The Information reported that Google will be launching an MVNO, reselling wireless service from the Sprint and T-Mobile networks. This has little chance of making a significant impact on the U.S. wireless market.
What is an MVNO?
To understand what Google is doing, it’s important to understand what an MVNO is. The acronym stands for Mobile Virtual Network Operator. These are companies that buy network service from companies like AT&T, T-Mobile, Sprint and Verizon at wholesale prices and then resell them to consumers at retail prices. Often, these prices are much lower for low-usage customers than the big brand names. The MVNO handles pricing, packaging, marketing, billing and customer service. (This is a simplification.)
Why do MVNOs exist?
There are three big reasons:
Carriers suck at specialty marketing. Virtually all of carrier marketing is focused on the mass market. Going after smaller segments isn’t their thing. With MVNOs, they let someone else handle that. Consumer Cellular (one of my favorites) targets seniors. Tracfone targets lower income customers. Even retailers like Target (Brightspot) and Walmart (StraightTalk) have their own mobile brands.
Carriers are focused on increasing ARPU, because that’s what Wall Street looks at. If you look at what gets marketed, it’s typically plans that start at $100. Their financials can separate retail from wholesale ARPU.
Carriers have excess network capacity and want to make money for it.
What Google lacks
Customer service. This is something that the company is not known for; although its service reputation isn’t nearly as bad as PayPal’s, it is a brand associated with little to no service. (To be fair, I had a great experience with Google Drive phone support.)
Retail. Google has no retail experience to speak of. There is a reason there is a wireless company store or three every block in urban areas — it works! Even Apple has its own, highly lauded distribution network.
Marketing. Having a hugely successful product with almost no marketing has resulted in Google’s almost complete inability to market products. See: Google Wallet, Nexus, Google TV, Google+…
Some have compared a Google MVNO with Google Fiber. Yes, both are in communications. But Google Fiber is bringing something unique to customers — extremely high speeds. A Google MVNO would have no such differentiation. Being on lesser networks would also make it harder to draw customers from the big two, substantially limiting the market.
It’s extremely bold for Google to try an MVNO. It’s something that not even Apple or Amazon has attempted. And both companies are much better at customer service and retail.
Google is telling us “No route found”. This a fairly simple request, from my location to Target. But Google didn’t check to see if there was connectivity before presenting the error message. This shows a common problem in UX design.
Such error messages should come from the server, not presented locally. If there isn’t connectivity, the user should be told to check connectivity or turn off airplane mode.
Although on the surface, Color seems to be another mobile photo sharing app, it is really the first incarnation of a ubiquitous location-aware sensor network.
Today’s cell phones are in many ways more powerful than laptops and desktops because they are packed with sensors. A modern smartphone has GPS, WiFi, Bluetooth, compass, gryoscope, light sensor, microphone and camera — at a minimum. All of these data can capture data to be analyzed.
Color is trying to take all of those inputs and layer social networks on to them.
If Color’s vision is fully realized (or my vision of Color’s vision), we can expect to see applications like these:
Breaking news. By detecting abnormal usage spikes, Color could quickly identify where news is happening. Because the app is automatically location aware, it’s possible to distinguish between people who are actually at the scene and those elsewhere who may be reacting to the event. See my post Adding Color to breaking news.
Race finders. Marathons and similar events today use chips to track runners. Imagine that Color is able to identify all of the spectators and runners with the app during Bay to Breakers. Based on your previous social interactions, Color would know who your favorite runners are. Not only would you be able to track their position on a map, you’d be able to zero in on the pictures that are being taken in the vicinity of those runners. It would also be able to provide you a map to reconnect after the race.
Person-to-person transactions. Going to a game at AT&T Park, but don’t have a ticket? Fire up Color and see people nearby who have tickets for sale. Tickets from people you know would be prioritized. Instead of sitting next to strangers, you might end up next to friends who have an extra seat.
Person recognizer. This could be a huge boon to people with a poor memory for faces. The person at the party looks vaguely familiar. You know you’ve seen them before, but you’re too embarrassed to ask for the name. Pull up previous interactions and find out their name and the contexts in which you’ve met.
Bar finder. When I go out, I often have a mood in mind. I may want to be really social or I may want to chill. With Color, I could pull up a bar and see what the feel is right now by looking through the photostream. If there are no pictures, I could potentially ping someone there and ask them to take to a picture. (It gives new meaning to “Would you mind taking a picture for me?”) Foursquare is providing a variant of this with Foursquare 3.0’s recommendations.
Search and rescue. Missions could be tracked automatically, making for more efficient operations. Pictures from a location could be used to identify victims, discover who may still be missing and to notify next of kin.
CalTrain tracker. Instead of the horribly inaccurate data provided by CalTrain, Color users would automatically crowdsource the data. You wouldn’t even have to check manually for updates. They would be automatically pushed to you.
