Initial Publication Date: 05 April 2021

Abstract

The pandemic has clearly been a forcing function for change, and enterprises have responded with innovation across the entire value chain. But this event is not the end of change, it’s an acceleration of the trends towards an adaptive enterprise that were already present in the market. To survive, enterprises need to innovate and the ability to effectively manage the innovation system is a key factor in success.

This is the third of our series of reports covering enterprise innovation. We initially described applying start-up principles to enterprise innovation, then provided an approach to applying a comprehensive reference architecture model to innovation program and we now will focus on execution. This report lays out the generally available tools and techniques to execute on your innovation programs as a holistic system in support of your business and innovation strategy.

Innovating is the act of transforming creative ideas into recognized value. Creating is thinking of something new. Innovating is implementing something new. Both are focused on the end goal of creating recognized value.

The way to think about moving an idea to realized value is to think about the idea as a product from the very beginning. Whether the idea is internal workflow change, exposing a new API, a product for the market, a business process improvement, etc., each follows a similar pattern as it moves along its journey to value. Depending on where the product is in its journey helps determine what tools and techniques are best to make progress.

We describe several well-known innovation methods and tools in this report, but there are many toolkits and innovation processes that will support your goals. The key is finding the toolset that works for your organization and evolve it as your needs change. We also define a series steps to take to make your selected toolkit fit within the context of your organizational culture.

Authors:

Gary Zimmerman Principal Consulting Analyst, CMO [email protected]

Executive Summary

The pandemic has clearly been a forcing function for change, and enterprises have responded with innovation across the entire value chain.  But this event is not the end of change, it’s an acceleration of the trends towards an adaptive enterprise that were already present in the market. To survive, enterprises need to innovate and the ability to effectively manage the innovation system is a key factor in success.

This report is the latest in our series covering enterprise innovation. It lays out our view of the best ways to utilize generally available tools and techniques to manage your innovation investments and efforts as a total system in support of the business strategy. Even if your enterprise’s innovation focus has been temporarily altered due to COVID, the direction is clear. Organizing and managing innovation as a system is the way forward.

Innovating is the act of transforming creative ideas into recognized value. Creating is thinking of something new. Innovating is implementing something new. Both are focused on the end goal of creating recognized value.

Practices such as Design Thinking, Lean Startup, Agile Development and Growth Hacking are all focused on iterative discovery, delivery, and improvement. They look at different domains, with diverse backgrounds and different practices. However, they are not a linear sequence of separate circles, like a waterfall of SDLC phases.

During the innovation journey, sometimes, the emphasis is more on design; sometimes, the focus is on validating hypotheses, and sometimes, the goal is to get stuff developed and delivered. In other words, an innovation can be moving through the testing of different hypotheses using these different practices at the same time because they may be focused on different audiences and different aspects of the business model.

It makes more sense to consider design thinking, lean, design sprint, agile, and growth hacking as simply a bundle of tools and techniques in your own toolkit rather than argue for one over the other. They can all create added value somewhere in the innovation spectrum, after all.

The way to think about moving an idea to realized value is to think about the idea as a product from the very beginning. Whether the idea is internal workflow change, exposing a new API, a product for the market, a business process improvement, etc., each follows a similar pattern as it moves to value:

1. Define the problem
2. Define a solution that solves the problem (Problem Solution Fit)
3. Test the solution to verify the problem is solved (Minimum Viable Product)
4. Confirm the practical aspects and arrangements of delivering the solution (Solution Market Fit)
5. Build a market-ready product (Minimal Marketable Product)
6. Build the business
   1. Evolve the product
   2. Evolve the go to market
7. Make it efficient (Market Maturity)

Determining where a product is in its maturity helps to determine what tools and techniques are optimal at that point in time.

Innovation is not the same as solving operational issues. As we first laid out in our report, “Applying Start-up Concepts to Enterprise Innovation”, building a sustainable enterprise innovation program requires a fundamental change in thinking. This report builds on that concept by asking you to think about each individual innovation initiative differently. Think about these characteristics as you plan out your efforts.

• Product not project – treat an idea as a product that moves through a lifecycle from a set of pure assumptions to realized value. It is not a typical work effort with defined tasks, linear progress, and budgets. It has unknowns that must become known and risks to be managed through experimentation and learning.
• Speed – complete small batches of work focused on falsifying hypotheses. Lean Startup, Agile, SAFe, DevOps, Growth Hacking, et al. are tools to drive risk out of innovation. The quicker you move to value (or failure) the better.
• Prioritization – tackle the most important (and risky) hypothesis first. Customer, Market, Business, Technology, in that order.
• Organization – each idea needs a permanent but evolving small cross functional “product” team with the proper skill sets applying the proper practices to move the innovation from idea to recognized value.

Finally, we describe several of the best-known innovation methods and tools in this report. But there are many toolkits and innovation processes out there. All of them share various features, usually include an innovation model, involve a step-by-step process, and provide specific templates that support each step. Most organizations can easily adapt and customize the various “open sourced” toolkits out there, whether from Stanford Design School, Intuit, Adobe, CSAA Insurance Group, or others. Whatever the specific tools, consider the steps you’ll need to take to make the toolkit part of your organizational culture when creating and introducing an innovation toolkit to support your broader innovation efforts

Introduction

Between the travel restrictions, quarantines, social distancing, and mandated shutdowns, we are in what many are now calling the new “shut‑in economy.” This has had a dramatic impact on corporate innovation efforts. Many corporate venture arms are moving from a “returns” focus to a “strategic” focus as companies increasingly invest in business continuity and resiliency.

Pre-pandemic less than one-fifth of the U.S. workforce were remote, and the unemployment rate was at historic lows. Today, white collar jobs have gone completely remote, unemployment has skyrocketed, and students at every level are learning virtually. Across the board, self-quarantine has forced people to change their behavior and rely increasingly on technology. And while vaccine breakthroughs are starting to roll out, predicted surges in infections means the shut-in economy is likely to be with us this year. And even after the crisis has passed, things will never go back to the way they were.

This has shifted some of the focus of traditional innovation investments. Technologies that promote teleworking in this new work-from-home environment have seen a surge in usage—and corresponding concerns about cybersecurity and data privacy. COVID-19 has fast-forwarded digital transformation trends that were already bubbling up—particularly technologies or companies that promote remote working, on-demand delivery, and online streaming.

As enterprises retrench, the “act” of innovation is becoming more tactically focused on things that help respond to the crisis and emerge on the other side more or less intact. They are responding to an increasing “market pull” in their efforts as their customers are changing their consumption habits as well.

The pandemic has clearly been a forcing function for change, and enterprises have responded with innovation across the entire value chain.  But this event is not the end of change, it’s an acceleration of the trends towards an adaptive enterprise that were already present in the market. To survive, enterprises need to innovate and the ability to effectively manage the innovation system is a key factor in success.

This report is the latest in our series covering enterprise innovation. It lays out our view of the best ways to utilize generally available tools and techniques to manage your innovation investments and efforts as a total system in support of the business strategy. Even if your enterprise’s innovation focus has been temporarily altered due to COVID, the direction is clear. Organizing and managing innovation as a system is the way forward.

Organizations are struggling

We know from research and practice that managing innovation activities can be very challenging in established companies and organizations. This is especially true when it comes to radical or disruptive innovations that are challenging the current ways of working, business models or organizational culture. Transformation and change are often an uphill battle.

A Systems approach necessary

We also know that innovation activities can be managed to a large extent by creating the right conditions, removing barriers, and engaging people in the organization. The ability for an organization to innovate is dependent on several interconnected factors such as leadership, resources, culture, structures, processes, reward systems and so on. This is why a systems approach is necessary for managing innovation activities.

An innovation management system provides a systemic and systematic approach for any organization to address their innovation challenges.

In their efforts to address opportunities and challenges, companies and organizations have been using many different innovation approaches. These include brainstorming sessions, idea management platforms, hackathons, design thinking labs, start-up accelerators and corporate venture funds, to name a few. Very often these efforts have not led to the desired innovation performance and they are therefore discontinued, or they simply fade away. Some of the reasons for these efforts not living up to expectations can be the lack of necessary resources and competences, not setting a clear direction to guide creativity, failure in providing the required organizational structures, missing appropriate measurements, insufficient senior management commitment or the lack of providing appropriate end-to-end processes or ways of working for the innovation initiatives to succeed.

Organizations are generally underestimating what it takes to make their innovation efforts successful, especially when they are seeking more radical, disruptive or transformative innovations. Innovation attempts tend to be fragmented, ad hoc and episodic. There is thus a need to find approaches that are more holistic, systematic and sustainable over time, and that changes the focus from singular events and projects to building longer-term innovation capabilities.

