Let’s say I want to create a better eat-at-home meal solution for work-from-home professionals. That’s not really a strikingly novel problem. Indeed, food and beverage services are some of the oldest businesses in existence. I will face fierce competition to do the job. Why will customers choose my solution and not those of my competitors?
Maybe because I do a better job of advertising, or because my product is more available, or because it’s priced lower. Those things are important for sure. But, much more significant, customers will choose my product if it better meets their needs.
The second diamond in the triple-diamond design process begins with a job to be done, understands the customer needs, and identifies one or more insights. There are two key goals of the second diamond. The first is to comprehensively catalog your customers’ needs. The second is to identify one or two latent needs – needs that are important but not yet addressed in the marketplace. We call these latent needs insights and they are useful in pulling a compelling solution concept when we get to the third diamond.
What are Customer Needs?
We aren’t here only talking about fundamental human needs in the sense of food, shelter, and belonging. Rather, for any given job to be done, customers typically care about 30 to 50 distinct attributes of a potential solution that if provided will result in greater satisfaction. We call these attributes needs, and they tend to vary a lot across individuals. For example, consider these two products. First, the Wendy’s Pretzel Bacon Pub Triple Cheeseburger. Second, the Soylent liquid meal. Both are solutions to the job “How might we provide lunch to hungry workers?” But, clearly the burger is better at meeting the primal craving for fat and salt, and the Soylent is better at meeting the need for high efficiency.
You have a focal customer, or type of customer, sometimes called your beachhead market, and represented by your customer persona. Once you have specified the target segments, a goal of the second diamond is to create a comprehensive list of user needs. There are entire textbooks that teach the methods for understanding user needs. In fact, I am a co-author of one of them (Ulrich et al. 2020). Here is an example of a comprehensive list of customer needs for a music player.
How to Identify Customer Needs
In sum, you create this list by doing a set of open-ended interviews with individual potential customers. You interpret what you hear and see in terms of the individual underlying needs.
You do your best to express these needs independently of solution concepts. So, instead of stating that the music player has a touch screen enabling a finger to rearrange the order of a playlist, you state that the music player allows the user to predetermine a sequence of songs, a statement independent of any particular solution concept.
Once you have a comprehensive set of unique needs, you arrange them into clusters. For example, the primary need “The music player lets me control the music” is supported by a cluster of secondary needs that are more specific and detailed like “The music player lets me easily find and play music I have enjoyed previously” and “The music player lets me reduce frequency of play of a song.”
The full needs list is important for the ultimate product design. After all, you don’t want to miss anything important. You may also at some point use more formal quantitative customer research tools in order to understand which needs are most important across different market segments and the relative importance of say price and convenience for a particular segment. But, in the triple diamond model of design, our goal is the development of a compelling solution concept, and for that purpose, we are doing an initial exploration of customer needs in order to understand the important unmet needs among our potential customers – what we call an insight.
What is an Insight?
A woman named Emily Harper recently posted a video on the social media site TikTok showing how she prepares ground beef for use in recipes. With the comment that all this fat is disgusting, she is shown first cooking the meat and then washing it thoroughly with hot water using a wire strainer. The video went viral with seemingly half the world aghast that she had removed all the flavor from her food, and the other half thrilled at this revelation of a new technique for healthy living.
Your job as a zero-to-one product leader is not to pass judgment on crazy customer behavior. Rather, you benefit from acting like an anthropologist and asking yourself what deep insight does this behavior reveal about the nature of your potential customers.
In thinking about the opportunity for 99 Bowls, a service providing immediately available yummy food for work-from-home professionals, I hope to observe Emily with a curious and open mind.
The insight I derive from watching Emily going to extreme lengths to adhere to strict dietary constraints is that some individuals feel a compelling need to tightly control the macro nutrient profile of the food they eat.
Among designers, the term insight refers to a user need that is:
Authentic means that the insight is based on an actual observation of users in the target market.
Non-obvious is self explanatory.
Significant means that if your solution addresses the need it would result in a meaningful enhancement in the value perceived by the customer.
In the second diamond of the triple-diamond model the designer achieves two goals: first, comprehensively identify the customer needs in the target segment and second, flag a small subset of those needs that comprise insights.
Four Categories of Needs
The result of a set of customer needs interviews is a comprehensive list of customer needs, usually 30-50 distinct items. These needs can be sorted into four categories, illustrated by a framework from the Japanese total quality movement of the 1980s and 1990s called a Kano Diagram.
The horizontal axis is the extent to which the need is satisfied by the solution. The vertical axis is the resulting change in customer satisfaction, or perceived value of the solution.
The “don’t care” needs are needs that are irrelevant to the customer. Address them or not – the customer’s satisfaction does not change. For example, for me, whether or not the food you deliver to me is gluten free, I don’t notice or care.
The “linear” needs are those for which the customer’s satisfaction is essentially directly proportional to the extent to which your solution addresses the need. For example, affordability is often a linear need. When the food is a little more expensive, I’m a little less satisfied.
The “must haves” are needs that if fully addressed do not result in dramatic improvements in satisfaction, but if not addressed at all, result in extreme dissatisfaction. For example, if a food container is microwavable, I don’t particularly notice. However, if the container immediately melts or sparks in the microwave, I’ll be very dissatisfied.
The “latent” needs are needs that if unaddressed are not missed, but if addressed result in surprise and delight. For example, if my food service allows me to precisely specify the macronutrients of my lunch, say 20% carbohydrates, 40% fat, and 40% protein, I’m thrilled.
While there have been some attempts to use the Kano framework quantitatively based on survey methods, it’s mostly conceptual. It gives you a way to think about customer needs and to direct your investment. Ignore the don’t cares. Deliver the must-haves. Invest at competitive parity in the linear needs. But, then, seek out the latent needs like hidden gems. The latent needs are by definition non-obvious. To the extent that they derive from your observation of users, they are authentic. Those that are significant – a big deal for customers if addressed – are insights. These insights will be used in the third diamond to pull compelling solution concepts.
How to do Customer Interviews
OK, but how do I actually get the information to identify the customer needs. Put simply, you get out of the office and interact with customers.
More specifically, you conduct at least 10 one-on-one interviews for each distinct market segment. For new zero-to-one products you’ll probably just have one beachhead market. Perhaps surprisingly, you need just 10 interviews to identify 90 percent of the customer needs that would eventually be revealed by interviewing hundreds of customers.
I recommend you do these interviews as follows:
Identify about 10 potential customers for each segment. These customers need not be typical. In fact, they could be extreme in some ways. After all, our ultimate goal is to find unmet needs, and sometimes the extreme users are better at revealing those needs. For example, if our segment is work-from-home professionals, we might interview some professionals who work from highly remote locations, say a cabin in the mountains. Or, we might interview workers who are extremely passionate about food, so called foodies, say those who are food writers. Or possibly those with extreme food regimens, say adherents of ketogenic diets.
Conduct the interviews either alone or with one other person. You can get by doing them alone, but you’ll find it easier to keep track of what you learn if you have a partner. The other advantage of doing interviews in pairs is that you can engage a lot of people in the process. For example, bringing members of your technical team on interviews is a very powerful way of developing empathy for the customer.
You can use an audio recording device, but honestly I rarely do. I think notes are pretty much just as effective. I think recordings are a bit obtrusive and they are rarely actually transcribed and used. Having said that, I do take a lot of photos and even some short video clips, as the visuals are very helpful in reporting on the interviews and in remembering specifics.
Do the interviews in the customer’s use environment if possible. If interviewing office workers about lunch, do the interview around lunch time at their offices. The reason for this approach is that you are going to observe as much as you are going to listen. You will develop an entirely different and better understanding of your customer if you observe them in their own world.
Plan on about an hour of unstructured conversation. I know this is a bit daunting for many people, but I’m pretty sure you can do it. I have four questions that I use to get the conversation started, but rarely need them. You simply start with an open-ended question related to the job to be done. For instance, “what’s your plan for lunch today?” Once you’ve asked a question, listen carefully to the response and look for an open door. For example, your customer might say, “well, I usually skip lunch.” That’s a huge open door. You know what to do – step through the door by asking, “really? Why’s that?” I guarantee a big fat customer need is about to be delivered to you with that question. The response might be “i’m trying to lose a few pounds” or “I don’t have time” or “I’m planning to eat a big dinner later” or “I forgot to bring my food” – whatever the customer says, you’ll learn a lot. If you walk through a bunch of doors and find yourself way off topic, bring the conversation back to the job to be done with one of your prepared questions. The prepared questions I like are “How do you currently do the job” which you’ll ask more naturally like “what do you do most days for lunch?” or “What issues do you consider when choosing what to do for lunch” or “what most annoys you about lunchtime?” or “what would be your perfect lunch experience?” Any of these questions will result in more open doors – then you step through them.
Whenever possible, ask the customer to show you as opposed to tell you about the question. For example, if the customer responds “I bring my lunch” ask to see the lunch itself. You’re going to learn a lot by observing them. For example, you might learn about office food storage, or dietary preferences, or portion sizes, all revealing of needs in a more direct and truthful way than would be an oral response to a question.
After your interview, sit with your partner and debrief. Revisit the conversation and identify as many needs as you can, even if obvious or obscure. This process results in a long list — usually 30-50 distinct needs. I say usually, but for some complex products there could be many more. I once worked on a project to design a better blood pressure monitor, and we identified about 400 distinct customer needs.
Finally, work to identify those needs that could be considered insights.
What About Large-Scale and Quantitative Market Research Techniques?
The triple diamond model of design is highly effective in understanding latent customer needs and in developing novel solution concepts in response to those needs. However, it has no mathematical underpinnings. There is no notion of statistical significance or estimation of the magnitude of a consumer response to a given feature. Many of you probably have backgrounds in engineering, economics, or mathematics and may be uncomfortable relying on such a qualitative process to create new products.
Of course your discomfort is justified. After all, in business we do really need to answer questions like how big is the addressable market? What should our price point and product specifications be? How will our new product fare in a competitive landscape?
These and other important questions are best answered with quantitative market research tools.
But be careful – the triple-diamond model is intended to engage you in a rich and multi-sensory way with the customer. You won’t achieve that goal with a web-based survey. Avoid the impulse to employ quantitative market research techniques until after you have used the customer-centered, qualitative approach captured by the triple diamond model.
Ulrich, Eppinger, and Yang. 2020. Product Design and Development. McGraw-Hill.
The triple diamond model is a user-centered approach to design and concept development. The first two diamonds really focus on better defining the “what” – who is our customer, what is the job to be done, and what needs are potentially most relevant to them. In this chapter, we turn to the “how”? Given an understanding of the customer and the job to be done, how can we create a great solution? For this, we use the third diamond, another cycle of divergent and convergent thinking.
