So What? Predictive Analytics for your annual planning

Well, hey everyone, happy Thursday. Welcome to So What, the Marketing Analytics and Insights Live Show. I am Katie joined by John over here. He's on the side today. Hello.

Yay. Chris is in Denver at the digital now conference. He's at I think he might actually be speaking right now as we're recording this. Yeah, I hope he's breathing well. There's not a lot of oxygen out there, man.

He's got it, he's trained for this. He's trained hard for this. Um this week we are talking about predictive analytics here, annual planning. Uh we're going to cover the basics of what predictive analytics is, uh, the use cases of predictive analytics and marketing and applying predictive analytics to your annual plan. Um so John, you've been with us for a long time.

And it's it's a type of analysis that I feel like is sort of the unsung hero of any kind of planning. Um what's your take on the usefulness of predictive analytics? Yeah, the you know, there is the challenge that it is kind of like 300 level marketing, right? Like if you're some if you're a team that doesn't really know what the heck they're doing and faking their way through it for the first time, you know, it takes a long time before you can get to predictive. But once you have things in order and you're looking for the next step, predictive is you know crucially important to figure out how to effectively allocate your limited resources because that's always the challenge is you know, there's never enough really there's never enough time.

I mean, you can use money to buy more time, but if it you know there's only so much money too. So you have to get as much done as quickly as possible with what you've got. And having predictive to point you in the direct right direction is really the make or break. And you know, for smaller companies, it's whether you flame out or not. And for bigger companies, the stakes are just huge.

I mean, if you're running predictive on your ad cycle, we've seen clients saving, you know, tens of millions of dollars on their ad spend because they're not, you know, burning it that week everybody's at the beach or you know, when that big trade show is so yeah, there's a lot of um ways you can use it to, you know, I there is a lot of hype about it, you know, being a crystal ball, but the reality is there are repeatable cycles that you can find and take advantage of, and you know, you've you've got to be able to do it. One of the first uh actually the very first talk I ever gave on stage was at Inbound in 2018, and it was like five use cases of predictive analytics, which at the time to me was felt very basic, but I tend to forget that I've been working with Chris long enough that these concepts are not new to us, but you're absolutely right to a lot of marketers, you know, it's it still feels like a crystal ball, but really it's just using their data, doing some math and projecting it forward. Yeah, it's uh you know, getting in and getting your own data, and I guess that's another part of it, it's just an extension of leveraging your own data. You know, tons of organizations have this stuff all ready to go, and they're just not putting it to use and squeezing the gold out of it. Um, well, I mean, and so that's a really good segue.

So we have um we have what we've called the data-driven marketers essential planning guide. Um, and so what this does is this kind of walks through. So John and I are just gonna use this as the foundation for our conversation about predictive analytics. Um, and so you can grab this deck if you want it at trustinsights.ai slash insights slash white papers slash data-driven marketers planning guide. Super rolls off the tongue.

Um, but we'll drop a link to this in the show notes as well. Um, and so what we have done here is we have a short deck that sort of walks through what predictive analytics is and how you can use it for marketing campaigns. I tend to think that this is one of the better tools for email marketing campaigns, uh, but you can use it for a variety of other things. So we we'll get into that. So the purpose in this case, the use case, is to tell you the good or bad times to launch marketing campaigns for the calendar year of 2024, uh moving into next year and the last quarter of 2023, which is what we're in currently.

Um's a bad time? When people are out of the office, people aren't around, they're not gonna see it. To your point, John, uh, understanding the timing and the seasonality of when people are sitting on the beach, probably not the best time for you to go full throttle and launch your biggest campaign of the year, and then nobody's gonna see it. Um, our methodology for this particular uh report is when they search for how to turn on their out of office auto-reply. Uh so we're using SEO data, we're using Google Trends data, um, and those two data sets together are fairly rich.

Google Trends, if you don't know, is a free tool for marketers. I'll actually pull it up, and I believe it goes all the way back to 2024. Uh 2024. That would be forward 2004. Um Google Ads summary, not helpful.

