The InfoCommerce Blog

Hard 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!

History Matters

A fascinating article in Technology Review highlights and quantifies a problem that is also an opportunity: disappearing data.

 

The article reports on the findings of a new research study of social media that finds that in an analysis of recent major cultural events (e.g. Arab Spring), 11% of the social media content had disappeared within a year and 27% within 2 years. Beyond that, the study authors have calculated that the world loses 0.02% of its culturally significant social media material every day.

 

Keep in mind that what the study authors are measuring is not the number of tweets that are disappearing, but the web pages that these tweets are linking to.

 

And while this study only looked at headline news events, you've probably had experiences similar to mine: links to news stories and press releases that are no longer active, businesses removing all traces of failed product launches from their websites, online stories that are undated and thus difficult to rely on, news outlets that continue to treat yesterday's news like yesterday's news - and the list goes on.

 

The short message is that historical knowledge is always valuable, and it becomes even more valuable as it becomes less available. And that's what we are seeing online. Whether by accident or design, more and more content is "aging off" the web, creating opportunities for those companies that hold onto it in an organized fashion.

Yes, there are amazing resources like The Internet Archive that attempt to maintain searchable, historical copies of the entire web, but as you might suspect, this is hard, and there is a limit to the amount of detail it can store. But taking on this task on a vertical market basis is do-able, and potentially quite remunerative.

Many data products are designed to provide the latest and most current data, and that's smart. But as you add new data, don't delete the old data. It's amazing what insights can be gleaned by watching the changes to a business over time, and as the art of data analytics evolves, this capability will only get more compelling. But you can't analyze data you don't have. More than a few data companies I know owe their strength and their profitability to the fact that they maintained historical data. And as you become the sole source for content that has disappeared elsewhere from the web, you create a proprietary aspect to your content, along with an often insurmountable competitive barrier.

So take a little time to think about what information (specific data points or full-text documents) you can preserve, and how valuable this information might become if it wasn't available anywhere else, because that's the trend I see. What's past isn't just prologue; it may also be a new profit center!

#hashtags

As Twitter continues to grow, so does the use of hashtags (those keywords and concepts in tweets preceded by the # symbol). But hashtags can help or hurt your goal of maximum visibility for your tweets. A couple tips based on observation and some quick research:

• Use them judiciously
• Use them authentically
• Their use/misuse reflects favorably or unfavorably upon your brand

Using one or two hashtags is about the most you'll want to use in a tweet, if their use is warranted at all. Tweets using a particular hashtag are classified together in a list. Within Twitter, clicking on the hashtag produces a list of all posts across the site. Using 3 or more hashtags means your tweet will appear on more lists, however the overall power and focus of your message is diluted and your content will look like gobbledygook. Remember, hashtags eat into your 140 maximum character count. The more you tag a tweet, the less you can say in a tweet.

 

Hashtags make content easily findable on Twitter, but for a relatively short period of time - which varies depending on a number of factors. This time-limited aspect means that hashtags are terrifically useful for current events - unusual weather events, breaking news, sports scores, product releases, conferences, etc.

 

Hashtags can be used as means of appearing on "trending" topics lists that exist to attract followers to content. For example, trending topics which appeared within the last week or so were: #thestorybehindmyscar, #mycatisajerk. As you will guess from these examples, a hashtag such as #softpaywalls (based on an actual recent InfoCommerce tweet), will never make a trending topics list. Which is to say, when it comes to social media for business, trending topics lists are generally irrelevant.

 

Hashtagging broad terms is not helpful either. Hashtags for generic words like #marketing, #money, #music, #internet, #ecommerce, etc. are nearly useless because the topics are so broad and so often referenced that they don't appreciably improve searchability. In this instance, hashtags mostly just gunk up a message and create the impression that the writer doesn't understand the concept of hashtags or the platform itself.

 

Sometimes hashtags are the rough equivalent to saying "like" or "ummm" when you speak. They seldom add to the readers understanding and reflect poorly on the writer.

Search engines being what they are - operating on ever-changing rules and keen on sniffing out baddies - are more likely to push heavily hashtagged content lower on search results pages because it has the look and feel of stuffing. "Stuffing" - in not terribly sophisticated or technical terms - is loading your message with unnecessary words in hopes one of them will trend and help your content be found. Generally, stuffing is something that: 1) people who don't know what they are doing do, and 2) something unscrupulous people do.

 

Finally, hashtagged posts are not necessarily read more often than posts without hashtags. Social media software provider Argyle Social researched hashtags and developed some interesting insights into their effectiveness. It is worth reading in full, but here are some key summary points:

• In 53% of the social properties analyzed, posts with hashtags
  actually performed worse than posts without hashtags.
• In 21%, there was no significant difference between posts with and
  without hashtags. And 26% of social properties had better performance in posts
  with hashtags.
• When averaging across the entire sample, posts with hashtags
  received 5% fewer clicks than posts without hashtags.