That’s the grand vision. In order for Color to accomplish any of these things, it will have to reach large scale. This is a challenge because Color is a seaparte application and not built in to the OS. Google can use Android phones to detect traffic because it’s baked into the OS. Likewise, Google and Apple get location and WiFi network information based on other things that people do on their devices.
Color needs to create an application that provides enough value that people launch it and enable all of those sensors. The application that’s out right now falls short of that goal. It doesn’t deliver an instant wow experience and by most accounts is confusing. Color has tremendous potential, we just need to see that demonstrated better.
Local search has changed dramatically in the last decade. Gone are the days when you could buy a generic database from a mailing list provider, slap maps on it and have a local search solution. Social networks, mobile phones and businesses themselves are changing and enriching local search.
These are the key factors that will define success in local search going forward:
User generated content and engagement
The best local search databases are content rich. They include attributes such as hours of operation, friendliness of the place to kids and pets, whether there is outdoor seating, etc. Many of these attributes are collected by users themselves. Increasingly, this is being done on mobile phones — people can update data before they’ve even left the place.
Users also help to maintain the quality of the databases. In my research, there wasn’t a single case where Yelp or foursquare didn’t have a place I was looking for. There were quite a few that I couldn’t find in Google Places and Facebook. For the U.S., the best database of restaurants and bars is at Yelp. New places are often in Yelp’s database as soon as the place opens. (Sometimes even before the official open, as people participate in friends and family dinners and soft launches.) Foursquare’s data are also comprehensive, but are cluttered by users who try to exploit the service’s game mechanics by creating extraneous venues.
Users can also report businesses that have closed, helping alleviate the frustration of driving to a business only to find out that it is no longer around. Check in data on foursquare and Yelp can also identify anomalies. (e.g. a check in stream that suddenly stops can indicate that a business has closed.)
Photos are key components of some of these databases. The growth of smartphones will only further this trend. Some venues on Yelp have a hundred or more photos. Yelp reports that its users are uploading a photo on average once every 30 seconds. Foursquare recently introduced photos. Google is sending professional photographers out to take pictures of top places. Specialized applications like foodspotting have small but loyal audiences who upload pictures of specific dishes at restauranst.
When it comes to mobile data collection, Google’s Hotpot is weak. New places can’t be added and photos can’t be uploaded. It supports ratings, reviews and identifying problems such as closed businesses and duplicate venues.
But providing tools isn’t enough; it’s important to provide the right incentives.
Yelp has done a great job of providing ordinary users incentives to contribute to the maintenance of its database. It uses both social reinforcement and more tangible rewards. Yelp makes it easy for members to thank and compliment each other for reviews. Selected reviews are featured in weekly newsletters. Review often enough and you become a Yelp elite and have a badge on your profile.
Yelp employs community managers in its markets to help reinforce the community. Frequent events (including Yelp-hosted parties) provide more incentives to review and create adhesion among the community. Only a small proportion of the Yelp user base does any of these things. But you only need 1 person to provide value to millions. Yelp’s dedicated, engaged user base will be a significant barrier to other competitors in the space.
Businesses are using these tools to communicate specials, announce closings (e.g. for private events), run promotions, have contests and just engage with their customers. I’ve even seen businesses helping businesses; one business had electrical problems and another business offered her electrician’s number in response. Here is a snippet from a Twitter list I created of restaurants in Portland:
This sort of real-time information can help sway a decision or prompt users to go out on a night when they would otherwise have stayed in. Radio Room in Portland does a great job of this with their Twitter feed.
The image to the right is an image from the Hops Cam at Beachwood BBQ in Seal Beach, Calif. It allows users to instantly see what’s on tap now. What spot now?, an iPhone app, allows users to see real-time cameras from various restaurants.
Although Google, Yelp and foursquare allow businesses the opportunity to claim their page, there is no mechanism to communicate with customers through their platform.
Businesses are claiming pages and providing enhanced attribute information. Nearly 2/3 of businesses I looked at have claimed their Google and Yelp pages.
To date, no one has done a great job of making recommendations based on a user’s preferences or social network. Local search has required users to sift through mounds of data or just go for serendipity (like in UrbanSpoon). Yelp and foursquare have had some form of social recommendations. Both will highlight recommendations from friends, but their social graphs haven’t been large or relevant enough.
This is a key focus area for Google Hotpot.
When you do a search, you might see recommendations based on other places you’ve rated.
Or you might see that a friend has rated the place. Unlike recommendations from strangers, this provides immediate context. I know some friends whose tastes are similar. If they like a place, I know the chances are good that I will like a place. Negative affinity can be helpful, too. There are a few people whose tastes are so divergent that I know not to go someplace they rate highly.