Here we go. Google Trends. So Google Trends free to everybody. You should be using it. It's a great tool.

Um, so let's say we looked for uh marketing, just as an example. Pretty broad. But what you have is you can break it down by uh demographic region. You have all the different countries, you have worldwide, you can do just the United States, you can go yep, all the way back to 2004, which is a fairly rich data set. Uh, we've run into terms like uh NFT and other terms that just didn't exist prior to a certain time frame.

So that's something that as you're searching to be aware of is did this term or this product or this catchphrase or whatever it is you're looking for even exist? Um, so we can go back, you know, the past five years, always good to show some seasonality. Um if you work in a certain vertical in a certain industry, then you can start to break it down to I only want to know marketing in the travel industry, or I only want to know in science. For us, we are agnostic of any of those industries, so we like to know all of them, and then this particular filter I think is highly underutilized because right now we're looking at web search, we're looking at what people search like when they search for marketing on a Google search, but you also have image search, you have news search, you have Google Shopping, and then YouTube search. So if you're trying to create video content, you can see that you can see the search trends for when people are looking on YouTube specifically, which is part of the Google universe.

Um, do you use Google Trends at all, John? Yeah, it's a good eyeball for just like you you said, you know, every once in a while you run into something like NFT, and you're like, okay, when did that start? And you know, where's the hype at? Um yeah, you can't beat it. It's really Google has gone out of its way to make sure that we don't get to see much of anything.

This is one of the rare exceptions where you can get a little peek, at least in relative terms of what's going on. It's hard, you don't have any hard data to calculate against, but you can at least get a feel for how like this list here is a perfect list, you know, a list of five things, and how do they compare what's on the way up, what's on the way down? Um you can see AI all over the place, you know, right now dominating unsurprisingly. Yeah, that I was tempted to put in. Well, let's just see what happens when I put in AI as a search term.

I would imagine you're gonna see a huge, yep, there it is. So basically, last summer people started to get wind of it. Last novel October, November, Chat GPT was like, hey, knock knock, I'm here, and then all of a sudden, whoosh, which significantly dwarfs the actual marketing and the actual advertising terms. And so, again, those are just really interesting things to see. This data you can download uh into a CSV and work with it, super handy.

How much can you run chat GPT uh yeah, chat GPT? I'd be interested because we've seen there was some stuff about actually user numbers dipping this month for the first time. Yeah, you can see that here. Yeah, but it's definitely still like on a tip. Weird that there was a dip there.

Oh, I guess that's just like the summertime. Yeah. Well, and what we don't have in here, what we haven't done is like full research on so chat GPT obvious, you know, picked up, picked up, picked up, and then dropped what else was happening here, what else was launching at this time that chat GPT got overshadowed for a little bit. But that's really helpful to start to understand. Once we have more than a year's worth of data with having a system like ChatGPT, we can start to see the seasonality.

And that's really where predictive analytics is helpful is understanding the seasonality, and it's not going to be the same for everybody. So we're looking at general search terms. So this assumes you know B2B, B2C, all kinds of consumers, but that's where you go back into the all categories. And if you work specifically in business and industrial, for example, you actually start to see very different results. Marketing all of a sudden picked up with AI and Chat GPT no longer really exists.

So they're all the way down here now because I switched the industry. Yeah, that's interesting to see it flatten out a little bit over there. Yeah. So if you have hobbies and leisure, for example, you're again, you're gonna have very different results. Um, you know, you still have that dip, but not as significant as when you look at everything all blended together.

So just a little bit of context. This is a data set that we use a lot when doing uh our predictive forecasting because it really gives you an understanding of user intent uh and their search history. And so it's a really good solid data set to anchor your predictive forecast to. Um, so when we go back to the actual forecast that we did, you can see in here is the example of Google Trends. So we looked at out of office, outlook out of office, Gmail out of office, past five years worldwide, because we wanted to see when people were going on vacation.

