{PODCAST} In-Ear Insights: Predictive Analytics for Unpredictable Events

In this episode of In-Ear Insights, Katie and Chris respond to a listener question: how valid are predictive analytics forecasts when you’re in the middle of massively unpredictable events? How do you deal with the anomalies of a black swan event, and how do you tell the difference between an anomaly and a breakout (continuing trend)? Plus, learn what exponential thinking is and how to apply it to your business forecasting.

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher Penn 0:00
In this week’s In-Ear Insights, we are talking about predictive analytics and how to deal with it when you have what’s called a black swan event, a event that is so unknown and so unforeseen that you cannot forecast it, but it’s going to have ripple effects on everything you do. We are, of course, in the midst of a pandemic right now. And it’s going to have long lasting economic and societal consequences, none of which we have prior data for all machine learning. All algorithms require prior data points. And the last time we had anything like this was over a century ago in 1918. And of course, there are no data sets, there’s no Google Trends. There’s no search volume from 1980.

Unknown Speaker 0:42
So, Katie, be cool if there was,

Christopher Penn 0:45
it would be interesting if there was we do have some data from 2008 through 2011 during the financial crash, but that was a very different system where, in that time people lost confidence in the financial system. And there were a number of interventions that happened then but this is not a case of that people actually don’t have a lack of confidence in the financial system. It is a a totally different thing where rightfully governments around the world have said we’re turning off the consumer engine to protect the consumer 100% right choice, but it will have significant effects that we cannot forecast. So one of the folks in our slack group analytics from marketers asked, How can we use these financial these these predictive forecasts when you have a completely unpredictable situations? Katie, what are your thoughts on some of this?

Katie Robbert 1:35
You know, it’s, it’s definitely an interesting question. And you’re absolutely right. So let me take a step back for just a quick second. And you know, if people are unfamiliar with how the predictive mechanism works, the way that we use it, you know, we use that historical data, usually search trend data, so Google Trends but we can use other types of data as long as it is as it is numeric and collected consistently and formatted, you know, in a weekly or daily or monthly manner. So, running a predictive forecast requires that historical information. And then we use the model that basically says, based on the trends that we’re seeing historically, this is we’re projecting what we assume what will happen, moving forward using a Rhema model. So you’re absolutely right. We have never what we’re experiencing now as a human race is not something that we have experienced before. There have been versions of it. So you had chrystia point you mentioned, you know, the recession from 2008 to 2011. We’ve also experienced other viral outbreaks such as SARS. And I think it was what am one and one or at one on one? Yep. You know, so there is some data available. That is similar to but not identical to what we are experiencing right now. You know, so the best that we as data scientists could do is take that historical data that is closer to and sort of start to input that as a training data set, but at the end of the day, what’s happening now has never happened before to your point. So it is that black swan event, you know, events such as 911, or you know, a war breaking out, those are considered those black swan events or tsunamis, natural disasters, things that you can’t necessarily predict will happen. And then you can’t predict the effect the ripple down effect that it’s going to have on the economy on, you know, people’s ability to do their jobs, and so, the best that we can do right now. So typically, we run these models like maybe once a month, maybe once a quarter, we need to continually run them to build up that train database and then make sure we’re answering annotating it in our, you know, notes or data set or wherever is appropriate so that we know when we look back during this time period, this is what was happening because it will impact all future forecasts.

Christopher Penn 4:15
Yep. And the other thing that I think is important is that the data inputs, you know, obviously, a lot of our predictive forecasts are based on search. So the question that we have to ask ourselves is, is the underlying human behavior changing? Right? If someone is searching for it, for example, you know, on premise, sat on premise appliance firewall appliance, right, that’s something that there will be less demand for, but the intent, the the underlying behavior probably will not change a whole lot, right? Because it’s a fairly cyclical seasonal thing. And there’s a defined need behind it that is probably not going to go away. People are not going to stop needing, you know, hardware security appliances for the computer networks. In fact, during a time like this whenever was your working from home, they may need more. On the other hand, there are things like your favorite sandwich shop where Yes, you’re gonna have a substantial change in the underlying human behavior. So one of the first questions you have to ask is, when I’m doing these predictive forecasts, is there an underlying human behavior that will or won’t change. And, to your point, using an algorithm that focuses and weights much more heavily recent data is going to change, you’ll be probably more useful. The other thing you can do is, as you mentioned, Katie borrowing from prior experiences when you have data from SARS from age five n one h one n one from the great recession and you have the ability to look back can you create essentially discounts saying like, Hey, there was this much impact. Can we discount the forecast by this much to see like, yep, we’re gonna look at this likely impact example, travel services during an after 911 were substantially impacted. In a pandemic, the World Health Organization and Johns Hopkins University forecasted A minus 45% demand change immediately. So you could take your travel forecast data, and essentially for the period that you project the pandemic occurring, forecast it down, minus 45%.

