So What? Holiday Predictive Forecasts

Well, hi everyone, welcome to So What, the Marketing Analytics and Insights live show. Uh I'm Katie joined by Chris and John. We are now in that weird in-between time of the last couple of months of the year where Thanksgiving has happened, uh Christmas has not yet happened, or any other real major uh religious holidays, and everyone is just sort of like, all right, let's just get through this. And so with that, you uh John and I were talking the other day about well, does every industry sort of follow the same kind of holiday predictive, like holiday seasonality? And the answer I guess we're gonna find out today.

So what we wanted to chat about was that holiday predictive forecast. And so uh what holidays, uh how the holidays look different for different industries, um, where to source predictive forecasting data from, uh and Chris, you're certainly going to touch on that, and then what actions to take. So, Chris, where do you want to start today as we're talking about uh predictive forecasts? I guess one of the more important things to start with would be to understand what the purpose of the forecast is. So, Katie, when you put together user stories, what would you say would be an appropriate user story for say a uh a cheese shop?

Let's go with that as a start. You know, what would a predictive forecast for a cheese shop, uh, there what would their user story be? Uh so as the manager of a cheese shop, I want to understand what cheese people are looking for on any given week so that I know what to be promoting on social media, have recipes for. Um, and then a second one might be as the cheese shop manager, I want to know what cheeses are people not looking for so I know what to put on sale in clearance. Oh, right.

And put on manager special. Manager special. Yeah, this cheese is blue when it's not supposed to be. Uh fun fact, manager special does mean that things are pretty much out of code. So fire beware.

Avoid the manager special. Okay. It's like uh saying always wash your your cans before you open them. Um with those stories, I think that's a great place to start because one of the things that you want to do is you want to use the various different tools that are available to marketers to see like what kinds of trend data could we get if we were to go out and get it said data. So let's look, uh let's go to our our two of our friends, right?

Uh let's go to friend number one is going to be Google Trends, right? Which is arguably uh one of the most useful pieces of software that is still available for free. And what this does, um, for folks who are unfamiliar is it you simply type in search terms and you can see the historical data going as far back as Google knows uh for terms. So let's go ahead and start with mozzarella, right? Because that's a cheese.

Um we're in the United States, which you can change the location, but we're the past 12 months. Look at the last five years. That way we can see, you know, are there kind any kinds of ebbs and flows in this? And so if you're a cheese shop manager, um, certainly just being able to look backwards and see what kind of what kind of things of things kind of happened here. Um, and you can see there are definitely some, you know, a kind of a heartbeat cycle, like a sine wave within the data.

So there is there is something of a trend here. So this would be one of the tools that you'd wanted to get started with just to try and understand like what can we even predict trend, right? Can because predictive analytics doesn't work with things that are unpredictable. Uh it seems kind of obvious, but but people forget that part. Um, it's like everyone's trying to predict the 2024 presidential election.

You can't predict something that hasn't happened, so right. And looking at historical events, uh, it's not a one-to-one match. So you're not looking at the right kinds of predictive metrics because it needs to be something that is consistent and reliable. And you know, for a lot of reasons, presidential elections are not those, and they're different every time. Different people, different, you know, circumstances, different variables.

So I think that that's a really good pro tip to keep repeating. Exactly. So that's part one. Part two is using the SEO tool of your choice. We use HREFs, but uh pretty much any tool that's got keyword stuff in it can do this.

Put in all of your search terms and just get a sense of you know this data is the last 30 days. It uh most tools default to the last 30 days, and you can see the number of uh searches. Uh by the way, John, I don't know if you hit mute or not, but that did not mute. Uh I was just gonna say the same thing. Oh, yeah, that's bizarre.

Um, but you can see the differ the the volume of all the different cheeses. So in the last 30 days, we see here uh Oaxaca cheese, cotilla cheese, mascarpone, cottage cheese, cream cheese, and so on and so forth. Lots and lots of all these different cheeses, and we see then the number of searches. So people search for search for cream cheese 94,000 times in the last 30 days. They searched for Gruyere 91,000 times in the last 30 days.

