  Recent researches have seen an upsurge in the analysis of consumer reviews.
Although, several dimensions have been explored, less is known on the temporal
dynamics of events that happen over the lifecycle of online products. What are
the dominant sales patterns? How are they affected by review count, rating,
helpfulness and sentiment? How is trust characterized and what are its effects
on sales and revenue? What happens during a market competition? When does a
takeover/recovery happen and by what percentage do sales increase on a
takeover? This work aims to answer these fundamental research questions based
on a sales time-series analysis of reviews of over 1 million products from
Amazon.com. We discover novel temporal patterns of sales and interesting
correlations of sales with the ratings. We find that trust and helpfulness are
important for higher revenue. Based on the analyses, we propose a model to
forecast sales that significantly outperforms other baselines. We then explore
the phenomena of market competition. Particularly, we characterize different
factors that govern survival/death of a product under competition and a model
for competition forecast. Experimental results on large-scale reviews
demonstrate the effectiveness of the proposed approaches.
