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E-commerce platforms facilitate sales of products while product vendorsengage in Social Media Activities (SMA) to drive E-commerce Platform Activities(EPA) of consumers, enticing them to search, browse and buy products. Thefrequency and timing of SMA are expected to affect levels of EPA, increasingthe number of brand related queries, clickthrough, and purchase orders. Thispaper applies cross-sectional data analysis to explore such beliefs anddemonstrates weak-to-moderate correlations between daily SMA and EPA volumes.Further correlation analysis, using 30-day rolling windows, shows a highvariability in correlation of SMA-EPA pairs and calls into question thepredictive potential of SMA in relation to EPA. Considering the moderatecorrelation of selected SMA and EPA pairs (e.g., Post-Orders), we investigatewhether SMA features can predict changes in the EPA levels, instead of preciseEPA daily volumes. We define such levels in terms of EPA distribution quantiles(2, 3, and 5 levels) over training data. We formulate the EPA quantilepredictions as a multi-class categorization problem. The experiments withRandom Forest and Logistic Regression show a varied success, performing betterthan random for the top quantiles of purchase orders and for the lowestquantile of search and clickthrough activities. Similar results are obtainedwhen predicting multi-day cumulative EPA levels (1, 3, and 7 days). Our resultshave considerable practical implications but, most importantly, urge the commonbeliefs to be re-examined, seeking a stronger evidence of SMA effects on EPA.

Social Media Brand Engagement as a Proxy for E-commerce Activities: A Case Study of Sina Weibo and JD