Build a Customer Dashboard in Tableau

Hello everyone and welcome to today's session. My name is Rhysa and I'll be your moderator for today. We are going to get started very shortly in a couple of minutes. We're just waiting so everyone has a chance to join. Uh if this is your first time at a data camp uh live session, welcome. We are going to be starting very shortly. We've got a few bits of accompanying info for you. Uh if you want to code along with us live today, so I'm going to be sharing those very shortly. We've got a data set and we've got uh some setup instructions for you. However, they are uh simple enough. So yeah, we'll share those via link very shortly. You we are going to be working with Tableau Public today. So if you don't have that uh set up already, then uh I'd recommend getting that set up now. Um I'll be back to repeat these messages very shortly, but if you haven't done so already, make sure you register for this session by scanning the QR code on screen. You can head over to dedic. com/weinars and I'll also post a link uh as well to that in the chat very shortly as well. Um that way if you register and you need to jump out, we can send you the recording as well as all of the accompanying info as well. We are also going to be answering your questions for the last 10 minutes of the session. So make sure you stick around for that too. My name is Ree and I'll be your moderator today. We are just about to get started. We're just waiting for the last few people to join. Uh if you want to cut along with us live today, I have just sent a link in the chat to the resources document that's going to uh contain a link to where you can get the data set and the setup info for what we're going to cover today as well as a few other useful links as well. So please do check that out if you want to join in live today. However, if you just want to watch along and catch up with the recording, make sure that you register, uh, you can do so by scanning the QR code that's on screen now, or you can head over to datacamp. com/webinars. And I'll also put the link in the chat for you now as well. Uh, but hopefully everyone who's here is already registered. So hopefully that's a little bit of a useless message from me. Regardless, uh, welcome and thank you for joining today. We're going to be getting started in about 30 seconds. I will just run through the housekeeping once more before we get going. Uh please do register if you haven't done so already. If you want to code along with us live, please do check out the resources document that I've sent in the chat. Uh that's got the link to the data set and the setup info that we're going to be using as well as a few extra bits in there as well. Uh and we are going to be answering your questions today. So if you have any questions or notes at any point uh during the session today, let us know and we will get back to you during the last 10 minutes of the session. So yeah, make sure you stick around for that. Brilliant. I think that's everything from me. So now I hand you over to your host for today's session, Richie. Richie, please take it away. Hi there, data scamps and data champs. Welcome back. This is Richie, and I'm very keen to be uh getting on with some Tableau today. So uh if you were here on Wednesday, we had a really nice session learning about dashboard design best practices. So today we're going to put those skills into action to build something. Obviously uh you don't need to have been here for uh Wednesday session. This is all going to be uh standalone but uh if you want to check out the recording uh yeah uh we'll send a link for that in the chat. Uh all right. So uh today we've got a really nice case study on building a dashboard for customer data. Obviously it's incredibly important use case. You need to make sure you understand your customers. Data is the best way to do this. Now I have not one, not two, but three guests for you and they are all data analysts uh from the same team at the telecom's provider Optimum. So uh first up we have uh Liz Singh. Hi Liz, welcome. — Hi. — Great to have you here. Uh next up we have Abe Gorten. Uh — hi Abe a great to have you here. — Thank you. — And last but not least we have Rashad Sha. So, uh, welcome, Russia.

