Ratings Wars

A new start-up called RentLogic has entered the New York City real estate market with a smart, simple idea wrapped around a proven business model. It provided a data product that rated rental properties on how well they were maintained. Unlike opinion-driven ratings sites like Yelp and TripAdvisor, RentLogic mines complaint and violation data from official government records, assessed them, and assigned every building a letter grade from ‘A’ to ‘F.’
 
It’s a clever idea and arguably much-needed. At the very least, it began to address asymmetric information exchange that has long characterized the real estate business. Put simply, your landlord wants to know everything about you prior to renting to you, but you know little or nothing about the landlord or the property itself.
 
RentLogic also was smart about its marketing. It hooked up with one of the largest rental brokerage firms in New York City, a firm called Citi Habitats, to match its database to its apartment listings. Pop up a listing in Citi Habitats, and you would see not only the standard apartment description and other details, but you’d also see a letter grade for the property and a summary description of violations and complaints. This was great exposure for RentLogic, and a differentiating website feature for Citi Habitats. And just to make sure that not too many landlord noses were bent out of joint, the two companies agreed that RentLogic data would only be shown if the property had earned an ‘A’ or ‘B’ rating.
 
All smart moves, and a win-win for both companies and apartment seekers. A ‘happily ever after’ story then? Not exactly.
 
Just eight days after rolling out the new ratings system, Citi Habitats pulled the plug and ended the deal, reportedly after receiving strong pressure from landlords. At least in New York City, landlords have lots of clout. While Citi Habitats makes it money from fees from apartment renters, it still needs access to apartment listings in the first place. And New York City landlords aren’t that into transparency, at least not about their own activities. Several landlords described the ratings system as “unfair” and “inaccurate.”
 
From a strategic perspective, RentLogic did everything right. In my view, its business model, drawing from official records, is far more defensible than sites like Yelp that aggregate largely anonymous opinions and turn them into ratings. But RentLogic missed one big item: the supply and demand imbalance in its market. RentLogic is trying to serve the demand side of its market (apartment renters), but given a shortage of apartments, the supply side (landlords) makes the rules. That complicates market entry for a disruptive market player, because with landlords closing down many distribution channels, RentLogic is left with selling its data direct to apartment seekers, a slower and more expensive path to growth.
 
But we’ll be keeping an eye on them to see how they evolve since more than one company has pivoted their way to a Model of Excellence. You can meet this year’s winners at BIMS. Here’s a preview, starting with TrendMD. 


Meet TrendMD at BIMS

Want to find out why TrendMD won an InfoCommerce 2016 Model of Excellence Award?

This year’s winners will be showcased at BIMS, November 14-16 in Ft. Lauderdale. It’s a peer-to-peer forum complete with exclusive tracks on Data and the unique opportunity to hear from the MOE founders firsthand.  Register now to attend!
 
Here’s just a taste of the brilliance behind TrendMD – be sure to attend BIMS to get the full story.
 
"It's difficult to fail if you actually talk to customers, " says Paul Kudlow, Founder of TrendMD. As he was going through medical school the last few years, Kudlow pictured his future: talking to patients all day, providing solutions, and working all hours. "It was still a massive transformation," Kudlow said, "I went through medical school and started residency—and then this came out of nowhere. TrendMD is a kind of Outbrain or Taboola for the medical world. TrendMD enhances content discoverability for readers by providing publishers with strong incentives to display relevant links to third-party content. We took that model and designed an article recommendation widget that's embedded in places doctors and other researchers use. Content producers can also promote their links on sites where the TrendMD widget sits.
 
"Unlike medicine, there's no playbook for startups," Kudlow said. "You kind of invent it as you go and see what fits. We offer value to readers. We're distributing content so it can get to the readers and give value to the authors. Before TrendMD, there was no way to push this kind of content to readers. Often we heard journals say that they get new readers through good SEO or posting content online," Kudlow said. "That's a bit like saying we printed the journal out it in one library hoping that people can read it."
 
Hear more at BIMS!

Don't Turn Strength Into Weakness

For some time now, the publishing world has been crying foul over the growing power of ad blocking software products. Several studies suggest that as many as 50% of all online users have some ad blocking software installed. Some see this as a death knell for the industry, which is already struggling to maintain viability living off so-called “digital dimes,” a term to describe how much less lucrative online advertising is compared to traditional print advertising which is in decline.

