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Making Introductions, Profitably

An interesting article in the New York Times highlighted a company called Legal Services Link. As you might infer from the name, the company works to connect lawyers with those who need legal services.

Lead generation? Yes, but with a twist. In this model, buyers are actively seeking sellers and the intermediary is attracting these buyers and adding value by actually matching buyer to seller. In many cases, the buyer is asked to complete a requirements survey, which is then matched to a database of qualified sellers. The intermediary (usually a data company), identifies usually from one to three vendors best qualified to help the buyer, and puts everyone in touch.

The benefit to the buyer is that a small number of pre-screened, qualified sellers make immediate contact with the buyer – enough sellers to have some choice, but not enough to be overwhelming or annoying. You may possible be surprised to learn that a hidden value-add of these matching services, is that they monitor the sellers to make sure they get in touch with the buyer quickly. Yes, even with sellers paying sometimes hundreds of dollars for a hot lead, they still manage to drop the lead on the floor!

What’s also nice about this model from the perspective of the intermediary is that there is no chance of “leakage” – a term for when buyers or sellers circumvent the intermediary, often to avoid paying a commission.

This model works well for both B2C and B2B. It seems to work best for high-value purchases that the buyer only purchases sporadically. This irregular buying pattern is key because it means the buyer can’t keep up with what seller offers what product, or even the products themselves. Markets with rapidly changing technology are especially good.

Since the buyer fills out the requirements survey with full knowledge she will be immediately hearing from salespeople, she makes an enormously high value lead. And since the seller has a good understanding of what the buyer needs before making contact, the initial conversation is more productive and the sale tends to close faster.

This is a strong model that makes more sense than ever in a world that’s rapidly getting used to apps that speed the delivery of everything. If you see the right fundamentals in your market, it’s a model that’s well worth exploring.

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The Next Data Gold Rush

A recent article in the Harvard Business Review, entitled “To Get More Value from Your Data, Sell It,” jumps on the data bandwagon, arguing that many companies own valuable data that can be monetized through sale to third parties. The authors do a good job pointing out that many companies don’t realize that the data they generate as a by-product of other activities has value. Even more usefully, they point out that some companies automatically treat all their data as top secret, and lose revenue opportunities as a result of this lack of discernment.

But where the article goes a bit off the path is its implicit view that almost all data are valuable. They’re not. As I pointed out in a post just a few weeks ago, there are a lot of reasons any given dataset may have little or no commercial value. And sometimes company data really is too sensitive to be resold.

Later in the article, the authors laud Cargill for building and selling a database of seed information. While the authors correctly note that getting into the data business is a risky move for most companies, the authors felt this was a low-risk move for Cargill because Cargill had “already developed a database to support seed development.” As every data publisher knows, having a database and being in the data business are two very different things. To create a saleable product, there is a lot of investment and work to develop a user interface. Then there’s the challenge of bringing a data product to market. Cargill knows farmers and Cargill knows seeds, but Cargill knows very little about subscription data products. It’s extremely rare when a non-data company, however good its data, suddenly decides it wants to be in the data business and finds success.

We can expect the idea of companies monetizing internal data to become mainstream. But once the hype settles down and reality kicks in, these companies will be very receptive to working with data publishers to optimize the value of their data, because they’ll see both the opportunities and complexities involved in monetizing it.

The flipside is that data publishers should start to look to non-data companies as potentially rich information sources. Many companies do indeed have valuable datasets built as a by-product of other activities. Finding and licensing these datasets could be a quick way to market for a data publisher, and can also yield that rarest of things: a high value dataset with none of the traditional compilation hassles and the possibility of licensing on an exclusive basis.

Finding internal company datasets isn’t easy, but as the concept of turning internal data into dollars gets more visibility, companies will start to actively look for potential licensees. Stay alert for these opportunities, and be prepared to move quickly because licensed internal company data could be the next data gold rush!

