So What? Marketing Analytics and Insights Live
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In this week’s episode of So What? we’re doing Trust Insights Wrapped! We walk through which episodes of So What were the most popular, how to set up tracking for your show, and what’s next for 2023. Catch the replay here:
In this episode you’ll learn:
- which episodes of So What were the most popular
- how to set up tracking for your show
- what’s next for So What? in 2023
Have a question or topic you’d like to see us cover? Reach out here: https://www.trustinsights.ai/resources/so-what-the-marketing-analytics-and-insights-show/
Katie Robbert 0:28
Well, happy Thursday, everyone. We’re doing Trust Insights Wrapped! Welcome to so what the marketing analytics and insights live show. I’m Katie, joined by Chris and John. This week, this is our last show of 2022. Next week is right before us Christmas if you celebrate and after that is right before the New Year, which everybody will experience regardless of what you celebrate. And so this is gonna be our last show for 2022. We will be back again in January 2023. With some fresh content, new ideas will be nice and rested. So what we wanted to do today for this week is to do our own version of wrapped. And so if you subscribe to things like Spotify, or Apple Music or any other kind of like membership, where you do a lot of activity, peloton, does it Strava does it, they sort of give you your end of year stats of what you did throughout the whole year. So you want us to do our own version of it with our own live stream. And do a little recap on putting your data into a look or studio dashboard. And just you know figuring out how to use this all of this information because at the end of the year, all of these subscriptions give you like, here’s your data. Here’s what you did. And we wanted to figure out the so what what the heck do you do with all of it? So this is our version of rap. This is Trust Insights wrapped. John, did you get your Spotify wrapped?
John Wall 1:54
No, you know, because I live out in the hinterlands here like I need to have all my music on local because you never know when you’re not going to have internet and you never know. Plus, I have like all these obscure and weird remixes to like I get all angry if there’s a song on Spotify that I can’t find. So yeah, I’m representing the Luddite community today.
Katie Robbert 2:15
What about you, Chris, I think you use Apple Music, right?
Christopher Penn 2:18
I do. So an Apple has apple replay. But I do. I use Spotify mainly for putting together playlists for other people. So there was a little bit of data that tomorrow, Friday the 16th however, on the Trust Insights blog, we’re going to have the Spotify mega wrapped post is the last post or 12 days of data, we’re gonna be looking at the contents of over 30,000 of the most popular Spotify playlists on the web, based on SEO data, and see what the most popular most used songs were across all these playlists around the world. So that’d be kind of a nice way to cap off the 12 days of data and keep it light hearted. You know, I might even be persuaded to put together like a playlist of like the top 20 songs that folks can can put into their own Spotify accounts. If you want to see what the planet listens to, I can pretty much guarantee it’s going to be to no one’s to exact taste.
Katie Robbert 3:13
isn’t about the beauty of diversity though.
Christopher Penn 3:15
It is the beauty of diversity. But you know, it’s like putting all your favorite foods in a blender and what you get out. And that’s so much.
Katie Robbert 3:23
Well, okay, moving on from that visual. Where should we start today, Chris?
Christopher Penn 3:28
Well, if we want to use Looker studio, then we should probably go ahead and pop on into that. If you’re not familiar Data Studio is now Looker studio, which is still the worst name product ever. And we’re gonna we’re gonna specifically look at YouTube analytics. So let’s go into the Trust Insights YouTube channel. Now the thing about Data Studio dashboards and stuff, and I just can’t say Looker is that we want to make it look at least a little bit nice. But it’s going to be challenging to do that with as much data as we have to work with. So we have to kind of think about what essentially the basic user story would be so what would that be Katie?
Katie Robbert 4:11
So as the producer of so what I want to know which episodes resonated the most so that I can build on those next year and my content calendar.
Christopher Penn 4:27
Gotcha. Okay. What What does resonated mean?
