ICYMI - the NYT Story on Facebook’s Chief Information Security Officer Leaving

So I came across a link to this story again this morning -- I'd seen references to it earlier in the week. The story, Facebook Exit Hints at Dissent on Handling of Russian Trolls by Nicole Perlroth, Sheera Frenkel And Scott Shane was published on March 19. I think that the first thing I saw about this article was some back and forth about the NYT had changing the article to soften the treatment of Sheryl Sandberg, but I didn't actually dive into the piece at that time. What drew me back to this piece was a reference that I saw to this quote from a former Facebook employee.

“The people whose job is to protect the user always are fighting an uphill battle against the people whose job is to make money for the company,” said Sandy Parakilas, who worked at Facebook enforcing privacy and other rules until 2012.

Not that that isn't apparent from the Mark Zuckerberg interviews from this week. Clearly the business is in full damage-control-spin mode. In fact, as you read through the piece, it's hard not to come away with the feeling that Facebook management is attempting to do everything they can to avoid really addressing this issue. And they certainly don't appear to be making substantive changes to their operations. At the heart of this is probably the recognition that these issues strike at the heart of their business model.

Personal Data -> Super Advertising Demographic Targeting -> In a Box
In reflecting on it, I'm reminded of this story from 2012, How Companies Learn Your Secrets, about Target's big data team. This is the story where they took all of Target took all of their purchasing data, linked it with a bunch of demographic data, and then statistical analysis, they were able to predict things like when a customer was pregnant based on their purchasing habits.

In some sense, what Facebook does is take all of this advanced technical work that Target did, and sell it to advertisers, pre-packaged and conveniently gift-wrapped. In many respects, the issues from the 2016 election, Cambridge Analytica, and Facebook are all stories about this aspect of marketing. Consider this quote from that story:

“With the pregnancy products, though, we learned that some women react badly,” the executive said. “Then we started mixing in all these ads for things we knew pregnant women would never buy, so the baby ads looked random. We’d put an ad for a lawn mower next to diapers. We’d put a coupon for wineglasses next to infant clothes. That way, it looked like all the products were chosen by chance.

“And we found out that as long as a pregnant woman thinks she hasn’t been spied on, she’ll use the coupons. She just assumes that everyone else on her block got the same mailer for diapers and cribs. As long as we don’t spook her, it works.”

Of course, as I've noted in the past, with Facebook and our broader experiences on the web, there's a built-in aspect of believing that "everyone else on the block got the same" page / view / experience. In the case of Facebook, their core platform and their business model is all about this data -- masked by the presence of family and friend photos so that they "don't spook" users.

Another Look At Twitter vs Facebook and Algorithms

Here's another story courtesy of the MediaREDEF newsletter. This link is from the Washington Post, again comparing feeds from Twitter and Facebook on Ferguson. What this does have is a great little bit of analysis about why you probably aren't being deluged with stories about Ferguson in your Facebook feed.

Content that causes dissension and tension can provide short-term rewards to Facebook in the form of heated debates, but content that creates accord and harmony is what keeps people coming back.

The article backs it up with some study data that suggests that when that guy you sort of new posts a bunch of anti-Obama political crap, not only do you regret adding him as a friend, it makes you uncomfortable -- and therefore less likely to log into Facebook.

More On US Data Monitoring vs. Cloud Computing

Here is another link to analysis on the potential impact of US data monitoring on the cloud computing industry. This one features numbers and links to an interesting white paper that looks at the potential revenue losses to cloud computing companies here in the states.

Snowden’s gone but his impact on cloud computing remains

Data, Relationships and Story: Marketing and NSA Monitoring

As the Snowden data monitoring story continues to live in the news, one theme that gets a lot of visibility is whether or not, by providing public visibility to these programs, he has exposed the secret inner-workings of a spying infrastructure, and that by exposing that, he has somehow weakened the protections it provides -- or provided, as the case may be.

