Too much attention is given to tech startups. What I believe should be emphasised is not start-ups or entrepreneurs in and of themselves, but what does matter is how we create high-growth innovative tech firms.
I think we all recognise there has been a focus on the startup phenomenon as a driver of economic growth, and little attention paid to scaling. Increasingly, there is greater attention being paid to the scaling journey too. First mover advantage doesn’t go to the first company that launches, it goes to the first company that scales.
When does a startup become a scaleup? A startup becomes a scaleup after it has validated its business model hypothesis, it’s beyond the search stage and into the execution phase of its business model. To scale and grow, you have to put processes and structure in place, but there’s also issues of culture, mind-set and risk to consider.
Another simple definition of a scaleup as an entity is ‘being able to accept increasing numbers of customers’ orders and grow the customer base’, but this highlights a number of potential barriers to this:
- Finding employees to hire who have the skills needed
- Building the founders leadership capability
- Having the insight to find new customers
- Accessing the right finance
A startup is an experiment, and whilst not embracing that same attitude, a scaleup aims to encourage everyone to take more risks, not fewer, to take advantage on another series of bets for growth opportunities. A scaleup is a development-stage business looking to grow in terms of market access, revenues and staff, adding value by identifying and realising opportunities.
However, there are some very practical challenges to achieve this, in addition to the points above:
- How to build scale into your vision?
- Can you build scale into your personal burnout?
- How many moving parts in your business model do you have to scale?
- Does scale create complexity in your business model?
Changing gears is a timing issue in terms of cash burn, amplifying the noise in the business, building and honing a repeatable customer process and a step up from emotion driven to execution driven. Scaling shifts your team’s focus, and it’s important you don’t let it hit existing customer experience. For example, developer focus previously on product development can now be distracted to customer on-boarding issues.
Scaling is expensive, it costs more to keep the lights on, the breakeven position is reset, and asks a lot of questions, for example, can you scale product-market fit with a repeatable sales model? The challenge is to ‘keep the main thing the main thing’, avoid complexity, do one thing brilliantly, and not lose sight of your key purpose.
How does a startup business scale in five years, from operating in one city location to a presence in over 280? Uber has done just that, disrupting the entire private car hire industry in turn. What scaling lessons does it offer other ambitious firms?
Uber is a local service operating on a global level. Its product is consistent across locations and customers, and thus needs to be relevant to widely-varying, small local markets. This requires strong overarching technology (the product) but also effective local insight on the ground to make it work.
When launching in a new city, Uber has ‘280 playbooks to work from’, templates and proven examples of how the service is working, the challenges faced and the lessons learned from its rollout. However, there have to be specific local knowledge, for example, the layout of the city, the demographics, existing competition and local transport networks.
Uber uses data to scale and drive product adoption and deliver customer value. Uber – as well as other high-growth startups like AirBnB, Amazon, and Netflix – has created powerful customer feedback systems. When customers use their products and take an action – for example: when you open the Uber app and order a taxi – all of the data from that interaction is captured and fed back into the system to improve the next customer’s experience.
In the traditional taxi example, you hold up your hand signalling you want a ride, the taxi driver sees you — not because he knew you wanted a taxi at that place and that time but because he just happened to be driving there. You take the ride, pay your fare, and get out. Other than knowing a ride happened and you paid your fare, no data about who you are as a customer, where you were or what you were doing is saved by the taxi authority to make future taxi experiences better.
By comparison, Uber takes your input action, measures your activity and behaviour, tracks the output, and uses all that information to inform the rest of the system. Uber knows the exact supply and demand for transportation in a given city in real-time, allowing it to optimise pricing for rides and match demand with supply (cars) to make the market more efficient.
Uber’s key scaling drive is thus data, the business model innovation isn’t just about radical industry reinvention, digital transformations can start with modest, pragmatic and incremental beginnings. In many ways, that’s exactly where startups like Uber first came from, they started out small, refined their customer growth systems, identified the inflection points, people and technology to make it scale.
The challenge for Uber’s platform is that both supply (drivers) and demand (riders) are dynamic and matching them efficiently in real-time is not easy. For Uber, availability is of paramount importance, as the cost of switching to competitors is low, so they decided to make everything retryable, which means making every operation idempotent, something which I suspect can be challenging in practice.
So what are the key learning points from Uber’s experience of scaling? Here are ten lessons learned.