Pictures also play a big part in decision making. Local search has long relied on textual data because it’s been easy and available. But visuals are a key part of the experience when it comes to dining and nightlife. They can answer questions like “Is this place fancy or a dive?” and “Would this place be a great place for an anniversary dinner?” much quicker than text reviews can. See Picturing a new vision for local search. Pictures are also much easier to go through on a mobile device.
Making intelligent recommendations requires having a lot of data. The easier you can make it, the more participation you will have. Few people will go through the trouble of writing detailed reviews, but 1-click ratings can provide important signals and will have a higher participation rate. See more on recommendation engines for local search.
No matter how good your content is, it doesn’t matter if you can’t get it in front of people. Here, Google has an indisputable advantage. Google sites serve 170 million people in the U.S. Yelp reaches 26 million. (Many of these come through the help of Google’s search results.) Foursquare claims several million downloads. The difference in scale is enormous.
Google’s distribution advantage extends to mobile with prominent applications on iPhone and deep integration within the Android OS. Facebook is also a large player here, with more than 150 million unique mobile users worldwide. When they set their eyes to local, they will be a big player to watch.
Local search often involves a shared experience. Plans are made and coordinated. So far, no one has really provided a great solution for this. Here’s a simplified version of how the process often works:
Step 1: Person A looks up a place on a local search site.
Step 2: Person A sends the place name via SMS to Person B (and C, D…).
Step 3: Person B gets the text message and looks it up in a local search site to find the address and look up information.
Step 4: Person B responds to Person A that it’s acceptable. (Or not, back to Step 1.)
Step 5: Person B then uses the site to generate driving directions.
This could be greatly simplified. Again, Google’s deep integration into Android provides an advantage. Person A could find and text the place information. The receiving phone would identify that the link is specially formatted and instead of presenting it at as an SMS, would present a Places page with pictures and reviews and an accept/reject button. Such sharing could also help Google build out a social graph.
Social search has been talked about for several years now as the wave of the future. We’ll get better information with the help of our friends. Local is the ideal place to prove that out:
Most people tend to have a lot of friends in their immediate area.
Local search revolves around everyday experience.
The “answers” are based on opinions.
Google’s entry into the space is Hotpot, a local ratings and recommendations tool. It is spending a significant amount of money to promote Hotpot in the Portland and Austin markets.
Hotpot is clearly meant to compete with Yelp. To a lesser degree, it competes with Facebook Places and foursquare. (It’s funny how much the local search space has changed in the last few years. AOL, Mapquest, CitySearch and newspaper Web sites have largely dropped off the local map in recent years.)
It’s important to set the context in this fight: Google is already the undisputed leader in local search. Despite the attention that other sites get, Google is the number one place people go to get local information. More than 20% of Google queries are local in nature. Google Search serves about 170 million users. I bet 99.99% of them have done a local query. Yelp serves 26 million users in the United States. But many of those users come through the Google front door. (Partly because Yelp is one of the best SEOs out there.)
There are two core problems to be solved in local search. Providing someone additional information on a business whose name they know and providing guidance to those who are open to suggestions on a business.
Business name searches
The first problem is largely solved, despite the fact that the scope of the problem has increased. Just a few years ago, it meant providing someone the name, address, phone number and a map for a business. Today, it increasingly includes providing hours of operation, attributes such as romantic, kid-friendly, links to make reservations and menu information.
Distribution and integration helps Google capture business name searches. You can use the browser’s search box and Google.com to get your answer. With an Android phone, it’s even simpler. Press a button, speak your search and the answer appears.
Google can answer most of the basic questions about many businesses in the United States. Yelp has the best data out there for restaurants and bars in the United States. I’ll get to the reasons why later.
Google has difficulty with non-standard venues. For example, in Portland, it does poorly with food carts. In most cases, I don’t advocate manually updating a database to address localized concerns. But given the amount of money that Google is spending on promotion in Portland and the importance of food carts in the city’s dining scene, they should follow the advice of an Oregon company and “Just do it.” A basic effort could be done in a day by using online resources. A street team could hit all of the major food cart areas and provide enhanced data such as hours and pictures in a few days. (While also handing out Google stickers.)
The other core problem in local search is discovery — helping to find an appropriate answer when they only have a few parameters or no clue what they’re looking for. These are the questions like “I want a kid-friendly pizza place nearby.” “I want to go to some place fancy,” “I’m looking for a special night out on the town.”
This is an area that Yelp excels at but Google generally sucks at. The problem with Yelp (and the opportunity for Google) is that getting the most out of Yelp requires a lot of work from the user. Yelp has an incredible amount of rich data on local businesses. But it’s too much. It’s overwhelming to see hundreds of reviews. Using Yelp also means trusting people you don’t know, whose tastes may be very different from yours. And it means dealing with the snarkiness of reviewers who often spend more time talking about their life stories and girlfriend problems than the business they’re supposedly reviewing.
