Business Model Canvas Uber
Uber is one of the most prominent platform businesses. They are admired by innovators & entrepreneurs and not so much by some others. And Uber has been in the news a lot recently. But not for technological prowess. They are the best example that innovation is not just about technology.
Over all the recent excitement, some of the most important elements of their business model remain under-reported and little-known. I am covering Uber’s business model in all important details. By the end of this article, you will know more about Uber than most people do.
The Business Model Canvas
The Business Model Canvas invented by Alexander Osterwalder is one of the most popular strategy tools. If you don’t know it, don’t worry. It’s just a great way for me to explain business models in a structured way. Let’s use it to understand Uber!
You can download the Business Model Canvas template as excel here. Find the completed Uber Business Model Canvas at the end of this article.
Let’s go through the canvas from left to right (from the supply side to the customer.
Uber has three types of key partners. Firstly, the drivers; secondly, technology partners; and, thirdly, those that help them become an established company (e.g. investors and lobbyists). The drivers are the supply side and help deliver the value proposition to the end customers. Technology partners do the same: help create a unique value proposition and remove friction between the riders and the drivers. Investors and other supporters, such as lobbyists, help navigate the rough waters of becoming an established company in the sharing economy.
Here are more details:
- The drivers are on the supply side of Uber and they can join or leave at a moments notice. It is essential to have a sufficient number of them to be able to provide the customer proposition (timely pick-up at low cost). They bring their own cars into the value proposition for which Uber does not have to outlay any capital costs. Without a critical mass of drivers, the crucial indirect network effects do not kick-in which is why Uber accelerates supply when they enter a new city.
- Uber has its own technology staff which I am listing under key (human) resources. Cloud providers (and many other standard technology and infrastructure providers) are not key partners if what they provide is easy to switch. Partners that offer leading-edge, proprietary (and ideally exclusively provided) functionality would fall into the key partner category. At this stage, there are e.g. Volvo on the self-driving car project; and Otto (now rebranded after previous scandals) or Uber’s self-driving group Advanced Technology Group.
- Investors/venture capitalists bring the initial rounds of funding to the table. The funding helps to develop the functionality, apps, algorithms, driver-less cars, but is also used for customers acquisition costs and other expenditures.
- Lobbyists are important for Uber at this stage of the company. Every time Uber enters a new city, they have lobbyists helping reduce resistance (in the US). Uber has left cities that have put up too high obstacles. The aggregate success (or failure) of their lobbyists can make a considerable difference to Uber’s trajectory. Once an established company, this group can move to the group of other partners. In countries where lobby activities are curtailed Uber needs to coral other support. From 2015 to 2016, their lobbying expenditures have almost 3-folded to $1.4m per annum in the US alone. In the first half of 2017, Uber spent almost as much as in the previous year.
- I would not add regulators or governments to key partners. Their job is not to partner with Uber but make a decision on behalf of their electorate. I cover them in more detail in the customer relations section.
Other partners that are important but not necessarily key partners:
- Other technology and infrastructure providers
- Cloud storage
- Data analytics
- Financing partners/banks (car loans for drivers)
- Hire car partners (Uber-ready vehicles)
Positive indirect network effects are the key ingredient of platform businesses to achieving competitive advantage. The key activities should revolve around enhancing positive indirect network effects and reducing negative ones.
- Remove friction from all interactions
- Scale driver and customer side to reduce idle times for drivers and waiting times for customers
- Reduce negative externalises, e.g. bad behaviours on both sides
- Grow the platform by getting more participants joining
- Keep participants engaged and stimulate ongoing participation
- Continue improving the value proposition, e.g. cheaper rides for regular commuters through UberPOOL
- Look out for complementary value propositions (e.g. car financing, new customer segments, etc)
- Deliver on the customer proposition
- Reduce churn (esp drivers)
- Expand to more cities (US and global)
- Analyse the data to fine-tune everything
- Enhance technological lead and intellectual property to steepen barriers of entry
The master resource of your platform are its network effects. It is the resource that needs to be built and the nurtured. The data, the algorithms and the capability to analyse and gain insights are essential. The latter also grows with the size of the network.
