C55 - Large Data Sets: Modeling and AnalysisReturn

The article contributes to the knowledge of customer lifetime value (CLV) models, where extensive empirical analyses on large datasets from online stores are missing. Based on this knowledge, practitioners can decide about the deployment of a particular model in their business and academics can design or enhance CLV models. The article presents predictive performance of selected CLV models: the extended Pareto/NBD model, the Markov chain model, the vector autoregressive model and the status quo model. Six large datasets of medium and large‑sized online stores in the Czech Republic and Slovakia are used for a comparison of the predictive performance of the models. Online stores have annual revenues in the order of tens of millions of euros and more than one million customers. The comparison of CLV models is based on selected evaluation metrics. The results of some of the models which use additional non‑financial data on customer behaviour - the Markov chain model and the vector autoregressive model - do not justify the effort which is needed to collect such data. The advantages and disadvantages of the selected CLV models are discussed in the context of their deployment.

We investigate commonality and heterogeneity of inflationary processes in ten Central and Eastern European (CEE) countries over the period 2001-2013. The research is important for the analysis of monetary policy as it helps understand the origin of price formation from both sectoral and country perspective. With a multi-level factor model we decompose product-level inflation rates into the CEE region-wide, sector, country, country-sector, and idiosyncratic components. The outcomes indicate that CEE region-wide and country specific components are more persistent than sector and product-level components, which is in line with similar studies for core EU countries. Regional factors explain about 17% of variance in monthly price changes, which is more than any other factors (below 10% each). This result is at odds with the assumptions of many sectoral DSGE models and empirical evidence on the importance of sectoral price shocks in developed economies. The difference may be related to the conclusion that the first regional factor is associated with common disinflationary process that occurred in CEE economies in the 2000s, whereas the second one reveals significant correlations with global factors, especially commodity prices and euro area price developments.