Product Details Lean Enterprise: How High Performance Organizations Innovate at Scale (2015)

Part II. Explore

Chapter 4. Explore Uncertainty to Detect Opportunities

It was darkness which produced the lamp. It was fog that produced the compass. It was hunger that drove us to exploration.

Victor Hugo

In this chapter we will cover practices to support the principles, discussed in Chapter 3, of exploring opportunities in conditions of extreme uncertainty, especially when considering new business models or products. We introduce the concept of Discovery to show how to quickly map out a business hypothesis to create a shared understanding of a problem and engage stakeholders from across the organization to buy in and align to our vision.

We will share concrete tools and techniques to safely create and test hypotheses to solve real business problems identified and validated in our customer development process.

Then, we will describe how to use a disciplined, scientific, evidence-based approach to experimentation to answer the fundamental question—not “can we build it?” but “should we build it?”

We will discuss how to test the riskiest assumptions of our hypothesis and generate empirical data to support our decision to pivot, persevere, or stop by creating safe-to-fail experiments using MVPs. Our purpose is to base further investment and portfolio management decisions on evidence, not science fiction. We will execute on opportunities by building the right thing at the right time and stop wasting people’s time on ideas that are not valuable.

Discovery

Discovery is a rapid, time-boxed, iterative set of activities that integrates the practices and principles of design thinking and Lean Startup. We use it intensively at the beginning of the explore phase of a new initiative.

In Lean UX: Applying Lean Principles to Improve User Experience, Jeff Gothelf and Josh Seiden state, “Design thinking takes a solution-focused approach to problem solving, working collaboratively to iterate an endless, shifting path toward perfection. It works towards product goals via specific ideation, prototyping, implementation, and learning steps to bring the appropriate solution to light.”⁶⁷

By combining the principles of design thinking with Lean Startup practices, we can build a continuous feedback loop with real users and customers into our development cycle. The principle is to invest the minimum amount of effort to get the maximum amount of learning, and to use the outcomes of our experiments as the base for our decision to pivot, persevere, or stop.

CUSTOMERS AND USERS

Although we often use the terms interchangeably, it is useful to distinguish between the customers of a product or service, who pay for it or invest in its development, and the users. Users do not pay for the product, but they contribute a great deal of value to the organization that builds the product, and often to the product itself (social networks are one obvious example). In an enterprise, people are required to use particular systems in order to get their work done, and organizations suffer real negative consequences when systems are hard to use. It’s essential to engage both customers and users as key stakeholders in the co-creation of products, services, or improvement opportunities.

During Discovery, we create a collaborative and inclusive environment for a small cross-functional, multidisciplinary team to explore a business, product, or improvement opportunity. The team should be fully dedicated and co-located to maximize the speed of learning and the effectiveness of real-time decision making. It must assume ownership of delivery and be empowered to make the necessary decisions to meet the objectives of the initiative.

When forming a team, it is key to keep the group small, including only the competencies required to explore the problem domain. Large teams are ill-equipped for rapid exploration and cannot learn at the speed required to be successful. The group must know their limitations and boundaries, taking responsibility to reach out and engage others outside the group for input and collaboration when appropriate.

The final—and too often forgotten—members of the team are customers and users. It is easy to fall into the trap of seeing them as simply a consumer of the solution we have created. In fact, they are critical stakeholders. Their input is the key ingredient and the most objective measure of how valuable our solution is or can be. Through the feedback they provide, customers and users are co-creators of value for any solution. Their needs must always be the focal point for everything we do.

Creating a Shared Understanding

When you want to build a ship, do not begin by gathering wood, cutting boards, and distributing work, but awaken within the heart of man the desire for the vast and endless sea.

Attributed to Antoine de Saint-Exupéry

When starting a new piece of work, it is imperative that the group creates an environment maximizing the potential of everyone involved. Based on the new information they are discovering, people learn, change, and improve when they are involved in a process that is energizing, interactive, and adaptive.

As Dan Pink argues in Drive,⁶⁸ there are three key elements to consider when building an engaged and highly motivated team. First, success requires a shared sense of purpose in the entire team. The vision needs to be challenging enough for the group to have something to aspire to, but clear enough so that everyone can understand what they need to do. Second, people must be empowered by their leaders to work autonomously to achieve the team objectives. Finally, people need the space and opportunity to master their discipline, not just to learn how to achieve “good enough.”