On one level, this story sounds like yet another one of those Internet privacy stories -- Facebook is watching you online, Google is reading your email, or your computer may actually be a zombie in a bot net. For much of the technology-challenged world, the headline embodies all of the frightening possibility of a campfire ghost story. But, as with other big stories with complexity and depth, this story is far more nuanced than a simple black and white, right and wrong.

The reality is that we live in a data driven world. From the moment you turn on a device and it connects to a network, there is an electronic discussion that takes place. Some of the communications may be innocuous, like a device handshake with the network to give it identity or your computer asking a server what time it is. Or when you pull up a web page in your browser, your computer talks to a server that then sends back the data that your computer needs to build a web page. In the process of sending you data, that server may verify who you are, then call a bunch of it's friends, tell them your name, and ask them to send you data as well. And in a world full of servers and electronic logs, each of these transactions is logged in journals, and your history in each may affect the other.

This is the electronic ecosystem. Fundamentally, some of these things need to work like this in order for things to operate. Below the web sessions or the phone calls, the core back and forth of devices and interacting requires identity, memory and structure.

Anonymity on a network is not true anonymity, what it really is is a disconnect between identities. In the brick and mortar world, it is possible to have essentially anonymous transactions. You can have a conversation with another person in an isolated room. You can go to a store in a different area and purchase something in cash. But electronic transactions are different. Each electronic communication is like a phone call from one location to another. Electronic payments are essentially promises to transfer funds with a number used to identify the person writing the IOU. While we might want to imagine electronic activities conforming to the realities of our experiential world, they don't. This can have both costs and benefits.

Logs, Logs Everywhere - In Pursuit of Real Identity
Web marketers have long known that, while it's interesting to see what pages people visit, it can be even more interesting if you know where somebody came from and where they go after they visit your site. Is this someone interested in your product? Are they comparing your product to a competitive one? Have they been to your site multiple times? This is the type of data that can be extracted from a simple cross-site tracking cookie. Within that, typically, we try to weave together a tapestry of data points. Can we get the visitor to complete a form and give us some identity or contact info? Did they download files?

In it's simplest way, these are elements that can be tracked from a basic web log on one site. Or, using something like Eloqua, Marketo or Pardot, tracked across multiple sites and marketing deliverables. As marketers, we look for every bit of data that we can get, every touch point, in an effort to build an identity. We want to invest all of our selling resources into the process of converting that potential customer into revenue.

And yet, for all of our efforts, our tracking and our analysis, our best efforts are still just a sketch. Our simple tracking can easily be fooled by someone doing research at home, then going into the office or maybe using a different email address.

This problem of identity has always been an aspect of the web that's been both celebrated and loathed. While we're happy to be 'anonymous' when we're looking at things we might not want people to know about -- competitor's web sites, job listings, embarrassing medical conditions or even online porn -- anyone who has been in a chat room, a forum or the comments section of a blog knows the evil of anonymous trolls posting irrelevant or hateful comments. Real identity is often a thematic solution for these issues, sort of a, "you wouldn't post that if everyone knew who you were" approach.

But in that way, you can see where a government program that reaches across services and joins the various data streams is not particularly mind-blowing in terms of technical scope. For an organization like the NSA, being able to sort through different emails and identify that even though the email address for crazy voice in the alt.discussion.terrorist-bombing-plans isn't the same one as the guy who just ordered 10 pressure cookers on Amazon.com, and even though one uses Gmail and the other uses Yahoo, they both actually originate from the same IP address.

Crafting Persona and The Importance of Story
If you were to look at your typical web site log, what you have is a series of events. Data points. But they are nothing without a story. Consider a typical goal path through your web site ending in someone filling out a registration form and downloading an electronic asset. If you have 100 people visiting the page with the registration form but only 50 downloading the file, you need to build a story that explains the two pools. For those that didn't download, maybe the form was too long. Maybe they just wanted to see browse. Maybe they were competitors.

While it may seem like an arbitrary process and difficult to imagine, we actually do this all time in real life -- it's how we build an understanding of events. Think about when you're driving and you see another car use a turn signal. In simple terms, it's a directional indicator, a single data point that tells you that this car is planning to shift in that direction. But, in order to really understand what they intend to do, you need to put it into context of a larger story.

