If you are looking to up your reporting and dashboard game this is the episode for you. With Annie leading the charge we cover a large number of tools, chart types and ways that we digital marketers are not helping ourselves.

We need to go beyond the same 3-4 chart types and do better and be better. So dig in with us as Annie drops knowledge for almost 45 minutes!

Data Studio vs Excel vs Google Sheets vs Tableau

Annie takes us down her path of finally out growing Excel, finding Google Sheets but also growing past that.

Eventually Annie fell in love with Tableau (this was before Google Data Studio was even available). That said if you want something with a low learning curve that allows you to create elegant visuals, Google Data Studio can be the solution you need.

Data Studio though doesn’t offer histograms and lacks the ability to do deep statistical analysis.

Histograms & Box Plots

If you get nothing else from this episode we hope you take a look at these two chart types and look to use them more for your marketing analysis and reporting.

Annie does walk through the upside and when to use these so be sure to listen (starting around the 12 minute mark).

Want some ideas? Crawl data, Web Core Vital Data and other examples are given by Annie.

Note from Dave & Matt

As what happens with guests from time to time we run into sound issues so as you will be able to tell Matt and Dave are turned down and Annie is highlighted. We always try and avoid this but sometimes it happens and we apologize!

Dave & Matt

Data, Reporting and Annie’s Resources

Full Transcript:

Matt Siltala: [00:00:00] Welcome to another exciting episode of the business of digital podcast, featuring your host, Matt  and Dave roar. Hey guys, excited to have you join us on another one of these businesses, digital podcast episodes. And today we have a fun one. Um, I would like to welcome any crushing, any thanks for joining us.

Annie Cushing: [00:00:23] Hey guys, it’s a pleasure to join you.

Matt Siltala: [00:00:27] I normally do like an intro or whatever, but like with you, you’re an old friend. So, um, it’s kinda hard to like put everything that I know about you and then like a five minute or a five second intro as to, uh, you know, um, who you are and what you do. And so maybe just to take a few minutes and just like, so our listeners can know who you are, at least what you want them to pull out you, and then we’ll just jump into it and go from there.

Annie Cushing: [00:00:57] Yeah, that sounds great. So I had an interesting journey to the digital marketing world. I actually started way back in the day in editorial and I was working for a publishing company and we just started. Of asking kind of big questions about where the traffic was coming from. And this was back in 2006 and there wasn’t an established, uh, analytics field, especially not web analytics.

It was the wild, wild west. So I just started learning everything I could. Like at night and just kind of, you know, experimenting and after about a year or two of doing that, I thought, oh, I wonder if other people have the same questions because there weren’t that many blogs at that time. And the ones that were out there were very technical, much more written for developers than marketers and analysts.

And so I decided to try a different approach and it kind of gained traction and. Yeah, my, my brand analytics has started as a joke on Twitter and became a brand, you know, quite accidentally. So, uh, and now, you know, I really focus on data. My tagline is I make data sexy. I wrote the books, making data sexy.

Yeah. Yeah. That was. Bucket list item for me, that was something I had wanted to do for years. And I just wanted to be able to give people kind of a desktop reference. It’s not a book that you’re going to read, cover to cover, unless you really are like a mega nerd. You don’t have much going out on, on Friday nights, you know?

Yeah. But for most people it’s kind of a desktop reference. Like I want to try something. Different with my data. And I want to see, you know, what other visualizations I could create with it. One of the things that I really enjoyed about writing the book is I didn’t just want to cover the charts that are.

Built into Excel. I really enjoyed kind of pushing Excel to a breaking point. There were times where I quite literally pushed to Excel to a breaking point, especially on the Mac, you know, just flip over one, a belly rub. But, uh, but there are quite a few charts in there that are total hacks and they weren’t hacks that.

I came up with other people. Yeah, other really brilliant people had these ideas and I really mostly just kind of sexy them up and, um, and wrote the directions in such a way that even if you’ve never used Excel before you would be able to, you know, kind of follow along. So, um, so yeah.

Matt Siltala: [00:03:52] Thank you for sharing that.

I know that we have, uh, likely to say, Hey today. He’s here somewhere here, but yeah, I’ll let you just kind of jump into it. Cause I know you have a direction you want to take with daddy and again, thank you so much for being with us today. So go

Dave Rohrer: [00:04:09] for it. And I think that is a great segue. I need to, one of the things that we wanted to talk about and specifically year is Excel in your love.

