122 | Dashboards on Demand: Mastering AI Tools for Rapid Business Insights with Isar Meitis

0:00Hello and welcome to another live episode of the Leveraging AI podcast. This is Isar Maitis, your host, and we have an exciting show for you today. We're going to talk about one of my favorite topics, which is data analysis and making data-based decisions, which in my eyes is one of the most critical aspects of being successful in a business. So a lot of people take or make business decisions based on gut or their knowledge or their understanding when the reality is most businesses has lots and lots of data that can help them make educated data-driven decisions that by definition will achieve better results.

0:44This is true across every aspect of the business, whether it's sales, marketing, HR, finance, and you name it, every aspect of the business, operations, manufacturing. If you have data of what's actually happening and you know how to analyze it, you can make better decisions and hence improve the success of whatever process that you're trying to do. The problem with data analysis is that we quote unquote are somewhat lazy. And the reason we're somewhat lazy is because every platform we use out there gives us some kind of data analysis. So if it's your CRM, if it's your marketing automation, email platform, inventory management, financial platform, whatever platform you're using, it has X number of reports that comes with it.

1:38And we just got used to living with these reports. In better cases, you have somebody in your company who is really good with Excel, and then some of the stuff happens either manually or quasi-automatically, and you can take the data from that tool and then run it into an Excel thing that you already have ready and get additional benefits and additional capabilities. The problem is there are many cases where we want to know more. We want to get specific insights about specific things and we're going to look at different examples that are available in many businesses. We're going to look at them today and we don't do that because it's just a lot of work and we have a lot of other stuff to worry about and to take care of.

2:22And what I'm going to show you today is that by using different AI tools, you can get both tables as well as charts and graphs of basically anything you want to know if you have the data, and we all have access to that data. So being able to use AI tools to do data analysis in a very accurate way and save yourself hours of report generation, and that happens in every single company, salespeople, finance people and so on spend an hour to half a day a week, every week in every single business in generating different types of reports, either for themselves or for their higher ups so they can show what happened in different channels.

3:05And all of this can now be automated or semi-automated with AI tools while getting better data analysis. So we win on all fronts. in the next few years AI technology will change our world dramatically whether you are a business executive trying to catapult your business forward or just somebody who refuses to be left behind and want to advance your career this is the show for you I'm your host Isar Maitis a serial entrepreneur and an AI enthusiast you'll hear invaluable practical tips from innovative business leaders AI practitioners, and some of the brightest AI minds in our world today on how you can leverage AI in ethical ways to advance your career and grow your business.

3:58What I'm going to do, I'm going to share my screen, and we are going to get started with the actual how to do the things I just talked about. Those of you joining just on the podcast, I'm going to explain everything we're seeing on my screen. So if you're driving or walking your dog and you can't watch the screen right now, that's perfectly fine. You can listen and I'll explain everything that we're doing on the screen. But feel free to join us. We do this every Thursday live on Zoom as well as on LinkedIn Live. I'm going to actually start with an example that we're going to dive to in the end.

4:35And the reason we're going to dive to it in the end, because it's the most advanced stuff. but I want to show you what's possible. So what I have right now is I have open in front of me a page of Claude. And what Claude has is it has a tool called Artifacts, which allows you to see or write code and see the outcome of that code right there and then on Claude. But there's also a tool in Claude that's called Projects. And what Project is these mini automations that you can create that will allow you to run them again and again. So what I'm showing you right now is I have a Cloud screen open in front of me.

5:14Those of you who are listening or watching and don't know what Cloud is, many people know ChatGPT. It's just a competitor to ChatGPT. It's a large language model. It's really good at several different things. One of them is what I'm going to show you right now. So what I have is I'm going to drag in here a file that is an export of one client from a CRM. So it's going to have the, of a specific account, everything that's under that one salesperson's accounts, different clients that he's managing, pipeline items, and so on. So it's basically a pipeline report out of the CRM for one specific account manager across multiple clients that he's managing.