I want to underscore that the tools and approaches in this chapter have two contexts for application. First, these tools are used to create a great solution concept in the context of zero-to-one product development – say the original concept for the Strava fitness app. Second, these tools can be used in the daily work of individuals and teams for any kind of design challenge, say improving the social networking features within the Strava app, or even for internal innovation problems like how we might increase retention of customers on the Strava platform.
What makes for a great concept?
The goal of the triple diamond model is to deliver a great solution concept. But, what makes for a great concept?
A concept is a preliminary description of how you plan to do the job for the customer.
For physical goods, it’s usually a sketch of the physical embodiment of the solution. For example, here are 10 concepts that were generated by two of the students in my product design class at the University of Pennsylvania to do the job of carrying an ID card, a key, and perhaps a mobile device unobtrusively underneath clothing. They called the resulting product the underwallet. They did a particularly nice job of illustrating these concepts by hand.
For services, the concept is often illustrated with a visual storyboard, or possibly simply with a paragraph of text.
For software, the concept is usually illustrated with the screens that comprise the user interface on a mobile device or desktop computer.
Not all concepts are equally good. Here are the four characteristics that I believe make for a great concept.
1. Addresses Needs
The first and most important characteristics of a great concept is that it addresses the needs of the customer.
Here’s an example. I love Google Docs. The concept is a word processing tool that runs in the web browser with no downloads or installation required. The file is stored in the cloud and is backed up automatically with a history of prior versions. The file can be accessed from any device, including my mobile phone, or from multiple devices simultaneously. Search works great, allowing me to find a document with just a few keystrokes. The software is clean, simple, and fast. I can share a file with anyone or everyone with a couple of clicks. I can export the document as a PDF file if necessary. Other than one tiny complaint about how bullets are formatted, this product is perfect for my needs.
2. Cost Efficient
The second characteristic of a great concept is that it is cost efficient. Here is an example of a cost-efficient packaging concept. Many of you probably know about GLIDE dental floss. It was pioneered by the company WL Gore. It’s made from Teflon or polytetrafluoroethylene. The compound is very slippery and so glide is particularly valuable to customers whose teeth are tight up against each other. Thus, the name Glide.
Glide itself is an interesting product, but it’s the package that I want to talk about for a minute. The Glide floss package is a single injection molded part designed with a living hinge that allows it to both fold into the deployed configuration, include an integral cap, and to be configured in a way that it can be molded as a single piece, and in a way that the part can easily come out of the mold.
My guess is that the manufacturing cost of this package is a few cents. Although the smooth pebble-like form is unobjectionable, the glide concept really shines not because of its beauty or function, but because of its extreme cost efficiency.
3. Wow Factor
The third characteristic of a great concept is what I call the wow factor. Here’s an example. I have an app on my mobile device called “Picture This.” I was riding my mountain bike this past summer and sat down for a rest. I smelled an interesting minty fragrance, which seemed to be coming from foliage near my feet. I snapped a photo using PictureThis and within seconds the app told me I was looking at Mountain Monardella, also known as mountain coyote mint. Who knew? Picture This has a bunch of nice features including the ability to keep a database of plants previously identified. But, the wow evoked when some image processing algorithm identifies a plant from a snapshot is magic.
The wow factor is valuable for at least two reasons. First in a commercial context, if you have some wow, then you typically also can achieve some intellectual property protection, usually in the form of a patent. This can provide a modest and temporary barrier to competitors replicating your design.
The second reason, and perhaps the more important reason, is that if you’ve got some wow in your concept, then you have something to talk about in the market, and you can get a user excited about the product. In a commercial setting it allows for your product to distinguish itself from the competing alternatives.
4. Aesthetics and Elegance
The fourth and final characteristic of a great concept is aesthetics and elegance. That is, as designers we should strive to create things that are beautiful. I can wax on about the beauty of purely functional objects, but automobiles are perhaps more interesting examples because they embody a complex bundle of function, identity, and meaning. When Volkswagen announced its all electric microbus, it revealed a concept to which a lot of attention had been paid to aesthetics, including a careful and tasteful reflection of the heritage of the original VW van.
OK so those are objectives. We want to create something that meets user needs, can be produced at low cost, has some wow, and is beautiful.
Concepts for Digital Goods
The four elements that make for a great concept are particularly important for physical goods and for services. In both physical goods and services, cost is critically important. However, for digital goods, cost is usually less important.
Furthermore digital goods can often be thought of more as bundles of features than as a single distinctive solution concept. These features can be added and subtracted incrementally over time as the product evolves.
Perhaps in part for these reasons, the development of a great concept tends to receive less attention for digital goods than it does for physical goods or services. Zero-to-one product managers sometimes feel that a software solution concept follows directly from the job to be done and the customer needs, and even if it doesn’t, the solution can be refined over time.
I think this view is a mistake. Digital goods offer almost unlimited flexibility in solution approach, so if anything, a thorough exploration of the solution landscape is even more important in digital goods than it is in physical goods.
Let me give you a few examples of distinctive concepts in digital goods.
Twitter – what’s the concept? A social network organized around the idea of followers in which an individual creates a virtual bulletin board, usually viewable by the public, in which messages of 144 characters or less are posted chronologically. Messages are pushed to followers and appear on a scrollable feed. (Of course many features have been added over the years, including direct messaging, the ability to respond to tweets, the ability to like tweets and so forth, but the original and still essential concept is quite distinct.)
Slack – what’s the concept? SMS/Txt for work, organized around topical channels. The big idea is to consolidate information and discussions into threads by topic to avoid the chaos of email. Again, many additional features have been added over time, including document sharing, emoji responses, push notifications and so forth. But, the big idea remains asynchronous communication for work organized by topic.
Tinder – what’s the concept? A mobile-only dating app focused on photos, in which individuals express interest in a potential partner by swiping an image to the right, and rejecting a potential partner by swiping an image to the left. When and if two parties swipe right on each other, the app declares a match and allows the two parties to start a conversation via text message.
Strava – a GPS-enabled fitness app, originally focused on running, in which an individual’s route and running time are automatically recorded, and then shared with a network of other runners. The route is automatically divided into segments and a leaderboard is established for the fastest times along those segments.
Because these concepts have been so successful, we take them for granted, even assuming that there could only be one way to do things. But, that perception reflects a hindsight bias.
There are a huge number of possible solution concepts for virtually any problem domain. For example, consider the simple task of listening to pre-recorded music using a digital device. What are the different ways that might be done?
One early solution, Apple iTunes, had these key elements. The iTunes store in which you could buy individual songs, originally for USD 1 each. Then, local storage of those songs in a master library on a physical device. Then, the creation of playlists from the master library. That was it.
A very different, and quite revolutionary concept arrived with the service Pandora. With Pandora, a user simply typed a single song or artist into a web-based application and Pandora created a virtual radio station based on other songs that shared the same “music genome” – Pandora’s name for the distinctive musical elements of a song. No need to create playlists. Pandora does it for you, and you can have as many stations as you want for free, if you are willing to listen to some audio advertisements, or if not, for a single monthly subscription fee.
Then, Spotify came along with an all-you-can-eat subscription service in which you could create playlists from extensive available catalogs, but without actually having to own and keep track of the digital content itself.
Today, YouTube Music is a significant player in the music space. The insight addressed by YouTube is that in many settings people really want the option of viewing video of the artists from live performances or highly produced visuals.
I know some of you are thinking that these different ways of listening to music are now blended together on many platforms. For example, Spotify allows for downloading of files for off-line listening and allows for the use of Pandora-like stations. By the way, I recommend the “dinner chill” station for nice music for dinner parties.
This convergence is a reflection of the massive amount of experimentation and innovation that competitors have engaged in over about two decades at the beginning of this century. At some point preferences of customers became clear, market segments came into focus, and the solution concepts matured and became fairly stable. This is the normal pattern of evolution in an industry.
The maturation of the approach an industry takes to doing a job is another reason concept development may not be emphasized as much as it should be for digital goods. So often the product manager joins the team long after the original solution concept was established. Anyone joining Strava today as a product manager would not likely be doing a zero-to-one design for a new app, but rather would be improving and tuning an existing app. Still, even in this context, the skill of concept development is important. Even if you are working on some small feature within a larger established product context, you should strive for a solution concept that meets needs, is cost efficient, has some wow, and is beautiful.
Concept Development – Basic Approach
The essence of the third diamond in the triple diamond model is another cycle of divergent and convergent thinking.
Concept generation and selection is essentially a mini tournament of ideas. We’re going to generate a lot of alternatives; a dozen or maybe even a hundred different solution concepts. We’re then going to select from among those in order to find an exceptional concept. For most people this does not come naturally. The good news is that as with most challenges in life, a little bit of process and practice goes a long way.
Before we get into the mechanics of how to actually do this, I want to emphasize three points.
First, I want to emphasize that concept generation is really hard. It takes much more effort than most people appreciate. I do not want to sugar coat this reality. Here’s an example to illustrate what I mean.
Some of you might have noticed a little stick figure in some of my illustrations. The stick figure is one element of one graphic in a few sessions of one of my courses. There are hundreds of such elements.
But that little stick figure concept is the result of deliberate exploration. I went back through my files and I found this sheet showing more than 100 different alternatives for what that stick figure might look like. I eventually found one that I thought worked quite well. Some of you are rolling your eyes and thinking how can it possibly be worth investing that kind of effort in a stick figure. You could be right.
But, the point I want to make here is that the tiniest design challenge when done well still requires tremendous effort. This is hard work. You need to be prepared to do the hard work to get great outcomes.
As a slight aside, let me make a comment about life and professional success more generally. You should embrace things that are hard, especially when by hard, I really mean just putting in the effort. If you can internalize the idea that great concepts are mostly the result of hard work, while others believe a spark of creative genius is required, then you have a superpower. You can be confident that if you do the work using a solid process, you are going to get better results than everyone else, and all you have to do is show up and do the work.
The second point I want to make about exploration is that we need to be open to ideas that come from anywhere and everywhere. Sometimes it’s even dumb luck that leads to a great design concept.
Here’s an example. I’m the inventor of a product called the Nexride bicycle seat. Let me tell you where the concept came from. A few years ago I was working on creating a better bicycle seat. As part of the process I was testing out several of the existing non-traditional products. I was out riding one of the saddles that’s essentially a bench-shaped form mounted perpendicular to the direction of travel.
So, I’m out riding about 30 kilometers from home when the clamp that attaches the seat post to the frame breaks. This meant not only that the seat fell down so that it was right up against the frame, but also that it was free to pivot side to side. I considered walking home, but then decided I should try to ride the bike anyway, as that approach would still be faster than walking. To my dismay I discovered that the pivoting action allowed by the lack of a rigid clamp made the saddle much more comfortable. It allowed the bench to get out of the way as my leg extended on the down stroke.
I immediately went home and created a prototype of a pivoting bike seat, and I eventually ended up inventing a new saddle, the Nexride bicycle seat.
