Excerpt: "Online investigators have exposed a network of hijacked computers that defrauded advertisers by generating billions of fake ad views. The so-called botnet scheme, which hijacked 120,000 residential PCs in the US and cost advertisers millions of dollars a month, highlights the increasing complexity and opacity of online advertising."
Excerpt: "Big Data is suddenly hot, winning Harvard Business Review’s recent “sexiest job of the 21st century” sweepstakes."
Very interesting but I wish people wouldn't brandish the Big Data buzzword at anything and everything. This is good old-fashioned analytics at work, but analytics does not equal Big Data (or vice versa).
Short answer: no. Facebook, Apple, Google and all the rest can eat their hearts out. The medical device industry really shows us how to capture and exploit important data from vulnerable users. Quote: "Because device makers see their primary market as physicians rather than patients, they are less motivated to make this information available to the people who actually live with the devices in their bodies."
Facebook just announced a new RTB-enabled ad exchange for advertisers to "buy impressions" in real time on a per-user basis. (RTB = Real-Time Bidding)
Either they've lost their minds or they're desperate to try something, anything to regain the initiative since the IPO fiasco.
Too many people than I care to count have made the point that Facebook users are on Facebook to communicate and socialise, not to buy products. Facebook themselves have touted their ability to help advertisers with the branding problem, not what is known in the trade as "performance media" (i.e., an ad that helps achieve a specific goal, whether that is a click-through or a lead or a sale).
But the very act of tailoring brand advertising to the individual user defeats the original purpose of branding as Don Marti lucidly explains here. The point of brand advertising is to shout from the rooftops that one's firm or product is of high quality. Shout? Why? Because:
When a firm signals by advertising, it demonstrates to consumers that its production costs and the demand for its product are such that advertising costs can be recovered. In order for advertising to be an effective signal, high-quality firms must be able to recover advertising costs while low-quality firms cannot.
Targeting users precisely for brand advertising leaves consumers unable to assess whether the ad comes from a reputed company or a guy in his underwear selling worthless widgets from his parents' basement, because web users know that web ads are cheap.
This is of course not the case when the brand being advertised online is a large, well established one. Every web user knows that IBM and Ford are big companies with reputations to maintain. But that is the crux of the matter -- those reputations were not formed by campaigns targeted down to the individual. They were formed via various forms of broadcast media. Try establishing a brand new brand (uhh...) exclusively with targeted advertising and without leveraging an established brand. Get back to me once you've run out of budget.
RTB is a good thing. Performance media is a good thing. But mashing those ideas with brand advertising is a bad idea in the long term.
I meant to write a longer post where I say more on this and a related topic but that will have to wait for now. I promise that post isn't about Facebook... or isn't exclusively about Facebook!
Quick update: the broadcast-advertising-as-signaling-mechanism concept is certainly true in India. Smart start-ups in India turn to TV, radio and print at the first possible chance when they can afford it, because the relatively conservative Indian consumer (especially in smaller towns where scams are not unknown) interprets being on TV as being high-quality.
Kaggle is a platform where companies, researchers and governments can host competitions to help solve huge data-related problems. About 50 competitions have been run to date and members take just days to solve problems that have stumped scientists for years.
"To date Kaggle has crunched data on dark matter, predicting which used cars are likely to be bad buys, improve the World Chess Federation's official chess rating system, and predicting the likelihood that an HIV patient's infection will become less severe, given a small dataset and limited clinical information," Kaggle claims.
[Founder Anthony Goldbloom] spoke to many CIOs who all said they wanted to do more predictive modelling but their enthusiasm didn't match actual adoption. Goldbloom concluded that the problem was that the product was too technical and the barriers to entry too high."I just became obsessed," said Golbloom. "It was so much better than trying to predict unemployment next month and getting it wrong."
The ideas of competition, wisdom of crowds, and cross-disciplinary analysis themselves are not particularly new but the application of these ideas to big data analytics is new, driven by advances in storage, parallel processing, cloud computing, etc, and by the increasing imperative for organisations to be smarter about what they do with the data they possess.
The phrase Big Data is trending big on the Ingternecks these days and I predict it will only continue to snowball in the next few years. May become as prevalent, even cliched, as terms like Tipping Point, BRIC and so on.
More than 60 years ago, in his “Foundation” series, the science fiction novelist Isaac Asimov invented a new science — psychohistory — that combined mathematics and psychology to predict the future. Now social scientists are trying to mine the vast resources of the Internet — Web searches and Twitter messages, Facebook and blog posts, the digital location trails generated by billions of cellphones — to do the same thing.
But a perceived hitch along the way:
Advocates of privacy rights worry that public data and the related techniques developed in the new Iarpa project will be adapted for clandestine “total information” operations. [...] The ease of acquiring and manipulating huge data sets charting Internet behavior causes many researchers to warn that the data mining technologies may be quickly outrunning the ability of scientists to think through questions of privacy and ethics.
Alright, but once Pandora's box is opened, how does one put everything back inside and lock it up? Not possible. Even ignoring this practical reality, if one looks only at the moralities and ethics of it, what they appear to be doing is taking information publicly available on the Internet and analysing it, just that this is being done with large-scale automated systems rather than manually. Is that wrong? Isn't this what private corporations already do today for profit?
Here's a massive opportunity for technology companies serving the financial services sector: roving bank tellers serve rural India. Has the potential to be a great "Bottom of the Pyramid" success story. Opportunity across several areas including banking software, analytics, security, mobile payments, CRM and so on. I imagine a similar opportunity exists in nearly every emerging market, not just India.
I am happy to announce a Series A investment in CustomerXPs Software Private Limited, a software company based in Bangalore.CustomerXPs is a rare example of a home-grown enterprise software company that aims to compete on the global stage with truly world-leading innovation.
The firm operates in the analytics space to provide human-like intelligence using real-time transaction data (for example, when a bank's customer calls the call centre with a question). A short-hand way of thinking about what the company does is, as one journalist said, CRM Version 2.0. CRM industry insiders would probably see this as Version 3.0, if we define Version 1.0 as contact management and Version 2.0 as analytics on historical customer data. Customer relationship is only one application of the company's technology. The technology is also being used for monitoring of fraud and compliance matters, business intelligence and other transaction-heavy applications.
Besides the team's obvious expertise spanning technical areas (artificial intelligence, data management, statistics, psychology and behavioural science), product management and sales, what struck us is the amount of commitment that the founders had to the long-term vision, with the discipline not to seek easy services revenues by becoming yet another Indian IT solutions company.
I hope to see more such product companies emerging and getting funded in India to allow the broader ecosystem to develop.
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