Mr. Graban talks broadly on the methods you can use to avoid measuring vanity metrics, and how to efficiently measure and represent your business’s growth using effective problem-solving methods. He gives examples from his work in the health sector on how he used his methodologies to improve the overall quality of care, ER wait time, and patient safety, while also reducing costs and improving the workplace experience.
0:53 – Dr. Jeremy Weisz shares the best solution for documenting standard operating procedures (SOPs), making it easy and efficient with SweetProcess and their 14-day free trial.
1:47 – Introduces the guest speaker, Mark Graban, who is a successful consultant, published author, professional speaker, and blogger.
3:22 – The guest speaker tells a real-life story about the worst way companies react to their growth metrics when they start measuring it.
5:58 – Mr. Graban shares a simple but effective method that will help you get off the rollercoaster that comes with measuring business success.
10:37 – Mr. Graban shares what you shouldn’t be doing when it comes to calculating data and metrics.
12:37 – The guest speaker tells you the best thing to think about when measuring success.
14:36 – An example of an organization Mr. Graban has worked with using his methods and the results they got.
19:29 – How the guest helps organizations to identify inefficiencies using various problem-solving methods.
17:20 – Here the guest shares, as another example, his thoughts on ER wait times and how to really measure and improve it.
31:59 – One thing you should always have in mind when you start measuring data to help represent your business’s growth.
Graban helps businesses improve their inefficiencies, boost performance, and also show them how to sustain those improvements. In his healthcare work, this means improving the quality of care and patient safety while also reducing cost and improving the workplace experience.
Speaker 1: Welcome to The Process Breakdown Podcast where we talk about streamlining and scaling operations of your company getting rid of bottlenecks, and giving your employees all the information they need to be successful at their jobs. Now, let’s get started with the show.
Dr. Jeremy Weisz: Dr. Jeremy Weisz here, hosted a Process Breakdown Podcast where we talk about streamlining and scaling operations of your company getting rid of bottlenecks and giving your staff everything they need to be successful at their job. Before I introduce today’s guest, who’s the author of Measures of Success which exactly about this topic actually, except for he goes deeper on it, I would say.
Dr. Jeremy Weisz: This episode is brought to you by SweetProcess, and if you’ve had team members ask you the same questions over and over again, and it’s the 10th time you spent explaining it, well, I think there’s probably a better solution, so you don’t want to bang yourself over the head, but a SwetProcesses is a software that makes it drop dead easy to train and onboard new staff and save time with existing staff. And not only do universities, banks, hospitals, and software companies use them, but first I discovered, Mark, the first responder, government agencies use them in life or death situations to run their operations. So you can use SweetProcess to document all the repetitive tasks that eat up your precious time so you can actually focus on growing your team and empowering them to do their best work. And there’s a 14 day free trial, no credit card is required, you can go to sweetprocess.com, that’s sweet like candy, S-W-E-E-T process.com.
Dr. Jeremy Weisz: And today I’m super excited. Today we have Mark Graban, and Mark is an internationally recognized consultant, published author and speaker. If you haven’t seen him speak, first of all, his YouTube videos are phenomenal, check them out. He helps organizations learn how to improve and sustain performance. He’s worked with such companies including GE, Dell, Honeywell, Division of Johnson & Johnson, and many, many more.
Dr. Jeremy Weisz: In his healthcare work, which is specifically, right now, important. This means improving the quality of care and patient safety, while also reducing costs and improving the workplace experience, he’s authored several books. His latest book is Measures of Success, React Less, Lead Better and Improve More. And it’s a management book about using simple yet practical statistical methods that help leaders at all levels overreact less to their metrics, and that helps you free up time for real focus, sustainable improvement, and Mark, everyone wants that. Thanks for joining me.
Mark Graban: Thanks. Yeah, it’s great to be here. Thank you Jeremy. I appreciate the chance to talk.
Dr. Jeremy Weisz: On so many levels, now is a perfect time to be listening to you. And whenever someone’s listening, now is probably the perfect time they should be listening to you anyways because help them with, like you said, react to changes in performance metrics in a actual productive way, I guess you could say.
Mark Graban: Yeah.
Dr. Jeremy Weisz: So, I want to start there, Measures of Success. Obviously you could check it out, measuresofsuccessbook.com, but talk about, do you have a favorite story from the book? And then we can get into a little bit about how people should react to changes in performance metrics.
Mark Graban: Well, there’s one story in the book that’s a bit of an amalgamation of situations that I’ve been in throughout my career. So I started my career, actually, it was a different two letter acronym. I was at GM, not GE.
