We describe the effect of social media advertising content on customer engagement using data from Facebook. We content-code 106,316 Facebook messages across 782 companies, using a combination of Amazon Mechanical Turk and natural language processing algorithms. We use this data set to study the association of various kinds of social media marketing content with user engagement-defined as Likes, comments, shares, and click-throughs-with the messages. We find that inclusion of widely used content related to brand personality-like humor and emotion-is associated with higher levels of consumer engagement (Likes, comments, shares) with a message. We find that directly informative content-like mentions of price and deals-is associated with lower levels of engagement when included in messages in isolation, but higher engagement levels when provided in combination with brand personality-related attributes. Also, certain directly informative content, such as deals and promotions, drive consumers' path to conversion (click-throughs). These results persist after incorporating corrections for the nonrandom targeting of Facebook's EdgeRank (News Feed) algorithm and so reflect more closely user reaction to content than Facebook's behavioral targeting. Our results suggest that there are benefits to content engineering that combines informative characteristics that help in obtaining immediate leads (via improved click-throughs) with brand personality-related content that helps in maintaining future reach and branding on the social media site (via improved engagement). These results inform content design strategies. Separately, the methodology we apply to content-code text is useful for future studies utilizing unstructured data such as advertising content or product reviews. Stanford GSB AbstractWe investigate the effect of social media content on customer engagement using a large-scale field study on Facebook. We content-code more than 100,000 unique messages across 800 companies engaging with users on Facebook using a combination of Amazon Mechanical Turk and state-of-the-art Natural Language Processing algorithms. We use this large-scale database of advertising attributes to test the effect of ad content on subsequent user engagement − defined as Likes and comments − with the messages. We develop methods to account for potential selection biases that arise from Facebook's filtering algorithm, EdgeRank, that assigns posts non-randomly to users. We find that inclusion of persuasive content − like emotional and philanthropic content − increases engagement with a message. We find that informative content − like mentions of prices, availability and product features − reduce engagement when included in messages in isolation, but increase engagement when provided in combination with persuasive attributes. Persuasive content thus seems to be the key to effective engagement. Our results inform advertising design in social media, and the methodology we develop to content-code large-scale textual data provides a framework for f...

We describe the effect of social media advertising content on customer engagement using data from Facebook. We content-code 106,316 Facebook messages across 782 companies, using a combination of Amazon Mechanical Turk and natural language processing algorithms. We use this data set to study the association of various kinds of social media marketing content with user engagement-defined as Likes, comments, shares, and click-throughs-with the messages. We find that inclusion of widely used content related to brand personality-like humor and emotion-is associated with higher levels of consumer engagement (Likes, comments, shares) with a message. We find that directly informative content-like mentions of price and deals-is associated with lower levels of engagement when included in messages in isolation, but higher engagement levels when provided in combination with brand personality-related attributes. Also, certain directly informative content, such as deals and promotions, drive consumers' path to conversion (click-throughs). These results persist after incorporating corrections for the nonrandom targeting of Facebook's EdgeRank (News Feed) algorithm and so reflect more closely user reaction to content than Facebook's behavioral targeting. Our results suggest that there are benefits to content engineering that combines informative characteristics that help in obtaining immediate leads (via improved click-throughs) with brand personality-related content that helps in maintaining future reach and branding on the social media site (via improved engagement). These results inform content design strategies. Separately, the methodology we apply to content-code text is useful for future studies utilizing unstructured data such as advertising content or product reviews.

Online product ratings are widely available on the Internet and are known to influence prospective buyers. An emerging literature has started to look at how ratings are generated and, in particular, how they are influenced by prior ratings. We study the social influence of prior ratings and, in particular, investigate any differential impact of prior ratings by strangers ("crowd") versus friends. We find evidence of both herding and differentiation behavior in crowd ratings wherein users' ratings are influenced positively or negatively by prior ratings depending on movie popularity. In contrast, friends' ratings always induce herding. Further, the presence of social networking reduces the likelihood of herding on prior ratings by the crowd. Finally, we find that an increase in the number of friends who can potentially observe a user's rating ("audience size") has a positive impact on ratings. These findings raise questions about the reliability of ratings as unbiased indicators of quality and advocate the need for techniques to debias rating systems. AbstractOnline product review as a form of online Word of Mouth (WOM) and User-Generated Content (UGC) has attracted much attention recently. While there are many studies relating online reviews and product sales, the interesting and important problems regarding user review generation processes have been largely ignored. This study analyzes how online movie user ratings are generated through a complex interrelationship between product information, marketing effort, and social influences. In particular, we examine the effects of comparable observational learning from the crowd or friends on user ratings. This study exploits sequential user movie ratings in an online community with user and movie level information, and constructs plausible latent variables for users' perceived movie quality and the heterogeneity at movie and user levels. Our analysis indicates that, on average, higher predecessors' ratings increase the likelihood of a subsequent user providing a high rating; in other words, herding occurs. On the other hand, the degree of herding behavior by prior friend ratings becomes relatively smaller. More interestingly, the impact of predecessors' ratings becomes weaker as the volume of friend ratings increases. This study contributes to the understanding of how social imitation and learning affect user rating generation and how online social interactions moderate inefficiency in product quality information created by online users.

