Odd Lots - How Traders Used Google Searches To See The Economic Recovery In Real Time

Episode Date: September 17, 2020

The use of so-called "alternative data" has been gathering attention for some time. Investors have been looking at things like credit cards or satellite photos of Walmart parking lots for insights into businesses before earnings or official government numbers come out. But during this crisis, alternative data has really come into its own. The speed of the crash and recovery happened so fast, it was clear that traditional numbers weren’t timely enough to get a read on what was going on. On this week's episode, we speak with Ben Breitholtz of Arbor Data Science, who explains how he's been able to monitor thousands of different categories of Google Search queries to know instantly when the recovery started to happen and what sectors of the economy were leading the way. See omnystudio.com/listener for privacy information.

I'm June Grasso, inviting you to join me for the Bloomberg Law podcast. Every weekday, we help you make sense of the legal stories that shape the nation and the world. Listen for complete analysis of the biggest court cases, the latest actions from Congress and regulators, and the legal moves driving the markets, from corporate law to constitutional law, and from state courts to the Supreme Court. At Bloomberg Law, we go beyond the day's headlines. We speak with top attorneys, judges, scholars and policy experts to break down what the rulings really mean. We do this every weekday,

then bring you the best conversations in our daily podcast. Search for Bloomberg Law on YouTube, Apple, Spotify, or anywhere else you listen. On the East Coast, listen as you start your day, and on the West Coast, catch up in the evening. That's the Bloomberg Law podcast with me, June Grosso. Subscribe today wherever you get your podcast. Hello and welcome to. another episode of the Oddlots podcast. I'm Joe Wisenthall. And I'm Tracy Allaway. So I don't know what day people are going to be listening to this episode, but, you know, the stock market hit a record high yesterday. Yeah, it's true. So all the losses that we saw during the COVID crisis have basically been erased and markets are back where they were

before all of this happened. Yeah, it's essentially six months. from the pre-crisis peak to this one. So I think the S&P peak on February 15th. And then we saw the new peak yesterday, August 18th. And in a sense, it really feels like we've compressed this sort of gigantic cycle into an extremely short period of time. Yeah, that's true. And I was looking at the latest fund managers survey from Bank of America. And it showed that I think fund managers have completely flipped from thinking that we're in a recession to thinking that we're in the early stages of a fresh economic cycle. And if they're right, to your point, it does suggest that we've just seen, you know, one of the shortest recessions of all time. Yeah, I mean, you could make the

argument that the recession recession in terms of the shrinking of growth was done by the end of March when most data points started turning up. And while the overall level of economic activity is still very depressed. And of course, unemployment rate is still above 10%. So hardly time to be declaring victory. We have seen steady improvement on a host of economic data points, basically since end of March, early April. That's true. But I also feel like there's something kind of weird going on with the data. Like there's the old stock versus flow argument, which we're seeing everywhere, but particularly in PMIs. So even when we get a big rebound in PMIs, it does, like, necessarily mean that we're getting back to the levels that we saw pre-crisis. But you're also

seeing just sort of weird indicators that are happening simultaneously. And I think one of our colleagues pointed out a really good one recently. And that was intentions to buy a house surging at the same time as mortgage delinquencies, which I mean never happens in an economic crisis. No, it's really weird. But I think because of all the weirdness that we're seeing, this sort of contrary indicators, because there's this weird gap between paces of change, which have been very fast and unexpected versus levels, which are still at very bad levels. And then also just the fact that it's so compressed, there's probably never been more demand for sort of alternative real-time data points in this feeling that the official economic

data points that we get, monthly jobs report, monthly retail sales report, they just, there's not enough of them. they're not timely enough to get a sense of what's going on, given how fast the changes have been both on the downturn and the rebound. Yeah, absolutely. And I mean, just on a very simple basis, everyone wants to know what's going on with the recovery, right? And everyone's tracking to what degree the economy has reopened. And some of the most useful indicators for that are arguably alternative economic indicators like open table reservations, things like that. Yeah, totally. I mean, that is like one of the things. we've been watching the most. It's like open table, they can keep track of people making reservations or doing inceasing dining. So if you want to sort of understand how behavior has changed or how people are doing different things due to the virus, that's been one of the sort of key data points,