• Do they intend to change lanes?
• Are they planning to exit the freeway?
• Are they making a turn?
• Did they forget their blinker and are driving down the road with their turn signal on?

That same story framework helps make sense of traffic patterns -- in the morning, there are a lot of cars trying to get off at this exit -- and creates stress when people behave unusually: Why is this person driving 20mph in a 45mph zone? Why are they shifting across three lanes now? While driving has a reality framework that makes it relatively easy to map cognitively, ultimately, you are still making guesses about what's going on in the other car, about what's going on in the engine inside the other driver's head. We analyze and we predict, but we don't know.

Understanding can be particularly challenging when you're looking at similar behaviors. Is this person weaving because they are drunk, dialing on their cell phone, or just being buffeted by crosswinds? In this context, one might be an ongoing threat, one a short term threat, and the other a broad-scale operational concern.

Building a story about online activity requires a much broader understanding of the landscape that the person is interacting in. Imagine the example of a single data point in your own system log, one where your system connected to an IP address in China. When your system connected to a server in China, did it go there because you loaded a web page with an ad network that pulled a file or a script from a server there? Did it connect there because your system has some advertising or tracking cookies on it from a previous visit? Did it go there because your system has malware running and it's compromised? Or did it just connect there because you're running Skype and there's a peer-to-peer link that connected there? Without having a broader tapestry of the transaction, this single data point is unintelligible.

Story and Data Correlation
In real life face-to-face interaction, understanding what's inside of someone's head can be difficult. It's potentially more problematic using electronic data. Even with a broad set of data points, algorithmically understanding intent and motivation often fall short. Consider Amazon. A visit to Amazon will get you follow-up emails with pricing deals on the things that you looked at. While this type of remarketing has higher clickthroughs than other programs, how often does it feel like you're being spammed? And, when those items that you searched appear on your 'My Amazon' page, how often do they actually help you get to the thing that you were interested in? On the marketer's side of the equation, that value is greater than zero so it counts as a win, but on you, the customer's side, it's far from a perfect match. If just having lots of personal tracking data was a slam dunk for understanding motivation, Facebook's advertising programs would be far more effective.

Ultimately, our story constructions are shaped by a variation of A/B testing and validation. First, there is the story, the hypothesis -- since this guy just activated his turn signal, I think he's going to exit the freeway. Next, we have the test -- does he get off at the exit? Once we've completed the test, we now have to evaluate the results and reinterpret our story.
Observations about data -- like the correlation between purchasing habits and pregnancy -- don't just bubble up from the data. They require a hypothesis and a framework for analysis. Consider this great article, In Head-Hunting, Big Data May Not Be Such a Big Deal, in the New York Times interviewing Laszlo Bock, senior vice president of people operations at Google. In the interview, Bock talks about some of the practices that Google used during the hiring process, and how well they correlated to their actual job performance. Essentially, he shoots down the value of famous Google practices like 'brain teasers' and asking all candidates for their GPA (I know what you're thinking, tell me something I didn't already know). Keep in mind, before they could evaluate this to see if it actually correlated to performance, somebody came up with the hypothesis that these things mattered. Google wanted to hire the smartest, best employees, so they defined a hypothetical profile of what those people should look like, then ran interview screening processes based on those. It's only after years of running this experiment that we see their hypothesis is being shot down.

Secrets, Lies, and the World of Cyberspying
Arguably, the most sensitive point in this whole Snowden story surrounds the secret, classified nature of the programs. Admittedly, it's difficult to measure a secret program. On the one hand, you have this big reveal that the government monitors electronic communications and activities, the great NSA version of Eloqua. Yawn. On the other hand, you have the government claiming that it is a national secret and officials saying that they didn't monitor communications.

The government's interest in having access to this kind of data is not new. While it seems like a rather simplistic idea now, remember the clipper chip? This was basically the government saying we can help American businesses with encryption, but we'll keep a key to the back door open so that we can monitor the bad guys using it. After the Bush-era telecom monitoring stories, are you really surprised that the government has an ear on the Internet and is hoovering up your electronic communications?