I say “invent” but really that concept came from a dumb accident – a broken bolt out on a bike ride. I’m not complaining. In fact, I’m emphasizing that you need to be open to the possibility of ideas from any source including random occurrences.
Lastly I want to point out that even though we have a process, and I am going to teach you some methods for doing exploration, please understand that concept generation tends to be highly iterative. It’s quite likely that as you generate ideas, the definition of your job to be done will come into better focus. It’s also quite possible that as you proceed to refine a particular concept and build and test prototypes, you’ll have additional ideas for new concepts. That’s normal. Don’t be alarmed. That iteration is fundamental to the way design happens in practice.
A few years ago I founded a company called Terrapass. We provided a service to offset the environmental footprint of driving a car. Given the complexity of understanding what and how we actually mitigated environmental damage, we settled on a solution concept that was very simple. For one fixed fee, USD 79 per year, we would provide enough carbon dioxide credits to offset the emissions of your car. See, it’s really, really hard to explain the product clearly. Still, what we discovered was that our customer was really interested in the details, and had a deep appetite for understanding the nuance of the environmental impact of driving. We found that we had to modify our concept, and actually increase complexity by adding a highly customizable emissions calculator to our website. This solution allowed the customer to precisely specify the make and model of their car, the amount of driving they did, and their driving habits. Iteration on the solution concept is usually required in order to achieve great outcomes.
I’ve mostly talked about the third diamond in generalities. Let’s dive into the details, starting with the divergent part. How can you reliably generate a lot of solution concepts?
The human mind is pretty good at thinking of solution concepts, especially after considering carefully the job to be done and engaging with the customer. So, before getting too fancy, just write down or sketch out any ideas you have from the top of your mind. In my experience, you already have at least three or four ideas.
Now comes the part that really differentiates expert creators from novices. Just put those ideas aside. They may eventually prove to be the best solution to the job at hand. But, experts know that those ideas are not going away. You’ve already captured them. So, just let them go for now. Move on and see if you can generate another half dozen or more alternatives.
You can use some simple techniques to stimulate your thinking. Here are five that I like.
First, pull from insights. You’ve already invested heavily in the second diamond, understanding the customer, and that effort should have produced a couple of insights. Insights are customer needs that are authentic, non-obvious, and significant. I guarantee you that if you identify an insight, a solution concept will just fall out of your mind.
Let’s try it. My hoped-for new venture 99 Bowls makes and ships frozen prepared lunch foods like soups, chowder, and chili to professionals working from home. In my customer research, I identified the insight that many people strive to tightly control the macro nutrient profile of their food. For example, some want higher fat and lower carbohydrates, and others low fat and high protein. Can you think of any solution concepts for 99 Bowls that would be responsive to that insight?
Of course you can. Here are a few. How about starting with standard recipes, say for chili (a mexican-inspired spicy soup), but whose ingredients can be adjusted in a custom production process to deliver a pre-specified macro nutrient profile? Want higher fat and lower protein? Increase the amount of cheddar cheese and reduce the amount of ground turkey. Want higher carbohydrates? Substitute corn kernels for the cheddar. Another would be to have a vast assortment of soups, but then to automatically filter the options to those that satisfy the customer’s desired nutrient profile. A third option would be a sort of bento box that assembles different food items – say sliced turkey, pita chips, hummus, and cheese – into a lunch portfolio that satisfies the overall target nutrient profile.
In fact, the whole purpose for finding those insights in the first place is to use them to pull distinctive solution concepts. This technique is the most powerful item in your concept generation tool kit.
A second approach is to apply the decomposition principle, focusing on just one element of the job to be done. You can decompose by customer needs, which is essentially what we do when focusing on an insight, or you can decompose by sequence of user actions or according to different sub-functions within a larger solution. For example, for 99 Bowls we might decompose the overall service into selection, ordering, delivery, and consumption. Now, focus on just one of those functions and consider all the different ways you might do it. For instance, consider delivery. 99Bowls could ship a carton of 12 individual servings that are frozen and placed in insulated packaging. Or, the customer could pick up a month’s supply at a regional refrigerated drop-off point. Or, a single serving could be delivered ready to eat at a pre-specified time each day. Or, the company could locate a compact freezer in apartment complexes and neighborhoods acting as a sort of vending machine for meals that have been pre-purchased. We could follow the same process for the other sub-functions, say selection. Once the decomposed problem has been tackled, pieces that work together can be assembled into complete solution concepts.
In a third approach, consider how an organization with a distinctive approach to product would solve the problem. For example, how would the Japanese household products company Muji implement 99 Bowls? I’m envisioning very tidy and beautiful containers, likely rectangular in shape that stack into a modular storage and shipping solution. The containers are either reusable or returnable. How would Nike do it? Maybe with selections endorsed by famous athletes – say the Michael Jordan meal plan. How would Netflix do it? They would make meal suggestions based on my eating history. You get the idea – take the best elements of the distinctive approaches of other companies and see how they might apply to your challenge.
Fourth, consider analogous problem domains. 99 Bowls is trying to solve the problem of what’s for lunch while working from home. What’s an analogous problem? Maybe the problem of what to watch at home in the evening. How do I solve that problem? I have different channels and platforms like YouTube, Disney, HBO, and Hulu. Maybe I can have different lunch channels, with varied offerings.
Fifth, set a numerical goal. I generally like to use a goal of 10 distinct solution concepts. Over thirty years of teaching product development, I’ve found that the number 10 is challenging, but achievable. If you have identified 10 distinct ways to do a job, you have probably done a decent job of covering the landscape.
All else equal, the easiest way to find a better idea is to generate more ideas. And, the easiest way to generate more ideas is to engage more people. Engage your team. Engage your engineers. Engage your customers. Ask anyone who will listen if they have suggestions or ideas about how to do the job.
One technique I like when working with a team, is to prepare in advance a list of what I call Emergency Stimuli. These are prompts that might dislodge new ideas. Here’s an example of some stimuli for the 99 Bowls challenge.
What food trends are taking hold in restaurants located around busy office complexes? Might some of those trends be applied in a home delivery service?
As increasing numbers of professionals work from home, what are the most common complaints about the experience? Might 99 Bowls address some of those pain points?
What innovations in food science are emerging, but perhaps not yet widely available? Might some of those innovations be incorporated into the 99 Bowls solution.
In my experience, almost any prompt will work to get team members unstuck. Just think of a handful of questions that can nudge people to think differently, and then use those questions when and if the ideation process gets stalled or people complain they are out of ideas.
The divergent portion of the third diamond is all about generating a lot of alternatives. There’s no shortcut, other than perhaps the trick of involving a lot of people, which certainly makes the task easier. Apply the process, do the work, engage a team, and you’ll end up with a rich set of solution concepts.
Harnessing the Power of Individuals and Groups
Some projects are completed by individuals working alone, but more typically you will find yourself in a team. How can you most effectively harness the power of individuals and groups to generate concepts?
The most common organizational practice is to call a meeting and to conduct a brainstorming session. You’ve all done this. You get a group of people around the table. Maybe you have a flip chart and an easel or a whiteboard, and someone facilitates the meeting.
I exaggerate only slightly in saying that this is probably the single worst thing you can do in order to effectively deploy your team.
Let me make an analogy. Imagine that you and your team are on a small plane that crashes on a remote island. Everyone survives but your first task at hand is to find food, water, and shelter – this is the metaphorical equivalent of looking for a great solution concept.
Now imagine two strategies. In the first, the team huddles together in a rugby scrum and you wander around as a group looking at the ground together. That’s the equivalent in organizational life to calling a meeting.
In a second scenario, everyone on the team heads off in different directions, with the mandate to come back and report on what they found after 30 minutes. Then, after sharing information you all go look more carefully in the most promising areas.
Hopefully your intuition is that the second approach will more reliably find the best food, water, and shelter. This same strategy of employing independent parallel exploration is also the best way to engage a group in concept development.
You don’t have to take my word for it. My colleagues Christian Terwiesch, Karan Girotra, and I have tested this idea experimentally. We compared two different approaches to ideation. In what we call the group approach, four people work together for 30 minutes. In a second technique, we call the hybrid approach, those same four people work for 10 minutes alone as individuals independently and in parallel and then those four people work together for 20 minutes exploring the ideas that they generated alone as individuals.
We did this study with 44 individuals divided into 11 groups of 4 and we had them work on exploring for alternatives for two different product design problems.
We found that with the hybrid approach the same 4 individuals could generate about 2 – 1/2 times more ideas if they took the hybrid approach than if they worked together as a group.
Not only that but the ideas they generated were actually better in quality as well.
We have unambiguous evidence that a hybrid process is better than a group process and that you need to have an individual phase for at least some of your exploration effort.
It’s very helpful to provide a numerical target for that individual phase. I usually use a goal of 10 ideas per individual.
Lastly, some people find out that it’s very hard in some organizations to actually get people to do their homework – that is to actually do the assigned work of generating 10 ideas working individually.
If that’s the case for you, then I recommended you go ahead and call a meeting, which is effective in getting people to allocate some time. But then after you’ve called the meeting and after you’ve got people together, you ask them to work alone for the first 10 minutes, after which you can proceed to work together as a group.
Just to be sure I’m clear, I want to reiterate that I’m not opposed to working in teams. Rather, you should cherish the value of the team resource and work to deploy it most effectively, by having your team members spend some time working individually and alone before you bring together team members into a group process.
Selection Methods and the Concept Selection Matrix
The third diamond in the triple-diamond model includes a convergence from many solution alternatives to a single plan for going forward. We clearly shouldn’t pick from our alternatives at random. How do we converge?
In most cases, the convergence comprises two steps. First, the team, without the benefit of any external testing, narrows a set of 10 or more solution alternatives to a few, say 2 or 3. Second, some kind of testing with potential customers is used to converge on the best single solution. Here I focus on the first step – screening and selecting internally.
The technique used most widely in practice is multi-attribute utility analysis — which is an overly fancy name for a criteria matrix. Even if you don’t know the technical name, you’ve probably used one of these tables before. In this example I use a google sheet to quickly create the matrix, but any table will do, even a marker on a whiteboard.
Just as an arbitrary convention, I like to use the columns for the different solution alternatives and the rows for the criteria.
A desirable characteristic of a structured selection method is that you can remember, codify, and communicate the logic behind a decision long after it has been made. The selection matrix is self documenting. So, if you do use a whiteboard, remember to snap a photo for archival purposes.
For physical goods, I find it is sometimes nice to draw a little sketch next to the textual description of the concept at the head of each column. You can also use a separate document with illustrations to document in more detail each of the concepts captured by the matrix.
The rows of the matrix are the selection criteria. I like the criteria to be the key customer needs. In this example I’ve shown three needs – quick and easy to use, removes all the ice cream from the container, and forms a nice ball. Almost always there are three additional criteria that apply, and they are cost or some measure of the economic efficiency of delivering the solution, wow, that is how fundamentally interesting and novel is the solution, and elegance and beauty. These criteria map directly to the universally desirable attributes of a solution concept.