- Network effect between the participants (drivers and riders)
- Captured data, algorithms
- Analytic capabilities
- Skilled engineering & other staff
- Platform architecture
- Venture capital to keep the business growing
- Brand name & assets
- The rider and the driver app and other products
Uber is a multi-sided platform and as such it has to have a value proposition to both sides, the passengers as well as the drivers. For riders, the value propositions are that it is the best way to get around, to be able to call a ride with one tap 24/7 and track the driver arriving. For drivers, it’s the opportunity to earn, the freedom of choosing their work hours and the ease of getting started. All propositions fall into what economists call reduction of search costs and transaction costs which is the predominant feature of multi-sided platforms.
In detail, for the customer (riders) this means:
- Fast pick-ups (often 3-5 mins)
- Lower prices than comparable taxi ride (exception: surge pricing)
- The App gives you an estimated fare and duration of ride
- No need to tell the driver the destination
- Cashless transactions (exceptions exist)
- Rating system that allows for feedback
- Secure and safe
Some of the value propositions for the drivers (supply side) are:
- Income generation
- Flexible work hours and ability to schedule own shifts and balance it with their family
- No boss
- A dedicated driver app that helps with earrings, navigation, etc
- Ease of joining (mainly: identification, background check, vehicle inspection), here: example Sydney
- No upfront investment in joining (pre-existing car or ability to hire through Uber)
- Ability to earn above average in peak demand (the driver app shows surge areas)
- Ability to get customers (passengers) at no cost to the driver
- No need to argue with passenger on any damages, spills, etc as platform manages this
- Insurance coverage through Uber (during the ride, driver still need to show they are insured at other times)
This also perfectly aligns with what a survey among drivers had shown as their prime motivation.
As a multi-sided platform business, Uber will benefit from segmenting both sides the customers (i.e. riders) as well as the drivers. Uber will use a combination of classic market segmentation combined with highly-targeted micro segmentation. As an example, look at Uber’s “helping cities” page which states “In London, nearly a third of driver-partners live in areas where unemployment rates are highest” (see image below). This is a great example for geo-demographic segmentation. Though the example is very location-specific, i.e. London, it can be data-verified for other cities that Uber serves and then aggregated up / used for targeting. An example for a micro-segmentation is shown in an Austin case study (see below), where Uber tracks trips by proximity to train stations to conclude that “nearly 60% of trips are one-way, meaning people are relying on Uber to connect them to other modes of transportation.” Again, an interesting insight that can be used for various purposes.
Here are the details for these examples:
Let’s conclude with some ideas how Uber might segment their customers and their supply side (drivers) meaningfully.
- Driver segmentation:
- Demographic: age, socio-economic status, family status, nationality
- Geographic: by city, suburb
- Geo-demographic: see above example
- Behavioural: preferred work hours & patterns
- Offering: UberX, UberPOOL, UberBLACK, etc; part-time vs full-time (>30h/week), etc
- Check here how a consultancy segmented Uber drivers [pdf] (but note that Uber does not have this data about all their drivers)
- Customer segmentation:
- Micro-Geography: home, work, typical locations (the Uber app will track your location even if it’s off unless you change the default settings)
- By usage patterns: regular, infrequent, etc
- Offering used / type of usage
- UberPOOL: Weekday regular workplace commuters in a car pool
- UberX: business travel users (e.g. airport to CBD)
- UberX: Casual users
- UberBLACK: Limousine users
- UberSUV or UberLUX: Weekend / recreational users
- Behavioural: spending habits (e.g. for those who are using an Uber credit card)
- and more
Uber needs to consider four elements to manage their customer relationships. Their relationships to (1) the customers(=riders), (2) the drivers, (3) the broader public and (4) regulators. 2017 has been a difficult year in terms of customer relationships (here’s a list) due to a significant amount of negative coverage and regulatory intervention, i.e. restrictions or bans. At its peak, it lead to the resignation of ex-CEO Travis Kalanick followed by an attempt to make good on their driver relationships.
On the more technical side, Uber is using Salesforce as their CRM software and Zendesk as their customer service software. For any platform business, it is crucial to firmly own the customer relationship, i.e. the customer details.