And again, it's not gonna be the same for every industry. If you want to drill down even more specifically, if you want to drill down to specific regions, specific states, um, that's really a good idea as you're getting into this predictive forecast for yourself. This is how predictive forecast is calculated. So John is our chief statistician. Uh do you wanna go ahead and just like sound it out and s and tell us exactly what we're doing here?

Yeah, give the rundown of this model here. No, this is yeah, this is definitely ugly math that uh I try to stay away from this will ruin your day. Uh a predictive forecast, if I recall correctly, and Chris isn't here to correct me, so I can say whatever I want, is uh a traditional predictive forecast uses an arena model, an SREM model for seasonality. Uh I do remember when I presented predictive forecasting in two 2018 on at inbound, I had down all the different steps what arena stood for. And it's I couldn't tell you what it is.

I probably should have looked it up, but I know it's like moving averages and seasonality. Um, and so basically the way that we describe it is sort of like a GPS. And so you need to know where you're starting and where you're going, but then the arema model starts to fill in all the different pieces that are actually happening. So you have, you know, weather, you have traffic, you have, you know, construction, other cars, and you know, timing. So you may start with it's a 10-minute drive, but as you start going and more data is coming into the model, it's like it's no longer a 10 minute drive.

Now it's a three hour drive you're probably better off walking. So let's see. And so when so in this example we pulled the three different data sets from Google Trends and you can see this is a very hard way to review it. But this basically starts to tell you as we apply the predictive forecasting model to the data so all the historical data what it does is it projects forward 365 days and we can start to see some seasonality. And so you have the peaks here are when people are searching the most for going out of the office.

So these would be the times when you wouldn't want to be planning big campaigns and these down here are the lowest meaning people are in the office and you would want to be planning campaigns you can start to drill in to the individual data points to say all right I have a campaign planned for uh January 1st 2025. Well you can see in January a lot of people are planning to not be around based on historical trends. So maybe you should either do it before or after that date. Yeah then you've got the peaks to jump on right that's always the the thing if you're looking for spend to get out in front of uh folks when the search volume is at its peak you're gonna well of course for this it's inverse for out of office but still there's um you know again you want to either go high and go low, and then the middle you you know tend to ignore depending on what's going on. Well, and it's interesting that you bring that up because it really depends on what your strategy is.

And so you could have a strategy where it's actually better to launch when people aren't around, if you're doing like a soft launch, for example. Um, so you want to sort of get it out there, test the waters, but you don't want it to make a big splash. Maybe it is better for you to launch something when people are less around to sort of like let it build up some steam. The other thing to consider is what are your competitors doing at that time? So are they laying low during those times when they think nobody's around?

So it's actually your time to strike because there's no competition, there's no you know, competitive search traffic, there's no other products going up at the same time, no campaigns to take people's uh attention away, so you could actually use it to your advantage. Yeah, that makes sense. You know, you keeping an eye on the rest of the spend is always that's all another poker game, but there's yeah, there's a lot going on on that front. Um, and so with this, we you know, we do the whole analysis. What we put together is uh a very simplistic way, we think it's simplistic uh way of looking at it.

And so these are all the weeks in Q4. So where are we now? It is November 2nd, so we are in this week. So we're sort of in a if you want to be sending email campaigns or doing um any sort of paid ad campaigns, you're in a good spot to do that. Um, we start to get a little bit more tricky as we get towards Thanksgiving and Christmas.

Understandably so. People take off a lot of time in November, December for the holidays in the United States, and so if you're planning on doing something, it's probably not a great time to do it. Or if you're like us, you sort of just do it anyway. It's interesting that there's not a huge dip around Thanksgiving, but I guess that does make sense because first of all, the rest of the world is not on board with that. And then it is a partial week.

