livestream header

So What? Solutions in search of problems

So What? Marketing Analytics and Insights Live

airs every Thursday at 1 pm EST.

You can watch on Facebook Live or YouTube Live. Be sure to subscribe and follow so you never miss an episode!

In this week’s episode of So What? we focus on solutions in search of problems. We walk through the problems with innovating, how to pivot once something is created, and how to understand if there is a use case. Catch the replay here:

So What? Solutions in search of problems

In this episode you’ll learn: 

  • How to deal with solutions or products developed without customer input
  • How to pivot an existing solution to a real problem with market research
  • Live development example of a problem for an existing solution

Upcoming Episodes:

  • Email stats
  • LinkedIn algorithms

Have a question or topic you’d like to see us cover? Reach out here:

AI-Generated Transcript:

Katie Robbert 0:23
Well, Happy Thursday again, welcome to so what marketing analytics and insights live show? I’m Katie joined by Chris and John. Today we’re talking about solutions in search of problems which in some ways it’s kind of the basis of so what it’s how it came about is why I, you know, say the phrase so often is, you know, when I’m whether it’s from Chris or anyone else, my first question is, so what what problem is this thing solving. So today we’re talking about that exact topic, solutions in search of problems. So today, we will talk about how to deal with solutions or products developed without customer input, which happens a lot. How to pivot an existing solution to a real problem with market research and live development example of a problem for an existing solution. So it, it reminds me when I worked at a different company, they created an innovation committee. And so this innovation committee was meant to come up with new product ideas or new research ideas that we could turn into grants or that we could turn into commercial products. And what was lacking was that customer input. So I feel like sometimes innovation is just a mask for solutions in search of problems, rather than innovation being new ways to solve existing problems. What do you guys think?

John Wall 1:49
It’s so if I didn’t have that creativity, as a committee is always a risky venture, you know?

Katie Robbert 1:55
And I should note, we never came up with a single idea. Okay.

Christopher Penn 2:01
That’s fair. Yeah. I mean, it depends on on what the source of the innovation is to, like when the topic we’re talking about today, came out of some code that I wrote, using Twitter’s API. And the reason it exists is because I was like, hey, I want to play with this thing. Because fundamentally, I’m still 12, right? I just want to play with toys all day. And started making things to the best of my knowledge, you know, looking at what’s possible in the API, what kinds of data you get out of it, what kinds of computations you can make, that created this sort of thing, that then I was like, cool, it exists. I don’t know what to do with it.

Katie Robbert 2:49
Well, and I feel like that’s a very common thing that happens is, I don’t know, it would be cool if it could do this, but there’s no real problem that the thing is solving. And that’s not necessarily a bad thing, a lot of really great innovations. And inventions come from just sort of playing around with stuff. But a lot of times, especially when your team has limited resources, limited budget, you know, trying to create solutions in search of a problem is the last thing you need to be doing. Another really good example of this is, you know, setting your strategic priorities for the year, you’re creating solutions, but you don’t actually understand what the problem is. And so you might have this disconnect, where you have the, you know, big thinkers and the business leaders and the thought leaders, and they’re coming up with these, you know, goals and ideas. And there’s such a disconnect from what’s actually happening, that they’re creating solutions, but it’s not solving the actual problem. They want to create this new, you know, innovative technological rigidity, du da platform, but everybody saying like, Hey, I can’t even get to work on time, because traffic is bad. So like, how is this thing going to solve the problem of people actually being able to get to work? So with that, Chris, what journey are we going on today?

Christopher Penn 4:12
Well, I thought we’d take a look at what the thing is, and then get your feedback. And for the folks who are watching, please do leave comments in the comments, wherever it is on whichever channel watching with your reactions, like when you see this go, Wow, that’s useless or Yeah, I could see some some features that and then once we take a look at this thing and sort of explore cells, we’ll look at some of the feedback we’ve gotten from folks we showed it to because our our first instinct after creating this thing was okay, well, what are what do other people see in this that maybe we’re not, so it’s doing things in the wrong order, the market research should have come first. But as is typical with, you know, coding projects that I do, as hobby projects, it’s, you create the thing like okay, now what? So let’s go ahead and and flip to The this thing. So ultimately, all this is is pulling data from Twitter’s API about, you know, a username or whatever. And they’re trying to do some analysis to see what’s happening. So this is the first of three screens. This is for the Trust Insights Twitter account, which by the way, if you don’t follow us go to, slash Trust Insights, you can follow us there. And then maybe someday you’ll be on here. These are the words and phrases that occur the most in our followers BIOS. So when with the other people who follow us, these are the things that occur the most frequently, so social media, digital marketing, co founder, content marketing, digital marketer, data science, and so on and so forth. So this is, you know, the first thing so when you see this, you know, John and Katie, what’s, what’s your reaction to this thing?

