Summary: With congressional and administrative attention now on the online advertising industry, the industry is responding with “principles” to continue self-regulation. Whatever the outcome of the debate now underway, TOUs and EULAs will change. To the business side of digital companies, these are boilerplate: To the lawyers, they should be seen as what they are–binding agreements with end users and that have serious consequences if they are too one-sided. You can read the entire report atwww.iab.net/behavioral-advertisingprinciples.
The online advertising industry has responded to the February 2009 FTC Staff Report on the topic (which is called “behavioral advertising”). That industry created a report on “principles” for managing these data. These principles represent an attempt to maintain the self-regulation structure now in effect–something that has not made regulators happy (rightly or wrongly).
You can read the report at the URL above. Here is our take on the principles:
- Ad Industry Mobilization–the mere fact that disparate industry associations have gotten together is good news, because these people have great experience and expertise to apply to a topic that is really pretty nuanced.
- No More Fine Print–well, everyone can dream. It is not so much that the industry will eliminate dense legalese in TOUs, but that the language is supposed to be drafted to be transparent–providing genuine guidance that end users can understand.
- Actual Innovation–The report includes one innovation: an “approval” toolbar on browsers.
- De-identification of data–this is the one we like the most. Finally, the ad industry is beginning to recognize that the data can be extremely valuable in their aggregate form, without recourse to knowing about the actual individuals. Pay attention to this one.
- Sensitivity–This principles recognizes that not all data are created equal and some are more sensitive than others. Think of medical records.
- Material Changes–gone will be the days of unilateral retroactive changes to TOUs. Actually, this is just a recognition that the Federal Trade Commission (FTC) will win on this point and that courts are moving in that direction, as well (See blog on Blockbuster case).
So What?
From a legal perspective, this will add more pressure on companies to change their TOUs, but from a strategic perspective, it is one more piece of the evidence of the growing appreciation–not of the data themselves but of their complexity and vast value. In other words, it is no longer an either/or debate: either the industry gets to collect everything or nothing.
Therefore, think about what kinds of data your company wants about use (and not necessarily about each end user).
(See some TOUs, etc., we have drafted: www.npbn.com and www.photospin.com for some examples.)
Smart Meters Go Wireless
May 2009
Summary: T-Mobile USA has hooked up with Echelon, a provider of smart meters, to make those meters wireless (for the US market). Smart meters are a key to upgrading the power grid and the wireless feature will simplify connection to the utility companies.
Echelon and T-Mobile recently announced that Echelon will embed a wireless SIM into its smart meters. T-Mobile’s value-add is also that the chips will be more durable than current deployments.
The wireless connection will improve the link into the utilities, providing them with real-time information on power usage, as well as problems with their networks.
So What?
This is a “shovel-ready” project to upgrade the grid that also seems to have a knock-on, or multiplier effect, not to mention improving efficiency of the power network. Apparently, the embedding has already begun. Echelon has already delivered some 100,000 of its smart meters in the US (to Duke Energy) and more than 1.6m around the world—though without the wireless connection.
The Knock-on Effect
The knock-on effect suggests that companies can provide data management applications for the utilities. The obvious starting point is the incoming data on power usage and network reliability. However, data miners could work with the utilities to monetize those data—with obvious and very careful attention to privacy matters.
Finally, imagine an app on your smartphone and/or your laptop, telling you your immediate usage. One the data are available on a wireless basis, then they can be delivered to any number of platforms (taking into account that the data are initially broadcast in cellular radio format).
Summary: Real life experience of data being collected for one purpose being used for other purposes–adding to the argument that data mining is just beginning. And, when you can do something about it (like own it or get access to it through a license in your agreements) then you should.
Two articles in a recent issue of The Economist added (at least in my mind) to the thesis that we are entering an era when the slicing and dicing of data (OK, OK, call it data mining) will yield actionable results and meaningful rewards. The issue was February 28, 2009.
To reiterate my point in other blogs, it is not so much the results of the studies that are interesting but the fact that data collected through new digital systems for one purpose were used for another purpose.
Permit me to address the first one, on social networks as a source for data analysis.
Facebook and the Dunbar Number (Hint: It is not 42)
First, forgive me if I get the facts wrong (but they are not the point here). Several years ago a professor posited an upper bound (on average) of the total number of people in a social network. That is the Dunbar number.
Later, a Professor Marsden confirmed common sense that there is a much, much smaller “core” network. The Dunbar number is 148; the Marsden number is around ten. (By the way, the Dunbar number has been surprisingly stable over history as an organizing unit for groups like armies, etc.).
Online social networks make social networking more efficient to create and sustain. (The conclusion of the studies cited is that they do not affect these numbers, but that is not germane to the point of this post.) Crunching the numbers from Facebook confirmed (pretty much) both the Dunbar Number and Marsden’s core. The average network is about 120 (close enough to Dunbar) and, by looking at proxies for interaction (proxies are another theme of mine), the core number worked out to be about seven for men and ten for women and in some circumstances somewhat higher. (Please keep in mind that these are averages: Your mileage may vary.)
The Method Is Not Madness
The analysts looked more closely to determine the core. This is where it gets interesting. They used responses as proxies for interaction–that is, leaving a comment or otherwise communicating with someone who has communicated with you. (It turns out that there are cultural or national differences, by the way. For example, in research for a long time on this core it has been known that American men tend to have a very small circle of people with whom they regularly discuss important matters–smaller than other nationalities (this is, after all, a British magazine).
You can imagine all sorts of variables that affect these numbers. That is true. There may be more interaction between two people flirting; there may be more interaction between family members; and there may be less interaction among co-workers based on their rank. Time of day, amount of alcohol–you pick–may also affect these numbers. (These points were not raised in this article–I am just mentioning them).
So Test for It
But that is not the point. You could test for–or control for–any number of variables. Now, think like an advertiser, or a publisher that wants to increase the value (and thus the price of advertising) for the advertiser.
(Note to readers: To anticipate one concern, the analysis does not have to include PII and can–and should–be done in full compliance with the strictest of privacy policies.)
You could cross-check any number of such users to see the overlap of interests. You could check those overlapping interests against geographic distribution over time and then over demographic data (of the group) such as number of males in given age cohorts and then demographic data for the region(s) such as the baseline of behavior for such cohorts, etc.
An advertiser could then check those results against its sales or marketing efforts in the region or, for that matter, its marketing efforts to that group on that social networking site at those particular times.
OK, these insights are not entirely new. Many search and ad companies have been doing this sort of thing for a few years. What has not happened is that advertisers (and their gatekeepers, the ad agencies) have not yet embraced the power of these kinds of data analytics. In other words, it has not yet gone mainstream.
So What About Dunbar Numbers?
I have not yet figured out the import of the actual Dunbar Number (looks like a constant to me) when it comes to monetizing data. One thought is a kind of “data threading.” Assume that people belong to several, if not many, groups. One could trace the overlapping interests across the groups, not to mention the overlap of group memberships, as well. Here we are not necessarily talking about social groups (“friends” in the parlance of Facebook and “Contacts” in the parlance of LinkedIn).
For example, I belong to some groups in which a few of those members also belong to other groups of which I am a member but they also belong to other groups of which I am not a member. Some number of those members share my interests; some number do not. These overlaps could be “threaded.” And, they could also be tested over time.
The Conclusion, Please
OK, OK, I am way over my self-imposed word limit. Perhaps you get the point. I will post on the second article soon (about crowds being analyzed by CCTV). This should add weight to my point that your agreements should get you access to anay data collected in any digital deals you do.
And, yes, as a result, expect CPMs (and monetizing online experiences) to rise.
Transactions & Strategy 