Hate.

Annie Cushing: [00:04:19] Yeah, that is fair. And was like, soon

Dave Rohrer: [00:04:23] as you said, something we were, as we always do, just kind of talking about what hell we were going to go into it and dive into this because we can go so many different directions with your background and just, you know, analytics. But do you want to talk about what we were just literally talking about before, about your progression from, you know, getting into Google sheets, you know, data studio, which, you know, as we were just talking about.

I laugh because I do use data studio because it’s easy and

Annie Cushing: [00:04:52] free,

Dave Rohrer: [00:04:53] but it is very limiting. And if it’s just for, you know, high-level, you know, directional, it’s awesome. But I think to your point, what you were going to dig into, I know it’s limiting. It really is. Yeah. Do you want to dig into that fun topic?

Annie Cushing: [00:05:11] Sure. So I’ll start with. I think I’ll take kind of a crawl, walk, run approach. Um, so when I started with Excel, I wasn’t even crawling, so I had taken Avinash, Kaushik, uh, web analytics certification course. And it wasn’t a prerequisite that you needed to know. Excel. I think this was back in 2009. Yeah. And so I did not know Excel at all.

And for our final project, we had to build a dashboard and like I had to look up how to, I think I could create like a really ugly. Line chart and bar chart, and that was it. And call and chart in Excel. I didn’t even know you could flip it on its side, you know? And so I really, really struggled through this final project and I was pretty salty about it because I was just like, oh, I don’t know.

Maybe put this as a prerequisite for the course, you know, cause I could have brushed up or whatever, but, um, Afterwards, you know, I did this presentation and I remember Avinash saying, if you pass and I didn’t hear anything, he said after that, like I was just like, I invested so much money into this course.

And I was like, if, if I pass what, oh, well, what are you talking about? So I remember getting the email. From them. And it was like this, you know, full analysis of your project and stuff. I didn’t even open it up. I just looked to see if I passed and I hit archive to this day. I’ve never tracked down that email to see what their feedback was, but yeah.

After feeling, sorry for myself for a while, for like a week or something. I was like, I am never going to let Excel humiliate me like that again, because then there were people in the course, they worked for major companies and I was like, right, you just did this yourself. No, they had someone from their company help, you know, build their dashboards.

And they were like beautiful and stuff, but, and I hated them, but, uh, so I just started, yeah. Everything I could about Excel. Like I literally gave up running and got a gym membership so that when I worked out, I could be reading Excel books. Like guys would come up to me and try to talk to me. And it was so intrusive because I was like, can you, can you see I’m reading here?

They got, it would be, you know, on a treadmill with an Excel book. I, in this went on for several years where I just was. Consuming everything I could because it just really gained traction. You know, in my mind, I was like, I really want to learn everything I can about data visualization. And I just thought like Excel would do it for me for the rest of my life.

You know, like it, there was always some new YouTube video to watch or some new book to read. I had stacks of Excel books. And, uh, but then I hit a point where I just started to, especially when I started building out dashboards, uh, it just wasn’t cutting it. Like it was too hard to get API data into Excel.

Uh, it was hard to. Share the dashboards. Um, there wasn’t like, you know, at least now there is an online version. Uh, and so it’s a little easier, but back then, there wasn’t. So you were sending files back and forth over email, and then sometimes the files were too big. So it was really starting to get cumbersome.

And I just never thought that I would one day outgrow Excel, but I was, and so then I moved over to Google sheets. This was before data studio. And I really liked Google sheets because I felt like it was much more conducive for marketers because. It had more functions that were good for text, you know, for strings Excel really tends to be much more of a tool for finance.

And so, you know, um, so Google sheets had like red jacks functions. Yeah. Yeah. And so I, I was really into that and it was so easy to share dashboards, but again, I was bumping up against walls because you also couldn’t hack it as easily as Excel, you know, so did that for a while and eventually started learning Tableau.

And that’s where I’ve really kind of settled in. So I already fell in love with Tableau when data studio came out. And I have to say, when it first came out, I was pretty snobby toward it. Like I, you know, I went in there, I threw some of my day. Then there were just so many things I couldn’t do that. I was just like, well, at least you get what you pay for.

And that was when it was free. Well, when it was part of 360, I was just like, this is never going to fly because this tool is too elementary. It is to, to your point, Dave, it is great for having a low, uh, learning curve. So if you don’t want to. Yeah, really focus on learning the ins and outs of data visualization.

