Category Archives: technology

14 Insights Into The Autonomous Vehicle Future

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Fleet Sizing, Fleet Management, and Strategic Frameworks are critical for a robot taxi platform’s proper execution. The below ideas are some of my favorite that span these topics.

FLEET SIZING

1: While market demand for rides drives fleet-sizing decisions (supply-deployed), so in turn, does supply-deployed drive market demand for rides. It is a circular reference that must be solved with iterative modeling.

Consumer demand for AV rides is a function of ride price that the platform charges, which will be a function of the platforms cost-per-ride. A platform’s cost-per-ride is a function of its scale, both: A) its total fleet costs and B) its volume (ie. rides). As a result, to determine a profit-maximizing fleet deployment, one must run iterative fleet-sizing models that incorporate a dynamic market demand response from changes in prices enabled by different sizes of fleets.

Generally, larger fleet sizes enable the fleet deployer to pay lower wholesale prices when procuring: vehicles, real estate, parts inventory, vendor contracts, insurance, even labor, etc. The below table is a hypothetical example of the effect of lower costs on ride demand and profitability.

2: The profit-maximizing fleet to deploy will fulfill <100% of demand.

Due to the non-uniformity and spikiness of the demand curve for rides (see below chart), there is an inflection point (i.e. a level of demand) at which the marginal revenue gained from fulfilling an incremental ride-demanded starts to decline. Fulfilling these incremental units of demand beyond the inflection point is more expensive because responding to large swings in demand requires disproportionately greater fleet sizes than that required to fulfill the more consistent and uniform areas under the demand curves. As a result, if profit maximization is the goal, a given AV team will not size its fleet to accommodate and fulfill all of a given market’s ride-hailing demand. The platform would be wise to let driver-based supply and competing AV supply fulfill remaining market demand.

Realistic demand curve shape modeled after today’s ride-hailing demand for a representative week.

3: Speed-to-market and network effects will be an even more important determinant of success for tomorrow’s robot taxi and delivery platforms.

The first-to-market robot taxi platform will choose to deploy a fleet size that serves a profit-maximizing share of the market. As a result, when an entrant faces making their own fleet-sizing decision, they will assess the market share already being served and the size of the remaining unfulfilled market. Deploying a fleet size aimed at serving more than the unfulfilled market demand is a financially risky endeavor due to the non-zero probability of failing to capture the incumbent’s market share, leaving the entrant with an underutilized fleet. This will deter the entrant from attempting to take market share away from the incumbent and enable the first-mover/incumbent to hold onto its market share.

4: Market for shared-ride products (ie. Uber Pool and Lyft Line) will shrink, and this should impact fleet-sizing decision.

Fares for point-to-point AV rides will approximate today’s Uber Pool and Lyft Line fares. As a result, much of today’s shared-ride demand will shift directly to point-to-point rides. Interestingly, this state change needs to be considered when using today’s market demand data to estimate market demand for robot taxi services.

5: Much cheaper and better performing solid state LIDAR sensors should be factored into longer-term fleet-sizing decisions.

According to Velodyne, solid state LIDAR sensors promise to be much cheaper (hundreds instead of tens of thousands of dollars) and available for sale in 2018, thus lowering the cost of the vehicle substantially. This changes optimal-fleet-size calculations.

6: Letting external AV suppliers on a platform would potentially be a risky move, especially in short-term.

By letting external AV suppliers on your ride-hailing platform, the AV supplier may be able to collect valuable trip demand data that could enable them to effectively launch a competing platform in the future. Similar consumer identification information could also be collected that would enable the AV supplier to market to the consumer and attract them to a different platform, whether theirs or a competing.

7: The time an AV spends on refueling and data bag transfers will be some of most important day-to-day inputs into the fleet sizing decision.

Estimating vehicle downtime is critical to estimating vehicle up-time, which is critical to estimating profit-optimal fleet sizes. The model for determining profit optimal fleet sizes is most sensitive to the following inputs:

  • Market demand for rides
  • Total cost of vehicle acquisition and ownership (including maintenance)
  • Range in miles of fully fueled vehicle
  • Average miles driven between ride request and ride pickup
  • Time it takes to refuel and clean the vehicle
  • Time it takes to pull “data bags” off vehicle
  • Time average vehicle spends in repair or tow

8: Ride-monetization products and services are a competitive advantage.