Now you just consider the relative performance of each concept relative to the criteria. The convention that I like to use for representing relative performance is a three level scale using a plus, a zero, and a minus.
Note that even though I use the term cost here, I adopt the convention that plus is always better, and minus is always worse, so a plus indicates lower cost.
Once you’ve evaluated all of the concepts relative to all of the other criteria, you can then summarize the net score by adding up plusses and subtracting minuses for each concept.
One positive outcome of the selection process is that it helps the team to realize when elements of one concept might be combined with another, or when concepts are actually quite similar to each other. For instance, as a result of this process, I discovered that concept E was conceptually very similar to concept G, a concept that I ended up pursuing further.
In this example, two concepts really stood out as superior, and so I focused on those two for further development. I often see three concepts that emerge as most promising. Rarely does a team have more than three really compelling concepts based on this internal selection process.
Now those of you who are more right brained have an objection. You argue that we can’t really reduce everything in life to a quantitative evaluation. Instead you need to make a more holistic judgment of the qualities of the concepts.
I hear you on this point, and I think it’s good discipline to see if you can get the concept selection matrix to be consistent with your intuition. That suggests that you’ve been able to capture what’s really behind your intuition, and that will benefit you when you go to communicate your rationale to other people, maybe to the more left brain of the stakeholders on the project.
[As a complete aside, I know remembering what those terms left brain and right brain mean is really hard – they are terrible labels. Here’s the mnemonic device I use. Remember that LEFT is LOGICAL…]
Those of you who are more left brained have your own objection. You argue that not all criteria are equally important and that it’s hard to reflect accurately the relative quality of concepts using just three levels.
Of course both of these concerns are valid. For this quick selection method to be effective, the criteria need to be roughly equal in importance, and in some cases, a crude three-level quality rating will mask some huge differences in relative quality of concepts. You can of course easily modify the criteria matrix to increase the number of quality levels, say using a 1 to 5 scale, and you can weight the relative importance of the criteria using a percentage weighting scheme or a point system. However, in my experience, if the goal is merely to narrow a set of alternatives to about three, then the simple criteria matrix will work quite well.
In the third diamond, we are sort of flying blind. It’s been a while since we engaged with our potential customers. For this reason, we need to do some concept testing.
Let me illustrate why this is so important using a simple example. Imagine you are designing a new hot sauce. (Have you noticed by the way that I use a lot of food examples? That’s because I love food.)
You can think of any individual as having an ideal point, which is their true preference, say on two dimensions of saltiness and spiciness. When you interview them and observe them, they tell you how much salt and heat they like – maybe they tell you they like it really hot and not very salty. Of course, you as the designer don’t hear them perfectly well, and they are not perfectly accurate in describing what they like. There’s inevitably some imprecision.
Then, you go to the lab and create a product based on what you understand, and there is a further translation error in that execution. By the time, you’ve gone through three phases of interpretation, what you have cooked up and what the customer wants are likely not perfectly aligned. Correcting any such mismatch is not hard. You give them a prototype of your hot sauce and ask them what they think. Yikes, they say, that’s too hot and tastes sort of bland. Hopefully you land pretty close though, and can incrementally refine your solution to hit the target.
Of course, I bet very few of you are designing hot sauce, but the same logic applies to creating a service experience or a piece of software. What the customer tells you, how you understand it, and the fidelity of your engineering process will inevitably result in a mismatch between what the customer actually wants and what you have actually built. Concept testing lets us estimate whether or not you have a good match before you actually develop the product.
There are three common types of concept tests. The first is informal qualitative feedback based on a quite schematic description of the solution concept. For example, this is the very first prototype of the Apple iPod. It was made by Tony Fadell from cardboard covered with a laser printed graphic. I bet it didn’t take more than an hour to build.
You can’t have too much feedback on prototypes. Even showing potential users hand-drawn screens on paper – sometimes called “paper prototypes” – is super helpful in clarifying where the solution concept misses the mark.
In a second type of concept test, you set up a forced choice among a small set of concept alternatives, usually three.
For example, here is a forced choice concept test for three ice cream scoop concepts. The 3 options typically result from an internal concept selection process using a criteria matrix in which 10 original concepts are narrowed to 2 or 3.
You can use this kind of forced choice in lots of different design settings, not necessarily just for overall product concepts. Here’s another example of a forced-choice survey I used in testing three possible names for the environmental services company I co-founded. The name Terrapass was the clear winner and we went with that option.
In a third type of concept test, you ask potential consumers to indicate their purchase intent. Purchase intent is almost always measured using a five-box scale, from definitely would not purchase to definitely would purchase.
Here’s one of the ice cream scoop concepts as it would be used in a purchase-intent survey.
Purchase intent surveys are notoriously imprecise in predicting demand, but they are the best single predictor of consumer acceptance of a new product at the concept phase of development. I believe they are best used for relative comparison of several concepts. In my own research, I have found that you can test up to 50 concepts with a single respondent without too much fatigue, and that you only need a sample of about 15 representative consumers to get a reliable estimate of how potential customers will react to your product.
Of course the way you represent solution concepts to your audience influences their response. For example here’s the same purchase intent survey with the concept illustrated with a photo-realistic rendering. In this case the vibrance of the color is much more evident than with a black-and-white line drawing.
You will probably not have the details of your concept fully worked out when you engage in concept testing, so you will have to just do your best in representing the solution. In my opinion, consistency in the fidelity of the concept descriptions across different concepts is more important than the absolute level of quality of the representation of your concepts.
More Iterative Refinement
If you are very lucky, you will find that the concept you select (1) meets the customer needs, (2) is cost efficient, (3) evokes the “wow” response, and (4) exhibits beauty and elegance. More likely, significant iterative refinement remains as you put prototypes in front of potential customers and refine your solution. That’s normal. Hopefully, however, the results of a deliberate concept development process get you close to the target and a process of incremental improvement will allow you to hit the bullseye.
Karl T. Ulrich, Steven D. Eppinger, and Maria C. Yang. 2020. Product Design and Development. McGraw-Hill. New York.
Laura Kornish and Karl T. Ulrich. 2014. The Importance of the Raw Idea in Innovation: Testing the Sow’s Ear Hypothesis. J. Marketing Research.
Karan Girotra, Christian Terwiesch, and Karl T. Ulrich. 2010. Idea Generation and the Quality of the Best Idea. Management Science. Vol. 56, No. 4, pp. 591–605.
Tony Fadell. 2022. Build: An Unorthodox Guide to Making Things Worth Making. Harper Business.
I’ve been a product designer my entire adult life. Here is one of the products I created, the Belle-V ice cream scoop. In full disclosure, I had a lot of help from a talented team. When people see the product they impute genius to the designer – wow, that’s amazing. How did you come up with that?
I’m using an example of a physical good for specificity, but I’ve experienced the same kind of reaction to digital products and services.
The reality is that I learned an effective process when I was in my 20s and I’ve applied that process repeatedly, sometimes weekly or even daily for 40 years. When you only observe the outcome, the results seem magical. But, the truth is that a fairly straightforward sequence of process steps can reliably lead you to a great result.
Design is just another word for the pull approach to innovation. All design processes are a sequence of steps that begin with some articulation of the “what” and result in some description of the “how” – the process moves from what to how.
Commercial phase-gate product development processes are just an elaboration of that basic idea, with lots of detail. My textbook on product design and development (Ulrich et al. 2020) is a comprehensive description of that detail. Most of you working in larger organizations probably use some sort of phase-gate process that is specific to your industry.
But, here I’m going to abstract a bit, and focus on the elemental design process – what is design at its very core. While design is the core problem solving approach within the product development process, design can be applied beyond product development. It’s almost a building block of being human – of dealing with life.
My goal is to describe the design process in a way that it can be used in myriad situations, from the creation of a new product from scratch, to the improvement of an existing product, and even for solving internal innovation challenges such as finding new ways to reduce waiting times in emergency departments.
To reiterate, the standard phase-gate product development process is a fully elaborated methodology that typically includes the roles of different functions within the organization. It emphasizes not only what to do in each phase, but the notion of a gate that must be cleared in order to proceed to the next phase. I am now going to boil that basic process down to its essence to give you a tool I call the triple-diamond model that can be used not just in zero-to-one product development, but also in almost any other problem solving situation.
To give credit where credit is due, the triple-diamond model is my extension and elaboration of the Double Diamond Model articulated by the UK-based Design Council, a non-profit organization with the mission of improving design practices.
The three diamonds correspond to three steps.
Clarify the job to be done in a jobs analysis.
Understand the needs of the customer or user.
Create a great solution concept.
In practice, a fourth phase is usually important – implementing that concept in a way that the organization can actually deliver the solution. This involves writing the code, designing the parts, and planning for production.
The three diamonds each represent a cycle of divergent and convergent thinking. For each diamond, the designer explores alternatives, and then focuses.
The first diamond answers the question, “What is the job to be done?” It starts with a target customer and the gap or pain point as you have first sensed it, and it results in a carefully considered reframing of the design problem in terms of a job to be done. In fact, one of the critical elements of an effective design process is not even really problem solving so much as problem definition.
The second diamond begins with a job to be done and develops a comprehensive understanding of the customer needs, which are those aspects of a solution that could result in satisfaction and even delight if satisfied. The convergent portion of the second diamond identifies one or a few insights, which are essentially important customer needs that were previously not known.
The third diamond uses those customer insights to pull many possible solution concepts and then selects one or a few for further refinement and testing.
Let me show you how the three diamonds played out for the Belle-V scoop. I started with a vague sense that ice cream was really hard to scoop. In diamond 1, I focused on the at-home consumer of ice cream and came up with the job to be done “How might we better dispense bulk ice cream into individual portions?” In the second diamond, I observed people scooping ice cream and noticed that the wrist angle was quite awkward, even painful for some people. That insight allowed me to pull several different solution concepts, including the one that eventually was embodied in the product, a more or less conventional scoop, but with the scoop angled relative to the handle.
Of course, really, it’s diamonds all the way down. The triple diamond model focuses on the concept development process, but when the team proceeds to build the product based around a concept, it will almost certainly use additional cycles of divergent and convergent techniques in order to solve downstream problems, say for establishing a product architecture, or implementing specific components of the solution. For example, even after we had converged on the solution concept of an angled scoop, we did a huge amount of exploration to find the final form of the object. Another diamond focused on the detailed design of the shape of the scoop and handle. And for that matter, there was another diamond when we considered the surface finish of the scoop – divergent exploration of alternatives and then convergence on tri-valent chrome plating.
Some of you are thinking that this model seems pretty tidy for a very simple piece of hardware like an ice cream scoop, but may not apply to more complex goods and services, say to enterprise software or to a hotel experience. I have a couple of reactions to those reasonable thoughts.