not something that people were really tracking before as far as I know on a meaningful level. So I think there's really important. I mean, I think obviously real time alternative data has never been more in demand than what we've seen over the last six months. But I don't think it's going away now. It's kind of another one of these things where real-time data points of all range of things will sort of be part of the conversation for a long time, even if and when we get back to something resembling a normal economy. Yeah, I think that's right. So today we're going to be talking all about alternative data, what it's showing, and more importantly, how investors actually use it in their process. And so we're going to be speaking with Ben Brightholz. He's a data

scientist at Arbor Data Science, which is part of Arbor Research and Trading. I've been following their stuff. They do some really interesting things with looking at Google search trends for lots of different keywords and trying to define an economic significance from them. So let's talk more about that. Ben, thank you very much for joining it. Yeah, thank you very much, Joe. Happy to be here. So let's just start big picture. What do you do? What is Arbor data science? Talk to us a little bit about your work. Sure. So over the years, we've gotten more and more into essentially this idea of filling the gaps between latent economic data and econ data that can be distorted like we've seen with unemployment data as of late. And also really trying to help our customers and the investment space in general deal with service. surveys that have been more or less leading indicators for quite some time, they've kind of fallen flat on their face. And this is something that's taken place well before even the current

episode we're going through now, looking back to around the financial crisis with really the polarization of the country and the world on a political space. And really, the advent of social media has created really this bifurcation in sentiment. It could be Republican Democrat or it can be more or less groupthink based on the use of Facebook, Twitter. We create all these small microcosms we essentially live within. And that has ultimately distorted the ability of survey data, for example, to have this leading nature that it used to have really for decades. And that's posed a significant problem for investors that are inputting this either on a subjective level or within their own modeling to then project forward where they think financial markets will go in the future. I have a really basic question,

which is what's the difference between big data and a large set of data? So big data is such a misnomer and nasty term. You know, most, I think big data is a term that's kind of slowly gone away. I think the initial idea is that it's unstructured data that's, for example, you can find all this wonderful information on the Bloomberg terminal. All right. And you can download it via an API or access it via your, via your windows or your terminal, all nice, clean, and easy to use, ready to input.

And big data, to me, this day and age, especially with alternative data, has to do with more or less unstructured, kind of ugly data. So this, for example, could be all just like us talking right now, or when you are all on TV, you have all of this text, this closed captioning that exists out there. And let's say, for example, it's in 15-second increments. And it can be ugly, it can be, have plenty of errors with a number. the data within the closed captioning. And essentially, we have to use algorithms and different processes in order to take that unstructured data and make it something useful and really turn it

into something that's more or less numerical in order to benchmark against financial markets, econ data, overall sentiment, and so on. So, you know, big data is kind of a word, I think, that's somewhat going away. But to me, again, it means somewhat of an unstructured data set. So I'm thinking about what you described as the problem with surveys. And I think it's either the University of Michigan Consumer Sintimate Survey or the conference board one. There's one of these data points. We have it on the Bloomberg terminal. And it's like they say, oh, it's now a good time to buy a washing machine.

It's now a good time to buy a car. There's even one that's one of my favorites is now a good time to buy a vacuum cleaner. But I guess what you're doing is you don't have to ask people. is now a good time to buy a vacuum cleaner because in 2020, if you know how to find the data, you can just look at searches for vacuum cleaners and that's presumably a lot more reliable than asking people in the survey whether now is a good time to buy a vacuum cleaner. Right. So within surveys, there's and there's plenty of studies on this as of late, showing that respondents will not provide really honest answers relating to their financial

hardship. So there's, there's large gaps in, you know, are things better now or worse? Are you going to spend, do you have the money to spend here moving forward on a vacuum, on a new wash machine, and so on. And there's always been a gap, for example, example between the web-based responses and phone-based. And we saw this, too, with the election. That's a whole other topic. But on a web-based survey, individuals are typically much more honest than they are regarding financial hardship than they are on the telephone or basically being put on the spot. So the idea here behind search activity, and this is something that I think that is improved in most recent years, is yes, we can get ahead of this intention of consumers. And we're not necessarily, we're not really going to lie to that little window on Google. We might lie maybe sometimes to our girlfriends or our boyfriends or husbands or wives.