All right. Now, I feel like, um, often times when I've got guests on, I'm like worried like maybe they have a bad internet connection or something like that. I think today, no problems. We got that covered hopefully. Uh, so yeah. Uh, no problems on the technical front. I probably jinxed it there. Anyway, uh, I want to call like the three of you like the Beasty Boys of data analytics, but I was thinking actually, no, Beasty Boys are a duo these days. We need a different band. Maybe like the yay of uh of data analytics. Wonderful. Uh so anyway, uh all three are Tableau and reporting experts. I'm going let you take it away. Uh Liz, I believe you're going first. — Yes. Okay. Uh welcome everyone. Really glad you're here today. Um, so what we're going to do in the next 45 minutes is walk you through a realworld business intelligence use case start to finish. We're talking about a problem that every major telecom company deals with every single day which is customer turn. Telecom is one of the most datarich, operationally complex industries out there. Your customers experience is shaped by dozens of factors all at once. The quality of their network, their billing experience, whether a technician showed up on time, whether a service issue got resolved. All of that data lives in different systems, different tables and even different teams and it all has to come together if you want to actually understand what's happening with your customers. That interconnectiveness is what makes telecom a fascinating and challenging space to work in from a data perspective. Which brings us to the churn analysis. churn is essentially customers cancelling their service. And it's not just a metric to monitor. It's a real business problem. And here's the part that most companies don't really talk about is they're usually more focused on acquiring new customers that they miss. What's happening on the other end? If you're bringing in an X amount of customer a month and then also losing that same amount within the month, you haven't grown at all. You're actually just running in the same place. And in business intelligence, we want to look past those surface numbers because churn is not random. And what we can do is go into the data, look at service history, billing patterns, how long a customer has been with the company, and start to identify whether there are patterns that explain why customers are leaving and probably even when too. And that's exactly the business case we're bringing to life today. We put together a simulated telecom data environment synthetic data structure to mirror what a real telecom data warehouse looks like to walk you through this end to end. I'm going to pass it over to my co-orker Rashad to take you through the data schema and show you how it all connects. Hey, hello everyone. Can you hear me? Sorry. So, just to switch as Liz mentioned like the customer data or the data in AL industry can be quite complex. Whatever you are seeing here is just a kind of a small mock data set which we created for the use case of uh this specific exercise and to showcase how complicated the data can be. So let's just start with a very basic layer we have currently at the bottom we have a network layer. Network layer is a store where store can store all our circuit information and all the location where we have installed those circuits and there can be a multiple more data sets on this but just for our simplicity we just consider this two table for today. On the top of network layer, we have a kind of an sales layer which allows us a user to sell that services uh which allows our sales from network providers and we can provide the customer data what services they handle into what type of uh products they are receiving and what type of billing and from the sales layer we go also it's all the sales stuff is connected to billing addresses or billing and orders so that we have different type of logs like what type of

subscription options they have what type of service order they were subscribed to. Uh the main difference between sales and a billing layer is like the sales might have a x amount of data but billing orders always will have less than x amount of data. In a real world there might be a multiple sales layer uh showing like there is a multiple point of sales in the in this specific case we are just trying to simulate stuff. So we only have a single sales layer moving forward as a data analyst to be to most of the stuff I would say at the end at the pre-production of the pre-transformation of the data. So we do have some analytics table which can help us to identify from the logs of disconnect and installation service order like how we can analyze and make our tableau more powerful. moving to a more deeper dive like how these tables are connected and what are the views you can see in this table. So this is a kind of a detail of like what we have used to connect this table. Uh what is a primary key and a foreign key in this table and this will be shared after the session I would say. And if you can see like this is an interconnectivity like from there is a table where we get a geographical context. There's installation status. There's a table which maintains who uh where we uh handle the sales. What is the loyalty of a customer or how long that customer has been a part of us all those type of different stuff. With the help of this data we can also enrich and see how our discounts are happening which are our market at risk what are the market we should go into. All type of analysis can be done for this purpose. Uh what we have done here is like we have created a kind of an executive analysis does uh dashboard for us to show like how important is that you understand the data and create the analysis and then improve the visualization that's a three-step process. So this is something after giving some thought and time what we can do is like we can create a detailed analysis of this. If you see this in kind of an executive analysis where you see what's active act active customer account what a disconnect specifically to focus on a churn uh type of data we created this data set is skewed with where we are losing more customer than we have active customers. So if you see we will have a high negative revenue and all those stuff what is the tenure for that skew we have set up to as like two to three years something can be more than that. Now if you see what is a disconnect trend at the base of the data set you won't have the reason categories you need to identify how disconnect reasons can be categorized together are they because of some kind of controllable impacts non-controllable factors. If there are something which can be controlled like something called find pricing or something or if it's something uh if some competitor is trying to poach uh customers then we should focus and create strategy around that. So what we are showing is like uh disconnect reasoning based on what can be the forecasted disconnect based on the prior data and what should be the steps for the strategy can be decided further and if you deep down this is a proper grouping which can be said like what's the reasoning group for the disconnects this shows the count the another point of view is alo also to analyze like where are we losing our customers are we losing for on specific apps. If yes, then why? What's the reason? And while talking about the just losing of customer, we don't want to see the number of losses. We want to see number of losses in compared to what they have a uh number of accounts they added. So if you see if anyone says sees this data and say okay this person has a 54% uh disconnect rate but I would say this per person is performing way better compared to this person having just 52 52% disconnect rate because the this person is handling 345 active accounts compared to this person handling somewhere around 245 active accounts. So this all become becomes a kind of a perspective what you want to show and this is also one kind of a high level analysis you can see like for each state where are we losing like money from which state we are losing money or who is a primary I would say point of contact where we are losing money. So this type of analysis can be done with this type of dashboards. There's even more you can do with this sample data set like you can even create a geoloccation maps. You can create multiple more things but for this code along I will pass to Abe who will show how you can set up and anal connect this data uh into to show some nice visuals.