First, as an aside, there’s a reason they call it HARD-ware – it’s hard. Even a simple object like an ice cream scoop presents a lot of complexity and challenges when it comes to actually getting it to the marketplace.
But, more substantively, for new, zero-to-one systems, software, or services, you must still devise an overarching solution concept. For example, consider LinkedIn – the top-level solution is essentially a user-created resume-like profile with the ability to establish a connection between two individuals, and then the ability to search 1st, 2nd, and 3rd order connections in the resulting professional network. Such an overarching concept could be developed with the triple diamond model.
For established systems, the triple diamond will be unlikely to be applied to the entire product or suite of products, but rather more likely to a feature within that more complex product. For example, once LinkedIn had become a successful product, the triple diamond model could still be applied, but to a new feature, say the creation of the follower feature, which allows individuals to follow another person and get updates that person publishes, but without requiring the individual to become a bi-lateral connection.
Problem Solving, Design, and Design Thinking
I happened to be on a holiday ski trip when I was preparing for this video. (I know, that doesn’t sound like that much of a holiday.) I kept thinking to myself, skiing is fun, but it’s a huge annoyance to actually get on the slopes. For most novice skiers, you have to procure skis, boots, poles, helmet, goggles, and warm clothing. Then, you have to put all that stuff on. Then, while fully dressed in really warm gear you have to walk awkwardly from transportation to a ski lift, sometimes navigating a flight of stairs. Then, you put on the skis. By then you are sweating and your goggles are fogged up. Next you wait in a line. Then you get on a windy and cold ski lift and become quite chilled. When you finally get to the top of the mountain, you stare at a map trying to figure out the best route down. Finally, you get to slide on the snow, which is actually quite fun. I’m an incurable innovator and so I found myself posing the question, “How might we improve the experience of getting skiers onto the slopes?”
If I were a trendy corporate consultant, I would call this a “design thinking” problem. But, I’m actually a bit of a crusty old designer. I’ve taught design for more than 30 years. So, I have to ask “what exactly is design thinking” and how is it any different from plain old design?
Well, first let’s first go back to the definition of innovation and design.
I define innovation as a new match between a solution and a need. Innovation can result from a push – starting with the solution and looking for a need. For example, what might we use the blockchain for? Or, it could start with the need and pull the solution, like I framed the skiing challenge. “How might we improve the skier experience?” Design is innovation anytime you are pulling a solution from a need.
So considering our definition, the short answer to what is design thinking is that it is design. Really. You apply the same process to creating a better ski experience as you do to creating a better ice cream scoop, or a better fitness app. In fact, the word design thinking annoys a lot of designers, because they are usually less interested in thinking about problems than in actually solving them.
Once I cool off a bit about the weird term “design thinking,” I realize there may be a gem of an idea in there, and that a bit of nuance may in fact be warranted.
A useful definition of design thinking might be that it is design of things we don’t normally think of as designed.
For example, here are some problems for which the design process could be used, resulting in solutions that would not normally be thought of as designed artifacts.
How might we improve the patient experience in the emergency department at our hospital?
How might we improve the convenience of using a bicycle for transportation?
How might we create a delightful food delivery service?
A lot of people talk about needing to apply more design thinking in business. I find myself wondering if the desire for design thinking is really just a reaction to the use of too many spreadsheets and PowerPoint presentations, disconnected from customers and from exploration of solution concepts. This reaction reflects a desire for a different and better culture of innovation.
I do think that good designers exhibit a few desirable elements of culture. Interestingly, most of these elements don’t need to really be confined to design. Here are five:
Designers exhibit a bias for action.
Designers tend to be optimists, exhibiting a culture of yes.
Designers tend to use exploratory prototypes early in the problem solving process.
Designers tend to be skilled at visual expression.
Designers tend to use empathic methods for understanding customers.
Despite my enthusiasm for all things design, I won’t argue it is universally the best approach to problem solving. For example, it would be a mistake to abandon elements of Six Sigma, Total Quality Management, the Toyota Production System, and data-based approaches. It would also be a bad idea to use a design process to find the volume of a geometric shape, a task better suited to an algorithm.
But, for a huge set of challenging problems, design is a great approach. It is fundamentally divergent and open-ended in its perspective on addressing user needs, and that’s useful whether you are designing a bridge, enterprise software, or an insurance claims process.
Karl T. Ulrich, Steven E. Eppinger, and Maria C. Yang. 2020. Product Design and Development. McGraw-Hill. New York.
Most investors agree that the two most important factors in a decision to invest are the market size and the quality of the team. This chapter focuses on market size.
The most common ways to think about market size, and the estimates most institutional investors expect to see are:
Total Available Market (TAM – pronounced as a word like “ham.”). Sometimes also total addressable market. The market value of the job to be done for all segments.
Serviceable Addressable Market (SAM as in a person named “Sam.”). Sometimes also segmented addressable market or serviceable available market. The size of the market you could reasonably serve with your solution and business model in the medium term.
Serviceable Obtainable Market (SOM, as in the nickname for a sommelier) – sometimes also share of market. essentially the market the company may credibly expect to obtain with available resources in the near term, say 24 or 36 months.
Some investors, advisors, and entrepreneurs (including me), prefer to define an additional category, the beachhead market. Getting a company up and running is really hard. Going after the biggest market segment is not necessarily the best strategy. Often the biggest market segment will demand aggressive pricing and thus significant cost efficiencies. Most start-ups won’t be equipped to tackle the biggest segments from the start. A beachhead market is one that provides the easiest access, allowing the fledgling business to get started, to develop a little traction, to build some scale, and to refine its products. The beachhead market is always a subset of the SAM. The desirable characteristics of a beachhead market are:
The needs of the beachhead market are not being served well by competitors.
You can access the customer using focused, efficient acquisition techniques as opposed to broad-based awareness campaigns.
Specific customers are readily accessible for engagement in refining a solution.
Your value proposition, even as a young company, is compelling.
For goods and services requiring physical operations and logistics, the beachhead market is often geographically close to the start-up location.
The boundaries of these definitions are somewhat arbitrary, and so my primary recommendation is to be extremely clear about your definitions and careful with your assumptions. One of the biggest sources of delusion in entrepreneurs’ pitch decks is wildly unrealistic estimates of market sizes.
Top Down Estimates
There are two ways to think about market definition and sizing, top-down and bottom-up. With the top down approach, start with all economic activity on earth – that’s the maximum size of the market for “do everything.” Then, successively narrow the definition of the market until you get to the “market for the job that we aspire to do in society.” That’s your total available market (TAM). Then, further narrow to markets for which you can offer a value proposition in the medium term with your solution – the serviceable addressable market (SAM). Then, further narrow to the first market segment you will focus on and for which you offer a value proposition — the beachhead market. Each narrowing is a defined subset of a larger market.
For example, let’s revisit MakerStock (originally described in the handbook as “University Panel Supply”).
The GDP of the entire planet (c2023) is approximately USD 100 T.
The subset of that GDP representing the market for plastic, wood, and metals used in making things, and excluding construction, is approximately USD 6 T.
The subset of that market for plastic, wood, and metal panels (e.g., large sheets of material) used in making things is approximately USD 700 B.
The subset of that market comprised of specialty plywood (which excludes softwood plywood used in construction), medium-density fiberboard, and acrylic sheet is USD 44 B.
The US portion of that market is USD 5 B. (Now we are focusing geographically.)
The US market for these materials in which the customer’s ideal format is a less-than-full-sheet custom size with delivery by conventional ground freight is USD 500 mm. This is the subset of the market for which a cut-to-size supplier of materials would offer a value proposition. This particular focusing step is where you might sense we are pretty much guessing. There is no place to look up market sizes for the “cut to size” panel market, and we are left to doing some back-of-the-envelope calculations. For this reason, top-down estimates become a bit shaky as additional focusing assumptions are made. Still, USD 500 mm is a reasonable estimate of the TAM for MakerStock — the market is certainly smaller than USD 1 B and it’s more than USD 100 mm. The definition of your TAM is somewhat arbitrary. For instance, this estimate of TAM is for the US only, but might we reasonably consider serving customers in Europe, for example? Given that many different assumptions are possible, just be very clear about the focusing decisions that underly your estimate of TAM.
MakerStock’s TAM includes cabinet makers and raw materials for factories producing finished goods in relatively large quantities. MakerStock is unlikely to be competitive for these segments in the medium term. The company is likely to offer a stronger value proposition to customers that prefer custom sizes and that order a variety of different materials, both wood and plastic, for use in their fabrication processes, largely laser cutting. The US market for such materials for university maker labs, do-it-yourself individual makers, and small- and medium-sized businesses such as sign making shops is approximately USD 100 mm. This is a reasonable estimate of the SAM for MakerStock.
Now, how will MakerStock get started? Where will it focus initially? MakerStock will focus on university maker labs. It will focus on those labs that can be served by FedEx ground freight within three days of Scranton, Pennsylvania. The beachhead market is for pre-cut specialty plywood, medium-density fiberboard, and acrylic sheet for university maker labs located within the three-day ground shipping territory from Scranton, Pennsylvania. Top-down estimates are going to be very approximate for the beachhead market. To do a top-down estimate, we might gather some estimates (guesses?) of a few knowledgable people and average them and assume that 10 percent of the SAM is the beachhead market, delivering a market size of USD 10 mm. Bottom-up estimates are much better for tackling this estimate, the approach I will explain below.
With each successive focus on a subset of a larger market, we are making a decision about which narrower market will be more readily addressable by the new venture. Each narrowing reflects a belief which may or not be correct. For instance, commercial sign makers may be a better beachhead market than university maker labs. So, as with any problem solving activity, considering alternatives and testing beliefs will likely result in better outcomes.
For the top-down approach, conventional internet search and current AI tools are probably the best source of raw data. By using a variety of queries and triangulating on the result, you are likely to be able to make estimates that are well within one order of magnitude of the actual value.
TAM and SAM depend on the definition of your core business
Estimating TAM and SAM requires that you assume some boundaries for your core business. For instance, if I conceive of my panel supply business as providing panels of any type to any user of panels globally, my TAM is USD 700 B. But I really have to stretch my imagination to believe I can address the need for particle board panels purchased by the Chinese factories that make the best-selling IKEA Billy bookcase. Those factories are likely purchasing many full truckloads of those panels per week directly from the mill that makes them. The definition of available and addressable determines your TAM and SAM. I like to think of addressable customers as those customers who have a job to be done for which the core business could reasonably be competitive in delivering a solution. For MakerStock, that core business is sourcing sheet goods by the full truckload, breaking the bulk delivery into sheets, cutting sheets to custom sizes, and delivering pieces by conventional ground freight. The job to be done is to deliver an assortment of the right materials, at the right time, in the right sizes, all with minimal fuss. The serviceable addressable market is the size of the market for which that value proposition makes sense, a market not of USD 700 billion but rather about USD 100 million.