It’s perfect. And you can create really elegant visualizations. I was very happy when they added in a drill down feature. I was like, okay. That’s you know, that’s good. And then when they offered, yeah. I think I’m getting the order wrong, but, uh, when they gave you the option to blend sources, I was a little more like, okay, I, let me see you data studio.

And, uh, but you know, but when I have to work in data studio, I hit frustration points very early on because now I do a lot more with statistical analysis and. Data studio offers very little in terms of being able to do any kind of statistical analysis, uh, especially because like it doesn’t even offer a histogram.

And I use histograms a lot, especially like with crawl data are. I just throw the data into a histogram. And that’s actually something that I started doing after taking these courses when I was learning, are I noticed in all of these courses, the instructor would start off with throwing the data into, into histograms, histograms, and box plots.

And that just wasn’t the box spots. They

Dave Rohrer: [00:11:51] all use the same.

Annie Cushing: [00:11:54] Yeah. So that was very unique to these are courses. And I remember being very surprised how they used box plots. And I was really glad that I learned R before writing the books, because I just didn’t have that much of an appreciation for box spots until I took all of these art courses.

So what they would do is. Throw the data into box plots. A histogram is like a close second box plot are amazing. And in the book, I really sell box plots because you can’t, let’s say you’re analyzing web data and your website has five main categories. Each of those categories can be a box and the box with the size of the box.

Indicates how much variance there is among the data points. So if you have a really short box, that means the data’s really consistent for that particular category. Whereas if you have a really long box, that means that there is a lot of variation. In that data. So let’s say you’re looking at conversion rates by landing page and you have those landing pages divided up into the different sections of your site.

I do this analysis a lot, and in fact, I’m doing this analysis with core web vitals. That’s a really good one to use as an example. So I have one client where I broke their site up into their intuitive. I pulled in the core web vitals, uh, really focused on the overall, uh, score and the quality score. And so, and, and each.in the box plot represents a landing page for that particular category of the website.

And when I did that, you could see, wow. The category of their site, that is the e-commerce category had one of the lowest like median, um, uh, quality score. And, but it was consistent, but it was consistently low, but then you also saw like these kinds of outliers. And so. Um, so box plots are absolutely amazing for one, just seeing the spread of the data, just to kind of get that hundred foot view and also for identifying outliers.

So with this particular client, they had one page. So I created, it’s sort of like a corollary grant, but not really, but I took all of the, all of the different, uh, Uh, I guess we’ll, we’ll use as an example, like the size metrics. So like the size of the CSS, the size of the JavaScript fonts, you know, so I grouped all the size metrics together and, and then weighed them individually against each of the metrics.

So, um, the, the performance metrics, like first. Uh, delay, uh, largest Contentful paint, first Contentful paint, you know, et cetera, et cetera. And I was looking for correlations, you know, so like I definitely saw the larger, the overall. Size of the Java script files, uh, the, the worst, the largest content, you know, the longer that largest Contentful pain was.

So that, that was a clear correlation. Whereas, you know, some things they shouldn’t correlate and they didn’t, you know, um, so. Anyway. So all that to say, uh, there was in all of the size and, um, count metrics. So it was like count of JavaScript, count of fonts, et cetera, et cetera. One dot that was such an extreme outlier.

It pushed all the other dots into that bottom left-hand corner, because it was up in this like upper right hand corner. Um, and it was tragic. It was creating like all of these problems, but it was consistently the same URL. So I pulled it up. It was a support page, but it had more than three. Hundred JavaScript files.

And I have no idea, but it was a part of the site that they had acquired from another company. And somehow this poor little support page had had nothing extraordinary. It had no video, no. Anything that you would say like, oh yeah, I guess I could kind of see, you know, how this right? Nope. It was, yeah, it was.

So unremarkable and yet that one little page was skewing all the data. So, you know, in Tableau you could just say, okay, exclude this dot. And so I did that and then my correlations all shifts. With just excluding that one page. And that’s the kind of thing that typically as marketers, we’re so used to reporting on aggregate data.

We love bar charts and line charts, but the problem with that is you don’t catch, you know, like those little foxes that could be trashing your data. Um, I’ll give one more example. I was working with a client and this was a large client. Did a lot with like taxes and stuff. And so, because they were so large, they had a representative from Google who every week would send them a Google trends, data for all of their keyword buckets.