Innovation and change should be iterative, decentralized, and started in small scale while receiving full support from top management.

Reviewing the Innovation Reference Architecture

The TechVision Research Reference Architecture for Innovation is a master template that identifies the innovation capabilities (rather than technologies) that can be improved or enabled, allowing business stakeholders and intrapreneurs to achieve a common language for innovation functions, which can then be refined over time. It provides the following benefits:

• Focus on efforts that provide strategic benefit. Even a successful innovation effort that is counter to the corporate strategy can be distracting at best, destructive at worst.
• A disciplined approach produces less waste. From problem identification and brainstorming to development and testing, following a set roadmap makes the most of innovation efforts. When the process is clear and repeatable, it makes it possible for an organization to measure their progress and efforts.
• Establishing a measurement system results in process improvements, better formed concepts, and ultimately a competitive advantage and a significant return on investment.

We have defined our innovation reference architecture to help our clients better organize and manage their continuous innovation process; and the details of the architecture are documented more fully in our report “Innovation Reference Architecture”. However, this reference architecture is not meant to be prescriptive. Each organization must develop their innovation capabilities according to their own strategy. What follows the diagram is a brief description of the components of the reference architecture.

Figure 1 – the TechVision Innovation Reference Architecture

Innovation Spectrum – The innovation spectrum defines the entire scope of innovation within the enterprise. It covers everything from incremental improvement of existing products and processes to those disruptive, breakthrough innovations that change the world. All innovation projects, regardless of where they are in the spectrum, are part of the innovation portfolio that is guided by an innovation thesis.

The innovation portfolio component describes the entire “book of business” of innovation investments. Innovation activities take place on a broad spectrum based on how much is unknown about the markets being addressed and technologies being adopted. This spectrum ranges from horizon 1 efforts, where we are focused on optimizing and scaling the core business, to horizon 2 efforts where you are focused on the next wave of growth, and finally horizon 3 efforts focused on the future of new markets, technologies and growth engines.

Strategy and Capabilities – When entrepreneurs set out to build a new venture, they start with a very limited means, often just who I am, what I know, and whom I know. Then, the entrepreneurs imagine the possibilities that originate from their means. As the picture shows, an established enterprise has a treasure of assets already at your disposal. You have a direction outlined by the corporate strategy. You have a proven business model. You have established and resourced business processes. A platform on which to execute. And finally, the knowledge built up over the years as to how you deliver customer value. To use a baseball analogy, the entrepreneur is at the plate trying to get that first hit while your business is already rounding third base headed towards home. Taking stock of your riches and taking advantage of them is key.

Methods and processes – For innovation to be effective, it needs to become a core competency, it has to be supported as any other business activity within the company. It needs to have a defined business process, specific tools and methods, resources, incentives, and training.

This section of the reference architecture addresses the innovation methods and processes that are needed to create and execute innovation projects. These are covered more fully in our report on “Innovation Methods”.

People and Networks – Innovation is not done by organizations – it’s done by people. For the foreseeable future, innovation will require people and their connections with other people to succeed. To succeed in internal innovation, the enterprise must be conscious of the soft factors, culture, employee skills, and motivation that drive behavior. You have to adjust these soft factors to match what’s needed to foster and nourish intrapreneurs.

And in an increasingly digital world, the ability to innovate is no longer just an internal exercise. While the enterprise needs to make sure innovations supporting core value remain in-house, external entities create a wider talent pool for innovation on any number of challenges.

Innovating with external parties tends to be approached in one of three ways – Open Innovation, Crowdsourcing and Co-Creation. These collaborative approaches to innovation are covered in depth in our report “Innovation Governance”. But many software developers are familiar with the products of such efforts. If you are involved in delivering software, you are most likely already using open innovation. Open-sourced software is running in 78% of enterprises today and that’s a prime example of open innovation where everyone contributes their ideas and efforts for common good.

Execution – Most people think innovation is all about ideas, when in fact it is more about delivery, people, and process. Ideas, big and small, are loaded with assumptions. Assumptions like “customers will buy,” “we can make it at scale,” “our organization can support it,” and many others. Innovation is what you do to commercialize ideas. Every organization has a number of techniques they use to commercialize their inventions. This report is dedicated to the tools and techniques supporting this layer of the architecture.

Digital Platform – While TechVision has authored several research reports focused many aspects of the technology stack, this innovation reference architecture highlights social, mobile, analytics and cloud, SMAC for short, as the four technologies are currently influencing business innovation.

The idea of SMAC was first published as part of a Search CIO Essential Guide and defines an ecosystem that allows a business to improve its operations and get closer to the customer. We believe that these technologies extend beyond commercial products and go deep into the innovation process itself because they are key to rapid experimentation and confirmation.

At our latest Chrysalis conference, we discussed a set of emerging technologies that we believe will start making their way into the digital platform as they become more mainstreamed. These include:

• Blockchain – Blockchain technology continues down the path toward broad adoption as organizations gain deeper understanding of its transformational value, within and across their industries. Blockchain is to trust what the web was to communication.
• Cognitive technologies – Cognitive is shorthand for technologies such as machine learning (ML), neural networks, robotic process automation (RPA), bots, natural language processing (NLP), and the broader domain of artificial intelligence (AI). Cognitive toolsets both augment human response and potentially automate the appropriate response.
• Digital reality – an umbrella term for augmented reality (AR), virtual reality (VR), mixed reality (MR), the internet of things (IoT), and immersive / spatial technologies that are redefining how humans interact with data, technology, and each other.
• Faster horses – Decentralized flash-based storage, 5G hyperconnectivity, and Quantum computing are disrupting the classic “transmit, compute, store” constraint cycle. As they mature, they will accelerate the previous emerging technologies as well as allow many others in autonomous / edge computing areas to become reality.

While the enterprise digital platform will continue to evolve over time, the need for technologies that facilitate the rapid experimentation and confirmation so critical to innovation needs to be nurtured and grown.

Coordination and governance – Much like a manufacturing plant focuses on producing product, an innovation system focuses on producing solutions that realize value. And in a similar way, the innovation system needs to be observed, measured, and improved.  That’s the role of the coordination and the governance function. Innovation governance starts with building a vision and strategy for innovation. The “why innovate” part of the equation. But it does not stop there as innovation governance is also concerned with the development of innovation-enhancing capabilities, not just hard skills but softer ones as well. In addition, it deals with the organization and improvement of the classic tasks linked with execution. These areas are covered in depth in our companion report “Innovation Governance”.

Innovation is all about execution

Innovating is the act of transforming creative ideas into recognized value. Creating is thinking of something new. Innovating is implementing something new. Both are focused on the end goal of creating recognized value. The figure below illustrates how a concept, an idea, moves through different states from theory to real product delivering recognized value. In each state, there are different tools and techniques that are leveraged to remove customer, market, business, and technical risk along the journey.

Figure 2 – The journey from idea to realized value

As shown in the figure above, practices such as Design Thinking, Lean Startup, Agile Development and Growth Hacking are all focused on iterative discovery, delivery, and improvement. They look at different domains, with diverse backgrounds and different practices. However, they are not a linear sequence of separate circles, like a waterfall of SDLC phases.

During the innovation journey, sometimes, the emphasis is more on design; sometimes, the focus is on validating hypotheses, and sometimes, the goal is to get stuff developed and delivered. In other words, an innovation can be moving through the testing of different hypotheses using these different practices at the same time because they may be focused on different audiences and different aspects of the business model.

The confusion is great when it comes to choosing the right innovation methods, frameworks and tools to foster innovation within a company. Questions such as “When should we use design thinking?”, “What is the purpose of a design sprint?”, “Is lean startup only for startups?”, “Where does agile fit in?”, “What happens after the <some methodology> phase?” arise all the time. Figure 2 tries to visualize how models like design thinking, lean, design sprint and agile flow from one to the other. The place where one method merges into the next is very controversial because there are too many similar techniques and there are simply too many overlaps.

Given this complexity, it probably makes more sense to consider design thinking, lean, design sprint, agile, and growth hacking as simply a bundle of tools and techniques in your own toolkit rather than argue for one over the other. They can all create added value somewhere in the innovation spectrum, after all.

The way to think about moving an idea to realized value is to think about the idea as a product from the very beginning. As shown in figure 3, whether the idea is internal workflow change, exposing a new API, a product for the market, a business process improvement, etc., each follows a similar pattern as it moves to value:

1. Define the problem
2. Define a solution that solves the problem (Problem Solution Fit)
3. Test the solution to verify the problem is solved (Minimum Viable Product)
4. Confirm the practical aspects and arrangements of delivering the solution (Solution Market Fit)
5. Build a market-ready product (Minimal Marketable Product)
6. Build the business
   1. Evolve the product
   2. Evolve the go to market
7. Make it efficient (Market Maturity)

Depending on where the product is in its maturity helps determine what tools and techniques are best to make progress.