But what we put into that search window is really truly what we're seeking and what we're actually trying to query. There's no one really looking over our shoulder. So our belief is that search activity really over the past five, six years has become kind of a great estimate or indication of the consumer's intentions of what they plan to do. Am I going to buy a wash machine? or if I'm in distress, what does it mean if I default on my credit card payment or I don't pay my credit card payment? Or what if I need to go out and search and find a bankruptcy lawyer? These are the type of things we can pick up on within this information to then create a kind of, you know, overall look at the consumer. And this can be all the way from the, you know, up towards the United States, the complete, you know, country level.

It can be worldwide and it can be drilled down all the way down to a metropolitan area. And again, the whole idea there is trying to get the most honest representation of the individual. And I'll also say that the growth in the Internet and really access to the Internet, both mobile and on the PC, has been a big boon for search activity. So you now have 59% of the world having Internet access and using it on an active basis. It's more than 4.5 billion individuals, which has really doubled, if not tripled, since the financial crisis. I think early efforts of using search activity is, I know a lot of it, pre-crisis, kind of fell on its face and kind of faded away. Google used to have these curated indices. I think they had 25 of them kind of showing how the economy was moving here and there.

I think that that, what didn't work as well, because we didn't have the ubiquity of Google searches and really Internet access. And as that improves, this type of information becomes that much more important, I think, the investing process. How much do you think the unusual or the extreme circumstances surrounding the coronavirus crisis are distorting survey responses? And I ask that because, again, I've seen a lot of criticism of the PMIs recently. And one of the things people are saying about those surveys at the moment is that respondents aren't really judging their experiences on a month-to-month basis, but they're sort of responding by comparing now to a period of relative normality. So everything's getting skewed. Do you think the unusualness of our current circumstances

might be skewing survey data as well? Yes, I think so. I think it's a combination, like you said, earlier with stock flow. It's what type of reaction have we had over the past couple months? I think has been more reflected within the survey data, and we're seeing that breakdown between search activity and surveys. And we also have this big group think, or almost circular reference that occurs within a lot of the sentiment data. So we all look to the equity market. We all know that we can use the equity market to essentially forecast where consumer confidence will be for the next month. And I think a lot of that's feeding into some of the more rosy consumer confidence numbers, as well as the PMIs. And again, that's somewhat of the distortion in why we seem to like to

rely on this search activity for the most part. A lot of short daily news podcast focus on just one story. But right now, you probably need more. On Up First from NPR, we bring you three of the world's top headlines every day in under 15 minutes. Because no one's story can capture all that's happening in this big, crazy world of ours on any given morning. Listen now to the Up First podcast from NPR. So let's talk a little bit more about that search activity. How do you take, how do you get the data, first of all?

What does Google make available? And then how do you present it in a form so that it's usable because there's obviously seasonality factors. You know, you can't just look at searches for a vacation and see whether they go up or down because people don't vacation at the same pace all year round. So how do you get the data from Google? process like and then what do you do to actually put it in a format such that it's not just noise for investors like just describe it overall how it works sure so we are able to access just like anybody

else via Google trends which there is an API to be able to grab that information and what we do is we avoid using the specific search term so if we're just going to say wash machine or vacuum that will include specifically that exact term. And we know that there can be multiple variations of those actual text terms. And so we want to pick up on that. The beauty is Google curates and creates two different types of groupings of search activity. And they do this for each and every country, essentially, which is going to take care of the major language barriers and issues that we'd run into as well.

