McKinsey Study: Location, Location, Location, Part 2
Greetings one and all and happy new year! As promised, part 2 of my post on McKinsey’s drill-down into the tremendous benefits location data offers to new businesses (and business models) as well as to all of us. If you need to refresh your memory (since the author was a wee bit late in meeting her stated publishing date), part 1 is available here. Certainly, the report, “Big data: The next frontier for innovation, competition, and productivity,” is chock full of illuminating ways that big data can be leveraged within specific industries, but personal location data is a somewhat different beast as it cuts across industries. For example, telecom, retail, and media (through location-based advertising) all stand to reap tremendous rewards.
Now, as I said in part 1 and will state again in part 2: I have a bit of angst around the collection and use of personal location data (see my many posts on privacy or our book on “Privacy and Big Data”). But that does not negate what can be gained if it is properly collected and used and with the appropriate regulations and guidance in place (my gosh—I am beginning to sound like one of the privacy policies I hate to read!). Put simply: all company’s data collection and usage policies should be clearly stated and always offered on an opt-in basis. Okay, privacy issues have been dealt with so let’s move on!
In part 1, we talked about the three primary sources of location data: data generated via GPS chips in your devices, cell tower triangulation data, and in-person credit and debit transactions. Of the three, devices with GPS chips are growing the fastest with McKinsey predicting that:
“By 2020, more than 70 percent of mobile phones are expected to have GPS capability, up from 20 percent in 2010. In addition, the number of automobiles equipped with dashboard GPS devices will continue to grow.”
McKinsey goes on to enumerate three major categories of applications that use personal location data. They are:
- Location-based applications and services for individuals (smart routing, automotive telematics, mobile-phone location services).
- Organizational use of individual personal location data (location-based advertising, electronic toll collection, insurance pricing, and emergency response).
- Macro-level use of aggregate location data (urban planning and retail business intelligence).
Of course, as a marketer I immediately focused on advertising as the major value driver. Imagine my surprise, when it came in second to something that is beneficial to all of us. In fact, we (singularly and globally) derive great value from it no matter where we live or what we do. It is smart routing—and not for IT networks but for automobiles.
In a recent EU commissioned study on the systems needed for automated driving, the benefits are clear:
“All these technologies open up new business opportunities for car manufacturers and their suppliers, road administrators, telecom companies, etc. while raising legitimate questions of background. The truth is that vehicle technologies evolve as we speak more and more toward assisting the drivers in difficult situations in traffic, achieving traffic decongestions, improving the safety on our highways and in urban environments, reducing the fuel consumption and exhaust emissions, and delivering a high degree of comfort while driving.”
Smart routing is based on real-time traffic information. In other words, imagine receiving up-to-the-minute (even second) information about accidents, scheduled roadwork, and congested areas and then changing your “route” to optimize travel time. From a road warrior standpoint, this is great news as we all benefit from less travel time and perhaps a smaller gas bill. But when you look at this on a global scale the benefits are mind boggling. According to McKinsey:
“All told, we estimate the potential global value of smart routing in the form of time and fuel savings will be about $500 billion in 2020. This is the equivalent of saving drivers 20 billion hours on the road, or 10 to 15 hours every year for each traveler, and about $150 billion on fuel consumption. These savings translate into an estimated reduction in carbon dioxide emissions of 380 million tonnes, or more than 5 percent a year.”
In this case, big data analytics drive a profitable business model (navigation applications) that represents value to the consumer (faster travel times) while benefiting the greater good (the environment). This is a win for everyone! Of course, for this to be fully realized digital maps and real-time traffic information (in the form of a technology infrastructure that includes hardware and transmission towers) must be globally (not just in developed but in developing countries) available.
Automotive telematics (you know, when your car “tells” you it needs servicing or repairs or when OnStar locates your vehicle if it is involved in an emergency situation or stolen) and mobile phone-based location services, like foursquare and Loopt, are also a part of this category and represent quite a “chunk of change” as well:
“The revenue model for such mobile LBS applications will be a mix of free services and applications supported by advertising and other revenue, including sponsored links from restaurants, bars, and other points of interests. Some mobile applications will feature embedded advertising or require a onetime download fee or ongoing subscription. In combination, we estimate the new value generated by such services could be $80 billion or more in 2020.”
Moving on, let’s look at the most known personal location tracking business model: geo-targeted advertising. Whether you love it, hate it, or are somewhere in between (like me), geo-targeted advertising is here to stay. Why? According to McKinsey:
“Compared with more traditional forms of advertising such as TV or print, geo-targeted campaigns appear to have a higher relevance to the consumer at the moment when a purchase decision is likely to be made and therefore boost the potential for an actual sale. Advertisers certainly seem to believe that this is to be the case, and they are paying increased rates for this service compared with advertising without geo-targeting.”
For example, ShopAlerts (by PlaceCast) connects advertisers with customers when they are near locations relevant to their businesses. According to McKinsey, ShopAlerts has 1 million users worldwide and reports that:
“… 79 percent of consumers surveyed say that they are more likely to visit a store when they receive a relevant SMS; 65 percent of respondents said they made a purchase because of the message; and 73 percent said they would probably or definitely use the service in the future.”
From a marketing standpoint, this kind of return is what we like to call golden!
There is also the area of electronic toll collection. Instead of investing in some sort of E-Z Pass, why not let your GPS-enabled cell phone locate the car and tollbooth, pay the toll, and charge it to your phone bill? No transponder device or additional bill payments accounts required. Now that could simplify our lives!
A bit more controversial is the area of insurance pricing. In a previous life (long ago and far away), I was a commercial property and package underwriter for a very large insurance company. In that role, I would have loved to have access to information about how my policyholders were driving their cars in order to “more accurately price the risk.” Today, as a consumer (and privacy advocate), I might take issue with that data making its way into my carrier’s “hands” without my explicit permission.
One area that realizes immediate benefits from the availability of personal location data, real-time traffic reporting and GPS telematics is emergency response systems. Certainly, faster response times by police, firefighters, and ambulance personnel is a for the greater good benefit!
Finally, McKinsey delves into two very different areas that could make use of the macro-level of aggregate location data (as opposed to each person’s specific location):
- Urban Planning. Planners can analyze this data to look at the impact of construction on roadways and on urban development with the idea of reducing congestion while reducing polluting emissions.
- Retail Business Intelligence. Retailers can use this data to analyze shopping patterns, looking at foot traffic speed and density to inform store layouts and merchandizing. Now as mentioned in part 1, certain data collection and usage methods are deemed controversial in the U.S. but are widely used in the U.K.
Taken in its entirety, McKinsey predicts that:
“… personal location data has the potential to provide more than $800 billion in economic value to individuals and consumers over the next decade… Geo-targeted advertising is emerging as a highly effective marketing vehicle that could represent more than 5 percent of global advertising spend by 2020.”
Certainly the area of personal location data has tremendous growth potential. But what might be far more interesting about this particular area is the unknown. From our perspective, current uses of personal location data represent the tip of the iceberg. We believe that the combination of geo-aware big data platforms like PAF with personal location data will spur new business models and ultimately, market value. By 2020, McKinsey’s $800 billion prediction may be a very low estimate of the real market potential. What do you think? And what uses of personal location data do you predict over the next 10 years?