Figure 3 – Innovation lifecycle

Discovery

But let’s begin at the beginning. Any idea is built on assumptions. Lots of assumptions. Because we are thinking about the idea as a product, the set of assumptions about the solution (product) and the use (market). If we turn those initial assumptions into questions, they look something like this.

Market[1]

• Who is our customer?
• What are their pain points?
• What job needs to be done?
• How are they doing this job today?
• Does the customer segment already have a solution to this pain?
• Is this customer segment really willing to pay for a better solution for this job?
• Is our customer segment too broad?
• How do we find our customers?
• How much will this customer segment pay?
• How do we convince this customer segment to buy?
• What is the cost of acquiring a customer in this customer segment?

Product[2]

• How can we solve this problem?
• What form should this solution take?
• How important is the design?
• What’s the quickest solution?
• What is the minimum feature set?
• How should we prioritize?
• Is this solution working?
• Are people using it?
• Which solution is better?
• How should we optimize this?
• What do people like/dislike?
• Why do they do that?
• Why do prospects buy from us?
• Why do prospects not buy from us?

To realize value, every idea has to have answers to these and many other questions to replace assumption “uncertainty” with firm answers.

As we illustrated in figure 2, iterative discovery, delivery, and improvement is how an idea moves towards realized value. Those iterations can be thought of as experiments trying to prove or disprove a hypothesis so that fact replaces uncertainty along the value journey. Many people confuse the words assumption and hypothesis so let’s define them.

Assumption – a thing that is accepted as true or as certain to happen, without proof (taken on faith).

Hypothesis – a supposition or proposed explanation made on the basis of limited evidence as a starting point for further investigation.

Now that’s an interesting difference, and it’s important because depending on whether we have an assumption or a hypothesis, we should do two different things.

• If we have an assumption, we accept the risk that the assumption is false and move on.
• If we have a hypothesis, we attempt to falsify

Assumptions should be challenged and clarified with research. Falsifiable hypotheses should be tested with experiments.

After we have clarified an assumption, we can either accept the risk or convert it into a testable hypothesis. – and in doing so, reduce the risk of that original assumption.

Generative Research

So how do you clarify an assumption? – through generative research. Generative research is defined as a method of research that helps researchers develop a deeper understanding of users in order to find opportunities for solutions and innovation. Sometimes referred to as discovery or exploratory research, the goal is always the same; understand the user, what problems they struggle with, and possible solutions. These solutions could be new products or experiences, or they could be an update or improvement to an existing one.

In order to identify new and innovative solutions, you must define the problem you are trying to solve. This requires you to truly understand how people “live”, including their environments, behaviors, attitudes/opinions, and perceptions.

When conducting generative research, the most important thing to do is to keep an open mind – you might not actually know the problem you’re trying to solve – yet.

Evaluative research

Evaluative research, can be defined as a research method used for assessing a specific problem to ensure usability and ground it in wants, needs, and desires of real people.

The goal of the evaluative research methodology is to test your existing solution to see if it meets people’s needs, is easy to access and use, and is hopefully even enjoyable. This type of research should be conducted throughout the development lifecycle, from early concept design (think rough sketches or prototypes) to the final site, app, or product. Evaluative research is conducted using experiments.

If we look at the questions we asked about the market and product earlier in this section, we can start to refine the research approach required. Some of the questions need further refinement requiring generative research, generally questions around “who” or “why”. Others are more quantitative and can be proven or disproven with experiments, generally questions around “how” and “what”.

	Market	Product
Generative Research	·      Who is our customer? ·      What are their pains? ·      What job needs to be done? ·      How are they doing this job today? ·      Does the customer segment already have a solution to this pain?	·      How can we solve this problem? ·      What form should this solution take? ·      How important is the design? ·      What’s the quickest solution? ·      What is the minimum feature set? ·      How should we prioritize?
Evaluative Experiments	·      Is this customer segment really willing to pay for a better solution for this job? ·      Is our customer segment too broad? ·      How do we find our customers? ·      How much will this customer segment pay? ·      How do we convince this customer segment to buy? ·      What is the cost of acquiring a customer in this customer segment?	·      Is this solution working? ·      Are people using it? ·      Which solution is better? ·      How should we optimize this? ·      What do people like/dislike? ·      Why do they do that? ·      Why do prospects buy from us? ·      Why do prospects not buy from us?

Table 1 – Approaches to Who, Why, How, and What Questions

Who and Why?

Generative research begins the innovation journey because we’re trying to figure out who has a problem and why we should be the ones to solve it.

The very first step is to listen and to understand to discover something that you can solve for people or companies. Design Thinking and Jobs to be Done are two great methods for generative research. Both techniques help develop a strong empathy for other people’s problems.

Design Thinking

Solutions teams are often tasked with solving a problem, but that scope usually ignores solving a problem in any larger or more unique way. Design thinking attempts to uncover a truly unique and useful improvement or solution. Most companies develop a product by eyeing a problem and creating a solution (often one they’ve presumed is needed). This solution might seem obvious, but it might entirely miss underlying problems.

Design thinking is a process that organizes the way we solve problems by breaking down the status quo and our own human biases. Anything that you’ve declared “That’s how we do things here” is ripe for design thinking. Asking more interesting questions, according to design thinking, results in more original ideas. After all, defining a problem in an obvious or conventional way can only yield obvious, conventional solutions.

In order to ask more interesting questions, design thinking implements:

• Diversity by bringing in a wide range of people to the design thinking team who wouldn’t normally be involved in change or innovation processes.
• Customer research by spending time thinking about what customers really need—and want—and encouraging us to break down our own biases and attachment to the status quo.

The step-by-step organizational process breaks down large problems into manageable components, so you can focus on any type of solution, no matter the size or scale of the problem.

Design thinking is a unique approach to innovation. By making it human-centered, it uses elements such as experimentation and empathy to come up with inventive solutions while incorporating the people’s needs, the prospects of technology, and what is required for the success of a business.

In Design Thinking, the emphasis is on the discovery of customer needs and designing potential ways to satisfy those needs. Design thinkers understand that you cannot design anything useful without creating it, testing it, and delivering it. And they know this is best done in cross-functional teams. It’s just that they have the most experience with the design part. It is true that a lot of design is needed in the early stages of a new product. But design activities never end! You still need to empathize with clients late in a product’s lifetime. Design only ends when the product stops evolving and improving.

That it is a most powerful tool and when used effectively, can be the foundation for driving a brand or business forward.

Figure 4 – Design Thinking Elements

Basically, Design thinking consists of four key elements.

Define the problem

Sounds simple but doing it right is perhaps the most important of all the four stages. Another way to say it is defining the right problem to solve. Design thinking requires a team or business to always verify the problem to be solved, and to participate in defining the opportunity and to revise the opportunity before embarking on its creation and execution. Participation usually involves immersion and the intense cross examination of the filters that have been employed in defining a problem.

First, you should employ empathy. This is the development of a deep understanding of the human needs that are in play. The next step is to look at problems from a new light by involving human-centric methods. Ensure that you come up with multiple ideas during the sessions. Next, come up with a prototype, which is a miniature version of the new product or feature. Finally, carry out rigorous tests on the finished product or feature, and make changes where necessary. We shall look at this in more detail later.

In design thinking, observation takes center stage. Observation can discern what people really do as opposed to what you are told that they do. Getting out of the cube and involving oneself in the process, product, shopping experience or operating theater is fundamental. No one’s life was ever changed by a PowerPoint presentation.

Design thinking in problem definition also requires cross functional insight into each problem by varied perspectives as well as constant and relentless questioning, like that of a small child, Why? Why? Why? Until finally the simple answers are behind you and the true issues are revealed. Finally, defining the problem via design thinking requires the suspension of judgment in defining the problem statement. What we say can be very different to what we mean. The right words are important. It’s not “design a chair”, it’s “create a way to comfortably suspend a person”. The goal of the definition stage is to target the right problem to solve, and then to frame the problem in a way that invites creative solutions.

Create and consider many options

Even the most talented teams and businesses sometimes fall into the trap of solving a problem the same way every time. This is especially true when successful results are produced, and the time is compressed. Design thinking requires that no matter how obvious the solution may seem, many solutions are created for consideration…and these solutions are created in a way that allows them to be judged equally as possible answers. Looking at a problem from more than one perspective always yields richer results.