So it was divided up into buckets and keywords. And when you’re this large of a client, I guess you. Googler who will do this for you. And, um, but so they were reporting in aggregate this client. I was like, I don’t even know why you’re using Tableau. It was just line charts and tables for days, you know?

And, um, I mean, it’s like buying a sports car and doing the speed limit, you know, but, um, anyway, so, so as. You know, they just wanted line charts and bar charts. I was like, okay, when I wrap this up, would you mind if I just had some fun with the data and introduced you to different types of charts? And she was like, oh, sure.

You know, so I built out a tree chart for them. Tree charts are great. Once again, for showing kind of distribution of data. Some people don’t really like them. I love them because they are easy for people who aren’t. Analysts to analyze, you know, like you said, I see a giant box and you look at it and you say, huh, that shouldn’t be that big.

And that’s that happened with this client. They had a bucket for ride shares. And because I guess you like a lot of people who drive, you know, Uber and Lyft, they’re having to report their taxes and, and things like that. The problem was the Googler included. Uh, Uber in the keyword bucket and that brought in and that, that square in the tree chart was so.

Big it made the car share bucket much bigger than it should have been. So when she saw the tree map, she was like, wait, that book, it shouldn’t be that that square to a tree map is just imagine having a box and all of these boxes within that box. And they’re all different sizes and you’re kind of playing Jenga with them.

And, and so first of all, that. Box for car share or ride share was much bigger than it should have been in comparison with some of the other keyword buckets that they had. But then you just saw this huge box within that. For Uber and immediately she was able to see, oh my gosh, this shouldn’t be in there.

And then that happened with another of their buckets too. And so she went back to the Googler and said, Hey, we need this taken out of the data and we need this, you know, reprocessed. And, and then, you know, their entire tree map shifted, but I just felt like, okay, now, now that we know that this data is clean, now we can report on it.

Dave Rohrer: [00:20:54] I think that’s a big thing is clean data. And also, um, and, um, I have open so on making data sexy.com/excel/chart-picker. As you were talking about all these different charts, I’m like going through the different, it’s like pagination on this page, on your site. Yeah. I’m like, I don’t see tree map on this one.

I was like, oh, wait. On the bottom. There’s 50, there’s 52 charts that any has on this chart picker. And these are the charts. Do we, most marketers use power points and reporting and dashboards like three of you

Annie Cushing: [00:21:34] four. Yeah. I

Dave Rohrer: [00:21:36] haven’t seen this treatment chart for, and it does, it is really handy for, um, and I’ve gotten this question before and it’s.

And I know you probably in the book, especially with your are like there’s sampling bias, there’s different biases that can be introduced into your data. And I see it all the time. And one of the biases is if you have, if you’re showing business line like reporting or keyword, like, um, category reporting or something, and it’ll show that this one category every month just destroys everything.

But what they leave out is that the price point and the average sale is like $10,000. And so they sell two a month. Whereas this other category sells hundreds of thousands of in converts and actually has like, you know, it costs them 10 cents, but they sell them for $2. So it actually, they make more money.

But it doesn’t look like it as much because you’re like, oh, well, you know, th there’s just some many different weight biases or, uh, with keyword recording and, you know, rankings, whether you like it or not, you might have different categories and you might be like, oh, well, look, you know, we’re up in these three different business lines, but a tree snap chart would show you that if you were to combine the traffic driven from those rankings or.

The impressions and clicks from Google search console or the data that’s given to you, to you by an Eros or Google keyword tool. It would show that yeah, you’re ranking for these keywords, but no one searches for them. Exactly. And even though you’re number one, there’s no opportunity there

Annie Cushing: [00:23:21] exactly. That

Dave Rohrer: [00:23:23] bias doesn’t show up, but in a tree map, which are, it would show you that yeah, your rankings aren’t as good for these, but the opportunity is actually.

Annie Cushing: [00:23:31] Bright. And I’m glad you brought up the chart picker because that’s one of the reasons why I added a column for the type of chart, because that helps you to assess well, what, what am I trying to do? Am I looking for a trend? If so then you can filter by trend and you might pick something that is a little different from what you’re used to, uh, you know, or if you’re looking for distribution, like what I’ve been talking about, you have some options and, you know, uh, if you’re looking for relationship, like I use, um, let me start over with that.