And so that is they create categories, which there are roughly 140 plus different. different categories, everything from accounting services all the way out to urban transportation, which would be things like Uber and Lyft. And then they have topics, and that can be anything from inflation or those talking about disinflation or gold bugs or Bitcoin. And that's going to then be more encompassing based on their mapping of numerous new, it can be hundreds, if not thousands, thousands in certain cases of different search terms and phrases that then get housed underneath those individual topics. Can I stop you and ask you a quick question right there?

Sure. The data that you're able to draw, just to make clear, is that the granular within those hundreds or thousands of terms, you're able to get data for each one of those. You can see beyond just the sort of general category. Yeah, so we can drill down. There are ways to drill down within the individual categories so we understand what the actual searches are within those categories. Yeah.

But in order to create a more. all-encompassing indication of what the consumer or business is looking for or thinking about, we do then pull in that search trend, essentially, that's going to be an aggregation of all of all those searches underneath a given topic or underneath a given category. And like I said, one of the greatest things about the way that Google sets this up is that you are then able to say, let's look at urban transportation, Uber and Lyft. And let's look at it not just here in the U.S., but let's go to somewhere like Germany, let's go to Australia, or let's go to Japan.

And they take care of, fortunately, a lot of the language barriers in that urban transportation that is translated into Japanese or whatever is being German and so on. So getting into the crux of how we then digest and use that information, like you said, is there's a high degree of seasonality, of course. It can be like with clothing, with back to school, or it can be accounting services coming into March, April, and October. So we do decomposition where we'll break down each individual topic or categories, search activity into three components.

And that is its overall trend component. You can think of it as kind of a slower moving average trend of that search activity. And then we have the seasonality that we're able to then strip out. And then we also have this thing. we call the residual or the shock. What's interesting about the experience that we've seen here with COVID-19 is we were never so interested in the shock component and the very quick shifts in search activity, either positive or negative, until COVID hit when we saw it's just substantial breaks from these

trends and what would be expected by seasonality. That can be anything from the searching for fiscal policy news, economic news, how individuals are searching on the line then for groceries and making those type of consumer staples purchases. But getting back to it, the idea is to break it down into those three components. So we get an idea of what is the long-term trend and shift really potentially in search activity. How does that relate then to what we're seeing within financial markets and overall economic data? And then what are these shock components regarding those big distortions or shifts away from those underlying trends? what does that have to tell us about how things may be abruptly changing in the near term

and what that could mean, of course, for potential volatility in equity markets, uncertainty in general from the consumer base and so on. And so what we do is we pull down those three pieces of information. That then gets used within our written content as well within our own models and our clients models and so on. So correct me if I'm wrong, but the data that you're using is, mostly public data. If investors all have access to the same data, how are they using that to actually generate outperformance? How do they differentiate how they're using the data versus how another fund or another investor might be using the data? Right. So, I mean, that's the question we probably get the most is since we do deal mainly, again, with public forms of data,

there's plenty of alternative data that is private in the credit card space and spending and so on. is we tried to uncover data we think that is underutilized. And in this case, with all of our dealings, specifically with fixed income portfolio managers, pension fund managers, and the like, the use of search activity on a broader scale, on a country by country, even a metro by metro level, we believe has been underappreciated

and non- internalized to the extent that it could be now, like you said, once something like this gets overused or gets used as a key benchmark, potentially to filling the latent gaps between economic data, potentially some of that alpha creation could evaporate. And that would mean we'd have to move on to some additional data sources. For this time being, in all our communications, the front offices of investment managers, banks, and so on, have not been heavy users of this search activity. I think that our early uses

of it prior to the crisis and during the crisis kind of fell flat. again, maybe the ubiquity of actual internet usage and those young to old that were using Google was not there as of yet. And what we've seen over the years, really since 2011, 2011, 2012, search activity's ability to fill the gap and really take the place of surveys has improved markedly year after year. And that's something we can measure statistically and via modeling for essentially those turning points as to when maybe search activity loses its flare, loses its ability to then forecast and now cast via GDP, retail sales, inflation, and the like. But we're not there yet. So obviously the demand for this data.

