So What? Third party data to inform your marketing

Well, hey everyone, happy Thursday. Welcome to another episode of So What? Uh, the Marketing Analytics and Insights Live show, which is easy for me to say. Uh, I am joined by Chris, I am joined by John. And today we're talking about third-party data to inform your marketing.

Um, you know, we often talk a lot about what you can do with your own data and making sure that the data that you're collecting is impactful, but we want it to take a little bit of a turn because we recognize that there may be other data sources that already exist that have already been collected that you can use to supplement your data or you can use to inform your marketing plans. And so Chris is gonna walk through what some of those data sets are that you should be paying attention to and that might actually inform some upcoming trends within your own marketing depending on industry. So, Chris, blow us away. Uh no blowing away just yet. Let's do this.

Because obviously you can just start jumping right in, but I want to take a few minutes to talk about uh how to think about this kind of thing because a lot of the time, and I I'm like the number one most guilty person of this, just kind of rush right in and say, let's play with stuff, you know, like a kid. And and there really isn't a point, and you can end up wasting a lot of time. So uh I'll go ahead and just bring up this uh diagram. Anytime we're talking about any kind of reporting or analytics and things. There's there's four things we need in order.

Um, we need to to have some kind of goal, right? What are we trying to prove? Um that's kind of like if you're thinking about baking a cake, right? That'd be like your recipe, like what's the cake supposed to look like? What's uh you know, what's supposed to taste like things.

It's it's uh Katie, you would call it requirements gathering, right? I would. Um, second thing would be the ingredients, which for us in this kind of thing is gonna be the data itself. What do you have, right? What do you have to work with?

And part of the reason why we're talking about third-party data, and we should be clear, we're talking about third-party publicly available data, not the third party in terms of like cookies and stuff. That's a totally different show, which was two weeks ago. Um this is like what data do you have and and what quality is it? And part of the discussion today is going to be around what data is out there and should you trust it or not. The third is the skills.

What what what do you know how to do? Again, if we're talking about baking a cake and you've never once turned on the oven, it might not go so well. Um, this it could actually go really badly. Um and the fourth, of course, are all the tools. What are the things that we need to do?

And the biggest mistake that we see people making all the time is they start at the tools, like, oh, what tools should I be using to do this? Like, no, it's like I have a really great waffle iron, right? But if I'm making soup, it doesn't matter how good the waffle iron is, it's it's gonna be the wrong choice. So when we're talking about third-party data, we have to go through these steps in order to make sure that we know what it is that we're trying to do. So, Katie, yeah, what do we want to cook today?

What kind of what kind of thing that would you would you find interesting as a an executive of a consulting company? As the executive, I would probably want to know if marketing budgets are going to start opening up again because we are marketing consultants, and therefore I want to know are companies starting to do better. Are they recovering post-pandemic? No, we're not full post, but you know what I mean. Like people are getting vaccinated, things are opening up.

And so I want to know will budgets be opening up, will they be bringing on consultants like us again? Okay. I think that's a great recipe. That's you know, we we know what kind of cake we want. You know, it's it's uh it's gonna be uh uh uh I don't know.

I have no idea. It's a cake layered with a hundred dollar bills. All right, that works. So the next thing we have, so that's the recipe. We got that down.

Next thing is what we have for ingredients, what's out there? One of the great things about the internet and and digital governments and stuff is that many, many, many different uh data sources exist that are credible data sources that you can go and get third-party data from. Um, I'll show you just a few of these. So this one is Fred, which is the Federal Reserve uh economic database, uh, which is operated by the U United States Federal Reserve Bank of St. Louis, which is part of the overall Federal Reserve system, tons of economic data.

If it's about money or things that affect money, it's in here, and there's 793,000 different data points, which is uh a phenomenal catalog. And this kind of thing exists in many, many different places. So uh the United States government operates data.gov where you can go in and find almost anything under the sun, literally under the sun. There's actually a data series from the Department of Energy that tells you the sunlight per square foot of the entire continental United States. So, like it's it's how much sunlight things get.

