The future of networking, like the future of practically everything else, is largely dictated by return on investment, meaning the balance of costs and revenues for all the players. I think 2017 is going to be a pivotal year, in no small part because network operators have predicted this will be the year where their revenue/cost-per-bit curves cross over. Probably it won’t happen as they predicted—exactly—because they’ve cut costs more than usual, but the reckoning is coming.
I’ve talked a lot about the cost side of the picture, about the fact that opex reduction is the only credible strategy on the table. I’ve also noted that the service automation steps that would be available for opex reduction could also improve the operators’ ability to earn new revenues. However, despite all the interest in things like elastic bandwidth and managed services, the big revenue opportunities have to come not by using technical features to change the pricing model, but by doing new stuff that operators can charge for. Since supply doesn’t invent demand but only exploits it, we have to look at the factors that will influence demand in 2017 and beyond to get a picture of revenue trends. That’s what I’m going to do in this series of blogs.
Starting with the biggest demand factor of all, the personalization of broadband. Consumer broadband and corporate networking have historically been about networking sites or fixed locations. Workers and consumers alike have come to their networked devices to perform functions, and this doesn’t “personalize” the service, it “devicizes” the people. Mobile broadband changes all of that, by letting us carry a connection with us everywhere. The impact that’s created on both networking and human behavior is profound, more so than the impact of “fixed” Internet services.
Social media has been the biggest single factor in broadband personalization. A smartphone and broadband connection lets people communicate almost as well as if they were face to face, and the result of this has been an explosion in social exchanges. It’s made every friend into an almost-constant companion, and every acquaintance into a friend.
Among the “youth” (under 35) category, social-media use has hit the 90% level among Internet users. As these people age, they carry most of their social-media habits into the older categories, and people currently in those categories are “socialized” by contact with youth, spreading the impact. By the end of 2017 my model says we’ll hit 80% social-media use across the entire base of Internet users. Among smartphone users, social-media use by youth is 100% within statistical limits, and 86% of the total population of Internet users.
You don’t turn your back on a companion or friend easily, so people don’t surrender social-media access willingly. About half of smartphone users say they use social media at mealtime (even in restaurants), at events, and even while “watching TV”. Youth, and parents of teen children, use them under more conditions than the rest of the market, but the data clearly shows that even in the over-65 category, social-media use is growing quickly.
If you establish your personal “society” through your mobile device and don’t want to be parted from it, then you also turn to the device more often because it’s familiar. We can see this in content consumption. Music is more likely heard on a smartphone than on a home stereo, and smartphones are the preferred means of viewing video among the young. This has happened in no small part because of the new social behavior pattern that’s been generated. You have to have your smartphone in hand to stay in touch, so it makes sense to view/listen using it.
What this means from a service opportunity perspective should be clear. There is nothing much you can shoot for in terms of incremental revenue opportunity that’s not dependent on social broadband behavior. Personalization and socialization are everything, even today. Think about what it will be like in ten years, and ten years is what operators should be looking at in terms of their planning horizon.
Personalization has two primary elements. First, it means relating to the user in a natural way. We already know that means speech recognition and speech generation, but we need a mobile device to sound the way we want. Second, it means understanding the context of the user, because smartphones are not phones, they’re virtual people.
We’re making significant advances in computer speech, as you can see just by looking at how personal agents (Siri, “Hey, Google”, Alexa, and so forth) are evolving beyond basic commands toward being almost conversational. It’s not “Hal” from 2001 yet, but we’re clearly heading in that direction. I did an impromptu survey of “youth”, asking whether they would like their phone to be able to carry on a basic conversation when someone called that they didn’t want to talk to. Almost half were ready for this even today, and nearly all thought it would be a natural development within five years.
Contextualization is more complicated because it involves going beyond conversational commands to keeping a conversational context, and because it extends context from voice to location, even sight. We are highly reliant on context, and if you look at the social conversations supported over a device link, you see that one of the major limitations is the lack of a shared context.
In terms of vision, it’s easy to find examples. I see something, but you’re not in the same place and so I have to “show” you by panning my phone (I had a conversation like that just recently). I expect to see integration of forward and rear-facing video to move toward creating a visually sharable context—if you keep one camera facing you the other is facing toward what you see if you look over/around the phone.
The ultimate in visual context would have to come from a headset or some camera (generally) aimed where our eyes are focused. That lets a device “see” where we’re looking, and if that vision is combined with a geographic position we could interpret what the objects in view were. “What’s that tall building?” would then make sense as a question.
Conversational context is also something we see regularly. How many times do we get jammed up because one person in a conversation has changed topics and the other doesn’t catch the shift? We could ease toward our goal by having a device “remember” past commands and notifications, then assume there was a relationship between those and the current situation. If a notice pops up, the next thing I do is likely related to it. If I’ve said to turn on a specific light, then “turn off” probably refers to that same light.
Geographic context is simple and not-simple at the same time. Since nearly all smartphones have GPSs, we know where a user is on the earth, and with a little more processing we can figure out what’s nearby in terms of major features or even happenings. I gave an example of a “What’s that tall building?” query, one that links vision and geographic location. You could extend this by adding in notifications of specific events, perhaps from sensors (IoT) or even crowdsourced. Now we could perhaps ask “What’s going on up there?” and have a process interpret a collection of smartphone users at a given point and the nearby facilities, and get an answer “Starbuck is giving away a free latte a block ahead on the right!”
Context leads to our other track to revenue—socialization. If we assumed that the majority of our relationships were mediated in part through our devices, we could assume that a service could know about those relationships and use that knowledge to add relevance to its capabilities. The ability to link a notice to a verbal comment is the simplest example; an SMS or email or call is a specific stimulus that could justify a presumptive link to the next verbal command. Socialization can run deeper though; call or message handling can be made to “learn” who gets priority, and regular interactions show a deeper relationship than occasional ones.
Socialization, like geographic context, has to be mapped in a sense. Everyone is a member of a number of “groups”, each of which establishes a social context. Most of us have, for example, a “family” group and an “office” group. We probably also have a number of “friend” groups, some that include people we regularly see, some perhaps divided by gender or age or a common interest. You could consider social mapping as a three-step process—first, identify the groups by mapping regular interactions, second placing the device owner in a group based on current behavior, and finally using the group data to make social-context decisions.
The importance of socialization, combined with the fact that both social relationships and geographic context are based on “maps”, raises the question of whether we should be looking at geographic and social contexts in terms of groups. Group membership, after all, could be defined simply as sharing a geography—“the people on Market Street” is an example. This could be very important because it could be a way of dealing with other “contexts” beyond the social, and doing so more efficiently than a one-off by user. If we can develop the context for an area of a street, or for a shopping center, and represent it as a group a user joins by moving there, we simplify contextual analysis. After all, anyone looking up from Market and 8th would see the same thing.
Social and group management were features of the original ExperiaSphere project, under the topic of the social-management tools called SocioPathTM and there are presentations on the concept HERE. The details of the approach are too much to present in a blog, but if you’d like me to blog on the concepts at a high level, please comment to that effect on LinkedIn.
It’s my view that the socialization, personalization, and contextualization of broadband services is the secret sauce for future revenue gains—both by operators and OTTs. The fact that these services are available to both camps means that neither can just sit back and hope the other will ignore the future revenues that could be gained. However, we all know that OTTs have been eager to exploit novelty and operators always fall into the trap of believing that “new revenues” are new models of old revenues. How many operators will make the jump? The answer to that may determine how many optimize their future profits.