The online market is increasingly gaining in importance. Consumers buy more and more goods on the online market due to the great variety of product offers, time saving and independence regarding closing hours of physical shops. For the German Consumer Price Statistics, which comprises the National Consumer Price Index (CPI) and the Harmonised Index of Consumer Prices (HICP), the Federal Statistical Office (FSO) collects approximately 10,000 individual prices for products on websites of online retailers. The share of these products on the overall basket of goods and services amounts to approximately five per cent and will probably be rising in the forthcoming years. Thanks to fairly easy to adjust prices on the internet, online retailers are able to react to market conditions or consumer’s behaviour by adjusting prices automatically in short intervals, applying algorithms that take into account different parameters. This phenomenon is known as dynamic pricing. First studies investigating dynamic pricing in Germany have shown that different variants of dynamic pricing exist and are very heterogeneous and not transparent. Dynamic pricing of online retailers may lead to a bias in the index calculation since the traditional way of price collection via internet is done generally at one time during the month and therefore cannot capture rapidly changing prices. Therefore, in order to display reliable price developments in the CPI/HICP, consumer price statistics needs to constantly monitor the pricing behaviour on the internet and apply methods to evaluate the large amount of data and integrate very volatile price developments into price indices. The FSO has gathered numerous experiences through former studies in the topical subject of web scraping and also conducted a study investigating the extent of dynamic pricing on the German online market. The present paper deals with the applied techniques to monitor the pricing behaviour on the internet, includes research towards handling of dynamic pricing within the price collection for CPI/HICP and gives an overview of suitable methods when calculating indices.