Dr. Jeremy Weisz: Oh, okay.
Mark Graban: So former general motors people have slightly different scars and wounds than former general electric people have, I’ve found. But I’m in the club of GM survivors. But there are certain scenarios that whether it was general motors, startup software company, other manufacturing companies, and then various healthcare organizations, there are certain dynamics that it’s a combination of human nature and the way people have been taught to manage, either formally in school, or informally by what’s modeled in the workplace.
Mark Graban: And there’s just this constant habit of reacting at every up and down in a metric. The metric gets better, we get excited, we celebrate, we pat people on the back, and then the metric gets a little bit worse, and managers get upset or they pressure people or they chew people out. The cover of the book has a drawing of a roller coaster and the swings of emotion that comes with our metrics it’s like a rollercoaster that just ceases to be fun after a while.
Mark Graban: So we’re trying to help people get off of that roller coaster. And you have some simple methods that are not rocket science, it’s not calculus, it’s simple math, to help us draw some guardrails around the fluctuations or changes in our performance measures over time so we can learn when it’s appropriate to react and react quickly, and start asking what changed in our business, or in our situation. And then as you were saying, I think the most important lesson, it’s not even what to do, it’s what to stop doing, and that’s to stop demanding an explanation or a root cause for every up and down in a metric. We want to help people get off of that roller coaster and use our time more effectively.
Dr. Jeremy Weisz: So what are some of the simple methods you mentioned?
Mark Graban: Well, so there’s a method I was fortunate to learn, I claim no inventorship, if that’s a word. I didn’t invent a method, but I was fortunate to learn, very early in my career, a method called process behavior charts. And if anybody has a background in Six Sigma who’s listening, a process behavior chart is a very specific form of what’s called more broadly a control chart or an SPC chart.
Dr. Jeremy Weisz: I need one of these for my kids Mark.
Mark Graban: So there are metrics that we could track in daily life. Maybe we can talk about that too. So this method, it’s a form of a statistical process control chart. The term process behavior chart is credited to a statistician, Professor Don Wheeler. And so basically if we just back it out, it’s a chart that helps us see the behavior of a process. Metrics are the output of processes and systems. And so with the process behavior chart, the first thing we do is visualize our data in what Excel would call a line charts. Sometimes this is called a run chart.
Mark Graban: So lesson number one is stop reporting two data point comparisons. We see this in the news, we see it in work, we can see it when we’re looking on the scale. My weight is 1.2 pounds lower than it was yesterday. What does that mean? Maybe nothing. So, first of all, if you weighed yourself daily, and you just put down in a line chart, you might start seeing that maybe your weight is just fluctuating around a stable average. Maybe it seems to be trending up, maybe it seems to be trending down. But a lot of times we see a measure is just fluctuating around an average.
Mark Graban: And then the other thing that we do is calculate an average over a period of time, and we add that to the chart. Now, you might get a different sense visually, seems like maybe half the data points are above average and half are below average. That won’t be precisely true, but again, you might start seeing that maybe this is fluctuating and not really trending anywhere. Then the other thing we do is calculate what are called the lower limit and the upper limit on the chart.
Mark Graban: So again, this forms guardrails. So we can tell if we now have fluctuation that’s abnormal. So if a data point goes outside of those calculated limits in the good direction or the bad direction, that’s a time where we would stop and ask, "Okay, wait a minute, what has changed?" And then we learn to stop asking that question with every up and down. Sometimes people have rules of thumb, like if you two or three data points in a row that are worse than average, you need to do a root cause analysis. Well, again, that could be fluctuation in the metric and that’s what process behavior charts help us sort out the difference between noise in a metric, and signals. We should react to signals, we need to ignore the noise.
Dr. Jeremy Weisz: I love that. Don’t report two point comparison chart. And it reminds me of something that’s happened recently, someone was saying, well if you take two people who are affected by a virus in this day and age, and one was 80 and one was 20, well the average age of someone infected was right in the middle. And I laughed but I didn’t realize really what that meant until you just said, don’t report two point comparison charts.
Mark Graban: Well, I mean, and this happens in the news, or even like right now with COVID-9, you watch the news and you see a number, the number of cases and the number of deaths, my goodness. Johns Hopkins or any of these other websites that will actually show you a line chart help us understand, are we still in exponential growth or has the curve been flattened up? You cannot tell that from a single data point. You cannot tell that from two data points or to say all the number of cases is up so much from yesterday. That’s factually correct, but it doesn’t really tell you much. So we need to look at the broader context of any data that we’re trying to learn from.
