A generalizable framework for evaluating new business opportunities

Whether an idea for a startup or new product or service offering at an existing company, the following is a framework I use to evaluate new business opportunities.

Usually, the truth is that we aren’t deciding between pursuing one opportunity or not; rather there are multiple opportunities available to us and we must prioritize which opportunities are best to pursue. To do this, our goal ultimately is to stack rank the new opportunities by quantifiably answering the following two questions: 1) the business value we could expect to gain and 2) our ability to execute on these opportunities. If there is only one opportunity available to evaluate, the process is simplified such that only opportunities with positive net business value and reasonable ability to execute are pursued. The challenge with this framework will be establishing the acceptable level of ability-to-execute beyond which you will choose to execute on the opportunity.

14 Insights Into The Autonomous Vehicle Future

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.

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.

 

The future of Whole Foods: Why Amazon’s acquisition makes so much sense

Amazon’s acquisition of Whole Foods makes sense. I enjoyed deconstructing why below.

1. Amazon will leverage their expertise in warehouse automation and delivery to eliminate the need for the ever popular “Task Rabbiter” shoppers who assemble pickup and delivery orders in the store. Visit a Whole Foods today, and you will see many contractor

In-store shoppers are extremely common and have been asked to stop wearing Instacart t-shirts so as to not change the feel of the shopping experience for other customers.

workers shopping for delivery-platform customers such as Instacart and Whole Foods own delivery service. These shoppers are extremely common in Whole Foods, and they have been ever since Instacart started doing deliveries from their stores. While they no longer wear t-shirts that indicate the platform for whom they are working (this isn’t an accident), these shoppers are pretty easy to pick out from amongst the crowd. They wear headphones, stare intensely at their phones as if reading a off a list, fill grocery carts with large orders, talk with store clerks as if they are colleagues, and move briskly from one item to the next. They become efficient shoppers, knowing where everything in the store is located and they have their own checkout lines in the store so that they can complete as many orders as fast as possible. But watching these shoppers go around and pick items to assemble orders, you can’t help but see this labor force as very replaceable by warehouse robots and machinery that Amazon already uses to assemble orders from large warehouses with thousands of SKUs.  Now that Whole Foods offers its own delivery service (delivery.wholefoodsmarket.com), expect Amazon to put its mighty marketing and operations competencies behind the delivery service to not only make them less dependent on Instacart going forward but to also move all assembly of delivery orders to a warehouse environment so as to improve the in-store shopping experience for  customers.

 

2. Overlapping customer bases enables better customer experiences for the Amazon and Whole Foods customer of the future.

Whole Foods stores are located in affluent neighborhoods of metropolitan areas.

Like Whole Foods stores, Amazon customers are located in more affluent neighborhoods in metropolitan areas. Walmart’s core customer lives in rural areas. Whole Foods and Amazon customers value convenience and a higher-quality experience over rock bottom prices; and they are willing to pay a higher price for it. Both Amazon and Whole Foods customers value pleasant and efficient shopping experiences that save them time. Amazon’s entrance into various product categories means that customers can efficiently procure many services and goods from a  single entity. Like Amazon customers, Whole Foods customers are accustomed to the convenience of high-quality, curated stores and SKU availability. As a result, many Amazon customers are already Whole Foods customers, so this acquisition will mean that Amazon can gain greater share of these existing customer’s wallet. With greater data collection and insight into each individual customer and their shopping history, Amazon will leverage this data to make better recommendations and inventory management decisions, both of which will also potentially lead to increasing total expenditures by each customer.

 

 

3. Massive cross-marketing opportunity to create new Amazon customers. Within the Whole Foods customer base, there a few segments who may not be Amazon customers yet. Imagine the older, not-so-tech-savvy segment of Whole Foods customers who shop there purely for the higher quality food and shopping experience. These customers are the type of people who don’t do a lot of e-commerce but regularly procure their food from Whole Foods because they have grown to trust the Whole Foods brand. Amazon will be able to leverage this brand equity, and cross market to these segments to attract new Amazon.com and Prime customers. And once the customer is brought into the fly wheel, there is a high likelihood that the customer will become a consumer of Amazon’s other stores and products, such as Amazon Video, Audible.com, etc.

 

 

 

One urban mobility market that Uber and Lyft might ignore

As we look to the future of urban mobility, one of the questions I am constantly pondering is “Is there an urban mobility product that can effectively compete against an autonomous vehicle fleet powered platform?” From our experience, we know that the bulk of the ride-hailing market cares primarily about the average cost and time he/she spends on the ride. An autonomous vehicle fleet is expected to decrease the cost of rides and hailing times, so the question then is “What product could compete along these two critical dimensions with an Uber or Lyft autonomous vehicle product?”

In my experience, the only product that comes close is a commuting shuttle or van, which usually features a larger 15-passenger vehicle and a driver, and benefits from pickup, dropoff and time densities of the riders during commuting hours. There are two primary variations of the model: fixed vs dynamic routes. My challenge with dynamic routes is that it is essentially Lyft Line and Uber Pool, but with a bigger and more expensive vehicle. Since the Uber Pool and Lyft Line products are operating at significant scale and the experience is generally pretty poor because of the uncertainty surrounding when the rider will arrive to her destination, I think it will be very hard to build a dynamic shuttle product that creates a better consumer experience at a lower cost structure.

So the question really then becomes “Can the consumer’s per-ride total cost and commute time for a shuttle product be more competitive than an autonomous vehicle solution?”* NOTE: Since consumers can use pre-tax dollars to pay for dynamic shuttle trips, we need to consider the post-tax cost to the consumer.

Below is a first attempt to model out the cost structure of both products, and which product is advantaged.

 

Cost Advantage
Driver Cost Autonomous Fleet
Vehicle Amortization Commuter Shuttle
Vehicle Maintenance and Repairs Uncertain or Neither advantaged
AV Hardware Maintenance Commuter Shuttle
AV Software Acquisition and Maintenance Commuter Shuttle
Vehicle Storage Neither advantaged
Vehicle Refueling Autonomous Fleet (assuming electric AVs)
Demand Planning Commuter Shuttle
Demand Matching (Dispatch) Neither advantaged
Marketing Spend Autonomous Fleet
Insurance Uncertain or Neither advantaged

 

Cost Count of Advantages
Commuter Shuttle 4
Autonomous Fleet 3
Uncertain or Neither advantaged 4

This is of course a rough attempt to map out competitive advantage between these two products. I not only don’t know if this is accurate but I also don’t know the magnitude of each of these advantages.

Much remains to be learned about how low these costs will decline over time or whether they will approach some kind of asymptote, but I think this is the right way to structure this problem, and that this is the right kind of question to be asking when we look to predict what the long-term future of urban mobility landscape will look like.

 

*Of course, there are some qualitative aspects of the experience that differ, but there is a price at which some market segments find these qualitative aspects worth the trade-off. I will ignore these and focus on the primary value drivers of total cost and time of the trip.

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

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

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.