  E-commerce platforms facilitate sales of products while product vendors
engage in Social Media Activities (SMA) to drive E-commerce Platform Activities
(EPA) of consumers, enticing them to search, browse and buy products. The
frequency and timing of SMA are expected to affect levels of EPA, increasing
the number of brand related queries, clickthrough, and purchase orders. This
paper applies cross-sectional data analysis to explore such beliefs and
demonstrates weak-to-moderate correlations between daily SMA and EPA volumes.
Further correlation analysis, using 30-day rolling windows, shows a high
variability in correlation of SMA-EPA pairs and calls into question the
predictive potential of SMA in relation to EPA. Considering the moderate
correlation of selected SMA and EPA pairs (e.g., Post-Orders), we investigate
whether SMA features can predict changes in the EPA levels, instead of precise
EPA daily volumes. We define such levels in terms of EPA distribution quantiles
(2, 3, and 5 levels) over training data. We formulate the EPA quantile
predictions as a multi-class categorization problem. The experiments with
Random Forest and Logistic Regression show a varied success, performing better
than random for the top quantiles of purchase orders and for the lowest
quantile of search and clickthrough activities. Similar results are obtained
when predicting multi-day cumulative EPA levels (1, 3, and 7 days). Our results
have considerable practical implications but, most importantly, urge the common
beliefs to be re-examined, seeking a stronger evidence of SMA effects on EPA.
