How Do You Rate?

Morningstar, the financial information giant, today announced that it will be licensing a ratings system from Sustainalytics, a Dutch company that assesses and rates public companies along three dimensions: environmental and social responsibility and governance. Morningstar will adapt this methodology and apply it to mutual funds.

Why the rush by Morningstar to add still more ratings to its data platform? And why license a ratings system when Morningstar already has demonstrated expertise in this area? Indeed, Morningstar has been rating mutual funds on their stewardship (akin to governance) for a number of years now.

The answer, in a word, is that ratings systems are hot. While they don’t look like much on the surface, they offer to users what they most want today: fast answers. You could even go so far as to say that the other reason ratings system are so popular is that they do the research – if not the thinking – for you.

Most importantly of all from a data perspective, a ratings system provides a consistent, normalized and sortable data point. This is especially valuable in the investment world, which is in the business of finding needles in haystacks. Ratings systems and other filters significantly streamline this process.

Imagine if someone asked to you identify the ten best restaurants in Dallas. Without Yelp and Zagat and the other existing restaurant rating services, this would be a nearly impossible task, particularly if you were looking for a comprehensive and objective answer. But these services in effect conduct mass-scale surveys, asking people to condense their opinions of restaurants into a predefined ratings scale. This user-generated approach to ratings has all sorts of imperfections, but most people believe that with enough people participating, the truth will present itself.

A step up from these open surveys are the professionally administered ratings systems. These distinguish themselves by identifying and rating companies against a fixed set of criteria. The goal of the exercise is to be objective as possible. That’s why data are used in place of opinions whenever possible. The more rigorous the system, the more valuable it tends to be. That’s because in addition to being normalized and consistent, these ratings systems allow you to make dependable comparisons. Companies rated “A,” for example, are all rated that way because they met a certain specified set of criteria. That means you can place more trust in the ratings system.

Interestingly, most ratings systems happily publish their underlying criteria and ratings methodologies. While this might seem to be their highly proprietary “secret sauce,” the reality is that nobody wants to undertake the same laborious ratings work if somebody else has done it, and publicizing the underlying methodology builds credibility and trust. In fact, the underlying methodology of most professional rating systems is central to their marketing efforts.

Rating systems reflect the fundamental shift we are seeing from data publishers selling vast piles of raw data to high value, more analytical datasets. The next opportunity is to actually do the analysis for them.

You can learn more about how publishers are using their data to produce a wide range of high value products at this year's Business Information and Media Summit. Hope to see you there!

How Zillow Spends Zero on Advertising

Doubtless everyone reading this is familiar with Zillow. We honored them as a Model of Excellence in 2006 .

They’re now a real estate listing behemoth that sports a market capitalization of $5 billion. We all know what Zillow does and how successful it’s been. But did you know that Zillow launched with virtually no advertising budget?

In a fascinating interview, Zillow’s Chief Marketing Officer, Amy Bohutinsky, explains Zillow launched with the classic “sell data with data” strategy. Using data to promote your data is – unsurprisingly – a marketing tactic available only to data publishers. And it’s a tactic well worth exploiting to the maximum.

Zillow launched itself with press releases aimed at the consumer mass market. It offered free access to data that was catnip to almost every consumer: instantly find the estimated value or your home, or anyone else’s for that matter. Zillow, after collecting and normalizing property assessment records from all 50 states, had developed an algorithm that looked at recent sales and area demographic data to calculate a home price valuation. Sure, it was necessarily imperfect, but the data was credible if not authoritative, comparable (all homes were evaluated the same way) and of course free. This quickly drove millions of page views, allowing Zillow to execute on its business model of selling listing enhancement to real estate agents.

But Zillow didn’t stop with this single gambit. Instead, it allowed consumers to sign up to receive email updates to their home valuation – every time the estimate changed, Zillow would send an email. This created critical ongoing engagement (important because the average person doesn’t buy or sell a home all that frequently), brand enhancement, and an important advertising vehicle (the email also presented information on nearby homes for sale).

Beyond this, Zillow regularly mines its own data to find newsworthy statistics that keep its brand front-of-mind and implicitly credential it as an authoritative and central industry player. It issues press releases on everything from the standard reports on where homes are selling most quickly and slowly, to offbeat data on the “10 biggest homes” or “10 most expensive homes,” and the like. Obviously there’s no shortage of material.

As we noted earlier, you’re most likely to get media coverage if you can provide facts and statistics. That’s hard news as opposed to opinions or transparent gimmicks to try to attract attention. More importantly, every piece of data you release reinforces your central market position, your authority, your knowledge and your expertise. You become generally understood to be the “go to” place for data in your market. There’s no better positioning than that, and best of all if you do it right, it’s practically free.

You can hear how another Model of Excellence winner, Capterra,  pulls of this trick when its CEO Mike Ortner joins us for our infamous “Excellence Revisited” panel at this year’s Business Information and Media Summit. Hope to see you there.

Is Your Data "Datanyzed"?

A new product by a cool young company called Datanyze is capitalizing on some well-established infocommerce best practices. Here’s how they did it.

The core business of Datanyze is identifying what SaaS software companies are using (sometimes called a company’s “technology stack”). To do this, Datanyze interrogates millions of company websites on a daily basis, looking for telltale clues as to the specific software they are employing online, and apparently a lot of categories of software can be divined this way. Datanyze aggregates and normalizes these data, then overlays company firmographic data (Alexa website rank, contact information, revenue estimates) to create a complete company profile.

Datanyze links directly to the Salesforce accounts of its customers, so it can add and update prospects on a real-time basis. At a basic level, the use case for this product is straightforward: a marketing automation platform like Eloqua could use it to find companies using a competitor or no marketing automation at all. But wait, there’s more!