— Thank [clears throat] you Rashad. Thank you Liz. Welcome everybody. So I'm going to share my screen really quickly. Let me just move this away and then share my screen. Entire screen. Please let me know when you can see the screen. We should be good. All right. So, like Liz and Rashar said, Liz set up the the case. Rash gave us a road map of as to how we put things together. So what I'm going to do now is to give you a detailed view as to how we can use Tableau for the analysis. So I'm going to just give you I'm going to [snorts] so this is I'm just going to set up a new data set. I think you have the data set. Uh you also have the instruction. So what you can do follow along if you can otherwise after this you can you will be able to go back and review it. Now, [clears throat] what we're going to do now is to bring the data range to Tableau, connect them, set up relationships, create um calculations, create the sheets, and then put them together on a dash. Put everything together in a dash. But we're not going to be able to do all of it for obvious reason in interest of time. So, I'm just going to run through it very quickly and just, you know, share this with us. So the first thing we're going to do with this [snorts] is to connect the data and then we're going to select the particular file where we want to bring in which is this one that we put together. Once this load, what we're going to do now is to obviously wait for it to load. Now we're [clears throat] going to bring the tables in. What we're going to do is we're going to bring the disconnect. This going to be our primary table. So it's going to be disconnect log. Bring it into the table pane or the Okay. So once we have this in, we're going to now connect the tables in. And then we're going to bring the disconnect enrichment. I could just simply type it in here to bring it as two is a table we want. So it's going to be disconnect enrichment. We're going to put this right on top of this and then set up the relationship. Now, if you're lucky and the connection is already established, we don't have to do anything. But this the connection is already established. Here we have the disconnect ID matched to the disconnect ID, the next we're going to do, we're going to do the geography or the disconnect reason I should say. So, this is going to be this table right here. We're going to put this right. We're going to connect that to the enrichment. And again, we have a match reason code to reason code. That is good. The next one is going to be the geography, which is this one. And we're going to connect that to the enrichment table as well. And then we have geo code matched to geo code. So far so good. We tracking in the right direction. And then we're going to want to connect the users because we want to know which users, as Rash said, are affecting the bottom line. So we bring this in here. And now we need to just connect this. For this one, we're going to connect the user ID, which is the user handle by ID for this table. And then we're going to connect the user ID. We have a match there. So the next step or the ne last table we're going to bring in is the account because we want to know which account we're going to tie this one to the disconnect log. So we want to know which accounts are active, which accounts have disconnected. And then with that one, we're going to connect the account number, which is going to be this guy right here. And We have it. And now we're going to now go ahead and build the calculations. Now, if you're following along, this should look similar to what you have on your the worksheet. So, we have everything connected. We're going to go now build calculations. Now what I like to do is [snorts] to if you're looking along you're going to see that some sections are marked in green. So we're going to we in part three now. We have part two, one, and two done. We're going to do part three. So I'm just going to set up the calculation. Copy this. Click on calculate field. Put that in there. Make sure we don't have any space at the end of it. and then bring the calculations in. All right, we have no errors, so we're going to chug along. We hit okay. The next calculated field we're going to create is the disconnected account. Again, we're not doing every single one of them. This is just a show and tell. So, we're just going to go through it. So, once we bring that in, again, make sure that we don't have any space at the end. bring the formula or the calculation in there.