 

 

 

 

 

 

 

 

Knowing More Than You Can Tell

Most of you have some familiarity with Gerson-Lehrman Group (GLG), the phenomenal success story that pioneered the idea of connecting experts on a wide variety of topics with those who needed fast, trustworthy and unbiased insights into a market, a company, a technology … whatever.

Not surprisingly, GLG found most of its clients in the financial sector, from hedge funds to private equity firms and others that needed expert insight fast to inform the often significant investment decisions they were making. These clients paid fat fees, and the experts were well paid for small chunks of their time, and it all went swimmingly for many years.

Where things got awkward is that some investors wanted more than background information: they wanted confidential information. GLG was very aggressive about policing this, understanding that it could damage its business. However, some GLG competitors didn’t have the same ethics, and differentiated themselves by playing on the often-murky line between public information and inside information. This potential to misuse the raft of expert services that now exist continues to cast a pall over another otherwise strong business model.

Enter a new start-up called Emissary. It’s an expert service, but rather than focusing on connecting experts to investors, it seeks to connect experts to salespeople. Want to know how to tailor your pitch to a particular company? Emissary can find someone who knows. Similarly, salespeople often find themselves wondering if they are dealing with a decision-maker or not at a particular company. Say hello to Emissary, whose experts may well have worked at the company in question.

Visit the Emissary website, and you’ll see a carefully crafted message: we’re just people helping other people. At one level, this is certainly true. And connecting a sales team to a recent former employee of the prospect company doesn’t seem to be rife with the same legal and ethical issues that exist for investors, but I suspect Emissary’s long-term success will depend on it also establishing an ethical line in the sand and policing it closely.

What also makes Emissary interesting is it’s a model that can be moved not only across verticals, but across functional areas as well. 

Not All Datasets Are Good Datasets

As someone who has been a long-time proponent of data, it is intriguing to see the number of new start-ups that have revenue models based partially – sometime entirely – on the sale of data, even though they are not data publishers in the conventional sense. Rather, they are seeking to monetize data they are collecting incidentally in the course of other activities.

A fashion website or app, for example, might realize that by tracking what new fashions its users viewed the most, they were collecting valuable intelligence that could be sold to fashion manufacturers. The early players in this area usually did, in fact, have valuable and readily saleable data collections and they had in fact identified an important new revenue stream.

But now “data” is transforming into a buzz-term, up there with “the cloud” and “social.” Purported data opportunities are being used to mask weak business models because everyone these days knows “it’s all about the data.” Just as start-ups these days feel compelled to be in the cloud and have a strong social component, so too do they now need a data opportunity.

Not every new business can create value from the incidental data it generates. Those that do represent the exception, not the rule.  Here are a few reasons why these data opportunities may not be as strong as the entrepreneurs behind them would like to believe:

1. You generate too little data. While everyone talks about quality data, there is still a quantity aspect as well. Even for things as valuable as sales leads, most companies will turn up their noses at them if you can’t deliver a certain volume of leads regularly and dependably. Depending on the data itch you’re trying to scratch, 100,000 or even a million users may not cut it.

2. You generate too much data. Having the most data about something can be as much a burden as an opportunity. Think Twitter. Everyone “knows” that the huge collective stream of consciousness that its  users generate is enormously valuable, but extracting that value is very complex and expensive, and much of the final output still represents conjecture and surmise.

3. You don’t really know much about the data you’ve got. I’ve been in numerous meetings where the issue on the table was, “we’ve got tons of data, but we’re not sure how to monetize it.” This situation naturally calls for advanced TAPITS (There’s A Pony in There Somewhere) analysis to assess value. More times than not, the chosen solution is simply to sell the raw data and hope that the buyer can find value. Of course, when you sell data by the ton, you have to charge for it by the ton too. It’s just not that valuable if the buyer needs to do all the thinking and all the work.

