Deals of Excellence

It’s been a banner few weeks for deal making for companies honored as Models of Excellence by InfoCommerce Group.

In the mega-deal category, we have 2006 honoree real estate data powerhouse Zillow, entering into a $3.5 billion deal to acquire arch-rival Trulia. This will of course put pressure on market leader Home.com, which operates the Realtor.com website. The whole real estate vertical has been one to watch from a data perspective. Zillow was not only an early innovator in the area of map-based user interfaces, it also blew more than a few minds by not only aggregating property data on almost every home in the country, but creating a home price estimate for every home as well. If this merger goes through, expect even more extreme innovation as these two giants battle it out for audience and advertising.

In the smaller (but hardly small) category, we have the $175 million acquisition of 2009 Model of Excellence honoree Bizo by 2004 Model of Excellence honoree LinkedIn. From a strategic standpoint, I’d rate this acquisition as nothing short of brilliant. At a high level, you are putting together “who” (the LinkedIn database, with “where,” (the Bizo B2B ad network). The potential opportunities are endless.

And while we’re in the world of high finance, a shout-out to 2010 Model of Excellence honoree SmartZip also seem in order, as they’ve just closed on a new $12 million financing round.

Where do these and other Models of Excellence companies meet each year to get their deals on? InfoCommerce Group’s DataContent gathering, now part of an even bigger show, the Business Information & Media Summit. See you in Miami!

Smarter Data is Right Inside the Box

Most of us are at least somewhat familiar with the concept of the “sales trigger,” something I lump into a larger category I call “inferential data.” If you’re not familiar with the concept, what we are talking about is taking a fact, for example that a company has just moved, and drawing inferences from that fact. We can infer from a recent company move that the company in question is likely to imminently be in the market for a host of new vendors for a whole range of mundane but important office requirements. So if we learn about this company move right after it happens (or, ideally, right before it happens), we have an event that will trigger a number of sales opportunities, hence the name “sales trigger.” But as I noted above, sales triggers in my view are a subset of inferential data. I say that because sales triggers tend to be rather basic and obvious, while true inferential data can get extremely nuanced and powerful, especially when you start analyzing multiple facts and drawing conclusions from them. Tech-savvy folks refer to these multiple input streams as “signals.”

Let’s go back to our example above. The company has moved. That means they likely need a new coffee service and cleaning service, among others. That’s fine as far as it goes. But let’s go deeper. Let’s take the company’s old address and new address, and bounce them against a commercial property database. If the company is moving from $20/square foot space to $50/square foot space, chances are this company is doing well. At a minimum, this makes for a more interesting prospect for coffee service vendors. But it can also be the basis for assigning a company a “high growth” flag, making it interesting to a much broader range of vendors, many of whom will pay a premium to learn about such companies.

Or perhaps we know this company has changed addresses three times in five years. We could infer from this either extremely high growth or extreme financial distress. Since this relocation signal doesn’t give us enough clarity, we need to marry it with other signals such as number of employees during the same period, or the cost of the space or amount of square feet leased. Of course, signals go far beyond real estate. If the company had a new product launch or acquisition during that period, these signals would suggest the address changes signify rapid growth.

You can see the potential power in inferential data, as well as the complexity. That’s because in the business of signals, the more the better. Pretty soon, you’re in the world of Big Data, and you’ll also need the analytical horsepower to make sense of all these data signals, and to test your assumptions. It’s not a small job to get it right.

That’s why I was excited to learn a company called – what else – Infer. Infer collects and interprets signals to help score sales leads. And it sells this service to anyone who wants to integrate it with their existing applications. It’s essentially SaaS for lead scoring. Intriguingly, Infer licenses data from numerous data providers to get critical signals it needs.

Inferential data makes any data it is added to smarter, which in turn makes that data more valuable. Many publishers have latent inferential data they can make use of, but for others, watch out for those “signals in a box” products from what I suspect will be a growing number of vendors in this space. It’s the smart thing to do.

Score Big with Rankings

We’re all familiar with the growing influence of user-generated rating sites such as Yelp and TripAdvisor. The power of these sites to determine which businesses thrive, while others struggle to stay in business, is well documented. Without a doubt, there is power in ratings and rankings. But you could be excused for thinking that this is all a very B2C phenomenon: consumers, retailers, restaurants and the like. After all, this is where all the noise and press reporting has been focused. But there are strong B2B opportunities in the world of ratings and rankings. And these opportunities don’t need to be at the scale of a Yelp or a TripAdvisor. Indeed, a simple list of the top players in a market can be absurdly influential, and where there is power and influence, there is usually also opportunity.

Consider this one compelling example. Bloomberg reports that two companies, Goldman Sachs and Morgan Stanley, were willing to forego millions of dollars in fees just to get credit as having worked on several large M&A deals. This “credit” in turn pushed the companies higher on a listing (often referred to as a league table) of the companies handling the most M&A transactions, and published by a third-party company called Dealogic.

Step back and consider, even savor, this for a moment. Two prestigious, successful and extremely savvy companies that hardly need more publicity or name recognition, are willing to trade millions of dollars in fees to push themselves higher in a list that ranks transaction activity. Clearly what’s going on is that these companies feel that the bragging rights and marketing value of ranking highly on this list will be worth many more millions that those they walked away from.

Now you may be noting that Dealogic, the transaction platform and data company behind this league table, didn’t see any of the millions of dollars. But monetization isn’t always direct. And in the case of the league table in particular, it shouldn’t be.