All right. So, this is showing us an error. We want to figure out what the error is. It said bad character. So, let's see what's happening. Uh, it doesn't like this one. So, I'm going to remove that one off the All right. So, we have these two set up. We're going to do the next three. which is going to be the active customer count. We want to know how many active customers we have. So, we put that in here. We bring the calculation. All right. Everything looks good with that. Also, make sure there's no space at the end. We're good with that one. Now we're going to know now that we have the this uh the C active account, we want to know how many accounts we have that are disconnected. So we do that as well. We counted extinct the ID. We have that calculation done. Now we want to know the disconnect ratio because we want to know how many customers we have versus the disconnector. So we can figure out the ratio between the active account versus the disconnected account. These are our insights that we think is necessary to have so we can run a better business. So we put that in there. Make sure everything is good. We good with that as well. And then like let's shop put we want to the revenue loss because we want to know of all the customers that disconnected what re revenue we lost if we had retained them. This is money we could have been making but because they disconnected this is money we have lost. So we're going to look figure out what the error is. Says reference. Okay. So that means one of these fields. Let me just fix that. Let's see what it says. Uhhuh. All right. This is very good. I'm glad we have this error. So what this means is that this is coming up as uh a string but we want it to be not a string. So we're going to look that up. We're going to bring it up. We're just going to change that the data type because it's revenue. I figure we're going to do decimal. So we're good. We fix that. Okay. So we have now we have all the calculations that we want to for this exercise we want to put in. The next step now is to build the sheets because obviously we have to have the sheets to create the dashboard. So now that we have these calculations in this is what we want to focus on for this exercise. It's a scale down version of what RAP show gave us a holistic picture but for the sake of this exercise we're just doing a scale down version of it. So we have in here. Then what we want to do is now bring in the disconnect event. So what I want to do just minimize this so I could see the disconnect event. I'm going to put that on the text. All right. So we have two 400 4,500. What I'm going to do now is to just name these sheets disconnect event for reference so I know which to call when I'm building the dashboard. Now we're going to do the disconnect ratio as well. Put that in here. We're going to bring the disconnect ratio. Put that also on the text we have in zero here. So let's fix that. I think that has to do with Let me just check into this one real quickly. Disconnect. All right, we we'll come back to that. Let me just build everything and we're going to come back to that and fix that. So, we have the KPI lost with that one. We're going to drag the revenue. We I think we're going to have the same thing here. Revenue lost. All right. We're going to format this to currency. I'll just do standard for now. And then we want to know the active customers. So we just bring in the active customers. These are our C we need to create so we can set up the dashboard to active customers. Active customer account. Okay. That's 1,700. So now let's go look to see why

the disconnect ratio which is this guy right here is showing zero. So we can look at the formula again. It looks good here. Let's go to this one. active account. Okay, I think I need to fix something with the disconnected account. Let me just bring this one back. Disconnected account. Yes, this might be it. All right. [snorts] In the interest of time, we're just going I'm just going to gloss over this and then we're going to set up the dashboard so we don't lose time. But once we fix this, everything should fall into place. So [snorts] now with this built right, what we're going to do now to see how we're going to put it together in a dashboard. We have this pre-built already with every calculation in the mix. So we're just going to bring it in. So I'm just going to minimize this right here. And what I like to do often times when I build set up templates. So I have a template. It makes it easier for me to just drag and drop um drag and drop things into them and format it the way that I want. So, we're just going to put this here. I'm just going to re label this to churn because that's what we trying to do. Let me just rename this turn. Oops. And then we're going to just rename this. Say ch analysis analysis. This should be good. No, I can't even spell. Okay, I think we should be good with that one. All right. [snorts] So, with this, these are the inside that we want. I think did show us we have disconnect event. We're going to put the I could put the disconnect event right over here. Then get rid of this one. Then we're going to put the disconnect ratio. Let's fix this by making sure this is entire Fix that entire width. Then what else do we need? We need the lost revenue. We get rid of this. We fix this formatting and then we put one more. What should we put? All right, let's put the active account. Now, you obviously can rearrange this however you want, but we're doing it this way for exercise purposes. So this dashboard gives us a view where we know how many account are disconnected, what the disconnect ratio is, what the revenue lost is and active customers. Now you might be interested to know why do we have active account 1700 but then discount account 4500 and the ratio. What we did on purpose just to bring visibility and insight into this is to figure out if the disconnect ratio is at 73% because we're doing the math based on the active versus disconnected. We want to know in essence we have more customer that are disconnected than we have active customers. So like Liz said, we don't want to bring more people in or more customers in while we're losing a whole lot more. That is not a business model we want to follow. So we want to make sure at any given point whatever we do we have more customers active than those that disconnected. So now that we have that high level overview what we want to do now bring some graphics into this. What we're going to do I'm going to put the reason breakdown the reason why customers are disconnecting then get rid of that. So this is the breakdown of the disconnect reasons. We also want to know I'm going to put the sales disconnect reason