The process of shaping the vision begins by clearly articulating the problem that the team will try to solve. This essential step is often overlooked, or we assume everyone knows what the problem is. The quality of a problem statement increases our team’s ability to focus on what really matters—and, more importantly, ignore what does not. By developing our team’s shared understanding of our goals and what we aim to accomplish, we improve our ability to generate better solutions.

Figure 4-1. Building a shared understanding as a team

Go Gamestorming

Gamestorming by David Gray et al.,⁶⁹ and the supporting Go Gamestorming Wiki,⁷⁰ contain numerous games that encourage engagement and creativity while bringing structure and clarity to collaborative ideation, innovation, and improvement workshops.

One of the fundamental techniques of Discovery is the use of visual artefacts, models, and information radiators to communicate and capture group learnings. Using graphical templates and exercises to externalize ideas helps our team to articulate, debate, and evolve concepts and ideas to form consensus (see Figure 4-1). It also helps to depersonalize and anonymize thoughts so we can safely debate ideas, not individuals—minimizing egos, HiPPOs (highest paid person’s opinions), and extroverts’ attempts to run the show.

Structured Exploration of Uncertainty

If you want to have good ideas you must have many ideas.

Linus Pauling

When exploring uncertainty, it is important to start broad—to generate as many ideas as possible to cycle through before narrowing our focus on where we will start.

lastminute.com is a travel retailer in Europe, operating in a highly competitive industry with major players and new startups trying to disrupt the travel marketplace every day. In order to stay relevant, the company needs to innovate faster and smarter than their competitors. They invited their customers to become part of the innovation process. For two days, they ran co-creation workshops that generated over 80 new ideas for online products aligned to their business goals. The team then set up an innovation lab in a hotel lobby for a week, rapidly experimenting with each idea to discard it or validate it as a viable customer problem to implement. Within days, the team identified three winning ideas to invest further effort in developing—resulting in an over 100 percent increase in conversion for their product.⁷¹

Divergent thinking is the ability to offer different, unique, or variant ideas adherent to one theme; convergent thinking is the ability to identify a potential solution for a given problem. We start exploration with divergent thinking exercises designed to generate multiple ideas for discussion and debate. We then use convergent thinking to identify a possible solution to the problem. From here, we are ready to formulate an experiment to test it (see Figure 4-2).

Figure 4-2. Structured exploration with divergent and convergent thinking

What Business Are We In?

Business models are transient and prone to disruption by changes in the competitive environment, advances in design and technology, and wider social and economic change. Organizations that misjudge their purpose, or cannot sense and adapt to these changes, will perish.

Organizations can be rendered obsolete by competitors that solve the same problem with an alternate or superior offering for their customers. Business definition and identification of future opportunities must be continually challenged and ever evolving. Allowing complacency to sneak in due to current success is the quickest path to failure for tomorrow. We only need to cite examples such as Blockbuster versus Netflix or HMV and Tower Records versus iTunes, YouTube, and Spotify to illustrate the point that no business model or competitive advantage is indefinitely sustainable.

Winning organizations continually experiment and test theories to learn what works and what does not, recognizing that the ones that do could have a massive impact on the business’ future fortunes.

Understanding Our Business Problem to Inform Our Business Plan

As Steve Blank, author of The Four Steps to the Epiphany⁷² and The Startup Owner’s Manual (K & S Ranch), says:

A business plan is the execution document that existing companies write when planning product-line extensions where customer, market, and product features are known. The plan is an operating document and describes the execution strategy for addressing these “knowns.”

The primary objective of a new business initiative is to validate its business model hypotheses (and iterate and pivot until it does). Search versus execution is what differentiates a new venture from an existing business unit. Once a business model is validated, then it should move into execution mode. It’s at this point the business needs an operating plan, financial forecasts, and other well-understood management tools.⁷³

It is critical to consider many different business models in the early stages of a new initiative. We don’t want to commit to a plan until we test the business model hypothesis and have evidence that we are on the correct path. The team must identify the riskiest assumptions of our hypothesis, devise experiments to test those assumptions, and increase the information we can gain to reduce uncertainty. The only assumption that always holds true is that no business plan survives first contact with customers.

