What Is Continuous Innovation And Why It Matters

That is a process that requires a continuous feedback loop to develop a valuable product and build a viable business model. Continuous innovation is a mindset where products and services are designed and delivered to tune them around the customers’ problem and not the technical solution of its founders.

Principle	Description	Example Company
Love the Problem, Not the Solution	Focus on understanding the problem your customers face before developing solutions. Avoid prematurely falling in love with your product idea.	Netflix
A Business Model Is the Product	Innovate your business model to align interests, create value, and drive scalability for all stakeholders.	Amazon
Traction Is the Goal	Strive for user and customer traction, especially in platform-based models. Traction attracts more participants and enhances the platform’s value.	Uber
Right Action, Right Time	Identify and prioritize key actions that matter at specific stages of your business. Allocate resources wisely and avoid distractions.	Tesla
Give Yourself Permission to Scale	Begin with a niche market and focus on acquiring your first customers before expanding to a larger audience.	Airbnb
Tackle Riskiest Assumptions First	Start with small-scale validation to address the most uncertain and critical assumptions. Adapt your approach based on early feedback and learning.	Dropbox
Think 10X	Gradually scale your product or service by opening to smaller user groups first. Gather feedback and refine your offering before scaling to a larger audience.	Google
Make Evidence-Based Decisions	Rely on data-driven decision-making by analyzing user behavior and engagement metrics. Use evidence to guide platform changes and feature updates.	Facebook
Validate Qualitatively, Verify Quantitatively	Combine qualitative user feedback with quantitative data to refine products based on user preferences and behavior.	Apple
Remove Failure from Your Vocabulary	Embrace experimentation and iterative testing. View failures as opportunities to gather valuable data for continuous improvement.	SpaceX

Table of Contents

Continuous innovation as a set of mindsets

On FourWeekMBA I had Ash Maurya explain why continuous innovation matters so much. Let’ start from the key principles!

Source: blog.leanstack.com

As Ash Maurya has highlighted continuous innovation is a set of mindsets moving along ten key principles:

Love the problem not the solution

Talking about problem-solution fit Ash Maurya highlighted how:

One of the biases that that many entrepreneurs fall run into is this premature love of the solution. Like the first principles in science, you almost have to deconstruct an idea. We have to start with the basics. In this case, when we look at our business, we have to break it down into customers and problems.

If you don’t have the right customers who are trying to get sorted and problem solved, and no matter what solution you build, it doesn’t matter because we know that unless you’re solving a problem, customers are not going to use it.

This is a common mistake, happening especially in the startup world, where it becomes easy to focus on providing technical solutions rather than focusing on the problem those might solve.

That is why you need to make sure to understand the problem first.

What problem are you solving? For whom? What alternatives are the people for which you will solve the problem using? Why and what can you do way better than existing alternatives?

Those are the right questions. Yet, many still focus on how to build a feature, product, and service without validating or understanding what problem is solving in the first place.

As Ash Maurya further highlighted when I interviewed him on FourWeekMBA:

They’re not going to pay money. Even if you can reach them.Even if you have a patent or an unfair advantage, it doesn’t matter at the end of the day because your customers don’t care.So that is the way we logically break it down, but that innovator’s bias is one of those sneaky things.

Keep that in mind!

A business model is the product

In a world driven by technology, it’s easy to think of technological innovation and business model innovation as two analogous concepts.

However, while technological innovation often focuses on the technical solution in its own sake. Business model innovation is about creating scalable companies and repeatable businesses able to create value by solving problems for customers, consumers, users, all the while creating value, in terms of financial resources for other investors and stakeholders.

In short, business model innovation aligns several interests, both monetary and not monetary for several players at once.

That is why it is such a powerful concept. It requires a deep understanding of the conflicting interests at play while keeping them aligned.

In a digital era, where technologies become commoditized as they get to the mainstream, what’s left is business model innovation.

That is why Ash Maurya highlights that the business model is the product. That is what creates a competitive advantage.

Traction is the goal

It might sound strange, yet one thing that aligns both investors and customers (often) is traction.