And thankfully, we're not the only ones who do this. If you are in the European Union, uh data.europa.eu. Again, tens of thousands of data sets from around the uh the EU on everything you could possibly imagine in languages that I can't speak, can't read, but they're in there with some great data sources. If you're in the United Kingdom, uh data.gov.uk, for example. Again, lots and lots of data sets of all sorts of things you can plan.

Uh, even smaller governments like New Zealand has its own data portal. So if you want to, you know, learn more about what's going on in New Zealand. So the nice thing is that for the most part, um, major nations have done a really good job of publishing data, and when you use their data, uh, they tell you where it came from, the provenance of it, the lineage, uh, which is all very important. Where you can start getting into trouble is when you start going off outside of uh I guess official government sets. So one of the most popular.

Go ahead. I have a question once you get past this. One of the most popular sites uh out there if that people love is uh Kaggle, right? Kaggle allows users and companies and stuff to publish data, um, you know, survival pay uh patterns of cancer, stuff like that, Marvel uh comics, characters, and sets. The challenge with some of this stuff is again lineage, which means where did it come from?

How did it was it put together? Who put it together? A lot of the time, these data sets are put together by people, so there may be some issues with credibility. Um, the other place that is relatively new that a lot of folks like um is Google data sets. So go just type in Google data set search.

And this takes us to datasetsearch.research.google.gov. So again, let's put in uh let's actually do management consulting. And here, you know, Google has uh all these different data sets of play of things it's found online uh that you could get. This is a case where again, you gotta be careful. Some of these are credible data sources, some of these not as much.

And some of these are even repackaged, like anything by Statista is data that they took from somebody else uh and is highly suspect. Um I would not use statista, especially because they also charge money to access publicly available data. So avoid them when you can. So, Katie, what was your question? So one of the challenges that I used to run into.

So when I worked on the healthcare side of the world, um, I would often try to supplement my data um with publicly available data. And so the sources that I had access to were things like SAMHSA, NESTA, TEDS, NSATS, Dawn. The problem with those data sets is that even if they're published a month ago, they're only good through the end of 2019. And so if I'm trying to make decisions or I'm trying to understand what's happening right now, I can't because the data has a one or two or even three year lag. It's not real time data.

So my question is, Chris, um, so you're showing us like Fred, you're showing us all of these other data.govs. How up to date are they? And how current are they enough that I can make decisions with it as of right now? It depends. And I'll show you how you make that determination.

Let's put in management consulting here. And with all these different portals, you get a sense of things like frequencies. How many and you could choose do I want data that's monthly, quarterly, annually, things like that? What are the sources, the releases, uh things like that? Was the geography?

So a big part is you can tell, and I get Fred even dates it in here. Uh, these are the time, you know, this is when the this last uh the last interval this thing was was run. And so you can get a lot of uh that freshness measure in here. If you need something that is faster, like say daily data, uh, you're gonna have to get really fancy. And by really fancy, I mean you're gonna have to take your existing data that you do have that has a time, you know, a significant time lag and run correlation analysis to data that you have as fast.

Say, like, you know, if you go to trends.google.com, you can get Google Trends for certain terms and see is there a strong enough correlation with the the fresh data and your existing back data to be able to say, like, yes, we can use this as a predictive uh tool. We can use this as a a fresher source of data until we get the back data and you're constantly going back and back testing and things like that. That would be the way to handle that more complicated one. So let's look in here. So we have uh 215 data series about management consulting.

Let's look at I'm gonna go with um producer price index by industry for management consulting services. So, for those who are unfamiliar, the producer price indexes what companies charge, what kind of money they charge for their services, right? So, in this case, management consulting services, you know, is really you know coming along. This is by the way, it's this is a scaled index where 100 is uh June 2006. So it's basically uh a pr almost like a percentage change in pricing over the years.

How much over that 2006 benchmark is this? And we see here things are kind of you know going a little bit down and in down then the pandemic hit, and then suddenly in January, everybody's like, hey, we need consulting help to understand like what to do now. Like the pandemic, you know, vaccines are rolling out uh in some locations and stuff. So what do we do? And so you have this massive spike in what consulting firms are able to charge.