The Business Model Canvas, shown in Figure 4-3, was created by Alex Osterwalder and Yves Pigneur along with 470 co-creators as a simple, visual business model design generator. It is a strategic management and entrepreneurial tool that enables teams to describe, design, challenge, invent, and pivot business models. Instead of writing a business plan, which can become a lengthy process, we outline multiple possible models—each time-boxed to 30 minutes—on a canvas.

Figure 4-3. The Business Model Canvas

The Business Model Canvas, freely available at http://www.businessmodelgeneration.com/canvas, outlines nine essential components of an organization’s conceptual business model:

Customer segment

Who are we targeting to create value for? Who are our customers?

Value proposition

What problems are we going to solve to create value for our customers?

Channels

Through what channels are we aiming to reach out to our target customers?

Customer relationships

What type of relationship does each of our customers expect us to create and maintain with them?

Activities

What activities will be required to support our value propositions?

Resources

What resources, people, technology, and support will be needed for the business to operate?

Partnerships

Who do we need to build partnerships with? Who are our key suppliers or who could be needed to provide support resources or activities for our value proposition?

Cost

What are the most important inherent costs with our business?

Revenue

For what value are our customers willing to pay? How much and how often?

By populating the individual elements of the template, we are prompted to consider any potential idea in terms of the entire business’ component building blocks. By populating the entire template, we are encouraged to think in a holistic manner about how these pieces fit together to support the greater opportunity. It is key to remember that each component of the canvas represents a set of hypotheses and associated assumptions that require validation to prove that our business model is sound.

Beyond the template itself, Osterwalder also came up with four levels of strategic mastery of competing on business models to reflect the strategic intent of an organization:

Level 0 Strategy

The Oblivious focus on product/value propositions alone rather than the value proposition and the business model.

Level 1 Strategy

The Beginners use the Business Model Canvas as a checklist.

Level 2 Strategy

The Masters outcompete others with a superior business model where all building blocks reinforce each other (e.g., Toyota, Walmart, Dell).

Level 3 Strategy

The Invincible continuously self-disrupt while their business models are still successful (e.g., Apple, Amazon).

Our ability to recognize what strategy we are pursuing when creating business models is the first step towards creating a shared understanding of what innovation approach will be most effective in helping us achieve our goals.

The primary objective of the Business Model Canvas is to externalize the business hypothesis and make its assumptions clear so we can identify and validate the main risks. The canvas provides a framework for understanding of each business model, in terms that are understood by all, thus building a shared sense of ownership and enabling collaboration throughout the organization. The Business Model Canvas differs from other canvases listed in Table 4-1 in that it doesn’t assume that product/market fit is the riskiest hypothesis that must be tested first.

There are a number of canvas created by others that focus on product development, as shown in Table 4-1.

Understanding Our Customers and Users

The single most important thing to remember about any enterprise is that there are no results inside its walls. The result of a business is a satisfied customer.

Peter Drucker

In order for any product or solution to be successful, people must want to use it, and indeed pay money for it. For a team to build a solution that addresses a real problem or need, it is essential to understand who we are trying to reach and why we are targeting them.

PUT A FACE TO YOUR CUSTOMER AND USER

A persona is a representation of the problems, needs, goals, and behavior of a hypothesized group of customers or users. Personas are based on relevant information and insight known to the creators. They are essentially collections of assumptions that must be tested and refined throughout our customer development process.

When creating a persona, remember the following points:

§ Define and brainstorm your initial persona very quickly to get alignment across the team.

§ Iteratively redefine your persona based on evidence from user research, testing, and feedback during the customer development cycle.

§ Continually realign the persona and the business/product vision as the product starts to emerge.

Personas are just a starting point that we use to create a shared understanding of our customers or users. They are never truly objective or empirical; that is not their purpose. We use personas to create empathy with our targeted group’s problems and move the conversation from what our own individual preferences may be to what the selected persona would perceive to be valuable—theirJobs-To-Be-Done.

