If you are not paying for it, you're the "big data" product

Andrew Lewis made a great comment on user-driven content back in 2010 in a metafilter.com exchange that since has become a Internet meme of sorts on the range of free Internet services being offered from Facebook, Google, MS, Yahoo and lot's of others: "If you are not paying for it, you're not the customer; you're the product being sold".

Great one-liner, but what does it mean? Well, it lead to Mr Lewis, of course, starting his own online shop offering t-shirts, coffee mugs and aprons with that same slogan, going almost meta on his own meta.

But in the setting of free or "free" Internet services like Facebook, Google, Yahoo and MS online services, running on some of the largest server and application platforms ever deployed and developed at a significant platform and man-hours cost one must assume, why is it offered for free?

One obvious answer is advertising and the development of personalised or context driven, online advertising on the Internet and mobile devices. Be it in the form of banner ads, splash ads etc or product content being adapted to user location or ZIP-code, time of day, device type and earlier browsing history for the user in question.  This gives advertiser a way to achieve much better and greater ad targeting than usual shotgun advertising of "manual" or analog media, and it's much easier to see ad hits, view times, conversion rates, also in real-time than with traditional media. So giving away IT-services like storage, communication services like email and IP messaging or content is a way to attract users, get them registered, build user base and attract more advertising dollars.

Another angle is collecting and aggregating user data per site, device type, ZIP-code or region and use this aggregated user and usage data for business analytics, trend watching, benchmarking new service offerings and competitors services.  That then goes into the continuous re-work and make-over that most large IT companies do all the time.  Analyzing customers and customer behaviour should lead to greater service offerings.  And we are over in big data and analytics territory.

And one of the most fascinating stories of using big data analytics to understand customer behaviour and wants, comes from Netflix and how the House of Cards TV-series got created, partly at least if we are to believe the backgrounder here.  Netflix has been very open and explicit about its plans to exploit user data logging and its big data capabilities to influence its programming choices well before the House of Cards TV-series was aired. Netflix has detailed viewer logs for any market they are in, broken down by content type, country, ZIP-code, time of day and device type and more.  Knowledge of Netflix subscribers viewing preferences pointed towards a political TV-drama with a number of defined attributes, among them starring Kevin Spacy for the lead, that would ensure high engagement levels and viewership through the Netflix recommendation engine, that is claimed to influence 75 percent of Netflix subscribers in viewer choice.  Big data logging and recommendation engines are a match seemingly made in heaven.

Other reasons for giving away IT and communications services for free are simply to stay competitive and doe service bundling and/or upgrades.  One guy is selling 2 GB of storage for $5 per month - that doesn't cover cost anyway and nobody actually uses 2 GB - why not give it away and attract more users, and then later on try to move them to more premium, paid service offers, presumably with better performance and higher service levels?

And that has been the approach for introducing Internet services or offers for the last 20 years or so.  Hasn't worked, best-effort free services worked too well in most cases - and generated tons of user and usage data anyways. that at least kept the marketers and advertisers happy.

Erik Jensen, 28.10.2013

Twitter IPO and Twitter message streams for market analytics: The real potential of Twitter for advertisers

Twitter, which filed for an public IPO with the US Securities and Exchange Commission back in June, that didn't become public before now in September, hopes to raise $1 billion with their public IPO. The SEC S-1 documents made public, stated that Twitter had $253,6 million in revenue for first half of 2013,  with net loss at 41 percent to $69.3 million and with some 215 million monthly active users.

The current and future revenue are said to come from three main ad-based sources:

1. Promoted tweets that appears in users message feeds
2. Promoted accounts (i.e. brands, companies, events etc) that appears on Twitter landing pages
3. Promoted trends, where advertisers can buy their way into trending lists, themes and developments

The Twitter "ad media universe" is somewhat limited and the advertising tools available for advertisers might seem limited as well.  But with the Twitter IPO being a confidential or "secret" IPO available for sub $1 billion companies (in revenue), all the available or future ad channels for Twitter hasn't been highlighted. To me there's one obvious one that is clearly missing (though I haven't read the full S-1 documents), and that is the big data mining and analytics opportunities with Twitter tweets and message flows for consumer tracking, audience sentiment tracking and overall market trends.

The Twitter streaming APIs gives developers or Twitter log collectors, applications or apps "low latency access to Twitter's global stream of Tweet data", either collecting all Twitter messages in a continuous stream, collecting single-user message streams or site streams . 

With more than 500 million tweets a day through Twitter, this gives market analysts, advertisers, companies and Twitter itself of course, a unique view into

• Trending themes and developments, i.e. new phenomena of all sorts, Internet memes, things going viral, movie or TV-shows releases, new consumer brands, pop stars, new albums, books etc
• Long-term development and standing of brands, products, product models, consumer sentiments
• Developing news and events
• National and regional break-downs of trends, developments and long-term standing 
• Cross-linked with mobile or PC access, client type, time of day, frequency of tweets or mentions etc

Utilizing Twitter message streams for near real-time market analysis and consumer views should be a no-brainer for advertisers, just as Netflix used their own data analytics to create House of Cards and other TV-shows - how long before advertisers catches on?