One of the more prominent ad blocking software tools, Adblock Plus, which is published by a German company called Eyeo GmbH, is somewhat less militant than some its competitors, and has come up with a concept called “acceptable ads” that allows specific advertisements to be whitelisted. Some third-party research has concluded that nearly one-third of all U.S. Internet users may be using AdBlock Plus.

Ad blocking software that allows some ads to appear? It may seem odd, but that’s what Adblock Plus does. And how does Eyeo decide what ads are acceptable? Well, that’s where things get really strange. You see, Eyeo will accept payment from “larger organizations” in exchange for whitelisting their advertising. Don’t ask about the specifics of these deals because they are not disclosed. Not surprisingly, some publishers refer to this as a “protection racket.”

If you’re starting to see that Eyeo is compromising its entire brand promise, hold onto your seat. That’s because Eyeo has just rolled out its own real time bidding platform for whitelisted ads. Yes, the company that built its business blocking ads is now in the business of selling ads!

Eyeo justifies all this is by allowing users to click on any of the ads Eyeo serves to them to rate them. How users rate various ads will determine what ads they see in the future. This ostensible innovation is supposed to make this initiative palatable to Adblock plus users.

You probably already see the issue. Having built a popular tool to block ads that may be used by as many as a third of all Internet users, Eyeo has a chokehold on almost every ad-supported website, giving it tremendous market power. And it exercised that power by accepting payments to allow ads to slip through its blocking software. It’s an approach that isn’t totally satisfactory to either Adblock Plus users or website owners. My experience has been that when you are not absolutely clear who your customer is, things end badly. It’s one thing to be a marketplace where you match buyers and sellers for a fee. It’s entirely another thing to try to get paid to match reluctant sellers to reluctant buyers. Indeed, it’s not even clear that what Eyeo has is even a marketplace at all.

The object lesson here is that having tremendous market power is always a two-edged sword and thus must be handled with extreme care. The more greedily and ruthlessly you wield your market power, the more likely you will ultimately lose it as you offend all the various constituents in your market. Through its actions, Eyeo may be sowing the seeds of its own demise. There’s a lesson here for data publishers. 

Make the Product, Not Just the Raw Material

Twitter exhausts me. Even though I feel I have been very selective in who I choose to follow, the volume is overwhelming. Every time I go to review my Twitter feed, I waste far too much time in an exercise to separate the wheat from the chaff to find useful nuggets of news or insight. Twitter ought to be incredibly valuable, but in its current design, users find that to overcome the sheer volume of tweets to get noticed, they have to pump out an increasing number of tweets themselves. It’s an endless game of volumetric one-upmanship that is ultimately self-defeating.

A recent article in the Wall Street Journal takes the view that Twitter is very good as a raw content creation platform, but a failure at making that content useful or even intelligible. We know that Twitter content has value: consider the number of companies looking for trends, breaking news and other signals to gain an edge and generate profits. But it is companies other than Twitter that are adding the value and making the money.

This got me to thinking. Many data publisher still focus on the quantity of the data they provided, not its value. And this inevitably leads to a mentality of selling data by the pound. These publishers deliver lots of data, and their customers figure out what to do with it. For a long time, this was a good business approach for publishers, but hardly an optimized one.

By wrapping their content in software, publishers have added value by allowing customers to act on their data more powerfully. But while data-software integration has been a boon for data publishers, there may still be entirely new products and even entirely new businesses hiding in your data. There are clues to this. Do you have lots of consultants buying your data year after year? Do they renew easily, rarely complaining about price increases? Chances are at least a few of them are productizing your data in some way. Get familiar with their specialties and their services, and you can often come away with new product ideas.

Have you ever changed your file layouts or stopped delivering a specific data field, only to get immediate panic calls from some of your customers? Chances are, they’ve built software around your content and are doing something very valuable with it. A few casual inquiries about how they’re using your data will often yield tremendous insights. Do you have whole categories of customers where you have no idea why they buy your data? Chances are, it will be worth your time to find out. It’s not unusual to find that markets you never considered are making valuable use of your data.

