Marketers and brands are scrambling in response to the “new normal” COVID-19 has created, responding to changing consumer needs by altering the way they do business. At Callahan, we have taken existing COVID-19 data from Johns Hopkins, the World Health Organization and the CDC, overlay it with consumer behavior data, and are identifying how coronavirus will influence consumer behaviors moving forward.

In this episode, Callahan’s VP of data strategy and marketing analytics, Zack Pike, and chief strategy officer, Jan-Eric Anderson, discuss how the overlaying of this COVID-19 data will help companies understand the current state of the virus, how it influences consumer behavior and how to use those connections to determine business decisions.

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Watch the video recording and dashboard demonstration from this podcast conversation

Links to additional COVID-19 podcasts:

What consumers expect from brands during coronavirus

How brands can make responsible coronavirus preparations now

Welcome to Callahan’s Uncovering Aha! podcast. We talk about a range of topics for marketing decision-makers, with a special focus on how to uncover insights in data to drive brand strategy and inspire creativity. Featuring Jan-Eric Anderson and Zack Pike.

Jan-Eric:

Hi, I’m Jan-Eric Anderson, chief strategy officer at Callahan.

Zack:

And I’m Zack Pike, head of data at Callahan.

Jan-Eric:

Zack, it’s great to see you again. Usually we sit a lot closer to each other than we are right now, but we’re holed up at home and trying to make it work in isolation in light of COVID-19, so thanks for finding the time to hop on the interwebs.

Zack:

Absolutely. Yeah.

Jan-Eric:

I know that you’ve been doing some really cool stuff with the team related to COVID-19 data, and I wanted to just catch up with you to see what you guys are doing and how you think it’s useful. So maybe let’s just start with, what are you doing? You’re pulling info in and some data in on what’s happening with the spread. Is that right?

Zack:

Yeah. Yeah. With all the stuff that’s going on, obviously everyone is trying to figure this out. The problem with that is that we’re reliant on all of these medical agencies, the individual counties, states, cities to report their data out. So it took us a little bit of time to find a reliable source. But actually Johns Hopkins has been working on this as well. We were able to hook into one of their data sources that uses the World Health Organization for all of our global data and then the CDC for all of our local stuff. But the cool thing about this data set is also tries to take into account local news reporting because there’s a delay in reporting for several factors and there’s really not a strong standardization in that reporting yet. So that’s the data source that we’re using, which is one that I think a lot of data science teams and analytics people are using right now.

Jan-Eric:

This is reminding me of another conversation I think you and I have had, which is around the vast amounts of free data that is available from third party sources that can be brought in and used for analysis, so yet another example of that. So your intention is not necessarily to try to be the reliable source of reporting for the world. The world’s not going to be coming to Zack Pike to understand what’s going on with the number of coronavirus cases. So what’s the point? Why are you pulling this info together?

Zack:

Yeah, well, so it started … I mean, my team was obviously very curious to dig in because this data provides additional information. I mean, it’s at the lat-long level, so it gives you a lot of geospatial type info, but it also tells you gender and where they think the infection actually originated from for the patient and all kinds of cool stuff.

Zack:

So it was a little bit of curiosity, but also there’s definite application to our clients. So we have clients in the restaurant industry, we have clients in the retail industry in all types of retail and knowing where the virus is, how quickly it’s spreading, when new cases are popping up, can, our assumption is, have big impact on business results in those areas. I mean, we’ve all seen the news on what’s happening with restaurants going to mostly dine out only type of transactions. Different types of concepts are set up differently for that and we’re still digging through the data, but our assumption is that in areas where you see hotspots of this virus activity, you will also see disproportionate impact on sales results. That’s just a hypothesis though. It could prove to be that it’s the same everywhere across the country because everybody’s seeing the same news. We’re all watching the same channels.

Jan-Eric:

So saying another way, as we may be in Kansas City, as we’re seeing information about much higher levels of numbers of cases and subsequent death rates happening, for example, in Washington state, we’re seeing those headlines here in the Midwest, that may be impacting our own behavior even though that’s not necessarily right in our backyard. Is that kind of what you’re saying? There’s a hypothesis that what’s happening locally may be influencing local consumer behavior, but we are all in this together. We may find that actually national news is impacting local behavior as well.

Zack:

Yes. Yeah. You could definitely say that. I think one of our other thoughts with this was if we could start to get a measure on velocity of growth of the virus in certain areas, that may help some companies or some clients get out ahead of what’s eventually going to happen. If you think about a restaurant, if I’ve got restaurants all over the country, some areas are impacted much heavier than others in New York or the Bay Area, I’m already shutting things down because I have to because the government’s making me, but in other areas of the country I’m not. And if I can see OGs in, let’s just say Kansas City, the growth rate of infection is running at the same pace as the Bay Area or New York, I may be able to get ahead of what the government’s going to make me do four or five days, right? And those four or five days could be critical to messaging and adjusting media budgets and just making sure that we navigate this as smoothly as possible.

Jan-Eric:

In your process, generally, I think what you’ll do is you’ll try to get data, relevant data, get access to relevant data, get it structured in a way where you’ve got it within the intelligence platform, the tool that you use, you’ve got it structured in a way that you can start to dig around and play with the data, look at it and through different views to really create some understanding about what’s going on with that data set. Your intention, though, as we’ve been saying, is not necessarily to be the furthermost or the best resource or the most advanced expert on coronavirus, but to understand where it is and how that’s shaping consumer behavior. So have you started to overlay client information or other relevant shopping information or consumer behavior information on top of coronavirus? Have you gotten to that yet? Are you still in the stages of collecting the coronavirus data?

Zack:

Yeah, we just found this data source on Monday, few days ago, and it took us a while to … because of course like any publicly available data source, it’s not clean and it wasn’t in a format or didn’t have the relevant data appended to it that we needed to connect it to that sales performance data. So if you think about sales data, usually the geographic breakdown you have for that at the most minute level is zip code. Well, this data set didn’t include zip code. So we have to take lat- longs, align them to zip codes and do some behind the scenes stuff to get there. But that is the next step is to take the current data that we have and start looking at that up against this virus growth type data.

Jan-Eric:

That’s fascinating. And so then when you start to overlay additional data that you have on top of COVID-19 data, you start to see