for this is we are trying to look to see of all the sales of all the disconnect which sales agent is predominant or it's an outlier. This way we can trace back and see is there something they are doing that is in essence the reason why people are disconnecting. Are they not giving people the right information? Are they being overzealous and just trying to game this g game the system and try to do things to get customers in and not really follow through or whatever the case is because I've seen situations where a sales agent will promise heaven and earth to the customer. They give the service and things don't turn out to be the way they were promised. So we want to trace back and look to see is that what's happening especially if we have a particular agent or agents that are outliers. So we can look to see where the gaps are and mitigate them. Again, these are all design ideas or design principles that you can follow to try to figure out. You're not just looking at data, but looking at situations or what's going to give you the better insight to identify the gaps and mitigate them. Because at the end, what we really want to do is look to see what we're doing well and capitalize on it and what we doing we're not doing so well and mitigate that. So, with this, we also going to probably want to bring, let me just put this in here. We want to do We have the sales agent. We're going to put the retention agent also here. I'm going to put this right Get rid of this. Again, this is also an insight that's giving us an idea to see which agents have an ally in disconnect, customer call to disconnect, or we're doing everything we can to retain them. Are we giving them incentive to retain them? What are we doing? Why is a particular agent agents have been being an outline and disconnect? So we can again figure out what the issue is and mitigate that. So the next graph that I want to show over here I suppose we can put yeah I think rear did show this as well. The region disconnect get rid of this and then do that. All right. So, we have this. We're going to see that we have these little thing poking out here. We're going to go to layout. Identify what that is. I think it's one of these. It's not that. Let me just go back down here. Okay. All right. What this helps you in the interest of time, I'm just going to skip through it. What this helps you do is identify which particular element I want to say or object you want to get rid of. just click through it, identify it, and remove that. So, what I'm going to do here is to pause on this one and see if we have any questions, insights, analysis, so we can talk and a better, you know, to be able to get a better insight as to how we put this together. So I would um I guess um Richie I guess we will just you know circle back and see if folks have any questions or any further insight that we want to get provided. — Uh thanks a lot. Uh so no questions from the audience yet but uh for anyone in the audience if you do have questions then please do ask them now. Uh in the meantime that was a very cool dashboard so I have a few questions for you while the audience is thinking. — Yes sir. Um so in terms of the target audience for this kind of dashboard uh so a lot of this is um it's a fairly technical dashboard. How would you adapt the dashboard depending on like whether it's another analyst looking at it or whether it's for example like um a customer life cycle retention manager looking at it. Um so if you got someone from the business side of things, how would you go about tweaking the dashboard for different audiences? — Very [snorts] well. So you're talking about the visuals, how to change the visual to suit a particular need. — Yeah. Or I mean even like the whole thing, would you change the layout? Would you just change like the format of individual plots? Like how would you approach uh customizing for different audiences? — Very good question. So I think me personally when I design dashboard I look at the audience um especially when I'm doing the dashboard for executives. The executive they just they don't want granular detail. They just want high level detail. So I guess to answer your question, it's all about figuring out what the audience type is. Uh even color scheme matters, right? And layout

matters. So I try to put that into perspective, take that into consideration to look to see what the audience is. So to answer your question, if I want to, let's do this. I could make a duplicate of this and then go here, delete everything. Delete all of that. Delete this and put this right in the middle. So this way what happens now is this. I have everything put in one gigantic view because often times sometime this is all the individual wants to see. Now, depending on the audience also, you probably want to set up a filter. You put up a filter here that can just go through each one of these and be able to zero in on what they want to see. So, I don't know if that answers your question or not, but I think about the audience first, what they're looking to do, how they want to have it laid out, and build it out, but that's that suits your needs. Sometimes if you're doing for multiple people for example, multiple department for example, you want to build in a filter that allows each department leader to select the folks to look at. — Okay. So in general, you want to I guess display only the information that people need to see in order to make the decision and you don't want any extra information that's going to distract them from that. — Correct. Yes. In that sense, that's exactly that's what I'm saying. Yes. — Okay. Nice. Uh and you mentioned color scheme. I mean since we've got some time uh do you want to talk us through how you go about like changing the color scheme for uh like a particular audience like I guess first of all like what colors do you pick? Are you going corporate color scheme or is there another principle uh behind how you choose the colors? — Very good question. So because we did this, you know, I just used the obvious the default for when we do it on our side. We usually like to mirror our corporate um colors, you know, um this is that's, you know, so to answer your question, yes, I pick colors that match the corporate theme. Um if I'm building it for my industry or my company, that's what we go for. But in a nutshell, I think that's what I'm talking about color scheme. So sometime color scheme can be very distracting. So if you don't pick the right one, it can be so that instead of folks identifying what the problem is with the data, they're talking color scheme. — Okay. Uh all right. I mean, if you want to demonstrate quickly just like how you change the colors, that'd be quite useful. — Okay. So we're going to go here. So let me just go into go to this particular sheet. We pick this. All right. Let me just bring this. Uh, okay. Oops. Which one is Okay, disconnect. Okay, let me just move this out of here. We're looking up but changing these colors, you mean right? — Yeah. — Okay. All right. So, [snorts] let me just move this a bit. Move this out of the way. I'm going to make a copy of this. Uh, duplicate. remove something like this works or — Okay. Yeah. Uh so I guess uh I guess the idea is that in is in that marks thing there's like the I guess the color thingy you map that to a different um uh do variable? Is that the main process? — Okay. I like it. I think I know what you're talking about. I think I misunderstood what you were asking for. So, let me just go back to which one is this one. Reason. I think it's this one. So, we go here.

