Ep. 71: Industry Trends in China’s COVID-19 Recovery with Mu Chen
Transcript
[0:00] Hi Tech Buzzers! As always, we hope you are healthy and safe. Amidst the continued global pandemic, and during this particularly chilly time in the U.S.-China relationship, our thoughts go out to those of you who have been personally affected, as some of our friends have. Please stay well, and take care of yourselves. In line with our summer of experimental episodes, today’s segment consists of a lightly edited version of a webinar that we hosted last month, which we had curated for investors, but left open to all audiences. In it, speaker Mu Chen of data intelligence firm BigOne Lab shared some thoughtful analysis surrounding data on digital and consumption trends in China post-Covid. Well, as post-covid as it gets these days, with the risk of new outbreaks at any given time. Have a listen, and send us some feedback!
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[1:27] For example, as you may know, we are working on an e-book on the most talked about Chinese internet company this year — Bytedance. In addition to our regular Extra Buzz newsletter installments, we’ve been publishing regular content on the company that includes translations of CEO Zhang Yiming’s interviews and most recently, a presentation from Kelly Zhang, ByteDance China CEO, on how Douyin, AKA TikTok in China, got started. Did you know that early creators helped co-create features and campaigns that made the app successful? Read these gems — and our commentary on them — by signing up for our weekly newsletter.
Absolutely. Go do that! But for now, are you ready to hear about how China’s economic recovery has gone? We’ll cover sectors such as delivery, recruitment, logistics, travel, online spending, and gaming. Let’s go!
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[4:37] Hi, everyone, thanks for joining. My name is Mu Chen. I’m the Founder and the CEO of BigOne Lab.
Today I’m sharing a deck which we prepared for some of our clients, that starts tracking the impact of Covid-19 and post Covid-19 since February. It’s been read by many of the leading private equity and venture capital [firms] across the globe to understand what’s going on in China.
Before I share the content, just some brief introduction about our firm. We are an alternative data firm that’s based in Beijing. Our goal is to build an intelligence gateway for China, as we realize there’s a lack of such gateways when it comes to understanding the economic activities and the financial activities in China.
[5:41] A little bit about our team: I used to work in the U.S. A few years of banking, and then switched from the evil side to the bright side, to a startup. I joined a startup in New York called YipitData, and spent a couple years there to build products, basically data trackers of public companies listed in the U.S., and provided data to U.S. investors. That’s when I realized data has been growing in China, due to the penetration of the Internet into the economy.
And I came back to apply different methodologies to collect the data, analyze the data, prioritize the data, and make it easier to digest.
Most of our teams are tech people. We continuously source and explore different kinds of data sources in China. We found out that as the internet economy is growing, a lot of data has been generated, different types of data. So far we have discovered about 20 types of raw data that can be used for research and analysis. And we build our algorithm on top of that raw data, and turn that into different kinds of metrics in our research team.
[7:00] Today we are sharing trackers to help you shed some light into the impact of COVID-19. This has been a popular dataset being subscribed to by our clients, because one, it helps you understand what’s going on in China; and two, it can be used as a reference for what will happen in the U.S. economic wise, especially during, I would say, the initial period of the outbreak.
I’ve prepared four parts. First part is Meituan versus Ele.me. This will shed light into one, the food delivery service, second, general consumption and local businesses.
And the second part is the job market. We are tracking the recruiting for different firms across four or five of the largest online recruiting job sites in China.
[8:01] Logistics and travel would speak to the underlying economic activities.
And lastly, I will compare the offline consumption versus online consumption.
Generally, the takeaway or observation is that things are back to 80 percent, 90 percent, of pre-outbreak. However, we are seeing some longer term structural impact of the outbreak in different sectors. People are buying different things. People are buying things differently. People are going online to play more games, watch more videos, and this is a structural or permanent change on their behavior.
O2O: Meituan vs. Ele.me
[8:58] First of all, I’m going to start with the O2O trackers. Here, this chart is based on our data that’s tracking Meituan and Ele.me. Our trackers cover about two to three millions restaurants, convenience stores, flower shops — so all the local businesses that are providing delivery services.
This is just some background: many, many local businesses are connected to the O2O platform to provide deliveries. And actually the types of merchants are more diversified than we thought.
When I was in the U.S., I usually used Grubhub, but basically mainly for food deliveries. But in China the online delivery service covers a broader set of businesses.
[9:35] From the two to three million data points that we tracked on a bimonthly basis, we saw who are open and who are providing services. And because we have merchant-by-merchant data points, we can cut it into different tiered cities.
The general trend we see here is that during the outbreak, it took about a month and a half, businesses were shut do wn to about 10 to 20 percent of the local businesses, so the merchants and the restaurants have shut down, or take themselves off from online deliveries.
And then slowly recovered: it took about a month and a half to recover to a 70 to 80 percent level. And it plateaued.
[10:31] The takeaway here is that we saw the 20 percent of the restaurants actually have shut down permanently during the pandemic. So that’s the first structural change. This outbreak has tested the resilience of the stores and basically washed out a lot of stores.