Argyle's summary conclusion of its research is one that I agree with as it relates to hashtags in any sense: Your followers are your primary source of attention. Tweets should be targeted at providing value to your existing followers, as they are the most likely to read and share your content.

 

-- Nancy Ciliberti

Micro-Monetization

Yes, I just made up a new buzzword, micro-monetization, which is meant to describe a huge trend online to create services to monetize things that previously couldn't be monetized because they were too fleeting, too small or too difficult to bring buyer and seller together. It's a fascinating area full of large, latent opportunities, and I believe B2B publishers can find opportunity here as well.

What they heck I am talking about? Consider one of the best-known examples of the genre, AirBNB.com. This service allows people to rent out their apartments or even spare bedrooms to strangers as a low-cost alternative to hotels. Not to be outdone, another service called Camp In My Garden actually allows people to rent out their backyards to campers. And it's not just real estate. RelayRides will let you rent out your car to strangers by the hour or by the day.

There are also numerous services that let car services sell their down time efficiently. Consider PlaneFinder.com and EmptyLegMarket.com, both of which sell empty legs on private jet itineraries. My point is that that there is remarkable activity, creativity and opportunity in this area, but the focus so far has been consumer-oriented. Can this work in business? Absolutely. Consider the example of GetLoaded.com, one of several services to help truckers find cargo to haul on what would otherwise be an empty trip home.

Companies such as LoopNet help businesses sub-lease excess space. Put your creativity to work. Does your vertical market have expensive capital equipment or facilities that typically aren't fully utilized? Are there inefficiencies you can address on a spot market basis - opportunistically matching buyers and sellers? Here are just a few top-of-head examples to get you thinking: would restaurants rent out their kitchens on the days they are closed to the growing number of people creating small batch products for sale? Could companies rent out their parking lots at night to truckers seeking a safe, hassle-free place to park overnight? Could companies rent out their conference rooms to other companies looking for a place for out-of-town meetings? Crazy ideas? Perhaps. But who would have thought that you could build a business letting people rent their backyards? And don't get caught up in liability and other legal concerns right out of the box - many of the companies cited above pushed ahead despite myriad legal vagaries.

So put your thinking caps on. You know the players in your market, and as importantly, they know you. That gives you a credible, neutral central market position that will go a long way to building out a micro-monetization opportunity in your market.

Sentimental Journey

Sentiment analysis represents a real opportunity for many data publishers to add new, high-value, proprietary and even real-time insight to their data products. But sentiment analysis has inherent strengths and weaknesses you need to appreciate when considering if there is a sentiment analysis opportunity for your data products.

A wonderful example of sentiment analysis at work is represented in a new service called the Twitter Political Index. Working with two respected political polling organizations, Twitter analyzes over 400 million tweets per day, to determine how people are feeling about the two presidential candidates. The key word here is "feeling," because that's what sentiment analysis is all about -- guessing how people feel about a topic. It's not easy, and it is particularly complex for tweets, which are short and often lack context. Moreover, most sentiment analysis tools go well beyond simply binary like/dislike assessments and try to gauge the degree of like or dislike. It's tricky stuff, but the potential applications are numerous and exciting.

This is often the point where many people will start questioning such issues as whether or not Twitter offers a representative sample, and what level of precision and confidence sentiment analysis can offer.

These are valid questions, but be careful not to fall into the trap described by research industry veteran Ray Poynter, who notes that all new research methodologies are invariably measured against the standard of perfection. This implies that all current research metholodogies are perfect, which is far from the case. When using tools such as sentiment analysis, you need to consider the application, then pick the methodology, seeking the best fit possible.

That's why data publishers should be thinking about sentiment analysis. You don't need to analyze every tweet; indeed you don't necessarily need Twitter at all. Sentiment analysis can be applied to research reports, blog posts and press releases. And if you can help your
customers better understand how the world currently views a company or product, for example, you can deliver a useful new layer of insight that differentiates you from the competition, makes your products more valuable, and can be acquired and implemented fairly quickly and economically.

And particularly with new research methodologies, I think it's useful to remember the saying, "don't let the perfect become the enemy of the good." Building powerful new data analysis tools is a long journey, one that both publisher and customer are taking together.

The Strategic Use of APIs

Looking for change, challenge, growth? Increased innovation across your organization? New content models and revenue? A new audience acquisition strategy? The ability to knock out the competition? Then think about giving third party developers to access to your content and data in a structured and open manner via APIs -- Application Programming Interfaces. APIs represent a way for publishers to develop new sources of revenue by increasing content distribution fueled by technology and bringing outside ideas in.

