Measuring the internet economy in The Netherlands a big data analysis 2016 | 14

an e-commerce website. Thus, some websites have low probabilities (5% for example) and
are thus most likely not e-commerce website, while some have high probabilities (95% for
example) and are thus most likely e-commerce websites. It remains then to choose a cutoff
point. Dataprovider chooses a cut-off point of 85%. During this research, we analysed
this choice by looking at the websites either side of the cut-off point. On this basis, the
performance of the machine-learning algorithm and the appropriateness of the 85% cut-off
point appeared to be performing satisfactorily. Below the cut-off point most websites don’t
seem to be an online store and above the cut-off point most websites were.
It is, however, possible that websites which are actually online stores can be missed using
this method. We therefore searched for other data with which we could test whether the
Dataprovider machine learning algorithm had identified at least all of the most important
online stores. The Dutch website jouwaanbieding.nl contains an up to date list containing
the most popular online stores. Within this list were several online stores which were better
placed in other categories, so we manually removed these from the list. From the remaining
website approximately 250 additional online stores were identified and added to category C.
Finally, we employed the Call To Action (CTA) dataset to further refine the category. Of the six
calls to action in this dataset (order, buy, view the shopping cart, make a reservation/booking,
subscribe or register), the following three are most closely associated with online stores:
order, buy and view the shopping cart. We analysed different combinations of these 3 calls
to action in order to find a combination which added a significant number of online stores to
the category without adding any websites which were not online stores. The best performing
set of calls to action was ‘buy’ and ‘view the shopping cart’. This choice of calls to action
identified around 5,400 online stores which were not yet categorised as such.
Comparison to SIC codes for online stores
The use of big data within this project facilitates a different perspective on the nature of the
Dutch businesses. It is particularly interesting to make comparisons between the nature of
businesses according to the SIC codes and the categorisation of businesses in this study. In
this box we analyse the SIC codes of all the businesses which are classified as online stores
according to our definition. The results are shown below.
5.1.1.1 SIC codes of businesses classiied as online stores
35,1%
1,9%
2,0%
2,5%
2,6%
12,7%
29,3%
13,9%
4791: Online stores
47: Retail excluding 4791 (Online stores)
46: Wholesale
62: Support activities in the field of ICT
74: Industrial design, photography, translation
74: and other consultancy
96: Wellness and other services: funeral activities
45: Sale and repair of motor vehicles
Other
CBS | Discussion Paper, 2016 | 14 18