Having empathy for customers and users is a powerful force. When we empathize, we enhance our ability to receive and process information.⁷⁷ Empathy in design requires deliberate practice. We must design experiments and interaction opportunities to connect with our customers and users in meaningful ways and challenge our assumptions, preconceptions, and prejudices. We need to assume the role of an interested inquirer, trying to understand the challenges they experience.

Creating a balance between empathizing with an experience and analyzing the situation allows us to understand our customers’ and users’ feelings and perspectives. We can then use that understanding to guide our identification of solution hypotheses and commence the experimentation process.

Go, Look, See

The design company IDEO,⁷⁸ famous for creating the original Apple mouse, runs workshops in which teams completely immerse themselves in the context where the envisioned product or service will be used. Their developers read everything of interest about the markets, observe and interview future users, research offerings that will compete with the new product, and synthesize everything they have learned into pictures, models, and diagrams. The result is insights into customers and users that are tested, improved, or abandoned throughout the iterative development process.

At Toyota, genchi genbutsu (“go and see”) allows leaders to identify existing safety hazards, observe machinery and equipment conditions, ask about the practiced standards to gain knowledge about the work status, and build relationships with employees. The objective of genchi genbutsu is to go to the gemba (workplace) to understand the value stream and its problems rather than review reports or make superficial comments.

Similarly, getting out of the building (a phrase popularized by entrepreneur and author Steve Blank) is a customer development technique to get feedback and focus early product development efforts around the early adopters through frequent qualitative inquiry (including structured interviews) with multiple potential customers.

People who cannot temporarily let go of their role and status, or set aside their own expertise and opinions, will fail to develop empathy with others’ conflicting thoughts, experiences, or mental models. The ability to listen and ask the right questions becomes a powerful skill, and the insights it brings are the foundation of effective problem solving and experimentation.

Turning Insights and Data into Unfair Advantage

The ability to discover and leverage critical insights is essential to high-performing organizations. We used to live in a relatively small data universe with high costs associated with collection, storage, and processing of data. The big data movement has provided technologies and techniques for reviewing, processing, and correlating large existing data sets. Organizations can gain additional value from insights into how and why their customers are interacting with their products and solutions. We can detect weak signals that tell us what is working well—or not so well—and use that information to improve existing services or create new offerings. When combined, software, analytics, and data form a key pillar of our organization’s intellectual capital.

Access to, and understanding of, existing customers is a significant competitive advantage that established organizations have over startups. Startups face the challenge of gaining market reach and traction due to the lack of access to known customer data. On the other hand, established organizations have existing market and customer data that can be reused and leveraged to unearth new opportunities.

Organizations are now able to ask questions such as, “Why are customers canceling their memberships?” or “How are customers related to one another?”, and run quick and inexpensive experiments to test their hypotheses based on existing data. This is a powerful technique to remove decision bias from our prioritization process and enable data-driven decisions.

Data analytics enables us to invert the discovery process—to look at how customers are using existing services and to do forward projections for new business model, product, or service opportunities.

HOW COMPANIES MINE DATA TO DISCOVER YOUR SECRETS

In The Power of Habit (Random House), Charles Duhigg writes: “Almost every major retailer, from grocery chains to investment banks to the U.S. Postal Service, has a ‘predictive analytics’ department devoted to understanding not just consumers’ shopping habits but also their personal habits, so as to more efficiently market to them.”

Target used this data to a particularly discomfiting effect in order to identify and market to pregnant women. When you’re pregnant, you need to prepare for your new child by buying lots of stuff. Target wanted to encourage pregnant families to do most of their shopping at Target, potentially capturing them as major customers for life. They analyzed their existing customer data to find a way to identify women in their second trimester of pregnancy who could be targeted for offers.