Yelp has introduced a number of tools over the years to alleviate this problem. It does data summarization across reviews so that you can see at a glance what are the things most frequently mentioned about the restaurant (e.g. popular menu items). You can see a distribution of the ratings to see how consistent a restaurant is. You can also see ratings trends to see if the restaurant is getting better or trending downward.
But often, people just want a few options. Too much choice and too much data is overwhelming. People don’t want to spend 30 minutes figuring out where to go. We’ve been getting recommendations from Amazon and Netflix for decades. “People who liked X also liked Y.” “Based on your previous ratings, here are places we think you’ll like.” This is especially important in mobile, where people are often more hurried and the screen real estate in which to read is limited. That’s what Google is trying to do with Hotpot.
In some ways, this is an easier problem to solve than Web search. If you’re looking up answers for Jeopardy, there is usually only one right answer. And if Google can’t find it, you know right away. For a discovery-oriented local search, there is more than one right answer. And if the answer isn’t what you were expecting, you won’t know for hours and you might not blame Google. (The restaurant might have had an off night.) For more details, see my earlier post about making intelligent recommendations in local search.
Picking the right social graph
In order to make the best recommendations, you need data. You need data from the user about their preferences and you need a good social graph from which to present options. The more data the better.
This is a significant challenge for Google. Other companies in the social space such as foursquare, Gowalla, Quora and Instagram, have piggy-backed off Facebook’s social graph. That’s not an option for Google. And I’m not willing to spam all of my friends to invite them to use Google Hotpot. The advent of Facebook Connect has made such spamming less socially acceptable. As a result, I have exactly one friend on Hotpot — and he’s a Google employee.
Foursquare’s social graph is OK, but it’s a bit small given the current focus on check ins. The number of people who I want to be able to see where I’m at in realtime is fairly small. But I’d be comfortable sharing historical data on reviews and ratings with a much larger audience.
Facebook’s social graph is ideal for this application. It has a lot of personal connections, including both close and loose connections. The loose connections are important because they help provide coverage that you might not have in your tighter friend circle. For example, the data to make recommendations for Indian restaurants in Paris might be from a former colleague who now lives in Paris.
In the next part of this series, we’ll look at some of the key success factors for local search.
A lot of companies have been spending a lot of time and effort in location-based services over the last couple of years. Whether it’s local search or check ins, the race to get people connecting with local businesses is on.
One ongoing challenge has been identifying where consumers are. GPS has issues with power consumption, time to first fix and doesn’t work indoors. Cellsite-based location is not precise enough. Even WiFi triangulation, which is the most effective way currently, isn’t precise enough given current deployments. In densely packed urban areas, you can still come up with a hundred or more businesses that you would have to pick through.
One way that Google (or Facebook or anyone with a strong brand) could solve this problem is to send WiFi beacons to local businesses. This is roughly how it would work:
Routers are sent to businesses. The MAC address of the router is recorded and correlated with the address that it’s shipped to.
The business receives it and plugs it into a wall outlet.
The router then transmits its information to nearby phones.
Those phones can narrow the list of potential businesses based on that information.
This doesn’t even require the business to have an Internet connection. The only requirement is that the device be powered. At scale, the device could be custom designed to eliminate the Ethernet jacks on routers. This reduces costs and makes the device look less intimidating to folks who aren’t tech savvy. If you wanted to get fancy, you could shape the device so it didn’t look like a router at all — maybe something like the Open sign that Google is giving away. This would have the added benefit of branding to the business’s customers.
With a per device cost of approximately $15 and a service life of about 3 years, we’re looking at a cost of $5/year. If you sent them to 500,000 businesses (the focus should be bars/restaurants in high density urban areas), it’s still a modest cost of $7.5 million to tap into the local market.
The pitch to local businesses would be something along the lines of “make it easier for Google users to find you.” It could be presented as part of a small business starter kit, complete with Google Places window decals, a guide to online advertising, personalized information on how the business is currently rated on Google and online advertising credit for use on Google. It could also serve as the validation mechanism for businesses to claim their Places page. In my experience, packages are more likely to be opened than typical direct mail pieces.
While there has been a lot of talk about NFC for searching or tagging, it would require a change in user behavior and is likely to take 2-3 years before a sufficient number of NFC-enabled phones are in use in the United States.
Not only would this sort of network enable easier local search and check ins, it could be used to generate real time maps of where the most popular places in a city are. People could also use it to generate automatic check ins when they reach selected favorite places.
The biggest challenge with this approach is the risk of bad press given the kerfuffle regarding StreetView vehicles capturing WiFi data by mistake. Although this is in no way equivalent, the media have a hard time understanding that. (Not to mention that the original issue was really blown out of proportion.) This could be offset if Google made the database open to the public. Not only would this improve results for Google applications, but could be used by a wide range of devices to improve position accuracy. It would be the equivalent of Google launching satellites for the public’s benefit.