5:57So I drag the file in there and all I do is I click go. I don't type anything. I don't prompt anything. I just drag a CSV file in there and I click go. And what you're going to see on the screen is that it's going to start writing code. It's actually going to write a code that's going to generate a dashboard that is replacing the handmade report that this sales manager had to write before. And the dashboard will have all the different things that he needs in order to present this to his team, to his boss, to himself, whatever he needs to do, without having to do anything other than dragging and dropping the CSV file into Claude once a week and click go.

6:40We'll give it a few more seconds to run. The cool thing about this, and you already have, if you're watching this, now you can see the dashboard on the screen. So we have total pipeline value of$33 million, average deal size$2.802 million, total opportunities 12, of. And then it's divided by the different types of the business, which domain it comes from. In this place, it's not defined. I have a pie chart that's showing the opportunity probability distribution. So 0 to 20%, 21 to 40%, 41 to 60, 61 to 80, and 81 to 100%. So basically, how many deals we have in the different probabilities of success.

7:19And then another graph that shows me how much money is in each probability for success. And all of this is happening. And on the bottom, there's an even more interesting thing. On the bottom, there's a decision support table that suggests what I would do with each and every one of the accounts. So the first account for accounts are really close to closing. I should focus to close, consider moving to the next quarter, consider moving to two additional quarters out. And the way he does this, I will show you afterwards in the end, because as I said, this is a little more detailed and sophisticated than some of the things we're going to look into.

7:58But I wanted to show you how incredibly powerful this could be right as we get started. All I had to do to see this dashboard that has a lot of detailed information, both in data as well as charts, as well as decision-making support matrix, by dragging a CSV file and clicking go, and about 30 seconds later, I have a functioning dashboard that I can also publish and give access to other people. So let's go back to the presentation and I'll really start from the beginning. If you are on this call and you find this interesting, I would love your feedback on would you like to know how to do this? If you're listening to the podcast, just wait till the end and we'll get to that as well.

8:42There's already a question, how does Claude know what to do with the file? So we're going to get to that at the end of this episode. I promise, as I said, this is a little more complicated. I'll show you exactly how I created this, all the steps that I took. So you'll be able to replicate the same thing yourself. But again, the idea was to show you how simple that can be once you do all the process up front. So we're talking about quantitative data analysis, right? So there's two types of data analysis that we do. We do qualitative data analysis and we do quantitative data analysis. They're both very important.

9:17But in this particular case, we're talking about data like numeric data, quantitative data analysis. And in the company, I want you to think for a minute how much quantitative data you have in your business. And the reality is you have a lot. So if you want to write in the chat, if you are on LinkedIn or on Zoom, just write in the chat what quantitative data analysis you have in your business. but I will start sharing here. And really, if you're driving and listening to this, I want you to think about it for a second. But there's multiple aspects of the business that has quantitative data. And that varies from financial information, obviously.

9:56Sales funnels, how many people are in different stages of leads and different stages of sales and how many proposals we've sent, how many we won, how many we lost. Customer service performance, how many tickets were open, how many tickets were answered, How many were closed in how much time? How many calls did we get? Inventory. How much inventory you have of each type? How long has it been sitting on the shelf? How much value is in their marketing? How many ads have we sent out? Conversions, funnels, et cetera. Compensation plans of employees, employee performance and evaluations and so on. It's really endless.

10:33every business have an endless number of data points that are all quantitative data. Now, as I mentioned, for every business, you have the data. The data lives in some kind of a system. It lives in your CRM. It lives in Jira. It lives on Monday. It lives in financial platforms, HR platforms. It lives in all these places. Almost each and every one of these platforms gives you some reports, but not all of them, but all these platforms allow you to export the data as CSV, right? So you can take the data that you have and export it in a format that you can open in Excel. And that's what most people do today.

