Walking Around Money

A young company called Placed is deep into Big Data analytics, but with a twist: it marries customer data with its own proprietary data to yield insights into customer behavior. Essentially, Placed wants to provide context around how customers use the mobile applications of its clients, for example, when do they use the app and where do they use it?

The “where” part of the analysis is what’s interesting. Placed could simply spit back to its clients that its customers are in certain ZIP codes or other dry demographics – interesting, like so many analytics reports are, but not particularly useful.

Instead Placed marries customer location with its own proprietary database of places – named stores, major buildings, points of interest. By connecting the two, Placed can tell its clients where mobile use of its app is occurring. For example, if a client’s customers utilize its mobile app in a competitor’s store, it might suggest competitive price comparisons. Knowing its customers frequent Starbucks and nightclubs might influence the clients’ marketing strategy or advertising campaign design. Knowing that the app is used most often when someone is walking (yes, Placed can tell you that) can be important for user interface design – you get the idea.

And therein lies an important insight. There are an endless number of companies offering Big Data analytics capabilities. But almost all of them expect their customers to bring both the problem and the data. That’s a sure recipe for commoditization, and as analytics software evolve, it’s also certain that the companies with the biggest analytics needs will decide to do the work themselves.

Solution? Big Data analytics players should bring proprietary data to the party. Placed is a perfect case study. It differentiates itself by providing answers others can’t. It adds value to its analytics by integrating proprietary and licensed data with customer data and its own optimized analytical tools. As I discussed in my presentation at DataContent 2012, there are lots of ways publishers can profit from the Big Data revolution -- even if they don't have big data themselves.

In a market where companies like Placed can make money by tracking people walking around, it behooves data publishers to walk around to some of these Big Data analytics players and suggest data partnerships that will help them stand out from the crowd.

Perfectly Measurable

An interesting new study produced in cooperation with eBay and sporting the weighty title “Consumer Heterogeneity and Paid Search Effectiveness: A Large Scale Field Experiment,” raises some interesting new questions about search engine marketing (SEM) effectiveness. The study involved eBay actually suspending various types of SEM on specific search engines, and then closely monitoring the results. The first big finding was that, at least for large companies, brand advertising via SEM (e.g. eBay buying the keyword “eBay”) was a waste of money. While it’s always good to see hard proof, at the same time, this finding strikes me as intuitively obvious. I see it all the time with big companies with strong brands: their paid ads appears directly above their organic listings. Drawn to the bold type and high positioning, consumers will often click on the paid click, enriching the search engines and yielding the advertiser a click they would have gotten anyway for free.

The second (and more controversial) finding is that SEM for non-branded keywords (i.e., products or services as opposed to company names) is also largely ineffective, because it tends to draw in far more existing customers than new customers, making SEM more of an alternate form of navigation than a true discovery mechanism. Indeed, the authors of the study go so far as to say, “Bluntly, search advertising only works if the consumer has no idea that the firm has the desired product.” In short, SEM only really works when you bring a user new information, such as the fact you sell a specific product, and the user didn’t know that information in advance.

The study is careful to limit its conclusions to large companies with big brands. And this notion of having to provide new information for SEM to prove effective would seem to be a perfect opportunity for smaller and less-known companies. Or would it?

While far less scientific, I have spoken with dozens of small B2B data publishers who have tried SEM and pronounced it a waste of money. While SEM reliably yields traffic (great if you are advertising-based), it doesn’t seem to dependably yield purchases (not so great if you are subscription-based). Yes, I know B2B is different, and site visitors often don’t purchase on their first visit. But my informal survey was of small publishers, who though they often lack sophisticated analytics, generally have a very good seat-of-the-pants sense of where their business is coming from. And for these publishers, they believe their business isn’t coming from paid search.

There’s no definitive answer here, but it does suggest we all take a much harder look at paid search efficacy, and consider the possibility we may be paying handsomely for clicks we would have gotten for free anyway.

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Education Data: Lessons Learned

A recent Reuters story described a new national database of student information. Reportedly built at a cost of $100 million, and backed by prestigious non-profits such as the Bill and Melinda Gates Foundation and the Carnegie Corporation, the aim of the project is to build a standardized database of information on all students in the country, grades K-12. No, this is not aggregate data. This is detailed, specific information on every student that can include such information as grades, learning disabilities, hobbies and interests. Surely this database doesn’t include student names and other identifiers you say. But in fact it does. And that’s the point. It’s also why this database is so exciting to so many companies in the education market. The goal is to jump-start technology-driven individualized learning for students.

According to the article, school administrators have long (and legally) maintained all sorts of data on students for educational purposes. And, as you would suspect, every school did things a little differently. They collected different data elements and held them in different formats in different locations. So if you were marketing educational technology to schools that tried to personalize the learning experience, you faced a painful data interface challenge for every new school you sold. Seeing a real impediment to growth for cutting-edge educational technology, several big foundations jumped. And rather than just developing a data standard which would take decades to gain widespread adoption, they invested to actually build a single database. Participation by schools is voluntarily and (currently) free, but lots of incentives have been created to spur participation.

We can draw a few fascinating lessons and trends from this initiative.

First, we see a wonderful acknowledgement of what I modestly call Perkins’ Law: no organization will voluntarily build and maintain a database if it is outside their core competencies and there is a viable alternative to doing so. The commercial data publishing business is really built around this law: data publishers succeed because people want the data, but don’t want to collect or maintain it themselves.