Dr. Jeremy Weisz: That makes perfect sense. I love the notion of what should we not be doing. So we should not be reporting two point comparison charts. What else should we not be doing when it comes to the data and the metrics?
Mark Graban: I would say, again, stop asking people to explain every up and down, that will quite literally waste people’s time. A different tip, I would stop using Excel to put a linear trend line on a graph. Two clicks, add trendline. That linear trend line is mathematically correct, but all it does is describe historical performance. The mistake people make is when they start extrapolating the linear trend line, there’s no guarantee that that trend will continue.
Mark Graban: And so if you’ve got something that’s trending in a good direction, the trap people fall into is, you extrapolate that line. Let’s say if you’re tracking some defect rate in a product or infection rates for patients at a hospital and you see a declining linear trend line, that linear trend line almost implies, well we need to do nothing but wait and eventually that number is going to get to zero. I wouldn’t believe that hypothesis. Where a process behavior charts show, what I think is more typical reality.
Mark Graban: You may have a metric that’s fluctuating around an average, and then you do something that’s intended to reduce infection rates, then you’d see a step function decrease in infection rates. And the process behavior chart can help you tell, not just visually, but with good statistical validity to say, "Yes, we’ve shifted that number downward in a meaningful way. And it’s maybe not fluctuating around a new lower average, so let’s go improve the system again." And so that series of step functions I think provides a better prediction of what future infection rates would be or what the range of those infection rates would be expected to be.
Dr. Jeremy Weisz: And then also on what should people do, I love the guardrail, don’t overreact but have something in place. What else should people be thinking about when it comes to measuring success?
Mark Graban: I think there’s two dimensions. In the book I talk about two key questions. So one, the question that everybody usually asks is, "How is our metric performing compared to our goal?" So we might have a goal of how many new customers our software business signs up every month. We could take that monthly number, we can put it on our process behavior chart, and if our range of performance, let’s say, a mature business that is just fluctuating around an average, let’s say there’s not high growth, but the business is stable. That number might not be where we want it to be. So here’s the difference in behavior.
Mark Graban: So, instead of asking, "Why did sales drop last month when it’s within that range of the process behavior chart?" We should refocus that time on asking the question of, "How do we improve the business?" So it’s a less reactive, more systematic thought process. Why does a number of new customers fluctuate around an average of 20. That’s a different question than saying, "Why didn’t it fall from 22 to 17 last month?" That drop from 22 to 17 might be noise, meaningless fluctuation. And we can ask, "Well, maybe we should change our marketing efforts." And I think this is a more proactive, strategic, systematic way of thinking instead of just being reactive. Why did the number get better, why did the number get worse, let’s improve our business.
Dr. Jeremy Weisz: What was maybe an example where you were consulting with the company, you worked with a company and they did actually change something that they should have changed based on what they saw?
Mark Graban: In terms of changing the behavior or changing-
Dr. Jeremy Weisz: Yeah, the behavior or process, because a lot of times the majority of people are overreacting to the numbers. When was the time they actually reacted properly because you advised them to react properly?
Mark Graban: One of the key measures in healthcare it’s called the patient experience survey. It’s sometimes referred to as patient satisfaction. So anytime you may have been to a hospital or had an outpatient procedure, they send you a survey with a bunch of questions. One of those questions might be, "How likely are you to recommend this facility to others?" It’s similar to a net promoter score. But hospitals will obsessively track the percentage of patients who gave either a nine or a 10, and they will plot that percentage over time. That metric, like any metric, will be noisy to some extent.
Mark Graban: And the typical pattern in hospitals is to say, well that number fell from 88 to 82%, we have a meeting, we have a task force, we use various problem solving methods to try to explain the root cause of that drop in patient satisfaction.
Mark Graban: And then what happens before anybody can really do anything meaningful, the next month data point comes in, and now it’s back up to 89. And people may take credit for fixing the problem, and the more realistic answer might be that if we ask what happened, the answer is nothing. The metric fluctuated back. It’s fluctuating within a range. So one thing that clients have said is very helpful is teaching them to put those numbers, whether it’s a monthly chart or better yet, is to look at weekly data points so we can learn to stop reacting to or asking people to explain the noise.
Mark Graban: And then when we look at data weekly instead of monthly, we can more quickly detect a meaningful shift in performance. So when I’m working with organizations, they’re not happy to see it just fluctuating around an average. And almost always that’s what’s been happening historically.