Um, what is it? Oh, I use scattered. All the time. I mean, all the time, it just looking for a relationship between two different metrics. And I was really glad once again, that I took that art class, because well, one of the, our courses, it talked about how one of the mistakes that people make with that particular chart is using the X and Y axis.

Interchangeably. And I noticed that they do that in GA for I, I saw, oh wow. A scatterplot. Whew. And, and they’re using the Y axis. For what should be the x-axis and vice versa. So the XSS is supposed to have something that it’s, it’s called an independent variable and then the Y axis is supposed to have the dependent variable.

So what that means is if I tweak, let’s say, if I tweak reading levels, Uh, and let’s say that’s along the X axis. Does that impact conversion rate or does that impact time on site? Or if I, uh, if, if, um, let’s see, what’s what would be a good example, word count. So I was doing an analysis for one particular client and we were looking at word count and this was from crawl data.

And I found out that. From, it was something like 200 to 400 words was like their, their kind of sweet spot. And then it dropped off. And then they had these long form articles that went, you know, 800 words and above, and, and those. Converted really well for bigger ticket items. But again, that’s where it’s really valuable.

One to understand statistics, to not just always use the same kind of trend, uh, you know, like use a linear trend. You like try some of these other models because you might find out like, if, when I used a linear trend line, it just showed well, Words there are on the page, the lower the conversions. And then when I switched up the trend line, And then I saw, oh, well it kind of curves.

So they, they max out and the, you know, like kind of picked up and then dropped off and, you know, in this one range, it was kind of no man’s land and people just weren’t really into it, but then you hit those long form articles and conversion rates shot back up again, you know? And so it’s just, it’s really interesting, but a lot of these things.

Our loss, if you don’t have the right tool for the job. And so, uh, so that, that was my, and I’m not promoting the book. I’m just saying one of the things that I did when I was writing the book is I thought, you know what? Been exposed to a lot of Excel tutorials, but a lot of times I was like, wow, this is a really cool tool, but I, you know, like a chart, not a, not a tool per se, but a chart.

But then I would be kind of sign me because I was like, I don’t know what to do with it. And so. For each chart type. I actually break down, Hey, here are some ideas for how you might want to use this data. And then I have a list of, you know, kind of caveats and things to watch out for and mistakes that I see a lot with that particular chart type and what I would do to kind of get more ideas with that is I would.

Search Google images for that chart type or looking for that type of chart. And I would just say, oh, oh wow. Oh, that’s bad. And I’d add it to the list. Like, you know, watch out for this, um, you know, get rid of your grid lines. Like if you don’t need it. Get them out. Anyone who’s followed me for a while. I hate gridlines.

If I absolutely have to have them, I will make them as subtle as possible. I have old eyes because I’m old. So I’m like, if I can see it then just about anyone can, you know, but I use like a light gray I’ll use a thinner. Uh, you know, um, line and, and things like that. But if you don’t need them, and then this is one of my complaints, actually, with data studio, like data studio, you can add data labels.

You don’t have a lot of formatting options. I don’t know if they’ve changed this, but I remember one time adding data label. So I was like, no, you will have two decimal places. I don’t care if those decimals are two zeros, you will have two decimal places. And so that was very frustrating, but the most frustrating thing was.

I you, I would add the data labels and the whole point in my thinking to adding data labels is good. Now we can clean up the axes. So we don’t have all of these numbers and all of these lines on, on the axis. And so, but in data studio, at least the last time I used it, which was somewhat recently, you could add the data labels, but you couldn’t.

Uh, then like get rid of the, the axis labels. And so then the data labels just made the chart even busier. So that’s something I really focus on a lot in the book is literally making your data sexy. You know, like all of those unnecessary pixels are distracting and they’re going to detract from the story that you’re trying to tell with it.

Dave Rohrer: [00:30:15] Which leads me into my last question, because I think that’s the one I was to, I was going to start yawning.

Matt Siltala: [00:30:20] So wrap

Dave Rohrer: [00:30:21] it up. I know the, um, I think that kind of ties into one of the things that was one of the reasons I wanted to have you on was just talking about presenting data, because so often if you’re in-house, you’re either fighting for budget, you’re fighting to show that your project worked.

Whether you’re in an agency, you’re trying to, you know, show that you look to the, the, the month was good for the client the quarter of the year, you know, that, you know, you need to re up, you need to, you know, spend more with us if PPC is working or not, um, or, you know, whatever, whatever you’re trying to, you know, either approve or sell.