Katie Robbert 4:31
And I said that purposely so that we could pick it apart because I guarantee that’s a user story that you know, most highly engaged well do you mean watch, do you mean like do you mean commented, and so for me, resonated would be the most watched, and I know that you can start to pick apart the the metrics within that have, you know, is it total watch time? Is it people who got to it at all, I think probably total watch time is a great place to start. But also number like total views.
Christopher Penn 5:13
Got it. Okay, since we’re focusing on so what we have gone ahead and connect our YouTube channel, one of the first things we’re going to want to do is we’re going to want to add a filter, recall this, so what filter and our video title should contain. But what I’m gonna help us,
Katie Robbert 5:34
it’s always a really nice plug for having consistent naming conventions. Because it makes, it makes these types of analyses so much easier to work with when you have that consistency.
Christopher Penn 5:47
Exactly. So this is SOTL total watch time, I don’t love that just a pure table. So I’m going to do table with bar chart, just because I think it makes it a little bit easier to understand what’s going on. And we’re also going to want to add our numbers on the chart. So and let’s just change the layout a bit, make the layout a little bit bigger to 1600 by 900, which is
Katie Robbert 6:11
I can’t read any of that. And I have Well, I would say I have good eyesight, but with my glasses, I have good eyesight.
Christopher Penn 6:17
Right. So let’s go ahead and just go quick hop into view mode here. So in terms of what got the most watch time all year is so what had to do a UX only fix the spacing on this resize columns fit to data and hop back out. Okay, then identifying red flags, the job market, AI assisted content marketing, and so on and so forth. So these are the the the episodes of so what? Well, one thing I was we should probably ask is should we slap a filter on this? So we’re looking at just 2020? Sue?
Katie Robbert 6:51
Yeah, I think so. I mean, I think there’s value in looking at both. And so but for the purposes of this exercise, I think we just want to look at this past year, because that’s what our route is comprised of.
Christopher Penn 7:03
Exactly. So still how to do a UX audit. But now, we’ve got other shows in here that have different watchtime. So using Google Data Studio with Google, Alex for data, Postmaster tools, and so on and so forth. So this is what our Go ahead. Oh, no,
Katie Robbert 7:20
John Wall 7:21
Well, that’s weird. I was just because I know that the Data Studio GA four, one that was going to be at the top of the list, I’m surprised to see the UX one because that didn’t show up in other stuff. So
Christopher Penn 7:33
exactly. So let’s flip this to top 10, just so that we’re not dealing with stuff running off the page, and put a quick summary row on just to make things look a little more tidy. So that’s one way we can look at this. Let’s take I’m going to duplicate this, you could do this a bunch of different ways. I’m going to do average watch time as our second table. I’m going to do a third table here. That’s there with total number of views. And let’s do last one here with video likes. Now, let’s hop back out. And so now we have from total watch time how to do a UX audit using Google Data Studio, Postmaster tools and so on and so forth. For average watch time. The people who watch the longest watch the YouTube algorithm, which I guess makes a whole lot of sense here. The AMA did did very well. We’re average watch time. 19 minutes on that episode, content marketing, the show that you and John did on subscription advertising where John was ranting.
Katie Robbert 8:51
Everyone loves John Wall rant.
Christopher Penn 8:53
John Wall 8:55
people need Rance.
Christopher Penn 8:57
Exactly. Accessibility and then pivoting your agency and clients to GA for so that’s our watch time, we go down to total number of views. There’s Data Studio, GA for postmaster tools, UX audit, and GF was a lot of GA for which no surprise, this was a big year for it.
Katie Robbert 9:16
So go ahead.
Christopher Penn 9:18
Okay. And then just in terms of number of likes the episodes got the likes GA for postmaster tools, more GA for social media benchmarks and applied data science and marketing. So this is kind of the four different ways to look at this. So what’s of this year?