Mark Graban: So, like last year I was working with an outpatient surgery center, where they knew one of the dissatisfiers, because there’s a different survey question about, if you were happy with your waiting time, and then you see freeform comments in the surveys where people tell you point blank. They weren’t happy about how early they came in or how long they waited past the appointment time. So we worked with the team using a lean methodology, as it’s called, to redesign aspects of scheduling, arrival, communication, trying to eliminate systematic causes of delays to patient flow.
Mark Graban: So the first thing that we could measure on a daily basis was the waiting time from arrival to surgery. And we had that in the process behavior chart. And guess what? Before our changes, that was just fluctuating around an average. And so then we made our improvement to the system and within days we could see a statistically meaningful reduction in the patient waiting time. So then the thing that we had to hold out and wait for was, "Okay, how’s that going to be reflected in the survey?" And we started seeing a statistical signal.
Mark Graban: So, our hypothesis, if you will, proved out if we reduced waiting time, the scores on the patient experience survey would go higher. So, that’s part of the methodology here. It’s, stop reacting to the noise, try to proactively or systematically improve the system in a way that shows, you could call it a positive signal in the metric. So as a team, we knew we weren’t going to overreact. We put this new process in place and the waiting time in the first day was below average. Let’s not overreact to that.
Mark Graban: But when we started seeing eight consecutive below average data points, that number eight is important, because when you look at the math behind the process behavior chart methodology, it’s very unlikely to have eight consecutive below average days occur randomly. It’s not exactly like flipping a coin, but think of flipping a coin. You could flip a coin heads five times in a row, six, seven, eight, nine, that becomes increasingly less likely to happen by chance. So process behavior charts and a handful of rules to help us evaluate those charts, help us stop confusing signal and noise.
Dr. Jeremy Weisz: So, Mark, is one of the things you help organizations to identify inefficiencies, and you have a hypothesis of which inefficiencies are going to move the needle the most?
Mark Graban: Yeah, that’s a good way of putting it. So, in the lean methodology, and a lot of this lesson comes from Toyota, we talk about root cause problem solving. And sometimes the examples that are used and [inaudible 00:19:57], the example I used in the book, just a classic example, is a relatively simple system where you keep asking why, and you find perhaps a single root cause to the problem.
Mark Graban: In a complex system, there’s rarely a single magical root cause. There’s multiple causes and lean, or in Six Sigma we might use something called the fishbone diagram to start brainstorming various causes. So if we were to step back and ask, "Why is the patient experience for lower than we want it to be?" There’s no single root cause. But we start identifying the different causes of dissatisfaction and prioritize those based on the various sub-survey scores. And we did something called the Pareto chart of looking at the types of freeform comments that patients were making. And then we talked about people who had a lot of experience in that surgery center, they talk to patients, they have a sense of what’s going on. So it was data-driven to try to prioritize, what do we think is the highest leverage point in the system.
Mark Graban: Now, second thing on the Pareto chart was people complaining about the building being a little bit run down. So that was a different level of problem solving to put a fresh coat of paint on everything, to fix chips on the floor, to replace some chairs that had gotten dingy. But we have to be careful in problem solving, if we change too many variables at the same time, it’s hard to prove cause and effect. And process behavior charts are one of those things that helps us test an assumption about cause and effect relationships.
Mark Graban: We did something, now what do we see in the metric? It’s possible you make a change to the system and you see the metric just continues fluctuating around the old average. Well, you might feel like that was a failure, but at least you learned something, and go and try something else that’s intended to shift performance in a good direction.
Dr. Jeremy Weisz: Mark, one thing I love about you when you talk in the videos is, you’ll say something, but it’s really deeply rooted in a lot of process and things. You’ll mention 80, 20 Pareto, you’ve mentioned did a video on Deming, and it’s just really cool to see that. And on this, you’re giving some great tips, but I do suggest people check out your website, check out the book to go a little bit deeper on these topics. I wanted to talk about, you did a lot of thinking around ER wait times. And I wonder if you’d talk about that for a second.
Mark Graban: So, that’s another key metric that hospitals measure. I’ve got a friend, I think he lives up in Winnipeg, and I’ve had a chance to go and teach and visit at a couple of hospitals there in Winnipeg. So the one dynamic, I’ve blogged about it, I think it’s also in the book, but every month my friend sends me a link to the local newspaper, and it’s not exactly every other month, sometimes two months earlier, you have a headline to say, ER waiting times are higher. Okay, that’s true. But what does that mean? And spokespeople and commentators and everyone’s speculating of why ER times were longer last month.