Target was able to identify changes in buying patterns for 25 key products, including nutritional supplements, cotton balls, and unscented lotion, that accurately predicted not only pregnancy but also due date. As a result, they were able to send pregnant women relevant coupons—advertently disguised amongst other vanilla offers so the women wouldn’t realize they were being targeted—to encourage them to do their pre-baby shopping at Target.⁷⁹

Big data is a tool, not a solution. Crucially, it does not replace empathy. We still need human intuition and innovation to improve the problem definition and identify customer and user needs and problems, so as to form hypotheses that can be tested against the data. Cross-functional teams,personas, and user interviews are all powerful tools that enable us to design experiments more effectively and rapidly. We need to learn how to listen and learn from data through unbiased analysis—otherwise our data is useless: “Data, like a flashlight, is only as useful as the person wielding it and the person interpreting what it shows.”⁸⁰

Using Insight to Inform Hypotheses and Experiments

During Discovery, numerous members of the cross-functional team will have—and should be encouraged to share—interesting and valuable insights into the organization, customers, business, channels, or markets. By sharing these insights with the team, we can generate new perspectives and inspiration for new products or solutions.

Ask those involved in Discovery to share whatever interesting insights and data they have to inform, create, or challenge problem statements based on a number of perspectives, using the canvas shown in Figure 4-4. For example:

Customer

What specific information does the group have about existing customers? What are their usage and engagement behaviors? How can those insights help to shape future opportunities within existing product offers?

Market trends

Industry trends of the market we are attempting to enter are key to understanding how and where opportunities exist—for example, mobile technologies, location-based services, mobile payments. What are the market trends for the product we are creating? How do we measure against them?

Organization

What specific information does the group have about our organization? Where is the organization focusing its efforts? What is the impact of those efforts? How much of the wider competitive landscape does it cover? Where is the organization most effective?

You will not believe!

Every company has individuals that are willing to share interesting and astounding facts about the business or its customer base. How can we test if they are true and/or offer opportunities to create new value propositions as a result?

Figure 4-4. The Problem Statement Canvas

By making this information visible and discussing it, we can attempt to identify new business models and value propositions appropriate for the business, given its current constraints and identified problem statements.

Accelerate Experimentation with MVPs

The Lean Startup movement challenges the assumption that customers must have all imaginable features available in a product before they will start to use it. Eric Ries coined the term minimum viable product (MVP) to describe a strategy of investing a minimal amount of resources to test the underlying assumptions of our hypotheses with customers. The objective is to eliminate the waste generated by overengineered solutions and accelerate our learning by testing a solution with early customers as soon as possible.

An MVP enables us to use a minimum amount of effort to generate the maximum amount of learning when experimenting with customers. The goal of using an MVP is to execute an experiment that tests the assumptions of our hypotheses as cheaply, quickly, and effectively as possible, in order to learn if our solution addresses the customer problem we have identified. It eliminates those parts of the solution hypothesis that create unnecessary complexity and consume excessive resources when experimenting with our initial targeted customers. The outcome of the experiment is learning, which enables us to make an evidence-based decision to persevere with our existing business model, pivot to explore a new way to achieve our vision, or stop.

It’s important to distinguish between an MVP in Eric Ries’ sense and the initial public release of a product, which increasingly takes the form of a public “beta” (Figure 4-5).

Figure 4-5. Minimum Viable Product: build a slice across instead of one layer at a time⁸¹

Confusingly, people often refer to any validation activity anywhere along on this spectrum as an MVP, overloading the term and understanding of it in the organization or wider industry. Marty Cagan, author of Inspired: How to Create Products Customers Love and ex-SVP for eBay,⁸²notably uses the term “MVP test” to refer to what Eric Ries calls an MVP. Cagan defines an MVP as “the smallest possible product that has three critical characteristics: people choose to use it or buy it; people can figure out how to use it; and we can deliver it when we need it with the resources available—also known as valuable, usable, and feasible,” to which we add “delightful,” since design and aesthetics are also as essential for an MVP as for a finished product, as shown in Figure 4-5.⁸³ Make sure that your team and stakeholders are clear on their definition of MVP.

SHOULD WE BUILD IT, NOT CAN WE BUILD IT?

JustGiving is an online fundraising platform that has raised over £2 billion for charities. JustGiving wanted to explore new business models to fund community initiatives that are not necessarily affiliated with a charity.

They formed a small co-located team to rapidly experiment with customers, running sessions with a prototyped version of a crowdfunding platform complete with real community projects that were seeking support. Based on the positive reaction from customers, they proceeded to create a concierge MVP, launching the same trusted community projects with real customers while manually handling back-office tasks such as project setup, payment processing, and collection, to see how the product would perform in the market.