Katie Robbert 9:32
And you know, it’s interesting, because you do see the trends very, very quickly in terms of what content resonated the most with our particular audience. And it’s mostly around Google Analytics, the marketing platform, specifically Google Analytics 4 and Data Studio. So if if you were to present this to me, what I would do is say okay, so it looks like people really like the Google Analytics 4 material. What else could we talk about? Oh, that is still helpful and relevant and timely? Or are there episodes where we just didn’t explore it deep enough? And we could revisit it? And sort of do you know, a bit of SEO to say, let me reference our own content and bring it into this new episode of content around the same topic and really just build on it. So this is a super helpful analysis for someone who’s planning out future content.
Christopher Penn 10:29
Exactly. And there’s, there’s a reason why there isn’t more of that. And the reason why is that there is a paid course. That goes into all that in much more depth. But so that would be the first section of this. The second section, I think it would be fun to do is more of that actual rap style. So let’s do a second one here. Let’s take our total watch time. Put that on here as a scorecard widget. We’re going to take our average watch time, average view percentage, we’re going to take our likes. Let’s look at comments. The number of people who subscribed, only want one video comments, not user comments. And let’s tidy these up just a little bit here.
And I apologize if you have OCD. I promised I don’t do the CEU intentionally.
Katie Robbert 11:38
He’s speaking to me specifically.
Christopher Penn 11:42
So let’s move our scorecard widgets to the corner here. Let’s put our date control on your end defaulted to the year to date so that it is this year. Why? And then maybe let’s add just one visualization for total watch time for the year just to see how the year looks. And
now we jump back into this
Unknown Speaker 12:21
way the menu bar. So this year, people have watched 900 hours. Oh, so what? That’s a lot of us. That’s that’s a lot of us. So I
Katie Robbert 12:31
don’t think I can handle us for 900 hours. But thank you.
Christopher Penn 12:35
Exactly. On average, people watched about three minutes of the show. 53 That’s the I don’t want us removed I want us added zero subscriptions added. We had 257 people subscribe to our YouTube channel. Shorter. 67 likes 32 comments, people on average got through 24% of an episode. And then when we look just across the total watch time spectrum here, actually total watch time is not as helpful as do just the number of views. How many views we get? So it’s very interesting. Just you have that big spike right in the middle there. Now, we all know what that is right?
John Wall 13:25
No, no? Is that a specific episode? Or what’s that?
Christopher Penn 13:28
So that was a pure specific period of time. That is when Google Analytics announced, hey, in one year’s time, you’re gonna lose access to Universal Analytics.
John Wall 13:38
The one year marker there?
Christopher Penn 13:40
Exactly. So there’s a lot of talk about that. So in terms of our raw stats for we’re wrapping up, so what this is what it looks like, we had a lot of people paying a lot of attention this year.
Katie Robbert 13:54
And it’s a good baseline in terms of if, you know, I had someone who was solely responsible for, you know, our video content, I would say these are the metrics where we, you know, this is where we shook up for this year, next year, we want to get average watch time to five minutes, we want to get average view percentage to 28%. You know, I personally don’t care as much about comments. But it may be something that the algorithm cares about. And so those would be the things that I would say, Okay, this is a really good dashboard for us to be paying attention to to see if we are improving or not over the next year, so I think this is super helpful.
Christopher Penn 14:40
Yeah, definitely. I think so. The one thing I like about being able to do it this way is because what you get in YouTube itself is not super helpful because it’s very canned. And while it’s okay, it’s it’s still not, you know, you can’t drill down very specifically and dig in just individual pieces of data. So using using Data Studio is definitely the way to go.
Katie Robbert 15:04
And this dashboard also has the so what filter on it?
Christopher Penn 15:10
Yes. Well, that’s, that’s why me it’s very hard to to restrict that. Well, I
Katie Robbert 15:15
know you put it on?
Christopher Penn 15:16
Oh, that’s a good question. Let’s check. Let’s slap, you know, consistency is key. That is true. And we forgot to put that filter on. So let’s go ahead and do that. That may change things quite a bit here.
Katie Robbert 15:30
I’ll bet it well.