Katie Robbert 5:54
My initial reaction is, this is an audience analysis. And so what we’re doing is we’re looking at our audience makeup, who makes up our audience? And do we have, are we attracting the right kinds of people to consume our content that we’re sharing? And so that’s the way that I would look at this. Now, my follow up question would be like, well, if we have the wrong mix, and on this on this analysis, it looks like we have a healthy mix of the right people. But if we had the wrong mix, like what would we do about it? How do we find those people who are the right mix? So that’s two different questions.

John Wall 6:34
Yeah, that is a great point, you know, like, you would love to see cmo on this list as a follower. Of course, the numbers aren’t there for it. And I’m just surprised that social media dominates so much. I mean, it’s almost two to one, or even just the next closest line. So that was very interesting to see that, you know, that’s such a big slice of what we do. But at least you know, digital marketing is strong and data science, actually, data science is stronger than I thought it would be for coming up on there. But yeah, so you know, okay, so now what, what do we do?

Christopher Penn 7:06
Yep. One of the things that doesn’t have, and it would need different coding to do it would be to explain at least a hint at a little bit of the context, because that first one could very well be views expressed on social media or my own. Right. So it’s not saying that person’s a social media professional, that might be part of their disclaimer and their bios.

Katie Robbert 7:26
That’s interesting. So it doesn’t, so the context with anything context is important. And so, you know, looking at that, again, you know, we don’t know, are these social media professionals? Or are they saying those kinds of phrases, co founders, great co founders of what, co founders with the right kinds of company, um, you know, data science, it could be, you know, this is a stretch, it could be, I don’t believe in data science, and just, you know, as an example, But to your point, Chris, we don’t have that context, because these aren’t the full BIOS, these are just the most frequent phrases.

Christopher Penn 8:02
That’s right. Now, in aggregate, though, I would agree with you this says, okay, you know, that you’ve at least gotten is that has some, some topical relevance. So that’s number one. Number two, is that we came out of this thing was, who does the Trust Insights account interact with interactions is the sum of replies, retweets and mentions, so add those three things up? You know, who does this account talk to or interact with them retweet the most? logically, it’s the company’s account. So you and I are interacting with the company account the most, followed by Gini Dietrich spin sucks our friends over Talkwalker inside big data, Harvard Business School, and then go on down the list.

Katie Robbert 8:51
So yeah, I guess is that so what? Like, Okay, that’s good to know that. So, you know, I look at this. And if Chris, you and I were not at the top of the list than the than the answer, they would be like, okay, we need to interact with our own content a little bit more, but otherwise, I don’t know what I would do with this information.

John Wall 9:15
John, how long is the what’s the time series on this?

Christopher Penn 9:20
So the the window of time on this is the last 5000 tweets. So it’s in our account, it’s effectively forever, because we haven’t tweeted 5000 times yet on our own account yet. But you could restrict this down if you had to buy time.

Katie Robbert 9:36
So then, in search of a problem, is this the type of report that I could use to say, Oh, it looks like John Jay wall interacts with my account a heck of a lot. But as an example, we do follow you, John. But if we didn’t, we’d be like, Oh, we should probably go see what his profile is all about. Follow him. And then if he has the type of audience that we want, we can try to engage them a little bit more. Is that what you were thinking in terms of how to use this kind of data?

Christopher Penn 10:08
That was one possibility. The other possibility that I always think about with influencers, in particular, is if you have somebody who’s, you know, a big influencer, let’s say the account was was unreachable influencer? Well, who are the people that they interact with? Because maybe I can’t afford to, or can’t, you know, break through to the influencer, but maybe I can interact with the account, you know, the accounts that are 3456 on the list here and say, Maybe if I can get on their radar, I can get my stuff to bubble up to the big influencer, who, whose attention otherwise, I’m probably not going to get, huh.

John Wall 10:43
That’s interesting. So it’d be more of a targeting tool that you’d go there and put the accounts that you want to get into. And now you’d be able to work the cloud around them.