That is particularly true for platform business models and those products which gain value as more users, customers, and people in general start using them.

Imagine the case of a platform like Airbnb, that has no traction. How can you make sure there will be the right room or home in the place you’re about to visit?

Unless it keeps gaining traction to have a wide variety of guest and hosts, how can customers be sure the platform will be valuable over time.

And as the platform gains traction and it sustains that, it becomes valuable for investors too!

Right action, right time

As Ash Maurya highlighted in a 2016 post entitled “My 3 Biggest Lessons on Entrepreneurship (so far):”

“At any given point in time, there are only a few key actions that matter. Focus on those and ignore the rest”.

When you’re building a business, the way you decide to spend your time is the most important decision you make.

Thus, deciding on what to focus, but most of all on what not to focus that is key.

Give yourself permission to scale

This comes from the fact that a startup launching might worry too much on reaching the largest number of people possible.

That is a massive mistake. I can’t stress that enough and that is why I wrote about niche marketing and microniches.

Before you can scale, you need to find your first customers, and you need to define them very well and start reaching them early on!

Which connects to the next point.

Tackle riskiest assumptions first

As Ash Maurya highlighted in the FourWeekMBA interview on continuous innovation:

I sometimes like to call that permission to scale, so whether you’re a startup, whether you’re a corporate, we have to realize that products go through a life cycle, and rather than trying to rush to get to scale prematurely and then making a lot of mistakes and getting overwhelmed with risk, if we instead give ourselves permission to scale, start with smaller numbers of customers and users and more systematically roll it out, it makes the process more manageable. It helps us tackle the riskiest assumptions first.

There are several risky assumptions depending on the business you’re building. For instance, if you’re launching something that might not be technically complex, then the riskiest assumption is whether people might want that or not, so monetization becomes important.

If you can monetize it means there is a need for that product and use case. Thus, you can move to the feasibility risk. But even if you’re thinking to build a technically complex product, you might want to check whether there is a use case for it.

Unless you’re doing pure research, or you have a lot of funds allocated for the sake of technological innovation alone.

But once again, if we’re talking about business model innovation, you need to understand whether people are waiting for that problem to be solved.

For instance, when I interviewed Alberto Savoia he told the story of how IBM changed its course when it realized:

Many many years ago IBM thought “we want everyone to have personal computers,” but there was no way (think about this is like 1980) that most people are going to learn how to use a keyboard. In those days who used a keyboard? Secretaries, programmers, and writers. So they thought, we need people to be able to operate the computer without using the keyboard, just by using speech to text into a microphone. Of course, they could not build the technology, they could not build the prototype for years because the technology was not there, computers were not fast enough…

…Now, this was not possible, the technology wasn’t there, so they could not have built a prototype. So what happened, in the room next door, instead of a computer that actually did all this processing, there was a human being, one of those people who know how to type very fast, got the input through the headphone, typed it on their keyboard, and so to the users it looked as if there was a working prototype, but there wasn’t…

…They learned very quickly and they got very valuable data that, while Text-To-Speech may be interesting, they better focus on accepting the fact that people will have to learn a keyboard. And, surprisingly enough, 40 years later we’ve all learned how to use keyboards even though, arguably, they are not a very efficient way of using a computer.

Think 10X

Ash Maurya 10x approach looks at what he calls a staged rollout or a place in which rather than opening to anyone you open to a set let’s say ten people first. A hundred. Then a thousand.

In this way you gradually scale, allowing enough feedback to avoid to kill your own product, too soon.

Make evidence-based decisions

As we flow into the ocean of data, that is among the most difficult things to accomplish.

The way you can gather evidence is by avoiding potential biases first. Understanding that most of what you’ll look at will be biased. Thus, you need to be aware of those biases and expose them to avoid falling into the trap of making decisions based on perception.

Validate qualitatively, verify quantitatively

This connects with the previous points. For instance, early on if you’re launching a product, it’s important to validate qualitatively by speaking to the first customers you get.

As the number of people accessing your product might grow, you can start looking at data which starts to having significance to understand how people consume your product or service.

Remove failure from your vocabulary

That is probably the hardest mindset to develop.

