LinkedIn: A D&B For People?

I joined LinkedIn in 2004. I didn’t discover LinkedIn on my own; like many of you, I received an invitation to connect with someone already on LinkedIn, and this required me to create a profile. I did, and became part of what I still believe is one of the most remarkable contributory databases ever created.

Those of you who remember LinkedIn in its early days (it was one of our Models of Excellence in 2004), remember its original premise: making connections – the concept of “six degrees of separation” brought to life. With LinkedIn, you would be able to contact anyone by leveraging “friend of a friend” connections.

It was an original idea, and a nifty piece of programming, but it proved hard to monetize. The key problem is that the people most interested in the idea of contacting someone three hops removed from them were salespeople. People proved remarkably resistant to helping strangers access their friends to make sales pitches. LinkedIn tried all sorts of clever tweaks, but there clearly wasn’t a business opportunity in this approach.

What saved LinkedIn in this early phase was a pivot to selling database access to recruiters. A database this big, deep and current was an obvious winner and it generated significant revenue. But there are ultimately only so many recruiters and large employers to sell to, and that was a problem for LinkedIn, whose ambitions had always been huge.

Where things got off the tracks for LinkedIn was the rise of Facebook, Twitter and the other social networks. Superficially, LinkedIn looked like a B2B social network, and LinkedIn was under tremendous pressure to accept this characterization, because it did wonders for both its profile and its valuation. LinkedIn created a Twitter-like newsfeed (albeit one without character limits), and invested massive resources to promote it. Did it work? My sense is that it didn’t. I never go into LinkedIn with the goal of reading my news feed, and I have the same complaint about it as I have about Twitter: it’s a massive, relentless steam of unorganized content, very little of which is original, and very little of which is useful. 

Today, LinkedIn to me is an endless stream of connection requests from strangers who want to sell me something. LinkedIn today is regular emails reminding me of birthdays of people I barely know because I, like everyone else, have been remarkably undisciplined about accepting new connection requests over the years. LinkedIn is also just one more content dump that I barely glance at, and it’s less and less useful as a database as both its data and search tools are increasingly restricted in order to incent me to become a paid subscriber.

Am I predicting the demise of LinkedIn? Absolutely not! What LinkedIn needs now is another pivot, back to its database roots. It needs to back away from its social media framing, and think of itself more like a Dun & Bradstreet for people. LinkedIn has to use its proven creativity and the resources of its parent to embed itself so deeply into the fabric of business that one’s career is dependent on a current LinkedIn profile. LinkedIn should create tools for HR departments to access and leverage all the structured content in the LinkedIn database so that they will in turn insist on a LinkedIn profile from all candidates and employees. Resurrect the idea of serving as the internal company directory for companies (and deeply integrate it into Microsoft network management tools). Most exciting of all to me is the opportunity to leverage LinkedIn data within Outlook for filtering and prioritizing email – big opportunities that go far beyond the baby steps we’ve seen so far.

I think LinkedIn’s future is bright indeed, but it depends on management focusing on its remarkable data trove, rather than being a Facebook for business. 

Crossbeam’s Mission Impossible

I write often about the opportunity for data companies to operate as central information exchanges because they have a central position in their markets, and this neutral market position makes them trustworthy.

Lots of sensitive market information gets exchanged through central data hubs. Companies routinely exchange credit data, pricing data, business metrics and much more. They do this because they know the data they submit will only be released in aggregate or anonymized form. As importantly, they do this because they need the answers that only data exchanges can provide.

This is why I got excited when I heard about a stealthy start-up called Crossbeam. Crossbeam wants to build a database that consists of company customer lists. Yes, they are asking companies to upload their entire customer files to the Crossbeam database!

Mission impossible? Not at all. Consider when companies discuss merging. One big, burning question is always how much customer overlap there is between the two companies. Even in merger situations, companies are reluctant to hand over their crown jewels to what often is a direct competitor. Crossbeam is offering to compare those two customer files on a confidential basis and report out the results, something that demands a neutral market position, and the trust that goes along with it.

You might think that this idea, while interesting, isn’t all that big. Think again. Crossbeam aims to be a business development tool for those in charge of partnering and strategic alliances. Using Crossbeam, a partnership manager can easily search out companies with a large overlap in customers – almost always the key to a successful partnership or business alliance. It’s an efficient, quantitative way to take the guesswork out of developing alliances, affiliates and business partnerships, because you know in advance you are selling to the same customers they are.

Crossbeam never releases customer data of course. It simply flags companies where there is a large overlap between your customer file and theirs. This is a wonderful example of the distilled magic of the central information exchange: companies contribute data that they would ordinarily not share because it provides back information they cannot otherwise get.

In the course of helping to accelerate business partnering, the other data and business insights that Crossbeam will be able to access are potentially staggering. Of course, Crossbeam also has the challenge of protecting all this sensitive data, making sure it can’t be used in unintended ways, and making sure it doesn’t kill the golden goose by mining all the data in its possession too aggressively. Still, those are manageable issues, and all part of the mission Crossbeam has chosen to accept!

Being in the Middle of a New Data Product

I’ve written before about the application model called the “Closed Data Pool.” In this model, companies (and many times they are competitors) contribute proprietary data to a central, neutral data company. The data company aggregates the data and sells aggregate views of the data back to the very companies that contributed it. Madness you say? Not really, because these companies get great benefit from those aggregated views (think market share, average pricing and other vital business metrics). It’s the neutral, trusted data provider in the middle who makes it possible. 

But there is another twist on the closed data pool that represents an even more profitable business for the data provider in the middle. Consider a company called The Work Number.