11:15If you want more than what the platform will give you, you will open it in Excel. In many cases, you even know what you're looking for, right? You know what additional information, what additional insights you want to find from that data. Sometimes that's not even true. You just want to know, I have all this data in my sales system or my CRM or my inventory or ERP or whatever. I want to learn more from it. What else can I learn? So sometimes we don't even know. And so even if you do know, don't always know how to look at that information, right? So what actions do I need to take to find additional information I want to get.

11:55But if you dive a little deeper, then there's questions you want to ask, let's say in this particular case about CRM, what actions do I need to take to close these existing opportunities that I have? What is the total value of pipeline that I have right now based on the probability that I have? These are just CRM questions. What should we focus on to maximize this specific opportunity we have right now. So we have 12 or 20, which one should we focus on in the next two weeks versus which one should wait for next quarter? All these things are sales related, but the same questions can be asked about every aspect of the business, whether it's manufacturing, whether it's inventory management, HR, and so on.

12:39So let's look at a few examples on how AI can help us take raw data and analyze it beyond what we can find in the built-in tools. So the first example that I want to give you is a CRM example. And what we're going to look at is pipeline data and how we can analyze it. So I'm going to open in this particular case, ChatGPT. And we're going to scroll all the way to the top. And we're going to take a look at what we have. So what I've done here is I took a CSV file that I exported out of my CRM and I wrote the following prompt. You're an expert data scientist with 20 years of experience in pipeline and sales data analysis.

13:29Please review the attached file and let me know what data it includes and what insights can we gain from it. So you can see I'm starting with a very general question, meaning I am not telling it specific things that I want to find out. I am not giving it specific information. I am just telling it to tell me what it finds. And I do this for two different reasons. One, it gives me new ideas that I didn't think of on information or insights I might find. But two, it helps me understand that it understands the data. So think about it before you would give this task to an intern in your company or even your head of business intelligence.

14:13If you have a new type of question, you want to understand that they understand the data that we're looking at. And so I always start these processes by asking a general question. And what you can see is that it analyzes the data. They tell me everything that's in this data set. So ID, account name, opportunity name, type, opportunity domain of the field it's in, probability, close date, open date, opportunity size, like all the information that exists in this data file. And then it tells me insights that we can find. Opportunity scope, opportunity types and domains, sales pipeline health. And for each and every one of them, it gives me a description of what that means.

14:50If you don't know what sales pipeline health means, it means the mix of probabilities and forecast categories can provide a health check of the sales pipeline. High probability opportunities in later quarters might need more attention to ensure closure. And so on and so forth. It gives me more additional ideas on what kind of insights I can gain. And the whole point is going from data to knowledge to insights, right? because insights are actionable. Data is just the raw information that doesn't really tell me much. So let's continue. So what I've done then, I wanted to know what information can I, what decisions can I make based on the information that I have?

15:39So based on where we are in the quarter, are we in the first week of the quarter? Are we in the middle of the quarter? or in the last week of the quarter. And based on the probability of the opportunity, I need to make specific decisions. And in the business, each business has its own guidelines or rules of thumb of what to do in different steps. But you can build a system that will do this. So what I've created, I created another file here that just exists as a Word document. And that Word document shows the matrix of what to do. So if you're in month one of the quarter, what to do in month two of the quarter, in month three of the quarter, what to do.

16:19And for each and every one of the months, the data shows what happens if the probability is less than 25%, 25 to 50%, 50 to 80%, and 80 to 100%. And you can see it's basically a table, a matrix, that shows what to do in each and every one of these scenarios. And underneath that, there's an explanation with an example. So the example says, if we are in the second month of a quarter and the probability to close an opportunity is 40%, the salesperson should move the close date of the opportunity to the next quarter. So it literally just looks at the sale to explain how the table works. So I attach that as another reference to ChatGPT, just uploading another file.