(1) Relevant for drivers & customers
- Manage any safety risks
- Manage bad behaviours (on both sides drivers and passenger) and improve rules continuously
- Deal with customer issues in an appropriate manner and timeliness (see “Channels” for more details)
- Transparent pricing, e.g. criticism on surge pricing by riders and decreasing hourly income by drivers
- Transparency around privacy (a number of repeat coverage over the years on insufficient data privacy, reports of security breach cover-ups)
- Portray the desired company image through social and other media
(2) Especially for the drivers
Customer relationships to the supply side (the driver) will be mainly defined by what the platform does for them, examples are:
- The platform’s ability to generate income (Uber is finally testing a long-standing driver request to allow tipping)
- Acceptable hourly wages (an Uber-contracted survey concludes that Uber drivers earn at least as much as taxi drivers, see below for a differing determination by the FTC that concludes only 10% of drivers actually achieve Uber-touted wages)
- Acceptable working conditions and hours (the afore-mentioned survey states that Uber driver hours were considerably less than taxi drivers, here the full pdf)
- Manage issues (accidents, damages or issues affecting earnings)
- Support in the on-boarding process where required
- Fair allocation of rides (algorithmic ride allocation avoids favouritism issues with the traditional dispatcher)
- Uber is working on a trial program for enabling affordable private insurance (medical, injury, disability, life)
- From Uber’s perspective: avoid groups of drivers to reduce risk of unionisation
(3) The public
Uber is working on portraying a positive image by claiming positive contributions to the communities:
- Pointing out positive impact on the environment, e.g. reducing emissions through UberPOOL(ing)
- Making communities safer, e.g. through reducing driving under influence
- How Uber puts pressure on regulators through their communication campaigns [pdf]
- Manage the platform’s image across the media and other relevant channels (workplace culture, leadership shadows)
(4) The regulator
Uber has faced massive public and political backlash that has put pressure on local regulators to look more closely at Uber’s business practices. Here are few example how Uber manages regulatory discussion:
- Referring to a FTC report, 1984 that shows the wasteful economic implications of the taxi medallion system
- A more recent FTC report, 2015 poiting out positive effects of Uber on existing taxi value proposition
- A Chicago case study showing positive effects of Uber
- An Austin case study of the positive effect on the local transport system (Uber had left Austin due to high regulatory hurdles and returned after these were reduced)
- Negative coverage on greyball which may have been used to deceive regulators
- Uber lost its licence to operate in London, collected over 500,000 petitions from users, appealed in court and with the case still in limbo, continues operating
- And most importantly (in the US) via lobbying (see above)
Uber, Airbnb and other sharing platform businesses can self-regulate using innovation before an overly negative public forms putting pressure on regulators to come up with heavy-handed, innovation-stifling regulations. Learn here how innovation can help do this!
Channels for the initial awareness and customer acquisition can be:
- Campaigns: free vouchers when Uber enters a new cities (e.g. haded out at public transport stations)
- Free media coverage based on the novelty factor (new-joiners have often soared even after extensive negative coverage)
- Word of mouth
- Social media, people sharing
- Digital ad campaigns
- App stores (iOS, Android) – through high ratings, ads and being featured
Channels for the daily transactions:
- One of the best visible customer relations channel is Uber’s Facebook page (13m+ followers) with an almost instant response to most direct queries, remarkable (check for yourself)
- Most transactions are managed fully automatedly through the app
- Webpages allow for sign-up and address the biggest obstacles to joining (the process of joining, how it works, any safety concerns and the collaboration with cities/communities – see above)
- Uber’s help pages
- Uber uses emails & notifications to engage, stimulate participation; reinvigorate/recover (special offers, reminders, etc)
- Tiered customer support channels (via Zendesk)
- Automate customer support for high-volume, low severity issues (e.g. forgotten items) to be rapid
- Multi-tiered customer support (ability to contact a human) for more severe issues
For many online platforms, the biggest cost element are customer acquisition costs (CAC). This is not different for Uber (and its competitors Lyft and Didi). Uber has fought a long bitter war with Didi in China to win on the biggest market in the world. The weapon of choice were customer acquisition “subsidies” (on both sides the drivers and the passengers).
It is assumed that the ride-hailing industry is not going to be very much segmented (other than the location itself). Some experts believe this will lead to a winner-take-all of. (In more clearly segmented markets more than one winner can emerge, so the theory. The question what a segment is, is not that clear. Is Snapchat in the same or a different segment to Facebook?)