Yeah. I can't remember exactly how it works. I know there's a color option there. And Oh, — well, we could just move it. — Okay. some somewhere in Tableau there is a way of changing the the color scheme. Uh all right m maybe we move on uh while thinking of that. So I guess uh one of the big uh points of this is around trying to figure out how do you prevent your customers from churning? — Yes, essentially. Yes. Do you have a sense of like what the most important um uh factors in that tend to be like uh because I know you've all worked on this and maybe you can all talk us through this like uh what tend to be uh the most important features there in that model of like why a customer churns or not. [snorts] — All right. So we have this built where we you know it's like so basically from my experience it comes down more with I think Rash touched base on it service issues and Liz also touched on the billing and um and uh bad experiences. So one of the models I feel you know we we kind of put together is customer loyalty where we can map out which customers are likely to disconnect because they have service issues. So we map out customers with service issues, customers with technician issues, customers with billing issues and customers with OSAT for example NPS net promoter score. If we can identify a particular area that has a high turn rate especially they have competition there we have different service provider there there's a very good chance these customers would turn if things are not going well. So the identification is trying to figure out which customers fall into these categories and what we can do with them. Do we provide them incentives like promotions? Do we make sure that when they have service issues we respond to them? Outages for example is a big part of this too where particular area that experienced a lot of outages especially with the competition in the area. These are all catalysts or things or variables that contribute to a customer having a higher desire to disconnect their services. So lots of things but I think the most important that I want to touch on is outages service issues which is obviously connected to outages and the experience that they have when they're calling to call center. Okay. Uh that makes a lot of sense that uh if you're having service outages then customers are more likely to change and I guess that's true in many industries if your product doesn't work then customers are going to not continue to buy your services. I like the way that this is going to show up in the data. Uh, all right. So, uh, getting back to Tableau, I guess. Uh, I'm curious as to whether there are any like, uh, Tableau features you think everyone should be aware of. Like, are there any things that, uh, like what's your favorite feature that, uh, you want to promote for people to use? Um, uh, Rash, do you want to take this one? — Sure. So, specifically for Tableau and like real examples, we couldn't show that in this specific uh, demo. Like I would say like anything to do with tab pi is a one of the good nice nice to have things on tableau like as you can see the data we what we have created a large scale we have some kind of data uh quality issues we need to handle them so we can have our custom Tableau the custom scripts for Tableau in Python and then in leverage both of things together for analysis there are some other tools like if you don't want to do adoc on Tableau we can always pre-clanse our data in the at the source and load but that sometimes we miss to do the pre-clansing and it's like too much a hassle to go back into your data storage platform and do the cleansing and reload the uh all the data sources it's better to like if it's just oneoff thing we can always connect to a tap pi server uh and then uh run our custom scripts on that — also with ty you can do a custom modeling if you want like some kind of statistical modeling or something like that No, Tableau is very cool. I do love the idea that like if you want to do some like uh complicated data cleaning or I guess if you want to like do some machine learning in there like you don't want to do that necessarily in Tableau itself, you just want to get all that crunched in Python and then uh leave Tableau for focusing on the visuals and creating the dashboard. — Okay. Uh Liz, how about you? What's your favorite Tableau feature? Honestly, more recently, I went to a um demo um where it showed that you can connect like Salesforce to Tableau and like all of the data gets to kind of upload there and it um regenerates itself there. So, I would say that that's probably one of

my more favorite newer features. — Okay. Yeah. Uh and of course, Tableau is owned by Cell for so it's about time they had some proper good integration between the two systems going on. So yeah, certainly if you're building dashboards based on uh like uh sales data or other customer data that's directly in Salesforce, you don't have to like export it to a database and then reimpport it into Tableau. — Okay. All right. Um actually, are there any AI features in Tableau these days? Uh I don't know whether they've been introducing those features yet. — No idea. Okay. — Not sure about that. No, I don't really think so. But maybe Rashad would know honestly. — Uh I know they were hyping it for a long time. I don't know what got launched. — I we can hear you. — Can you hear me now? There we can so for sure there are some kind of Tableau agents and all those thing but I would say we don't use that much in our daytoday as a day-to-day analysts kind of stuff because sometimes what happens like you have a so large schema while you're working with that it's little difficult to explain all the stuff to agents you need some kind of a I would say middleman to explain that stuff and it's always faster to write that script on writing manual compared to going to agent and ask him to