Ad Blocking in Perspective

There has been tremendous anxiety in the media world around Apple’s move to allow ad blocking software on iPhones and iPads. After all, eliminate ads from mobile devices, and you take a big bite out most publishers’ ad revenue. Publishers are describing this move by Apple in near-Apocalyptic terms. But let’s get a grip.

First, we need to be clear that this ad blocking capability applies to the mobile web, not to apps. In that respect, this move by Apple is really just a big kick in the pants to build an app and get your audience onto it as quickly as possible.

Second, this move makes a lot more sense when you consider what’s driving it. Apple doesn’t make money from mobile search advertising; Google does. Apple doesn’t like Google for a variety of reasons, hence this aggressive move cuts into Google’s main source of revenue. We’re all just collateral damage in this war of the titans. But this perspective also helps you understand why apps are (and will likely remain) protected from ad blocking technology. The Apple ecosystem depends on apps, and Apple makes a lot of money from apps. Apple is not really against all mobile advertising; it’s against mobile advertising that benefits Google.

Third, some of these new mobile ad blockers will reportedly strip out some content as well as advertising (not text, but some things such as bloated masthead graphics). Indeed, the new breed of ad blockers are really less focused on eliminating advertising than improving the mobile user experience by speeding up page loads as much as possible.

Fourth, once again, publishers are feeling the pain of a self-inflicted wound. By junking up their websites (and by extension their mobile websites) with all manner of trackers, ad networks, auto-play video, re-targeting ads, overlays, and perhaps most ironic of all, ads to get the user to download the publishers own app, we’ve junked up the mobile experience quite thoroughly. When was the last time you recall having a satisfactory (as in fast and easy) mobile web session?

I certainly agree that a lot of people are using ad blocking software out of a sense of entitlement – they truly believe they should have limitless access to content without fee and ad-free. Of course that’s another self-inflicted wound (a topic I’ve discussed many times over the years). But the more important reason that users are flocking to ad blocking software is that it actually improves their online experiences. That’s a sad statement, but the resolution of the problem is firmly under our control.

Tapping Into Phone Data

For all marketers, B2B marketers in particular, the telephone has long been both a great friend and a big problem. Telephones are a great friend, because someone who calls you, particularly if it’s in response to your advertising, is a top quality prospect. At the same time, telephone calls resulting from ad campaigns have remained difficult to count, measure and evaluate.

And it’s not for lack of trying. I go back in this industry long enough to remember the glory days of “key phone” numbers. In essence, publishers would convince advertisers to use a dedicated phone number in each ad campaign as a crude way to track results. This approach worked, but because they really only yielded call counts, all they could do is prove a point for the publisher. Key phone yielded very little insight into the nature and quality of these calls.

Lest you think key phones are a dated concept, it’s interesting to note that this is essentially what Google is doing with its recent launch of call tracking for AdWords. Intriguingly, Google hasn’t really advanced this technology much – it’s all about using dedicated phone numbers to count the calls generated by your AdWord campaign.

Yes, for 30 years, call tracking technology hasn’t advanced very much. At least that’s what I thought until I recently ran across a company called Convirza.

Convirza offers basic call counting. But it goes much, much further. It has developed software that analyzes every incoming call (most companies already announce that incoming calls may be recorded, putting to bed any privacy issues), actually listening to each call to provide a call quality score. It can measure the outcome of the call, presumably by listening for keywords, to measure call conversion rate. It can even flag calls where it feels the salesperson left money on the table by not trying to upsell or cross-sell the customer. More generally, it can provide a quantitative assessment of the quality of each salesperson’s selling skills.

But wait, there’s more. Convirza integrates with marketing automation software, and can even be used to adjust online ad spending in real-time. If a particular program is generating a solid percentage of calls that convert, that program can be immediately scaled up.

This isn’t even everything that Convirza does, but you get the idea. By analyzing and deconstructing recorded phone conversations, Convirza is generating high-value, actionable data where none existed before. And stunningly, it’s left Google in the dust, because while Google is fine for counting calls, Convirza solves for the “last mile” problem: whether or not that call converted.

We should follow Convirza’s example and expand our thinking about how to extract data from unconventional sources to solve real-world business problems. It’s also a technology that advertising-based publishers could likely adapt to provide not only proof of performance, but a remarkable level of added value to their online advertisers.

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.

Deriving Data from Images

Google recently acquired a company called Skybox Imaging for $500 million. While admittedly a small deal by the standards of Google, the potential of this acquisition is so mind-boggling it is amazing it has not received greater attention. You see, Skybox makes satellites. And it’s just a year or so away of having six satellites orbiting the earth that will be able to photograph, in high resolution, every spot on Earth and do it twice a day.

In many respects, the innovation here is speed of refresh, not resolution of the images. There is nothing particularly special  about the optics used by Skybox. The excitement comes from the frequency of update. That’s because if you check on things frequently, you can see changes easily. And from those changes you can infer meaning.

Already, we know that satellite photos can be used to calculate the square footage of a building (based on the roof surface), a potential boon to roofing contractors, who can now do estimates from their desks. But you can also start to see angles for data providers as well: the size of a company’s facility can let you infer a lot of valuable things about that company. And Skybox will potentially take this concept to the next level.

Overlay satellite photos with map data (something Google routinely does now), and you now know who owns the property you are looking at. Checking the number of cars in the parking lot twice a day could allow you to infer number of employees. Over time, you could infer if the company is growing or shrinking.

One hedge fund (allegedly) now uses satellite photography to check the number of cars in lots at big box retailers to infer sales. It’s suggested that Skybox can assess the quality and yield of crops while still in the ground, as well as the amount of oil being pumped around the world (by analyzing storage tanks). Consider construction data, where new home starts and completion rates could be accurately measured on a daily basis. Consider measuring the truck and rail traffic into manufacturing plants over time to assess financial conditions. Let your mind roam, because that’s what Skybox is all about.