I see this, and I say, hey Katie, we should probably raise our prices. I would agree. Um so now the question is what do we do with this? How do we get this information? Because it's good to have this bookmarked, and this alone tells you a lot, but it would be nice to be able to have it someplace more consolidated.

So there's a whole bunch of different ways you can do this. Um, for the easy version, like if you want this in a in a nice dashboard, um, you can just download the data. Um, in this case, I'm gonna download it as a CSV file, and then go over to uh utility of some kind, like Google Sheets, for example. Pop into Google Sheets here. And yeah, this is the no coding, very low tech approach.

Um import the thing I just downloaded. Anytime today, Google. As I say, this is live without a net here. This is yeah, this is live with with nothing. Um and my thing that I just downloaded, I'm just gonna drag that CSV file in and import my data.

And now we can see our series. There's our date, and then there's our series name. Now let's call this management consulting data. And now if I want to make this into a dashboard, I've got my Google Sheet it's saved. I can go over to something like Google Data Studio.

What do I just do? There we go. And I'm gonna connect my to a Google Sheet. And I should see. Come on, Mountain View, you can do it.

There we go. Well, you know, they're having their big management marketing uh big show today, yeah. Okay, so now I've got my table here, and I can change my metric and now start turning this into, say, like a nice line chart. And now we've got essentially what was in that fret interface in a Data Studio dashboard. And this is one of many things.

I could put in you know, multiple pages in my Google Sheets. Now, this is a very manual way of doing this. This is good for like an ad hoc analysis of something that you have to you know present once. Like if you go into into Google uh Data Studio now, you can actually uh switch over to view mode here. Um you can say I want to present this like a slideshow almost, and it'll you you can you know step through your dashboards and stuff, which is kind of fun.

So, Chris, a question for you. Yeah, what's the purpose of doing this if you have the Fred data? Couldn't you just screenshot the Fred data into your presentation? You absolutely could. However, if I want to bring in more than one series, or I want to bring in, say, some data from like my Google Analytics instance, or if I want to bring in other data sources, data studio will let me put it all in one place, which should be better than you know 20 different slides.

You absolutely can just take a screenshot. There's nothing wrong with that, um, as long as it communicates the information intelligently. The other thing you may want to do is you may want to do some kind of manipulation on the data that um isn't necessarily available in just the Fred data. So, for example, uh with this here, if I on my just on my styling alone, let's say I want to toss in a trend line. Let's do a polynomial trend line, and let's make it a little easier to see and make it red.

So I couldn't line. But you can you can see um, I can start to do a little bit of data analysis with this, not a ton because it is still a visualization tool first and foremost. But this would be the basic way of getting at this data and and putting it into some kind of system. Now, if you want to get fancy and be more technical, um, Fred and many of these other so services allow you to connect to them via APIs, right? So, and they have a whole list of all the data you can pull uh and even different programming tools uh that will help you in the language of your choice to automatically pull this date in.

So, one of the things that um I do is I have a script that I wrote in the R programming language using this library, and for about 40 or 50 different economic indicators, I pull them in and I analyze them. So that would be one, I think, really good way of using a lot of this data. And again, all these different services have APIs, you can sign up for them, they're all free. Well, I mean, your tax dollars pay for them. Um, and then you can turn that into into useful information on your dashboards.

Do they not even care about the calls, or is it one of those deals if you started hitting it 10,000 times a day, somebody would come looking for you? There are limits, there are limits that that uh you can have. Um sometimes, though, you get data that is just you you've got to manually do something with, and and there's no getting around that. There's it it just requires a little bit of you know, mostly copying and pasting. So let me show you an example of this that is a really good opening metric, but is not in a user-friendly format.

This is the TSA. Uh if you go to TSA.gov, this is their checklist of of traffic travel uh through TSA checkpoints, uh, and they have you know, buy day for the last two years. This is terrific. This is really useful information because unlike things that are speculative, like the stock market uh or cryptocurrencies or whatever, uh, you can't fake the number of people walking through a TSA checkpoint. Uh you can't just you know just just make that up.