write. So yes, there are some innovation which tablo is doing in the agent side like all those stuff but still it still will take some time to in just in day-to-day life. — Okay. So um it's a sort of coming soon probably useful in the near future thing but for now it's like uh just sort of uh watch it and see — nice to have feature. — Okay. All right. And I'm just curious like uh since you're building a lot of these uh dashboards uh I guess like uh who do you who is your sort of main audience for these like how like who do you build most of the dashboards for then? So that I would say that type of categorizes two type of audience once you get where the audience one can be like uh they want just a summarized view of everything what's happening. There might be a second audience which we need like lot more time to develop those type of dashboards like have everything dynamic. So they want to view what is their performance their sales and everything like a kind of a personal dashboard sheets. So there's like two ways of dashboard we create like kind of an executive dashboard and a day-to-day dashboards. And both of have their like their own approach of creating their those type of dashboards. — Kind of like to Abee's point like sometimes like it would be for you know VPs who have no interest in like looking at things that are more granular and they want to see everything that's very high level. So then that would be a different type of view. And then if we're, you know, have a different type of ask and it's like for a sales leader for example, then you know they want to get down to the nitty-gritty of everything. [snorts] — But predominantly we build it for leaders leadership right like they I don't want to use the word ground level leadership but immediate leadership that look at agents or look at reps you know predominantly that's what we go for but like it's like depend on who the audience is to build it. If it's a person like you on the suit in the seauite, they don't want the clutter. They just want the meat and potatoes. Yeah. — Okay. Uh very simple stuff. Uh so actually that leads me uh to one of the big problems with analytics is you build all these dashboards and then people use them for a week and then they abandon them uh and you feel like you've wasted your time. So, I'm curious, how do you uh make sure that um you're building dashboards that will get used? Uh do you have any techniques for like ensuring you're building the right thing? — Me personally is having constant update and modification. Um sometimes changing the schema around, sometime moving this around, especially if it's something ongoing, then you know people are going to have to come back to it and visit because it's ongoing. They have to keep track of it. But if it's something like a oneoff where it is more or less very high level and you know like especially when you do KPI dashboard for example those are pre pretty much consumable because leaders have to constantly go and look at it to track performance and coach it as necessary as necessary. But when it's more like the executive style dashboards, people just go in like once a month to look to see what the, you know, what the trend lines are. So I tend to sometime once in a while, you know, change the um the layout just to because when people come and something is different, they see movement, right? They see something has

changed, so they want to come back. is when they go and they look the same all the time it feel like nothing has changed so they stop coming back. — Okay. Uh yes I can certainly tell there's a big difference between something where it's a very operational dashboard and it's like well you want to know is something breaking right now in real time versus it's just something about well what's changed this last month this last quarter or something. You're only going to expect occasional views. All right. Uh we got a question from the audience. So uh the enthusiast asks uh how is churn being defined in this data set? Uh is it with an activity downgrade etc. Uh I don't want yeah do you want to talk through this and you might explain differently like net retention rates gross retention rate all these sorts of things. Yeah, this are total disconnects, right? So, yeah, I'm glad you brought it up because a customer could have multiple services and they disconnect one, but this is account disconnect. Flat out disconnect. Okay. Um, and do you have to build like how do you take account of different metrics then uh or endpoints in this like what um would you ever would you want different dashboards for different types of churn or would you try and build them into the same dashboard? How do you divide up what you're going to do uh within a single dashboard? — Very good question. So we can bring it all in one view and then have one dashboard that just gives us the entire disconnect. So we know the customer disconnect and then also look another dashboard that explains downgrades right so we can see what product people are downgrading or removing from their services and trace it back. So I think it's going to be important to have a high level in the grand scheme of things how many customers we have that are disconnected. But while we worried about that we also want to worry about within the internal those active customers which one of them is [snorts] removing services so we can try to figure out why that is. Is it a particular product that has been downgraded? Why we're not doing well with that product? Are we failing on our promises with that product? So we can look to see what the insights are and how to mitigate them. So I think a lot of times people are focusing just on the disconnect but then they fail to realize that within the active customers people are disconnecting the particular services. We want to track that and figure out why that is and how to bring it back full