And you mentioned maybe search data is sort of relatively newly being incorporated into investment processes. But for years, we've been hearing about satellites looking at parking lots in Walmart or satellites looking at trains or credit card data that's been out there as a thing for a while. How intense is the search, basically, for new data sources, either on the buy side, the investor side, or you as sort of a data vendor, so to speak, to just constantly be coming up with something that's relatively underappreciated. What does that process look like? The use of alternative data within the investment world, really the investment world was very late to using alternative data, you know, compared to healthcare, even education, and the like. And we initially saw this, you know, in our routines of going out to big banks, for example, and discussing with their teams, you know, how they're utilizing alternative data.

It was almost always in the back office. So it could be anything to do with their customer relations. It could be chatbots in terms of creating natural language, natural language processing, and pulling the data for that. It could be trade matching, all kinds of different things that were done in the back office. They were trying to basically bring in machine learning, bring in better data to create better predictions. And it could have to do again with their customers,

which customers to call, not call, who's going to potentially provide the best avenue for new business. And so on. But what we've seen, I'd say, you know, starting roughly in 2017, 2018, we started to see a, with the advent of more alternative data available via numerous vendors, the increase in transfer to the front office has happened rapidly. And I would say that now with COVID-19 and the inability for econ data to keep up with the actual happenings of the economy and really the needs of investors to understand and adjust what's going on with how rapidly things are changing. The demand is just intense.

And so it calls to us and calls, I know, to many of our competitors and similar alternative data providers has just shot to the moon. And you can see that again. Bloomberg, of course, offers some of this alternative data. There's plenty of other repositories to grab it. But I would say that the degree of interest has increased tenfold since it's. It's beginnings in 2017. The news doesn't stop on the weekends. Context changes constantly.

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to the podcast. That's Bloomberg this weekend. Saturdays and Sundays starting at 7 a.m. Eastern. Make us part of your weekend routine on Bloomberg television, radio, and wherever you get your podcasts. What's been your favorite alternative data set during the crisis? What has either surprised you or what has been most useful in judging the direction of the overall economy? We've been benchmarking a lot the mobility data that's available via Apple and Descartes Lab is another one. Google and benchmarking that off of search activity. And I've been absolutely shocked at how well search activity has been able to predict two things.

And that's been retail sales on a month-over-month basis and also inflation on a month-over-month basis. A lot of our kind of point forecasts looking forward based on what we believe are the most unique and important search activity have done very well in predicting the rebound in May. For example, the heavy damage done to transportation, energy, and apparel within March and April to CPI, for example. We had noticed the heavy degree of rebound in all three of those categories in particular within apparel, which ultimately led to an 180% rebound in a overall apparel spending in May, which then ultimately translated to a higher inflation that was reported in June. And so the search activity that we've been able to use most utilize, which I think

Joe featured in a chart a number of weeks ago, has to do with a series of key categories. And that can be everything from beauty and fitness, which is we found to be a highly leading indicator, as well as just the general public searching for economic news and fiscal policy news revolving around welfare and unemployment and jobless benefits. Welfare and unemployment itself has been a highly leading indicator. And then also, one of the things we picked up on very early was the incredible drive for home improvement that really began in the final weeks of March. And what we had seen was this fervent search activity, looking across all the major metros and all the major states of the United States, a heavy degree of need for, or not need, a desire to replace

appliances, to paint their homes, to get a new roof, new siding, new carpeting. And this is something that really took place ahead of the CARES Act being signed on March 27th. It began really two weeks before that, which I think was a leading indicator that the consumer would be stronger and potentially spend more than those, the naysayers and that we had expected to see given the calls for a recession and potential depression, given the full stop to the economy. And it's really striking just this week. We've seen Home Depot and Lowe's post-extradinary sales, home improvements, just been one of the monster stories of this recovery, how much spending and how sustained that's been. I just want to drill a little bit further down. I mean,