Within seven weeks from the start of the initiative, JustGiving had validated a repeatable business model that they could start to scale into a business in its own right. The product has now become YIMBY⁸⁴ with success stories that include purchasing basketball wheelchairs for teams, tools to expand a community garden, and saving the 140-year-old Kettering Town Football Club.

MVPs, as shown in Table 4-2, do not guarantee success; they are designed to test the assumptions of a problem we wish to solve without overinvesting. By far the most likely outcome is that we learn our assumptions were invalid and we need to pivot or stop our approach. Our ultimate goal is to minimize investment when exploring solutions until we are confident we have discovered the right product—then, exploit the opportunity by adding further complexity and value to build the product right.

How Do Our Vision and MVP Work Together?

Cagan stresses that vision and MVP are intimately related but not the same. Cagan defines vision as the shared understanding that “describes the types of services you intend to provide, and the types of customers you intend to serve, typically over a 2-5 year timeframe.”⁸⁶ As such, it provides a roadmap and context for MVPs, and we should be prepared to create many MVPs as we search for a repeatable and scalable customer development process aligned with our vision.

Early evangelists, particularly in enterprises, should buy into our entire vision, not just our first MVP experiment. They will need to hear what our organization plans to deliver over the next 6 to 18 months. They are bought into the vision of what we are trying to achieve and so are able to fill in the gaps in our solution as they feel the pain of the problem we are trying to solve. By offering a taste of the solution we are aiming to build, we give them evidence that it works and provide an opportunity for feedback on the solution we are building.

Leveraging business interaction and engagement is very important at the early stages of a new initiative. Feedback and evidence gained through the use of an MVP provide better insight and learning into customer behavior than aggregate measures of success such as total revenue or total transaction value. The MVP allows us to focus on the right thing to build and provides valuable information on how to evolve, adapt, and pivot to meet customer needs discovered through experimenting, as shown in Figure 4-6.

Figure 4-6. The MVP mindset and experiment evaluation loop

Starting with the question of what we want to learn from the experiment, we can define how we will observe and measure it, and finally create the cheapest, quickest, and simplest MVP to test our assumptions, measure the effect, and use that learning to formulate next steps.

A fundamental point with new initiatives is preserving cash and iterating rapidly while teams are testing hypotheses to identify a repeatable solution. Once those fundamentals are understood, and we achieve a product/market fit, cash preservation becomes less important than spending and we can begin to create a scaleable solution.

The One Metric That Matters

When designing MVPs to experiment, it is important to identify one key metric that will tell us if the assumptions in our hypothesis are valid. Lean Analytics (O’Reilly) authors Alistair Croll and Benjamin Yoskovitz introduced the concept of One Metric That Matters (OMTM). OMTM is a single metric that we prioritize as the most important to drive decisions depending on the stage of our product lifecycle and our business model. It is not a single measure that we will use throughout our product lifetime: it will change over time depending on the problem area we wish to address.

We focus on One Metric That Matters to:

§ Answer the most pressing question we have by linking it to the assumptions in the hypothesis we want to test

§ Create focus, conversation, and thought to identify problems and stimulate improvement

§ Provide transparency and a shared understanding across the team and wider organization

§ Support a culture of experimentation by basing it on rates or ratios, not averages or totals, relevant to our historical dataset

It should not be a lagging metric such as return on investment (ROI) or customer churn, both of which measure output after the fact. Lagging indicators become interesting later when we have achieved a product/market fit. By initially focusing on leading metrics, we can get an indication of what is likely to happen—and address a situation quicker to try and change the outcomes going forward. For example, customer complaints are often a leading indicator of churn. If customer complaints are rising, we can expect that customers will leave and the churn will increase. Our OMTM should always evolve as we learn more about the problem we want to solve.

The purpose of the OMTM is to gain objective evidence that the changes we are making to our product are having a measurable impact on the behavior of our customers. Ultimately we are seeking to understand:

§ Are we making progress (the what)?

§ What caused the change (the why)?

§ How do we improve (the how)?