At the same time, remember the environment that you live in. There are malware exploits out there in the wild that allow non-government entities to monitor your computer, log your keystrokes, even turn on the camera and microphone in your computer -- some criminal or 15-year old pervert could be watching you through your laptop as you read your morning email. There are foreign governments that have exploited your electronic systems to gather intelligence on you, on your business, and on your technology. And the other day, as you drove home chatting with your significant other, you actually broadcast all of those secrets over the radio. Admittedly, it was a cellular radio designed with controls to make it more secure and more private, but did you really think that it was equivalent to the two of you speaking intimately in your bedroom?

This is the reality of the environment that we live in. The fundamental nature of these technologies means that electronic data is available and it can be monitored. But just as in the real world where you're unlikely to physically prevent an armed police officer from entering into your house and searching your premises if he wants to force his way in -- saying "you can't come in" doesn't actually prevent a search. Instead, historically, we have opted to disincentivize forced entry behavior by making any evidence collected without a warrant be inadmissible in legal proceedings. With electronic data right now, we've essentially handing the review of this over to a secret process. Rather than simply handing over the decisions surrounding the implications of this to secret, hidden elements of the government, we need a more open discussion of the potential and the impact of these types of programs. We need to define a framework for what we establish and rights and protections in this modern data environment. Otherwise, what happens when the government starts sending you Target-like coupon books because you might be guilty of a thought-crime?

Again, the real problem here isn't the data, it's the application of the data and the potential for abuse. It's not the terrorist plots that you stop, it's what happens if you know what porn site Mitt Romney looked at. It's not just the government that you fear; instead, it's what happens if the non-profit that you work for finds out that you 'anonymously' publish a blog about your sex life. Or what will your neighbors think if you order birth control from an online pharmacy because your local pharmacist has moral reservations and doesn't fill those orders?

In the vast and expanding world of our digital breadcrumbs, we all have moments that would rather not share with our friends, our colleagues, or our government. In the court that governs shame and embarrassment, there is no way to disincentivize moral outrage. There is no statute of limitations. Whether you're Paula Deen, Lance Armstrong, or (one of my favorite controversies) Sasha Grey, the perception of who you are and what you stand for lives in a sliding scale world that changes over time. Sometimes that time period can be short, sometimes decades.

Our electronic data is a lot like our DNA. With the evolving understanding of DNA, increasingly we know more and more about a person from their DNA. We can understand their genealogy, we can diagnose what's wrong with them, and we can even make predictions about their future. This type of information is so powerful that, as a culture, we attempt to be very careful with the availability, distribution, and use of it. We fear -- and probably rightly so -- discrimination, exclusion, or a wide range of potential limitations to life, liberty and the pursuit of happiness. Because we can imagine the dark potential inherent in all of this, we are cautious. In that same way, we need to approach our digital data in the same way. 

Marketing, NSA Monitoring and Big Data

In light of the numerous overblown 'scandals' running through the recent stories in the news, the NSA data monitoring story offers a tale that's actually worth looking at, reflecting on, and thinking about in greater detail. The story is an interesting look at both the amazing upside of our modern technology world and a reminder about how difficult it is for us to truly conceptualize the full scope of big data.

Remember this story about how Target figured out a teen girl was pregnant before her father did? It's an amazing story about what a corporate data group was able to accomplish with records that they were able to obtain from within the public domain. But, again, the key aspect of this story -- and one that's fundamental to the broader big data story -- is that the individual data points can look trivial because they don't tell the story. It's not the data points, it's what happens when you have lots of data points and you can use them to make correlations. In Target's case, it's not that you bought unscented moisturizing lotion, it's multiple factors that make that an indicator of a stage of pregnancy. One data point that statistically correlates to many, many others in a demographic model.

