Is It App Time?

In a move that further signals the remarkable growth and increasing importance of mobile devices,
Google has announced changes to its search algorithms to prioritize apps.

Starting April 21, for all searches on mobile devices, Google will present search results that identify and prioritize mobile-friendly sites. That means those with mobile-friendly sites should rank higher in search results conducted from mobile devices. Further, if you wish, Google will also begin to index content on mobile apps that may not appear on your website. And to close the loop, Google will allow app developers to use mobile search results to guide users to either the website or the app (provided the app is installed on the mobile device, something Google will check), wherever you the content owner think they’ll have the best experience.

These are cool if not world-changing new features from Google, but they indicate clearly the rapid evolution of the mobile ecosystem, one where the quality of the displayed information is becoming nearly as important as the information itself. This means that mobile-friendly websites are important, but the future lies with apps that will become an increasingly seamless part of the search experience. Think about it. Google will now check (on Android devices) what apps you have installed, index the content of those apps, present this content in regular Google search results, and allow you to seamlessly view that content in the installed app.

If your website isn’t already mobile-friendly (and it should be just as a best practice), Google’s giving you a big incentive to do so by pushing you up in mobile search results.

And if you’re wondering about the value of apps for your products, consider how quickly they are moving from handy appendages to the mobile experience to becoming central to that experience. If your data makes sense on a mobile device (and it doesn’t always), it’s probably time to stop thinking and start coding!

Customer Privacy: Get Serious

You may have noticed the news last week that AT&T is rolling out new, ultra-fast residential Internet service in Kansas City. But along with that announcement came a novel pricing structure: the service is $70 per month, or $99 per month if you want your online activity to remain private. Leave aside the ethical and moral arguments for a moment and just look at the optics. There it is in black and white: AT&T will monitor your web searches and browsing activity in order to serve up tailored advertising unless you pay a hefty premium to avoid this. Unsurprisingly, the press uniformly reported this as a “privacy premium” or “no-spy fee.” You are left with a creepy feeling about AT&T, and this pricing approach certainly doesn’t work to burnish the company’s brand. Also, is a typical residential customer really worth $350 in advertising revenue? This feels more like a penalty fee than recovery of foregone revenue.

And what about the ethics and morality? Many will argue, plausibly, that this is no different from what Google, Facebook and many others do – offering you services where they monitor your activity in order to better target advertising. All AT&T is doing is giving you an (paid) opt-out opportunity.

The small but important differences I see are two: the AT&T service is paid, and AT&T is in a privileged position as the on-ramp for its customers. If you offer a paid service, the business model is explicit and understood by both parties. Trying to further monetize your customer is good business, but it’s also a delicate business because you risk killing the golden goose. And when you put yourself in the position of having access to sensitive customer data (even if you don’t think it’s all that sensitive), you are in a trust position. When trust is lost, it’s very hard to get it back.

The implications for B2B data publishers?  Paid subscription services come along with a customer expectation of privacy. After all, your subscribers are using your databases to check on competitors, look for acquisition candidates, plan business strategy and lots of other sensitive activity. Even the perception that you are peeking into their activities for anything other than system maintenance represents a huge breach of trust that can seriously damage your brand and your business. Consider, as just one example, the blowback Bloomberg experienced when its customers learned that Bloomberg editors could and did access their accounts.

Think hard about your own approach to customer privacy. Don’t fall into a common trap of thinking that because all this customer data is so accessible to you, it’s yours to use. It even filters down to everyday activities such as managing customer engagement. Contacting customers that haven’t logged in in 60 days is one thing; calling them up to discuss their recent queries probably crosses the line.

Privacy doesn’t get discussed much in the context of B2B data products in large part because it is an implicit customer expectation. But if pricing models such as this AT&T model proliferate, publishers that are serious about customer privacy will likely have a strong competitive advantage.

Reviewing the Reviewers

You are likely familiar with Yelp, the local business ratings and review platform. It's been a phenomenal success, but it also has a large number of detractors, in large part the businesses that are the subject of those ratings and reviews. Yelp is a business that, if examined dispassionately, really should never have succeeded. The primary reason was its business model: let anonymous users pick apart businesses in published reviews, then try to sell advertising to those same businesses. Yelp made this challenged business model even tougher by introducing a secretive filtering algorithm that would decide what reviews got published. The objective was to weed out spam, but all it did instead was to spur conspiracy theories among businesses that felt good reviews were being swallowed while bad reviews always seemed to get published.

Tough business, right? Well it gets tougher, particularly because Yelp showed little interest in mediating disputes (for example, there are documented cases of restaurants getting bad reviews on dishes they have never offered), essentially admitting it was too much work. Mix into this the inexperienced sales force Yelp fielded, giving rise to stories of reps offering to make bad reviews disappear  in exchange for advertising, with a raft of lawsuits claiming extortion quickly following.

Things seem to have calmed down for Yelp in the last year or so, but it's hard to imagine that the rift between the business community and Yelp has fully mended. Yelp is more powerful than ever, and can make or break a business. Yet it maintains as a core principle that it is a consumer empowerment tool, even though Yelp generates no revenue from consumers.

That's why I find it surprising that Yelp just announced the acquisition of Eat24, a service that lets people order food for home delivery. Yes, the company that controls the reputation and success of restaurants now wants to control their order flow as well. I see nothing to suggest that Yelp has become a friendly, trusted brand to the average local restaurateur. Yelp brings scale, but a lot of baggage as well.

