What is the most meaningful way to understand and measure a dialogue? As marketing transforms from a broadcast model to a conversational one, which constructs should be captured and how do you measure them? Is it necessary to make a distinction between the metrics used to tap into the value of a conversation per se and the ROI of a social media marketing campaign?
The proposed presentation offers new strategies to think about and tap into the depth of interactions and emotional connections people have online. Beyond buzz levels, sentiment, and other core metrics typically provided by brand monitoring solutions, the presentation will offer methods to understand a conversation: how emotional is it, how in synch are the constituents, how intimately do they relate to the brand or product? How much trust do the constituents reveal?
Marketing efforts that take advantage of technology to enable community and collaboration render traditional metrics limiting, at best. Traditional metrics have been optimized for more passive exposure to a specific message, frequency of exposure is considered a proxy for relevance; and, the premium is on reach over quality.
Primarily due to its more participative dynamic, a conversation engages constituents unlike static messaging. As many in the industry have noted, a natural development is to measure engagement. However, there is little consensus on what engagement means and how it can be measured. Often it is calculated by merely adding traditional metrics, assuming more is better.
The presentation will introduce new constructs and present case studies with empirical research demonstrating more valuable, still measurable constructs than the core metrics currently in use.
Kate is part of the founding team at Dachis Corporation, an Austin based start-up delivering social business design services.
She joined from Nielsen Online, as the VP of Measurement Science, liaising between Research, Product, and R&D. Kate focused on developing internal methodologies and supportive technology, introducing established Nielsen data to consumer-generated media, and consulting on innovative client requests.
She completed her Ph.D. in Social Psychology at the University of Texas. Her research and publications concentrate on the role of language in relation to personality and interpersonal perception, including the development of Linguistic Inquiry and Word Count (LIWC), a probabilistic text-analysis software program. Her doctoral dissertation examined personality based on everyday behaviors and language.
Kate has spoken at industry events and conferences including WOMMA, AdTech, MediaX, ARF, Linkage Leadership Forums, and ICWSM, and published in several academic journals including the Journal of Advertising Research, Annual Review of Psychology, Journal of Language and Social Psychology, and Handbook of Positive Psychology.
Read more about her interests at http://socialabacus.blogspot.com
Marc Smith is a sociologist and Chief Social Scientist at Telligent Systems, a provider of fine quality social media platforms and systems. Smith specializes in the social organization of online communities and computer mediated interaction. He founded and managed the Community Technologies Group at Microsoft Research in Redmond, Washington and is now leading the development of social media reporting and analysis tools for Telligent. Smith lives and works in Silicon Valley, California.
Smith is the co-editor of Communities in Cyberspace (Routledge), a collection of essays exploring the ways identity; interaction and social order develop in online groups.
Smith’s research focuses on computer-mediated collective action: the ways group dynamics change when they take place in and through social cyberspaces. Many “groups” in cyberspace produce public goods and organize themselves in the form of a commons (for related papers see: http://delicious.com/marc_smith/Paper). Smith’s goal is to visualize these social cyberspaces, mapping and measuring their structure, dynamics and life cycles. He developed the “Netscan” web application and data mining engine that allows researchers studying Usenet newsgroups and related repositories of threaded conversations to get reports on the rates of posting, posters, crossposting, thread length and frequency distributions of activity. This research offers a means to gather historical data on the development of social cyberspaces and can be used to highlight the ways these groups differ from, or are similar to, face-to-face groups. Smith is applying this work to the development of a generalized community analysis platform for Telligent, providing a web based system for groups of all sizes to discuss and publish their material to the web.
Smith received a B.S. in International Area Studies from Drexel University in Philadelphia in 1988, an M.Phil. in social theory from Cambridge University in 1990, and a Ph.D. in Sociology from UCLA in 2001. He is an affiliate faculty at the Department of Sociology at the University of Washington and the College of Information Studies at the University of Maryland.
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