You actually have to uh buy an airplane ticket and and you know take your shoes off and all that stuff. So, one of the things that we would like to be able to do is how do we get at this data? Well, the ugly answer is there is no clean way to get at it. Uh what you have to do is you have to fire up the uh spreadsheet software of your choice, and as painful as it is, copy and paste it into that software and then you know maintain it somehow. Um so I'm gonna go ahead and I have my last entry here was the 17th.

So let's make a little bit of room. What's today? Today is 27th. 27th, yeah. Ah, all right.

So we have data, I think, for the 26th. Yep. And now, ugly as it is, and as low tech as it is, and as painful as it is, um, we now have this data into in into a format that can now be processed. I like how you're calling it ugly, even though it looks exactly the same as the other spreadsheet that you just put together. And in terms of painful, that maybe took you 30 seconds to do.

But it's not automated. That's where it's not. There's a human there, that's ugly. Exactly. You know, there's there's an ugly human pushing the keys, and we don't like that because again, these are things that I would have to set myself a reminder, like, hey, make sure that you go in and do this kind of thing.

Now, what do we do with this information? Well, again, this is a case where uh we could upload to Google Sheets or or you know the sort of software of your choice. I'm gonna uh uh actually have uh run it through R, because I have an R script that can process that. Um, and now we zoom into this, we can see our plot, uh our trend line of our travel data. And now what we're seeing here is two years and change of the number of passengers going through these TSA checkpoints.

You can see pretty clearly when the pandemic really ramped up. Um, and more importantly, the blue line, that blue trend line really now starting to get to the point where you know it was uh prior to the pandemic. So travel has really started to open up. And this is all travel. This is business, this is leisure, this is you name it.

If you walk through a TSA checkpoint, you're counted in this. So the idea or the ideal would be to bring this data into Google Data Studio as well. Um in fact, let's go ahead and do that. I'm gonna open up a second tab here. And I'm gonna take my TSA travel data.

Just gonna do a straight up copy paste for now. There we go. Go into Data Studio, and I'm gonna add in a resource. I'm gonna add in the second page of this sheet. That's not refreshed, has it?

Okay, let's refresh that. What I like about these uh publicly available data sources is that it's a good way to validate your assumptions. And so the assumption as people are getting vaccined is that they would start traveling again. And now instead of just making a statement and having people say, Well, how do you know? You actually have the data to back up what you assume is happening so that you can make those decisions uh around your marketing, around your different uh efforts to reach out to your audience.

Exactly. So let's go ahead and choose our sheet two here. There's our date. Oh, that's not sheet two, that's sheet one. There we go.

So we have our date, we have our 2021 traveler. Oh well. Um see, this is what my charts usually look like is the data configuration error. The point being having those two data series side by side now uh would allow me to see, oh, okay, I the things are opening up, right? Which we saw from our our R chart, and that prices management consulting firms are charging has gone up as well.

Uh so we know that demand is up uh based on the travel data. We know that supply is restricted because prices have gone up, right? If supply was plentiful, prices tend to go down when there's more supply than there is demand. And so from a strategy perspective now, the question for us would be well, how much more could we charge? Um, how much more demand is there?

How much faster should we we be ramping up our our sales and marketing? And one of the proxies, a really good proxy for that particular question would be things like let's go back to management consulting. And we're gonna look here now at labor data, right? So this is gonna be labor data, and let's just look at the last year. Right.

So the number of employees, you know, there was a uh obviously a big drop. Let's actually switch to five here. Um there was a big drop, and now suddenly not only we are we back, did all those people get rehired, uh, but now there's even more. So there's a lot of demand here for um you know that that companies are hiring that many more people to deal with the demand, right? So we've seen the increase on the pricing side, we're seeing the increase here on this side on this side as well.

Let's look at things. Let's see. Uh professional and business services. That's a a good data series to look at. Again, five years.