Like people are definitely working, you know, most people are working Monday, Tuesday, and Wednesday. Yeah. I was looking at our calendar, and we only have Thanksgiving, which is the Thursday, and the Friday marked as out of office, but the rest of the week and then the following Monday will be around. I think people tend to save that time more for the actual Christmas, um, Hanukkah, other holidays that happen in December. Yeah, yeah.

If you don't have it done by that Friday before all the Christmas stuff starts, that's usually it for the rest of the year. You're throwing in the towel. Mm-hmm. Um, and then we can just go through the other quarters pretty quickly because you'll start to see some seasonality. Uh, and so you see back second week of January, that's a great time for that quarter.

Um the last week of March is the worst time, but you start to see in late January and then in March. And so, John, I don't have kids, but I'm assuming that this starts to coincide with like February vacation and spring breaks and uh all that kind of stuff. Yeah, there's always these competing um weeks. You know, there's some schools that will do a week in February and a week in March, and then there's usually a different set that's uh often tied to a more collegiate group of taking two weeks in April. But yeah, there's all kinds of variations with that.

Um I know that, yeah. See, like it's weird that near the end of February, that's actually, I know that that's like a dead week at Disney World randomly, like because all the vacations fall around that. People are actually working heavy that week. And then as you get to that 310 week, it gets ugly. Um, and then yeah, I think with the 4-3, you're just like square in the middle of spring break.

Um, you've got both college and primary, secondary schools off then. So yeah, that there's people trying to get out of the cold, and um, there's a lot going on as far as those weeks. So uh the other one is trade events too. Like trade events finally start to pick back up in March. You know, they they go dead during December, and then January, February, everybody's doing planning internal stuff, and then you usually see end of Q1 into Q2 uh business travel stuff picks up again.

Well, let's see what that looks like. So this is Q1, this is Q2. So first whole month. So you have April and then the first week in May, all good to go. At least based on historical data, people should be around.

So if you're trying to launch something end of Q1, early Q2, April seems to be the way to go. Um, again, you know, this isn't a specific uh analysis. This is fairly straight, fairly general. Um, but we do offer the ability to do more specific analyses if that's something that people are interested in. Umsurprising when you start to get into May, you start to get into at least in the United States, you have Memorial Day, kids are starting to get out of school, and then you have June and the summer months.

And so June, it looks like is just don't even bother. Everybody's like, try to wrap up school, head out on vacation, get the heck out of their office. Like, don't bother launching anything then. It's a terrible time. If that's what makes sense for your company.

Yeah, that back up of June. It's you know, graduations and then yeah, summer vacations start to kick off and everybody's on the road. And so let's see if that trend continues into Q3. Yeah. Oh, and unsurprisingly it does.

So you have July, you have the first part of August, and then mid-August, people start to come back. Now, depending on where you are in the United States, some schools actually start in mid-August. I know uh my in-laws live in Arizona and come like the second week of August, they're already back in school. Yeah, you've got the that seasonal thing is it is really weird. And that's you get people, those people bailing at the front end too.

But then it's interesting to see that 915 week, you know, right back in action. That's always where it's like HubSpot inbound and content marketing world. And there's like five shows that run over the next two weeks right there. Like everybody is right back on the job at that point. And yeah, it's interesting to see Fourth of July being the high watermark for people bailing.

And you know, what's interesting about a predictive forecast is here, you know, and you said this earlier, John. It's not a magic ball, it's not, you know, tea leaves and crystals. It's really a confirmation of the anecdotal evidence that you already have about your consumers. And so we can say, you know, we think that the week of July 4th is probably the worst week to send an email. But once we run to the day when we run the data, we can confirm that.

And then that is a decision that we can stand behind because we can say the data shows what we already know. So it's a really good way to just sort of confirm what you know about your audience anecdotally anyway. Yeah, and you know, we do these kinds of analyses all the time as far as okay. So this is just on a general term, you know, based on search data, but you can run the same thing on your email performance. You know, you can look at what your email how your email campaigns have done.