Many times, we are not aware of the filters we may be burdened with when we create answers to problems. In this stage opportunities appear. The trick is to recognize them as opportunities. Multiple perspectives and teamwork are crucial. Design thinking suggests that better answers happen when 5 people work on a problem for a day, than one person for five days. Designers have an advantage in the use of two- and three-dimensional tools to demonstrate solutions and new ideas — tools which are almost always far more effective to demonstrate what is meant, than words.

Refine selected directions

A handful of promising results need to be embraced and nurtured. Given a chance to grow protected from the evil idea-killers of previous experience. Even the strongest of new ideas can be fragile in their infancy. Design thinking allows their potential to be realized by creating an environment conducive to growth and experimentation, and the making of mistakes in order to achieve out of the ordinary results. At this stage many times options will need to be combined and smaller ideas integrated into the selected schemes that make it through. Which brings us to stage 3.5.

3.5 Repeat (optional)

Design thinking may require looping steps 2 and 3 until the right answers surface.

Pick the winner, execute

At this point enough road has been traveled to ensure success. It’s the time to commit resources to achieve the early objectives. The byproduct of the process is often other unique ideas and strategies that are tangential to the initial objective as defined. Prototypes of solutions are created in earnest, and testing becomes more critical and intense. At the end of stage 4 the problem is hopefully solved, or the opportunity is fully uncovered.

Design thinking describes a repeatable process employing unique and creative techniques which yield consistent results – usually results that exceed initial expectations. Extraordinary and disruptive results that leapfrog the expected are often seen using this approach. This is why it is such an attractive, dynamic and important methodology for businesses to embrace today.

Jobs to be done

The Jobs-to-be-Done (JTBD) framework has emerged as a helpful way to look at customer motivations in business settings. Conventional marketing techniques teach us to frame customers by attributes—using age ranges, race, marital status, and other categories that ultimately create products that focus on what companies want to sell, rather than on what customers actually need.

The JTBD framework evaluates the circumstances that arise in customers’ lives. Customers rarely make buying decisions around what the “average” customer in their category may do – but they often buy things because they find themselves with a problem they would like to solve. With an understanding of the “job” for which customers find themselves “hiring” a product or service, companies can more accurately develop and market products well-tailored to what customers are already trying to do.

The classic example of a JTBD focus is captured by this quote from Theodore Levitt “People do not want a quarter-inch drill, they want a quarter inch hole.” It focuses on the result not on the approach. Of course, even this assumption can be questioned, why do they want a quarter-inch hole? Perhaps to hang a bookshelf which might be the real job to be done.

Using JTBD, you can develop an offering that a customer can “hire” to complete their job. Office workers hire word-processing software to create documents. Surgeons hire scalpels to dissect soft tissue. But few companies keep this in mind while searching for ideas for breakthrough offerings, and simply asking people what jobs they have is unlikely to result in any insightful answers, as people themselves often don’t realize or can’t verbalize what is frustrating them or what they are trying to accomplish. What companies really need are insights, not opinions.

The JTBD framework helps companies get the true insights from real people about the challenges and frustrations they are facing. By segmenting these people, it may be possible to find an innovative solution to meet a number of the challenges which together mean a product could do the entire “job”, and therefore make it much more appealing to a customer.

In the end, customers don’t buy products and services. They hire different solutions at different times to get a variety of jobs done. We’ll now further define of the categories of jobs from a customer perspective.

Types of Jobs to Be Done

There are different types of jobs to be done from a customer’s perspective:

1. Functional Jobs describe the tasks that particular customers want to achieve. These jobs are the basis for all products and services because they are their primary function why they exist on the market. For example, “getting to a destination on time” is a functional job. Many intrapreneurs focus only on these types of jobs. But there are other jobs that make up the entire experience.
2. Emotional jobs are related to feelings and perception. So, they are subjective. But what are some of the emotional jobs (or goals) that people have when trying to get to a destination on time? Perhaps they want to avoid feeling anxious about being late for a meeting. They most likely want to feel safe while getting there. The heightened emotional repercussions of not getting to a destination on time are what gives context to how a functional job is fulfilled. If the functional job is executed poorly, it negatively impacts the emotional job and creates negative emotions and anxiety for the customer.
3. Consumption jobs are what a customer has to do to utilize a solution. If you are trying to get to a destination on time, to use Google Maps as a solution, you will have to interface with a mobile app while driving. “Interface with a mobile app while driving” is a consumption job. Tasks that include verbs such as learn, install, maintain, repair, dispose of, unbox, etc., are often consumption jobs.

All three types of jobs are important to identify and analyze because together they contribute to your customer’s full experience of your product.

There are two ways to think about JTBD. One way is to observe a customer attempting to do something and discover the inefficiencies and gaps in how they are doing the work. In other words, uncovering “Do” objectives.

• The Jobs-As-Activities model suggests that people buy a product because they want to “do work” with the product. Therefore, your efforts should be to improving how they use a product.

A broader interpretation of JTBD is to think in terms of making progress towards positive change. In other words, achieving “Be” objectives.

• The Jobs-As-Progress model suggests that people don’t want to “do work”. What they do want, is to make a positive change in their life – i.e., “progress”. Therefore, your efforts should focus on helping them make that change. Ideally the person wouldn’t have to do any work to make the change.

The Jobs-as-Progress model suggests that a great deal of disruptive or radical innovation is about eliminating tasks and activities – not designing for them. IKEA’s innovation was to create furniture that you could order and assemble yourself without needing to “cut a straight line”, “drill a quarter inch hole”, or even “want a quarter inch hole”.

Moreover, in 2017, IKEA bought Task Rabbit. Why? Because they also understand that people don’t want to “assemble/build furniture” either. Rather, they want to be organized, express their individuality, and feel comfortable in their home.

Any innovation project which your company is starting should aim to investigate the potential jobs to be done for a customer. It is most effective at the early stages of an initiative when a team should be going out and investigating how things currently run. This involves going out and meeting real people, observing them in a neutral and unbiased way and trying to get insights by learning about their behavior and frustrations, rather than their opinions.

Design Sprints (Google Ventures)

Once you have a pretty good handle on the customer and the job to be done, the next phase is working to develop a solution. A quick way to iterate through possible solutions is a design sprint. The design sprint is a five-day process for answering critical business questions through design, prototyping, and testing ideas with customers. Developed at Google Ventures, GV, it’s a “greatest hits” of business strategy, innovation, behavior science, design thinking, and more—packaged into a battle-tested process that any team can use.

Working together in a sprint, you can shortcut the endless-debate cycle and compress months of time into a single week. Instead of waiting to launch a minimal product to understand if an idea is any good, you’ll get clear data from a realistic prototype. The sprint gives you a superpower: You can fast-forward into the future to see your finished product and customer reactions, before making any expensive commitments.

Former GV design partner Jake Knapp began running design sprints at Google in 2010. He worked with teams like Chrome, Google Search and Google X. In 2012, Jake brought sprints to GV, and the rest of the team chipped in their expertise to perfect the process.

Braden Kowitz added story-centered design, an unconventional approach that focuses on the customer journey instead of individual features or technologies. Michael Margolis took customer research—which can typically take weeks to plan and often delivers confusing results—and figured out a way to get crystal clear results in just one day. John Zeratsky helped teams start at the end and focus on measuring results with the key metrics from each business. And Daniel Burka brought firsthand expertise as an entrepreneur to ensure every step made sense in the real world. Here is how a typical design sprint is executed.

Monday – Monday’s structured discussions create a path for the sprint week. In the morning, you’ll start at the end and agree to a long-term goal. Next, you’ll make a map of the challenge. In the afternoon, you’ll ask the experts at your company to share what they know. Finally, you’ll pick a target: an ambitious but manageable piece of the problem that you can solve in one week.

Tuesday – After a full day of understanding the problem and choosing a target for your sprint, on Tuesday, you get to focus on solutions. The day starts with inspiration: a review of existing ideas to remix and improve. Then, in the afternoon, each person will sketch, following a four-step process that emphasizes critical thinking over artistry. You’ll also begin planning Friday’s customer test by recruiting customers that fit your target profile.

Wednesday – By Wednesday morning, you and your team will have a stack of solutions. That’s great, but it’s also a problem. You can’t prototype and test them all—you need one solid plan. In the morning, you’ll critique each solution, and decide which ones have the best chance of achieving your long-term goal. Then, in the afternoon, you’ll take the winning scenes from your sketches and weave them into a storyboard: a step-by-step plan for your prototype.