What is the correct business model for a ratings and review business? There is no easy answer, especially as the consumers who typically provide the reviews show little appetite to pay to access them. One exception is Angie's List, which sells subscriptions, but even Angie's List now makes more money from advertising than subscriptions. Fortunately, Angie's List found a middle path that allowed this revenue pivot without compromising its credibility and integrity.

TripAdvisor is another reviews site with many of the same issues as Yelp. But TripAdvisor makes most of its money by selling eyeballs, a traditional media model. This means its doesn't have to rely on hotels for revenue, though it recently started to push in this direction.

The real estate website Zillow posts its estimate of a home's value right next to the (almost always higher) asking price. One can presume that's not helpful to making the sale. Awkward? Well, Zillow now asks consumers to rate and review the real estate agents to whom it sells advertising.

In many respects, the jury is still out on what does and doesn't work for review sites. What we've seen to date is that if you can build a big enough audience, the advertising dollars will follow, no matter how upside-down your business model. But just because they pay you doesn't mean they have to like you.  And this may come back to haunt these companies, if not in their core business, then by ultimately limiting their growth and expansion potential.

How Do Your Customers Rate?

It’s recently become known that Uber not only allows its customers to rate its drivers; it also allows its drivers to rate its customers. If you’re loud, abusive, demanding or otherwise unpleasant, you might find yourself silently boycotted by Uber drivers, none of whom are obliged to pick you up. Is this scary, unfair, an exercise in pure democracy or a wake-up call to consumers, who have to date exercised sometime life-and-death power over all types of small businesses with their comments, ratings and reviews, often furnished anonymously? It’s too early to know if this move by Uber foretells a trend, but it’s worth exploring.

Spend just a few minutes online, and you’ll quickly learn of the outrage of small businesses against rating and review websites such as TripAdvisor and Yelp, not to mention the periodic lawsuits. These businesses see an issue of basic fairness: why should unknown strangers (who may even be my competitors pretending to be customers) determine the success of my business in a manner in which I cannot even defend myself?

On the flip side, should businesses be empowered to rate their customers? In some cases, it’s simply not possible: customer transactions are largely anonymous. But for big dollar B2B transactions, rating and review platforms already exist.

An early example of this is a site called TheFunded.com. It had the audacity to let entrepreneurs rate venture capitalists, anonymously. It created a firestorm in the industry, with venture capitalists up in arms about the unfairness of anonymous reviews. In fact, the outrage really stemmed from the upending of the power dynamic in that business. Suddenly, entrepreneurs were no longer supplicants.

Business credit website Cortera has an interesting approach, creating online forums for credit managers in specific market areas to exchange information on companies. It’s a good concept, but one where it’s difficult to get a critical mass of interactions.

The idea of businesses rating customers is not completely new. Indeed, companies like ChexSystems operate “bad customer” databases used by banks to judge whether or not they want to do business with you. And in the apartment rental industry, numerous databases exist to report bad tenants, some with catchy names like badtenantslist.net and donotrento.com. These are not credit rating databases as much as they are places to report poor behavior.

So maybe widespread customer ratings will come along faster than we think. And if they do, a nifty data opportunity will arise: aggregate the ratings of customers that can then be used to weight the ratings these consumers assign to businesses. In algorithms, veritas.

How Many Ways Can You Monetize Data?

I watch the real estate sales vertical with great interest. There’s a lot of data, and money here, which in turn means a lot of innovation and competition. Companies like Trulia, Zillow (which are poised to merge shortly), Move (which operates the Realtor.com site) and a host of fascinating and scrappy regional players such as PropertyShark makes for endless creativity and impressive user experiences. The first thing you notice about all the online real estate information services is that none of them is trying to disintermediate real estate brokers. Indeed, these services typically have business models that depend on agents for revenue. Thus what has happened in this very unusual market is that customers have taken on the primary work of discovery (formerly a big part of the agent’s job), even though agents haven’t reduced their commissions to reflect this.

The second thing you notice is the wealth of structured data that is available for parametric searching. Search by zip code, price range, bedrooms, lot size, and much, much more. In fact, such powerful searching is table stakes now. Map integration? Done. Alerts? Done. Rich multimedia? Done. So what’s left to innovate?

Zillow burst onto the scene (beautifully timed to coincide with our late, great real estate boom a few years back) with its audacious system that put a price valuation on every home in the country. That brought it tremendous visibility, but also introduced consumers to the power of predictive analytics.

Trulia later upped the ante by overlaying neighborhood crime statistics on its database. Not to be outdone, its competitors overlaid school district boundaries to map the schools nearest to each home. Trulia then upped the ante again, licensing data from our Model of Excellence winner GreatSchools.org, that showed the relative quality of each school. And that’s where the market seems to be headed today – qualitative assessments of neighborhoods, along with more predictive analysis.

As you might expect, qualitative assessment starts with Census demographic overlays. Real estate site Movoto.com is already there, with zip-level income, education and ethnicity. Some other sites are hesitating because of the vagaries of real estate anti-discrimination laws. But that is not an impediment to third-party data providers such as Onboard Informatics, which provides a raft of local data, including an innovative “lifestyle search engine.” Other sites like neighborhoodscout.com provide sophisticated demographic views of local areas. And we’d be remiss not to acknowledge diedinhouse.com for those who need to know if former home occupants left on their own power or not.

But what’s most fascinating is that this lifestyle analysis of neighborhoods has even been elevated to a personalized, consultative model. The New York Times recently profiled a New York area firm called Suburban Jungle that helps homebuyers target areas based both on demographics and deep market knowledge. Suburban Jungle doesn’t sell real estate; it refers its clients to real estate agents in exchange for a fee-share, another great example of how many different ways data can be monetized.