Bottom-Up “Counting Noses” Approach
The top-down approach is a useful way to put the business into a larger context, and to reveal possible additional market segments, and their sizes. However, the bottom up approach is usually more credible and less speculative. WIth the bottom-up approach, you start with a hypothesis about the beachhead market — the very first market you will tackle. You estimate the size of that market by creating an inventory of every potential customer in that market. This approach is called counting noses.
For example, the beachhead market for MakerStock is university maker labs located within the geographic region served by three-day ground shipping from Scranton, Pennsylvania, the initial location of the business. The business was located there because I as founder already had a facility and an available team member in that location, and because that location is a logistics hub, serving about one third of the US population within two days by conventional ground shipping. The geographic cut off reflects a belief that freight costs would be decisive in the value proposition to our customers.
Once that beachhead market is defined, counting noses is easy. First, there are about 5300 colleges and universities in the United States. Now, look up the ground delivery time map that FedEx provides. Identify those universities within the three-day shipping territory. That subset is about 2500 institutions. This set of 2500 potential customers can be identified and cataloged precisely and entered into a database. For each potential customer, say PennWest Edinboro University, an internet search can reveal whether or not it has a maker lab, and possibly unearth a name and contact information for the lab manager. In fact, an internet search for the PennWest Edinboro furniture design program yields a great description of the program, including photographs displaying specific details about the equipment and furniture in the shop. That search even produces a name of the program director, Karen Ernst, along with her contact information. We can now count Professor Ernst’s nose in our beachhead market. Doing a comprehensive search for all 2500 potential customers is probably not a good use of time until the business is actually launched, but doing that search for a sample of potential customers, say a hundred, produces an accurate estimate of how many labs would eventually be found in all 2500 universities. For this example, a preliminary search suggests that about 64 percent of these universities have at least one maker lab, a potential customer base of 1600 labs.
At this point, some assumptions are required about the average level of spending on speciality plywood, medium-density fiberboard, and acrylic by each lab. That estimate can be based on interviews with a sample of customers. By talking to about five potential customers, we found that the average spending seemed to be roughly proportional to the size of the university, with a university spending an average of about USD 4 on these materials per year per student enrolled. The labs serve an average of 1250 students each, so their average spending on materials is about USD 5000 per year. These figures lead to an estimate for the total size of the beachhead market of USD 8 million per year (i.e., 1600 labs at USD 5000 per year). This a pretty solid number. The real value might be USD 10 million or USD 6 million, but it’s not USD 50 million or USD 1 million.
Consider Adjacencies for a Bottom-Up Estimate of SAM
Once you have identified and characterized a promising beachhead market, now consider the immediately adjacent markets. The adjacencies might be geographic. So, after considering colleges and universities in the northeast, consider those in the mid-Atlantic region, or the mid-west. The adjacencies might be in the goods and services offered, for instance extension of the product line from wood and plastic to metals. The adjacencies might be in the types of target customers, for instance from colleges and universities to secondary schools, or to small and medium-sized businesses using laser cutters. These adjacencies taken together comprise your SAM, as estimated using the bottom-up approach. As a matter of good planning hygiene, consider how your bottom-up estimate of adjacent markets compares with your top-down estimate of your SAM. These two ways of getting to SAM should not deliver wildly different results.
Recall that SOM is the market you plan to actually serve within your planning horizon, usually 24 or 36 months. For my taste I prefer estimating SOM with a bottom-up, counting noses approach of the beachhead market integrated with some estimates of the rate of customer acquisition of the go-to-market system.
Recall that we estimated that there are 1600 potential customers in this beachhead market. I believe we can acquire an average of two new repeat customer per week with the likely sales and marketing resources we will have, net of churn. (See the chapter on go-to-market systems for how to think about this.) Thus, within three years, we will have approximately 300 university maker labs as repeat customers. We estimated earlier that each lab orders approximately USD 5000 in materials per year. Thus the recurring revenue for the customer base at year three will be approximately USD 1,500,000 per year, at which point we will have about 19 percent market share of the beachhead market of USD 8 million. Of course, even though we will focus on the beachhead market, we will unavoidably reel in some customers from adjacent markets, and likely will also sell to some do-it- yourselfers via the MakerStock website. It’s even possible that an adjacent market will prove to be a better fit for the company than the beachhead market we had originally envisioned. Still, the bottom-up nose counting along with some realistic modeling of the go-to-market system is a grounded approach to forecasting the scale of the business.
Fables Versus Realism
None of you readers are too excited about recurring annual revenue from the beachhead market of USD 1,500,000 after three years. Investors likely would not be too excited either. Yet, that’s actually an above-average outcome for a start-up in year three. I have invested in over 50 start-ups. About five of those have achieved recurring annual revenue of USD 1,500,000 by the end of year three.
Every founder faces many dilemmas. One of the most pressing is whether to spin a compelling fable in the pitch deck, which probably represents a 95th percentile outcome, or to provide a realistic forecast of what you believe to be a highly obtainable outcome. I believe the level of conservatism in the forecast depends on the audience. If you are planning to be a closely held, bootstrapped, company, then you should strive for hard, cold realism, even pessimism. That was my approach with MakerStock. I funded the business with my own money. I wanted to have a realistic sense of how the business would unfold and how much cash I would need to burn to get to positive cash flow. MakerStock actually did substantially better than the forecast, largely because do-it-yourselfer and small- and medium-sized business customers turned out to be fairly easy to acquire and serve alongside universities. That was a pleasant surprise easily accommodated as the business developed.
If, however, your audience is institutional investors, then they are highly conditioned to see forecasts at about the 95th percentile. My advice is to be excruciatingly clear about your assumptions and logic in estimating TAM and SAM. Those need to be highly credible. Then, the estimate of SOM should likely skew a bit optimistic, assuming that you have all the resources you need, including that investor’s capital, and that things go well. Your SOM is going to be discounted by institutional investors seeing your pitch, so you should account for that fact in your presentation. (Yes, I realize this is a pernicious cycle of optimistic forecasting and assumed discounting by the investor. I don’t see a clear way around it.)
Market Size Graphics
The most common way market sizes are shown in pitch decks is with nested circles, sometimes called an onion diagram, in which the area of the circle is proportional to the market size. Data graphic purists may prefer bar charts of some kind, but I think the nested circles work pretty well and you probably want to avoid bucking convention here. You can, of course, annotate this graphic heavily to support the numbers.
I enjoy cooking. I’m not one for recipes, and I especially like making savory stews and soups from seasonal vegetables, and with interesting spices, especially cumin. I usually make a big pot and after enjoying a nice dinner, I’ll stow away a few leftovers in serving-size containers in my freezer. Few moments give me greater happiness than when I remember I have something yummy in my freezer ready to heat up for lunch.
A while back I had an idea. Why don’t I create a service, which I’ll call 99 Bowls, which periodically sends me a carton of 12 containers of interesting soup? That way I always have something yummy to eat when I’m on my own at home for a meal.
I’m a serial entrepreneur, and this is how a new venture often starts for me. I’ve got some itch myself and I conceive of a company to scratch that itch. Let’s call this the raw opportunity.
A jobs analysis is a good first step in exploring an opportunity. It results in a clear and deliberate articulation of the job to be done for a focal customer (Christensen et al. 2016). The jobs analysis also comprises the first diamond in the triple-diamond model of design and design-thinking.
Let’s first talk about that focal customer. Before you even consider how to solve a problem, you really need to think about “for whom”? Returning to the 99 Bowls example, the product I would create for delivering pre-made meals to a school cafeteria would be dramatically different from the one I would create for a professional office worker or for a graduate student.
Two concepts are useful in identifying the focal customer. First, for zero-to-one products, you should identify the beachhead market. By this I mean the very first group of customers you will target. The best beachhead markets are those with the biggest need, the most acute pain point, and those that you can most easily reach. These markets are usually not the biggest markets. (See this chapter on defining markets for more.) The biggest markets usually have to wait until you have some experience and have started to spin up the flywheel of greater cost efficiency in your business. For 99 Bowls, a beachhead market could be relatively affluent professionals working from home at least two days per week.
The second useful concept is the persona. A persona is a description of a hypothetical customer that is highly representative of your target market. This is useful in making the challenge real to your team and to other stakeholders. A persona is usually constructed with specific attributes like age, gender, professional role, and even personality characteristics.
Let me tell you about the persona for 99 Bowls. Her name is Grace. She works as a product manager at Facebook. She’s 36 years old and lives alone with her cat Milo. She’s super fitness-conscious religiously going to a yoga class near her house in San Francisco every day. She works at home most days, although occasionally takes the Facebook shuttle to the main office about an hour away in Menlo Park. Grace is Korean-American and finds most prepared food too bland. She’s a foodie and likes a lot of heat and flavor. Makes it real, doesn’t it?
Your target segment will probably change, and after launching your solution you will almost certainly discover that there are other customer segments that your solution can address. You’ll also inevitably want to expand into adjacent segments. But, you have to start somewhere and specificity helps guide the creation of a solution.
I do a weekly podcast with entrepreneurs and my informal tally of past guests suggests that for about half of them the genesis of their business was a need that they themselves experienced. This raises the question of whether or not it is good practice to use yourself as a target customer.
Here are a few thoughts. First, you will find yourself in many professional situations in which you are not the target customer. If you are creating heavy equipment for the mining industry, you probably are not also a mine owner or equipment operator.
Second, when you are representative of your target market, I believe you are highly likely to create a product that satisfies at least one customer. You. That’s actually no easy feat. It’s a real luxury in entrepreneurship to have an immediate intuition about whether a solution is near the mark or not.
Still, designing for yourself does not guarantee a big market. You represent one data point and in most cases you aspire to serve thousands or even millions of customers. Disciplined and experienced entrepreneurs are able to leverage their own deep intuition about the job to be done while understanding that they need to be able to assume the perspective of the broader market for long-term product success.
Jobs Analysis and the Abstraction Ladder
In one of the most highly cited articles ever in Harvard Business Review, Theodore Levitt wrote “customers don’t want a ¼ inch drill, they want a ¼ inch hole.” Levitt’s insight was that when customers consider your product, they have a job to be done. They don’t typically want your product per se, they want the results of a job it can do for them.
But think about this example a bit more. I don’t know many customers who really want a ¼ inch hole either. They want to fasten a bookshelf to the wall. And do they really want to fasten a bookshelf to the wall, or do they want to store their books?
Every gap, as you first sense it, exists within an interconnected network of alternative problem statements, some more abstract and some more specific.
Here’s a technique called the abstraction ladder for elaborating the alternative ways you could state the design problem. You use the abstraction ladder for divergent thinking, to consider alternative ways you could frame the job to be done.
First, state the problem at the top of mind using the phrase “How might we…” For example, recall the 99 Bowls opportunity, which I could state as “How might we periodically deliver containers of prepared soup to work-from-home professionals?” You might write this on a self-stick note and place it on a wall or your desk.