From a human perceptual perspective, it's really difficult for you to extract yourself from the personal significance of that transaction moment and extrapolate that as a data point in a bigger picture. We all see ourselves as individuals, not as similar patterns. It's part of what makes a big data surveillance program frightening. Instead of seeing the aggregate data and what the potential impact of that could be, we're more likely to worry about those times when we drunk-dialed our exes late and night, and what might happen if somebody found out. Or all of those times where we viewed the web sites with the lingerie models.

On some level, we all break the rules. Whether it's driving 70mph sometimes, even when the posted speed limit is 65 or downloading an episode of that epic cable television show that everyone is talking about because you're unwilling to pay the crazy annual subscription price for one show that only lasts a few weeks. Most of us understand that there's a cloudy area that separates the letter of the law from violating the spirit of the law. In most cases, we -- as members of a society -- hold to a set of values that keep us within that cloud, within our perceived sense of the spirit of the law. And so, while we might tolerate someone driving 10% faster than the posted speed limit, we tend to wish for law enforcement when we see someone operating outside of that range. And so, if some unrestrained adult tweener recklessly drives his Ferrari though your residential neighborhood, you're probably going to be outraged.

The Threshold and the Target
With all of the data available on these networks, it's hard to imagine not aggregating intelligence from it. As with the Target story, there are aspects of this process that are somewhat common in the modern Internet age (which is probably one factor in why there isn't an overwhelming amount of outrage surrounding the privacy concerns). At the same time, because these practices are so common, it's almost more surprising to hear the outrage from some government officials over these programs being revealed -- sort of like unveiling the secret that Facebook tracks your activity and builds an electronic profile of you. I would describe the outrage as laughable were it not for the mysteries that still remain cloaked -- both the Guardian and the Post have only published limited details of the program.

So, if it's helping keep you safe from the terrorists and it's commonplace, what's the concern here? Well, first and foremost, is the application of that data-gathering intelligence. While it seems surprising that Target can make good guesses about whether a woman is pregnant based on her buying habits, imagine what the government might be able to profile and demographically target. While international terrorists might avoid electronic communications, what about some of these lone wolf crazies? Imagine if you could build a profile to identify and monitor them? The upside of all of this has the potential to capture bad people and prevent bad things. At the same time, what happens if you're one of those people who thinks that the Occupy Wall Street participants are terrorists? Or those people from the Tea Party showing up at political rallies carrying guns? Maybe part of Code Pink and potentially going to heckle the President?

And think about the Target story again. Remember how Target was sending it's pregnant prospects coupons? Understanding that this is a potential use of the data, how does the government handle it? And at what stage of their 'terrorist' or 'criminal' pregnancy term do they target the person profiled? And when does behavior or electronic communication become 'thought crime' or 'pre-crime'?

Ultimately, I think that this is where we need more sunlight and a more publicly visible oversight of the process. While the overall problem is complex and probably difficult for the 'tubes' using Internet public, the potential to abuse this process is vast and powerful. After all, one of the biggest differences between the profiling that Target does and the profiling being done by the government -- if we don't like what Target does, we can opt not to shop there. We can opt to not participate, to not give them data. With the government, we don't have that option.

Internet Moneyball: Why Android, Facebook and Twitter are Overrated

Over the Thanksgiving break, I finally got around to watching the Moneyball movie. I enjoyed the movie and was also amused by this transformation of a business book into a good entertainment piece. It's also a great reminder about how statistics and data can be a far more powerful measure of reality than conventional wisdom.

Which brings me to these interesting posts from Business Insider that capture some great analytics data from the black Friday weekend world of online shopping:

Is It Time To Conclude That Android Gadgets Are Bought By People Who Don't Actually Do Anything With Them? This post is an interesting look at online traffic and, more specifically, the difference in the number of Android devices in the world versus the percentage of web traffic. Consider these numbers from the article:

Android phones now account for nearly 75% of the global smartphone market. The next closest competitor is iPhones, which have about 15% of the market.
In the U.S., Android is clubbing iPhone 53% to 34%.

Contrasted against these traffic stats:

A recent survey of mobile web usage found that a staggering 60% of mobile web visits came from iOS devices, while only 20% came from Android.

