Social Media Marketing: How Data Analytics Helps to Monetize the User Base in Telecoms, Social Networks, Media and Advertising in a Converged Ecosystem - Couverture souple

Jaokar, Ajit; Jacobs, Brian; Moore, Alan; Ahvenainen, Jouko

 
9780955606977: Social Media Marketing: How Data Analytics Helps to Monetize the User Base in Telecoms, Social Networks, Media and Advertising in a Converged Ecosystem

Synopsis

Social Media Marketing - How Data Analytics helps to monetize the User Base in Telecoms, Social Networks, Media and Advertising in a Converged Ecosystem Social media marketing is a mechanism to interact with a set of online social media conversations from a marketing perspective, based on converged media (since conversations span technologies and media). Social media marketing is measurable via a set of social media metrics. These metrics function as the proverbial 'air traffic control' monitoring the domain in almost real time. Based on the data driven dials of this interface, the marketer monitors these many way conversations. Many way conversations take place between the marketer and the participants in a social network - but also amongst the participants themselves. The marketer benchmarks the insights gained from these conversations against a set of transactional data (sales, surveys etc) to monitor and tweak a series of narrowcast (long-tail) campaigns. Thus, instead of having one large 'broadcast' campaign - we have many small narrowcast, interactive and ongoing campaigns. The campaigns and conversations are based on a feedback loop, hence they are iterative and form an ongoing learning experience. The extent of social media marketing campaigns include social media advertising, but could also encompass product development, trend monitoring, reducing churn, benchmarking and so on. Specifically, social media marketing can be used as a part of a two stage process: first, to identify certain patterns in data, secondly to verify those observations by specific social media campaigns which also seek permission from the customers. The provider sends personalised messages to the receiver, and over time, the visibility of the participant's digital footprint grows and leads to better personalization. Therefore, we start with passive digital footprints (based on data patterns) and transition to active digital footprints (based on trust). Data and privacy form the bedrock

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