Bob Rietveld

Visual-based social media are growing exponentially and have become an integrated part
of the customer engagement strategy of many brands. Prior work points to the textual
message content as a driver of customer engagement behavior. So far, little is known
about the impact of visual message content, specifically visual emotional and informative
appeals. We extract emotional and informative appeals from Instagram posts using
machine learning models and use a Negative Binomial… Meer weergeven

Visual-based social media are growing exponentially and have become an integrated part
of the customer engagement strategy of many brands. Prior work points to the textual
message content as a driver of customer engagement behavior. So far, little is known
about the impact of visual message content, specifically visual emotional and informative
appeals. We extract emotional and informative appeals from Instagram posts using
machine learning models and use a Negative Binomial model to explain customer
engagement. We test our model on 46.9K Instagram posts from 59 brands in six sectors.
Our results show that visual emotional and informative appeals encoded in brand-
generated content influence customer engagement in terms of likes and comments.
Specifically, we demonstrate that positive high and negative low arousal images drive
customer engagement. Informative appeals do not drive customer engagement with the
exception of informative brand-related appeals. These findings help brand managers in
developing an effective customer engagement strategy on visual social media. Minder weergeven

Social media has become an important tool in marketing for companies to communicate with their consumers. Firms post content and consumers express their appreciation for the brand by following them on social media and/or by liking the firm generated content. Understanding the consumers' attitudes towards a particular brand on social media (i.e. liking) is important. In this paper, we focus on a method for brand popularity prediction and use it to analyze social media posts generated by various… Meer weergeven

Social media has become an important tool in marketing for companies to communicate with their consumers. Firms post content and consumers express their appreciation for the brand by following them on social media and/or by liking the firm generated content. Understanding the consumers' attitudes towards a particular brand on social media (i.e. liking) is important. In this paper, we focus on a method for brand popularity prediction and use it to analyze social media posts generated by various brands during a specific period of time. Existing instance-based popularity prediction methods focus on popularity of images, text, and individual posts. We propose a new category based popularity prediction method by incorporating the spatio-temporal dimension in the representation. In particular, we focus on brands as a specific category. We study the behavior of our method by performing four experiments on a collection of brand posts crawled from Instagram with 150,000 posts related to 430 active brands. Our experiments establish that 1) we are able to accurately predict the popularity of posts generated by brands, 2) we can use this post-level trained model to predict the popularity of a brand, 3) by constructing category representations we are improving the accuracy of brand popularity prediction, and 4) using our proposal we are able to select a set of images for each brand with high potential of becoming popular. Minder weergeven

Brand-related user posts on social networks are growing at a staggering rate, where users express their opinions about brands by sharing multimodal posts. However, while some posts become popular, others are ignored. In this paper, we present an approach for identifying what aspects of posts determine their popularity. We hypothesize that brand-related posts may be popular due to several cues related to factual information, sentiment, vividness and entertainment parameters about the brand. We… Meer weergeven

Brand-related user posts on social networks are growing at a staggering rate, where users express their opinions about brands by sharing multimodal posts. However, while some posts become popular, others are ignored. In this paper, we present an approach for identifying what aspects of posts determine their popularity. We hypothesize that brand-related posts may be popular due to several cues related to factual information, sentiment, vividness and entertainment parameters about the brand. We call the ensemble of cues engagement parameters. In our approach, we propose to use these parameters for predicting brand-related user post popularity. Experiments on a collection of fast food brand-related user posts crawled from Instagram show that: visual and textual features are complementary in predicting the popularity of a post; predicting popularity using our proposed engagement parameters is more accurate than predicting popularity directly from visual and textual features; and our proposed approach makes it possible to understand what drives post popularity in general as well as isolate the brand specific drivers. Minder weergeven

It’s becoming commonplace for consumer companies to listen to what their customers are saying on social media, but the big question is: What do they do with the results? In a lot of cases, managers merely circulate them within the marketing department—after marking them with a prominent “FWIW.”

That’s because they don’t know what this information could be worth.

The beauty of listening to social-media chatter is that one picture or one comment can have an outsized impact on your consumer knowledge and, as a consequence, your profitability. But a lot of people in business don’t appreciate that. The article is about why companies should.