A study IBM did of Black Friday online sales showed much the same thing--except that it was even more skewed.

iOS (iPads and iPhones) accounted for nearly 20% of Black Friday sales.
Android devices, meanwhile, accounted for only 5.5%.

Of course, this traffic disparity is no surprise to anyone looking at their analytics traffic. In the back of my mind, I used to chalk this up to the newness of Android or the lack of market penetration. Most of the people that I know use iPhones or iPads, so there's also a first-person perceptual sense of the market. But the reality of these stats seems to point to something more significant. Like Moneyball, the analytics stats reflect a different reality than conventional wisdom might suggest, one that underscores a measurable difference between iOS and Android. In this case, unit volume isn't much of a measure of the demographic and, unless you make components that go in these handsets, the number of Android devices doesn't really matter. There is a disconnect between devices and 'users'.
 
Guess What Percent Of Black Friday Online Sales Came From Twitter Referrals? There are a lot of interesting online sales stats in this post. But for me, there are another two highlights from this post that are worth noting:

Only 0.68% of Black Friday online sales came from Facebook referrals--two-thirds of one percent. That was a decline of 1% from last year.

and

Commerce site traffic from Twitter accounted for exactly 0.00% of Black Friday traffic. That was down from 0.02% last year.

Again, these are just data points, but they point to a disconnect between how these platforms are being pitched and how they are being used.

There's more good stuff in each post, so take a minute and dive into each one -- it makes for some interesting holiday leftovers.

How Facebook Advertising Should Really Work

A while back, Facebook's stock price got hammered by their recognition that they were struggling to monetize mobile. Not long after that, more news came out of Facebook that they were focusing more of their advertising on retargeting.

The retargeting story was an admission that letting advertisers target audiences based on their profiles was a rather ineffective method that delivered poor ROI. With retargeting, a technique that had been used elsewhere on the web for quite some time, Facebook was seeing doing a better job of delivering conversions and seeing better monetization of their traffic. Across the online advertising world, there was sort of a collective, "duh". Among the analyst community, there was a small howl of, "what happened to the best minds of our generation?"

Fundamentally, the real problem here is that for all of the "potential value" of Facebook's big data, its massive volume of user tracking data is disconnected from its advertising programs. Think about that, Target knows enough about its customers to be able to determine how pregnant a woman is based on what they've purchased, but Facebook's most intelligent answer for advertisers is whether the user has self-identified as male or female on their profile page.

This is the great Facebook illusion. On the one hand, they have this user interaction platform that's great for harvesting user data. It's something that everyone talks about. And yet, for all intents and purposes, they haven't come up with a way to bottle that stream and make it easy for any advertiser to get a good value from it.

Contrast that with Google and their Adwords program. With Adwords, they came up with a way for advertisers to benefit from the underlying value and functionality of their platform, to integrate ads with search.

This is the "Unified Theory" problem that Facebook needs to solve. Admittedly, there are a host of challenges to balance, not the least of which being privacy and utility, but if the can crack that nut, then Facebook will be worth something closer to the hype. Until then, it's not much more than an Internet broadcast network.

Facebook Engagement = Logging in to Update My Settings

It strikes me as funny that the biggest driver for my engagement with the Facebook platform has become a semi-annual log-in to update some aspect of my Facebook account settings. This week, it's updating my email because Facebook decided that I would probably prefer a "Facebook email address". In the past it's been to make sure that the data that I thought was private was private -- I know, that's actually more than once.

What always surprises me is the way that Facebook seems to want to ignore this idea of opt-in to change. Instead, it's always we make a change that's good for us, then you can go turn it off if you can find the setting and think you want to undo the change. They might want to blame it on the nature of the cloud or modern software, but I think that the reason that so many people are troubled by the company's behavior when it comes to this issue goes back to the freedom to choose to opt it.

When you download an updated version of software, you choose to opt in. When Salesforce.com updates the way that their platform works, most of the time you have the option to enable the feature or not -- particularly when it may affect your data and your relationship with your customers. If there were a software bill of rights, your right to choose whether to opt in would probably rank high in that list.

