Uh, thank you, Hue. That was a fantastic presentation.

25:52 Uh, we have a couple minutes still. Does anyone in the chat have any questions for Hue that we would like to field?

25:59 I would love to, to give a few minutes for Q&A if anybody has any thoughts.

26:05 Matt Huff: I, I know, I know there was a question about filtering on dates for your sales dashboard.

26:14 Hue Vuong: What the questions?

26:16 Matt Huff: Uh, one of them was if your sales dashboard allowed you to filter on dates. I'm trying to figure out… I'm trying to find the exact question, but it's buried in the chat somewhere.

26:27 Blake Feiza: And it looks like Aman Kumar is requesting presenter mode. Is that to, to ask a question, Amar?

26:34 Let me… I'll add him in here for a moment.

26:38 Do you have a question, Aman?

26:41 Okay.

26:42 We'll go with, we'll go with no. And Aman, if you do have a question, feel free to ask it in the chat and we will get to it.

26:48 Uh, we do have a couple questions coming in now. Uh, one from Cooley here: "Do you use any tools, Hue, to prepare the Excel mapping that you showed?"

26:59 Hue Vuong: Uh, for the Excel files, right?

27:02 I, uh, use the, um, the warehouse layout, and then I add the, the map into the Excel file, and then I, uh, map them from the number of one, two, three, et cetera, until the end, and then, uh, using that to define the locations, the X and the Y coordinate. It's quite manual at that time, but now with the support of AI, I hope that I can find a better and faster way.

27:31 It's also a challenge at that time as well because each customer, they have a different warehouse layout.

27:37 And, uh, when we work with a different requirement, we need to prepare a new master file of the warehouse layout locations.

27:49 Blake Feiza: Wow. Yeah, that sounds pretty difficult to do at scale if you have a bunch of different warehouses to map that out by, by hand. So yeah, hopefully AI can be helpful in the future if that's something that you have to go back to.

28:02 Uh, we do have… Let's see. I see one more question here from Aaron.

28:06 Uh, he says: "In the warehouse dashboard you showed, how scalable is it, and are there ways to automate some of the data mapping, or is it all updated manually when products change locations?"

28:17 Hue Vuong: Oh, that's a good questions. For the, uh, warehouse mapping, it's quite really scalable.

28:23 The first step takes really ti- time-consuming. It's just the, uh, the master file to map the actual warehouse layout to a master file of all the locations. And after that, all the automatically, for example, the operational data is extracted from the SAP, and then I automate schedule it on the weekly basis. So for every Monday, the supervisor, they can have the visualization on the dashboard, and then they can allocate a task to their operators and have the better labor planning in advance.

28:59 Blake Feiza: Fantastic. And it looks like we've got a question from Aman now in the chat here. Sorry you were having trouble there with the mic, Aman. Uh, but it looks like his question is: Uh, "How can a user navigate for last six months or last year? Only if this was applicable for this dashboard." So essentially, is there a way to navigate between last six months or last year, uh, with the dashboard?

29:27 Hue Vuong: For navigating the last six month, as I understand your question, is relating to the operational file, right?

29:35 And when it comes to the operational data, it, uh, come from the data of the customer.

29:41 Uh, how, how much the data the customer share to us, it can be a what, a year or six month or more than that. So any time that the data refresh, uh, we can have the archive data and also refresh the, the latest update on the dashboard at the same time.

30:02 Blake Feiza: Fantastic. Amazing. Thank you, Hue, for the presentation and for jumping into some Q&A there at the end. That was incredible.

30:11 Much appreciated.

30:13 Uh, so as we mentioned, following our star speaker of the day, uh, we do have a quick plug for the Tableau Community Slack workspace, and in particular inside of that space, the new fancy-schmancy, uh, industry retail Slack channel. So we essentially have partnered up with Edward Beaurain over here on the, uh, Salesforce sales side.

30:36 Uh, to make sure that we can get, uh, tons of activity going on in this channel and make it a great resource to keep this conversation going from today, uh, and provide a bunch of value to the retail plus Tableau community.

30:48 Uh, so Ed, we've got you on today. Do you actually want to-

30:51 Edward Beaurain: Yeah

30:51 Blake Feiza: … say a few words about the channel?

30:53 Edward Beaurain: I would love to do that, and I would also love to demo it. Uh, can I share my screen if possible? All right.

30:58 Blake Feiza: 100%.

30:59 Edward Beaurain: Thank you. Thanks for presenting. And, um, so I run our retail and consumer goods business for, um, Tableau and, and Slack, um, for a certain segment of customers in the US. So first of all, thanks for participating in the Tab- Tableau, uh, retail user group. I know that, um, Ojo, Matt, and Blake have been kind of hard at work in bringing that together.