Christopher Penn 15:34
Because we also are other major publication on our YouTube channel, podcast,
Katie Robbert 15:38
and our podcast, there was a podcast in June, that was our most popular podcast that was around content marketing. So that may change. That’s true. The stats,
Christopher Penn 15:51
right? You are correct. There we go. All right. And so what’s much more consistent
Katie Robbert 16:04
it, which makes more sense. And so what’s interesting is when you add the podcast and the live stream together, which is the majority of our videos, that’s where we have 900 hours, now we’re about half of that a little less than half of that, which tells me that, you know, the podcast, and other pieces of content, because we have, you know, talks and other things up there, make up the bulk of that. But we’re still, you know, not doing that bad in terms of live stream,
Christopher Penn 16:34
exactly for a market that is very, very crowded. The other thing that I think is really important to take a look at is taking a look at this data from a Google Analytics perspective, too. Because all this is great. But you also probably do at some point want to say, Well, is it? Is it turning into any kind of business for us? So you’d want to fire up your Google Analytics 4 account? And just very quickly dig and say, year to date, just conversion paths? Is video at all showing up anywhere? And the answer is yes, sort of a mid touch point here, it does show up in some of the mid touchpoints. It also does show up later on AM. But the this chart system doesn’t allow you to see that in greater detail. This is why this is why we wrote our own attribution modeling, which is this, this is kind of a pain. That said, it least video our video does shops or as the middle in the middle of the funnel. So combining this with our Data Studio dashboard, we can see that video is is working for us.
Katie Robbert 17:40
And you know, full disclosure given that we will be adding on to our own promotion next year. But in general, our promotion of our own materials is lacking for lack of a better term. I would say that this is pretty good for very little promotion of the stuff that we do.
Christopher Penn 18:03
I would agree. So that is us wrapped. Now. Just kidding. From a marketing perspective, what comes next? What do you want to know? As as someone who’s setting the tone for what we’re going to work on next?
Katie Robbert 18:21
It would be interesting to sort of see, I mean, benchmarks is such a lousy term, but benchmarks is also something that we would want to know of. Okay, this is what we look like. But what do we look like compared to our competitors? I know that that’s harder data to get a hold of, we would probably have to look at their data within YouTube because we don’t have access to their channels. But just the sheer volume of content that we put out. Is this good?
Christopher Penn 18:56
Yeah, I yeah, I don’t know. Actually, I have I do know where we could get some of that data, but it would take a lot of digging to do it. Talkwalker has that data in its platform. So if let’s go ahead and go to Talkwalker. Let’s go to our search and let’s put in specifically Google Analytics 4 Because that’s what we’re talking about earlier.
Katie Robbert 19:34
The reason I bring it up in terms of like benchmarks, you know, and maybe those are the wrong things to look out. But, you know, in terms of cadence, we do one show a week pretty much every week with the exception of a handful. So let’s say you know, we do 45 shows a year. Is that enough? Is it too much? You know, should we increase the cadence, decrease the cadence and so those are the types of things in terms of resource Is I would be looking at in addition to the topics themselves,
Christopher Penn 20:05
right? Okay, so we have on any given day this, there’s a handful of videos about it, you’re talking about 30,000 engagements, potential reach about 700,000 total, let’s take a look at some of the listings.
So you have Google Search Central, best call tracking, just add some bounce rate stuff, such as tutorials. This is sorted by engagement. So in this particular the highest rated, the highest engagement video came out in March end of March from Google itself. 518 likes 33 comes to a total of 551 engagements overall, and looks like a reach of about 15,000. So 15,000 views?
Katie Robbert 21:00
Does it say how long the video is?
Christopher Penn 21:03
It does not.
Katie Robbert 21:05
Because that would be the other thing that I would be interested in. As, you know, if I were planning this to say, okay, great. So we don’t we do like a 30 to 45 minute show, depending on the topic is that if we were to change the length of the show, would that change? The average watch time? Would that change, you know, people’s ability to consume the content? Because it’s too long? Or you know, whatever? Those are the questions that I would be asking.