As an aside, we use the phrase “How might we…” really just to put ourselves in a divergent frame of mind, considering many possibilities, some of which may not even be feasible.
Now, ask yourself what desirable outcome would be achieved if you solved the problem as stated. Or, in other words, why is that problem worth solving?
For example, we hope to deliver prepared soup so our customer always has something readily available to eat for lunch. Now, use that desirable outcome as the foundation of a second, more abstract, problem statement — “How might we provide a work-from-home professional with something to eat for lunch that is always readily available?” As these motives for doing the job come to mind, just write them down on separate notes and stick them higher up on your work surface.
You see what we’ve just done? We’ve moved up a rung on the abstraction ladder to state our problem a bit more generally. Now why is this a good thing?
Put simply, abstraction opens up additional solution concepts. For example, we’ve now opened up the possibility of a pre-scheduled daily lunch box drop-off, or of a meal kit that allows quick preparation of a fresh meal, or of a club in which a group of five people take turns making lunch for each other once a week. By broadening the definition of the problem, we have opened up the possibility of many more alternative solutions.
But why stop there? What is the benefit of providing readily available lunch? Well, perhaps a key motive is to increase available work time during the day? So, we could rephrase the job to be done as “How might we increase available work time during the day?” That would open up even more solution directions, maybe a virtual personal assistant to help with mundane tasks, professional or otherwise.
Of course, even this statement can be broadened. How might we be more productive? How might we better provide for ourselves and our families? As we ask why we would want to do a job, we broaden the problem definition until eventually we end up with jobs to be done like how might we improve well being in society?
Note that these increasingly general statements are not always strictly arranged on rungs of a linear abstraction ladder. There are typically many motives for any particular job to be done. Maybe a more significant motive for our target customer is to be able to control diet, or to introduce meal variety, or to enjoy more delicious food. Any given statement of the job to be done is located in a network of alternative problem statements, some more general, and some more specific.
So far in this example, we’ve always moved from more specific to more abstract, the why direction on the abstraction ladder. But, you can also step down a rung or two on the abstraction ladder by asking how — or what approach might we take to do the job. For instance, for the job to be done allow work-from-home professionals to enjoy more delicious food, we could take the approach of providing them with freshly cooked meals. That’s a more specific statement of the job to be done: How might we provide work-from-home professionals with freshly cooked meals?
When is the job to be done too abstract?
Stating the job to be done more abstractly, and thereby opening up additional solution concepts seems like a great thing. But, at what point is the job to be done too broad, too abstract? If you state the job to be done as “How might we improve well being in society?” then a solution might be to provide free neck massages in waiting lines at the airport. Although I might use that service as a customer, somehow that solution would not motivate me to quit my job and start a company. You, specifically, as an entrepreneur have a vision, and if the job to be done doesn’t align with your vision, you’re veering off track.
When you set out to create or improve something, it’s motivated by a gap you have sensed. You own that gap. If you embark on a process to create something that improves the well being of individuals in society, and it does so, but it does not achieve your goals, then you have defined the job to be done too broadly.
Step-by-step process for using the abstraction ladder
State the problem top of mind using “How might we…” For example, how might we periodically deliver a supply of prepared soup to work-from-home professionals?
Now, ask yourself what desirable outcome would be achieved if you solved that problem, and use that desirable outcome as the foundation of a second, more abstract, problem statement — “How might a work-from-home professional always have something available to eat for lunch?”
Now, repeat step 2… For example, “how might we increase the available work time for work-from-home professionals?” Repeat again and again until the problem statement is something like “How might we increase well-being among members of the community?” the most abstract possible motive for solving the problem. In this step, you might use self-stick notes placed on a wall with each note capturing a different job to be done. Place the notes in a hierarchy or network with more abstract statements higher on the wall. Remember that there may be several motives for doing a job — so your abstraction ladder may branch out as you consider alternative how-might-we statements.
Don’t be too hung up on the details of the process. Your goal is to explicitly articulate and consider several alternative problem statements, some more abstract and some more specific. If an alternative job to be done comes to mind, just write it down and put it on your work surface.
After all that divergent thinking, it’s time to converge. Deliberately choose the most abstract statement of the problem that if addressed would still satisfy your personal vision and goals as an entrepreneur. That statement is the job to be done.
I’ve described this process as a single effort that is one and done. In reality as you proceed in developing the opportunity you may find that you benefit from further broadening or focusing of the job to be done to better align with your mandate and vision as you better understand it.
Christensen, Clayton M., Taddy Hall, Karen Dillon, and David S. Duncan. “Know your customers’ jobs to be done.” Harvard business review 94, no. 9 (2016): 54-62.
Karl T. Ulrich | Original Version June 15, 2018 | This Version November 1, 2022
This is a concise note describing the alpha asset framework, and intended for students and busy professionals. It may be reproduced and used for non-commercial purposes with citation. I am writing a book Alpha Assets: Building and Sustaining Competitive Advantage, but it’s not yet finished. Here also is a PDF version.
Here are all the restaurant companies that are publicly traded in the United States. You’ll recognize many of these names, and in case you don’t know, Yum is the parent company of KFC and TacoBell among others.
As a group, they earned an average cash flow return on investment over the ten year period of 2006-2015 of about 6 percent per year. This measure of financial performance is the return on invested capital above and beyond each company’s weighted average cost of capital. This measure is sometimes called economic value added, or EVA.
But, why did those at the top earn so much more than those on the bottom? To some extent this is the most important question in business. For a given job to be done, how and why can an organization sustain competitive advantage and therefore earn an exceptional return on invested capital?
Of course something idiosyncratic can destroy a company or propel its fortunes. Yum brands made a huge bet on Asian markets and that bet paid off. The great recession in the United States hurt some sit-down restaurants over the first five years of this period.
But fundamentally the only way one company can earn more than another over the long term is to experience some combination of greater demand, which is reflected in higher revenue, OR greater efficiency, which shows up as lower cost. Higher demand or lower cost. That’s it. But, how can one company enjoy greater demand than another, or lower costs? To do so, it must possess some resource or asset that enhances performance but that can not easily be acquired by its competitors. Yum has to have something special that PotBelly can not easily get.
Two such assets, fairly self-evident from the list of companies are strong brands – say Starbucks – and huge scale – say McDonalds. I call these special types of resources a company’s alpha assets, a concept that is based on a key idea from the academic field of competitive strategy called the resource-based view of the firm.
I define alpha assets as resources owned or controlled by an organization that (a) enhance performance and that (b) are hard for others to acquire. Let’s take those two conditions in turn.
The presence of a performance-enhancing asset can uniquely cause significant improvements in profits relative to not having the asset, for most competitors. For example, electricity is a performance-enhancing asset for a restaurant company. Without electricity, your restaurant would likely be nothing more than a food cart on a street corner.
But can access to electricity be a source of competitive advantage? Probably not for a restaurant. Performance enhancement is a necessary condition for an asset to confer advantage, but it is not sufficient.
For restaurants, access to electricity can not be an alpha asset because that asset is not hard to acquire. All competitors can acquire electricity and so there will not be meaningful differences in its possession. (Although in some industries, say for computing infrastructure, perfect reliability of power is actually not that easy to acquire.)
An asset is hard to acquire when doing so requires a large amount of time, effort, or money, or is not possible at all.
For example, McDonald’s scale is essentially impossible for In-and-Out Burger to acquire in any reasonable amount of time or with any reasonable investment. And because scale is also performance enhancing, it is definitely an alpha asset.
Some assets can be hard to acquire, but not be performance enhancing. For instance, Starbucks is headquartered in Seattle. It would be expensive and take a long time for Luckin Coffee, a Chinese competitor to Starbucks, to move to Seattle. But, doing so would not significantly enhance performance, so the Seattle location is not an alpha asset for Starbucks.
I don’t have a precise technical definition for how much time and money comprises “hard to acquire” but you can think about required investments of say greater than 10 percent of revenue and/or time periods of five years or longer.
Let’s look at another example from another industry. Consider Amazon.
We’ve already discussed electricity and a Seattle headquarters – neither of these can be alpha assets for a restaurant or for Amazon. However, the Amazon brand confers advantage and can not be acquired by others. It’s an alpha asset. Amazon’s huge assortment of retailers on its platform confers advantage and is not easily acquired. It’s an alpha asset. Amazon’s vast trove of customer data is an alpha asset. Amazon’s massive scale is an alpha asset. And, although Amazon is embarking on a leadership transition, the founder, Jeff Bezos, has unambiguously been an alpha asset.
Some of you may criticize this framework for being unreasonably static. You are right. Almost no asset can remain alpha forever. That Chinese company, Luckin Coffee, pioneered the use of a mobile-first, all digital transaction system. In fact, I remember once trying to buy coffee in a shop in Shanghai and not having the app. The barista eventually abandoned the effort to set me up with the app and just gave me the coffee for free. Despite the barriers it imposed for foreign visitors, Luckin’s mobile-first strategy and implementation was an alpha asset for years. But, today, most coffee shops are fully app-enabled.
Where do alpha assets come from? In some cases, they come from an inventive spark or the conditions of the founding of the company. In other cases they are the result of good fortune. For example, if I can invoke an analogy, one of the alpha assets of Michael Phelps, the world champion swimmer, is his body dimensions, including his feet, which are US size 14 (or European size 48). Phelps didn’t do anything to acquire that alpha asset – it was endowed on him at birth. Similarly some alpha assets for companies are essentially random endowments, like when DuPont accidentally discovered the molecule polytetrafluoroethylene, which became its proprietary product Teflon.
But, more interesting are alpha assets that can be developed over time and as a result of deliberate action and managerial attention, such as cost efficiency or product performance. Fortunately these are the most important alpha assets in most organizational settings. These alpha assets are often the result of accumulation of strength via five mechanisms that I (and others) call flywheels. They represent tremendous sources of strength and resilience for organizations that can create them.
The Five Flywheels
In 2015, taking inspiration from Warren Buffet, I bought stock in Wells Fargo, one of the “big four” US banks. Oops. I’m now strictly an index investor, having learned my lesson watching the company’s stock decline over the next five years. Scandal after scandal plagued the bank. It seemed that every quarter some new crisis would arise. Yet, Wells Fargo has operated continuously since 1852. It seems pretty nearly impossible to kill this organization. In fact, it’s recovered again from its recent setbacks and its stock price is back up. (This of course after I sold my position. Go figure.) While few companies last as long as Wells Fargo, incumbent companies are remarkably resilient. If a company survives infancy, its prospects for living a few decades are very high, either as an independent company or as an operating unit within an acquiring company.
Why are incumbents so powerful, and how can a start-up chart a path to such strength?
I call the sources of incumbent power flywheels. In a physical system, a flywheel receives power and stores it as kinetic energy, which can then be tapped when and if necessary to overcome episodic demands.