4. A sample of none. Online businesses want lots of traffic and lots of users, the more the merrier. This is good for business generally, but not necessarily great from a data perspective. If your user base is too disparate, the aggregate insights from the data they generate may not be all that valuable. And if your user base is largely anonymous, good luck with that.

5. Buy me a drink first. Many times, an online company is in possession of extremely detailed and valuable data. Unfortunately, this typically means that these data can only be had by violating the trust if not the privacy of the user. It’s even more complicated if the company built its business with a strong privacy policy that prohibits it from ever selling all this valuable information.

6.  Exclusive insights. These days, if you said you have “near-real-time insight into bus station storage locker utilization rates” it will be automatically assumed that you've tapped a huge data opportunity. Every bus station certainly needs this information, bus lines probably have a use for it, there’s probably a government market, some hedge funds will want it and there might even be a consumer opportunity as well – think of an app that shows you available storage lockers nationwide! But in reality, every market is not a viable data market. The market might be too small, marginally profitable, too localized or too consolidated. It is absolutely possible to have data that nobody cares about or that too few people care about to create a meaningful revenue stream.

7. Competition. Your data may indeed be valuable, but chances are, you don’t have the full picture. This means your data is less valuable than a company that can supply the full picture. That means the market for your data may be the one company that knows more about the market than you do. Yes, there’s revenue to be had in this case, but you won’t get rich.

8. Raw data follies. Typically, companies trying to sell the data they collect incidentally want to sell the data, get the money, and get back to their core business activities. But if you don’t clean and organize your data, you’re leaving lots of money on the table. And if you decided to get serious about your data, you’re moving into a different business, one you probably don’t understand very well.

I could keep going, but hopefully you get the point: the chances that the incidental data you generate from some other business activities are valuable is pretty low. And even if you have valuable data, getting maximum value from it generally demands getting a lot more serious about your data, which starts to move you into a totally different business.

 

 

 

  

Data Insights from Bitsight

A Boston-area start-up called Bitsight is pulling in investor money so quickly, a total of $95 million, that it doesn’t know what to do with it all … yet.

And what does Bitsight do, to justify this level of investment? It examines company websites, evaluates them for the quality of their website security, and assigns them a rating, much like a credit score.

How do they do it? There’s a bit of proprietary secret sauce in how the company evaluates the security of a website, but what’s particularly interesting is that they do it all with publicly available information. And that raises another fascinating aspect of the business: the companies that Bitsight rates are not its clients. Bitsight is not an online security consultant with an automated assessment tool. Indeed, it has evaluated over 60,000 websites to date, and ultimately may evaluate tens or even hundreds of thousands of websites.

Why would anyone want this information? The uses for this data are surprisingly numerous. You can sell it in the form of a benchmark products to the companies you have rated. What IT manager wouldn’t want to know how their company stacks up against its peers? A better opportunity is to help insurance companies properly price data breach insurance policies.

But perhaps the best opportunity is to help big companies evaluate and manage risk with their vendors – a huge issue as a number of headline-grabbing recent data breaches resulted from a company’s network being penetrated via one of its vendors that was connected to it.

While Bitsight may look like a cutting edge analytics company, what’s significant is that so much of its business model is drawn from very basic approaches used by many other data publishers. It is aggregating publicly-available data into a database. It normalizes this information, then applies an algorithm to assess it and produce comparable company ratings. It sells this data product for internal benchmarking, risk management and due diligence applications.

In short, despite its high tech trimmings, Bitsight very much has data publishing DNA. It is also a great example that data products don’t have to be perfect right out of the gate. By relying on public information, Bitsight can’t possibly know everything about the security of a company’s website. But by relying just on public data, it can quickly build a large database of comparable company ratings using a credible methodology and solve market needs that require a certain scale of coverage. If you’re the first data provider serving a serious market need, you can launch with good-enough data and improve it over time. Trying to perfect your data prior to launch can mean missing the opportunity entirely.