Christopher Penn 10:53
Exactly. Okay. And number three, is, when we look at the followers, the people who follow the Trust Insights account, here’s the last time that they tweeted, so 417 of them tweeted today, right? So as they publish something on Twitter today, 170 on publish something, at least a day ago, 70 published something at least two days ago, and they go all the way down to the bottom and there. The 653 accounts that follow us I have not published anything on Twitter in at least a month. So there’s that sort of the, the either at the very least is your got lurkers. Right. But they might also just not be active accounts.

Katie Robbert 11:36
So my first gut instinct to this report was, I don’t see how this is useful, because it doesn’t tell you who. But if you think about it, in that context of influencer marketing, you know, if you’re looking if you’re potentially vetting influencers for your brand, or your campaign, or whatever it is, I could see how this would be useful. Because when we talk about the different kinds of influencers, you know, if you have someone the example, Chris, I think he used like a Kardashian, if they’re just screaming into the void, then how useful is that if their audience isn’t necessarily engaged, versus someone who might have a lower number of followers, but their followers are super engaged with everything that they’re doing?

John Wall 12:23
Yeah, when we share these with the marketing over coffee listeners, because you would run this for four people there, and everybody was just blown away by that 31 plus days old. And it was interesting to see how some accounts it wasn’t. I mean, it was still huge, it was maybe a third of their total followers, but other accounts, it could be well over 60 70% of their followers. So there’s a lot of junk. I think there’s a bunch of stuff to explore there. Like it would be really interesting that you said grab a Kardashian or somebody like, you know, Chris Brogan people in the hundreds of 1000s of followers range and see what those numbers look like. Are you able to pull beyond 31 days? How far back? Can they go?

Christopher Penn 12:59
Oh, yeah, I mean, you get the actual timestamps is just in order to make this chart readable and not like this long, right? I basically bucketed everything after 31 days.

Katie Robbert 13:09
So Chris, what were you thinking? Chris? What were you thinking? What were you What was your train of thought when you were putting these charts together? Other than, oh, I wonder what that data looks like.

Christopher Penn 13:25
That’s exactly it. So that that’s, you know, the head solution in search of a problem wasn’t necessarily solving a problem so much as exploring what’s in here. And this is obviously something that you can do with any kind of data, like, you know, it’s so this is something that, for example, the logic and the pieces of things, you could take this and use your CRM data, right, and see what was the last time any of our prospects have been active? You know, we’ve tracked something activity in the CRM, if 90% of your prospects have no activities, like you may, on paper have a lot of pipeline, but if they’re not interacting with you, is that pipeline toast?

Unknown Speaker 14:04
Well, so there’s,

Katie Robbert 14:05
I think back to your point, though, Chris, it’s all contextual. And so, you know, we do have one comment that I think is actually interesting. So Peter Knight is saying, I have a Twitter account just for reading stuff, never post anything. And I think a lot of people use Twitter as a newsfeed. And so, you know, maybe awareness is good, but engagement is low. And so you know, I think it depends on the purpose of your Twitter account, what you’re trying to accomplish. And so if you’re just sharing news and information, maybe the lack of engagement is okay, because people are seeing the stuff but they’re not actually doing anything. Or, you know, I guess I can, I can kind of understand where having a really good purpose for the Twitter account is probably important for this kind of analysis.

Christopher Penn 14:59
Right and The flip side of that would be your attribution analysis to say, okay, all of the stuff that you know, all the channels are out there on. Even if someone like Peter never tweets, never post anything, never likes anything, never favorites never retweets, if they’re just reading, if they’re clicking on our stuff, and in going to our website, we should be able to see at least the traffic generated from that. So that would be a case where, like you said, you want to have that parallel context. Yes, we have an active group of people here. And then for the lurkers, we may not know it’s the same person. But if you see that you have a big dormant chunk of followers. From an activity perspective here, and you’re looking at attribution analysis, you’re getting no traffic from Twitter, then you’ve probably got a problem.

Katie Robbert 15:46
Feed that makes sense. And that’s not data that’s being shown here. So you need that secondary data source in order to understand Is this okay?