An organization can also be thought of as a system, a machine for doing a job. It also has ways of accumulating energy.
There are four flywheels that are almost always present to a greater or lesser extent in every company.
I’m going to add a fifth flywheel I call Organizational Capability and Culture. This fifth flywheel will reflect a focus of an organization on doing a few things well or on a distinctive culture.
Let me illustrate the first four flywheels with a graphic.
All businesses deliver a solution to a customer, and this essence of a business is what powers the flywheels. In order for the company to acquire customers it must offer a compelling value proposition and deliver on that value proposition through its customer experience. When and if the value proposition increases, the company’s rate of customer growth will also increase, and of course if the value proposition decreases, so will the rate of customer growth. Implicit is the reality that the net rate of customer growth depends both on acquiring new customers and on retaining existing customers, or minimizing so-called churn.
Delivering solutions with value is the engine powering the flywheels. Now, the flywheels themselves.
1 – Customer Network
The first flywheel is the customer network. This flywheel is important for products that enjoy network effects. Growth in new customers increases the number of customers in the customer base. For products with network effects, as the customer base increases, the value proposition of the product increases. This further increases the rate of customer growth. Not all products exhibit network effects. I enjoy drinking Peet’s coffee. The value proposition of Peet’s Coffee to me does not depend at all on how many other customers enjoy the same coffee. However, I also like the mobile payment app Venmo. It was useful to me when just my college-age children were users. But, Venmo became even more valuable when the guy who removes snow from my driveway became a user. Venmo is a product with huge network effects and really benefits from the first flywheel. (See James Currier’s article for a very thorough taxonomy of types of network effects.)
2 – Cost Efficiency
The second flywheel is cost efficiency. Cost efficiency improves with deliberate effort and the application of good process, but the biggest explanatory factors in lowering costs are cumulative units delivered and the current rate of production. Any organization that does something repeatedly can enjoy lowered costs because of the learning that occurs over time. This is called the learning curve. For example, just consider what happens as you take on any new task, say making a cup of coffee. The first time, it takes you 10 minutes as you figure out how to measure out the beans, heat the water, filter the brew, and clean up the mess. Now imagine you make 100 cups every morning working at a coffee bar. I’m pretty sure by the time you’ve made 1000 cups, you’ve figured out how to make each cup in less than a minute. That same kind of learning occurs in all organizations as the cumulative number of units produced increases. A second type of cost efficiency comes from scale economies. This is not the result so much of the cumulative number of units produced, but rather from the rate at which you produce them. If you are making 100 cups of coffee each hour you will adopt very different methods than if you are making 1 cup each hour. For example, you’ll invest in a big coffee grinder and massive drip brewing system. Those investments reap huge productivity improvements, but are only possible as the rate of production increases. Put this all together and you see the second flywheel at work – the unrelenting improvement in efficiency that comes both from accumulating experience and from investing in better processes enabled by increased scale.
3 – Product Performance
The third flywheel is product performance. Only the most tone deaf organization fails to improve its products with experience. A company observes where the customer complaints are and seeks ways to enhance its performance relative to rival products. These opportunities become a queue of product improvement projects and over time the product just keeps getting better.
In some settings, that product improvement flywheel leads to nearly insurmountable product performance advantages. For instance, the product of semiconductor company TSMC is a fabrication service it offers to chip designers like NVIDIA and Apple. Its primary alpha asset is the accumulated know-how and trade secrets embedded within its semiconductor fabrication process. For TSMC, product improvement is a flywheel propelled by powerful positive feedback – as its processes become more capable, its value proposition increases, and it wins the most demanding production challenges, further increasing its scale and learning, and making it even more likely to garner future orders. Some people believe that what TSMC does is the hardest single task in the world. No one else comes close to being able to do it. That product performance advantage is likely to persist for decades.
A proprietary product can in relatively rare settings be an alpha asset because of a legal monopoly obtained through patent protection or trade secrets. Patents as alpha assets exist primarily in the pharmaceutical industry in which a patented product is identical with a specific molecule for which regulatory agencies have granted approval for use. In such cases, the product performance advantage arises not from a flywheel of continual product improvement, but from a regulatory barrier. In most other settings, patents serve more as a deterrent to blatant copying, or as a tool for intimidating much smaller competitors with the threat of expensive legal action.
4 – Brand Equity
The fourth flywheel is brand equity. Especially for engineers, the notion of brand is pretty strange. Why is it that a label on a product can enhance the value proposition of a product? The short answer is that brand serves as a proxy for attributes of product quality that the consumer can not directly observe, brand reduces the time and effort required for a consumer to find and select products, and brand directly confers desirable brand associations to the consumer as a kind of badge. While these benefits are all intangible, brand is a shockingly powerful and therefore valuable alpha asset. Brand is a flywheel because the value of a brand, its brand equity, accumulates over time, and typically increases with market share, time in the market, and with improved product performance.
5 – Organizational Capability and Culture
Remember I said the fifth flywheel was the focus on a few organizational capabilities or elements of culture. Let me give you an example. When I was the Vice Dean of Entrepreneurship at Wharton, I led an organization of about 30 people. Early on, I placed a heavy emphasis on building an organization with a highly diverse workforce. I felt this was important in order to foster creativity and innovation and to best serve our student and alumni stakeholders, who were themselves a highly diverse population. A group of 30 middle-aged men who grew up in rural America like me was not my goal. As we worked to attract and retain staff who did not look or think like me, the diversity of our team increased. This diversity in turn made it much easier to attract and retain additional team members who didn’t fit the mold. After three or four years, the composition of our workforce became a source of high performance for us, and we became one of the most sought after places to work within the university, further enhancing our ability to attract and retain a talented and diverse team.
The fifth flywheel might comprise a culture of innovation, a culture of customer service, a passion for environmental sustainability, a focus on reliability, or almost any other distinctive element of organizational capability or culture. Such elements are very hard to acquire or build, and to the extent that they enhance performance, they can be alpha assets.
The Role of New Products in Kick Starting the Flywheels
Even Wells Fargo was once a start-up with flywheels at a standstill. How does a new enterprise get the flywheels going in the first place, and when does that company stand a chance against incumbents?
An idiosyncratic endowment of any single resource could in theory kickstart a flywheel, and therefore a company with advantage. For instance, when the musician John Legend co-founded LVE Collection Wines, he himself was the alpha asset for the company and immediately spun up a brand flywheel. However, this strategy is not available to the rest of us. More typically, a better mousetrap – a new product resulting from the invention of a new technology or the recognition of an emergent market need – is the alpha asset for a new company.
Peter Thiel famously wrote in his book Zero to One “as a good rule of thumb, proprietary technology must be at least 10 times better than its closest substitute in some important dimension to lead to a real monopolistic advantage.” The 10x rule is mostly rhetorical, but I do agree that when there is some disequilibrium in technology or in the market, then an organization has an opportunity to move with speed and agility to take advantage of that disequilibrium and to create a product that is dramatically better than the pre-existing alternatives. At the dawn of the covid pandemic of 2020, the videoconferencing company Zoom offered a product that just worked. It didn’t require registration. It didn’t require a download. It didn’t require any special gear. It just worked. Despite the fact that there were dozens of other solutions in the market at the time, including BlueJeans, Skype for Business, Google Hangouts, and WebEx, Zoom was able to seize the market and gain significant share. This was almost entirely because Zoom had a better product, at least on the important dimension of ease of use and reliability.
Better product is often an alpha asset for a relatively brief period following some type of disequilibrium. But, the organization must use this precious window of product superiority wisely in order to oversee the acceleration of the other flywheels for sustained advantage. Indeed, Zoom took advantage of its initial product performance and prospered. But, predictably Microsoft eventually followed with an improved enterprise product, Teams, that was at parity on many features and superior in others. Google was jolted awake and improved its product Meet as well. Zoom remains a key player, but its brand and customer network, perhaps even more than its product per se, have become its alpha assets.
In very dynamic markets – those for which some combination of enabling technologies, competitive actions, or customer behavior are changing very quickly – the organizational capability of product management can itself be an alpha asset. For example, consider the fitness app Strava. Strava does weekly product releases, which include incremental improvements and, less frequently, substantial product changes. Any particular version of the Strava app could likely be easily replicated by a team of developers and so the product per se is not much of an alpha asset. However, the system that Strava employs to engage its users, understand opportunities for improvement, and prioritize the changes in its product roadmap, benefits from experience with millions of users and a refined organizational process of product management. This organizational capability is an example of a fifth flywheel and a compelling alpha asset.
I need to be clear that not all things that are important are alpha assets. For example, an excellent sales process is very important for enterprise software companies. That doesn’t imply that a sales process could necessarily be a significant source of sustained competitive advantage. It’s more that if you fail to do sales well, you are unlikely to be successful in enterprise software. We could say the same thing about operational competence for a restaurant, or accurate and timely finance and accounting processes in a bank. None of these things are likely to be sources of sustained advantage, yet they all need to be done competently to ensure success. In the same way, good products and effective product management are critically important for all companies, even if not alpha assets for all companies.
My central concept of alpha assets is essentially a reframing of the dominant intellectual paradigm in the academic field of competitive strategy, the resource-based view of the firm, first articulated comprehensively by Professor Jay Barney. Jay is an affable and enthusiastic man, now a professor at the University of Utah. We worked together on an interesting consulting project in healthcare, and have occasionally kicked around ideas on entrepreneurship, innovation, and teaching. The resource-based view (or RBV in the alphabet soup of obfuscating acronyms so often found in specialized fields) attempts to explain competitive advantage, and I think it does a pretty good job of doing so. But, it is far from clear. For instance, RBV argues that VRIN resources are the sources of competitive advantage. (Got it?) VRIN refers to valuable, rare, inimitable, and non-substitutable. (Got it now?) I made a good faith effort to use the VRIN framework in my book Innovation Tournaments (with Wharton colleague Christian Terwiesch), but it’s just too wonky for a non-academic audience. The resource-based view of the firm is both one of the most powerful ideas and one of the most poorly marketed ideas in social science. A makeover and rebranding is needed. As a first step, let’s just relabel VRIN assets as alpha assets, evoking some of the oldest tricks in branding, the use of alliteration, alphabetical primacy, the soft flowing sounds of a greek word, and the positive associations with the word alpha. Next, let’s simplify and make more intuitive the definition of VRIN. Alpha assets are (a) performance enhancing and (b) hard for others to obtain. This reframing now seems to work in the classroom, at least for me.
While there are a lot of antecedents to RBV, this is the paper that explained the concept most clearly: Jay B. Barney, (1996) The Resource-Based Theory of the Firm. Organization Science 7(5):469-469.
Here is the content in video format. I don’t think the graphics (which I did not create) are very good in these videos, so I suggest referring to my graphics in the note above. These videos are from my Wharton on-line course on Product Management and Strategy. Reach out if you need a referral in order to get a discount.