Via economist.com -
Telecoms operators naturally prize mobile-phone subscribers who spend a lot, but some thriftier customers, it turns out, are actually more valuable. Known as “influencers”, these subscribers frequently persuade their friends, family and colleagues to follow them when they switch to a rival operator. The trick, then, is to identify such trendsetting subscribers and keep them on board with special discounts and promotions.
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Companies can spot these influencers, and work out all sorts of other things about their customers, by crunching vast quantities of calling data with sophisticated “network analysis” software. Instead of looking at the call records of a single customer at a time, it looks at customers within the context of their social network. The ability to retain customers is particularly important in hyper-competitive markets, such as India. Bharti Airtel, India’s biggest mobile operator, which handles over 3 billion calls a day, has greatly reduced customer defections by deploying the software, says Amrita Gangotra, the firm’s director for information technology.
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Of course, companies have long mined their data to improve sales and productivity. But broadening data mining to include analysis of social networks makes new things possible. Modelling social relationships is akin to creating an “index of power”, says Stephen Borgatti, a network-analysis expert at the University of Kentucky in Lexington. In some companies, e-mails are analysed automatically to help bosses manage their workers. Employees who are often asked for advice may be good candidates for promotion, for example.
Ellen Joyner of SAS, an analytics firm based in Cary, North Carolina, notes that more and more financial firms are using the software to uncover fraud.
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Last year an American government body called the Recovery Accountability and Transparency Board (RATB) began using network-analysis software to look for fraud within the $780 billion financial-stimulus programme. In addition to the internet, RATB combs Treasury and law-enforcement databases to uncover “non-obvious relationships”, says Earl Devaney, its chairman. The software works very well, he says. It has triggered about 250 ongoing criminal investigations and 400 audits.
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The Army Criminal Investigation Command already sniffs out procurement fraud by scanning text in e-mails. The software, developed by SRA, an American firm, can correlate numbers and phrases written in nine languages with financial databases. If a person discusses a particular Department of Defence payment with an individual not officially linked to the deal, SRA’s software may notice it.
The police department of Richmond, Virginia, has pioneered the use of network-analysis software to predict crimes.
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Party plans turn out to be a particularly useful part of this picture. Richmond’s police have started monitoring Facebook, MySpace and Twitter messages to determine where the rowdiest festivities will be. On big party nights, the department now saves about $15,000 on overtime pay, because officers are deployed to areas that the software deems ripe for criminal activity. Crime has “dramatically” declined as a result, says Mr Hollifield.
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Network analysis also has a useful role to play in counterterrorism. Terror groups are often decentralised, so mapping their social networks is akin to deciphering “a big spaghetti picture”, says Roy Lindelauf of the Royal Dutch Defence Academy, who develops software for intelligence agencies in the Netherlands. It turns out that the key terrorists in a group are often not the leaders, but rather seemingly low-level people, such as drivers and guides, who keep addresses and phone numbers memorised. Such people tend to stand out in network models because of their high level of connectedness. To find them, analysts map “structural signatures” such as short phone calls placed to the same number just before and after an attack, which may indicate that the beginning and end of an operation has been reported.
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The Telegraph UK has another related article on data mining and its affect on us all...
Makes me wonder, if we will see the development of companies that are designed to predict and tweak the predictive analysis results for an individual willing to pay the money. This would require a sort of personal data warehouse with constant feedback, leading to behavioral adjustments habits (e.g. spending habits, people in your social circle, etc) over time.
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