While nearly every AV team have similar costs of capital, each team does not have the same ability to create ride-monetization products and services that enable the ride-hailing platform to generate further economic rents from a ride. The most obvious example of ride-monetization opportunities is in-car advertising. These additional economic rents create a virtual cost structure advantage because it enables the platform to charge lower ride prices and still make a profit off each ride.

9: Brand loyalty will continue to be an opportunity.

Robot taxi platforms and the autonomous vehicles themselves may quickly become undifferentiated commodities like the airline industry. Most consumers are not loyal to any particular airline and happily switch to the lowest cost provider. Similarly, we don’t check the type of plane used for flights before booking. For most travelers, the purchasing decision is driven primarily by length and cost of the flight. Lack of differentiation and low switching costs — as perceived by consumer riders — could persist as a major challenge for the platforms. To avoid a “race to the bottom” and low margins, ride hailing platforms need to find an effective way to keep consumers from switching to competitors. There are many possible opportunities here. Two of which are subscription-based models and integrations with external products.

FLEET MANAGEMENT

10: As some of the largest potential consumers of fuel, AV platforms may vertically integrate downward and upward in the energy supply chain to acquire refueling stations and fuel resources — whether the fleet is electric or hybrid vehicles.

Because time spent refueling and driving to refueling stations are key drivers of vehicle downtime, controlling fueling stations in close proximity to ride demand is not only attractive but likely required. AVs will not be able to refuel themselves (thus necessitating humans to plug in or refuel the vehicle) and AV fleet operators will need to know exactly which refueling stations are available for refueling in real-time. The remaining question is whether gasoline or battery refueling stations will be needed.

11: Fleet managers may not be the fleet owners.

A robot taxi platform (e.g. Uber, Lyft) might manage the third party owned fleets to maximize fleet uptime and know precisely when the vehicles are available for use. Conversely, the platform or the manufacturer may own the vehicles, and outsource fleet management to a third party. All models are on the table, but some certainly provide operational and readiness advantages.

STRATEGIC FRAMEWORKS

12: Computing profit-optimal fleet size against a non-uniform demand curve requires a model that iterates through the demand expected in each narrow time interval (e.g. hour) over a large representative period of time (e.g. two weeks).

13: Fleet-sizing and infrastructure (e.g. personnel, real estate, equipment, etc) questions are best structured/solved as a “capacity-planning” problem, wherein demand for the resource is quantified as units of time needed over a period of time.

14: When choosing between internal and external fleet management options, the most critical and difficult variable to compute is the value of getting to market faster than the competition.

These ideas are all my own and not in any way those of my previous employer. Please shoot me a note or add a comment with feedback or if you would like to discuss. I read them. Thanks for reading!

 

Why Apple has to make inroads in the autonomous self-driving car space, and fast.

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Autonomous vehicles; not wearables will be the new mobile platform.

Autonomous self-driving vehicles (AVs) will be the new mobile platform, on top of which a host of new products and experiences will be developed. Like Apple’s iOS and Google’s Android mobile operating systems, on top of which multi-billion dollar iTunes and mobile advertising businesses have been built, connected autonomous vehicles will enable a multitude of new products and experiences that the consumer will be purchase and/or consume during the ride.

Ride-monetization opportunities are valuable competitive advantages to a robot taxi platform.

As we have learned first-hand in the ride-hailing business, any ability a ride-hailing platform possesses to keep ride fare prices low is a strong competitive advantage. The reason for this is simple. Consumer demand for a given platform’s rides is highly sensitive to ride prices/fares it charges. That is, the platform that offers the lowest prices and wait-times will enjoy the most demand. The ability to generate revenues on AV rides — on top of the ride fare — provides extra margin and room for the robot taxi service provider to lower ride fare prices and still be profitable.

Waymo, Apple, Amazon and Uber possess the best ride-monetization opportunities.

Google’s Waymo is the AV team arguably best positioned to create in-car ride-monetization experiences. Nest, Google Express, Youtube, and of course the digital ad business provide a multitude of opportunities for Waymo and its parent. Perhaps the next best-positioned providers of ride-monetization experiences are Apple and Amazon.

Apple needs to move fast in the autonomous vehicle space.