Thursday – On Wednesday, you and your team created a storyboard. On Thursday, you’ll adopt a “fake it” philosophy to turn that storyboard into a prototype. A realistic façade is all you need to test with customers, and here’s the best part: by focusing on the customer-facing surface of your product or service, you can finish your prototype in just one day. On Thursday, you’ll also make sure everything is ready for Friday’s test by confirming the schedule, reviewing the prototype, and writing an interview script.

Friday – Your sprint began with a big challenge, an excellent team—and not much else. By Friday, you’ve created promising solutions, chosen the best, and built a realistic prototype. That alone would make for an impressively productive week. But you’ll take it one step further as you interview customers and learn by watching them react to your prototype. This test makes the entire sprint worthwhile: At the end of the day, you’ll know how far you have to go, and you’ll know just what to do next.

The ultimate point of generative research through Design Thinking, JTBD, and Design Sprints is that you iterate through the process to develop a set of hypotheses in terms of the who, what, when, where, how, and how much of a problem and its solution. It defines the attributes of a preliminary business model needed to solve the problem which is further developed in the development phase as follows.

Development

A business model is a document or strategy which outlines how a business or organization delivers value to its customers. In its simplest form, a business model provides information about an organization’s target market, that market’s need, and the role that the business’s products or services will play in meeting those needs.

Business model design, then, describes the process in which an organization adjusts or creates its business model. Often, this innovation reflects a fundamental change in how a company delivers value to its customers, whether that’s through the development of new revenue streams or distribution channels.

Business Model Canvas

A Business Model Canvas is a great tool to describe, design, challenge, and pivot your business model and test out new business models, vital when developing innovations. Developed by Strategyzer co-founder Alex Osterwalder, it enables you to succinctly lay out the various aspects of a new offering and determine how the components will fit together to form a business model. This allows you to see potential hurdles which need to be overcome, gaps in the offering or even potential gaps in the market. Often this can provide a much clearer overview of an offering than other summaries, like traditional business cases.

The business model canvas is a shared language for describing, visualizing, assessing and changing business models. It describes the rationale of how an organization creates, delivers and captures value. While to classic business model canvas is good, the Lean Canvas proposed by Ash Maurya provides a more intrapreneurial focused approach to capturing the assumptions and hypotheses around the idea as described below.

Figure 5 – Lean Canvas by Ash Maurya

As you work through each box, you’ll fill in your assumptions and hypotheses in each aspect of the business you are defining.  The work done in the discovery phase is the main input to this effort.

1. Problem – A brief description of the top 1 – 3 problems the solution is addressing, including how the customer is solving those problems today.
2. Customer Segments – Who are the customers/users of this solution? Can they be further segmented? For example, amateur photographers vs. pro photographers. If there are multiple target customer segments, focus on the segment that is most likely an early adopter. Look to cover other segments in different canvases because the “who” can change the “how” and “what” of value realization.
3. Unique Value Proposition – What is the product’s tagline or primary reason you are different and worth buying?
4. Solution – What is the minimum feature set (MVP) that demonstrates the UVP up above?
5. Key Metrics – At any given point in time, there are only a few key actions (or key macro metrics) that matter. Failure to identify the right key metric can lead to wasteful activities or running out of resources while chasing the wrong goal. Initially these key metrics should center around your value metrics and later they shift towards your key engines of growth.
6. Channels – What customer acquisition channels (traction channels) are most likely to demonstrate traction[3]? These are the preliminary marketing and distribution channels that can lead to real customer growth.
7. Cost Structure – List out all your assumptions about fixed and variable costs, including any supply and distribution costs.
8. Revenue Streams – Identify the revenue model — subscription, ads, freemium, etc. and outline your back-of-the-envelope assumptions for customer lifetime value, gross margin, break-even point, etc.
9. Unfair Advantage – This is last because it’s usually the hardest one to fill correctly. Jason Cohen, a smart bear, did a great 2-part series on competitive advantages. Most intrapreneurs list things as competitive advantages that really aren’t. This box wasn’t intended to discourage you from moving forward on your vision but rather to continually encourage you to work towards finding/building your unfair advantage.

At this stage of the innovation journey, the business model and its implied hypotheses are tested and de-risked through applying Lean Startup techniques.

Business Building

Lean Startup

The Lean Startup version of the iterative cycle emerged in the startup scene. Startup teams have a lot of experience doing crazy stuff and trying things out. Lean Startup places less emphasis on design and more on turning assumptions into hypotheses and validating (or falsifying) them through experiments. Risk is the characteristic that distinguishes a startup from any other type of business, and frankly, the most transformative ideas coming out of an existing present the same characteristics.

At its core, adhering to the lean startup methodology is all about creating a sustainable business with minimal waste of both time and money. At the core of Lean Startup is the Build-Measure-Learn cycle.

Build, Measure, Learn

Build

This methodology begins with the creation of a minimum viable product. The minimum viable product (MVP), as defined by Eric Ries, is a learning vehicle. It allows you to test an idea by exposing an early version of your product to the target users and customers, to collect the relevant data, and to learn from it. For instance, to test the viability of using ads as the major revenue source, you could release an early product increment with fake ads, and measure if and how often people click on them.

As lack of knowledge, uncertainty, and risk are closely related, you can also view the MVP as a risk reduction tool. In the example above, the MVP addresses the risk of developing a product that is not economically viable.

Since the MVP is about learning, it’s no surprise that it plays a key part in Lean Startup’s build-measure-learn cycle.

The MVP is called minimum, as you should spend as little time and effort to create it. But this does not mean that it has to be quick and dirty. How long it takes to create an MVP and how feature-rich it should be, depends on what you are trying to prove. But try to keep the feature set as small as possible to accelerate learning, and to avoid wasting time and money – your idea may turn out to be wrong!

While the MVP should facilitate validated learning, it’s perfectly OK to work with MVPs such as paper prototypes that do not generate quantitative data, as long as they help to test the idea and to acquire the relevant knowledge.

Figure 6 – different types of MVPs

In fact, as shown in figure 6, The MVP might actually include different kinds of build efforts each designed to prove or disprove hypotheses. Ultimately, the MVP will turn towards the envisioned product and the build step will include features required in the initial launch of the product, something described later as the Minimum Marketable Product.

Measure

When looking at the measure component of this methodology, it’s essential that you effectively measure the results of your minimum viable product while you continue to develop the product. The feedback that’s provided to you by these customers can be used to fine-tune the product and make it more feature rich. If you find that the very idea of your MVP isn’t gaining traction with the customers that the MVP was provided to, you should be able to get rid of the basic product without having used too much of your resources.

The feedback that you receive can be measured in a variety of ways. If you are creating a business that exists solely online, you would gain feedback by providing customers with surveys and by looking at the analytics for your website to determine what you’re doing well and what could be improved.

For an actual product that’s being tested before it’s placed on the market, the feedback can be easier to obtain by asking testers questions about the product that they’re using. Once you’ve obtained data on your minimum viable product, you can start learning from this data, which is the third component of the lean startup methodology.

Learn

It’s not enough to measure the results from the product that you’ve received and to obtain feedback from the initial customers. If you want to eventually create a product or service that’s ready to be placed on the market, it’s important that you learn from the data and feedback that you’ve received, which isn’t always an easy thing to do.

For instance, some of the feedback that you receive might not lead to the creation of a successful product. However, this feedback is best used to identify which aspects of the product aren’t working and which ones may need to be refined. If you can effectively learn from the results of the product testing, you should be able to develop a product that meets the needs of your target audience.

The overlap with Design Thinking / Design Sprints

The goal of Build-Measure-Learn is not to build a final product to ship or even to build a prototype of a product, but to maximize learning through incremental and iterative engineering. (learning could be about product features, customer needs, the right pricing and distribution channel, etc.) The “build” step refers to building a minimal viable product (an MVP.) It’s critical to understand that an MVP is not a product with fewer features; it is the simplest product to show customers that can provide maximum learning and discovery at that point in time.

While classic Build-Measure-Learn models begin with an idea, new ventures (both startups and new ideas in existing companies) don’t start with “ideas” – they start with hypotheses (our assumptions about the business we are trying to prove).

As discussed earlier, the words “idea” and “hypotheses” mean two very different things. For most innovators the word “idea” conjures up an insight that immediately requires a plan to bring it to fruition. In contrast, a hypothesis means we have an educated guess that requires experimentation and data to validate or invalidate.

That fact that lean startup begins by acknowledging that an idea is simply a series of untested hypotheses is in itself a big idea. It aligns the build, measure, learn iterations with a set of prioritized hypotheses you want to test. And that testing is done through developing several iterations of minimum viable product (MVP).