it's clear that like, okay, if someone identified that trend at the end of March and saw what was going on, there were huge investment opportunities. Because like, again, like I said, Home Depot lows, et cetera, huge beneficiaries. Their stocks have been extraordinary runs due to this desire for people to like renovate and fix things in their home while they're working from home and so forth. How then do in your clients and when you talk to them, how do they actually make a decision buy or sell based on the data and the context that you're giving that. What is the, you know, that's sort of the last mile question, so to speak. They can get the data from you, but then how are they actually using it to form a view

and take a risk? Both on a subjective and also on an algorithmic basis. We have many, many clients that are effectively now casting. And so they're now casting the econ data, the econ environment, and then as well, the impact on the actual financial market in terms of producing their own actual forecasts of where things will be one week to six weeks to 12 weeks later. So the search activity is one that we found provides a lead time that's more kind of medium term as opposed to ultra high frequency short term. So within the searches just like survey data, we're not going to be able to help

someone effectively make a decision for that day, you know, what is the next 24 hours of economic activity? Are people buying more watch machines? They buy more cars. Are they buying more apparel? That's not exactly how it works. It's a more medium term focus of varying lead times, typically from one week to eight weeks. So we have things, for example, like apparel that will have a lead time of days to a week. And then we'll have things like building materials or roofing that will have a lead time of seven to eight weeks. So then what our customers and our clients are doing is taking that information in, understanding those lead times,

and then either inputting it to their own subjective decision-making process in order to affect their decision. It could be a risk management one in regards to their actual book or their position to determine if there's something that could be disruptive to their position, or it could be on the flip side, someone that's actually using it on a more tactical basis that is then inputting it to their own now casting forecasting process and then coming up with their own conclusion of how well that supports or doesn't support their general idea. But with this data, along with a lot of the natural language processing data that we work with, does not have a high frequency basis.

This is something that's more medium term, if not long term, in nature. Ben, last thing, like, where do you see? What's the next big thing for you in terms of, I just thinking back to when you said, okay, at some point the search data will get more used, it'll get more commodified, the alpha from having access to it will theoretically diminish. What are the next frontiers in terms of data that you think are interesting and potentially still underappreciated or underutilized at this point? So I think the advent of mobility data, for example, with like Descartes, Descartes labs that's able to zero in on specific retailers and look at the actual foot traffic

that's occurring coming to them, going away from them. It can be also down to parks and specific locations within different metros or rural areas. I think this mobility data, which we don't have a high degree of historical data to work with, is something that moving forward will become more and more of the leading indicator that they can vigils will seek for. Unfortunately, Apple, Google, and Descartes Labs have, you know, they sell this data so it's not necessarily publicly available. But I think as we build a larger and larger track record in order to benchmark that against anything, it began search activity, survey information of how consumers are operating where they're moving and what they're doing, I think that is more or less the kind of the cutting edge and leading edge of understanding the consumer

and then how they're interacting with retail, interacting with people around them, using urban transportation, and so on. And obviously, in this environment of COVID-19, with how much we were not moving around in March and, April. I think it'll be critical here moving forward to get a better grasp on how much of a revival economies are seeing and how mobile people have become or it will be. One of the fun ones we didn't talk about that I know it's definitely fringe too, just because it's such a strange space is that the Twitter sentiment is one that's become, I think, more and more useful in terms of gauging actual investor sentiment. It's been pretty wild to watch the number of accounts. And even formal central bankers that have popped up on Twitter that use it pretty voraciously.