 

Consider Twitter and the constellation of products created by third party developers in its orbit. Twitter provides users up-to-the-minute content on a continuous basis and generates ad revenue through sales of promoted tweets. Twitter is a relate-able and familiar model to publishers.

 

By allowing third party developers access to its content, Twitter invited innovation from the outside in, increasing the use and value of its content and boosting its revenue. Third party development using Twitter’s API makes Twitter even more useful and draws a larger user-base to its content. Twitter’s ongoing evolution holds valuable lessons for those producing and distributing content.

 

Innovation, Increased Data Use, Expanding Audiences - APIs provide external talent the ability to develop novel useful new pathways to your content which increases data use and revenue and helps companies innovate and evolve past its competition. Providing access to content and data in a structured and open manner for third party development provides the opportunity to design entirely new ways for existing customers as well as new customers to experience content.

Successful publishers understand the importance of aligning content to the capabilities new technologies bring. It’s a tough job since publishing as an industry has traditionally under-valued and under-funded R&D and struggles with accepting external ideas. APIs represent the next step in developing new ways of presenting and pricing content as well as meeting the expectations of an audience which is constantly growing in technological sophistication.

 

Monetizing APIs, Controlling Access to and Pricing Content -APIs offer endless possibilities to monetize content which are limited only by the imagination of app developers. Technology exists for controlling the access to and securing content as well as the tools necessary for monetizing it.

Old-timey revenue and pricing models publishers are already familiar with: ad-supported, transactional and subscription as well as somewhat newer models like DaaS (Data-as-a-Service) can be implemented in conjunction with systems for tracking and billing for data usage.

 

APIs and Expectations - Across industries and businesses APIs are redefining how companies develop their products and conduct business and the steadily escalating growth of APIs will influence and shape expectations about how content is accessed, used and priced.

 

-- Nancy Ciliberti

 

Measured Progress

Monitoring what's going on with online start-ups is not only a great way for me to identify interesting new data-driven products and new market opportunities, it's also a touchstone for assessing how the publishing industry is doing relative to the best and brightest online innovators. Here are just three recent examples, each interesting to me in a different way:

 

Mixpanel, an undeniably hot and respected web analytics company recently gave an interview where the founder proudly stated that, "The next leap we're taking is being able to tie data to an actual user." If that sounds like an audience database, that's because it is. And the publishing industry has seen the opportunity in creating comprehensive user/subscriber profiles for years now, and poured significantly resources into this effort. And based on the audience database projects we've been involved in, I am pleased to report that many publishers are well on their way to databases that will truly be cutting edge in the targeting and insight they can deliver.

 

Another hot new start-up, Retailigence, does one thing: it helps big companies put their store inventory data online. This isn't a new concept; companies like Milo have been offering similar services to smaller retailers for years. But what is intriguing about Retailigence is that it is selling to big companies, those with big SAP applications and the like. The lesson here is that even the largest companies, those with the resources and the incentive to do it themselves, will still turn to a third-party vendor for a well-crafted solution that helps them quickly address a business need. And with all that product data (and information on what is selling where and how fast), one has to wonder if Retailigence has a data opportunity at least as large as its software opportunity.

 

Finally, here's an elegant data product that solves a small but important problem: how can online retailers easily offer discounts to senior citizens, college students and active duty military personnel? It's something that is done so routinely in bricks and mortar retail that nobody thinks about it. But online, it's next to impossible to do this in a seamless, hassle-free way.

 

Enter SheerID, a new database that helps automate this process for online retailers, right inside the shopping cart. The need is clear and the concept couldn't be simpler. What jumps out at me is that something like this is just being addressed in 2012 - a powerful proof statement that for all the amazing innovation and progress we've seen on the web, there are sizable infrastructure opportunities still to be found, and many of these opportunities will be data-driven.

 

So should you ever start to feel that all the good opportunities have been mined, or that you're falling behind the technology curve, take a good look around you. You're sure to find both re-assurance and opportunity just about everywhere.

Twitter and LinkedIn: It's Complicated

On June 29, Twitter and LinkedIn decided to end a partnership that began in 2009. Before the 29th, tweets had the ability to flow seamlessly from Twitter to LinkedIn. That's no
longer possible. Twitter has restricted its API to prevent tweets from posting to LinkedIn user profiles. LinkedIn users can still create updates to publish to Twitter - it is a matter of clicking a button and it happens.

 

More significantly, the separation is a story which illustrates the difference between how collaboration looks on paper and how it plays out in practical terms when collaborating
companies mature and change and business models uncomfortably bump up against
one another.

 

If you are a regular reader, you are likely an information provider. As an industry, publishers are familiar with business model conflict and the Twitter and LinkedIn the
split is not surprising.