And if you've got multiple years of data, you can actually see, okay, when are the clicks and opens happening? And you can create basically the same report for yourself, but it will be a lot more in line with the performance that you're getting from your email campaigns. Yeah, absolutely. And I think that that's a really good takeaway is you know, an analysis like this, like the one that we created, that again you can get from our website is a really good starting point. But your best bet is to use your data because your data is going to be specific to your audience and your seasonality.

Um and that's absolutely something that we can help with. And so we started with Q4 2023. So we knew that um, you know, basically December was the worst time. And you can see reflected here, sort of the same thing. So you have, you know, you sort of go through October, October's pretty good.

We're in this week here, uh, moving into next year. And we actually had the same sort of neutral response beginning of November, because I think people are they're thinking about taking some time off, they can't take too much because they want to save before the end of the year, but you know, attention spans are low. So as you're planning out your campaigns, November, maybe start winding things down, unless you're in the retail industry, and then you really need to be amping things up. Um, and then you have December, which is pretty much for B2B, not a great time. And again, that sort of goes back to what is your industry?

Who is your audience. So for this, if you're if you work in retail, if you work in travel, this isn't the best analysis for you. But if you work in B2B, um, and you work in tech, this is a really great analysis because it tells you when people are paying attention, uh, when they're sitting physically sitting at their desks reading emails and looking at campaigns. And so, you know, we asked the question, what do you do with this information? Well, there's a lot of things that you can do with it.

And so, John, you were sort of talking about this a little bit. Um, you can do general financial planning, you can do keyword topic research, uh, content calendars, ad budget planning, attribution analysis. The ad budget planning is an interesting one. When Chris and I worked at the agency, we had a very large B2C brand that we worked with, and they were spending upwards of six figures a month on paid ads, uh, which is not an insignificant amount of money. And so, in order to get really good results, we actually applied this predictive forecast technique to their keyword data to help them tune up and tune down the spend on their ads.

And it was really interesting to see. It was one of my first introductions to predictive forecasting about eight years ago, um, in its infancy in terms of this application, and just the different outcomes we got when we used the forecast and when we didn't was just insane. Because a lot of companies, and I'm sure you can speak to this too, John, a lot of companies are like, let's just keep throwing money at it until something happens. Which is it's not that's not a strategy, that's just a panic move. But if you actually use something like a predictive forecast to go, oh, now I know when marketing is trending versus when it's not trending, let me sort of adjust my budgets accordingly.

Yeah, the panic double down is never a smart play. You know, we see that a lot where again just throwing more money on the fire, and there's just certain weeks where it's not going to make any difference. Yeah. Um, you know, we talk about it in terms of content calendars as well. This is something that we've been using with a lot of our clients.

And so if you have an editorial calendar, if you're writing content for your website or articles to get placements elsewhere, understanding what kind of content consumers are looking for and when is a very powerful tool. And so with generative AI uh becoming even more sophisticated, we've been able to get really in depth with our predictive forecast for content specifically. And so we'll have keywords and suggested uh topic titles and then all of the other pieces. And so we can go so far as to write a whole predictive calendar that has keyword, what week it should go, and then here's a draft or an outline of the content, which is such a powerful tool because then you just hand that over to your content writers and say, This is exactly what we need to do, and then we needed to have it posted by this date, and then that's when people open for it. You should theoretically see your search volume start to really go up.

Yeah, that's huge, you know, because pretty much anybody that's doing any kind of content marketing has a list of the topics and things that they want to rank for, and to be able to run that through these same models and see which weeks, you know, they get the most action that can really make a huge impact in the amount of spread and organic, you know, diffusion that you get of your stuff because yeah, you could have you know five or six topics, but if two of them are highly seasonal, there's times of the year where it makes no sense to be writing, you know, you don't need your pumpkin spice articles dropping in March. Like that's not going to do you any good. Um, and whatever your industry is, there's sure enough, there's some kind of pumpkin spice terms. Now I'm like my brain is like, what are the pumpkin spice terms? Like for, you know, it's basically the jargon, like the you know digital transformation.