Founder of Intuit Scott Cook says that founders should focus on “love metrics,” for example, how much people love the product, or how often they come back, or how delighted they are in the early stages. “If you’re not getting high activity from the users you already have, it’s time to pivot.” Choosing OMTM provides clarity, alignment, and focus for teams, thus enabling effective decision-making, especially during early stage initiatives.

Use A3 Thinking as a Systematic Method for Realizing Improvement Opportunities

A3 Thinking is a logical problem-solving tool to capture critical information and define the focus and constraints of the team. Later, it becomes a measure to test our outcomes against. An A3 report (so called because it fits on a piece of A3-size paper), composed of seven elements, embodies the Plan-Do-Check-Act cycle of experimentation:

Background

Capture the critical information to understand the extent and importance of the problem. Tying the background to the goal statement reduces waste by limiting opportunities to focus on wrong areas.

Current condition and problem statement

This is the problem the business stakeholder wants to address, in simple understandable terms and not as a lack-of-solution statement. For example, avoid statements like “Our problem is we need a Content Management System.”

Goal statement

How will we know that our efforts were successful at the end of implementation? Ideally we need one key metric for success. For example, “Our goal is to reduce system failures compared to the previous test results of 22 major issues; our target is to reduce this by 20%.”

Root-cause analysis

Detail the hypothesis and assumptions, or a set of experiments performed to test for cause and effect.

Countermeasures

List the steps of an experiment to be implemented to test the hypothesis.

Check/confirmation effect

Define a method for assessing if the countermeasures have had an effect.

Follow up actions and report

Identify further steps and share what you learned with the team and wider organization.

For more on A3 Thinking, read Understanding A3 Thinking: A Critical Component of Toyota’s PDCA Management System by Durward K. Sobek II and Art Smalley.⁸⁷ Other examples include the elevator pitch⁸⁸ and the Five Ws and One H (Who, What, Where, When, Why, and How).

Remember, metrics are meant to hurt—not to make us feel like we are winning. They must be actionable and trigger a change in our behavior or understanding. We need to consider these two key questions when deciding on what our OMTM will be:

What is the problem we are trying to solve?

Product development

Are we attempting to create new products or services that involve customers? How will we know that we are engaging them and they are interested in our product?

Tool selection

Are we attempting to select a tool for using in the organization? How will we know that it is the best tool for the process?

Process improvement

Are we attempting to improve our internal capability and efficiency? How will we know if our changes are having the desired impact?

What stage of the process are we at?

Problem validation

Are we trying to identify that a problem exists by talking to people to see if they are experiencing the pain of the issue we’re trying to solve?

Solution validation

Are our people demonstrating alignment and buy-in for the problem we are aiming to solve through qualitative interviews?

MVP validation

Are we creating experiments to quantitatively prove that our solution is working to solve the problem we have identified?

OMTM is a helpful tool for simplifying the complexity of analytics. It specifically tells us if our solution is succeeding or not. Once we have defined the key metric on which to focus, we can identify supporting metrics that provide insight into other areas and support decision-making.

As a good example of OMTM, at LinkedIn, the team does not talk about “total page views” but only “profile views”—the number of people using LinkedIn who search for and find other people, and the number of LinkedIn profiles they viewed.⁸⁹

Conclusion

Discovery allows us to safely explore opportunities in conditions of extreme uncertainty—especially in new product development and business model innovation. Discovery concepts and tools let us invest the minimum amount of effort to obtain the maximum amount of learning to make measurable progress towards exploiting validated opportunities. Discovery creates a clear vision and a shared understanding of the problem we are trying to solve within our organization.

We must adopt the mindset in which all our ideas are hypotheses based on assumptions that must be tested, and that most of these assumptions will be proved wrong. By basing our decision-making on information gleaned from fast, inexpensive experiments using MVPs, we can make better investment decisions. The earlier we can pivot or fold on bad ideas, the less time and resources we waste, and the more we can devote to ideas that will deliver value to our customers—or create new ones.

Questions for readers:

§ What is your current business hypothesis and how would you create an experiment using an MVP to test it?

§ Do you ask “should we build it” before pursuing “can we build it”?

§ What experiments would your team perform and what evidence would they gather to decide when to pivot, persevere, or stop?

§ What is your One Metric That Matters?