 

The pairing made sense for convenience reasons: compose once and publish twice. A seamless flow of tweets from Twitter to LinkedIn added aspects of community that LinkedIn, with its origins as a structured database, had lacked from its inception.

 

As a website for professional networking, LinkedIn succeeds in its ability to connect people. Once connections between people on LinkedIn are made, the ability to share
information is limited. LinkedIn Groups have found wildly varying degrees of success. (InfoCommerce LinkedIn Group members: please check out my colleague Megan's question posted earlier this week regarding how helpful you find LinkedIn Groups and weigh in).

 

But is this loss of seamless "tweet flow" truly a big loss for LinkedIn? Arguably not.

 

Although the collaboration enabled sharing of information between LinkedIn connections, the pairing was not without its problems. Pacing and content between the sites were a less than ideal match. Overall, LinkedIn is much slower paced than Twitter. The Twitter partnership produced significant amounts of content for LinkedIn. Yet Twitter users who tweet often (say 15 or more times a day) tend to stand out and can crowd or eclipse LinkedIn generated updates displaying on the site. Perhaps this is the reason why hiding tweets on LinkedIn was an option.

 

Further, tweets aren't entirely consistent with that which should be shared on a professional networking site. Twitter content that doesn't play well to an audience of
business connections could carry more significant consequences than just personal embarrassment. And even anodyne tweets, because of their economy of space, still offer ample room for miscommunication.

 

LinkedIn and similar sites using Twitter's API have created a range of value-added products from Twitter clients to analysis tools. These products have improved Twitter's
value and reach. Even though LinkedIn was never really a destination to go to read tweets, LinkedIn and others using Twitter's API may have funneled some traffic away from Twitter which presents a challenge when money is in the mix. Twitter's revenue model relies on ad money (promoted tweets).  

 

As International Business Times' Valli Meenakshi Ramanathan notes: "Though the end of the partnership was nothing new in the social network landscape as search giant Google moved away from the microblogging sweetheart recently, leaving Twitter to spruce up its search function to stay on track, the changes did call for LinkedIn having to redefine
its strategy and operations."

 

Bottom line, this is a battle of eyeballs, user loyalty and control of content. And while LinkedIn might look like the loser, it probably is time for LinkedIn to put more effort into enhancing its value proposition, rather than papering over the issue with a tidal wave of tweets.

-- Nancy Ciliberti

 

The Outlook on LinkedIn

LinkedIn is one of the most important data products ever created, and the recent Fortune interview with LinkedIn CEO Jeff Weiner is chock-full of insights and fresh thinking. I can't do justice to the full interview in one post, but here are a few points to whet your appetite:

 

     
1. Data Beget Data: As Weiner puts it, "One of the most exciting parts of the LinkedIn platform and the LinkedIn ecosystem is that the more members we attract, the more deeply they become engaged, the more data is being generated. And that data can be leveraged to create more relevant experiences for our members and better return on investment for our customers. Data really powers everything that we do. So, it powers algorithms that will suggest people you may know, so you can build out your network. It can suggest groups you may like that you can join and share information and knowledge."

 

The notion that participatory, open databases can achieve a network effect, where every new participant makes participation in the databases more compelling to others, is hardly new. But LinkedIn takes this to another level, using the data it collects to fuel more engagement and more participation. This is the unique capability of structured content - it is more susceptible to algorithmic alchemy.

        
2. A Global Labor Marketplace: LinkedIn believes that if it can collect enough information about people and companies (and it already has over 2 million company profiles), it can create a global labor marketplace that efficiently moves the right person to the right job, something that could fuel tremendous new levels of productivity and economic growth. How often do you get to wrap a big business vision with a clear social good? As importantly, this thinking supports my belief that many data products can pivot from information repositories to true industry marketplaces. 

 

3.  Core Competencies: LinkedIn has clearly set its sights on becoming a provider of internal corporate directories, something big companies need, but rarely execute well. And what a beautiful idea: companies get better and more powerful internal directories while LinkedIn reaps huge new numbers of public profiles. And might I add that this is a wonderful example of Perkins' Law: "No organization outside the data business will voluntarily maintain a database if there is a viable alternative to doing so."

           
4. LinkedIn and Outlook: In an attempt to stir the pot, Fortune suggests to Weiner that Microsoft should buy LinkedIn and integrate it into Outlook. Weiner demurs in response, but this raises what I think is an hugely under-appreciated possibility for LinkedIn: to become the world's best spam filter. LinkedIn is uniquely able to assess the legitimacy of someone sending you email, as well as your interest in receiving it. The notion of building an identity layer into LinkedIn, and a lot of other vertical market databases, remains an area of huge opportunity.

 

My happy conclusion is: a lot of these opportunities are not unique to LinkedIn. There are lots of exciting opportunities for smaller, vertical market data products as well. Read the full article and put on your thinking caps!