Data-software integration is great, but in the majority of cases, publishers are simply helping their customers better manipulate their data. But there’s a whole additional of level of value that can be created by turning your data into finished products. And while I am not arguing that you should try to run all your customers out of business, if some of them have found a way to make money by re-formatting, augmenting or manipulating your data to add value to it, I’d argue that such opportunities properly belong to the owner of that data. And your subscriber file is often the first best place to look for clues to such opportunities.

Credit Scores: Not Just for Credit Anymore

A credit score, like it or not, is something that exists for all of us. Pioneered by a company called Fair Isaac (now just known as FICO), the credit score provided powerful advantages to credit granters in two key ways. First, using massive samples of consumer payment data, FICO analysts were able to tease out what characteristics were predictive of an individual’s willingness to re-pay their debts. With this knowledge, the company built sophisticated algorithms to automatically assess and score consumers. This approach is obviously more efficient than manual credit reviews by humans, but it offered consistency and dependability as well. Second, FICO reduces your credit history to a single number in a fixed range. The higher the number, the better your credit. This innovation made it possible for banks and other to write software to offer instant credit decisions, online credit approvals and more. Moreover, a consistent national scoring system made it easy for banks to both manage and benchmark their credit portfolios, as well as watch for early signs of credit erosion.

There’s little doubt that credit scoring was a brilliant innovation, but is it so specialized it can’t be replicated elsewhere? Well, it appears that creative data types are seeing scoring opportunities everywhere these days.

Consider just one example: computer network security scores. There are several companies (and FICO just acquired one of them) that use a variety of publicly available inputs to score the computer networks of companies to assess their vulnerability to hackers. Is this even possible to do? A lot of smart people in the field say it is, and pretty much everyone agrees the need is so great that even if these scores aren’t perfect, they’re better than nothing.

You may also be asking whether or not there is a business opportunity here and indeed there is. Companies buy their own scores to assess how they are doing and to benchmark themselves against their peers. Insurance companies writing policies to cover data hacks and other cybercrimes are desperate for these objective assessments. And increasingly, companies are asking potential vendors to provide them with their scores to make sure all their vendors are taking cybersecurity seriously.

While scoring started with credit, it certainly doesn’t end there. Are there scoring opportunities in your own market? Put on your thinking cap and get creative!

Ebay Revamps By Adding Structure

Ebay, the giant online marketplace/flea market, is reacting to lackluster growth in an interesting way: with a new focus on structured data. The goal, simply put, is to make it easier for users to find merchandise on its site.

Currently, eBay merchants upload free-text descriptions of the products they are offering for sale. This works reasonably well, but as we all know, searching on unstructured text is ultimately a hit-or-miss proposition. And with over one million merchants on eBay doing their own data entry with very few rules and little data validation, you can imagine the number of errors that result, ranging from typos, to use of inconsistent terminology to missing data elements, etc. The consequence of this is that buyers can’t efficiently and confidently discover all items available for sale, and sellers can sell their products because they are not being seen.

It may seem odd that after several decades in business, eBay is just getting around to this. But in fact it hasn’t been standing still. Rather, it’s been investing its resources in perfecting its search software, trying to use algorithms to overcome weaknesses in the descriptive product data. And while eBay has made great strides, this shift to structured data is really an admission that there are limits to free text searching.

Granular, precise search results can’t be better or more accurate than the underlying data. If you want to be able to distinguish between copper and aluminum fasteners in your search results, you need your merchants to specify copper or aluminum, spell the words correctly and consistently, and have agreement on how to handle exceptions such as copperplate aluminum. Ideally, you also want your merchants to tag the metal used in the fastener so that you don’t have to hunt for the information in a block of text, with the associated chance of an erroneous result.

While we’ve come to believe there are no limits to full-text search wizardry, remember the best software in the world breaks down when the data is wrong or doesn’t exist. Google spent many years and millions of dollars trying to build online company directories, before finally admitting that even it couldn’t overcome missing and incorrect data.

Databases and data products are all about structure. Cleaning up and organizing data is slow, expensive and not a lot of fun, but it is a huge value-add. Indeed, one of the biggest complaints of those working in the Big Data arena is that the data they want to analyze is simply too inconsistent and undependable to use.

These days, anyone can aggregate giant pots of data. But increasingly, value is being created by making these pots of data more accessible by adding more structure. This is the essence of data publishing, and something successful data publishers fully appreciate and never forget.