Um, they search for uh Parmesan cheese. So again, Katie, if you were that the cheese shop manager, this would at least give you a starting point. The challenge is this is all data that looks backwards, right? The last 30 days, which uh as everyone knows, tastes change, you know, circumstances change. We're about to go into uh the the winter holiday season, and there are probably cheeses that you would serve at Christmas that you wouldn't necessarily serve other times of the year.

Like there's always that one that comes in the like the sausage and cheese basket that looks like it looks like a spreadable cheddar cheese, but then it's got like nuts and stuff on the outside. It's like the cheese log roll shrink wrapped thing. Cheese ball, yeah. Yeah, cheese balls, uh Hillshire Farms and stuff sells those things. And I mean, again, that's not stuff that you're ever gonna serve outside of the holiday season, but when it you know, when someone gives you the the Hillshire Farms box, the Harry and David box, you're like, oh, there's there's the cheese ball with stuff on the outside.

Thank you very little. So all that says is we want to be able to look forwards, not necessarily backwards because circumstances can change, particularly when you're dealing with consumers. With B2B, things are a little more cyclical because of things like stock markets and quarterly reports and how budgets work and things like that. Things are a little bit less volatile. Okay.

So those will be the first two data sources I would start with. The challenge here now is we have to kind of glue these things together and then build forecasts from them. Well, I you know, it's funny. I remember, gosh, it must have been six, seven years ago now when you rolled this out to our then marketing team. And I remember my first thought was this is a Facebook thing.

And I think everyone on the team was also sort of confused of like, what does Facebook have to do with this? And so um knowing that uh the developer community has taken this open source uh code and modified it to be more accurate is super helpful. Yep. Um Facebook uses this a lot for their advertising, and the reason why it is valuable is the the methods they chose um incorporate a lot of seasonality, but they incorporate a lot of weighting towards more recent trends. Obviously, if you were you know selling advertising space in an app, you want to you want to give more preference and weight to forecasts based on recent data than than legacy data because again, things change, people change.

If you think of a service like Instagram, there may be a new hashtag or a new challenge or a new trend that appears overnight, and you don't want to be trying to forecast and do advertising bid pricing on trends from even seven days ago because you want to take advantage of this new trend like eating Tide Pods. So the catch with software like this is as you can see it's available in a couple different programming languages, uh R in Python. If you don't code in either of those languages, there's not really a way to use the software. Um there are products that do this out of the box. Uh IBM Watson is an example, um, but they those obviously do come with a bit of a price tag.

Well, I would think too uh they're also harder to customize for you know what you need, or just more difficult to understand how the algorithm is actually working versus building it yourself. Yes. And one of the things that uh not enough tools do, and we actually had to write this ourselves in our own software is to say do a statistical check to see if there's a trend before you write a forecast for it, right? Because we don't want to try and forecast something um that doesn't exist. So let's do uh forecast for be real, right?

The app you can see over the last five years that you know basically there's some you know mistakes, some typos, but it wasn't until really uh yeah, March of this year that this app really took off, and then they got the Saturday Night Live placement and all that stuff. Um there's not a trend here, right? We see a shape that sort of looks a little bit like a bell curve, but from a statistical perspective, there is no cyclicality, there is no seasonality, so this cannot be forecasted because it's sort of a uh once in a lifetime thing. Uh, really good example. Um god, uh oh, Clubhouse.

I was just I was gonna say, wasn't there that thing? I think I was gonna call it chat house, which is definitely not what it is, right? Um it definitely is not what it is, and what you see is pretty clearly, yeah, there was there was sort of that big spike, it sort of came and went not a trend. Nope, by definition, which means that you cannot forecast from it. However, things like mozzarella cheese or parmesan cheese, these are things that absolutely do have uh seasonality and cyclicality.

