Identifying influencers in a social network: The value of real referral data

Abstract

Individuals influence each other through social interactions and marketers aim to leverage this interpersonal influence to attract new customers. It still remains a challenge to identify those customers in a social network that have the most influence on their social connections. A common approach to the influence maximization problem is to simulate influence cascades through the network based on the existence of links in the network using diffusion models. Our study contributes to the literature by evaluating these principles using real-life referral behaviour data. A new ranking metric, called Referral Rank, is introduced that builds on the game theoretic concept of the Shapley value for assigning each individual in the network a value that reflects the likelihood of referring new customers. We also explore whether these methods can be further improved by looking beyond the one-hop neighbourhood of the influencers. Experiments on a large telecommunication data set and referral data set demonstrate that using traditional simulation based methods to identify influencers in a social network can lead to suboptimal decisions as the results overestimate actual referral cascades. We also find that looking at the influence of the two-hop neighbours of the customers improves the influence spread and product adoption. Our findings suggest that companies can take two actions to improve their decision support system for identifying influential customers : (1) improve the data by incorporating data that reflects the actual referral behaviour of the customers or (2) extend the method by looking at the influence of the connections in the two-hop neighbourhood of the customers.

Emotion and sentiment analysis from Twitter text

“…Detecting influential people from a social network is a very lucrative research area for referral marketing to spread the information of any product and reach highest possible nodes of a network [92].…”

Brand engagement in self‐concept and consumer engagement in social media: The role of the source

“…The third increasingly important source of branded content on social media relates to the users or fans, who influence other individuals’ perceptions and behaviors through social interactions (Roelens, Baecke, & Benoit, ). It takes the form of posts (text, images, audio, and/or video) by potential, actual, or former customers of a brand (Chen et al, ; Hennig‐Thurau, Gwinner, Walsh, & Gremler, ).…”

“…It takes the form of posts (text, images, audio, and/or video) by potential, actual, or former customers of a brand (Chen et al, ; Hennig‐Thurau, Gwinner, Walsh, & Gremler, ). Brands aim to leverage their influence on users (Roelens et al, ), as this type of source has the largest impact on the reach of social media posts (Lipsman, Mudd, Rich, & Bruich, ). In this sense, users also exert a major effect on the brand, its associations, and its social media presence (Booth & Matic, ; Pentina et al, ).…”

Fashion Promotion on Instagram with Eye Tracking: Curvy Girl Influencers Versus Fashion Brands in Spain and Portugal

“…The curvy girl audience often finds greater understanding from these micro-influencers than their physical friends, since they transmit their social identity related to positivity and self-esteem, providing support to their community [27]. The social interaction and interpersonal persuasion exerted by influencers facilitate this task of promotion [28] to their followers. Therefore, this activity has aroused ethical concerns regarding the identification of a mere spontaneous promotion versus a promotional activity financed by a brand, for which a specific regulation of this activity has already been suggested [29], as has happened in other conventional media with regard to product placement.…”