Uber’s cost element are:
- Cost of customer acquisition, CAC: free vouchers, one-off subsidies, digital advertising, etc
- Weighted average cost of capital, WACC (professor Damodaran assumes this to be 10%for Uber)
- Development of new features, ongoing fine-tuning of algorithms, etc
- UberPool driver costs (but none of the other drivers)*
- Legal cases and settlement costs
- Lobbying, regulatory compliance
- Transaction fees (credit card charges)
- Salaries for staff and share-based compensation
- Expansion to more cities and countries
- Infrastructure costs, computing power, bandwidth
- Customer support
- Insurance costs
- Research & development, e.g. autonomous vehicles
- Expansion to adjacent niches (UberEATS, etc) initially cash negative
- And more
* You may be surprised not to see driver wages as a cost element. But Uber revenues only include what Uber takes as transaction revenues, i.e. 20%-30% of the fare. Thus, there are no driver costs or payouts as such (this is being incorrectly listed as the main cost in most publications). Here is Bloomberg to vouch for this “Revenue includes only the portion Uber takes from fares, except in the case of its carpooling service; the company counts the entire amount of an UberPool fare as revenue.” This way of accounting also aligns with how other platform businesses account. The reason for accounting UberPool rides differently is likely because Uber’s share on these rides may exceed the drivers share.
Since inception in 2009, Uber has spent over $8b – a huge number even for the biggest start-ups.
Despite all the setback that I have outlined above, Uber reported strong revenues in 2016:
- Value of all fares: almost $20 billion at a growth rate of 126% from the previous financial year. As mentioned above, this is not their revenue. They show their Q3 and Q4 revenue below.
- A 28% growth in revenues in Q4 2016
- And a stagering net loss of $991m in Q4 2016 axcluding the losses in China
The details behind the revenue model
Now we are getting to an interesting question. On the surface, Uber’s revenues are a mere percentage of the transaction fare of a ride (i.e. a share of the $20B in 2016). But the real question is how a ride plus a transaction fee (of 20-30%) is cheaper than a traditional taxi ride without transaction fees?
Uber’s business model would not work if their rides were not considerably cheaper than a taxi ride such that even with the addition of the Uber commission it still remains somewhat cheaper than the comparable taxi ride.
Where do these savings come from that enable Uber to be cheaper than taxis even when extracting value for themselves? That is the real question behind Uber’s revenues. The answer is that the cost structures are different. Though there are differences by country and then even on state, city/municipality, here are some of the key aspects.
Comparison to taxis
- Utilising existing assets:
- Most commonly, drivers utilise their own, existing cars
- With this, Uber spends no capital costs on these assets, has no associated cost of capital and no ongoing depreciation charges
- For the driver, it is an opportunity to get some contribution towards what normally would be an asset parked for 95% of its time. And they still have the personal utility that they bought the car for
- Drivers who may have bought a more expensive car for the purpose of driving for Uber would expect to have at least some coverage of the incremental capital costs (principal) / cost of capital (interest). Though I am not sure if many track this kind of stuff
- Drivers will expect coverage of incremental operating and maintenance/servicing cost
- Ultimately, Uber (and their customers) profits from a higher utilisation of an existing asset in this case
- It is different if a driver buys or leases a car for the purpose of working for Uber
- Uber offers various ways to get a car: (1) Rent; (2) Rent-to-own; (3) Buy
- And has a location-specific marketplace (example Sydney) for these options
- And Uber also facilitates financing options
- In any case, whether facilitate through Uber or sourced through another channel, these kind of drivers will see the cost impact immediately and would calculate their net hourly wage quite differently
- On a cost basis comparison, note that most independent taxi drivers also have to finance their own vehicle plus pay (for) license costs (see below) that Uber drivers don’t incur
- Depreciation costs (and resale value) are closely linked to the above and complicated
- In summary, in case 1, Uber drivers have a cost advantage to traditional taxi drivers/private chauffers. In case 2, they have a comparable cost base (though there is always subjectivity involved in terms of personal utility of the vehicle in times not used for earning money). Essentially, we can assume lower input costs for Uber on this aspect on aggregate.
- Most commonly, drivers utilise their own, existing cars
- Operational & maintenance costs:
- Both taxis and Uber drivers have much of the same costs, such as petrol, insurance, servicing, cleaning, tyres, general wear and tear, phone. It seems in some countries, taxi drivers have higher insurance costs due to regulation
- In a 2017 decision of the Federal Trade Commission, Uber has been fined $20m for misleading drivers on the earning opportunities “The FTC alleges that Uber claimed on its website that uberX drivers’ annual median income was more than $90,000 in New York and over $74,000 in San Francisco. The FTC alleges, however, that drivers’ annual median income was actually $61,000 in New York and $53,000 in San Francisco. In all, less than 10 percent of all drivers in those cities earned the yearly income Uber touted. The FTC also alleges that Uber made high hourly earnings claims in job listings, including on Craigslist, but that the typical Uber driver failed to earn those advertised hourly amounts in various cities.”