And I think the one big thing is, is picking the charts or just, if you’re like looking for crawl data, you’re looking for things to help you improve. I think like you were talking about the scatterplot. Um, I don’t use Tablo. I use, um, power BI a lot, cause it’s free and I don’t use it that often, but I love using it to combine my PPC and SEO data and conversion data and start looking for trends or keywords that are in both and doing well or not doing well.

And. As a marketer, we always are looking for trends in any tool you can use to find those trends in diggin. Um, or, but also like you said, to not make the wrong assumptions or to read the data incorrectly. I think looking at other charts, looking at other ways to display data, um, not trying to tell the fo a false narrative or.

Um, make it look better than it does because yeah. You might do a scatterplot and go wow. Or a conversion sock. Oh, look at this chart. Oh, it doesn’t look so bad now. Um, so someone eventually will look at that data and they’ll find the hole in it. But I think that’s the big thing. Is that just everything you were talking about using the right tool and going beyond, like, I hate Google sheets, um, for those that, like, it bless your soul, but when I work with 300,000 rows of data, In Excel, it’s painful.

And I know I should be using like access or CQL and you know, different databases and stuff. I’m, I’m almost to that. Um, and sometimes I do use it for BI I’m just not as good and quick with it, so I haven’t made that transition, but yeah know I need to, um, or chaplain even. Yeah. Yeah. I think that’s so to actually ask my question, um, what tips would you give other than the things that you’ve talked about?

How to internally or externally use charts and data and sell it, um, or what would be your one, like thing that you’ve learned over the years that helped you go from not knowing Excel, Hey, loving Excel, hating Excel, and kind of,

Annie Cushing: [00:33:10] so my answer might seem a little unusual, but

Dave Rohrer: [00:33:16] he listened to our show before.

Annie Cushing: [00:33:20] Do you even know me? One of the biggest walls that I ran into, again and again, early on, and even kind of mid point in my life. Was just not being able to verify the efficacy of the data. So I would pull data from a particular tool, but a lot of times, I wouldn’t know just how clean that data was or. I would run into walls just because like combining data using V lookups would then add so much lag that, especially when I got to a point where I was really building out a lot of dashboards, that would be problematic.

Like I would build these super interactive dashboards in Tableau, but. I’d be like, oh no, don’t touch that filter. Ah, man, this is going to be awhile, you know, because it just wasn’t streamlined. And so, uh, about, I, you know, there were a few times where I started learning SQL like enough to be able to use query the query function in Google sheets, but I kind of was shying away from it.

And in the last year and a half, especially now I use sequel. All the time, because it empowers me to be able to go into, I use, um, big query. I can go in there and I can so easily combine different data sources. Like for example, I’m com I’m usually combining based on URL, but it’s just opened up a whole new world to me, like case in point I had one client, they wanted to know how.

Was impacting their net promoter score, survey data. So I was like, okay, so giveaway,

Dave Rohrer: [00:35:12] their SEO was impacting their NPS.

Annie Cushing: [00:35:15] That was their question. So it was, yeah. Right. And so it was a large site and, uh, They wondered if they had definite international SEO issues, which we had to tackle some of that stuff, because if you don’t have HR flying down right, then you just have all of these pages from different countries.

You’re going for the ball at the same time. And they were dealing with that. So they wanted to know if when a visitor lands on a wrong country page, does that impact their MPA? And I swear, I was just like, okay. Challenge accepted. But there was so much data between their MPS survey data. Google analytics.

And then it was a large client and, you know, lots of different countries. So I was like, I have to trim this data down before sending it to Tableau. And that was one of the first big projects where I was like, okay, I need to do this in big query. And so in big query, I trimmed everything down. I combined the data sources.

Trimmed everything out that I didn’t need and then built these visualizations. And sure enough, there was a strong correlation that when someone landed in the wrong country there, their MPS score was lower. And so, I mean, it was a pretty intense analysis, but that just for me, that was

Dave Rohrer: [00:36:54] kind of. Any SEO work that they thought wasn’t going to be worthwhile?

Annie Cushing: [00:36:59] Yeah. Yeah, exactly. Because for them that’s what put SEO well for the, yeah, exactly. For them getting that answer gave SEO a seat at the table. Because they really cared about their NPS data. And they were like Google analytics. It was like, oh, well, okay. And, and in, so doing, I went in into their Google analytics and saw that the red jacks that they were using to identify the countries was also wrong.