17:04And then I gave it the following prompt. Let's start by focusing on account one, because we have multiple accounts. This is obviously fake data just for the purpose of this presentation. In your life, it will be an actual name of an account. We are in the month of July of 2024. Our quarter ends at the end of September. I would like you to use the attached, and then I tated the name of the document, Pipeline Status to Shizu's Making Matrix, and create a table with the following columns. Suggested action, opportunity name, probability, current close date, suggested close date. So all the information I want to see either for myself to make the right decisions or that I want to present to my boss or as the head of the department that I want to share with my team so they can execute based on that.

17:58And then I explain what each column is. So suggested action is based on the attached pipeline status decision making matrix. Opportunity is the opportunity naming dataset and so on. I keep on explaining each and every one of the columns so the AI knows what it is. And then I ran it and it created a table that shows for just the first account, so account one, so one account manager, each and every one of the opportunities, its probability, its current close date, its suggested close date. So the suggested close date is if the suggestion is to move it two quarters out, what is the new close date two quarters out or one quarter out and so on?

18:43Or if it's not changed at all. And then it gave me the list of each and every one of them. What you can see in this table is that it's all over the place because it literally went by whatever order the data was versus based on what makes sense as far as decision making. and so I ask it to change it. I'm like, please aggregate based on the first column. In other words, merge all the cells in the first column that are the same action and keep all the other columns as separated rows. And then it created as the fixed table. So in the fixed table, what I have is I have all the opportunities that I need to push to close, which are between 80 and 100%.

19:26and I have four of those in my table. And then I have five opportunities under the category of push hard or move to next quarter. So you can analyze them one by one and you know which ones you can have a higher chances of closing. And then I have consider moving to next quarter. I only have two opportunities. So this is a really powerful tool to help you with decision-making based on a rule of thumb that your company has that you don't have to do manually to present, Again, either to your team, to yourself, or to your boss. You can literally do this in seconds, time and time again, once you have this up and running.

20:02I then went further ahead. I said, great work. Now let's do a broader pipeline analysis. Let's go and look at all accounts. I would like you to show me a chart that shows how much money we have in the pipeline for the current quarter, categorized by probability. And I told it which probabilities I want to have. So less than 25%, 25 to 50, 50 to 80, and 80 to 100. That's just the way we look at opportunities for our business. And you can see that it created a chart that shows me how much dollar value I have in each and every one of the probabilities. That shows me that I have a huge amount of money, too much money in 25 to 50%.

20:47So relatively low probability, I have, I don't know, about 30 % of the potential pipeline of the business. And all I had to do is ask the question. So I uploaded a CSV file and I asked questions in plain English and it generates the chart for me. Now, yes, you need to know how to tell it what you want in the chart, but literally explain it like you would explain to an intern or a data scientist that you have. What do you want on the x-axis? What do you want on the y-axis? What do you want the ratios to be between things and so on? And it will create to the chart. And if it's not exactly what you wanted, you can continue explaining in plain English what you want.

21:27So let's continue. So now I know I have too much money in lower probability. So what can I do next? So I said, okay, great. Now I would like you to show me the 25 to 50 % category, just that category where I have a lot of the money and show me the opportunity size broken down by account name. Use a similar bar chart as before. So it created the bar chart for me. So there's a question by Rosemary, if you can do similar things within Google Sheets. Yes, you can do the same within Google Sheets. And thank you, Rosemary, for asking. The way I do this in Google Sheets, and I probably need to do a complete separate session on that because it will take us a full hour to go through that.

22:12But what I do in Google Sheets is I integrate AI tools through their APIs into Google Sheets. I actually do it through a third-party platform called Open Router. And Open Router has access to multiple large language models, and each and every one of them has pros and cons and what it's good at and what it's not. And I can pick four different stages of the analysis, different tools to call from within a cell. I know that sounds really a magical to some people and many really confusing to others. But yes, the short answer is you can do a similar thing in Google Sheets while bringing in large language models into Google Sheets itself.

