Content marketing and brand engagement on social media: a study of Facebook´s posts in the ecommerce industry in Brazil

Content marketing refers to marketing format that involves the creation and sharing of media and publishing content in order to acquire customers. It is focused not on selling, but on communicating with customers and prospects. In today world´s, a trend has been seen in brands becoming publishers in order to keep up with their competition and more importantly to keep their base of fans and followers. Content Marketing is making companies to engage consumers by publishing engaging and value-filled content. This study aims to investigate if there is a link between brand engagement and Facebook Content Marketing practices in the e-commerce industry in Brazil. Based on the literature review, this study defines brand engagement on Facebook as the numbers of "likes" "comments" and "shares" that a company receives from its fans. These actions reflect the popularity of the brand post and leads to engagement. The author defines a scale where levels of Content Marketing practices are developed in order to analyze brand posts on Facebook of an ecommerce company in Brazil. The findings reveal that the most important criterion for the company is the one regarding the picture of the post, where it examines whether the photo content is appealing to the audience. Moreover, it was perceived that the higher the level of these criterion in a post, the greater the number of likes, comments and shares the post receives. The time when a post is published does not present a significant role in determining customer engagement and the most important factor within a publication is to reach the maximum level in the Content Marketing Scale.

Multistep stochastic mirror descent for risk-averse convex stochastic programs based on extended polyhedral risk measures

We consider risk-averse convex stochastic programs expressed in terms of extended polyhedral risk measures. We derive computable con dence intervals on the optimal value of such stochastic programs using the Robust Stochastic Approximation and the Stochastic Mirror Descent (SMD) algorithms. When the objective functions are uniformly convex, we also propose a multistep extension of the Stochastic Mirror Descent algorithm and obtain con dence intervals on both the optimal values and optimal solutions. Numerical simulations show that our con dence intervals are much less conservative and are quicker to compute than previously obtained con dence intervals for SMD and that the multistep Stochastic Mirror Descent algorithm can obtain a good approximate solution much quicker than its nonmultistep counterpart. Our con dence intervals are also more reliable than asymptotic con dence intervals when the sample size is not much larger than the problem size.