31:17 I've been with Tableau for 11 years, um, so I know that the value of the community is, is that special sauce. Um, and we need to continue to invest in it and bring people, uh, that are in these verticals, learning this technology, into this kind of opportunity to improve, to share successes, to ask questions and do those things. So, um, Raj just went ahead and shared the Tableau community on Slack.

31:37 I'm gonna go ahead and share my screen and just make sure that, you know, while we're on, all on here, we should probably all just kind of get into it. But this is how you go ahead and join the community workspace right here. So, um, he just go ahead and posted it below, but if you go ahead and, and, and click on that link, it'll go ahead and open it up to that Slack workspace that you're in.

31:54 What's special is, is that, uh, and you can tell I was just kind of demoing the product, so you can tell I, you know, I do, do, do show and, and, and love Tableau. Um, but when you go into this, it's got 23,000 members in this. So we moved this over to Slack. Many of you guys might be using Teams or whatever, but you can join this with your personal email if you need to.

32:12 And one of the things that we did is we went ahead and created a industry retail channel. This is the only industry channel in the entire global workspace. They're testing it out with us to see if we can kind of have success in bringing this community together. So we're up to 55 members, which is exciting, which means that you are all joining as we speak.

32:33 But imagine this as being a place where we can share best practices, where we can kind of ask specific questions as it relates to the retail and consumer goods space. We can have dialogue and just really be in this, in this kind of channel where we can do our best work. Now, the power of this is, again, I said that there's 22,000 members in here.

32:52 So one of the other things that's really powerful is, is when you join this space, you have access to a thing here called Slackbot.

32:58 So I kind of go back to the questions that I just saw right here where it said, h- um, uh, my only question was how can a user navigate for the last six months, last year? So that was a specific one there. But maybe I go ahead, um, and say, like, this question right here was from Aaron. Are there ways to automate some of the data mapping, or is it updated all manually when products change locations in the warehouse, uh, as it relates to, um, Tableau Desktop, uh, and retail analytics?

33:24 Whatever. I'm taking a question that was specific to this presentation that hasn't been loaded in, but the concept is, is where you have these questions specifically about Tableau, it's gonna go through all of these posts that are happening on a daily basis globally, and this AI tooling is gonna be fully available for you to go ahead and take advantage of.

33:43 And the beauty of it is, is it's going to go ahead and link you to the specific areas where these questions have been answered in the entire Slack experience, right? And so as you're going through this, it already seems like the Slack webinar here took it specifically from it. It links you into here, and it links you into the conversation that we can keep going.

34:01 But hey, I'm trying to learn about LOD calcs.

34:06 What is the best way to learn this, for example? And so going through these and seeing kind of these channels right here where this is kind of our questions page. As you're going through, these are all the questions that have been asked and answered through the history of the workspace.

34:20 I think if I were a Tableau user and I'm trying to go ahead and invest in the community, share best practices, learn how to improve this, I think this is a workspace that can scale. I think Slackbot is an accelerant on top of it, and I think we built a channel here for industry retail where it's small right now, but now it's up to 56 members. It's growing like crazy.

34:42 We're gonna be at Tableau Conference next year in San Diego. We're gonna use this to all meet each other, and we're gonna go ahead and build the retail community.

34:50 You gotta ask the people within your organization to go ahead and log in. You gotta get them in this. We gotta ask questions. So my ask for you is if you're in this, ask a question, share a tip.

35:01 You gotta force the engagement early on, and then naturally engagement will follow.

35:06 But my ask to all of you is just to be aware of this, hopefully part of this, and please share it with other people that are looking to improve their Tableau skill set, 'cause it's a powerhouse of knowledge across this community, and that's what I wanted to share with you today. Any questions? Are, are folks able to go ahead and see this?

35:25 Yeah, you're in the, in the workspace?

35:28 All right.

35:30 Sweet.

35:31 I'll stop sharing.

35:33 This community is number one. Let's make the industry retail cha- channel a powerhouse and go from there. All right, Blake, back over to you.

35:40 Blake Feiza: Amazing. Thank you so much, Ed, and thank you for the demo of Slackbot. I have yet to play around with that thing. But just from that quick demo, that looks really powerful. So I am, I'm, like, itching right now to try and find some questions to throw in there and, and see what I can do after this session.

35:55 Edward Beaurain: So-

35:55 Blake Feiza: Uh, so a quick… Oh, Matt, do you have some thoughts too?

35:59 Matt Huff: Yeah. So Viraj, you had posted something in the channel here about the connecting Tableau Slack using the headless analytics. That, that is something I think that was announced earlier, um, in the, in that announcements channel. That, that also is probably something to check out. So,

36:20 uh, that was it, so.

