Roy Morgan Research
May 26, 2020

More people are visiting shopping centres as restriction ease

Topic: Press Release
Finding No: 8519
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A special analysis of movement data at shopping centres across Sydney and Melbourne shows the number of devices picking up gradually over the last few weeks as restrictions have eased.

Roy Morgan has partnered with leading technological innovator UberMedia to aggregate data from tens of thousands of mobile devices to assess the movements of Australians as we emerge from the restrictions imposed in response to the COVID-19 pandemic. The interactive dashboard below tracks the movement data from a number of shopping centres in Melbourne and Sydney during 2020.

The number of devices seen at these centres bottomed over the Easter long weekend in early April; there have been increases every week since that low point. The traditional weekend movement spikes have also reappeared in May as people return in greater numbers to shopping centres.

However, despite the movement data trending upwards, it remains below average levels earlier this year, before the COVID-19 pandemic.

Michele Levine, CEO of Roy Morgan, says Australia’s successful handling of the COVID-19 pandemic has allowed an earlier than expected emergence from the restrictions introduced in late March, but there are distinct differences in the rate at which different groups return:

“Roy Morgan’s partnership with UberMedia provides aggregated data not just on numbers but also on the types of people frequenting Australia’s cities and places of interest such as shopping centres.

“The latest aggregated data on movement shows an increase over the last few weeks as restrictions on movement have gradually been relaxed. (Restaurants and food outlets have been allowed to seat up to 10 customers since Friday May 15 and from next week this limit will be expanded to up to 50 customers as NSW continues to open up.) 

“The latest figures show the younger, socially aware, culturally diverse group dubbed Metrotechs make up an increased share of people at shopping centres.

“With bricks-and mortar-retailers facing the double challenge of managing social distancing logistics and luring back customers who have become accustomed to shopping online, understanding the evolving behavioural patterns of different demographic and psychographic segments of Australia’s population will be key to reaping the benefits of a newly re-opened post-pandemic world.”


Returning shoppers are drawn from two lucrative Helix Communities
A number of shopping centres in Melbourne and Sydney are attracting a highly desirable customer base, with the majority of those visiting belonging to the lucrative 100 Leading Lifestyles and 200 Metrotechs Helix Personas communities.

• 100 Leading Lifestyles: Focused on success, career and family, people in this Community are proud of their prosperity and achievements. They are big spenders and enjoy cultured living to the max.

• 200 Metrotechs: Socially aware, successful, career-focused and culturally diverse, Metrotechs are also trend- and tech-focused. They are committed experience seekers, willing to spend big on the best of city life, and thrive on being out and about in the world.

Roy Morgan’s Helix Personas (www.helixpersonas.com.au) uses deep psychographic insights, far beyond simple demographics, to segment consumers into targetable groups. The tool incorporates values, beliefs and attitudes which are the best predictors of consumer behaviour, so you can reach your customers most effectively with messages that resonate.


Daily Analysis of Movement Data in 2020

tableau

Source: Roy Morgan collaboration with UberMedia who provide anonymous aggregated insights using mobile location data.

Margin of Error

The margin of error to be allowed for in any estimate depends mainly on the number of interviews on which it is based. Margin of error gives indications of the likely range within which estimates would be 95% likely to fall, expressed as the number of percentage points above or below the actual estimate. Allowance for design effects (such as stratification and weighting) should be made as appropriate.

Sample Size Percentage Estimate
40% – 60% 25% or 75% 10% or 90% 5% or 95%
1,000 ±3.0 ±2.7 ±1.9 ±1.3
5,000 ±1.4 ±1.2 ±0.8 ±0.6
7,500 ±1.1 ±1.0 ±0.7 ±0.5
10,000 ±1.0 ±0.9 ±0.6 ±0.4
20,000 ±0.7 ±0.6 ±0.4 ±0.3
50,000 ±0.4 ±0.4 ±0.3 ±0.2

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