Look how many more job openings there are now in professional and business services. So the pandemic happened, right? February 2020. We got back to where we were around November ish, and now we're substantially up in terms of the number of openings. So not only are prices going up, we not only are the number of people employed in the field going up, but the hiring is even higher, right?

So we we saw from the earlier chart, there's a 30% increase in demand. We're now seeing a point where there's a squeeze. So there's just not enough people to fill these these particular jobs. So from a strategy perspective, Katie, you've got a strong demand, yeah, there's insufficient supply in the market, and prices are going up. This is a pretty good situation for a consulting company to be in.

It is interesting, and I think that having some sort of understanding of what the data is telling you is helpful too because you can look at all this data and go, I don't know what to do with it. So there's more jobs. So what? Or so there's more people traveling. How does that apply to me?

And really being able to think through sort of that, I guess, journey of how, okay, if travel is opening up, but I have nothing to do with travel, does that impact me at all? Exactly. So one of the things that, you know, for example, it's good to know sort of the indicators in your space and to know the indicator is in your client's space. Like we have clients that are in travel. So that you know the TSA thing is obviously very, very relevant for them.

We have clients who are in the broadband space. So understanding broadband usage data would be really helpful. So let's take a look here. Nope, there's no nothing in Fred from broadband. So let's try 603 data sets here.

Broadband summary uh API availability by municipality, national broadband map providers. There's a ton of data. So for that particular client, we could probably find some data that would be very useful for them to forecast as well. But there's even data series that for us, because we're a B2B company, tells you about the overall health of the B2B sector. Let's look at BDI index data.

So this is we're gonna be looking for what's called the Baltic Dry Exchange. The Baltic Dry Exchange is the price it costs to ship a container, a cargo container. Um let's go ahead and bring this up here. This is not speculative. In order for this index to go up, the the somebody has to buy a container space on our cargo ship.

And it's not something you do for fun, right? This is something that you've got to move goods from point A to point B, and you have you got to do it, you know, in as cost effective manner as possible. So this is an index saying what's going on with shipping. You know, there is 2020. You actually saw um uh pretty substantial increases in the early months of the pandemic.

No surprise, the world locked down and suddenly this huge demand uh for everything, you know, masks from wherever they're made. Um we saw some supply chain disruptions, and now in 2021, uh we see that this index has gone really high. It's higher than it's been in five years. Um, so we know from a B2B perspective, this tells us that B2B is doing really well because you're shipping stuff, you're moving stuff around. So logistics companies um are in strong demand.

And again, this is yet another indicator that we would want to take into account as a consultant firm to say, like, yeah, there's really strong B2B demand right now. So we need to spend some time looking at the different markets um and see what's happening. Now, how would you know this if you didn't spend a lot of time in you know macroeconomics? You wouldn't, um, which is why it's really important to talk to clients, uh, to talk to customers, to say, like, what's happening in your business? A really good example of this um would be if you were talking to a client, it's not like you know, four weeks ago, they're like, oh shit, every you know, our our business is really screwed right now because we've got you know 440 tons of you know of ore on a ship that's stuck in the Suez Canal.

Right? It's like uh what do we what do we do? Um, and that would tell you, oh, my client is affected by shipping. Now I know, okay, well, what shipping indicators are there? Uh, if we were to look at, say, one of our uh our clients is based in the city of Minneapolis.

Well, great. What are some that tells us, you know, Minneapolis and Minnesota and that state has things that are happening economically? Are there indicators like hiring, like the tightness of the job market that would tell us hints about that client? Um if we look at one of our clients is a med tech company. Well, how many more medical claims are being submitted now?

How are people going back to the doctor or are they doing you know treatment at home? That would be data that we would then know to go with search and all these tools to say, okay, can we find data that matches those things? Well, and I think that you're illustrating the point of why business requirements are so important to do and not skip from I think it was what recipe to tools, because you're describing a lot of things that feel like I have to have an economics degree in order to even think to make the leap from you know, I'm a B2B consulting firm to let me see what the cost of a shipping container is, but really you're just playing a logical game of okay, what is it that I want to know? And then you're really challenging the people that you're working with to like really in detail explain how you get from point A to point B. So it's almost like trying to dissect the whole process.