Let's remove you can see even with Parma's uncheese. If I remove the the mozzarella here, um there is sort of that that heartbeat uh rhythm. So you would your next step would be to feed this into predictive analytics software and get a forecast of some kind, get an output. And so it's we're talking about, let's start with the cheese one since we're talking about different industries. Mm-hmm.

Let's go clear this and switch to our cheese database. There you go. Cheese. Our terms, our numbers, and let's take a look just at 2023. And let's turn this into a line chart.

Rick term our color and bring this out. And so we get yes. Well, for each of these cheeses now, we can look at at these cheeses either in bulk or yeah, all or individually. You saw the in bulk with the the trut looked like spaghetti. And understand, okay, here's you know, here's when in the next year are American cheese is likely to peak, right?

Uh right around July, uh second peak around November, another peak uh December, and then a peak in early January. So you have these these ebbs and flows in American cheese. You could take Havardi. Oh, uh yeah, let's go to Havardi. Havardi, a bit different.

Thanksgiving and Christmas, but you don't have that summertime bump, right? It's it's not as popular cheese. If we look at mozzarella cheese, mozzarella, you get a decent summertime bump. You know, why, of course, people are cooking in their pizza ovens that uh that they have outdoors. Well, it's also mozzarella uh goes really well, and it's what the mozzarella, tomato and basil capracy summer caprese summer salad, thank you.

Exactly. So, you know, it's interesting because you know, you're doing the whole United States, which is a really great place to start, especially if you have like an online business. And so I would say that, you know, if you are like a brick and mortar store, um drilling down into just your state is also going to be really helpful, which you can do through Google Trends, because the trends that you see for the entire United States might be different than the trends you see for just your state. So, you know, you when you're talking about things like food and like the seasons and seasonality, the way in which the weather happens in your state is going to impact that. So in Massachusetts, in July and August, the humidity is off the charts, and people don't want to turn on their ovens and you know, sort of sit inside with like this hot, hot food.

So they're likely to eat more of the Caprese salads and other things that are easy to prepare outside. And so you might see that uh trend different from you know, uh Alaska, for example, which is in the United States, but the weather is different at that same period of time. Exactly. So that's if you were a uh sort of a cheese person, uh if you were in the architecture industry, architecture engineering and construction, again, very similar. Uh, let's go ahead and take our term and turn it into a series of pages here.

So people search for architects um at very different times of the year, right? So people search for architects, you know, early in January, uh mid-April, we see uh August, and then outside outside that. So those during those times of uh periods of time, that's when people are looking for uh that when people looking for engineering firms, uh they don't. Uh well, probably like structural engineering. Exactly.

Here's so looking for structural engineering near me. Here we have again January, March, uh then we have May, July, August, September. So again, sort of which which goes against what you would think for like normal B2B, normal would be to be like office work. Uh we know that people are out of the office during those times, you know, they're on summer vacation, but people searching for structural engineers, that that's actually a summertime thing. Why that is, I don't know, because I don't I'm not a structural engineer, but I would imagine it probably has to do with budgets and project timelines.

Well, you know, if you think about you know what you need structural engineers for, I would personally, if I were, you know, in this space, I would be looking for, you know, what are the trends of structural engineer near me versus contractors near me, because those tend to go hand in hand. So for example, I needed to look at uh I needed to have a structural engineer to look at the back of something on my house before I could have some work done because of the type of landscape that I have. And so typically when you look for one, you're looking for the other. So if I were a contractor, I would want to know when people are looking for structural engineers and vice versa. Um, and that would also sort of tell you, especially again, depending on what part of the country you're in, you know, contractors tend to be busier in New England during the spring, summer, and fall months.

Winter's just, it's tough to do that work versus someplace where it's perpetually summer, like California. Exactly. One of the interesting things in this um forecast is that the overall macro trend is on the increase, right? If you look for just even simply eyeballing it, uh you can see that in terms of popularity, this search is increasing in popularity over a long period of time. So if you're a structural engineer or working at a structural engineering firm, this might be a very good thing for you for you and your business.