The smartphone will continue being a key tool in our lives, but Apple must play a role in this future mobile platform otherwise it risks massive declines in its enterprise value. Apple may be late to AV game, but there is hope for Apple to win. With its suite of entertainment, content, and home-system products, Apple is one of the best positioned to create ride-monetization solutions. Seamless cross-product integrations and experiences are what Apple does best. The autonomous vehicle will be just another one of the products with which Apple products will need to integrate. If Apple doesn’t win a major stake in the AV future, Google’s Android — through its integrations into the autonomous vehicle ride experience — could create a better-integrated mobile OS and capture not only Apple’s share of that market but also share of adjacent markets in hardware and content.

As it stands now, Apple has made its AV strategy clear, and it will partner with car manufacturers to provide the autonomous hardware/software technology needed to make a car drive from point A to point B. The challenge will be for it to get the technology working fast enough and to set up the manufacturing partnerships before other teams beat them to it, and there aren’t many car manufacturers left who haven’t already partnered with an autonomous vehicle technology team.

Very strong network effects will be at the core of the autonomous vehicle revolution, which will hinge largely on robot taxi platforms instead of direct-to-consumer vehicle sales. The first teams to get a working product to market will have significant advantage over late bloomers, who will struggle to profitably create a product that offers value in excess of the significant switching costs that the would-be customers would incur to leave its existing AV tech provider.

 

Does it make sense for OEMs to be investing in AVs and urban mobility products?

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TLDR: AVs and robot taxi services are not a near-term (3-7 year) threat to existing OEM business lines, but they are a longer-term (7+ year) threat to OEMs; and this is why they are investing so heavily in AV technology.

Investments being made by large car manufacturer (OEMs) in self-driving autonomous vehicles (AVs) and urban mobility solutions are receiving a lot of attention these days, and for good reason. AVs promise significant societal benefits and economic opportunity, but I want to contemplate and deconstruct why OEMs are making these investments.  What is the business rationalle.

The going sentiment or explanation seems to be A) that AVs and these urban mobility solutions threaten the OEMs existing business and/or B) that the OEMs’ ability to design, manufacture, and distribute vehicles makes them a natural necessity in the future value chain of AVs. While I agree with the latter explanation, I think there is room to elaborate  on the former.

 

Vehicle sales will not decline significantly over near-term (3-7 years) because of robot taxi services.

First, the majority of cars and trucks are sold to consumers and businesses located outside dense urban city centers. Second, as I’ve explained in a previous post, robot taxi fleets will be economically constrained–over the near-term–to being deployed within dense urban city centers. As a result, over the near-term, if anywhere, robot taxi services will reduce car ownership rates within dense urban centers, but car ownership rates in urban centers are already low, therefore little impact will be made on the number of vehicles sold by OEMs. Simply put, car ownership is already low in the areas/populations wherein AV-based robot taxi services will be launched over the next 3-7 years. It won’t be until robot taxi services slowly make their way out to lower density suburban areas that car ownership there starts to be impacted.

I would suggest that vehicle sales by volume are declining not because of ride-hailing services, but rather vehicle build quality is improving. Cars are being built better and are lasting longer, and hence need to be replaced less frequently. Again, for most people living in the suburban US, ride-hailing is not a substitute for car ownership. And for most people living in urban environment, car-sharing (eg. GetAround) and short-term rental (eg. ZipCar) services are more likely to be a substitute* for car ownership than ride-hailing service is. Sure, hailing a ride is better than renting a car for two hours in most situations (that’s why ZipCar’s business is declining significantly), so ride-hailing services might be substitute for some urban market segments but I would argue that most car owners in urban areas own their car because they have consistent weekly inter-city transportation needs. Most people decide to buy/lease a vehicle because they have consistent transportation needs that cannot be solved with a better alternative. Getting to/from work or shuttling the kids to/from five days a week, for example.

The same holds true for suburban car owners. If you live outside dense urban areas and decide to buy/lease a vehicle, it is because you have consistent transportation needs that cannot be solved with a better alternative. Getting to/from work or shuttling the kids to/from five days a week, for example. For most people, car ownership is a better alternative for these consistent trips than Uber and Lyft.

As a result, I would argue that car ownership has not been impacted significantly by ride-hailing services–in neither urban nor suburban population.

Ride-hailing platforms have become a complementary transportation solution; not a substitute. They have become a substitute only for traditional taxi services. For urban dwellers, it complements mass-transit utilization. For suburban dwellers, it complements car ownership, as a solution for infrequent trip needs such as getting to/from the airport or nights on the town to avoid drinking and driving.

Although I don’t think robot taxi fares will come down to the level of mass-transit fares, the already low car ownership rates in urban populations means that I see robot taxi trips being used by consumers as a substitute for an occasional mass-transit trip; not permanent car ownership.