- Let’s say, that by and large, these input costs are quite similar for Uber and taxis
- License fees:
- In some (or maybe even in many?) countries, there are license fees for operating taxis which go to the government/municipality.
- In New York City and Chicago, you will find so-called taxi medallions. Here in Australia, there are the so-called taxi plates.
- In whichever form they come, some of these schemes are very expensive. In Chicago and Australia in the vicinity of $300,000. In New York City, the medallions have been traded for over $1,000,000 at some stage in 2013. Moreover, they are being traded on respective marketplaces, thus subject to speculation and price volatility
- Here in Australia, the taxi plates cost around $300,000. A productivity commissionestablished by the government has found that these schemes offer no benefit to the consumer. The drivers have to work them off for decades to come. In the Australian case, this equated to an average of $2.37 (inflation adjusted) for the consumer for an 8km trip. This alone is a saving that a regular passenger will notice immediately
- In the US, the Fair Trading Commission (FTC) also saw little justification for the medallion scheme (FTC report, 1984)
- These are high fees that add no value to the customer (nor to the driver). Uber is free of these artificial barriers to entry that limit supply and drive prices higher. In New York City, drivers that don’t own a medallion can lease it for $100 for a 12-hour shift. Divide this by the number of rides a driver will be able to make in this shift (say, 50 for arguments sake or $2 per ride)
- Nothing has changed (in Australia) in the 18 years since the productivity report delivered these clear findings
- Nothing has changed in the US in the 35 years since the FTC findings. Worse yet, the number of medallions in New York City today is lower than it was in 1937 when the medallions were introduced and this despite increasing population and mobility needs and traffic
- One investor explains “How the TLC & Medallion Owners Created Uber“
- Employee entitlements:
- Uber engages drivers as contractors. Thus, they do not accrue annual/sick leave, nor do they contribute to social security, pensions or other entitlement.
- There are some savings here compared to taxi companies. But there are vast differences between countries what taxi drivers are entitled to.
- This is obviously one of the most contentious aspects of the Uber business model. But it is not black and white as it is often portrayed. Neither is this discussion is not limited to Uber. It affects most of what has been titled “gig-economy” and I will dedicate a separate article to this topic sometime in the next few months. In any case, Uber is trialling affordable sickness, injury, life insurance partnerships for their drivers.
- Economies of scale:
- One of the potentially most interesting cost savings comes from Uber’s ability to achieve better prices for their drivers’ input costs.
- Uber staggers the benefits depending on the activity of the drivers. Those that drive more, can achieve more savings. There are discount levels from bronze to platinum.
- Note, that some of the 3rd party discounts are also available to the provider’s retail base, e.g. their loyalty members. In some cases, the Uber obtained discount may be higher or the driver can join the program without membership fees or incurring other expenses to get the discounts. It is definitely an economic benefit for drivers and a strengthening of Uber’s business model in that scale can lead to lower unit cost.
The most important insight should be that the revenue is not just the sum of transaction fees. The question will always be if a platform can create enough cumulative value for its participants so that it can capture value for itself.
Uber’s Business Model
Uber uses the platform business model and leverages positive indirect network effectsbetween the supply side (drives) and the demand side (customers/riders). As the number of participants grows in a city, the benefits enhance for both sides and for the company (professor Damodaran sees strong local network effects contributing triple as much to market share prospects than no network effects, see valuation excel ‘Input’ sheet, column ‘E’). Drivers have less idle time and thus higher hourly wage (i.e. they can work less hours for the same take-home pay). Customers have shorter waiting times. Uber’s input costs are lower as it utilises already purchased assets, pays no license costs and doesn’t pay employee entitlements. Uber’s value proposition to its customers is compelling. It removes significant amounts of search and transaction costs [pdf]. And their revenue growth has been unstopped despite a string of negative coverage (though their Dec ’17 funding round indicates that their valuation has taken a hit).