Second, we see another great example of a “data pipe,” where one organization provides data that developers can tap into via APIs to build applications driven by that data. The data provider seeks to become an information utility, while dozens or even hundreds of different developers can identify and mine niche opportunities faster and better than any single data publisher. This is a relatively young model, but it’s quickly gaining a following.

Third, valuable data is more often than not sensitive data as well. As this database hits the radar of parents and civil liberties advocates, the inevitable questions around privacy and security are being asked. And the answers to date, according to the article, do not seem particularly robust or reassuring. The non-profit managing the database makes all the appropriate noises about protecting the data, while at the same time the database exists in large part to benefit commercial entities. While the goal of the database is laudable, we have a classic example of a database that will likely succeed only with strong governance and privacy policies. This is something that commercial data publishers will need to become attentive to in years to come.

It’s a fascinating initiative, and one where we can all learn by example.

When It Pays to License Data

I have long advocated for something I modestly call "Perkins' Law." It holds that "No company outside the information industry will voluntarily build and maintain a database if there is a viable alternative to doing so." Over the years, I have found this posit to hold up pretty well. Indeed, when I find an industry where multiple companies are collecting largely the same information themselves, there is usually a data opportunity. This dynamic drives off an even more fundamental truth: building and maintaining a quality database is hard and expensive work.

So when companies start bragging about building databases when their primary business and expertise is elsewhere, watch out. Back in the early 1990s, a very successful software company decided it wanted to take on media information company SRDS. Their software was impressive. The company had keyed all the necessary listings data. I saw a demo of the new product on a giant screen in a huge conference room. The software folks prattled on about their cutting edge code. The marketing folks described endless features. I have to say, I was impressed. Then I asked about what type of editorial group they had put in place to maintain the data. The room got very quiet. Apparently, the content had been viewed as a one-off activity. The product was quietly scuttled a year or so later.

This, and many other related experiences over the years came flashing back to me this week when I read that high-flying Internet darling Airbnb was busy building its own global city guides that will assess such factors as nightlife, quality and quantity of restaurants, etc. Yes, pretty much the same information you can get 100 different places online. And if you have any remaining doubt that Airbnb is in over its head, note that to build its database, it found a need to fly "neighborhood experts" in from all over to world to help build its database. Further proof: it's working on printed guides as well.

An isolated case? Maybe not. Consider the well-publicized debacle of Apple launching its own map product with its own business and points of interest database. Apparently Apple, having read too many of its own press clippings and deciding it could do no wrong, went out and promptly did wrong.

When companies not in the data business decide they need data, the smart answer is to license whenever possible. Data is hard. Some companies build databases out of ignorance of options; some build them out of hubris. But you should stay alert to both such situations, because whether educating a company or bailing it out, Perkins' Law can be a dependable source of attractive new revenue for you.

Unlocking the Value of LinkedIn

TechCrunch posted a long and thoughtful analysis of LinkedIn this week. In short, it suggests that LinkedIn is at risk from a growing number of vertical market professional networking sites, and faces a "death by a thousand cuts." The risk of being "sliced and diced" is real. Any horizontal provider of information stands at perpetual risk of a competitor targeting an attractive vertical segment and doing a better job. The same holds true for professional networks. It might be nice that you could theoretically interact with any other professional anywhere, but in reality, most of your interaction is with others in your specific industry.

But is LinkedIn really at risk? I don't think so, primarily because I don't think of LinkedIn as a professional network.

Blasphemy? It may sound like it, since LinkedIn has routinely been classified as a social media play almost since its inception. Indeed, If you think back to the early days of LinkedIn, it was explicitly designed as a novel attempt to codify the concept of "six degrees of separation" among business professionals.  If you continue to think back, you will also remember how quickly that concept fell on its face. Nobody used LinkedIn as originally intended, but it was in the right time and place to become the largest and richest biographical database in the history of the world, and that's where its money does and should come from.

LinkedIn has reached a stage where your failure to have a LinkedIn profile raises questions about you in many circles. It has quietly grown its company profiles (all cross-linked to individual profiles) to the point where a number of business information providers are taking worried notice. Many individuals base their job hunting on well-burnished LinkedIn profiles. In some professions, having the largest number of connections is a sign of success. Increasing number of data publishers are tapping the LinkedIn API to gather, augment and maintain their own databases. I could go on, but in its own weird, wacky and wonderful way, LinkedIn has become part of the fabric of business.

Right now, LinkedIn profits handsomely selling access to its structured database to recruiters and others. But this is just the beginning. LinkedIn is poised to become a critical backbone database with numerous uses. In one obvious application, CRM software companies are all rushing to integrate LinkedIn into their applications. But think beyond the obvious. For example, LinkedIn could be a hugely powerful trust and identity tool. After all, it's hard to fake more than a handful of connections, and are you likely to blatantly misrepresent your career in front of all your friends and colleagues who you invited to link to you? There is very valuable confirmation and verification locked up in all this linking that remains to be fully exploited.

The LinkedIn database could be used in a number of ways to tune up spam filters. You might even use it to prioritize incoming messages from your connections. LinkedIn is ever-eager to suck in your contact list from your computer, but what if it maintained that list for you (remember Plaxo)? Suddenly, LinkedIn would become the central database of business. And let's get inferential for a moment. I am certain that someone somewhere is trying to correlate the number and quality of LinkedIn connections with creditworthiness. And what about evaluating the quality and prospects of a company based on the extent and quality of the LinkedIn connections of its management team?

Crazy ideas? Maybe. But all I am trying to do is illustrate that while the social elements of LinkedIn are nice and necessary, don't lose sight of the fact that it has only just begun to mine one of the most remarkable databases ever created. There's gold in them thar hills!