I would call a pumpkin spice term. Yeah, and for us, you know, it's totally about um, you know, when people are looking at events or um if they've got a product releases, you know, things like that. There's all kinds of cyclical stuff that you've got to work around and line up and yeah, knowing what the heck is going on in the industry and what that hidden wave is is where you get instant lift without having to do any different work. You're just putting it in the right sequence. Yeah, that's a really good point.

Um, so Brian, our buddy Brian Piper, he has a question. Are you using Chat GPT's advanced data analysis tool or other AI tools to help with any predictive analytics and forecasting? Um I know I can at least say for myself, I have been playing around a lot with Chat GPT's advanced data analysis tool. Um historically, Chris has written his own code in R. And so it's always been a proprietary tool for Trust Insights.

That said, it's becoming more and more accessible to marketers as Chat GPT and other generative AI tools are available. But I do believe that we still run ours using our own code. But the topic titles, uh, suggested summaries, those things, we are using Chat GPT for those. Uh, once we actually have the analysis done, we're using Chat GPT to help us take it that step farther to create the outlines and to create the uh things that we would hand over to the writers. Yeah, we have a lot of that stuff in-house, but yeah, I haven't had a chance to play with the data analysis tool.

I know that that was recently rolled out. That's something else that uh I need to spend some time getting into. We have seen, I've seen Andy Christadina doing some stuff with that, and Chris has done some stuff with that too, where you actually upload tables of your own stuff and have it give you, you know, reporting back. I don't know as far as, you know, does the thing have enough in like the corpus to be able to call out seasonal trends for industries where you're not feeding any data? That's uh that's a different story.

Um, I actually used Chat GPT uh the data analysis in this past week's newsletter. If you want to subscribe, trust insights start AI/slash newsletter. Um, I used the advanced data analysis tool to help me analyze qualitative survey data, feedback data to help understand uh what people had said on our one question survey. It actually the analysis came out pretty good. Now, the to the question uh that Brian had about are we using Chat GPT?

So uh you're right. Chris did build his own tool and he is using those math formulas that we showed earlier. That is absolutely 100% what's happening. One of the reasons for that is um, you know, up until recently, ChatGPT didn't have this advanced data analysis tool. So we had been building it all in house, as John said.

But the other thing to keep in mind is that the timeline of ChatGPT, the data only goes back so far. So number one, you have to be providing the data yourself. But you know, I and I'm gonna try not to bungle this too hard, but basically, my understanding is that yes, Chat GPT can do the data analysis, but it may not fully understand seasonality as its own historical database may not go back far enough. It's maybe just looking at your data. If your data doesn't go back far enough, if your data only goes back a year, you're not really going to understand true seasonality.

So I would say it's worth experimenting with to see what kind of results you can get, but know that there may be limitations for now on what chat GPT can do with that kind of historical data and forecasting. Yeah, and it would be interesting to, you know, given that it's kind of constantly adjusting. I mean, because you could ask the same question a number of times and see how much variation you get in the answers you get, I'd be interested to see how that works. I would say if you're going to ask ChatGPT for a predictive forecast without providing it data, then you're definitely not going to get something that you can take action on. You should at least have your own core set of data that you're getting to it to ask for assistance with that.

The other thing I learned through Andy Crestedina, and I applied this again in the newsletter this past week was you can ask Chat GPT to show its work. And so if you give it a data set and you say, This is this is what you're doing, this is the type of analysis, show your work, it actually gives you the snippets of code as it goes through each step. So you could take those code snippets, put them in your own code language, like Python, I believe is what it uh typically writes in, and you can start to build your own models that way. Uh, I also my understanding is also that the code is usually about 90% correct, and so there is some error in it. So you would have to then know what you're looking at to be able to key way it properly and put it into a cohesive script that you can run repeatedly, but it is a really good place to start if you're just building those things you know from scratch.