Mark Graban: Now, sometimes there’s a good reason, they’ll say, "Well, it’s flu season." So there’s seasonality in a metric like that. So, there’s just this constant up and down, and they’re comparing last month to the previous month, and they’re comparing last month to the same month to the year before. So sometimes the number is higher compared to the last month but lower compared to last year. What sense are we supposed to make out of that? But when you do, and actually the Winnipeg region started publishing line charts on their website instead of those two or three data point comparisons.
Mark Graban: And now you can start seeing a trend, and then you can overlay, a few years ago they had a very specific initiative intended to reduce ER waiting times. And you look at the chart, "Yeah, that seems like that actually had a positive effect." With or without the seasonality, you can see it was fluctuating in certain range, and now it’s dropped and it’s fluctuating in a different range. And you can apply the process behavior chart methodology to that to prove out how has it shifted.
Mark Graban: And then the other thing to look out for is performance creeping back up. Now, let’s say you’ve shifted performance down, people in the region and the hospitals should feel good about that. Now you have two months in a row that are above the new average. That might not be worth anything worth freaking out about, and it wouldn’t be worth saying, "Oh, our program is a failure now," Or "People are backsliding to their old way." That might not be true. And we make that error far more often when we’re looking at just two or three data points. Looking, again, a simple line chart or better yet a process behavior chart helps us better evaluate what’s going on.
Dr. Jeremy Weisz: Mark, I would love to see a new show. They say their thing about the statistics and overlay of you afterwards go, "Well, that doesn’t mean anything." What does that actually mean? I’m just thinking, tomorrow deaths are at all time high. Well, yeah, because you compared today to yesterday, and so if it’s one more death, today than yesterday, then they’re at an all time high.
Mark Graban: I’ve done this on my blog, here’s a news report, TV ratings for the Oscars are at an all time low. Well, that doesn’t mean, well, what went wrong last year? Well-
Dr. Jeremy Weisz: They blame the host?
Mark Graban: They are always blaming the host, or it was because of this host. And then they bring the host back a second year and ratings drop and then, "Oh, well, people must be tired of that host." That could be really faulty cause and effect analysis. [crosstalk 00:26:23].
Dr. Jeremy Weisz: So if the Oscars are listening, hire Mark to figure out why-
Mark Graban: Another thing that happens a lot is, somebody will report that metric is the lowest, but it’s been for three months. That does not mean it’s a statistically meaningful data point. Every metric-
Dr. Jeremy Weisz: Because of the guardrails. It may be still inside the guardrails.
Mark Graban: It’s within those guardrails. And any metric will have one data point that’s the lowest data point in that timeframe, doesn’t mean that it’s a meaningful statistical signal. Now, if we look at things related to COVID-19, unfortunately, now we’ll bring it back to a much more somber topic. If you look at the number of deaths per day in New York city, what’s been happening recently is surely a statistical signal, caused by what? The Coronavirus and COVID-19. Where if we were to look back a year ago, and you look at deaths per day in the city or the number of traffic fatalities in a week, metrics like that in the short term tend to be fluctuating around an average.
Mark Graban: Now, in the longer term there are things like seatbelts, and automatic braking systems, and different technologies that you might call a change to the system that would lead to a shift over time. I’ve seen reports that say traffic accidents are down quite significantly recently. I haven’t done a process behavior chart to see if it’s a statistical signal, but it might be reasonable to assume that, well, people are driving far less than normal, so it wouldn’t be surprising.
Mark Graban: So if you looked at a rate of fatal accidents per million miles driven, that number might be the same, or maybe that number’s a little higher. Is it a statistical signal? You don’t know until you drop the chart. So there are some metrics like the number of new COVID-19 cases that I would not expect to be fluctuating around a metric, they’re in a period of exponential growth.
Mark Graban: Again, my mentor on this, Don Wheeler, has shown, and there’s a little bit more complicated math, but you take something that’s an exponential curve, plot it on what’s called the logarithmic charts and it becomes a straight line. And then you can use a process behavior chart type method to detect when has the curve flattened. Because I’ve seen people overreact. There’s one day where the number of new cases didn’t go up, and people said, "Hooray, we’re flattening the curve." Well, maybe not yet. And then the data point goes up the next day, "Oh, we haven’t flattened it yet." I think methodologies like this can be applied in different ways.
