We are all struggling to understand what a post COVID19 world will look like. Of course, this phase isn’t even with us yet, but at least we can begin to see what has been impacted and how, with the ease of the lockdown. In this paper, one of the countries that has had limitations eased is Germany – since the beginning of May. We aim to deliver the first observations on the changes (or not) in online behaviours.
On observing web navigation data1, we can start to draw a comparison between the online behaviour during and after lockdown. We observed 4 different situations.
1. Negative impact
Here we have websites whose number of visits went down during lockdown and which kept on decreasing once the lockdowns was eased in Germany. At this stage of the crisis, the category of websites which is still negatively impacted by the situation is “dating websites”. It is understandable that the lockdown ease hasn’t changed anything here. We can’t even hug our own parents, so it is very likely that dating websites users struggle to meet unknown people in this context.
2. Back to normal
Some websites have been highly visited during lockdown; as the range of possible leisure was pretty limited, easy ways to get entertained are to watch TV shows or movies. As of now, either we have more options to be entertained (typically we can meet friends), or we have to get back to work, there are less visits on entertainment websites than there were during lockdown. Two best examples for this are Justwatch (streaming service) and kimcartoon (streaming service specialized in cartoons).
We made similar observations for the usage of the Netflix app: on average it was used 18% more during lockdown, and from the beginning of May, usages are far less intense2.
3. Normalization
Some industries have been stopped by lockdown. Typically, the travel and tourism industry. With the ease of the lockdown, the situation is getting better, however the thread of the pandemic (and consequently of a second lockdown), is not yet back to normal. We noticed the same phenomena for sports: Bundesliga was stopped and it is about to start again (in empty stadiums), so the interest came back too but not fully. In a lesser extent, it is the same for job searches: impossible or useless during lockdown, they start again once the lockdown eased.
Here some examples for famous travel websites: Airbnb (+124% since lockdown ease, but still 44% less visits than before lockdown) , bahn.de (+25% since lockdown ease, but still 43% less visits than before lockdown), booking.com (+21% since lockdown ease, but still 52% less visits than before lockdown). Holiday preparation started, but they are not guaranteed yet!
4. Positive impact
Last but not least: those who took advantages of the crisis and still keep the benefit from the situation. Here we found internet services directly related to the recommendation to work on remote: mediasharing services (wetransfer for instance), video conferences…
Less expected – we find real estate websites. Either for leisure, or because lockdown has triggered the need for a different place of residence (lifestyle?), desire to move is still on!
Zoom is of course the perfect example here: +300% increase in number of visits during lockdown (compared to before lockdown) and +14% post lockdown compared to during lockdown!
To conclude, at this stage, main changes in online behaviour are still given by the specificity of the situation: sanitary risk, working in remote styles, release of some past constraints. Hence, it is very likely that online behaviour will change again in the next few weeks according to the evolution of the situation. One counterexample though: the usage of real estate websites which could indicate emerging trends for the next few months in rental or sale of property business.
respondi Deutschland
1 Since 2016, a part of our German panel has accepted to share their navigation/app usage data with us. They all have installed a software/an app which monitors their online activities. Here we use the data collected from March 2020 on. During this timeframe, we used a n=1583 nat rep sample (age, gender).
2 We prefer to wait until the end of the month to deliver measures.