Christopher Penn 15:55
Exactly. So, we this is sort of the process that I went through initially, when we’re creating these things, and it was still very much, you know, going in search of a problem. So the next thing that we did was we asked the folks in analytics for markers, and a couple other friends, what’s your feedback? When you see this stuff? What do you see? What are the things that come to mind? arsham was asking, Hey, could we slice and dice the BIOS over time to see for the people who are following you? Is there a change in the words of the people that that are following you. So was maybe social media was really popular in BIOS from, say, two years ago, but in the last 90 days, more people with Tiktok were, you know, like, I’m a Tiktok, influence, or whatever is showing up in your BIOS will be a way to detect relatively new trends based on the people who are following you. Brian said, Can you then see who they interact with? So of course, we’ll create a network map? And the answer to that is yes, you can, it would make the software, you know, gigantic in terms of the amount of data you’d have to pull in. But it is a doable thing. to at least look, you also do get when you when you address the API, you get the last tweet that somebody made. So you can also just do a quick summary of all the people who follow you, you know, what the what is the general topics of your this this cohort from their last tweet? Let’s see going down here. Kristoff says, These might be useful to identify who might have been behind an anonymous Twitter account to see who the account interacts with Catherine Phelps is saying cross referencing with BIOS and recency of activity to see, can we identify those topics and discussion points, and then do some outreach to them. And thing is a way to identify which of these accounts might be bots, not just inactive, but just outright bots. And there’s actually separate software for that. There’s a library called bot or not, that looks at essentially patterns, things like stuff as silly as your profile picture. But things you retweeted or shared recently, the timing like is, is your account sharing on such a regular schedule, that it’s always exactly the same time every day as looking for automation. Then we asked for a couple of our friends who do influencer marketing. And I thought was very interesting. So one, one person said, I’ve never seen that sort of inactive audience pool before in any other tool. It’s it’s kind of new, but it might be a good way to judge influencer fraud. If an influencing I’ve got a million followers like yeah, I’m 20 interact with you 99%. And in that 31 days post, again, to Peter’s point, they might some of presented are going to be lurkers right button, not like 90% of them.

Katie Robbert 18:52
So I guess I’m still struggling with a very clear use case for this software. Like, I feel like it’s still a solution and search for problem. And we’re searching pretty hard to find a problem that it solves. I think what we’ve been able to come up with so far is that it could potentially be a useful tool specifically for those who were doing influencer marketing. But beyond that, it probably doesn’t have a lot of utility. It sounds like we could maybe stretch and think about how to use it for a building community coming up with content ideas, but even that feels like there’s better ways to do that. Am I am I on or off track?

Christopher Penn 19:32
Well, so I would say this is the point where someone with product marketing experience would take this feedback that we’ve gotten, and the capabilities we have now and sort of the wish list of things that we might be able to see okay, well, is there a way you know, this is the whole pivot into into something, you know, new, we’ve got ingredients. We’ve got some tools, what we we’ve got talent. What we don’t have is a recipe and an outcome. So the question I posed to you, Katie, as someone who’s done a lot of product management is, given this these raw materials, how would you turn this into a coherent product that someone like John could then go sell?

Katie Robbert 20:14
You know, the first thing I would do is a little bit more research. So I would take this and sort of put it aside for a minute. And I would start asking those communities, what are some of the challenges you have with influencer marketing in terms of getting good data? What tools do you are you currently using? So we could at least start to understand who the players are out there the potential competitors for the thing that we’re creating? Because one of the things that, you know, we would want to do just like everyone else is, why is ours better? Why should you use ours instead of what’s already out there. And one of the things that I know, we lack a lot of times, if any kind of an interface. And so that right, there already kind of puts us at a disadvantage. But if our data is, you know, more robust, more timely, more detailed, more intuitive, whatever the thing is, then, an interface becomes less important for people who can read a spreadsheet and do something with it. So I would start with doing a little bit more market research to see what are the existing problems, to getting any kind of information about the influencers that you’re trying to wrangle and then see if there’s any sort of clear matchup with the thing that we’ve created. But then you also need to package to your point, we need to sort of package the thing of, you know, here’s your problem. Here’s our approach. Here’s the output, here’s, you know, everybody wins solution, kind of a thing. And so you need to put it together that way, as well. So that’s where I would start is I would start with more research.

Christopher Penn 21:51
Okay. And, John, what about you in terms of thinking about how, if at all, you could sell something like this, when you think about the conversations you’ve had with folks around the topics of social media? How does that map to what is here? And what might be possible?