Mark Graban: So one other quick example. A hospital that’s been a client of mine, that’s in a part of the country that has not really been hit by COVID-19 yet, they are charting the number of employees who call in sick every day. Because they don’t want to be overreacting to noise and that metric. And if they start seeing signals, then that might be something worth reacting to as maybe a bit of an early warning that a wave of COVID-19 patients might be coming.
Dr. Jeremy Weisz: Mark, last question, and thank you. Everyone should check out measures of success, and you can go to measuresofsuccessbook.com. Are there any other places we should point people towards outside-
Mark Graban: So, that’s a good place. And thank you anyone who’s toughed it out and is still listening. We’re talking about a very visual method. You have these charts in an audio which is difficult. You don’t see my body language, Jeremy can see, I’m waving my hands up and down to try to draw charts in the air, and maybe that’s not helpful to you, but-
Dr. Jeremy Weisz: You’ll just have to get the book.
Mark Graban: So what I mean, it’s there on the book. I blog about this a lot. So if people go to leanblog.org and do a search for the phrase process behavior charts, there’s a lot of information that I’m happy to make freely available. I’m happy if people buy the book. But this has been a passion of mine for going on almost 25 years. Because this methodology really isn’t that complicated, but it’s something that people don’t get taught about in school. Typically, they don’t get taught about it in their workplace. And so it’s been this passion to try to share something that I found incredibly helpful.
Mark Graban: And the other catch or challenge is that it’s arguably a solution to a problem that people don’t see. So, of course, I react to every up and down in the metric, what’s the problem? I’m being a data driven leader, I’m driving results. Well, it could be overreaction. So you can be a data-driven leader and drive people to improve the system, which would improve the average performance in that metric instead of just having people run around trying to explain every up and down in the metrics. So I would invite people, you can go to leanblog.org, and you could learn this methodology or see other examples of it in the book or elsewhere.
Dr. Jeremy Weisz: So Mark, my last question is, what’s something that people should start doing? I know you mentioned a few things, but what should be their next step after listening to you? Should they maybe think about deeper employee in their organization?
Mark Graban: I would ask people to start questioning when the news or somebody at work presents a two data point comparison. There’s a group in the UK and the national health service that has done amazing work in this area of using process behavior charts. And if you want to see what they share on Twitter, they use a hashtag that I love, hashtag plot the dots. If in doubt, plot the dots. And a line chart with two data points is not helpful either. So you want to go back and have 12, 15, 20 data points. I mean, sometimes people start the new year, and let’s say, at the end of 2019 they had a monthly chart that illustrated January through December. Then they start the new year with literally a blank chart on the wall. Don’t do that.
Dr. Jeremy Weisz: That’s funny.
Mark Graban: [crosstalk 00:33:06] 2020 is an extension of your business from the previous year. So don’t lose the context by starting the year with a blank chart. I mean, now that we’re into April, that’s not the most timely advice. But if somebody right now is running a business and they’ve got a metric that shows January, February, March, three data points, go add last year’s months on there, 15 data points will show you a lot more than one, or two, or three. So that would be maybe one quick piece of advice of something you could do right now. Plot the dots.
Dr. Jeremy Weisz: I just smile because I can imagine the stock market starting over each year. When you look at that, you look over a three year period. I mean, you see the trends, right?
Mark Graban: Well, I mean, I’m not a stock market expert, and I don’t know if these methods apply. This is not construed as financial advice, whatever these disclaimers are. But there are times where I just roll my eyes where you see, in normal times, the Dow was up 300 points. And the media always has to say it’s because of such and such. No, then the next day it’s down, 250, because of something. I mean, just stop explaining the ups and downs. Now, if we have a day where the market falls by 2000 points, that’s probably worth explaining. Do we really know the cause and effect? Maybe, maybe not. But that’s another way-
Dr. Jeremy Weisz: I’m surprised no financial organizations have brought you in to help them put in guardrails and analyze this stuff.
Mark Graban: Yeah, that’s a good point. I mean, I think people would probably use different quantitative methods. I’d be curious what would happen if you have a stock that seems to be just fluctuating within a range, I don’t know what lessons you would draw. You asked a good question though.
Dr. Jeremy Weisz: Well, you’re busy enough as it is with healthcare and all these other companies. But Mark, thank you. Everyone should check out measuresofsuccessbook.com and all this stuff he has on YouTube, his website, and Mark, I appreciate you.
Mark Graban: Jeremy, thank you. I appreciate it. Thanks for having me here.
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