Christopher Penn 21:33
Right? Okay, let’s take a look and see if that data is even in the file for for usability. So YouTube.
Katie Robbert 21:44
So John, when you’re looking for videos, you know, does the length of the video sway your decision in terms of whether or not you’re going to watch it?
John Wall 21:56
Well, you know, there’s two kinds of use cases that come to mind. I mean, one is, if you’re searching for a specific answer, then this is the kind of traffic you really expect to see. And in fact, YouTube is doing a better and better job of Chapter ring and showing when people are jumping to points, so that you can just get the specific question you want answered, you know, you don’t have to watch the whole thing. That’s pretty normal. And then the other side of it, though, is for pure entertainment videos, you know, something where people want to sit down and actually watch the whole thing. And yeah, I don’t know, look at it from that side, it seems like given the average length time, we could compress a little bit, you know, try and get tighter and see how that would affect the overall number of views and number of likes, and things like that, to see if that would change things. But I don’t know, it’s always hard to gauge that because, you know, 20 minutes is like classic entertainment length, but 40 minutes is more kind of standard academic deep dive real content, you know, so it’s, and maybe just something to play around with do there’s no reason we couldn’t do you know, alternate weeks of shorter and longer to try and kind of see if that affects anything or where it goes. And then yeah, it was interesting to see open q&a Doing well there. You know, that’s kind of like grab bag stuff that does tend to pull some people’s that was interesting to see just show up in that overall list.
Christopher Penn 23:15
There is no variable within the data export for video length, we just have for YouTube, specifically of likes, dislikes and views as the primary parameters, we can get the dimensions of the video, but that’s that’s about it.
Katie Robbert 23:29
No, I think that’s fine. I was just I was you know, it was a curiosity thing. You know, just shorter videos do better than longer videos. And, you know, I think John, you gave a really solid answer of, well, it depends on what you’re after. If you’re there for entertainment, maybe you want it to be longer versus if you’re there to for like a how to, you probably just want to get your question answered and move on.
Christopher Penn 23:54
Now, of these videos, the median number of views on a video about Google Analytics 4 is 40. Right. So that is the median number in this huge data set. So when we go if we go back to our Looker studio profile, let’s see. So we’re hovering prior around the 15 to 20 for as a median number, so we’re not that far off from from from the category as a whole.
Katie Robbert 24:25
Okay. That’s good to know.
Christopher Penn 24:29
And what’s interesting when we look at the table itself is even though we’re focusing on Google Analytics 4. These videos are not all laser focused on a lot of it’s like Roundup news like Google Search news and things like that. Google Tag Manager for beginners, so on and so forth. So it’s not just GA four.
Katie Robbert 24:51
Right? Well, and I think as you know, as the buzz around GA four starts to wear off. People will start to look more at the well rounded Google Marketing Platform. And so that’s something that, you know, we should be thinking about of have we tackled all the pieces of Google Marketing Platform as they feed into a Google Analytics instance. And I can say, with 98% confidence, we really haven’t tackled AdWords, for example, or Google ads, whatever it’s called these days, they keep changing the names of these things. And it just makes me feel old and out of touch, because I never get it right anymore.
Christopher Penn 25:34
I’ll put a little cheer emoji and chat. Yeah, I think those things are interesting. Now, here’s the other challenge that from a video content producer. That’s hard is this is what we know. Right? So the bigger question from a content production standpoint is, what are the topics or ideas or things that we’re not doing, that we could be doing? That might do better?
Katie Robbert 26:06
That is a great question. And I think that that’s the question. That’s the burning question that, you know, content marketers, video marketers are always wondering is, what else? What’s next? What’s new? What do I need to be thinking about? You know, for us, Google Analytics is a sweet spot, because we know it so well. But where we are having the same conversations that everyone else is having? So what’s the new spin on Google Analytics, or, you know, anything else? And so I see that you’ve just put chat TPT in there, which is, like, the fresh hot thing. But how long is that going to last for? And everybody’s writing about it? Everybody’s talking about it? We’ve talked about it. And so again, it’s another topic that is being so heavily covered, like what? What are the spaces in between?
