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Thoughts and Predictions

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Nice Try, Moody's

For over a decade I have watched with interest as a company called CoStar became the largest player in commercial real estate data. It achieved this feat – and a market cap of over $13 billion – by old-fashioned data compilation, well-timed acquisitions, and aggressive litigation to keep competitors at bay.

What resulted is an effective monopoly in the commercial real estate space. CoStar achieved this by never cutting corners on primary data collection. As just one example, at one point it had a fleet of trucks snapping photos of every commercial property in the country. CoStar was never shy about getting on the phone too, collecting valuable leasing data from brokers and property owners nationwide. Marry all that proprietary data with public records data and a strong online platform, and you have a business that is highly profitable and nearly impregnable.

 Data companies in this privileged position do sometimes suffer at the hands of competitors, but nin times out of ten, it’s because of self-inflicted damage. Companies that become data monopolies have to be endlessly vigilant about not becoming arrogant or charging extortionate prices, because being hated by your customers provides an opening for competitive players. So too does complacency, and a failure to invest in the business to enhance the value of its products and keep up with changing market needs.

It doesn’t seem that CoStar has made any of these mistakes, but it is feeling new competitive heat anyway from another information giant, Moody’s (market cap $31 billion).

Moody’s (through its Moody’s Analytics division) has never been a big player in commercial real estate data, but having decided it wants a piece of this market, it has been spending heavily on acquisitions to buy its way in. The centerpiece of its acquisitions was the $278 million purchase of commercial real estate analytics firm REIS last year. Moody’s also made “strategic investments” in a number of other industry data providers.

So is it curtains for CoStar? I think not. Moody’s has spent huge amounts of money to position itself to compete for only a small portion of the market CoStar serves (think banks and real estate investors). Moreover, Moody’s will be in large part dependent on data it doesn’t own, sourced from companies selling into the same market, meaning that a lot of the data Moody’s will offer will come heavily restricted. 

Perhaps most importantly, CoStar’s proprietary data (commercial real estate inventory and listings data) remains proprietary and untouchable. My take is Moody’s has over-spent for the opportunity to enter a bruising battle with an established company whose smarts and street-fighting skills are well established. Moody’s will build a business here, but it will be one much smaller than its ambitions, and one that will take relatively little revenue from CoStar. Data franchises are strong and it usually takes more than a large checkbook to bring them down.

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Relationship Scoring

No, this is not about online dating.  I am referring to the growing use of consumer scores to help companies determine how much time and energy to invest with individual customers.

We’re all familiar with credit scores that yield a single number meant to reflect how dependably you pay your bills. A high credit score can mean easy access to credit, often at lower interest rates that reflect your low re-payment risk. A poor credit score can mean limited access to credit and loans, in addition to higher interest rates.

The folks behind the credit scores have been relentless in their work to find new markets for their product. With the notion that a credit score is also a reflection of someone’s level of personal responsibility as well, credit information is increasingly used in hiring decisions. You’ll also find credit scores used to determine pricing for such things as automobile insurance, the insurance companies having concluded that if you pay your bills on time, you likely drive carefully as well.

But credit scores are not the only consumer scores out there. In parallel with credit scores, a number of companies have been building out consumer scores based on Customer Lifetime Value (CLV). The CLV concept has been around forever. What’s changed recently is increasingly easy access to a wide variety of input datasets (a/k/a/ “signals”) that work to increase the precision of these scores, along with increasing computer power that makes it possible to access and act on these scores in real-time.

And how are these scores used? A recent Wall Street Journal articles suggests that CLV scores are increasingly used by companies to determine how they will interact with their customers. A higher scoring customer may actually get faster and better customer service. Companies will offer bigger incentives and better deals to their best customers in order to retain them. CLV scores start with numeric calculations of the likely dollar value of a customer over the entirety of the projected relationship (and yes, your score typically declines as you get older because … less lifetime). More recently, these relatively simple calculations have been enhanced with demographic overlays and a wide array of lifestyle and even behavioral data points. For example, customers who complain too much or call customer service too often may have their scores reduced as a result.

Currently, companies implement their own CLV scoring systems, sometimes with the help of third-party vendors. CLV scores as a data-driven way to make sure better customers are treated better sounds benign. Where it could take a more worrisome turn is if a third-party vendor tries to centralize all of this information to build a single CLV score for all consumers. This would be a fraught undertaking, especially since it would likely not be subject to any regulatory scrutiny and control. Such a scoring system would also look uncomfortably similar to the social credit system recently introduced by the Chinese government, the implications of which are not yet fully understood but are likely to be profound.

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. 