John Wall 22:10
Yeah, I mean, you know, the hook is always the pain, right? We’ve got to find a pain point that this solves. Otherwise, we can show it off. And it’s good to show that we have, you know, the facility with which we can chew through data and show somebody results. And so we can get to things. But yeah, without a pain hockey gets really tough. One thing that did jump out, I mean, those interesting things, social media show up in the BIOS, one thing is scanning BIOS, you know, keeping a track for keywords, you know, anybody that’s got like marketing Ops, or performance marketing, some of these terms that, you know, have just surfaced in the past couple years of folks that are usually data hungry, that’s an interesting angle. But that’s more of a kind of alerts type thing. You know, instead of choosing single accounts more like me, basically, you just search for that kind of stuff. It’s, it doesn’t need such a huge tool. But that is definitely a point where you’d be, you know, qualifying leads better, you know, you kind of know from your Twitter account, who to actually reach out to and get over into the slack group and things like that.

Christopher Penn 23:06
gotchas, you could say, take that extract of those Twitter BIOS, maybe do what’s called seated LDA analysis and say, Okay, these are the eight topics that we know, we can serve effectively, well, let’s look at the BIOS that exist and bucket our users into eight, those eight containers, and then the other container and see, okay, these 40 people who are all about Google Analytics, let’s make sure they get you know, someone dmws them and gets them into into the slack group. You know, first, is that kind of what you’re thinking about?

John Wall 23:37
Yeah, definitely. Because they’re, you know, it’s people with those titles, we know for sure that they want to hear about what we’re talking about, and they’re more engaged with the kinds of things we’re doing. Because, yeah, we see a lot of folks that are, you know, social media experts, but they’re not really interested in charting large amounts of data, you know, they’re more just content creation, people, it’s not the same. It’s not a right fit as far as lead.

Christopher Penn 24:00
Okay, when you think about that, Katie, because I know one of the things that we wanted to talk about today was, if you’ve got the thing already, maybe you know, the product is about to launch or maybe the products already in market. And you don’t have the opportunity to start over. How do you pivot while the was was the expression you fixing the plane while you’re in flight?

Katie Robbert 24:21
Which is always an excellent idea, by the way. No, I mean, that’s, that’s exactly it. It’s sort of what we used to terribly say, or what my engineers used to terribly say is, you know, putting lipstick on a pig, kind of dress it up and, you know, do the best you can with it. And so, yeah, I think that, you know, John is definitely on to something of like, Okay, so we have the thing. You know, in this example, we have somebody who has already signed off on selling it, and we’re just saddled with doing something with it. So, yeah, you want to start to create a little bit of buzz and so why not use what we have to our advantage and find out who those people are see if we can get them on as maybe early adopters, maybe they can beta test it, maybe we can get some early testimonials from the both people. And so there’s definitely ways to use the information in this specific tool. Now, if you’re talking about, you know, other tools that have been created with no real use case, I mean, it just really depends on what it is.

Christopher Penn 25:26
Right? Well, you know, we’ve talked earlier this week on the on the podcast about Google Analytics for Google Analytics for is has been rolled out. And we all as the end, users don’t really get a whole lot of say, into into what goes into it, we can let folks and pay on Google’s team know, hey, this seems to work seems to not work or where this thing go. But we don’t really have a voice in in it. It’s just part of the the tools that we’re given. So again, that you know, outcome, recipe, ingredients, tools and talent, we have, we are given the tools, we don’t get a choice. So the question then becomes, well, how do we adapt? You know, it’s, you have to make soup, but you’ve been given a frying pan,

Katie Robbert 26:07
it’s still it’s still very unclear to me. What Google Analytics four is four is opposed to Google Analytics three, I mean, I, we don’t have to go down that rabbit hole. I know that there’s specific reasons. But that’s a really good example. Because the way that it was rolled out has not been explicitly clear. Here’s how you use it. Here’s how it maps to the old thing. Here’s the problem. Is it solving that you the user have asked us to solve? It’s very unclear in that kind of communication.

Christopher Penn 26:42
Now, with the product feedback that we were given so far, could you could you use this if this is all you had to work with? Maybe you’ve gotten some customer feedback from social media, whatever? To what extent could you use this to to reverse engineer those those problems that we’ve created solutions for that we didn’t maybe identify.

Katie Robbert 27:05
So the way that I would approach that is I would take a look at this feedback and pull out those, you know, three or four use cases that people have listed. And I would try it with different data. So I would take a look at, you know, known top influencers across different industries, and see if you sort of get the same kinds of results, Time after time after time. And so I’d be looking for more of that consistency across one use case. So I would take it one use case at a time and continue to run the data to see Can I get a usable result? Every single time? And if I can’t, what is the percentage that the data is not useful? So is there some sort of like a threshold that has to be this much, or these kinds of people, and so it’s only this industry where the information is useful? So that’s how I would start to back into the use case is I would basically have to do a lot of product testing, to see, you know, does it hold water? Is there anything there? Or was this just an anomaly? Because we ran it for ourselves? And we know what the data should say?