Unknown Speaker 6:20
Right. But

Christopher Penn 6:23
ultimately it comes down to understanding what the person is going to do like and that I think it’s not something you can do to predict or forecast that’s something you have to do with market research.

Katie Robbert 6:33
Right? Well, I mean, if you think about you know, people who are thinking about blizzards, for example, so when the weather team predicts Okay, so next week, we’re going to have a blizzard, you’ll probably be stuck at home. For you know, a few days people go out grocery shopping and they stock up on essentials, with the news of the pandemic and you know, restaurants and schools. enclosed. It’s very similar mentality to a blizzard of I’m going to be stuck in my house. And so consumer goods, such as paper goods, and you know, milk and bread, and those types of things, have all been flying off the shelves in mass quantities for some reason. And you know, but if you think about it from that sense of a market researcher, and what’s happening within search data people are searching for, you know, pizza delivery near me much more often, or toilet paper in stock, or bread near me. And so people are using Google as that beacon of hope to say, Where can I find the things that I need? near me? Now, of course, this is reliant on the businesses keeping their data up to date. But if you look purely at just the search trends, you are going to have those anomalies in your data for people searching for consumer goods. You know, you’re gonna have other things such as, how do I, you know, do x How do I I create my own homemade hand sanitizer, or how do I, you know, wash my hands? Or how do I keep myself safe? And how do I keep my kids entertained for three weeks at a time. So there’s going to be a lot of new information, a lot of new inputs into that search data. And those, Chris, to your point about the human behavior are things that you will have to know in your data sets as you move forward, because they are those anomalies, they are those black swans. So let’s fast forward six, you know, 1218 months from now, and let’s say I’m, you know, a consumer goods marketer, and I’m trying to figure out when to put toilet paper on sale, I’m going to have a big anomaly in my data set, because all of a sudden, it’s going to look like Well, every March, people are rushing to the stores will actually notice that one time point that we had this thing going on. And so those are the types of considerations you have to have. So as much as you can’t use a predictive forecast right now. To figure out what’s going to happen, as you’re using them in the future, you have to remember what happened now and note that for your future data set, yep,

Christopher Penn 9:08
there’s a couple of different ways to handle that particular situation. We have a known anomaly. But one of the ones that ordinarily would seem like a really dumb thing to do, but it did have anomalies, actually, quite wise is using what’s called imputation. And you take it says, Take the anomaly, just delete it, delete the data, put a big gap there and say, okay, computer based on the five years of data you had prior, guess what the normal volume would have been, and then use that as your training data moving forward to say, Okay, well, let’s restore seasonality. Now, the challenge with that is if you have what’s called a breakout, and anomaly is when you have a big spike in a data point and it goes back to normal. A breakout is when you have a change and change sticks around as a macro trend that’s changed. One of the things that for example, we expect to be a substantial consequence of these modern events is you may see companies That say like, hey, this will work from home thing actually worked out pretty well, you know, people got their work done. We didn’t have to, you know, watch them like a hawk 24 seven, you know, have you know, have the you know, the guy stopped by with his coffee cup and the suspenders say, hey, to get that TPS memo and so if you’re looking for things like work from home software, video conferencing software, there may be breakouts that where there’s a permanent change in behavior because of what’s happened. So we have to be able to distinguish between the two datasets and delete the anomalies, but then deal with the breakouts as an ongoing trend.