But let’s tally up the benefits to Dealogic. It certainly needs name recognition more than the big name companies in its ranking, and it gets that recognition in spades as the producer of this important list that drives deal activity. Secondly, the league table is inherently a highly summarized product. Dealogic can easily sell the underlying data at a premium price to those who want to do more granular analysis. Third, the league table has a halo effect on other Dealogic products. As a producer of critical industry data, every Wall Street player will be receptive to hear about all the other products and services that Dealogic offers. Indeed, many of these Wall Street players will be regularly reaching out to Dealogic to make sure they are properly reflected in these league tables. As a neutral producer of this relatively small dataset, Dealogic has built strong market authority and credibility, and is able to reach and sell to the biggest names on Wall Street more as an equal than an obscure vendor.

The power of rankings and ratings is undeniable. But the really important lesson here is that the rankings don’t have to be elaborate, and the market doesn’t have to be huge for them to yield outsized benefit.

The Hidden Data in Invoices

The data business is one of creativity, and what could be more creative than asking companies to send you their invoices and other types of billing data so you can get them into a database and sell the aggregate results back to them? Now, why would something like this ever make sense? Well, in many industries, there is nothing more useful or important than pricing information. Yet the pricing information that many companies publish (if they publish it at all) is almost always the list price. And in many industries, the list price is close to meaningless, since every customer will have a special deal and varying discount. So how do you develop a database of what companies are really paying for specific products and services? Ask to see their invoices!

There are lots of spins on this intriguing model, so let’s take a look…

The question already on your mind quite likely is, “Why would any company let me look at its invoices?” The simple answer is what I often refer to as “strength in numbers.” A company will happily give up its individual data (properly secured and anonymized) in exchange for access to the aggregate results. And they’ll pay for that access, and that’s exactly the play here.

A great example of collecting, normalizing and reporting out information drawn directly from the internal systems of advertising agencies can be found in a company called SQAD. Its NetCosts product collects data for media purchases from advertising agencies worldwide, generating what may be the only honest look at what broadcasters are charging for media buys, and even what they have charged into the future. You can immediately see how valuable this information can be.

Thomson West has a product called Peer Monitor that does the same thing in a slightly different way: rather than work with the recipients of invoices, it works with the senders of invoices, in this case law firms, to collect similar data to be used in similar ways.

If it sounds like a lot of work, it is, or probably was. That’s because SQAD now receives most of its purchase data digitally, through interfaces with client systems. And while those interfaces were doubtless painful to build, at the same time SQAD has built an almost impregnable franchise, because as long as SQAD doesn’t get greedy, nobody can justify the time, cost and pain to try to compete with them. The same holds true for Peer Monitor.

There’s also what might be called the pre-order model. Here, your objective is to gather RFPs and proposals to get a true look at what companies are proposing to charge for their products. One advantage of picking up information at this stage is that there is often a lot more detail, allowing you to collect even more granular product price data. A great example of this model is MD Buyline, a company that collects price quotes and proposals on medical equipment to build a high-accuracy pricing database.

Lots of variant models, but the objective is the same: gather data on actual prices being charged in the marketplace, the more granular the better. Your job is to aggregate, normalize, and report back to the marketplace, while protecting the anonymity of those who participate.

It’s important to note that the need for pricing data isn’t equally compelling in every market. The dynamic seems to be a reasonably large pool of both buyers and sellers, and a solid tradition of haggling over price. It won’t work for everyone, but it’s certainly worth considering if it would work in your market.

A Business Model Detour

TrueCar.com started out as a data and analytics company, offering insight to consumers as to the actual prices being paid for specific makes and models of cars in their local area. The idea was to aggregate multiple data sources, including actual sales data from dealers themselves to build as much precision as possible into this pricing information. In many respects, TrueCar was duplicating the approach taken by established industry powerhouse Edmunds.com. What TrueCar didn’t duplicate was the business model of Edmunds. Indeed, TrueCar took an entirely different route.

TrueCar moved beyond providing estimates of new car prices to delivering actual prices that dealers would accept. Simply hand your TrueCar number to the dealer, and the car would be yours for that price. It was a fresh approach, and particularly compelling to those consumers not fond of haggling with car dealers.

The idea took off. TrueCar signed up thousands of dealers to accept its pricing. Then, according to published reports, it started marketing itself as offering the lowest prices for new cars. Turns out, its dealers weren’t thrilled with that positioning, in large part because they weren’t offering the lowest prices, and large numbers of them canceled their affiliation with TrueCar.

TrueCar recovered from this, but in an odd way. It now represents itself as offering “fair prices” instead of lowest prices. And from a quick look at its site, you can see that it has morphed into a lead generation service for car dealers. I asked for a price on a car from dealers near my zip code and was presented with three prices from three dealers. That’s a big move away from presenting objective pricing based on aggregated sale price and other data.

So, the TrueCar value proposition has pivoted from providing objective data to providing consumers with a price in advance that certain dealers will honor, thus avoiding the stress and uncertainty of having to negotiate a price. If you look at the TrueCar website now, you’ll be repeatedly assured you are getting a fair, competitive price, but if there’s any data to back up that claim, the company’s no longer talking about it.

TrueCar claims to be responsible for 2.3% of all cars sold annually in the United States, so it seems to have tapped into a real need in the marketplace. At the same time, it’s a rare pivot away from a data-driven business model, to a model that as far as I can see doesn’t require any data at all.

Of course there’s a great lesson here in profiting from adversity, but there’s another lesson here as well: if you dive into the data business without a clear business model, you’ll probably find yourself needing to make an expensive and dangerous u-turn.