Five forces analysis
Let’s assess Uber’s business model within its industry setting. I am using professor Michael Porter’s Five Forces framework:
- Input costs: are low by comparison. I have shown the reasons for this in the revenuesection above
- Bargaining power of supply side: is weak at this stage as there is no unionisation, something that Uber is closely monitoring. I have not yet seen elasticity data for the supply side, i.e. how much higher would hourly rates need to be to attract X number of new drivers? A concept by professor Judy Chevalier in an Uber study, called “reservation wage,” though is a good starting point
- Switching costs for supply side: are low. Some drivers are multi-homing by driving for Uber and Lyft (or other ride-hailing companies) at different times. But given hourly wages are similar (and there is no reported shortage of drivers), there is no bargaining power gain for drivers
- Value proposition for supply side: compelling. Due to the indirect network effects and the scale that Uber has reached in some cities, they can offer low idle times which lead to comparable per hour wages as taxi drivers but in less absolute time on the street. This may also increase switching costs for drivers if Uber takes a larger market share
- Barriers of entry for supply side: It is easy to join Uber and other ride-hailing companies as a driver. But the lower switching costs make it easier for new drivers to join (no multi-year apprenticeship, certificates, etc) effectively reducing bargaining power of the supply side and – interestingly – increasing the value proposition for new joiners at the same time
Buyers / customers:
- Pricing: lower than traditional taxis due to the considerations that I have explained in the revenues section above
- Bargaining power of the customers: reasonably high at this stage as there is existing ride-hailing competition, alternate means of transports and taxis. Beyond a certain market share, Uber may have a better pricing position (and in any case, they may decide to grow through complementary offerings)
- Switching barriers for the demand side: are low. Similar to the drivers, customers are multi-homing. But this may change if the industry ends up becoming a winner-takes-(almost)-all. Due to the indirect network effects waiting times would increase for competing platforms
- Value proposition for customers: are compelling. Lower transaction and search costs, shorter waiting times and lower costs
- Barriers of entry:
- On the surface, they are low. Anyone can program an app. But will you be able to scale it up?
- Any new entrant needs to get to critical mass. This is often costly in terms of acquiring the supply side and the demand side
- Uber has spent billions in demand generation. Customer acquisition costs are very high as seen in the battle with Didi. Will investors be willing to fork out capital for a new entrant to fight an already established brand like Uber?
- Will a new entrant be able to critical mass on the driver side to provide a comparable value proposition (low waiting times)?
- The most likely scenario here is not that another global Uber emerges but rather several local competitors (Ola in India, Didi has managed to fend Uber off in China, Lyft is now concentrating its resources to the US). A lot of locally-focused entrants may dilute Uber’s strength (i.e. financial resources) enough to capture enough market share in those regions.
- Could new entrants come from unexpected areas? Maybe Apple, Microsoft, Ford, Toyota, Volkswagen or other companies that already have a huge customer bases and a brand who can mobilise them at low marginal costs? Possible, but Uber is moving into many adjacent/complementary areas, such as freight, meal delivery that may lead to better asset utilisation which other players may not want (or be able) to enter.
- Economies of scale:
- Can Uber scale up in a way that they have lower unit costs that makes it very hard for new entrants? The answer likely is yes
- Can this help Uber increase their lead? Same drivers could work for UberEATS or other conceivable ideas (the Uber of X)
- If they are able to negotiate better terms for operational, maintenance and servicing for their drivers, this is something that can bring unit cost further down
- Some of the economies of scale will pertain even with driverless cars (and most importantly the indirect network effects)
- Brand equity: while tarnished temporarily, it is still a major asset and in the long term.
- Car sharing companies such as Zipcar
- Self-driving cars: many people debate what self-driving cars will mean for the entire transport industry. I am not going to join this speculation. As you certainly know, Uber is investing a lot in self-driving technology themselves
- Better public transport: seems very unlikely. I have not heard of any large cities with any success stories on this front
- More people working from home: it is hard to assess if mobility requirements will reduce due to technological penetration but worth keeping an eye on
Porter’s Five Forces
Michael Porter’s Five Forces are the most important strategy tool when it comes to understanding a company within its industry setting. Some think that in today’s fast-paced times strategy is irrlevant. Applying this tool in-depth on Uber I am showing that is has not lost any of its fascination or relevance.
The opposite is true!
Innovators who know this tool will have a significant edge to others trying to come up with similar ideas. They can idenfity and finetune their ideas for success:
Uber Business Model Canvas
Finally, here is how Uber’s Business Model Canvas looks like.