Katie Robbert 10:36
I 100% agree. And I think that, you know, right now, we’re so new to what’s happening that that sort of long term ripple effect thinking isn’t really happening and a lot of places, you know, obviously you and I are sort of obsessed with how the data works. So we are thinking about those things. And like, well, if this happens, then this might happen. If this happens, then this might happen. And that’s something that you know, As Black Swan events happen in the world, they affect your data. So you as a marketer, you know, as you have time can start to put together, you know, some of that planning to say, what would it look like if we lost power for two days? What would it look like if everybody started to work from home and wasn’t working on site like, that’s where you can start to future proof. Your data sets from Black Swan events, the Black Swan events will still continue to happen. But you will have a better sense of this is what I can expect from it and just play out those different scenarios. You know, think about things like your Google Analytics data, you might suddenly see a drop off of website visits because people are distracted doing other things worrying about themselves, or depending on the industry that you’re in. You might start to see huge spikes in your right exactly in your datasets. And so just being aware of But that is a possibility that could happen for your business will make it less shocking when it does happen of, oh, what’s going on with the data? Well, this is, you know, we had sort of thought through these different scenarios. And so you know, as I mean, Black Swan events happen, they just flat out happen. We don’t know when we don’t know how we don’t know what the impact is going to be. But thinking about a few different scenarios, you know, think about small businesses right now. They’re trying to figure out financially, how am I going to stay afloat? What do I need to do? So a lot of businesses, ourselves included, are talking about pivoting the way that you know, we’re looking at services or what can we offer off the shelf or what what are some of those lower cost things that people can purchase from us? or How could they work with us differently. And so having those backup plans to your strategic planning is really going to make working through some of the things that are happening now. A little bit Easier to help honestly keep you sane. Exactly.

Christopher Penn 13:03
There’s actually a term for that methodology of thinking it’s called exponential thinking. And it is very similar to the way kids behave. If for those of you who have not had kids, or had experience with them that at some point in the toddler years, they just keep asking the same question over and over again, they say, why don’t you explain why explain. But that is sort of what exponential thinking is where you’re trying to understand second, third, fourth, fifth order consequences of an event. So everyone’s working from home. What does that mean? It means that kids are going to be home. Well, what does that mean? It means that the product demands for things like art supplies may go up, certainly, device usage will go up, what does that mean? and so on, and so forth. And being able to think five or six or seven steps ahead, gives you that planning ability that other people who were focused only in the moment, can’t see. I don’t know if that’s something that you grow up with, if it’s something that’s intuitive, or if it’s something that you can learn Learn as fluently as like a regular skill, but it’s something worth practicing to see like, how far ahead can you think in advance with the understanding that and this is where humans and machines converge. You have that training data, your own experiences, what you’ve learned from history, what you’ve learned from reading past experiences, and even the time lag that other countries have had, you know, they’re what’s happening as we record this in the United States has already happened in other nations. So being able to look ahead at them them eight weeks ahead. You can pull lessons out of and say, oh, that could work that could not work and be able to plan for that.

Katie Robbert 14:39
A really good example that people will will should resonate with them of that exponential thinking is when a few years back or probably more than a few years now, the blackout happened during the Superbowl. And so there were a lot of consumer good companies, I think it was Oreos, specifically, you can still dunk in the dark. I was ready for that type of scenario because they had done that exponential thinking of, well, what if this happens? What if this happens? Because on social media, you need to respond instantaneously, or else it’s no longer relevant. You can’t respond the next day people have already forgotten what’s going on. And so there are a lot of companies, specifically ones focused on their brand on social media that do practice that type of exponential thinking. Now, we’re talking about it in a slightly different context. But the basic premise is the same thing. So if you’re applying it to your social media, or your crisis comms, like, what if this happens, what if this happens, think about applying it in a slightly different context. When you’re looking at your employees when you’re looking at the business when you’re looking at product development when you’re looking at, you know, variety of different things beyond just, you know, planning for what do we say on social if this happens?

Christopher Penn 16:00
Yep. So to sum up, predictive analytics forecasts are still useful, especially if the underlying behavior is not going to change. It will obviously be different. If things have substantially change the behaviors that lie them have changed. Be aware of that in your data, look at your data, understand what’s happening in your data, and to the best of your ability. Try out doing that those exponential thinking exercise even when there isn’t a major crisis because it’s one of the best ways to help you plan ahead. If you have follow up questions on this you want to talk about drop on in at the analytics from marketer slack go to Trust insights.ai slash analytics for marketers can stop with over 900 other analytics professionals who are also working from home and want to chat about all things analytics. And of course, if you have comments on this episode, please head over to Trust insights.ai you can leave a comment on the blog posts that goes with this episode. Thanks for listening. We’ll talk to you soon please stay safe.

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Trust Insights (trustinsights.ai) is one of the world's leading management consulting firms in artificial intelligence/AI, especially in the use of generative AI and AI in marketing. Trust Insights provides custom AI consultation, training, education, implementation, and deployment of classical regression AI, classification AI, and generative AI, especially large language models such as ChatGPT's GPT-4-omni, Google Gemini, and Anthropic Claude. Trust Insights provides analytics consulting, data science consulting, and AI consulting.