circle. — Okay. Uh so there's often a disconnect between I've built a dashboard and then I'm going to take some action to solve a business problem. And it sounds like uh you want some level of diagnosis in between there. But talk us through how do you go from like that uh I've got a churn dashboard to I'm going to have an impact on th those churn metrics. I think you know um when we bring everything in remember we were talking about connecting it to the outages people have connecting it to um NPS how the customer is rating the service that we're providing them via the customer interaction with an agent. um if a customer has a tech visit them and we uh there's something that I think most people forget is when you do a dashboard and you look at the surface level details as to this person had disconnected. You know what's happening you don't know why. The why often comes via looking at the pattern the customer has had. So we have a model that when a customer calls in, we track the interaction. We can review that to look to see these customers that have disconnected. Here are all the interaction they've had with the with our agents. What is the common denominator? How are our agents responding to them? How are they responding back to us? Figure out the common denominator. I believe we cannot be 100% at anything, but if we could come close to it as possible, we can mitigate it. Hence the reason why we figure out and one more thing that I think most people miss is when we're doing well, we just assume we're doing well, so we just keep doing it. But we don't know why we're doing well. We got to figure out what the why we're doing well, the common denominator and then and and amplify that or explo exploit that, you know, sorry to use the word exploit, but manage that and make it better. If we're doing bad based on the common denominator, we communicate it as well. — Yeah. And I think like to Ape's point, like looking at the patterns is the most important part. like we're never going to get everything 100% right and like

you know completely eliminate churn, you know, like people are always going to leave for whatever reasons and some reasons we may not even be able to track. Um, but it can fall into a bunch of different buckets. Like it could be a geographical issue or to Abee's point an outage or it could be, you know, a sales rep in um a particular space or just a specific sales rep where we would have to go in maybe do extra training or have a conversation. And it's not even, you know, sometimes about like pointing fingers. It's just, you know, having a conversation and seeing maybe like what we're doing wrong and how we can fix it. — Uh, absolutely. Yeah. I mean, uh, sometimes you get a bad like salesperson. I used to work in a call center. It's like, uh, okay. Yeah, there are some people who are useless, but also sometimes it's like I just had three customers in a row who's like yelling at me for like half an hour and it's like, okay. Yeah. Uh, it's tricky like trying to pin down exactly what's going on. I guess uh the data ought to be able to tease out some of these things. All right. — That's why we don't focus on the one-offs. We focus on a like a for a three four five for the entire year. You know, you map it out that way so the oneoff don't be doesn't become noise that you're chasing. — Absolutely. Uh okay. Uh we've got another question from the audience. Uh Runa is saying uh as an analyst should dashboards be standalone or do you want to report to accompany them? Uh good question. Um — I would say like for those type of stuff like two type of dashboards. One is executive dashboards where it's better to have some kind of report or summary but where there's a dashboard which is for like day-to-day monitoring and tracking. That type of dashboards won't require any kind of summary or reports. That's kind of a day-to-day usage dashboard. But that dashboard needs to be uh modular and we need to create in such a way that can be used in different devices like either iPad phones or laptops. But whereas an executive summary you need a proper snapshot and a dedicated summary which goes with that explaining what are the positives or what are the negatives of that specific things and report on those uh I would say KPIs. — Yeah, it's a tricky one. Like I have to say I've seen quite a lot of reports where it's like oh there's like a screenshot of a dashboard in there somehow. It's like well you know you you're kind of getting the data from one place to another but um yeah if you need a narrative then you got to have a report because dashboards more like designed to be explored or interpreted uh by a human. So yeah um if you're sharing a story with lots of people about what's going on then you probably want report format — right? So the dashboard is more or less what's going on. The report is why this is happening. — Oo, I do like that distinction. That's good stuff. Um, all right. We are coming up to time now. Before everyone dashes off, I'm going to say next week we've got a couple more sessions on a similar theme. So on Tuesday, uh, we've got a session on how to create a marketing funnel using uh, Microsoft Excel and Copilot together. On Wednesday, we got a session on data visualization for data storytelling. So uh we've got uh Brent Dyes who's like a very big naming data storytelling coming along for that. So please do come back for the sessions. Uh then on April 1st we've got the radar virtual conference. It's one of our biggest events of the year. Uh please do sign up for that if you've not signed up already. All right. Uh with that uh thank you Abe, thank you Liz, thank you uh Russia even the you're invisible at the moment. Uh but uh yeah that was a really great session. Uh very much enjoyed that. All right. Uh thank you to everyone in the audience who asked a question. Thank you to everyone who showed up today. See