The wise understand Google’s predicament. Google’s growth is slowing as the customers who matter the most monetarily to Google are now online in some form via a PC, tablet, or smartphone. Monetizing this customer base is how they will grow meaningful revenue. Adding another billion people using their services is not the short term answer to Google’s revenue problems. So what do they do? This was the question underlying the I/O Conference for many of us who study the industry. After watching the keynote and then having the luxury of being at an event with many smart analyst colleagues to kick around ideas, I’m both skeptical and optimistic for Google.
My skepticism has always been tied to their business model. My conviction is free-with-ad-supported business models can only take you so far. I feel Google has reached the limit of that model. Google may sense this also, even though they may still believe the free-with-ads model can take them farther in the consumer space. In an interview with Adam Lashinsky of Fortune Sundar Pichai noted Google can afford to be patient. My interpretation of this statement and other parts of this interview is he says it because Google has a growing business, cloud platform, and enterprise apps segment. This business is not free — using their enterprise apps and cloud platforms are solutions businesses pay for. Similarly, Pichai made this point in his interview:
“First, if we help users get information, a lot of which is inherently commercial, monetization opportunities arise. Second, history always shows that if you build something millions or billions of people end up using, that builds a lot value too.”
The underlying tone of this statement is essentially where the bull case for Google can be made. They are ultimately a machine learning company, arguably the best in the world. Leveraging their machine learning capabilities and monetizing data in many different ways for both commercial and consumer customers is key. Much of the focus on Google has been on their consumer facing stuff like Android. While Android is not a money making platform for Google as an endpoint, it has helped them feed their machine learning engine. However, more emphasis on Google analysis must move toward the commercial side of their big data story.
How this story manifests itself is the tricky balance. You can see the strategic imperatives all over Google’s moves with Android. A good example is their newest feature which gets users out of the app and into the browser when clicking a web page link. Google is essentially trying to un-bundle the web from the app with this tactic. Google is not collecting data so long as people are in non-Google apps. This is why they want to do everything they can to get you out of the app and back to their browser. Their business model incentives are all over this move. Arguably, this is also not a bad move, user experience wise, but it does showcase how their model drives endpoint experiences in things like Android. But there are times when this can and will hurt the user experience as well. Which is where my skepticism in free-but-ad-supported business models lie. They are simply not always aligned with the best interests of the customer in mind.
With a basis of Google more effectively leveraging their machine learning expertise, optimism can be found in their willingness to be more horizontal than they previously were. Bringing their new Photos app to iOS is a good example of this as is bringing their updates of Google Now with Now on Tap to iOS via their Google search app. But these moves may ultimately be a challenge on iOS since tight OS integration is the key to a better experience. The vast majority of consumers will use what is integrated more times than not over what they have to install and set up themselves.
Google Now on Tap was perhaps the most interesting thing I took away from Google I/O. I’m extremely bullish on artificial intelligence engines (I call them “anticipation engines”) and what they bring in terms of a much deeper “assistant” experience to our smart devices. However, I also believe Apple’s and Microsoft’s ability to develop an anticipation engine are being underestimated. A number of use cases Google pointed out for Google Now on Tap are being done on iOS now. For example, iOS will already keep track of where you parked your car and help navigate you back. It also pro-actively shows you your boarding pass the moment you reach the airport. I experienced this for the first time yesterday while traveling home from Austin, Texas — my boarding pass showed up on my lock screen the moment I got to the airport. Now on Tap certainly goes deeper than this, but my point is building a comprehensive anticipation engine will not be exclusive to Google.
But the greater point is Google Now and the new On Tap features are a step closer to the future of smart devices. Underlying artificial intelligence, residing on both the local hardware and the cloud, will help predict, anticipate, and surface value for us before we realize we even need something. Perhaps a good example of this is where Google Photos can go. While no explicit advertising or data collection is specified from Google at the I/O conference, you can imagine in the future Google may notice a picture I took with my car in the background, see my tire is low on air, and recommend I get it fixed or looked at by a professional — even offer local tire shops who have available deals. To many, this will sound or seem creepy, but it is also convenient. There are certainly many security conscious people out there who will hate this but, for the mass market, I’d argue usefulness trumps creepiness in many cases. This may also be one of those things were we need to opt-in to if we want these predictive features or not.
The IoT platform is one I’m very skeptical about. Google does not have the influence over customers the way SoC companies do to drive IoT standards. Apple is truly in the driver’s seat here since they have the customers to drive connected IoT products while the market is still immature. In 3-5 years, we will see where these IoT standards are but it is still early days.
After WWDC, I’ll do a greater ecosystem contrast between what Google and Apple are building as foundations for the future. But the consumer facing stuff from Google I/O was not particularly earth shattering and even the things they are doing with Now are way off from being mass market. But the moves Google is making in deepening their machine learning and monetizing that in commercial segments is a source for optimism.