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SmartData Collective > Big Data > Data Mining > Social Networking, as seen by The Economist
Data Mining

Social Networking, as seen by The Economist

DavidBakken
DavidBakken
5 Min Read
Social Networking, as seen by The Economist
Illustration generated with FLUX.1 [schnell] via Cloudflare Workers AI.
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The current issue of The Economist (January 30 -February 5 2010) features a 15-page special report on social networking.  Typically thorough, the report covers history, the differences between major players (Facebook, Twitter, and MySpace), benefits for small businesses, potential sources of profit for social networking sites, and some of the “peripheral” issues–such as the impact on office productivity and privacy concerns.  For any marketers who’ve been caught by surprise by the emergence of social media and social networking as marketing forces or been watching out of the corner of their eye, this special report might be especially informative.

The potential for making money from social networking services–that is, for the owners of Facebook and other sites–lies in selling advertising.  The Economist points out that the unanswered question is whether the social networking industry can find something as successful as search-based advertising.  More important from my perspective is whether either the social networking industry or the customer knowledge industry will come up with a way to exploit the information contained in the behavior of social networks.  A few hints of the insights potential are found in bits and pieces of data sprinkled throughout the report (mostly provided by MR companies that track web behavior, like comScore or by the social networking companies themselves).

“Listening” to consumers via social media is getting a lot of buzz these days and as a result new, mostly technology-driven services are springing up to capture content from blogs and other online conversations.  Semantic filtering, key-word search, and more sophisticated text-mining tools allow counting and categorization of these conversations.  One example is “brand monitoring” or “listening” to conversations about a brand.  

Content represents the first level of information contained in a social network.  In the case of brand monitoring, this could include brand name mentions as well as sentiments (positive or negative modifiers) that appear with the brand mentions.

The second level of information is source.  Who generates the content?  How does content vary by characteristics of the source?  Some companies that capture content claim to capture demographic information about the source, but this may be highly restricted or “approximate” information, akin to appending census block demographics (the census-based “average” values for a defined geographic unit) to customer transaction records.  It would seem that the social networking sites like Facebook are in the best position to match social media content with source information.  That might become a new source of revenue–and a big threat to the market research industry.

The third level of information is connectivity.  Who is talking to whom?  What do the networks look like?  Content combined with source and network structure will be very powerful information indeed.  One of the obstacles to understanding and leveraging word-of-mouth has been the difficulty of simultaneously measuring these three dimensions.  

The social networking companies will have the data.  What models will emerge for transforming those data into new and more powerful insights for marketers?

Copyright 2010 by David G. Bakken.  All rights reserved.

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