AfterPay Insights is an interactive knowledge platform for e-commerce professionals. With our data we hope to give professionals a better understanding of consumer behavior and attitudes as well as the reasons behind shifts in consumption and preferences. Whatever the reason for these shifts may be – trends, public opinion or a pandemic like COVID-19 – AfterPay Insights is here to help businesses understand the e-commerce landscape.
Methodology for pandemic impact on e-commerce study
The data and analytics underpinning the results in this blog is based on a quantitative online tracking survey conducted by an independent research agency, Odyssey, using validated and representative online panels supplied by Dynata.
The research scope was defined as the general public aged 18 and above in Germany, the Netherlands and Norway. Target group criteria was having made at least one online purchase during the past 12 months.
First wave data collection was completed during March 24th to March 30th.In total, 3,345 interviews were conducted, distributed as follows:
• Germany: 1,178 interviews
• The Netherlands: 1,111 interviews
• Norway: 1,056 interviews
In the continued data collection, 500 interviews per country will be conducted on a weekly basis, with fieldwork periods Thursdays through Sundays, in order to release a fresh batch of data Wednesday every week.
As data quality is of utmost importance, every wave of data is weighted vs national census data on age, gender and region to ensure representativity. Based on selecting a 1,000 respondent sample in the interactive dashboard, this will result in a maximum c:a 2 pct point margin of error. I.e. if 50% of this selected sample is of a certain opinion, the true value in the full target population is between 48% and 52%.
The vast majority of KPIs displayed in the interactive dashboard are presented as cross-sectional percent with changes in percentage points. I.e. percentages displayed reflect the share of individuals the target group that you select.
There is however one exception. The KPIs related to change in number of purchases (and when broken down per product category) is presented in percent, not percentage points. This means that the percentage displayed is the relative increase or decrease compared to the previous data point – for example the increase in total number of purchases during the past 2 weeks vs. the number of purchases conducted before the Corona outbreak.
In the interactive dashboard, you will see us refer to different time perspectives. Recent purchases refer to what respondents have bought in the past 2 weeks from day of interview. Future purchases were defined as respondent projections of the month to come, and pre-Corona purchases was framed as ‘a normal month’ preceding the Corona outbreak. When comparing pre-Corona purchases with recent purchases, data has been transformed to equalize the timeframe making comparisons valid.
Enjoy your analytics. And if you have any methodological questions, please do not hesitate to shoot us a question.