Is that something that you want to take into account with trends of forecasting as well? Um, so I think one of the things that we wanted to demonstrate is that depending on the industry, the trend is likely going to be different. So not everyone is going to have the same holiday rush that sort of we've come and know for the retail industry. Um, as we're seeing with structural engineers and architects, there is no winter holiday rush per se. It's more of a spring-summer thing.

Uh, cheese, depending on the cheese, the type of cheese, it's definitely going to vary between summer and winter. You have American cheese, which is really popular at summer barbecues, hot dogs, hamburgers, you know, all of that good stuff that goes on the grill versus in December, American cheese isn't as popular because maybe you're not grilling outside as much. And you're wanting more of those like festive, like aged cheeses. I'm kind of making that up. I'm black hose intolerant.

Um a really good example is vacation rental. So this is a very popular term, right? Vacation rental, that season for if you were if people start searching right around spring break, right? Right around the the April vacation week, uh just after Easter. Um and so in 2023, uh mid to late April is when people are really gonna start looking for that, and that peaks, the searches for it peak a sort of end of June, after that's on the backside.

And that is pretty obviously like people trying to find where they're gonna go on vacation, right? But you see, there's a couple of early peaks in January and in February as well. So, you know, those may correlate to uh to school vacation weeks. Uh again, looking for accommodations. And again, towards the very end, December, we see people who are looking for vacation rentals um uh for the holiday season.

But again, a very, very different look, uh, you know, very different industrial look than the other two. John, what do you think? Yeah, well, it's just you know that's the most important thing to keep in mind is that for your industry you've got to have the data to be able to see these kinds of curves because you know, if you just think it's gonna be the classic seasonal thing of it's gonna be quiet in December and nothing's gonna happen in the summer, you you can totally get destroyed. Um and we see this too because it's you know, we always talk about how we think July, August, September will be dead, but for us that's for our customers, that's prime planning time where they're actually free to go into other projects. So what's normally a sleepy time in B2B, you know, in other fronts ends up being really busy for us.

So yeah, you you know, if you don't have this data, you're just kind of flying blind. And I there's a lot of bias with this too. You know, there's people that think when things should be big or not big, and um, you know, only the data can really get you on the right track. Exactly. Other things you can forecast, we've not done this here because it requires uh a bit more um lifting, is you can for uh essentially if you've got any data that goes by time, you know, uh cashier cashier register receipts, uh shopping carts online, your Google analytics data, your email marketing data, your you know, terrestrial radio uh ad impression data.

If as long as there's dates and there's and there's numbers and it's regular and frequent, you can forecast it. And that's that's the important thing to remember about these technologies is they are agnostic. You don't just have to use Google Trends, you can use any uh time series data. And the the thing about forecasting is is like everything else, the more specific it is, the better. So if it's your Google Analytics data or your Adobe Analytics data or your Hubspot data, you're gonna get a better forecast for that is unique to your business than taking it in an industry-wide forecast.

Exactly. Um, you know, Google Trends is a really great place to start. So we that's where we started when we first launched Trust Insights, we didn't have that historical data. Um, so we were reliant upon systems like uh Google Trends and SEO tools to tell us what people were searching for. But now that we have our own, you know, five years basically now worth of that data, our goal is to be using our own data to see how people are searching for us and what kinds of things they're finding us for, and then use that data.

And so it's a constant evolution because what we see in our own data might differ from what Google Trends was telling us. Exactly right. And the the thing that we have to keep in mind too is that you know you may be in a situation where yeah your data is a hot mess. That so don't rule out search data even if you know things are are a little bit on the messy side because you might be able to to use uh public search data if if your data is just a disaster. Let's take a look at management consulting.