We even have like Christia Freeland. You just took over a finance minister in Canada. There's just such noteworthy individuals. And it's become something that's become more and more predictive, I think, not necessarily of the direction of equity markets, but more or less a gauge of uncertainty and, you know, financial market volatility. So we've built a lot of algorithms to. Is this like another thing where it's kind of like, because I know people were like interested

that 10 years ago, but there probably wasn't enough interest. There weren't enough people on there for Twitter or social media to be representative, but sort of like kind of like search where you can actually get a big enough cross section that it's meaningful. Exactly. So that's the, what's absolutely wild is the number of people providing original content and the speed by which they are actually tweeting has accelerated just demonstrably. So we saw this incredible crescendo in Twitter activity in FinTwit through really the middle of March. And it's just held there ever since with this COVID pandemic, everyone at home and really grasping for information.

So it's been fun to be able to break down all the different components of Twitter, which we do into is based on clustering prior to the financial crisis. We break it down into permables, bears, pragmatists, economists, and the like. And then able to grab out, you know, how are they? feeling about liquidity in the market how are they feeling about the equity market COVID-19 the consumer and so on and it's amazing pulling in the information like you said prior to just three or four years ago its ability to actually get ahead of and forecast you know volatility and maybe a little bit of financial market direction is is improved significantly so it's it's been an

interesting space to dabble into and that was great Ben brighthold really appreciate you uh joining us This feels like such a big area. There's such a clear explanation of how it all works. So thank you for coming on an oblo. All right. Thanks, Joe. Thanks so much, Ben. It was really interesting.

I thought that was great. You know, I do feel like just from us, from a media perspective, we've never used alternative real-time data as much as we have over the last six months. And so I thought it was great to hear how it's actually collected and then how it's actually used to put into an investment process. Yeah, it's funny, like, thinking back to this now, but I remember in, I guess it would have been February telling someone about how we were tracking movie bookings in, or theater bookings in South Korea because of the COVID outbreak there.

And the person I was telling it to just thought it was like so unusual and so amazing. But of course, now, everywhere around the world and especially in the U.S., people are looking at all sorts of those kind of things, from restaurant bookings to the mobility data that Ben was talking about, it's kind of become normal. Yeah. And I'm really fascinated by the sort of, you know, the speed with which sort of alpha deteriorates. You can imagine the first person who really discover is that search indications for certain terms has some predictive value. There's a lot of money to be made in that. But look, I mean, we're talking about it on the podcast and Ben's active and Pretty soon you have to figure that'll be table stakes and people will be searching for the next thing,

that that is a process that will essentially never stop. Yeah, I think that's right. But also I think what becomes clear from speaking with Ben is that understanding the data, how it's collected, and how you can actually apply it is really, really important. So even with something like the mobility data, it's very useful at the moment, but I think it's benchmark to early January or something like that. So it's really good to be aware when the summer comes around that the benchmark that you're comparing the data to might not be completely applicable to warmer weather.

So there's all these quirks in each data set that you really have to get to know. Yeah, totally. I mean, even with the Google data, just having the sort of experience to adjust for seasonality, takes, those are all things that if you were to say, if I were to just look on Google trends and look at vacations, it would be hard for me to get much signal unless I really like understood the data and had experience working with it. Yeah, exactly. All right, shall we leave it there? Yeah. Okay, this has been another episode of the Odd Lots podcast. I'm Tracy Allaway. You can follow me on Twitter at Tracy Allo. And I'm Joe Wisenthal. You can follow me at the stalwart. And you should follow

our guest, Ben Breittholtz, on Twitter. He posts tons of interesting charts from the Arbor Research work that they do. Follow him at Ben Breittholtz. Follow our producer on Twitter, Laura Carlson. She's at Laura M. Carlson. Follow the Bloomberg head of podcast, Francesca Levy, at Francesca Today. And check out all of our podcasts at Bloomberg under the handle at podcast. Thanks for listening. I'm Francie Lacqua, an award-winning journalist. And I've got a new podcast. Leaders with Francine Lacroix from Bloomberg podcasts. I've interviewed everyone from Heads of State to fashion icons about the news of the moment. But I've always been curious who are these people as leaders.

I don't think there's one right way to be a leader. Make decisions. A poor decision is always better than no decision. Listen to new episodes every other Monday. Follow Leaders with Francine Lacroix wherever you get your podcasts.