As none like to fail. However, if we have a model to build a business, we can develop a process to fail as well. So that each failed attempt and experiment will work as feedback.

If you just move the logic from personal (I failed) to procedural (the experiment designed through this process failed), you can definitely gain a better perspective!

Case Studies

Love the Problem, Not the Solution:
- Netflix: Netflix transitioned from a DVD rental service to streaming because it recognized the customer problem of convenient and on-demand entertainment. They focused on solving this problem with streaming technology, shifting their business model successfully.
A Business Model Is the Product:
- Amazon: Amazon’s innovative business model extends beyond e-commerce to include Amazon Web Services (AWS). AWS revolutionized cloud computing by providing scalable and cost-effective cloud infrastructure services, creating a new revenue stream and business model.
Traction Is the Goal:
- Uber: Uber’s success is built on the idea that more users attract more drivers and vice versa, creating a network effect. This traction makes the platform more valuable to both riders and drivers, establishing Uber as a dominant player in the ride-sharing industry.
Right Action, Right Time:
- Tesla: Tesla strategically focused on developing battery technology and electric vehicles (EVs) when the market was ready. Their timing allowed them to become a leader in the electric car industry, capitalizing on increasing demand for sustainable transportation.
Give Yourself Permission to Scale:
- Airbnb: Airbnb started small by offering air mattresses in a shared apartment. This allowed them to validate their concept and gain a deep understanding of their niche market before scaling to become a global lodging platform.
Tackle Riskiest Assumptions First:
- Dropbox: Dropbox began with a Minimum Viable Product (MVP) that addressed the risky assumption that people needed a simple and seamless way to share files across devices. They validated this assumption before expanding their feature set.
Think 10X:
- Google: Google often adopts a staged rollout approach when launching products or features. They start with a limited user base, gather feedback, and gradually scale up. This process ensures that products are refined based on real-world usage.
Make Evidence-Based Decisions:
- Facebook: Facebook relies heavily on data-driven decision-making. They analyze user behavior, engagement metrics, and other data points to make informed platform decisions, such as algorithm changes and feature updates.
Validate Qualitatively, Verify Quantitatively:
- Apple: Apple combines qualitative user feedback, often collected through direct engagement and usability testing, with quantitative data on user interactions. This approach helps them refine their products based on both user preferences and behavior.
Remove Failure from Your Vocabulary:
- SpaceX: SpaceX embraces iterative testing in the development of space technology. They view each test and experiment as an opportunity to gather valuable data, even if it results in failure. This mindset allows them to continuously improve their rockets and spacecraft.

Key Principles of Continuous Innovation:

Love the Problem, Not the Solution: Avoid prematurely falling in love with your solution. Focus on understanding the problem and identifying the right customers before developing a solution.
A Business Model Is the Product: In a technology-driven world, business model innovation is more crucial than ever. Business models align various interests and create value for stakeholders beyond just the technical solution.
Traction Is the Goal: Traction is a unifying factor for both investors and customers, especially in platform-based models. The growth of users contributes to the value of the platform, benefiting all parties involved.
Right Action, Right Time: Identify the key actions that matter most at a given time. Focus on these actions and ignore distractions to make the best use of your time and resources.
Give Yourself Permission to Scale: Before aiming for mass scale, focus on finding your first customers and defining your niche. Niche marketing and catering to a specific audience help in establishing a solid foundation.
Tackle Riskiest Assumptions First: Start with smaller numbers of customers/users to systematically validate and roll out your product. Address the riskiest assumptions early on to manage challenges effectively.
Think 10X: Adopt a staged rollout approach. Gradually scale and gather feedback to prevent prematurely killing your product.
Make Evidence-Based Decisions: In a data-driven age, be aware of biases and strive to make decisions based on evidence rather than perceptions.
Validate Qualitatively, Verify Quantitatively: Initially, validate your product or service qualitatively by engaging with early customers. As your user base grows, analyze quantitative data to gain insights.
Remove Failure from Your Vocabulary: Shift the perspective from personal failure to procedural experimentation. Treat each failed attempt as valuable feedback to refine your approach.