We actually saw that another 20 percent, 30 percent of the new stores have joined the platform. So net, I think more businesses are doing deliveries or running online deliveries nowadays, post-outbreak.
[11:07] This is another slide showing the landscape between all the competition dynamics between Ele.me and Meituan. Generally, Ele.me has strengths in the coastal areas as well as select provinces like Heilongjiang 黑龙江. The rural areas, the more share Ele.me has. And the orange is more share for Meituan.
Market shares have fluctuated during Covid-19, but Meituan has actually gained 1 percent of market share during the outbreak. That’s mainly because Meituan has a broader penetration across the different tiered cities. And it may have gained shares in the types of cities in which Ele.me is weaker.
[12:02] Generally the takeaway for this part is that there’s a structural change in the local business, 20 percent of local stores have been washed out. And an additional 30 percent maybe were forced to get on the online deliveries.
And my personal experiences were that some of the restaurants that were more high-end were not doing deliveries before. They didn’t want to or they didn’t have to. But during the outbreak, they were forced to get on it. Now I can order delivery from those high end restaurants. That represents an additional 30 percent of restaurants that were never on the platforms, and they now join the platform.
So we may observe that structural change, where online deliveries increase penetration into local business. We may observe that in the U.S..
Job Market
[13:19] In terms of job markets, it’s a good indicator of the entire economic health, right. And it basically gives you a time series tracking of the economic activities during the outbreak, at a very high level point of view.
In this chart, we show the time series for new job postings in some of the main provinces in China: Guangdong, Hubei, Jiangsu, Shandong and Zhejiang, as well as benchmarking them against the nationwide level.
[13:56] You can see that most of Guangdong, Jiangsu, Shandong, the manufacturing big provinces, their recruiting levels are back to pre-outbreak levels. So things are actually recovering: the economic activity, the manufacturing activity. Manufacturers, employers are expecting a healthy recovery post-outbreak. So it actually shows a strong sentiment or confidence from the employers.
And if we break down by industry, again this is a good indicator for employer sentiments. We can see that hospitality — hotels, travel agencies — they are hiring less. Their recruiting activities have not recovered back to pre-Covid levels.
[14:58] And the “Industrial & Manufacturing” which we use as a call [signal] for China’s economic activities is one of the few sectors that have fully recovered in terms of recruiting and in terms of employer sentiment.
Most of the sectors are back to about 80 percent level in terms of recruiting numbers. So those are healthy indicators.
Travel & Logistics
[15:30] And next, we’ll look at logistics and travel.
Logistics basically is a good indicator for again, the job posting indicates the employer sentiment — kind of the “supply side” measurements, and the partial volume or logistics is kind of the transaction side, or the “demand side” indicator.
We are glad to see that, even though during the Covid-19 outbreak parcel volumes were down by almost 90 percent, we see a “V-shaped” recovery in terms of parcel volume, and now we bounce to around 90 percent during April. And we’re back to normal after the 618 [June 18] shopping festivals, one of the “Black Fridays.” Actually we have two “Black Fridays”, one is the double-eleven, and the other is “618”. It actually led to a recovery of the consumers’ activities.
[16:37] Another indicator we’re tracking is travel. It’s been very much impacted by the outbreak. We can see that international travels from China have not recovered. And that’s indicated by our outbound and domestic flights tracking.
And you can see domestics have recovered to a 50 percent level, but outbound and international flights are still at a low percentage, compared to pre Covid-19. People are not traveling.
[17:15] Another indicator is Macau. Usually when outbound travel is growing, Macau and Hong Kong are one of the few first destinations for overseas traveling. And you can see Macau is still flat and low in terms of visit count.
Here I would like to highlight how we get the data. For the flight trackings, we got it from a public app and web where we can see the seat map for different flights. And our system will go on to the app or the website to track the seat map, how many seats have been taken, right before the flight has taken off. And then we calculate the occupancy rate.
[18:05] And for Macau, we are using GPS trackers. We check GPS data to count the foot traffic in and out from all the casinos in Macau. That’s the technology that has been developed by the map providers, like Baidu Maps, Gaode Maps (高德地图), Tencent Maps, as well as third-party data trackings, like TalkingData.
They [TalkingData] also track the minute-by-minute GPS location of different mobile devices. And we use that to proxy the foot traffic or the location of different individuals.
[18:40] A quick note here for our audience on the podcast. Mu, you were introduced to us by Min Dai, who is currently the Alliance Director at TalkingData, and we’re really grateful. He had mentioned that you work together closely, and he’d planned to be with us on this call tonight, but the TalkingData exec team is traveling in Wuhan on urgent business. They’re following a recent uptick in demand from local governments for digital transformation and data-driven city governance, which is a relevant observation for today’s topic. Anyways, Mu, please continue! I think we’re now on online consumption data.
[19:18] Lastly, I’d like to share a little bit about comparing our data against online offline data.
Here, we track the online sales numbers of different products on Tmall, JD, and Taobao. And we are continuously tracking almost a hundred million products: their price, their units sold, and their sales for individual products. And we aggregate it by category and by brand.