The Work Number came into being because a lot of credit grantors need to be able to quickly verify employment status and income. At the same time, companies hated getting an endless stream of calls from creditors seeking to verify employment data. The Work Number came up with an ingenious solution. It went to big companies and said that they could outsource all these nuisance calls to The Work Number. All the company had to do was supply a feed of its payroll data. 

The Work Number then went to major credit grantors such as banks and said that instead of those painful verification calls they were making, credit grantors could just do a lookup on The Work Number website and instantaneously get the exact data they needed.

The best part? The Work Number was able to charge credit grantors for access to the database because of the big productivity gains it offered. But The Work Number was also able to charge the companies supplyingthe data because it increased their productivity as well by eliminating all these annoying verification calls. Yes, The Work Number charges both to collect the data and provide access to it!

If this sounds like an interesting but one-off opportunity to you, it’s not. Opportunities exist in vertical markets as well. Consider National Student Clearinghouse, which does the same thing as The Work Number, only with college transcripts.

Is there an opportunity in your market? Look for areas where relatively important or high-value information is being exchanged by phone or one-off emails or even by fax. If the information exchange constitutes a serious pain point or productivity drag for either or both parties, you’ve probably got a new data product. 

Say Yes to Market Neutrality

A few weeks ago, Zillow, one of the leading real estate listing sites, made a surprising announcement: it was going to enter the business of flipping homes, the process of buying a home, fixing it up and quickly reselling it.

This immediately raised two questions in my mind: why and why?

First, good things generally don’t happen when you as a data platform or provider give up your market neutrality. No matter the specifics, you are putting yourself in competition with your customers. That means your customers see you as putting yourself first, which makes them very receptive to taking their business elsewhere.

Second, there’s nothing about this new venture by Zillow that gives it any market advantage. Zillow has no unique insights, no privileged data that others lack. It sees listings only when an agent posts them, so there is no timing advantage. In short, Zillow could have quietly invested in a company that flips homes and nobody would have blinked. But Zillow is integrating this right into its main website. Again, Zillow’s function is real estate discovery. Simply knowing a property is for sale at the same time as everyone else confers no market advantage.

Zillow has a slightly different prism though. It sees this new business as a feature that will differentiate it. Just as eBay went from strictly running auctions to adding a “buy it now” button, Zillow sees itself as adding what is essentially a “sell it now” button on its website. But to appease its advertisers – real estate agents – it plans to pay commissions to agents on every house it buys and sells, eliminating any price advantage it might get from buying directly from the seller. The more Zillow contorts itself to make this new business palatable to real estate agents, the more complicated and less attractive this business opportunity becomes.

Even if this venture is really more about adding some sizzle to drive site traffic than a serious source of new revenue, it’s probably not a good idea. That’s because even the appearance of favoritism or self-dealing can put a real dent in your business. And if this new venture really isn’t about making money, then it’s positioning itself for the worst possible outcome: not making any money while simultaneously confusing/annoying/scaring your advertisers.

Does this mean a data provider or data platform can’t ever consider related sources of revenue? Absolutely not. Had Zillow decided, for example, to get into the mortgage business to streamline the home buying process, it would have been rewarded with more site traffic and happier advertisers – the classic “win-win.”

As a data provider, you should say yes to market neutrality. 

When Algorithms and Advertising Collide

You may remember when real estate listings firm Zillow first burst on the scene back in 2006. While there are many online real estate listings sites, Zillow distinguished itself with its “Zestimates,” an algorithmically-derived valuation for every house in the United States. Many Americans amused themselves throughout 2006 checking Zestimates for their own homes, as well as the homes of neighbors and friends.

Zestimates were never intended to be appraisals. After all, Zillow has no idea what is on the inside of any home. But the Zestimate algorithm does use many of the same approaches as appraisers use, including comparisons of recent sale prices of similar houses and historical sales trends. To the average consumer, they sure looked and felt like appraisals, and in a sense, that’s what really matters.

While Zestimates were unquestionably a brilliant way to launch a new website in a crowded vertical (Zillow become one of the highest traffic websites virtually overnight), Zestimates have always been an awkward fit with the Zillow business model. That’s because Zillow is an advertising-based business.

Think about it from the perspective of the real estate agent – the advertising buyer. The agent is attracted by Zillow’s huge traffic numbers and pays for an enhanced listing to get even more prominence. But Zillow automatically (and prominently) displays its Zestimate right near the asking price. Imagine asking $1 million for a home when the seemingly authoritative Zestimate pronounces the value of the home to be $700,000. As an agent, you’re not going to be happy.

Zillow’s stance is basically, “hey, it’s just an objective data point.” But advertisers don’t want to hear it. And that’s the essence of several recent lawsuits. In one lawsuit, the plaintiff argues that Zillow damaged her selling prospects by posting a lower Zestimate near her asking price and doing so without her permission. Another lawsuit goes further, saying that Zillow agreed with certain real estate agents to “de-emphasize” (read: hide) the Zestimate within the listing, meaning that some agents were getting a more attractive listing presentation, and those that didn’t pay an advertising fee were being disadvantaged.

This may sound like a problem peculiar to Zillow but it’s not. Yelp has dealt with a similar issue for years. In short, Yelp is finding it hard to sell advertising to customers whose listings are chock full of negative reviews. Yelp has been repeatedly accused of “de-emphasizing” (read: hiding) these negative reviews to satisfy advertisers.

The simple lesson here is that objective data and advertising don’t always mix, and that creates complexity and legal exposure unless you are aware of the issue and identify a solution that works for everybody. Those solutions can be hard to find.