China, South Korea and New Zealand are exiting their coronavirus lockdowns, with many more countries also heading steadily toward easing restrictions. As restrictions ease, suppressed demand will begin to be released for many companies.
Yet we all know the return of demand will be fragmented and varied, with new catalysts to uncover in its recovery. Data scientists all over the world are hard at work in coronavirus recovery teams trying to identify the rate their company’s demand may recover, while their strategy peers identify additional levers they can work with to drive up demand directly.
Identifying your coronavirus demand recovery rate is complex. In this eWEEK Data Points article, Dr Xuxu Wang, Chief Data Officer at demand intelligence provider PredictHQ, outlines six considerations for a company's recovery team to study.
Data Point No. 1: Companies need a coronavirus recovery rate.
Building the data science capability to identify and iterate on coronavirus recovery rates is essential. While historical event data is used by systems to aggregate and verify millions of events, and rank them all by predicted impact, it is very unlikely a conference that used to draw 15,000 people will return at the same scale immediately. Due to this, companies need to adjust their predicted attendance and impact rankings models by adding a recovery ratio so that customers can use the combined impact of the event data.
Customers need intelligent event data to inform their own recovery rate identification and iteration. Many companies will need to re-hire or train up staff again to meet demand, as well as engage their supply chains before demand commences to ensure they are ready.
Data Point No. 2: Acknowledge the challenges of identifying a coronavirus recovery rate.
The impact of COVID-19 is unprecedented and varies by country, state and industry. While government restrictions and flight bans are easy for systems to track, factors such as willingness to spend and attend events without a vaccine have the ability to throw a wrench in these rates.
Human factors such as fear or hesitancy, in addition to the impact of the economic downturn on people's ability and willingness to attend events will only become knowable as markets open. Models will need to update rapidly based on new information. This will impact both attended events, such as conferences and sports games, but also non-attendance based events, such as people celebrating public holidays, observances and school holidays.
Data Point No. 3: Analyze the varying impact of COVID-19 on events.
Smaller attended events will come back faster, such as local concerts or fun runs. Events attended by a lot of international visitors may look very different for a while, because international travel is likely to be low for some time.
These are all complex factors to decompose into discrete problems to build models to solve. However, this is only the tip of the iceberg, because new models are continually being built to source, verify and incorporate substantial amounts of new data, such as public transport data, trends data and much more. While almost every company out there is watching its spending, chief data officers shouldn’t be reluctant to invest in high-quality data sources; this is exactly the time when your strategies need to be data-driven to filter out all of the noise.
Data Point No. 4: Identify a recovery ratio for each event subgroup in each category at a state of country level.
Different categories of events have been impacted by the novel coronavirus in different ways. For example, some people mistakenly claim that events aren’t happening. While it is true scheduled attended events such as conferences, concerts and sports games are postponed with a small proportion cancelled, other events that impact demand continue. This includes school closures and holidays, public holidays and observances, as well as unscheduled events that impact demand such as severe weather, terrorism or natural disaster.
This recovery ratio also considers community events such as farmers' markets, which are continuing albeit in a more socially distanced way for the time being. These events can cause incremental, displaced or decremental demand and should be tracked.
Data Point No. 5: Build models that can accurately identify and sort every attended event.
There are three buckets: mostly domestic attendees, mostly international attendees and events with a good mix of both. Proprietary entities system and extensive verified events metadata means companies need to acknowledge and sort millions of events by the percentage of international attendees. This is critical because international travel bans are likely to remain for some time, and the airline industry has been hit particularly hard, so we can anticipate most international attended events will take longer to recover.
Even then, not all events attended by mostly international visitors will recover at the same rate.
Whereas events made up of mostly domestic attendees, such as community events, but also massive sports events and concerts, are likely to recover earlier.
Worth noting for attended events such as conferences and expos, it is anticipated that many larger events may change location to territories with higher attendee limits. For example, if a recovered San Francisco has a lower maximum attendee event than Las Vegas, we may see a shift of events into Las Vegas for the recovery.
Data Point No. 6: Update coronavirus recovery rate models constantly.
New logics and enhancement need to be evolved as soon as new information is available.
Enterprises should focus on developing data science models that can iterate swiftly, and also review every model each week. For attended events, this will involve noting the differences between search volume, ticket sales and actual attendance figures of events so we can build models to start to quantify the more human and fluid variables. These will, of course, need to keep iterating per market, based on latest information.
This is an unprecedented time for all, and companies should consider thinking carefully about how powerful intelligent event data might be helpful in the forthcoming recovery preparation.
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