And lest you think I am alone in this geeky view of things, consider this statement by Skybox co-founder Dan Berkenstock, “"We think we are going to fundamentally change humanity's understanding of the economic landscape on a daily basis.”

The key to all this magic is software that is smart enough to interpret photographic images. This is where images get turned into data. And once that data is overlaid on maps, giving it both context and other data such as ownership, you quickly move to actionable data.

I’m focused on commercial applications for Skybox. For those considering the consumer implications, privacy concerns abound. For the moment it seems, we have to rely on Google not to be evil. And in the interim, there’s still a lot of work to be done to get this infrastructure fully in place and to determine what can be measured as well as what is worth measuring. But as a potential new source of high-value business intelligence in structured form, Skybox is painting a very pretty picture of the future.

Edmunds.com Yields Multi-Million Dollar Revenue Opportunity from its Free API

APIs, which stand for Application Programming Interfaces, are all the rage these days. APIs, which can be described as online back doors into your database, allow programmers to seamlessly integrate your data into their products, particularly, but not necessarily, mobile apps. Increasingly, customers are asking companies selling subscription data products for “API access” to their data. The reason for this is that these companies want to integrate commercial datasets into their own internal software applications. So you’ve got application developers looking for API access to your data in order to build it into software products for resale. You’ve also got companies that want API access to your data to power their own internal software. If you are charging a high enough price for your data that reflects the convenience and power of API access, as well as the expanded audiences your data will reach, APIs are nothing but great news for data publishers. But can you also make money giving away API access to your data for free? A growing number of companies think so. We recently spoke with Ismail Elshareef, Senior Director, Open Platform Initiatives for Edmunds.com. Edmunds makes its data available via API for free, and can directly attribute millions of dollars in recurring revenue to this initiative.

According to Ismail, Edmunds.com launched its API about two years ago, primarily as a way to get more exposure for the Edmunds.com brand. The second objective was one we often hear from those with open APIs: a desire to encourage innovation. As Ismail puts it, “We can’t hire all the smart people out there.” The goal is to put Edmunds data in the hands of a broad array of talented developers and see what they can do with it – whether it’s new applications software to leverage the data, or even entirely new and unintuitive uses for the data itself.

The additional brand exposure for Edmunds worked exactly as planned, according to Ismail, who said it has become “a huge differentiator.” Edmunds displaced a number of competitors who were charging money for equivalent data, and with the “powered by Edmunds” attribution on so many different products, Edmunds saw immediate brand benefit, not the least of which was more advertisers specifically acknowledging the reach of Edmunds in sales meetings.

Overall, Edmunds has found a number of partner deals came together more quickly as well, “because using the API, they can get comfortable with our data first.” A great example of this is a major deal Edmunds put together with eBay. Ismail emphasized the growing popularity of this “try before you buy” approach to data content, and that publishers need to respond to this growing preference among data buyers.

Ismail is careful to note that Edmunds wasn’t seeking to actively disrupt paid data providers in its vertical; the free data it offers simply reflects lower barriers to entry, and to an extent, the increasing commoditization of much of data it offers for free.

And while additional market exposure is clearly beneficial, as Edmunds saw it, the big upside opportunity was to see what dozens or even hundreds of talented, motivated independent developers would do with the data. And that’s exactly where Edmunds found gold. Acknowledging that of the apps developed around its data, “only 1 in a 100 is really interesting,” Ismail noted that one really interesting application emerged after only seven months of offering the free API. An independent software provider in the Northeast built a cutting-edge application for automobile dealerships. But while they had a great solution, they didn’t have a sales force to market it to dealers. Edmunds contacted the CEO of the software company, struck a partnership deal, and already the product generates millions of dollar in annual revenues.

One of the keys to Edmunds’ success is that while its data is free, it isn’t free for the taking. Every developer who wants to use Edmunds data has to adhere to a terms of service agreement, which specifies the attribution that Edmunds is to receive, as well as reserving the right for Edmunds to cut off data delivery to anyone who acts irresponsibly, though Ismail notes that most developers are very responsible and “know what’s cool and what’s not.” Also important to the Edmund’s model is that it initially only provides enough free data to developers for testing purposes. Before raising a developer’s API quota, Edmunds looks at each application to make sure attribution and back-links are correct, and that the application overall is using the data correctly (not incorrectly labeling data elements or incorrect calculations) and that the application is a quality product that Edmunds is comfortable being associated with.

As guidance to other data publishers interested in pursuing an open API, Ismail feels it is essential to use a service that provides an API management layer. After extensive research, Edmunds went with Mashery, which stood out to Ismail in particular because “Mashery already works with major publishers like the New York Times and USA Today, so they know the issues that are specific to publishers. They also have a huge developer outreach program, now over 100,000+ developers, which made it easy for us to get the word out in the developer community.”

Internally, Ismail notes that the Edmunds API was initially a tough sell. Not everyone believed in the concept, so executive support was a huge factor. It was only because the company’s chairman was such a believer that the API became a reality. As Ismail notes, “ultimately a free API is a leap of faith.” Ismail also noted the difficulties in getting the concept cleared by the company’s lawyers, who simply weren't initially comfortable with exposing our data to everyone." Executive sponsorship was key to ultimately clearing these legal impediments as well.

Launching the API involved “a lot of small steps in the beginning.” Initially, Ismail worked by himself on the API program. Now, his team consists of four engineers and a designer. And just yesterday, the Edmunds API has been certified for “Best Developer Experience” by Mashery – more evidence of how far Edmunds has come so quickly.