Um, and so John, that kind of brings us to our favorite part of predictive forecasting, which is the cheese of the week. And so we started doing the cheese of the week in 2018, actually. And so we try to refresh this every year because this is a really great concrete example of what a predictive forecast could look like. Um, and so this is from 2022, so uh it is a little bit outdated, but uh the basic idea is the same. And so if you were a marketer who was trying to um use a predictive forecast, this is how you would do it, and so you would have all of your keywords over here, and so you would want to have your keywords, not just a general set, um, but things that are specific to you.

In this case, we're looking at all the different kinds of cheeses. So we have Munster, Cheddar, Swiss, Parmesan, Asiago, Parm, Parmigiano, Cottage Cheese, American Cheese, so on and so forth, because everybody loves cheese. And so then you have all of the different weeks. And so, what we would do is we would say, all right, so for uh the week of October 16th, what are the terms that are going to be trending the highest so that we can, you know, over here in August, maybe start playing out our content calendar and say, what content should we have ready for the week of October 16th? Well, when we sorted it, it looks like we should have content around Munster and Cheddar and Swiss and Parmesan.

And so what we might do is say, all right, do we have recipes? Do we have, you know, buying guides? Do we have storage guides? Do we have other, you know, history of where it comes from, the different types, all the different kinds of helpful content that will give your consumers something to really, you know, learn about and search for. So, you know, they could be like, where do I buy munster cheese?

How do I use munster cheese? Can I grow munster cheese? Answering all of those questions. And a predictive forecast gives you the time to do that so that you're not getting to the week of the 16th, going, oh man, everyone's looking for a munster and we don't have any content around it. Yeah, this kind of stuff we had run for other food manufacturers and some other verticals, but and it was so cool when we had run it, and we realized, well, you know, of course, we obviously can't share any of that stuff.

So that was where cheese came into the thing. And yeah, it highlights all kinds of crazy stuff. When I'm talking about this with clients all the time, I always bring up Halloumi, you know, the fact that you see it, you know, it basically spikes in the heart of the summer. And I knew nothing about it prior to this. And the fact is it's grillable cheese, right?

So that's July, August months when people are firing up the grills. Suddenly it gets the spike. So if you were a cheese, you know, if you ran a cheese manufacturer or you're a retailer that was looking for what to focus on for the next couple months, this kind of report shows you what the cycles are. And yeah, having played around with it for a bunch of, you know, other examples, you know, about the Super Bowl, you get um Monterey Jack springs up because you've got everybody doing nachos and uh around Thanksgiving and Christmas, you've got the cheddar weeks because people are, you know, making apple pies and doing other stuff that they can bake with cheddar. So yeah, it gives you this look at your data where you know, when does the rest of the world search this stuff out and when are they trying to find it?

And it's just fantastic because as somebody in the industry, you usually end up being blind to this kind of stuff after a couple of years working there. So you don't think about what people who know nothing about your product, like what are their first steps and when are they looking and what inspires them to search? And this can surface all that. And yeah, and then this is just easily back solving to your content calendar. You know, it's like, okay, you know that Hulumi's hot July, August.

So that means April, May, you've got to start getting the editorial going as far as writing these up and figuring out what's supply gonna be like and what are the recipes that people normally dig up, and let's make some videos showing how it works. And yeah, this kind of stuff can again it's um you're you're gonna do all the same amount of work, but the way you sequence it can drastically improve your results and get you where you want to be. Yeah, we um we actually we need to refresh it. Uh, we use it for trust insights for our own editorial calendar, because you know, every Monday morning, Chris and I kind of stare at each other and go, so what should we talk about on the podcast this week? What's going on with AI?

What's going on with marketing? And we had fallen off the uh habit of doing it because we were running into a lot of repetitive topics, and what that tells us as marketers is we need to expand our keyword list. It was too narrow. Um, because then you're gonna start to get every other week is something about digital marketing, or every other week is something about how do I use AI and marketing, which is great, but you you get you definitely get to a point where there's only so much content that you can create that is high quality. Um so when you start to run into those issues with a predictive forecast, it means you need to start expanding your keyword list beyond the basics, and that's where you really need to do that keyword research, audience research, asking people what's going on, what do you need?