36:21 Edward Beaurain: Yeah, I think, I think what I'm seeing on, on this end, just so I sell Tableau and Slack, so there's a benefit to me of introducing people to a Slack workspace and showing them Slackbot. Think about Slackbot as Cloud OEM at a fixed cost in Slack, which Slack is becoming kind of the operating system of choice for Anthropic's wall-to-wall, Open AI's wall-to-wall.

36:37 In fact- The, the person responsible at Microsoft for, for Copilot, um, turning that business around, the first thing he did was give his team Slack and rip out Teams, right? So there's, there's great examples in general, and I have the Fortune article that I can plug into this.

36:51 But the power of this is as Tableau users, you know, when you think of the value of the headless experience and the published data sources and the work that's happening with the data sources, the semantic modeling, all the metadata that you're doing, you can now start to go ahead and connect directly in Slackbot to those published data sources, giving the conversational analytics experience in the window that is going to be most prevalent, which is Slack.

37:12 So I think there's definitely value here in the team to learn more about that piece, and a great way to learn about it is getting engaged with it. So I was technically done, and pass it back over to you, Blake, but wanted to jump back in.

37:24 Ojo Basu: I, I wanna pitch in something. Um, sorry, Blake. This is very topical.

37:29 At the retail bank client that I'm working with, they were a Power BI shop and they swung… they leaned towards Tableau, and one of their top leaders is questioning, "Do we have a Copilot plugin or an API?" And, and I moved the discussion over towards, "What? We don't need Copilot. We have Slack here." So let's use what's natively possible.

37:51 To that point, Ed, I feel like the, the work in the flow piece with Tableau's integration with Slackbot is, is a winner, and we wanna advise our clients whenever we can to stick to the platforms that Tableau natively works amazing with. Data source, Snowflake, Tableau, Slack. That's your entire life cycle. Back to you.

38:14 Blake Feiza: Fantastic. Yeah. And, and always feel free to jump in, guys. Uh, it's not, it's not just my mic, so happy to share it around.

38:20 Um, back to the deck over here. A couple quick call-outs for the group that we have today. Uh, as you all saw, Hue had an incredible presentation. Uh, and we know that of the 18 people here live with us, and everyone who's gonna watch this as a recording, tons of you have fantastic content as well. Uh, share it with us. There's no prerequisites. You don't need to be a certain status in order to, to get up here and share the stage.

38:43 Uh, we'd love to hear from you and, and give back to that retail community that, that we've just preached about. So reach out to us in that Slack workspace, uh, or LinkedIn, whatever is most convenient for you, and let us know that you're interested in presenting. We'd love to have you.

38:57 And without further ado, what kind of a close to the, the Retail TUG would it be without showing you the QR code for the next meeting?

39:04 Uh, so I will grab a link as well to share in the chat for those that are with us live. But if you've got something that can scan a QR code, this will also take you there, uh, to that link for our October session coming up. So if I recall correctly, I believe, Matt, you are scheduled to speak in October, uh, covering some of the H-E-B retail media experience.

39:29 Uh, so if you work in retail media or you've hear-heard the buzz about retail media, uh, definitely will be a talk you don't wanna miss.

39:36 Yeah. Matt, any, any thoughts on, on what to expect in October? Or Ojo, thoughts from you while I'm grabbing that link?

39:44 Matt Huff: Yeah. Nothing, nothing too wild and crazy. I, I think as, for, for anyone who isn't aware, right? As we think about, as we think about retail problems, and Hue, you, you brought up a bunch of them earlier around inventory, sales analysis, that kind of stuff. Um, retail media is relatively new within the retail space.

40:04 Uh, it's been, it's been something that's been around for several years, but the, the fundamental problem of retail media is a little bit different in terms of trying to connect, um, customer behavior all the way through from a ad exposure to a transaction. And so there's a… If you think about what that means, there's a pretty interesting data problem to solve.

40:24 And so that's, that's gonna be what we're, what we're covering next time and, and starting to think about how do we, how do we connect those dots, and what does that all look like, and what is retail media, and just kind of giving you a background. So it should be a really good industry, industry-focused example. So…

40:43 Blake Feiza: Fantastic. And yeah, if you're on your computer right now, I dropped that link in the, uh, chat. Uh, otherwise, the QR code is available here. And I will flip ahead to my thank you slide. So thank you all. We did, uh, get through the curriculum a little bit quick today, which is always good to give some time back. Uh, I don't wanna keep you all here for, for no reason.

41:04 So we'll flip the screen off. Thank you all for joining, and see you in October.

41:10 Thanks, guys.