Not All Platforms Are Created Equal

In the data business, the prize positioning that everyone seeks is to become integrated into client workflow. Having achieved this enviable goal, publishers know that extraordinarily high renewal rates are certain and profits are assured, because clients in effect are dependent on these workflow products to do their jobs and sometimes to run their entire businesses.

Workflow integration is assumed to be a B2B thing. After all, consumers don’t have workflow. Or do they?

I got thinking about this after having read several articles suggesting that Amazon may be considering getting involved in the sale of financial products such as mutual funds, perhaps even offering a robo-advisor service that would use software to manage the investment portfolios of their customers. This is a big, scary thought for online brokers and investment managers. And while Amazon hasn’t yet made any concrete moves in the financial services area, it’s a big, juicy target for Amazon, a company not known for its timidity or lack of ambition. As several industry observers point out, Amazon already has made moves into the massive and regulation-heavy pharmaceutical industry, seeking to become the nation’s pharmacist, with potentially even grander plans beyond that.

What allows Amazon to even consider entering the financial services market? It’s the fact that Amazon has a massive consumer platform. Many people consider Facebook a platform too, yet Facebook isn’t launching online pharmacies and the like. What makes the Amazon platform different is that it is a commerce platform.

Of course, Amazon is no ordinary commerce platform. It wants to sell you everything you need and deliver it to your door. It even will automatically ship its customers consumable products on a regular schedule. Amazon has also built a strong brand based on fast shipping and low prices. And because Amazon has so deeply embedded itself into the lives of its customers, delivering remarkable product breadth along with remarkable convenience, Amazon has achieved -- wait for it -- consumer workflow integration.

This takes me full circle. Does Amazon’s success with B2C workflow integration suggest big opportunities for those with B2B data products that have deep workflow integration to become commerce platforms? I am not convinced. The Amazon journey to success was long and expensive. It also started by delivering something unique and valuable: a universal bookstore. My guess is that most B2B data products, even if deeply embedded, can’t really transition to becoming commerce platforms. Their usage is too specialized, as are their audiences.

Deeply integrated B2B workflow products driven by data may look like platform opportunities if you squint enough. But if you squint too hard or too long, you’ll end up needing glasses, and you can find a great selection of them … on Amazon. 

Searching for a Better Recommendation Engine

My first experience with recommendation engines was with Amazon in its early days. Then, when you bought a book, Amazon would tell you that people who bought the same book had also bought these other books. It was simple, brilliant, and most importantly, it worked. When Amazon later started selling CDs, the recommendation engine worked even better. I got to enjoy music I never knew existed, and Amazon sold more CDs. It’s a classic win-win, and you would think Amazon would put its substantial resources into making its recommendations even better. 

But apparently not. After buying an introductory book on Photoshop a while back, the recommendation engine started showing me every Photoshop book ever written (there appear to be hundreds of them), and crowded out every other book recommendation for nearly a year. These were lazy recommendations, and disproportionate to the one book I bought – ever – on a specific topic. And Amazon recommendations have gotten even lazier since then.

You may also recall the Netflix Prize, announced with great fanfare back in 2008. A $1 million prize was given to anyone who could improve the efficacy of the Netflix recommendation engine. It was an impressive commitment by Netflix, and it showed they deeply understood the importance and value of recommendations to their business. Fast forward to today. Having watched every single episode of Arrested Development on Netflix, how did I learn about the arrival of new episodes? I read about it in the newspaper. Has Netflix brought these new episodes to my attention? Not yet. Somewhere along the way, Netflix seems to have stopped caring about the quality of its customer recommendations.

Move over to the search engines – all of them. You may know that you can force a search engine to search for a specific phrase by putting quote marks around it. Typically, your first search results will be web pages containing that exact phrase. But then the search engines actually remove the quote marks and toss in results that have the requested search terms, but not necessarily together. Then they toss in pages that have some but not all of your search terms. Since I didn’t ask for these search results, I think it’s fair to consider them as recommendations. And they are (predictably) lousy. It’s as if the search engines assume I don’t know what I am doing, so they give me every possible type of result. Yes, more is better with search engines, but only if they are giving me more of what I want. 

Contrast this with the music service Pandora that I’ve been raving about since 2007. Despite a tough revenue model, Pandora has not forgotten that it lives and dies by the quality of its recommendations, and it’s built to over $1 billion in annual revenue by staying focused. Hopefully they'l maintain that focus as it continues to grow.

When companies get big, it’s very easy for them to get distracted and lose interest in what made them big in the first place. There are more voices now saying that Google search quality is in decline. And remember when Yahoo got bored with search and decided to outsource search while it chased bigger dreams? These distractions create opportunities for smaller players to do search better, and some are finding success.