22:53And you can even use it for qualitative data analysis to analyze customer reviews, to analyze emails, to analyze proposals, and so on. And I'll maybe do a separate session about this in a few weeks, that's not a bad idea. So thank you, Rosemary, for asking that question. So now let's go back to our thing. We asked ChatGPT to, and by the way, to do this kind of analysis, ChatGPT does it best as of right now. And the reason for that is ChatGPT has a data analysis capability. And what it does in the background is it writes Python code. So every time I ask it to do something, which in this case is to take the category of 25 to 50 % opportunity and give me the accounts, the specific opportunities in each and every one of them, it will write Python code in the background, will execute it, and then will share the outcome with me, which guarantees that it's going to be an accurate outcome.

23:54So what we see here, this is, as I mentioned, it's fake data. In the fake data, I created one account with a lot of data that I can play with in my different presentations. It ruins my graph because this one has 130 million in pipeline as far as account one, and all the other accounts are really small on the bottom, and I can't really see what's going on. All I did is literally ask it a simple question. Please remove account one from the graph. So now it removes account one, and that now scales all the other accounts to something that we can see. So what we see in the graph right now is we see that between 25 % and 50%, we have most of the money broken into two potential client accounts.

24:35In this case, it's account two and account 30. So now I'm like, okay, now I know where I need to focus my sales team's efforts because these two accounts, only two accounts, that's it, holds most of the money that's stuck in relatively low probability. So the next obvious thing is to understand what opportunities are in these two accounts that we need to try and push forward. So it's literally what I asked for. And now I'm asking for something a lot more granular. Please create a table showing all the data from the original data set, but only for account 30 and account two that we have identified based on a graph.

25:18And what you can see is that it did it. So now I have a granular level broken down table that shows me each and every one of the opportunities that I need to look at in order to work either myself or with my team or to show my higher ups that we need to focus on in order to increase the probability of our pipeline. And the whole process took me about five minutes knowing nothing about my CRM, knowing nothing about how to do it in Excel by asking simple questions in English. This can be applied to any aspect of the business. As I mentioned, you can bring in a CSV file for your employee reviews and the scoring that they've done, that they've given to each and every one given to each other.

26:04You can bring your financial data and do the same thing. you can bring your funnel information and do the same thing. Just ask questions in English. You can build tables, you can build graphs, and all that information is available to you. And then you can export that information as a CSV if you want to do additional analysis or just copy and paste information to wherever you want, or just use it as is. So this was just one example. I want to show you another example from a different field in the business just to get an idea what else you can do with this. So this is going to be relevant to you if you're a software company, but if you're not a software company, it doesn't matter.

26:41I'm going to explain how this is relevant. So in this case, I took information from Jira. Jira is a platform that most software companies are using in order to manage their development. So what features are being developed, what bugs are being opened, what bugs are closed, and so on. And in this particular case, I gave it the following prompt. You are an expert data analyst in the SaaS industry. Look at the attached data and let me know what information you can find in it and what business technical insights can I gain by looking at this data. Same exact concept as before. I want to see that it understands.

27:23So it gave me all the data that's in there, a very long list. And then it gave me ideas on what kind of information and insights can again, priority and severity, status, aging, suggested actions, improve external quality control, and so on and so forth. So it gave me a lot of ideas. So then I ask it the question, which I really doing every now and then. So we know bar charts, and we know line graphs, and we know pie charts, and that's where it ends for most of us. But the reality is there's dozens of types of different graphic representations that you can use to show different things that you may not think of because we don't use them regularly.

28:05But these platforms like ChatGPT, they know all of them. So I asked it, what would be the best graphics way to show the time it took to close different defects by severity? So it gave me ideas. Box plot, histogram, line graph, stacked bar chart, or scatter plot. Each and every one of them, it explained what are the pros and cons for each one. So I can choose which one is right for me. I can pick all of them and see which ones makes more sense. But then I ask it to create it and it created the code. And the first two ones that it gave me actually did not make sense to me. So then I ask it for something else.