So management consulting this looks like traditional B2B. Right. So you have uh your first quarter lots of interest you have sort of uh you know second quarter SAG, you have planning there's your planning season people, you know August September, and then uh you have uh you sort of just fall off the cliff here after Thanksgiving. So that's that is classic B2B um very very easy to forecast. So you can see you know they each industry has its own rhythms.

Well and I think the so what of that is you know if you are just making assumptions about the seasonality based on what traditional you know consumerism looks like you have your holiday season you have your major, you know, things like Valentine's Day and that kind of thing, that may not be the seasonality that works for your business. And that's why doing this kind of forecast specific to at least your industry is important to understand, you know, those peaks and valleys. And so now I think we're at the point of like, all right, we have all this data. What do we do with it? What can you do with this information?

Right. And that's and that's the question is it depends on what your user story is, right? That you're the user story should be telling you. Um, this, for example, is Google Analytics 4. You know, it's it's on a pretty clear direction as to people's interest in it.

Um, but there's some big spikes um that are going to be happening in sort of April, and then again uh in July, which we know for the July one is when Universal Analytics just turns off and stuff. But we also know that that April spike is probably going to be a bunch of people going, uh We only have one quarter left to do it. Exactly. So based on the user story, if we go back to the cheese shop uh example now, let's uh actually duplicate this and remove our term and go into our tabular data. Let's say you're the cheese shop owner and it is let's do the week of Valentine's Day.

Uh cheese and love are in the air. Um I mean, who am I to judge? Exactly. Um, provolone cheese, all by Pecorino, followed by Swiss. Um, those are the cheeses that are gonna be most popular that particular week uh in February.

So as you're planning out your Valentine's Day theme in your store and your point of sale displays and all that stuff, those would probably be the you know, Provolone Pecorino is supposed to be the ones you'd have up front. And you have to decide as a as a business person and based on how you know people use those cheeses, whether you should be doing you know a discount and a sale or raising prices because demand's going to be there. Now, how far ahead do you feel is you know safe for someone to be uh putting that information out there? So obviously it's gonna differ by the type of channel that you're using. Um, but so let's say we know around Valentine's Day that provolone, you know, is gonna be the thing.

Should I start promoting provolone for Valentine's Day in December? Is that too far ahead? Uh, should I start doing it at least two weeks out, or is it doing it just that week, you know, good enough? Oh, that's where we would look at the data and see like what you see in each of these data points is sort of inflection points where things bounce. Generally speaking, you want to promote just after the inflection point.

So here for Provolone, there's a soft spot in uh in May, right? That's where that search volume kind of ends. Then the following week, the end of May, you go on this upward trend as search volume goes up. So we have to remember the data source too. This is people searching, which means they have intent.

Someone is looking for stuff about provolone increasingly until you get to uh you know, sort of this this mid-June thing. So if you were that store owner, as you know search intent is increasing, that's when you should probably start putting out maybe some digital mailers, uh, maybe uh some emails, some social posts asking people, hey, what's your favorite provolone cheese recipe? You know, things like that. And then as you get sort of to that that the midpoint of that curve, or even you know, earlier, early to midpoint, start running uh keyword ad based ads and pay-per-click advertising and on social media where uh keywords are part of the ad targeting when you get to this top this peak here that may not be when people are going to use the cheese now with cheese that's probably the case because it's a it's a perishable whereas something like vacation is going to have a more of a time lag um that's when you could say okay yeah maybe we can start to back off our our most expensive paid channels for example because we know that that uh demand is is going to start to to gather but that's where I would say you don't want to look just at that week you want to get the context for that term or those basket of terms and say okay here's here's what's happening and when I should get started versus when we know everybody's gonna be in in in the knife fight makes sense I'm still trying to figure out like what's romantic about provolone cheese but that's that's where I'm stuck provolone this is wildly guessing because I am in no way a cheese expert I love wild guesses so let's hear provolone's a soft cheese that you can melt and you can melt with other cheese and it makes for great fondues. Ah again lactose intolerant I wouldn't know that um you can also use it on pizzas uh it's not as good um yeah it's kind of a weird cheese smoked provolone is very good that that you can eat by itself what would you think about ad spends like my gut would be you definitely want to be dumping spending when you hit the inflection point when search volume is going up, would you actually turn it off or dial it down once you hit the next inflection point and it starts to fall off?