It’s a long-term play for OEMs.

Robot taxi services are not a near-term threat to OEMs, but I do think they are a long-term threat (7+ years). For the reasons stated above and in this post, robot taxi ride fares over the near-term will remain prohibitively high for suburban populations, where car ownership is highest. Eventually, in the longer-term (7+ years), the technology and economic constraints will change such that serving more sparsely populated areas can be done profitably, and robot taxi platforms will start serving these areas.

This is why I think hundred-year-old OEMs are investing in urban mobility and AV tech. OEMs don’t and shouldn’t care about being relevant over the next decade; they’re aiming to be relevant over the next 100 years.

 

*According to Jeffrey Rifkin, “Some 800,000 individuals in the U.S. are now using car-sharing services. Each car-share vehicle eliminates 15 personally owned cars.”   

Quick guide to great mobile strategy design

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Create mobile optimized site(s).  Whether responsive or adaptive design, make sure users have a good mobile web experience. If you can’t make your entire site mobile optimized or responsive, then focus on those pages to which users will be directed while on a mobile device. For example, the URL in an email that will be opened on user’s phone should put the user on a mobile optimized site.

Take advantage of mobile tools to create unique services.  Three factors make the mobile device a game-changer in terms of understanding user’s context. These are: 1) sensors (e.g. GPS, microphone, accelerometer, clock, etc.) onboard the device, 2) data flowing from wearable tech and other devices and systems into the mobile device, and 3) the fact that mobile device is always on the person.  My advice is to think outside the box, and create an experience that takes advantage of mobile platforms and tech, and that is unique to other channels. Your mobile web and apps do not have to offer the same functionality that is available in all your other channels.  Decide what existing and new services make most sense to be on mobile.

Think local, but don’t not strictly!  (mobile—especially with the GPS sensor and internet connection—is a tool we use on the go and to find things physically near us) but don’t forget that mobile is not just on-the-go experiences anymore, as our mobile devices are powerful, light-weight computers with bigger screens and more sensors that are increasingly being used at home and in the office to consume long-form content as well!

Think time sensitive tasks and job/task-focused users.  The natural ubiquitios nature of mobile means that it often is the first thing we reach for to solve urgent problems. Hence, content should be organized and reshaped so that it is to find and digest via mobile.   For example, organize content by jobs, use search instead of lots of navigation bars, shrink content into smaller file sizes, make content lower resolution, etc.

Include mobile in your marketing plan.  Whether advertising, customer referrals, customer feedback, customer support, etc.

Enable integration of third party data/channels in omnichannel execution.  Mobile sits at the center of an omnichannel experience that includes sensors and a range of digital and physical touchpoints, from store clerks to geo fences.  Don’t forget about integrating data from third-party channels, as this can be an opportunity to create value-add experiences for users!

Constantly analyze how users are using your mobile assets.  Are people not using certain assets?  How long are people using assets? Where are they using them? When are people using them?  On what devices are people using them?    Work towards putting IT infrastructure and organizational processes that enable you to track users as they move through and across channels.  This is by no means easy to do but it is very to doable today and it is where the world is moving very quickly.

The largest lost-and-found in the world

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Tile.

Source: http://www.thetileapp.com

I just came across the Tiles and the Tile smartphone app product (watch video demo here), the product/company that will create the largest lost-and-found in the world.

Below, I’ve highlighted both why I think this is a noteworthy invention and app that we should all download, and two quick ideas for how the value proposition might be further extended.

 

What I really like about Tiles and the Tile app:

Image

  • App harnesses the power of mobile and sensor technologies to remember where it last “saw” a given tile.  I guess the app is remembering when and where it last was within a certain proximity of a given Tile.
  • App “surfs the crowd” to locate Tiles/items that have been reported as lost/stolen.   Much like the winning MIT team from the DARPA Network Challenge, Tiles and the Tile app harness the fundamental power of mobile technologies and the crowd.  I wouldn’t be surprised if the Tile app soon becomes a top-downloaded/used app of all time, demonstrating people’s comfort with trusting services that–in theory–could be used to infringe on one’s privacy when when the value they are receiving in return is large enough.  Working in the mobile technology field, I know that it is easy enough to design the system and tech such that all personally identifiable information is safeguarded, and so I predict Tiles will be a great success so long as the company (Reveal Labs) addresses customer privacy in an opaque and easy-to-understand manner.
  • Company makes it easy to recycle Tiles.  Since Tiles last one year and electronics are highly toxic to the environment, I like that the company reminds you when it’s time to order new Tiles and also sends you an envelope to recycle your old ones.