So this is a good indicator of online consumption behaviors for Chinese consumers. And by observing the GMV by category, it gives us a lot of insights.
[20:12] First of all, on a high level, we can see that online consumption has recovered back to normal level. On the purple line, that’s the YoY growth of gross merchandise volumes, GMVs, for online consumption.
Actually the growth has exceeded last year’s level. That means online consumptions are growing despite the outbreak, which, it’s not surprising, basically confirms our hypothesis that now, due to the social distancing and lockdown policies, people have to — actually, they are — buying more online and boosting the growth of ecommerce.
But what is driving the growth? Are the people just buying the same thing more, or they’re buying different things? That’s where our category data comes into play. So we break down our GMV data by category and see their trends.
[21:24] And here, the general takeaway is that despite the rapid growth for online ecommerce purchases, actually different categories have different performance. And overall, a general takeaway we have is, there is a structural change in online consumer behavior.
What we see is that food and beverage is growing at a rapid speed. “Sports & Health,” “Home Decoration,” “Home Appliances,” and “Computers & Office” are also growing at a high speed. However, the more outdoor related consumptions are growing less, actually some of them are not growing, they are shrinking.
So the takeaway is people are buying more stuff. There’s a structural impact of the outbreak on consumers’ behavior right now. Now, most of us are out of the outbreak. We are doing our normal activities, daily activities.
[22:22] However, our consumer growth patterns are different. People are buying more in-house related stuff. They are cooking more in the house, they’re actually decorating their homes, they’re buying more appliances at home.
And somehow social distancing has had a more perpetual impact than we thought. People are not expecting to go out as much. Clothing, jewelry, all outdoor related stuff [has declined in sales]. That’s an interesting observation and we are actually curious to see what will continue.
Additional Consumer Trends
[23:11] And lastly, I will share the more micro-view of consumer behavior.
Here is a tracking of Haidilao, which is the largest hotpot franchise in China. It basically represents the health of offline consumption. Many shopping malls will recruit Haidilao to open a shop at their shopping mall to draw more consumer traffic. So the health of Haidilao has a direct relationship to the consumer traffic and the health of different shopping malls in China.
We can see that the takeaway here is that on the left side is the operating number of Haidilaos: how many stores have opened. We can see that it is basically back to normal.
[24:07] However, on the right side, we see “Peak Queuing Index.” What that means is that we’re tracking the highest queuing number for Haidilao on average, nationwide. And the number actually has not gotten back to pre-outbreak level. Haidialo actually reopened most of its shops in May, April. But the queuing index has not yet recovered.
So, that’s a general observation for local businesses, is that as we can see employers are hiring, expecting a normal recovery. However, if we see the demand side, if we see the consumer behavior side, it’s not yet recovered — we may not get back to the normal level.
[24:56] Again, this is just another kind of data point for the hypothesis that we found. One, hotels are heavily impacted by the outbreak. And other businesses are reopening; we can see that they are back to 70 percent or 80 percent level. But on the demand side, we have yet to see back to normal recoveries.
So, we believe that there may be a structural change. One, on the supply side: only 70 to 80 percent of the stores across different categories have survived the outbreak. And the demand side: people’s behavior have permanently changed.
How will that impact offline businesses? How that will shift the physical world has yet to be seen, because the recovery of consumers’ confidence and consumers’ activity have been slower than the supply side. So our data will continue to track it.
[26:06] Awesome, Mu, thank you for the presentation and for staying so on point. I want to ask you to give a little bit more background about your firm. How is it doing amidst the pandemic? And what do you think that reflects — what is demand like for data from China? How’s your business going?
[26:26] Sure. In the last six months, our business has doubled from last year. We are expected to basically triple or more by the end of this year. Internally, we have not actively gone out and grown our business — just a lot of inquiries and inbound demand. Since the end of January, we have been seeing many inbound demands every week, every day. We just sit there and there’s an investor coming to us and asking us for data, until now, still. We had like two or three inbound inquiries yesterday from major internet companies and investment funds.
We believe that this is another structural change in our clients’ community, in the research community, which is that people are forced to adopt a more digitized way of running research. Basically, they are sitting at home. They cannot go out to do real research, to run surveys, to count cars at Walmart. So they are forced to figure out “how do I run research?”
[27:42] So, basically through us, they can get different information digitally. So investors — our clients — are thinking about “how do I upgrade my whole research methodology and research teams framework to a more digitized way?”
And we are observing that many leading institutional investors, many leading internet companies are building what we call “Research 2.0,” where there’s a systematic approach to research rather than a very conventional, you know, desktop research.
They are building a system internally: to collect data, to collect news, to record expert calls and put them together into a system — record them and try to synthesize the information more systematically. Rather than relying on manual PowerPoint-making, Excel-making, everything is being automated, and everything is being digitized. So it’s actually really beneficial for us.
[28:45] All right! That was the end of our session with Mu Chen. What did you think about what he had to say? Send us your feedback!
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