I mean, in theory, it depends. It depends on how much demand there is and how much competition you've got. One of the advantages of predictive analytics is that like if you know you're going into uh a major season, let's go to our our here, let's go to what be good. Um inbound marketing. Right.

Um if you know that you've got a major competitor, say like HubSpot, uh that's going to be, you know, just flooding places with money. You might want to go just at the bottom of the inflection point and just that early start, because you might be ahead of them. Um, because yeah, most people who go by gut and assumption go don't have the exact you know days or weeks. So you could maybe get a market opportunity by going a week ahead of a competitor and get some really share. Setting the expectation that because search volume isn't as high, you might not get as much performance, but you might get folks who are who are starting to think about it but haven't started searching for it yet.

Uh, but you know it's going to be on their mind. Yeah, that's funny. You totally see the inbound show there in November, right? That's I would say with anything paid, you have to take into account competition. Um, because the other thing you have to take into account is inventory.

Inventory is an issue too. Um this past year, if you were doing holiday gift guide advertising, you were paying a fortune um in September or October for your for your ads. Not because other people were doing that, but because the inventory is being consumed by politicians running ads. They were just flooding every ad network with as much money as they possibly could um you know everywhere they could uh for all the different elections in the midterms and that just take you know that just soaks up inventory and so everybody's advertising gets more expensive when you're in an environment like that yeah thankfully for those who are forecasting 2023 you don't have to worry about that but in two more years in 2024 you know that until you know whatever November 7th of that year there's going to be increasingly less inventory during a presidential uh election in the in the United States and it your ad your ad budgets have to be uh accounted for and that's I think a really important type of predictive analytics is being able to look at the macro picture uh and understand okay from a macro picture here's what's happening that's going to affect everything not just certain keywords but everything um when we look for example at the Federal Reserve Bank's uh 10 year to uh three month treasury rate which we've talked about on on the the show in the past that indicator when it goes below the zero line uh means that investors have very very low confidence in the market and a recession usually occurs within a quarter uh you can see uh as of this morning when I ran this this is you know fairly significantly red so investors are very very bearish right now on the market um and that also means that you know companies will be spending less so again if you're setting ad budgets and stuff um if it hasn't happened already you will probably get the call saying yeah we're cutting our our budgets for 2023 and you're like uh like we just talked to a client yesterday, you know, we talked to them three weeks ago they're like, oh yeah, we want to do this, what this, and this. Talk to them yesterday, like, oh, our budget's got cut.

Like, okay, here's why. So it sounds like you know, the big sort of takeaways, the bullet points are, you know, if you are looking at trying to understand seasonality, make sure that you are comparing yourself to the right industries, the right companies. If you don't have enough of your own data, there are data sources that you can use, such as Google Trends, you can use SEO tools. Um, those are really great places to start in terms of understanding consumer behavior, your customers' behavior of how they're searching for you. And then as you are getting more of your own data, start to transition into using that data as a supplement to the uh organic search data because it's it's going to give you more granularity into your specific customer base.

Exactly right. Um, and keep an eye on the macro picture, keep an eye on the big picture, the things are going to be influencing stuff in the long term because we are we are perpetually and ever increasingly living in a more unpredictable world, and we will be over the next 50 years, right? Um we saw with the last three years, you know, hey, someone parked a ship the wrong way in the Suez Canal, international trade stops for 14 days. Um, and you know, and pandemics and and billionaires buying social networks and all sorts of crazy things have been happening. That unpredictability means that your forecasts have to be taken with a grain of salt.