So, you know, as an example, let's say um, you know, at the beginning of the pandemic, there was a shortage in flour because everybody was at home baking bread. And so you might say, okay, well, it's because of the grocery store. Well, is it because the grocery store to take it a step back? Is it because of the their ability to get it? Is it the prices?

Is it the supply? Is it the actual wheat crop? Those kinds of things. And so really starting to walk it backwards step by step and checking in on all of those different data points. And so, Chris, you know, you're able to pull up, you know, flour per pound in the city average, it's more than just cost, it's obviously demand.

And so if more people are buying something that wasn't ready for that kind of a demand, then obviously there's going to be that gap. Exactly. It's funny if you look at this chart, April of 2020 is missing. Why? Yeah, is that pricing?

Right. So even something like that, you're like, huh. Well, what the is that? Yeah. The point being that if you're not sure, like how in a publicly available data set applies to you, then start to walk it backwards and think through all of those different scenarios of well, I'm in a B2B space and I get a lot of my business from you know being on stage and speaking.

Okay, is travel opening up again? Those kinds like starting to think through how it really does affect your business from every angle, and then you can start to look at those different data sets. Well, travel is opening up, therefore Chris is likely to get back on the road again, therefore he's likely to have more events coming his way, therefore, we're likely to have more leads in the pipeline, therefore, John's gonna be really busy soon. Exactly. At the end of the day, what we're actually really talking about is supply chain analytics.

We are talking about understanding and and decomposing the supply chain to understand is that what's happening upstream from your business, you know. You know, so you to you you sort of went downstream with that, Katie, right? You know, it's travel is doing this, so this will happen, so this will happen. So we can go the other direction too. Travel is opening up.

So what's the impact on our clients? What's the impact on their clients? You know, if a conference um is in a place where uh they're starting to realize, like, hey, all of our speakers and 70% of our audience is vaccinated. Great, we can open up, you know, and then you start to take a what say, okay, well, why? Well, because insurance agencies don't want to ensure something that's known to be dangerous, right?

Their job is to take your money and never give it back to you for any reason. Uh so the the next logical step there would be okay, well, what are insurance prices look like in the entertainment industry? You know, if as they start to trend, you know, in a different direction, you know, and then take a step further. So again, it's all supply chain and demand analytics to figure out up and down the scope what's happening. Um got an uh interesting question here.

Chip was asking, can you speak to the challenge of analyzing data in turbulent times and trying to understand the difference between a blip and a trend? Um that comes down to math. Um, specifically, it comes down to understanding change, uh, change and change of change. So when you have a blip, when you have something unusual, you have typically have a very rapid change and then it reverts to the mean very back very quickly. You know, it's a big spike and goes back to normal.

You see this a lot a ton in things like your Google Analytics account where like, hey, you had a big traffic spike one day and you're back to having five people visit your website every day, right? That's a that's an anomaly. A breakout, which is a trend, is when you have a change and it sticks, right? So you have that something happens and that and it sticks. So if we go back um to this management consulting one, go ahead and produce a price index.

So we've got a five-year spectrum here. If January 2021 had been 127 and then reverted down here, we'd be like, yeah, that was a blip. That was an anomaly, right? But in February and March and April, it's kind of stuck, right? So that price change has happened and it's it is starting to decline, but it's not reverting back to you know the one 105 or 110 level where it historically has been even before the pandemic, you know, it was it was around 110 for a good year.

We're at 130, almost 130 now. So that is an example of something where this is a breakout towards a trend as opposed to an anomaly. And generally speaking, you know when something is a trend, when you've got some sustained momentum over a certain period of time. What that period of time depends on the time series and and your measurement window. For your website, you might be like, yeah, seven days is important to us because we're an e-commerce company and we sell stuff all the time.