The minimum viable product you’ll need to build to find the right customers is different from the minimum viable product you need for testing pricing, which is different from an MVP you would build to test specific product features. And all of these hypotheses (and minimal viable products) change over time as you learn more.

So instead of a classic Build-Measure-Learn, the diagram for building minimal viable products in a lean startup looks like Hypotheses – Experiments – Tests – Insights.

Figure 7 – A revised Build-Measure-Learn model

So, in the discovery phase, Lean Startup can be used to help confirm the customer segments and the problems they face as the team completes it generative research efforts. Towards the end of the discovery phase and into development, Design Sprints can be the method used to falsify hypotheses and develop the prototypes to identify possible solutions. In the end, Lean Startup yields one or more MVPs.

An MVP is the smartest approach if you are navigating significant change to the product/content, service design models, pricing and means of delivery. Testing and learning with an MVP can give you better guidance for moving your existing customers into a new arena such as mobile, while also making sure you capture the attention and acquisition of dedicated mobile users. Test and validate your MVP among both existing and potential users and you’ll find minimal marketable product that can meet the needs of both, thus maintaining an existing revenue outlet while expanding into a new one.

The Minimal Marketable Product

By definition, a startup starts with an idea or hypothesis aimed at a problem, identifies who experiences the problem and builds a business and product from the ground up that solves the problem. This implies a need to experiment, test, validate and narrow focus so the best place to start would be with an MVP or a series of MVP experiments to validate multiple assumptions.

A minimal marketable product (MMP) differs from a viable one; it is complete enough to be ready for general release. What’s more, launch preparation activities have to take place for an MMP, for instance, starting growth hacking experiments, or for some products, gaining certification. So, some of your MVPs are likely to be throwaway prototypes that only serve to acquire the necessary knowledge; others are reusable product increments that morph into a marketable product.

The key to creating a successful MMP is to “develop the product for the few, not the many,” as Steve Blank puts it, and to focus on those features that make a real difference to the users. To discover the right features, the MVP is a fantastic tool, and Agile is the means to deliver them.

Agile

The Lean Startup approach combines customer development, which is helpful when you don’t know the problem, and agile development, which is helpful when you don’t know the solution. Both approaches provide ways to iteratively validate assumptions and learn through feedback.

Agile Development came from a bunch of software guys whose primary experience was building software products for large clients. They indeed recognized that you shouldn’t develop software without considering design and hypotheses. And these developers definitely promoted the importance of cross-functional teams. But they were developers, so they focused mostly on the development practices, while they understood that agile development is relevant and on-going from the day a new product is conceived to the day it is phased out. And while it is in the market, i.e., being used by customers, the notion of continuous integration and delivery through DevOps extends the iterative cycle into the market.

Agile software development refers to software development methodologies centered round the idea of iterative development, where requirements and solutions evolve through collaboration between self-organizing cross-functional teams. The ultimate value in Agile development is that it enables teams to deliver value faster, with greater quality and predictability, and greater aptitude to respond to change. Scrum and Kanban are two of the most widely used Agile methodologies.

Agile development at the team or small organization level has emerged over the last 20 years as a really powerful way to improve delivery, engagement, and quality. Successfully and repeatably Scaling agile to medium and large organizations has been a problem, though. The Scaled Agile Framework (SAFe) has emerged as the leading solution to that problem. SAFe is a collection of principles, structures, and practices that has been shown to consistently and successfully scale Agile practices and deliver the benefits of Agile to organizations that had been working in waterfall or ad-hoc methodologies.

While Agile and DevOps started as independent methodological movements, they share a number of traits focused on improving the efficiency and speed of teams. As organizations become more Agile and refine their project management skill sets, they increasingly depend on technical teams being able to keep pace and maintain a certain flexibility.

This is where DevOps comes in. The DevOps approach helps development groups utilize new tools, automation, and different cultural strategies to change not just how they work themselves, but how they work with others. It becomes a symbiotic relationship where product teams work hand in hand with developers and testers and the like to ensure everyone has more contextual awareness. This promotes a greater overall quality of deliverables in a shorter period of time. Many of you are familiar with Agile and DevOps. For those who want to learn more, our report, “A Practical Guide to DevOps” can be helpful.

Growth Hacking

To continue the components of figure 2, we focus on Growth Hacking, the practice of experimenting with different ways to find, engage, and retain customers for whom the innovation was built.

“Because it is its purpose to create a customer, any business enterprise has two—and only these two—basic functions: marketing and innovation” – Peter Drucker

Many people confuse the typical marketing organization with growth hacking. There are five differences between marketing and growth hacking:

• A growth hacker works the entire funnel, where most marketers only look at Acquisition.
• A growth hacker runs experiments; they test which traction channels work best, where a marketer often focuses on established platforms and channels.
• A growth hacker is data driven in that they are always looking to measure the traction of their efforts. That is not the case for most marketing departments.
• A growth hacker has some technical skills, such as programming, tooling and automation so they can run their experiments more or less independently.
• A growth hacker is involved in the product, because, among other things, they have to pay attention to the retention of active customers.

Full funnel

Venture capitalist, Dave McClure, coined the acronym AARRR, which is a simplified model that enables us to understand what metrics and channels to look at each stage for the users toward becoming customers and referrers of a brand. This is a simple tool for business growth. When said, the acronym, makes you sound like a pirate, hence the name “Pirate Metrics.”

Regarding this funnel, the difference between a growth hacker and a traditional marketer is:

• A marketer mainly focuses on Acquisition[4] – How do I get more brand recognition and more visitors on my website?
• A growth hacker focuses on all phases of the AARRR funnel – Where is the biggest opportunity to grow our revenue?

Figure 8 – the Pirate funnel

Experiments

One of the most important insights that a growth hacker must have is the realization that nobody knows what efforts will work to move customers through the funnel and which will fail. This applies throughout business building because different tactics may be needed to continue growth as you reach deeper into the customer segment (early adopters, early majority, late majority, etc.). That’s why you’ll have to experiment.

By trying a channel / tactic in a small experiment, you can avoid losing time or money. If during an experiment it appears that it doesn’t work, then you do not have to invest in it. If it does, you continue exploring. In this way, a growth hacker ensures a higher ROI (“Return on Investment”) by spending time on the most effective channels. Growth hacking employs the same type of iterative cycle as lean startup and is complimentary.

Data driven

The experiments are structured so they can be measured. The growth hacker uses the tools and platforms at their disposal to measure the effectiveness of any experiment. For example, a proper experiment might be written as follows:

“Placing targeted display ads on LinkedIn will cause a ten percent increase of new users visiting the homepage in three months.”

It describes the change, the metric, the impact and the timeframe. The growth hacker can use the platforms to track click throughs, test messages, and ultimately prove whether the “display ad” channel is driving acquisition. The data drives the next action.

Technical skills

As with everything related to innovation, speed to result is important. As a growth hacker, you need technical skills so that you can make progress independently. If you are dependent on other people (specifically developers or designers) for each experiment, then that will slow progress tremendously. Therefore, a growth hacker should understand the following:

• Build and optimize landing pages. Building landing pages is hardly technical since the emergence of all kinds of drag-and-drop landing page builders, but it is still a necessary skill for growth hackers in 2021.
• Front-end code. If you have mastered the basics of HTML and CSS, that is enough for 90% of the growth hackers.
• Web scraping for the collection of potential B2B leads.
• Be able to work with lots of different software tools. Creating, executing, and monitoring experiments is made so much easier with the growth hacking tools available in the market that help you work different channels, automate workflows, and report on results.

Conclusion: a growth hacker has more need for technical skills than most marketers.

Involved in the product

As I mentioned above, a growth hacker works in the complete pirate funnel. Activation, Retention, Revenue, and Referral are also keys to growth. These phases are influenced less by marketing channels, such as advertisement, email, or content, but are more influenced by features within the product itself.

Activation focuses on the first experience your customers have with your product or service. Keep a keen eye on where your drop-off points are. A big part of what makes the AARRR funnel so useful for businesses is that it makes it easy for brands to identify their flaws. Look at where exactly on your projected activation timeline that most people are abandoning the product, service, or app altogether. Is there a roadblock that makes it difficult to perform a certain function in the app? Is there a point at which using the product does not serve as a solution to the consumer’s problem? Identify drop-off points and experiment with solutions to fixing them, and track what increases activation. This can go hand-in-hand with retention is some scenarios.

Retention measures how many customers you’re keeping around, and how many are dropping your product altogether. Why are they leaving? What’s keeping others dedicated? Look for pain points and places where engaging customers more deeply are important.