Christopher Penn 27:03
The spaces in between, I think, are what we talk about all the time, which is the so what, a lot of these things, Google Analytics, 4, Google Tag Manager, chat, TPT, NF Ts, whatever, is okay. It’s the thing that’s like frying pans, you know, soup, pots and things. And so what does he have? What do you do with it? How do I make this thing useful? Rather than having it just be yet another thing on my to do list like, how do I how do I use this thing to be helpful? And so for all these things, I, I firmly believe there is a very large market that is untapped. Have to show me how to use the things and get one more thing off my to do list. I’ll give you an example. Earlier today, I got a request from our friends over at Agorapulse and said, Hey, we want we want you to write an article for our site, about your how people should think about their analytics workflow. And I’m sitting here thinking to myself, yeah, I could do I have time to do it? Do I want to do it and stuff like that? And they’re like, well, we like, we’d like our friends at Agorapulse, we should probably do something. How much time do I want to spend on this? So I sat there and thought, well, what if while I’m cooking breakfast, I just turn on my voice memos. I turn on the otter app, and I just foam at the mouth for 15 minutes about my analytics workflow. And so I do, and I get the really rough, messy transcript. And I’ve just stuffed that into into OpenAI G GPT-2 Playground, which is the engine that powers with it gets you to the model faster than the chat version, the same thing and says, Please rewrite this. Please write this. So that’s not a coherent rambling from somebody for 15 minutes, but it actually is a coherent article. And it does, it preserves what I told it to preserve certain things. Like I want you to keep all the acronyms that I used. I want to keep the textbook doesn’t want to be ripped from professional technical tone of voice. I want it to be college age readability. And it turns probably 4000 words of rambling into a compact 700 word article that was coherent. And I looked over and I shipped it out. And that process took six minutes. So instead of what would probably take me an hour, maybe two hours to stand on, bang out the words and second guess myself. I recorded 15 minutes talking took six minutes to run through the engine and now I was able to produce a good enough article for for guest posting. So one of the things Katie that we have on our list for next year is how can we do more guest posting and writing and content production off our site? This is the process I’m going to take into it. So that would be the recipe. And so yes, the shiny object syndrome part of chap GPT will wear off right, you can already kind of see it. There’s a big spike in that purple line, and it’s already dropping off pretty fast. Clay Shirky said something a long time ago that I love and I respect Often once a tool becomes technologically boring, it becomes societally interesting, right? Once Once the shiny objects factor has worn off, then if it’s got legs, we’re going to start using it. So for the content we need to create and the stuff that we want to show on, on. So what and other things is gonna be focused, right. Okay. It’s great that this thing exists. Here’s how to use it.
Katie Robbert 30:25
And I think that, you know, we always try to focus on the how to aspect of our topics. But we could certainly do a better job with that. And, John, to your point, how do we make it a little bit more succinct? You know, sometimes, like just happen, we kind of go a bit off topic of the original concept of the show. And so maybe it’s a little bit more preparation ahead of time to make sure that we’re hitting certain points and really driving home. Here’s the so what, here’s the so what, here’s the so what? So that, you know, when people are searching for things on YouTube, they can very quickly find how to integrate OpenAI into your content marketing workflow. Like that’s a terrible title. But that’s essentially what the gist would be. Exactly. And they could open the video and immediately have an answer.
