AfterPay Insights is a knowledge platform for e-commerce professionals. With our data and analyses, we hope to give you 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 you understand the e-commerce landscape.
The data underpinning our results are based on a monthly quantitative online survey conducted by Odyssey, an independent research agency, using validated and representative online consumer panels supplied by Dynata.
The research population is defined as inhabitants of the Netherlands, Germany and Norway, aged 18 and above, with a clear distinction made between online and non-online shoppers. Data collection started in March 2020 and was conducted on a bi-weekly basis until end of February 2021. From March 2021 data collection continued on a monthly basis and is based on 1,000 completed interviews per country each month. To harmonize time-series, all bi-weekly consumer data points from the 1st year of data collection have been aggregated to monthly data points.
Data quality is of the utmost importance. Every data wave is weighted versus national census data on age, gender and region to ensure it is representative of the general population. 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. 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 variables displayed in the dashboard are presented as cross-sectional percent with changes in percentage points: percentages displayed reflect the share of individuals within the target group that you select. Percentual changes in the analyses always refer to times pre-dating March 2020. 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 month vs. the number of purchases conducted before March 2020.
In the dashboard, you will see us refer to different time perspectives: the ‘Past Month’ and the ’Coming Month’. 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.