The added value of social media data in B2B customer acquisition systems: A real-life experiment
作者:
Highlights:
• We assess the value of social media information in a B2B acquisition context.
• We use data of nearly 10,000 prospects in a real-life experiment.
• Our models show social media is the most informative data source.
• Social media data is complementary with previously investigated data sources.
• We demonstrate the financial benefits of using and integrating social media data.
摘要
Business-to-business organizations and scholars are becoming increasingly aware of the possibilities social media and predictive analytics offer. Despite the interest in social media, only few have analyzed the impact of social media on the sales process. This paper takes a quantitative view to examine the added value of Facebook data in the customer acquisition process. In order to do so, we devise a customer acquisition decision support system to qualify prospects as potential customers, and incorporate commercially purchased prospecting data, website data and Facebook data. Our system is subsequently used by Coca Cola Refreshments Inc. (CCR) to generate calling lists of beverage serving outlets, ranked by their likelihood of becoming a customer. In this paper we report the results, in terms of prospect-to-customer conversion, of a real-life experiment encompassing nearly 9000 prospects. The results show that Facebook is the most informative data source to qualify prospects, and is complementary with the other data sources in that it further improves predictive performance. We contribute to literature in that we are the first to investigate the effectiveness of social media information in acquiring B2B-customers. Our results imply that Facebook data challenge current best practices in customer acquisition.
论文关键词:Social media,Business-to-business,Customer acquisition,Experiment,Predictive analytics
论文评审过程:Received 16 March 2017, Revised 8 August 2017, Accepted 30 September 2017, Available online 1 October 2017, Version of Record 14 November 2017.
论文官网地址:https://doi.org/10.1016/j.dss.2017.09.010