Christopher Penn 31:22
Exactly. It’s just like, we’ve talked for years, how do we get people to use attribution reports better? Right. So this is the Trust Insights report for the last year from Google Analytics 4. It falls in the category of Okay, that’s cool. I see all the things that’s nice. The so one of it is we look at something like Facebook 2% of our conversions for this conversion came from Facebook. are we investing 2% of our time and effort in Facebook? No, we’re not we’ve we’ve actually specifically decided not to market on Facebook at all, and yet is still delivering something. So the question there is, do we need to do we do we consider that we invest a lot of times in Slack, and about three and a half percent of the places and times we spend on investing in marketing? Is that the is that a good use of our time? I started number five converting source probably so YouTube’s kind of down here, right? It’s it’s point three 1% of for this particular conversion for people filling out thank you. Now if we were to look at a different conversion, like new users to the website, that might be different. Right? But that’s what you do with an attribution report. You don’t just stare at it. It’s not it’s not cubicle art, it is use these percentages to make decisions. I know I’m investing X percentage of time, money and resources into this channel, am I getting the same or better results out? You know, how much money do we invest in the almost timely newsletter, right? And we get 56% of our conversions from it for this year. That that one that would also be over 50%, at that point where like, is that a risk? Thankfully, no, because the publisher was also the co owner of the company. But if this was an external channel, that wasn’t under our control, like that’s a risk, that’s a real risk. If that channel goes away, we have a big problem.
Katie Robbert 33:19
So when we go back to the purpose of this particular episode, which is, you know, Trust Insights wrapped, you know, we sort of started that lightweight, hey, let’s look at our show. But really, what we’re seeing is, it’s it’s a way to organize your data for your past year that maybe people can, you know, understand a little bit more and make a little bit more actionable. You know, one of the challenges that we’re always trying to help ourselves and our clients with is that because I think what you call analysis paralysis is, there’s so much data to look at, like you exported data from YouTube, not from Talkwalker for YouTube, and then pulled it in, and it was just the sheer number of metrics we could look at. If we didn’t have a clear cut question, then we probably would still be there looking at it now. So I like I like borrowing from these more popular platforms like Spotify, or, you know, pretty soon I’m hoping to get my peloton one to tell me, You know what I did in the past year, because that’s always fun. John, you’ll probably get one from Strava. Chris, you know, I think you use a couple of different fitness apps as well. And so it’s always fun to see that data for yourself. So thinking about that, in terms of how do you bring that into your organization into your team, to you’re still telling the same story, but you’re telling it in a little bit more lightweight, you know, maybe easier to understand digestible. pieces. So borrow from how these other apps are doing it as you’re presenting it to your, you know, C suite to your stakeholders as to the decision makers, like, Hey, this is marketing team wrapped. Look what we did this year, and so giving you an opportunity to highlight everything, but then also say, so wouldn’t it be great if next year, we could get to 25%? You know, average views, or 10%? Watch Time. And that’s what we hope to see and are wrapped up next year, for example.
Christopher Penn 35:31
Exactly. And I think that that is the other thing that wraps does. I think so well, and this is an example of one right? is all about you. Yeah, it’s all about you. As John and I were talking about this yesterday, the reason why so many of these different AI tools like the you know, the puts you into a portrait of you as a Viking whatever. The reason why it’s so popular is it’s it’s your favorite person, it’s you. Alright, it’s you, you’re wearing fairy wings, it’s you as a as a no action hero. But it’s the center of it is is you. And so when we think about our reporting, right, you take your average report. Is it about you? Is it about the the stakeholder, right? Is it presented from the perspective of the stakeholder? Or is it presented from the perspective of the tool or marketing team or whoever and maybe that’s what these that’s what these wrap things get so right? This is all about you and I like to feel good about myself.
Katie Robbert 36:32
John, did you get one for marketing over coffee?
John Wall 36:35
No, I did not. I was basically ranting. I just can’t get myself over the hump of like Mee Mee Mee Mee Mee Mee Mee Mee Mee, you know, you just like, don’t want to pay that. But I’ve seen some that are so amazing. Now, I’m slowly starting to lean. So maybe I’m getting pulled out of the Luddite quadrant today, here, we’ll see how that goes.
Katie Robbert 36:54
That’s, that’s our 2023 goal is to you know, bring John into at least the 18th century. Shoes, which is funny, because John is Mr. Technology, Mister, you know, I play with all this stuff. But then when it comes down to actually, you know, using it for your own stuff that I think that’s where you, you know, dig in your heels a little bit.