Understanding, like, yeah, it these are great tools for for making plans, but be ready to just hit the, you know, fling the forecast out the window and say, all right, we gotta go with what's happening right now because this happened and nobody's, you know, this was not on anyone's bingo card. Well, and I think that that lends itself to running a forecast, you know, for a year ahead a year out is okay for planning, but you're better off doing it in smaller periods of time and running it more frequently as things that are now in the past, like the beginning of the show is now in the past. It's already happened. And so that's not something we can predict anymore because it's gone. It's you know, bye-bye.

So running your predictive forecast once a month, once a quarter, depending on how reliant you are on it for planning is a really good idea. Running it once and saying, Great, this is the plan, it's never gonna change, not a great idea. Yeah, think about like the weather. How often do you check the weather forecast, right? Um, yeah, we'll look at the 10-day forecast for like two weeks, but you check the weather every day.

Like you know, you don't say, okay, in two weeks I'm planning this big vacation because the 10-day forecast looks good, and then you never check the weather again until then. You're like every day, like, oh, hey, look, now it says it's gonna rain. Oh, now it says great tornadoes. Who knows? But all forecasting is like that.

There is there is uncertainty. And in environments of uncertainty, it's it's a really good idea to check the forecast more than once, you know, more than just once. You would not, I would hope, just you know, pull a weather forecast for a year in advance and say, okay, I'm all set for the I know exactly what to wear on every day of 2023. Like, I don't know how that's gonna go for you. Is that how you use the weather forecast, John?

You look at it once a year and just gonna say YOLO. Right, yeah, exactly. There's there's no bad weather, it's just bad clothes, right? That's if I want to get some of these reports. I mean, I don't have time for this.

I'm busy running a cheese shop. I don't have time for these reports. Where do I get this stuff? Um if you have the the resources to do so, obviously we do them clearly. You know, if you don't have uh the the reports, because they can be reassuringly expensive, um there you can still look at historical data, and if you know your industry well, you can make some decent guesses, right?

Um if you're a cheese shop and you look at the last five years worth of of cheese data and you see trends in it, you can eyeball it, it will have less accuracy, right? It'll be less accurate, but you can say, like, yeah, you notice it looks like every May Provolone starts to pick up. So I know it at some point I should probably be doing something of provolone uh sooner rather than later. And so in those instances, if you are willing to accept less accuracy, uh which we just talked about on the podcast this week, um, that is that isn't acceptable if you cannot afford the the full data-driven, you know, machine learning powered forecasts. But if you can, you can contact John Wall directly, he will take your calls.

John, John's John's down for the standing by. I always get wrong, but yeah, John's curious. Yeah, that's right. Mirror camera getting you there. It really is.

Um and again, the macro stuff, everyone should be paying attention to that, right? Um I I generally suggest to people that they read news sources like Reuters or AP News, places they don't have a political spin, just like here's the news. Um, because knowing what's going on, like hey, um in the early days of the pandemic, India, certain a couple of big population centers in India were heavily affected. Those population centers make the precursor ingredients for a seed of minifin. So if you know your industry and you know how things work, so how supply chains work, when that population center gets shut down, you know that three months down the road there's gonna be a Tylenol shortage because of just the way everything is interconnected.

So if you have a lot of domain expertise, if you know that this year um dairy herds were severely impacted by the drought, um, you know that you know cheese prices are are gonna go up. And if it's an aged cheese, like an aged Parmesan cheese, it's aged for three years or whatever. You know that come uh you know September, October of 2025, there's gonna be a parmesan cheese shortage, and you're gonna need to you A, you should be stocking up your stocks if you have you know the ability to carry long-term inventory, like stuff it in the deep freezer. Um, and B, expect that you're gonna have to charge higher prices, and you might may have customers who are impacted. Um so a good part of the macro of predictive analytics is knowing what's happening right now that's gonna have second order effects down the road.

All right, I'm good for the parmesan cheese shortage of 2025. We're ready. Put it in your bunker. That's right. In the vault.

All right. Well, I think until next time. Uh, that has been your cheese talk. Thanks, everyone. We will see you next week.

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