Scale the personalised customer experience Uber understands that the journey doesn’t start when the customer gets in the car, and doesn’t end when the customer gets out of the car. Every company should connect to customers the way Uber does, with highly personalised experiences through mobile devices, apps, and connected products. Behind every device, behind every social post is a customer. That’s why it is more important than ever before to make the most out of every customer interaction by transforming single moments into personalised customer journeys.
Scale simplicity Uber is a classic example of elegant simplicity solving a single problem. It was an uncomplicated idea based on an uncomplicated need – disrupt a simple, legacy, sizeable market with latent demand but poor customer experience – the kind of thing that made you say ‘get it’ when you first heard it. Uber has weathered its storms, from law to politics to etiquette, because at the end of the day it’s a simple, uncomplicated user-oriented service.
Scale through validated learning How many moving parts in your startup do you need to scaleup? Maintain validated learning, be the best at getting better, recognise growth is never done. Maths and metrics don’t lie but they don’t tell us everything. Take extraordinary measures to understand why customers bought from you, and over engage insanely with early, customers. The Uber experience became a vector for growth, on the back of a single ride a growth engine was created, using data as highlighted above.
Scale the right mindset for the right time When your startup enters the scaleup stage, ensure you’re not just thinking about the numbers — customers, users, headcount, you need to have the right mindset for your current phase of development, and ensure the people you’re working with are on the same page too. Scaleups are especially vulnerable to mindset mistake when things are working well. For Uber, it’s clear that the big idea, executed flawlessly is the true engine, and the founders had the right mindset.
Scale unit economics At the end of the day you need to reach a point where unit economics start making sense. What a startup bet on is the business model attaining the scale/critical mass beyond which the unit economics starts making sense. Although it’s difficult to accurately predict the unit economics to begin with, but a rough analysis of where the cut from every transaction is going to go before hitting the critical mass and after hitting the critical mass helps to decide the viability of the model.
Scale transaction frequency Uber’s success can be partly attributed to repetitive high margin purchases. According to reports, a large number of Uber users spend more than £100 every month on the service. There are some services which translate naturally to this metric, there are others which are used sparingly. The beauty of on-demand products is convenience, which leads to high retention rates and scaling to the critical mass.
Scale thoughtfully Scaling means ensuring everything moves together. There is no shortcut leading a startup from 10 customer locations to 200+. Each function must mature at the same rate, or at least close to it, staying in line with equal attention to detail and support. Uber has achieved this. By avoiding the trap of ‘one-size fits all’, and smaller thinking, and iterating on one element of the taxi experience, they were able to create a ‘wow’ experience that has totally redefined what it means to use a taxi service, sparking an avalanche of word of mouth and press.
Scale things that don’t scale The early days are the perfect opportunity to do things that don’t scale, for example you can dedicate yourself to cultivate intimate relationships with each customer, sending personal emails or talking to them about their experiences using your product. Keep doing this as you scale for as long as you can. Startups become scaleups because founders make them take off, but the ease of use of Uber’s service meant scaling wasn’t complex.
Scale things out Subtract as you add. Scaling is all about more – adding employees, customers, revenue and processes, Often, this drive toward more masks what you are losing and what you should lose. Scaling is actually a problem of less, there are lots of things that used to work that don’t work anymore, so you have to get rid of them. There are probably a bunch of things you’ve always done that slowed you down without you realising it. You have to be aware of necessary subtractions even as you keep your eyes fixed on additions.
Scale Out. Think of scaling out as building the base of a pyramid, the foundation upon which everything else is built, and you know that it will hold. Most startups focus on scaling up customers and revenues, but you need to build your architecture in an intelligent way that will allow you to grow to realise your potential, without over taxing your team or endangering your roadmap.
This potential is the primary reason that Uber has garnered so much attention from investors to enable its scaling strategy. The economic, environmental, and everyday implications are huge. They are changing the way that people think about using personal transportation, making it less about everyone living in cities purchasing his or her own car, and more about purchasing rides as we need them.
Uber is a fascinating scaleup case study, because it is one of those truly disruptive ideas that completely redefine an industry and changed the way people consider long-entrenched beliefs and habits. Uber shows that building a pro-consumer product that completely reinvents the experience can be scaled and lead to sustainable growth. Uber’s growth provides insights on what it takes to find the scaleup growth a startup seeks.