What are you thinking about? What's new? What aren't you thinking about? Using tools like Google Trends to see are people even talking about this thing. Um, another really great way to use this is you know, at least it used to be when you should be posting your content on social media.

Uh, social is a little bit of a dumpster fire at the moment, as Chris would say, but there are still social channels that are working. Um this can help you with your video content, when to post that on YouTube, uh, this can help you when to post your other videos on TikTok or Instagram or any of the other wide variety of social platforms out there. But essentially, you should be using this as what should I be doing today? What do people care about today? How do you know how do I help them get to the answers they need today?

Um it gives you the opportunity to be a little bit more proactive as opposed to reactive. Unfortunately, as marketers, we tend to be super reactive to everything. We kind of put something out there and wait to see what happens. But with a character forecast, you can really get ahead and be like, this is this is my plan. And now I'm going to measure this plan.

Yeah. And then the exception is, you know, with what we've been doing, and as you're looking for topics to hit, right, there's always black swans. It's like, what is the thing that popped up this week that we have to talk about and kick around? And of course, there's zero history on those topics. So we you have to adapt and move on the fly to accept those.

But then for every other week, yeah, you should have your solid list of like, okay, if nothing crazy happens between now and then, we know that this is the stuff that we want to serve up for that week. Um, and so with that, John, you know, anything you think we didn't cover on predictive analytics. You know, one huge segment that we didn't go near at all is financial, right? If you're any kind of SaaS service or um any company that's reached a point of you're over 10 million in revenue and you've got some kind of renewal cycle, you can run these same analysis on just what's your normal buying cycle, when are people renewing when you know you'll get a feel for okay, we should be running renewal campaigns on these months because you know, two months past that is when we tend to get the normal renewals or get the churn, you know, you can do churn analysis to see what's going on. So yeah, it's you know, we talked, we showed off a great array of tools and how they work.

And the important thing to remember is that these work across every front of data on every type of business that you're doing. So um, same deal with you know who's talking about you on social. You know, if you want to look about when are the trends for when you need to staff up to be able to handle customer service issues and things like that. Uh we've done the stuff for call centers. There's a whole bunch of other options.

So yeah, no, I think we did a killer run through on what we've got. And just remember that you know this can be applied to just about any set of data that you've got lying around. I think especially now, um, you know, with that sentiment, John, is we know marketing budgets are still being squeezed. We know that layoffs are still happening, we know that teams are overworked and burnt out. And so at least from where we sit, a predictive forecast, if you have budget to spend, is a really great, you know, multi-purpose tool that can tell you a lot about your business.

And if your team is really stretched thin, why not help them focus in rather than feeling they like they're scattered all over the place? Or if you're just bringing on contractors, you know, it gives you a really solid plan of like, okay, this is how we need to execute for the next three months. We know what we have to do. We're not gonna guess. We have really tight budgets, every penny counts.

So let's make sure that we're really using the most of what we have. And you have your data. Now you just need to project it forward in the right way so that you can make these plans and keep people focused and you know, not waste money. Yeah, that's all that advice. That's the way to roll.

All right. Well, I don't know what we're gonna talk about next week, but I think that's it for this week. Um, if you want to join us in our free Slack group, analytics for marketers, um, around 35, almost 4,000 people are in there. Uh, we were talking about predictive analytics earlier this week. Uh, there's a question of the day every day.

Today was about um large enterprise size tools for your tech stack versus small ones. A lot of things going on in there, so definitely check us out if you want to continue the conversation, if you have questions. Um and then otherwise, yeah, John, I think that's it. We'll see everybody next week. That sounds good.

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 our weekly email newsletter at TrustInsights.ai slash newsletter. Got questions about what you saw in today's episode?

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