Upping the Data Ante

Step back a bit from the fray and you’ll see an interesting evolution in the world of data: from providing lists of people or entities that might be prospects, to lists of people or entities that should be prospects, based on something they have done (think sales triggers). Now we’re beginning to move squarely into what used to be the realm of science fiction: identifying prospects before they have done anything at all.

We’re blazing new trails here, and pre-prospecting (for lack of a better name) depends heavily on lots of input data and Big Data analytics. The 800-pound gorilla in this space right now is a company called InsideSales that calls its analytical secret sauce “Neuralytics.”

All hype, you say? Well some level of hype is a given these days, but the company has raised over $139 million to date, and Salesforce.com in particular has fallen hard for the company’s pitch, and actually led its most current funding round, that also included Microsoft.

I don’t have any inside knowledge of what InsideSales is up to, but from the tantalizing tidbits that have surfaced in the press, it seems to be a combination of obvious inputs such as social media feeds, plus less intuitive things such as weather patterns and sports team scores. I can only guess that you’re a somewhat better prospect if it’s sunny out and your team won last night, but perhaps these data are being used in a more subtle and sophisticated way.

The other hint I picked up is that InsideSales depends on “email and phone records” to perform its analytical alchemy. Needless to say, these tend not to be public records, so to deliver the holy grail of sales prospecting, InsideSales apparently depends on the holy grail of input data as well!

I’m not dismissing InsideSales, primarily because I am doing some big league speculating here. But I will say there are data sources available today that get us a long way towards the notion of pre-prospecting. What excites me the most is what is going on today with online ad re-targeting. Ad re-targeting is based on what might be described as networked cookies. Visit a site, and a common cookie is placed on your computer. As you move to other sites that are part of the network, ads can be displayed based on sites you’ve previously visited. More importantly, your travels around the Internet can be centrally stored, creating a wealth of information about you, your interests, your habits and much more. While not easy, it is a straightforward leap to start learning about not only what interests you but also what are the early signs that you are beginning to contemplate a purchase.

Privacy isn’t the issue in re-targeting (at least for now), because nobody needs to know who you are for re-targeting to work. But as your movements around the Internet are recorded and analyzed, it is entirely possible that we’ll someday know when you’re thinking about buying something, and perhaps even a little before.

The next generation of sales insights likely isn’t all that far away, so now is a good time to do some pre-pondering on what it might mean to you and your business.

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.

CQ Roll Call: Going Big on Analysis

The superheated interest in Big Data and associated analytics continues unabated. As we have long noted however, while there are many ways that data publishers can tap into opportunities created by Big Data, they themselves tend not to be sources of Big Data. That’s not a bug: it’s a feature. So much of the value-add of data publishers come from distilling, organizing and synthesizing of data. Indeed, those data publishers that continue with the old-style model of delivering giant data dumps to their customers are the most challenged players in this more sophisticated and demanding environment.

But despite this intense focus on Big Data and analytics, let’s not forget that there is still real value in all types of analysis, no matter how it’s created. I was reminded of this today while reviewing a new offering from CQ Roll Call. If you’re not familiar with CQ Roll Call, you can think of it as a giant legislative tracking service. For the inside players in Washington, it’s a must-have: it’s the place to go to hear developments first, along with authoritative analysis. But CQ Roll Call also has an “inside sports” aspect to it: an endless series of details wash over you, and if you’re not deeply immersed in a specific piece of legislation, for example, it’s hard to get up to 40,000 feet and see what’s going on over time and in context.

CQ Roll Call is starting to address this need (and likely a latent market as well), with a series of detailed backgrounders, all on high profile topics. They’ve produced 45 of these policy backgrounders to start, with more to come.

What really caught my eye is that these backgrounders, unlike some many that are cranked out by publishers, are designed to be dynamic: they stay up to date. Also of interest is that they are not divorced from the company’s core data product: they link to CQ reporting, the actual text of each bill and other CQ research. It’s an elegant way to tie together a lot of the company’s products with a simple overview designed to encourage drill-downs by those who want to get deeper into the subject. CQ Roll Call touts these reports as a sort of “Wikipedia for policy wonks.”

The simple insight here is that even in the era of Big Data, ALL analysis products remain useful and valuable, and even more so if your content is fast-changing and intricate. Drawing meaning from your content is what your customer do with your data: do if for them and they’ll not only thank you for it, they’ll probably also pay you for it!

Adwords Now Promote Tele-Measurability

Google has just launched a new Adwords feature that offers lots of useful applications for online marketers. It’s so slick, I am a little surprised it hasn’t received more press. In a nutshell, Google Adwords can now track and report not only how many people click on an ad, but how many people call based on an ad.

Yes, you’ve doubtless heard claims like this before, but this one seems pretty solid and pretty powerful as well. Here’s how it works.

Google Adwords  dynamically inserts a key telephone number (supplied by Google) into all places you specify on your landing pages or even your full website. Put another way, anyone who gets to your website via Adwords will see a Google-supplied phone number and everyone who gets to your website any other way will see your regular phone number. You can format the Google numbers to match your website look and feel, and the phone numbers will be dynamically supplied for up to 90 days.

While Google doesn’t mention B2B applications specifically, you can immediately see the benefits. Few B2B buyers click through from an ad and immediately place an order. More likely than not, there’s a phone call involved, and even then, the phone call might not happen immediately. With this new feature, it’s possible to measure and track this unique aspect of B2B buying which is well understood, but has been devilishly difficult to measure.