With Facebook, configuration and privacy settings are kind of like one of those mental dream puzzles in Inception, an ever shifting landscape. Considering that distrust of the Facebook platform is so strong, it leads you think that trust is not an overriding goal in their brand strategy.

Unless, like Inception, there is a bigger, hidden factor behind the ever-shifting landscape of distrust -- yet another engagement algorithm mining your behavior data?

Gamification: Check-ins and Updated Foursquare

With E3 going on, gamification seems like an good topic. Over at Pando Daily, Erin Griffith has a post on Foursquare's newly updated app and their changing relationship with the check-in.

Perhaps you remember 2011 — it was the year the check-in died. That sentiment wasn’t lost on New York’s resident Lord of the Check-ins, Foursquare.

The company witnessed slowing user growth and a backlash brewing. But rather than ignore it and continue to congratulate themselves on achieving darling status and a crazy-rich valuation, Foursquare did the right thing:

They disassembled the entire app and put it back together again.

As someone who has been using Foursquare for a couple of years, I've personally seen some interesting dynamics associated with the app -- call it anecdotal analysis of behavior patterns and use case. While you might call Foursquare a deals site or focus on the social aspects, gamification is an important part of the app and plays a key role in many of use patterns that I've seen. Here are some lessons that I've taken from Foursquare's gamification.

Badges and Mayorships
Badges and 'Mayorships' can help drive early adopters or provide a small reward for some exclusive social circles (possibly location-based), but they become somewhat irrelevant with long term use or when the number of users exceeds an early adopter threshold. Sure it might be cool to be the mayor of the local Starbucks, but with hundreds of people checking in every day, do I really care about bragging rights over the guy behind me in line? And what do I get for it? Maybe something like a free cup of coffee, but more likely nothing.

The Point System
Foursquare's point system is a great driver for competitive behavior, but their point scoring model also has issues. Foursquare's scoring system points to one of the great challenges of gamification -- the differing interests between new users and experienced users. Foursquare scores new check-in locations with higher point values that repeated check-ins. This means that if you go somewhere for the first time or you are a new user, you get more points that if you go to your usual restaurant.

This model is great for drawing in new users with a sense of competitive behavior. Over the past couple of years, I've seen several people use Foursquare with the goal of beating me on the scoreboard. And when they are new users, this gives them a bump of excitement as they outscore the veteran user. But as they become a more regular user, their scores fall off and they fall into the veteran scoring system. Depending on the user, I've also seen this result in a fall-off of engagement with the app.

In that same way, a good game needs to provide a good entry point for new users, but deliver an increasing level of challenge based on use and experience. At the same time, if there isn't a correspondingly increasing reward system, regular users will fall off. A simple way to restate that would be put in context of the structure of Diablo 2:

• As you play the game more, you get more experience. 
• The battles you fight are against tougher creatures, but the treasure and the items that you get are also increasingly greater.
• Later in the game, you could go back to the areas that you went through earlier, but the battles offer little challenges, the rewards are usually too small, and there just isn't any real challenge, so most players don't go back.

The Foursquare point system doesn't really provide any substantial growth path or increasing level of return. Instead, it's almost like it was designed to serve as nicotine hook -- to get you habituated to a behavior of checking in. Unfortunately, from what I've seen, there isn't the same addiction and new users get bored with the application as their point-rewards start to drop.

Location-based Flash Deals
While this initially held some promise, I've yet to see more than one or two deals that got me to log my check-in with a business. While this could be a result of the challenge of selling local, I also think that there's a difference between the kind of people who seek out deals (and use something like Groupon) versus those that happen to be at a location, check-in, and then are surprised by some added reward for that behavior.

Forget About Santa: Target Uses Big Data to Know if You're Naughty or Nice

Here's an interesting couple of items that I came across on Linked In. They are some great insights into the power and pitfalls of big data analytics and it's impact on marketing.

How Target Figured Out A Teen Girl Was Pregnant Before Her Father Did by Kashmir Hill with Forbes.

And the original New York Times Magazine article that her post is based upon:
How Companies Learn Your Secrets by Charles Duhigg