Growth happens when people are talking about your product and recommending it to people who trust them. Look for ways to measure and recommend changes that impact referrals. For example, WhatsApp users come for free, rich, secure messaging. Each message sent is therefore an increment of value. If the total number of messages is growing, this is most likely a positive sign that the company is accomplishing its mission. And because that metric depends on others being on the network, the application encourages its users to invite others to the conversation.

Finally, there is Revenue. It’s where you evaluate the customer long term value (LTV) against the acquisition costs (CAC) and reevaluate the previous steps individually and as a whole to see where there is friction and work with the product team to discover and experiment with solutions.

Growth hackers capture and understand usage data, invite user feedback and suggests tests and experiments to grow customers at each step of the funnel.

Maturity

At some point in its lifecycle, the market growth of a product starts to slow, and the focus of the business’ effort turns from growth to profits and efficiency. Taking out costs and increasing profits generates the funding for future innovation. Two ways to do that are adopting lean methods and automation.

Lean

At its core, Lean is a business methodology that promotes the flow of value to the customer through two guiding tenets: continuous improvement and respect for people.

Lean methodology is not a new concept, but its modern application to business is constantly evolving. Before Lean was known as a business methodology, it was an approach to the manufacturing process.

Roots in Manufacturing

Lean methodology originated with the Toyota Production System, or TPS, which revolutionized the manufacture of physical goods in the 1950s, ‘60s, and beyond. Lean maintains its hold in manufacturing, but has also found new applications in knowledge work, helping businesses in all industries eliminate waste, improve processes, and boost innovation.

Expansion into Software Development

Lean methodology’s first applications outside of manufacturing appeared in software development, in a discipline we described earlier, Agile methodology. Conceptually, Agile software development is a Lean development methodology for optimizing the software development cycle.

Software development is a natural application of Lean methodology because, much like manufacturing, it:

• Generally, follows a defined workflow
• Has some defined conditions of acceptance
• Results in the delivery of tangible value

Over time, the success of applying Agile and Lean principles to software development piqued the interest of other departments and other industries. Today, Lean development methodology is being applied to knowledge work that follows a process – which is essentially all knowledge work.

Continuous Improvement

Continuous improvement is the ongoing improvement of products, services or processes through incremental and breakthrough improvements. These efforts can seek “incremental” improvement over time or “breakthrough” improvement all at once.

Continuous improvement, as with the other methods highlighted in this report, depends on iteration.

Figure 9 – the lean methodology improvement cycle

While there are many diagrams depicting the continuous improvement cycle, the original came out of the quality movement, and is still the best representation of the effort. The PDCA cycle is described as follows:

1. Plan – Recognize an opportunity and plan a change.
2. Do – Test the change. Carry out a small-scale study.
3. Check – Review the test, analyze the results, and identify what you’ve learned.
4. Act: – Take action based on what you learned in the check step. If the change did not work, go through the cycle again with a different plan. If you were successful, incorporate what you learned from the test into wider changes. Use what you learned to plan new improvements, beginning the cycle again.

When some people think of Lean methodology, they equate it with the elimination of waste. While it’s true that Lean organizations aim to eliminate waste (defined as anything that does not deliver value to the customer), the goal is not elimination – it’s value creation.

How do you create value? You deliver quickly. When you deliver quickly, based on what you know about the customer, you are able to get feedback quickly. And whether what you deliver is a failure or a success (or somewhere in between), you gain valuable insight into how to improve. This is how you achieve business agility; this is how you, through the process of creating value, eliminate waste. The continuous improvement cycle helps organizations practicing Lean methodology differentiate themselves from competitors.

Respect for frontline workers

Often, the best ideas come from the people with their hands on the product. In most organizations, decisions are made at the top of the organization and trickled down to the frontline. Lean thinking encourages allowing everyone, especially those closest to the product and the customer, to have an equal voice, to ensure that the voice of the customer, and those doing the work, is heard. Lean thinking says that good people want to do their best work and are motivated to make decisions that optimize their time and talent to create the most value for the customer.

Lightweight leadership

Elevating the voice of the frontline worker evolves the role of leadership. In an organization structured around a command-and-control form of leadership, the role of the leader is to set the course of what to do, but also how and when.

Lean leadership empowers employees with the autonomy to make decisions, the opportunity to master their craft, and the purpose (the “why” behind the work) to understand the value of their efforts. The role of the leader is to define the goal at hand, and then allow their talented employees to discover the most appropriate course of action toward that goal.

Leaders are charged with the task of bringing the best out of their employees and removing any obstacles that could prevent their team from delivering value to the customer. Lean leadership is better defined by what it is not than what it is. It’s not command-and-control, it’s not micromanaging, and it’s not driven by ego or the power of position. It’s leading in the truest sense.

Lean Methodology Summary

In short, Lean methodology is a way of optimizing the people, resources, effort, and energy of your organization toward creating value for the customer. It is based on two guiding tenets, continuous improvement and respect for people. Teams all over the world, from sales to software development, are using Lean methodology principles to sustainably deliver more value to their customers, while building healthier, more resilient organizations.

The Tools of Lean

The Lean Methodology has many tools that can be used by practitioners to deliver value. Here are some of the more popular ones:

Theory of Constraints – The Theory of Constraints is a methodology for identifying the most important limiting factor (i.e., constraint) that stands in the way of achieving a goal and then systematically improving that constraint until it is no longer the limiting factor. In manufacturing, the constraint is often referred to as a bottleneck.

The Theory of Constraints takes a scientific approach to improvement. It hypothesizes that every complex system, including manufacturing processes, consists of multiple linked activities, one of which acts as a constraint upon the entire system (i.e., the constraint activity is the “weakest link in the chain”).

Kanban – In order to improve your process, it’s critical that you fully understand it. Kanban is a visual process workflow tool that enables individuals, teams, and organizations to manage work through a shared understanding of process.

Kanban can help teams identify opportunities for process improvement. As teams use Kanban boards to manage their work, they automatically generate data they can use to assess the impact of their continuous improvement efforts.

Lean Six Sigma – Lean Six Sigma is a fact-based, data-driven philosophy of improvement that values defect prevention over defect detection. It drives customer satisfaction and bottom-line results by reducing variation, waste, and cycle time, while promoting the use of work standardization and flow, thereby creating a competitive advantage. It applies anywhere variation and waste exist, and every employee should be involved.

Automation

The other path to efficiency is to apply automation to the process of delivering and supporting the product. This can be thought of as simply automating manual processes as part of process improvement, but in today’s world, it can mean much more. The advent of AI and robotics can totally replace or dramatically improve more mundane activities. For example, we are seeing the use of AI/ML/Robotics…being leveraged with a company TechVision worked with recently, HDS Global (Louis Border’s company, the founder of WebVan) as they reimagine an automated grocery ecosystem. As shown in figure 10, the combination of AI and robotics can impact the efficiency of practically any work, regardless of the value it provides or the amount of physical precision it requires.

Figure 10 – the application of AI. And robotics in the workplace

In the upper left quadrant, the jobs require a high degree of specific knowledge, but do not require physical precision. The jobs in the lower left quadrant require less knowledge and require less precision. In the lower right quadrant, the jobs require a higher degree of physical precision, but less knowledge or training. And finally, jobs in the upper right quadrant require a high degree of both knowledge and physical precision. The following examples highlight jobs that fall into the different quadrants and how AI and robotics can contribute to those areas.

Legal – The legal profession will be significantly impacted: typical support services in a legal context have to do with document handling / classification, discovery, summarization, comparison, knowledge extraction, and management – tasks where AI agents can do a great job. In this example, Specific AI is used to perform the monotonous work so that the lawyer can concentrate on high-value knowledge work. This kind of automation is not limited to traditional professional services. Think about the growing attraction of low code / no code application development where business knowledge workers can generate sophisticated and integrated business applications without the need for IT programming services.

Customer Service – The workstream of “handling a customer request in the best possible way” can be broken down into separate jobs which are repeated over time and across different types of requests, for instance:

• customer identification,
• customer history retrieval,
• request understanding and classification,
• problem identification and mapping to a solution space,
• forwarding or escalating to another team,
• customer document retrieval
• and finally, the decisioning based on the suitable corporate policy.

All the above can be covered with increased effectiveness from A.I. algorithms — they prove to be faster, more accurate,reliable, and cheaper than the corresponding team of humans. A properly trained A.I. system:

• Can understand customer requests in natural language
• Identify the mentioned or implied entities (for instance, which product or service the request refers to)
• It can estimate customer’s intent early enough (for example, to activate a service or ask for help)
• It can instantly process large volumes of data and apply the corporate policy in order to identify the best action/ decision for the particular case
• The decision can then be communicated to the customer in natural language.