Christopher Penn 28:10
Gotcha. Okay. Well, now we’re at the, essentially the end of this process. So now the question is, Katie, what do you want me to do with this thing? Since this was supposed to do here?

Katie Robbert 28:24
You know, if you’re, if you’re looking for the absolute honest, you know, doesn’t matter that there’s a slew of people watching us, I would say this right now, is probably not a high priority. Because when I see the feedback that we’ve gotten so far, I don’t see very strong use cases, like I see that there are some use cases, I think there’s a lot more work that would need to go into it, in order to make this a viable product. The second question I would have is, yes, Trust Insights is really good at processing large amounts of data, because of the way that we’ve structured the company. But are we interested in getting into the influencer marketing realm? For example, we typically don’t, you know, just maybe sort of the one off, but is that a service that we want to get into? I don’t know the answer to that the answer is maybe. So I guess I would say for now, what I would ask you to do is, you know, maybe go back to this group of people or branch out, and we can do a little bit more market research to ask them, what are the challenges that you have with your influencer marketing data, so that we could just learn more because maybe there’s a version two of the code? Maybe there’s other ways to think about it from that information other than putting the solution in front of them and saying, what would you do with it?

Christopher Penn 29:46
Well, I think then, for folks who are subscribed to the Trust Insights newsletter expected to be a poll or a survey or something coming to you in the relatively near future asking that and other questions because obviously you want to do Hey, not waste time, but B, get a sense of what the problems actually are. Because the problems actually are Oh, well, you know, the Kardashians are just too expensive that there really isn’t a solution for that. Well, actually, there is an AI solution for that. And it’s, it’s just have to license the image and then build a bot that can simulate a Kardashian. But short of that we are

Katie Robbert 30:18
not doing, by the way, for the record. does not do that for

John Wall 30:25
multiple reasons. But

Christopher Penn 30:30
yeah, so look, for that coming. I guess any final thoughts about pivoting a solution and social revolve into something that the market would actually buy?

John Wall 30:42
I think it’s the magic of project product, you know, Product Marketing is, you know, you’ve got to get in front of a bunch of people and interview and drill and kind of find out like, okay, here’s these things we can do. And try and surface what the problems are, you know, it’s really up to you to try and connect the dots and get to where their pain is. And if you can spend enough time in front of them, usually, you can find the pain pretty quickly. But it’s, you know, earning the, the right to get in front of them. That’s the challenge.

Katie Robbert 31:10
Yeah, I think that’s spot on, John. And I would also say, if possible, like Don’t, don’t try to quell innovation, I think innovation is an important thing for any company to be doing. But base your innovation on the data that you have. So, you know, maybe you come across like, Oh, we don’t have a solution for this problem. What could we do about it rather than let’s build a thing and see if there’s a problem for it. So I would definitely encourage you to be innovative, but really stay grounded in the existing game that you already have. And if you don’t have a lot of customer feedback, go out and ask for it. You know, ask your customers, ask your community, post something on Twitter and see who responds but that would be my hard and fast like, don’t do solutions in search of a problem. It’s always more difficult. It’s always more expensive, and it’s just it’s a pain in the butt for us product marketers.

Christopher Penn 32:10
Alright, well, I shall I shall retreat to my layer then. And Hi, folks, thanks for tuning in. I will talk to you next time. 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 slash ti podcast and a weekly email newsletter at Trust slash newsletter. got questions about what you saw in today’s episode. Join our free analytics for markers slack group at Trust slash analytics for marketers. See you next time.

Transcribed by


Need help with your marketing AI and analytics?

You might also enjoy:

Get unique data, analysis, and perspectives on analytics, insights, machine learning, marketing, and AI in the weekly Trust Insights newsletter, INBOX INSIGHTS. Subscribe now for free; new issues every Wednesday!

Click here to subscribe now »

Want to learn more about data, analytics, and insights? Subscribe to In-Ear Insights, the Trust Insights podcast, with new episodes every Wednesday.

Leave a Reply

Your email address will not be published. Required fields are marked *

Pin It on Pinterest

Share This