.NETinst.org, is a non-profit institution devoted to research on network industries, electronic commerce, telecommunications, the Internet, "virtual networks" comprised of computers that share the same technical standard or operating system, and on network issues in general. This paper examines the effect of recommender systems on the diversity of sales. Two anecdotal views exist about such effects. Some believe recommenders help consumers discover new products and thus increase sales diversity. Others believe recommenders only reinforce the popularity of already popular products. This paper seeks to reconcile these seemingly incompatible views. We explore the question in two ways. First, modeling recommender systems analytically allows us to explore their path dependent effects. Second, turning to simulation, we increase the realism of our results by combining choice models with actual implementations of recommender systems. Our main result is that some well known recommenders can lead to a reduction in sales diversity. Because common recommenders (e.g., collaborative filters) recommend products based on sales and ratings, they cannot recommend products with limited historical data, even if they would be rated favorably. In turn, these recommenders can create a rich-get-richer effect for popular products and vice-versa for unpopular ones. This bias toward popularity can prevent what may otherwise be better consumer-product matches. That diversity can decrease is surprising to consumers who express that recommendations have helped them discover new products. In line with this, we show it is possible for individual-level diversity to increase but aggregate diversity to decrease. Recommenders can push each person to new products, but they often push similar users toward the same products. We show how basic design choices affect the outcome, and thus managers can choose recommender designs that are more consistent with their sales goals and consumers' preferences. †

Sponsored search accounts for 40% of the total online advertising market. These ads appear as ordered lists along with the regular search results in search engine results pages. The conventional wisdom in the industry is that the top position is the most desirable position for advertisers. This has led to intense competition among advertisers to secure the top positions in the results pages.We evaluate the impact of ad placement on revenues and profits generated from sponsored search using data for several hundred keywords from the ad campaign of an online retailer. Using a hierarchical Bayesian model, we measure the impact of ad placement on both click-through rate and conversion rate for these keywords. We find that while click through rate decreases with position, conversion rate first increases and then decreases with position for longer keywords. The net effect is that, contrary to conventional wisdom, the topmost position in sponsored search advertisements is not necessarily the revenue-or profit-maximizing position. Our results inform the advertising strategies of firms participating in sponsored search auctions and provide insight into consumer behavior in these environments. Specifically, they help correct a significant misunderstanding among advertisers regarding the value of the top position. Further, they reveal potential inefficiencies in present auction mechanisms used by the search engines. the likelihood that a consumer will buy a product) and advertising costs.2 Thus, the net impact of ad position on overall revenues and profits is not well understood.In this paper we address this question by empirically analyzing how ad position in sponsored search impacts an advertiser's revenues and overall profits. We use a unique panel dataset from a Search Engine Marketing (SEM) firm that catalogs daily clicks, conversions, and cost data for multiple keywords sponsored by one of its clients. One of the challenges with sponsored search data is that clicks and conversions are sparse. In order to address this, we use a hierarchical Bayesian model to analyze the click and conversion probabilities in this environment while accounting for heterogeneity across keywords. Our findings suggest that, contrary to conventional wisdom, the topmost positions for keywords in our dataset are associated with lower revenues relative to lower (and less expensive) positions. Our results confirm that ad clickthrough rate decreases with position. However, we find that the conversion rate and revenue initially increase and then decrease with ad position for longer keyphrases. For shorter keyphrases, the revenue decreases with position. However, the costs are much higher in the top positions resulting in higher profits at lower position.Our paper makes two main contributions. First, our paper provides key managerial insights for advertisers. A common assumption in the industry is that the value of a click from a sponsored search campaign is independent of the position of the advertisement. Our results indicate this is not...