Partly Google could do us a favor by letting us build filters based on continent. We can’t do that, but. But you, I was able to recommend some cleanup of their country filters and that was a piece of the puzzle. Uh, but then also really nailing down the HRF Lang. And that was what they needed to justify the lift of going in and hiring someone to come in and clean up their HR.

Dave Rohrer: [00:38:13] And so that’s why, when anyone comes to me and I see a data project or Google analytics project, and I looked at it and go, Nope.

Annie Cushing: [00:38:21] Yeah, but he just, he just tosses it over the fence.

Dave Rohrer: [00:38:27] She’s got you. She’ll get you covered, especially if you love GA and the data and that stuff. But I think that, that is like, we talked about it, Matt, with Dwayne, you know, recently.

Yeah. You need to tie SEO or PR like T it’s easier for PPC, but social media, SEO, PR anything you’re doing in marketing, you need to figure out a way to tie it to money. Likings, not traffic, not any of those other metrics, but you need to tie it to money so you can get more money. And that in the end, that’s what it comes down to.

Is it crazy that you, by digging into that data, you actually did find that it does impact the MPS, which does impact people. And if you don’t know what NPS is, one through six means, um, basically they’re not going to recommend you. Uh, they’re not in your favor. Nine and 10 is good. Seven and eight means, eh, they don’t love our HQ.

So really if you’re not getting nines and tens, they’re not out there spreading word of mouth and telling you, oh yeah, go talk to so-and-so or go talk to this guy.

Annie Cushing: [00:39:28] Exactly. And you know, if you were a large company with a board and the board really cares about NPS data, you tie everything to MPS. So that was a fun challenge, but it was, it was a good example just because it really demonstrated the, the point of if you really want to like, be an analyst really focused on data visualization.

It’s fun to play with, you know, different chart types and stuff. But at some point you’re going to have to be able to tell. Like how to clean up data, how to ha how do you create categories when they’re there don’t appear to be any and you have to use, I use red jacks. I just created a massive guide for how to use rejects.

And I put it in simple terms. Right? So some of these kind of ancillary skills. Really come to play in your ability to kind of take it to that next step of, okay. I don’t want to be intimidated by the data. I don’t want to be caught off guard if I present something to a client and at the end for this particular client, when I did this presentation, they had a team of analysts just ready to pounce, you know?

And, um, and, and so, you know, When it comes to that con that level of analysis, that’s where you’re going to have to be able to pick up new tools like Tableau prep, builder, like that will, it is an amazing tool for cleaning up your data and kind of getting that a hundred foot view to see are there issues in the data?

And I’ve been surprised how many times I’ve found. Just because like dates are formatted differently or Google analytics there times they RMS and dates absolute the API, uh, formatting for dates. There is no formatting, it just comes in as eight numbers smashed together. And so those types of things can cause problems down the road.

Especially if you then try to marry that data with data from another data source and. Yeah. That’s what I, that’s what I would say. Uh, Google has its own, uh, version of Tableau prep builder. I forget what it’s called, but it’s part of the cloud. Um, but yeah, so tools like that, they just have. Yeah. So I don’t do that much cleanup in, uh, using SQL.

I use SQL when the data’s all cleaned up and I put, I want to pull it, but, um, but the, you know, using some of these tools, it goes a long way to make sure that the data is. Organized and, uh, you, um, presented the way you want it to be presented. And that’s not the fun side of data visualization, but it is very important.

Matt Siltala: [00:42:41] Awesome. Well, we really appreciate you taking the time to go over all this. I think, uh, Dave, uh, you got all your questions answered,

Dave Rohrer: [00:42:52] at

Annie Cushing: [00:42:52] least for this setup.

Matt Siltala: [00:42:55] Yes. Thank you so much for, for doing this. And, uh, we’ll put all the links to everybody that’s listened to the book that was referenced to her.

Exactly. All that stuff we’re going to be out there. So, uh, thanks again, Annie. And, um, we will, we would love to have you on again at some point down the road. And so, um, thank you again for that for Annie. Um, and Dave roar, I met several and thank you guys for joining us and we’ll get you on the next one.

Dave Rohrer: [00:43:24] Thanks.

E196 – Presenting Data With Annie Cushing Hosts:




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