The ironic thing now is that Tiles and the Tile app (as a system for locating my most prized possessions) now make my smartphone an even more critical tool in my life, so what do I do to locate my lost/stolen smartphone and the tile I put on it?

 

Two ideas for further strengthening the value proposition:

1. Integration with police systems

Also, now that Tiles, the Tile app, and cloud can be used to locate stolen items, can a feature be built into the app that enables one share a live feed of a lost/stolen Tile/item location information with police? Or another feature that could enable the owner of a given Tile to request police assistance for retrieving an item at a specific location?

2. Sponsorship by insurance companies

If Tiles and the Tile system can be shown to reduce theft and/or increase recovery of stolen items, will insurance companies be willing to compensate customers who use them? Didn’t car insurance companies reduce premiums for customers who used LowJack?

P.S. The company is currently raising investment funds via Selfstarter, and expects to begin shipping Tiles to customers winter of 2013.  You can pre-order Tiles here (limited quantities).

Give a Coke campaign: A powerful example of digital marketing

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Google and Coca-Cola teamed up to deliver this awesome global campaign, which was a re-invigoration of classic hilltop commercial.   Of course, the messaging was incredibly well aligned to the brand, but I also liked how the power of our modern digital technologies were highlighted.  Watch the video here:  http://youtu.be/45Z-GevoYB8

This campaign, like one of my other favorites (Coca-Cola’s interactive vending machine on the India-Pakistan border), utilized technologies to create consumer/brand experiences that would not have been otherwise possible.

Bogota uses mimes instead of traffic tickets!

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A primary purpose of this blog is collect and share ideas that provide supportive evidence and examples of how great business can be as simple as returning to older, tried, and fundamentally important ways of doing business.  Hence the title of this blog “By Hand”.

This post is about how a city government used shame, a fundamentally powerful and effective force for influencing our behavior, to get people to obey laws and act more safely.   Peer pressure still works long after junior high school!  I came across the story while listening to a June 21, 2012 Freakonomics podcast, and a synopsis of the story is as follows…

In Bogota, Colombia, the capital city’s mayor at the time, Antanas Mockus, hired mimes to essentially make fun of citizens who were walking or driving unsafely in city streets.  A pedestrian running across the road would be tracked by a mime who mocked his every move. Mimes also poked fun at reckless drivers.  By publicly drawing attention to the citizens as they drove or walked unsafely, the Mayor’s brilliant idea was to use peer pressure and our natural desire for social conformity to cause these unlawful citizens to feel shame and change their behavior.   The city government did not hand out traffic tickets with financial penalties to these jaywalkers or unsafe drivers; rather it used shame prompt behavior change.  As a result, the citizens learned Gazette 2004 03.11 Photos 1-Mockus1-450lessons learned by their heart instead of the wallet; and the rate of traffic accidents in Bogota were greatly reduced!

According to a BoingBoing.net article, initially 20 professional mimes shadowed pedestrians who didn’t follow crossing rules, but the program was so popular that another 400 people were trained as mimes.

 

 

“Computers are already better than us at playing chess, but we are still better at recognizing a photo of our parents or children”

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As I was leaving Tom Mitchell’s office, he says to me in the kind of hurried speech of a brilliant individual who has perhaps made the statement before:

“Computers are already better than us at playing chess, but we are still better at recognizing a photo of our parents or children.”

Many consider Tom Mitchell, chair of the machine learning department at Carnegie Mellon University, to be a leading pioneer and expert in the field; and I just met with him to get a better understanding of machine learning, its limits, and its future.

Some major takeaways include:

  • We should not be afraid of machine learning replacing humans, at least in the near term
  • Even the area of “unsupervised learning” still requires humans to tell the computer what relationships to tease out of data
  • The lack of understanding possessed by businesses and marketers about machine learning consistently causes them to come to machine learning experts, such as Dr. Mitchell, asking these computer scientists to make sense of these large datasets.  These are under specified and arguably therefore useless questions for which machine learning is of little use.  What is always needed is context, a hypothesis you wish to test, and an ability to gather/capture useful datapoints.

At the end of the day, humans are still needed to perform one very critical function: defining the dependent variable and the range of possible independent variables that explain/affect that dependent variable.