Datanyze’s new product essentially flips this service. Now, Datanyze clients can have Datanyze analyze their existing best customers, and Datanyze will build a profile of these customers that can be used to predictively rank all their prospects, current and future. Here are the best practices to note:

• The transition of Datanyze from a data provider to an analytics provider, something that’s happening industry-wide
• The shift from passive (we supply the data, you figure out what to do with it), to active (here are top-rated prospects we’ve identified for you), and the associated increase in value being delivered by the data provider
• The tight integration with Salesforce means that Datanyze customers just need to say “yes” and Datanyze can get to work – no IT involvement, no data manipulation, no delays
• Datanyze is pouring leads into critical, core systems of its customers, a strong example of workflow integration
• The use of inferential data. Boil down a lot of the analytical nuance, and Datanyze has discovered that companies that buy expensive SaaS software are better prospects for other kinds of expensive SaaS software. Datanyze doesn’t know these companies have big budgets; but it does know that these companies use software that implies they have big budgets

Datanyze offers a concrete example of how data companies are evolving from generating mountains of moderate value data to much more precise, filtered and valuable answers. Are you still selling data dumps or analytics and answers?

Shine a Light on Your Hidden Data

If you watch the technology around sales and marketing closely, you’ll know that beacon technology is all the rage. Stores can purchase beacon broadcasting equipment, and when shoppers enter their stores with beacon-enabled apps, the apps will respond to the beacon signals – even if not in use. Stores see nirvana in pushing sale offers and the like to customers who are already on the premises. And of course, it is expected that some mainstream apps (Twitter is often cited, though this is unconfirmed) will become beacon-enabled as well.

Beacons represent a concrete manifestation of the larger frenzy surrounding geolocation. Everyone wants to know where consumers are at any given moment, as epitomized by big players such as Foursquare, which has evolved from its gimmicky “check ins” to become more of a location-driven discovery service.

That’s why I was so intrigued by Foursquare’s most recent product announcement called Pinpoint. Shifting its focus from where people are now, Pinpoint is going to mine valuable insights around where people have been and let companies use it for precise ad targeting.

Details about Pinpoint are scarce right now, but Foursquare is smart to start mining its historical data. At the lowest level, it means that Foursquare can help, say, Starbucks target lots of Starbucks customers. Useful, but not too sophisticated. If Pinpoint can roll up businesses by type (such as pet food stores), it starts to get a lot more interesting. But the real home run would be to be able to divine purchase intent. If someone visits three car dealers in a short period of time, you suddenly have an amazingly valuable sales lead. And mining insights like this is now practical with Big Data tools.

But the real insight here is that your history data isn’t just ancient history: it provides the multiple data points you need to find patterns and trends. Knowing that a company replaces its CEO every 18 months or so is a hugely valuable insight that you can identify simply by comparing your current data to your historical data. At a minimum, you’ve got a powerful sales lead for recruiters. But that level of volatility might be a signal of a company with problems, thus creating useful insights in a business or competitive intelligence context. We’ve all heard about the predictive powerful of social media sentiment analysis. You may have equally valuable insights lurking in your own data. All you need to do is shine a light on them.

How Starbucks in Mall of America looks to Foursquare

The Award for Outstanding Performance Goes to Internet Movie Database

We awarded the Internet Movie database a Model of Excellence  in 2003, and it is still a standout in terms of innovation and best practices.  

The Internet Movie Database (often called by its acronym IMDB) originally started in the UK as a non-profit undertaking, and it may well be the earliest and most successful example of crowdsourcing – well over a decade before the term was even coined. Very simply, the IMDB was a site for movie buffs worldwide to build an enormously detailed database of every movie ever made. And we are talking about a serious level of detail. Want to know who was the hairstylist for the co-star of an obscure French drama from the 1950s? Well, IMDB was the go-to source. What also made IMDB interesting was that from its inception it was a true database, and despite the inherently unruly nature of crowdsourcing, there were enough committed volunteers to take on the unsexy work of removing duplicate entries and normalizing the data.

In 1998, IMDB was quietly acquired by Amazon and turned into a for-profit company. There are some great best practices to be observed here. Taking over and commercializing a site built by tens of thousands of unpaid, die-hard movie fans was a risky proposition. The backlash could have killed the business in short order. But Amazon left IMDB alone, infusing it with editorial resources so the database got bigger and better every year. Better data, less work and all free. Not much here to get upset about!

But Amazon (surprise!) wasn’t in this to be charitable. First, it started marketing to the substantial audience of IMBD users with links to its site. Like the movie? Great. Amazon can sell you a copy.
Amazon’s next move was sell sponsorships to movie studios eager to promote upcoming releases. From there, Amazon launched a subscription-based Pro version of the database that offered enhanced searching and even deeper content to movie industry professionals for research purposes. The core site remained free, meaning Amazon was a pioneer with the freemium model, well before that term had become popular. 

Is Amazon now resting on its laurels? Absolutely not. To support both its Kindle and Amazon Prime offerings, Amazon has launched a service called X-Ray, powered by IMDB. Amazon also selectively licenses this new data capability. What X-Ray does is link movies to the IMDB database, so users can visually identify actors in the film, find movie trivia, explore the movie soundtrack and much more, right while watching the movie.  It’s not all software magic, by the way. Amazon is doing a lot of the necessary linkages manually, but it already has thousands of movies coded. Also of interest, it’s touting its “X-Ray Enabled” badge that if it plays its card right, could someday become a differentiator for new movie releases.

Endless innovation. Strong support of its core e-commerce platform. Deft handling of often prickly enthusiast community. Endless monetization. This is where data is going!