Viewing entries in
Business Models


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.



Sharing in Private

While there are many, many B2C ratings and review sites where consumers rate and otherwise report their experiences with businesses, there are relatively few B2B sites where businesses rate other businesses. There are multiple reasons for this, but prime among them is that while businesses tend to have a strong interest in using this kind of information, they typically don’t want to supply this kind of information. In short, they see competitive advantage in keeping their vendor experiences confidential.

One fascinating example of this in the legal market is a company called Courtroom Insight. Originally founded with the simple and reasonable idea of creating a website where lawyers could rate expert witnesses (experts hired by lawyers to testify in court), the company hit this exact wall: lawyers didn’t want to tell other lawyers about which experts they did and didn’t like.

Rather than close up shop, though, Courtroom Insights pivoted, in an interesting way. It discovered that large law firms were very sloppy about keeping records of their own expert witnesses. So, Courtroom Insights built a database of expert witness from public sources and licensed data. It then went to large law firms an offered them an expert witness management database. Not only could lawyers search for expert witnesses and verify their credentials, it could flag those experts they used, along with private notes that could be shared freely within the law firm, but not externally.

This pivot created a nice business for Courtroom Insights but it wasn’t done. Since all of its large law firm clients were sharing the same database, but also individually flagging the experts they were using, could Courtroom Insights convince them to share that information among themselves? Recently, they offered this “who’s using who” data to its clients on a voluntary, opt-in basis. And it worked. While not every client opted in, enough did so that Courtroom Insights could make another level of valuable information available.

While this is just my personal prediction, I think Courtroom Insights will ultimately be able to offer the expert witness ratings that it originally tried to provide. How? By using the protected space of its system to let lawyers trade this high-value information with each other. It will probably start small: perhaps lawyers could click a simple “thumbs up/thumbs down” icon next to each expert that could be shared. But I also suspect that if Courtroom Insights can crack the initial resistance to share information, the floodgates will open, because lawyers will realize they are communicating only with other lawyers, and because the benefits of “give to get” information exchange becomes so compelling.

The Courtroom Insights story provides a fine example of the power of what we call the Closed Data Pool in our Business Information Framework. Sometimes data that nobody will share publicly can in fact be shared among a restricted group of participants, with of course, a trusted, neutral data publisher making it all happen.