While the RPA and chatbot capabilities are examples of Specific AI, the natural language processing is an example of the more robust General AI, as is the fact that the system also knows early enough if it can handle the request with confidence or not; in the latter case, it knows where to redirect the request as an exception, for a human team to handle it. And all these, in milliseconds, as part of a chat or voice session between the customer and companies’ agent.

Food Prep – Today, robot chefs are essentially food processors and appliances that need minimal supervision in the entire cooking process. This is an example of Specific Hardware partnered with Specific AI. For instance, Miso Robotics invented an AI cook that can work either the grill or the fryer. The AI technology can recognize and monitor food items, as well as adjust its cooking times for the optimum result. It continuously learns how to do so through a cloud-based monitoring system.

Autonomous Driving – Transportation is already in a transformation mode — fully autonomous cars will be soon a reality — and they will be safer, more efficient, and more effective. Professional drivers (taxis, trucks, deliveries, and more) will see the demand for their skill set dropping as more vehicles are guided by Specific AI and Software driven automation.

Surgery – It’s hard to imagine a service (product) that requires such high levels of knowledge and physical precision as a surgery. Many surgeons perform the latest minimally invasive surgery using robotic-assisted surgery because it extends the capabilities of their eyes and hands. In robot-assisted surgery, the machine gives the surgeon control of the instruments they use to perform your surgery. The machine provides magnified views of the surgical area, and the surgeon uses tiny instruments that move like a human hand but with a far greater range of motion and smoother precision. It’s a prime example of human / machine interaction.

While these examples are very diverse, the jobs that are used to build or support your product can be viewed as possible candidates for automation and using a model that allows you to categorize them for research and experimentation can make the inclusion of these emerging technologies feasible.

To learn more about AI and RPA, read our reports on “State of Robotics Process Automation (RPA)” and “Artificial Intelligence: An Enterprise Level Set”.

Fault Lines

Your product growth is slowing, and you are managing to drive costs out and value up. It’s time to check to see if the original premise for the innovation/ product has shifted over time. When this happens, it’s time to cycle back into the previous stages of value realization and reset your assumptions and hypotheses and experiment your way back into growth. Otherwise, the product moves into decline and needs to be replaced by something else in order to keep your customers.

As we mentioned in our report, “Applying Start-up Concepts to Enterprise Innovation”, a general principle first articulated in the Harvard Business Review: A company cannot endure in the long term without reinventing itself. Which means leaders have to be vigilant in discovering and fortifying “fault lines”—the weakening foundations in your business model, or the shifting needs of your customer base. Fault lines include your business model, customer needs, performance metrics, industry position, and ultimately, internal talent/capabilities. The fault lines focus on the fundamentals: whether the business serves the right customers, uses the right performance metrics, is positioned properly in its industry, deploys the right business model, and has employees and partners who possess the required capabilities^[5].

Check your fault lines, there may be opportunity hiding in what you find.

Conclusions and Recommendations

Think Differently About Innovation Initiatives

Innovation is not the same as solving operational issues. As we first laid out in our report, “Applying Start-up Concepts to Enterprise Innovation”, building a sustainable enterprise innovation program requires a fundamental change in thinking. This report builds on that concept by asking you to think about each individual innovation effort differently. Think about these characteristics as you plan out your initiatives.

• Product not project – treat an idea as a product that moves through a lifecycle from a set of pure assumptions to realized value. It is not a typical work effort with defined tasks, linear progress, and budgets. It has unknowns that must become known and risks to be managed through experimentation and learning.
• Speed – complete small batches of work focused on falsifying hypotheses. Lean Startup, Agile, SAFe, DevOps, Growth Hacking, et al. are tools to drive risk out of innovation. The quicker you move to value (or failure) the better.
• Prioritization – tackle the most important (and risky) hypothesis first. Customer, Market, Business, Technology, in that order.
• Organization – each idea needs a permanent but evolving small cross functional “product” team with the proper skill sets applying the proper practices to move the innovation from idea to recognized value.

Innovation Toolkit Success

Every team requires a toolkit to move an idea along. And in this report, we described a few of those. Here is a summary of the tools described in this document and where they can be used along the journey to realized value.

Tool	Discovery	Development	Business Building	Maturity
Agile / DevOps		X	X	X
Automation			X	X
Business Model Canvas		X	X
Customer Development		X
Design Sprints (Google Ventures)	X	X
Design Thinking	X	X	X	X
Growth Hacking		X	X
Jobs to Be Done	X	X
Kanban				X
Lean Methods				X
Lean Six Sigma				X
Lean Startup		X	X
Theory of Constraints			X	X

Table 2 – Reviewed tools and methods

Table 2 identifies the tools and methods we introduced in this report with the primary phases of innovation in which they are used, but the tools are not used exclusively in a particular phase. As an innovation moves through its lifecycle, you may discover an unknown (or change) about the customer, problem, solution, or business model. When that happens, the proper tool can be applied to gain knowledge and reduce risk regardless of phase.

We have introduced only a small sampling of the tools and methods available to the innovator, however there are many toolkits and innovation processes out there. All of them share various features, usually include an innovation model, involve a step-by-step process, and provide specific templates that support each step. Most organizations can easily adapt and customize the various “open sourced” toolkits out there, whether from Stanford Design School, Intuit, Adobe, CSAA Insurance Group, or others. Whatever the specific tools, here’s what to consider when creating and introducing an innovation toolkit to support your broader innovation culture efforts:

1. Take the best and reinvent the rest – find an existing open-sourced tool kit and modify it to fit the language and innovation process of your own organization. But make sure it covers the proper stages of the innovation journey so that teams understand where to apply it.
2. Build the innovation brand – give your toolkit a compelling name and design a brand image that resonates with people, just like Intuit named their toolkit Catalyst and Adobe created Kickbox.
3. Rally people around real problems – provide people with opportunities like workshops and hackathons to try out the toolkit, apply it to a real challenge or opportunity, and learn to use it in a “safe” environment.
4. Tap the energy of early adopters – identify the people who get most excited by innovation overall and see the toolkit as a way to express their creative spirit. Track their progress using the toolkit and recruit them to help others to see and experience the value by facilitating workshops and training sessions.
5. Relish in the early wins – consistently communicate small, medium, and large success stories to help the broader organization see the value of the toolkit and internalize that innovation is desired and valued.
6. Get out of the way – provide the time and space for individual contributors and teams to use (and even modify) the innovation toolkit in whatever way helps them achieve their current and future goals.

As with anything, some people will dive in and others will remain on the sidelines until the toolkit proves itself to be more than an organizational fad. Leadership’s number one mandate should focus on highlighting how the toolkit is just one component – albeit an important one – of a broader organizational effort focused on driving a culture of innovation. Ultimately, a toolkit’s success can be measured by how ingrained it becomes as a symbol and accepted approach for innovation across the organization, and, of course, what business results flow from it.

Creating a culture of innovation is an inherently ambiguous process. When done right, an organization’s innovation toolkit and culture can become the “invisible advantage” that leads to a continuous stream of incremental improvements, major advancements, and if you’re lucky, even a disruptive innovation.

About TechVision

World-class research requires world-class consulting analysts, and our team is just that. Gaining value from research also means having access to research. All TechVision Research licenses are enterprise licenses; this means everyone that needs access to content can have access to content. We know major technology initiatives involve many different skillsets across an organization and limiting content to a few can compromise the effectiveness of the team and the success of the initiative. Our research leverages our team’s in-depth knowledge as well as their real-world consulting experience. We combine great analyst skills with real world client experiences to provide a deep and balanced perspective.

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TechVision Updates will provide regular updates on the latest developments with respect to the issues addressed in this report.

About the Author

Gary Zimmerman is an experienced executive known for helping companies deliver new offers and expand markets. Accomplishments include launching four companies, 20+ products, building high-performance organizations, and generating millions in sales.

His experience at Neustar, Respect Network, and Sovrin allows him to provide a broad perspective on a variety of subjects including self-sovereign identity, blockchain, enterprise data management, and the data brokerage industry.  His experience both enterprise and startup product development give him a unique perspective on innovation.

[1] In this case, “market” refers to any question or element that is mostly or completely connected to the identity of the user (customer) segment.

[2] If the product is for internal use, you would replace the word “buy” with “use” as the problem of adoption is similar.

[3] Traction is quantitative evidence of customer demand.

[4] Newer iterations of the Pirate Metrics include another “A” for Awareness. While it is important for top of funnel activity, it can be considered part of Acquisition for this exercise.

[5] “Knowing When to Reinvent Detecting marketplace “fault lines” is the key to building the case for preemptive change.” by Mark Bertolini, David Duncan, and Andrew Waldeck, Harvard Business Review, 2015