For us, you know, as a B2B consulting firm, 90 days might be the the minimum time where we start to say, yeah, it's a trend because if you know sales cycles are longer, etc. So you have to you have to use some judgments to what your window is and then use anomaly detection or breakout detection. Um, you know, how fast is the change reverting uh as you're determined as is it an anomaly or is it a breakout? I feel like we could cover trend spotting in a whole different episode because what occurs to me as you're describing this is it's very easy to get behind in terms of what's trending, because to your point, Chris, you need to see if it sticks. Um, and so are you constantly chasing those anomalies or are you tracing chasing true trends?

And I think that that's something that we should dig into on a different episode in terms of setting up your data so that you can spot those things a little bit more easily. But it's interesting. Um, because it does look like it was more of a trend than an anomaly, because even though it is declining, Chris, to your point, it didn't decline immediately. Exactly right. And you know, we know also from industry knowledge that consulting firms are really busy right now because people want to know what to do, right?

So uh there's there is uh there is a logical demand to it too. So one of the things that we would as a next step want to do, maybe send out a client survey or a a prospect survey to our customer base and say, hey, are you facing any new challenges other than the usual ones that you haven't faced before? If so, how can we help? You know, um we were just watching uh two hours ago the keynote to the Google Marketing Platform talk where they said, hey, here's all the cool new things that are coming in Google Analytics. And of course, they're showing it only in Google Analytics Forward.

And we're all going, well, it's pretty clear which direction we need to be going with this, like the new uh imputation of lost data and their new forecasting tools like, oh, that's so cool. Oh, it's only Google Analytics for. Um, which means that for us as a as a marketing analytics company, we should be looking at that and going, well, time of to put the uh turn the ads back on on our Google Analytics for migration service. Yeah, that I mean, that's a whole other conversation that we can have again. I feel like it's very easy once we start to get into these episodes to start to digress down the other roads of things that play into understanding uh the so what of the episode that we're in.

Exactly. So in this case, our our key takeaways are this. One, you've got to follow the process, right? Know what the outcome is you're after, know what data you have to work with. And if you don't know, take some time to explore it.

Get to know your industry, get to know your customers, get to know what they care about. You know, something as simple as calling up, you know, the the decision maker at your your best clients and saying, Hey, what do you read every day? Like, you know, how do you get your business news and things? Just something as simple as that can at least give you a hint of, oh, these are the things they're paying attention to. What skills do you have and how well can you process the data?

Uh, we looked at a couple examples today of real simple, do it in a spreadsheet all the way up to run some code to process the information, and then make a decision about the tools and how you're going to visualize these things. And when it comes to the data sources, again, your tax dollars have paid for an awful lot of this information. Uh, and other citizens in other nations have paid for their tax dollars. So, New Zealand, thank you. We appreciate your contribution to uh humanity as a whole.

We would love to live there because you have a a you you did the pandemic well. Uh does help to be an island nation. Um use the data that's out there and be careful with the data sources you have. Uh some of them out there less credible than others. And once you've got the data, make use of it, make decisions with it.

It's really cool and fun to have, you know, dashboards and spreadsheets and stuff that show with all this stuff. But if you're not using the data, it's just a distraction, right? It's a it's a decoration at best. Any parting words, uh guys? Uh you know, one thing that I do want to throw out there is there you can find a holy grail in this space, you know, if you're in a mature B2C space.

Um, I've done stuff in the insurance industry where we could call out our annual goals a year or two in advance every year. We would hit our goals every year because we knew which end economic indicators were going to affect what the business was, and we could just you know define those goals to make sure we always had two or three percent of cushion and hit the number like clockwork every year. So there's there's huge ramifications if you get this right. There's huge opportunity. Exactly.

Yeah, and spend some time digging into the data into what's out there and and what impacts your your business. Again, all the stuff is free. It's out there. You just gotta know how to find it and know how to make use of it. Uh all right, so thanks for tuning in today, and uh we will catch you all next week.

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 TrustInsights.ai slash TI podcast and a weekly email newsletter at trustinsights.ai slash newsletter. Got questions about what you saw in today's episode? Join our free analytics for marketers Slack group at trustinsights.ai slash analytics for marketers.

See you next time.