John Wall 37:17
Yeah. Well, the tech you contrast with the curmudgeon, you know, it’s the old man thing that gets in the way of just buying everything all the time.
Katie Robbert 37:27
So as we are wrapping up Trust Insights wrapped, I’ll ask you both, and then I’ll answer, you know, what do you hope to see for so what moving into 2023? Chris?
Christopher Penn 37:41
I would like to see us build a cookbook. Right? So if we focus on in this episode, here’s how you use Data Studio to analyze YouTube data. Here’s how you use Google Analytics 4 data to understand which page is the most valuable by the end of 2023. With all these new cool tools and technologies and things that allow us to summarize and fix transcripts and stuff, I would love to see a so what the cookbook, right of here’s 50 marketing recipes that you can take to the bank today. And, and, and make a difference in your organization. I think that would be a really cool takeaway for everybody to have from a show where this is what we do every week.
Katie Robbert 38:26
I like it, John.
John Wall 38:29
I’d really like a picture of myself as a Viking. That’s number one. But after that, no, I think I don’t know, I just think we’ve got a lot of great content and just finding a few ways to kind of change it and figure out does it need to be more live streaming? Or does it need to be more q&a? Or what can we do with it? I think, yeah, it’s just like any kind of content is like you do the first draft. And you kind of keep playing with it until you find something and, and I think, yeah, eventually we’ll hit something where it will jump up to the next curve, you know, and that’s what I would really like to do kind of figure out where it needs to be next. And yeah, I can see having a recipe book where, you know, there’s 40 segments of it, like each episode is a piece of that overall book or something like that. Yeah, maybe that’s somewhere. We could stop next, because that does seem pretty interesting. Yeah.
Katie Robbert 39:23
I would like to figure out, you know, so we do this show live, and then people can watch it afterwards. I would like to figure out, you know, if we want to continue to do it live, how do we get more audience participation? How do we get more people showing up, you know, during the recording so that we can get more of that interaction. But then also just, you know, really digging into what’s working, what’s not working. And I think that that goes down to Chris, what you’re talking about, of, you know, the real nuts and bolts of how tos and outlining it so people know what’s coming, what they can expect. And maybe they can pre order, you know, the playlist ahead of time or the book or however we, you know, package it together to because they know, each piece is valuable. But when you put it all together, they can, you know, entertain for 50 people.
Christopher Penn 40:19
Exactly. So Data Studio crudities.
Katie Robbert 40:25
So with that, you know, if you’re watching this, thank you for being along this journey with us. This is, as I mentioned at the top of the show, this is our last episode for 22. We will return in January 2023. With some new content, maybe a whole new content strategy, who knows we have a couple of weeks to think about it. Final thoughts, Chris John?
Christopher Penn 40:50
Drive safely. Don’t Don’t Don’t do analytics while driving.
Katie Robbert 40:57
What if I just recite my multiplication tables?
Christopher Penn 41:01
Sure. No, I think thanks. Thank you too, for watching, for tuning in. Whether you’re watching live, whether you catching the recording whether you just read the transcript, we appreciate the intention knowing that in the coming year, in even this year, the sheer amount of content people are creating is greater than ever. And so any attention that any of us earn as a marketer, we have to be thankful. So thank you.
John Wall 41:28
That sounds good. Be sure to show up in 2023. We’ll be looking for you. I’ll be events we’re going to be covering a lot of ground next year. So hope to see Alright, well,
Christopher Penn 41:36
happy holidays everyone and Happy New Year. Thanks for watching today. Be sure to subscribe to our show wherever you’re watching it. For more resources. And to learn more. Check out the Trust Insights podcast at trust insights.ai/t AI podcast, and a weekly email newsletter at trust insights.ai/newsletter Got questions about what you saw in today’s episode. Join our free analytics for markers slack group at trust insights.ai/analytics for marketers See you next time.
Transcribed by https://otter.ai
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