When companies want to innovate, there are three things they can change – products, services and business models. Products are usually the first, second and third priorities, services, though they have a tighter connection with customer and are more lasting and powerful, sadly, are fourth priority. And business models are the superset and the most powerful of all, yet, as a source of innovation, are largely off limits.
It’s easy to improve products. Measure goodness using a standard test protocol, figure out what drives performance and improve it. Create the hard data, quantify the incremental performance and sell the difference. A straightforward method to sell more – if you liked the last one, you’re going to like this one. But this is fleeting. Just as you are reverse engineering the competitors’ products, they’re doing it to you. Any incremental difference will be swallowed up by their next product. The half-life of your advantage is measured in months.
It’s easy for companies to run innovation projects to improve product performance because it’s easy to quantify the improvement and because we think customers are transactional. Truth is, customers are emotional, not rational. People don’t buy performance, they buy the story they create for themselves.
Innovating on services is more difficult because, unlike a product, it’s not a physical thing. You can’t touch it, smell it or taste it. Some say you can measure a service, but you can’t. You can measure its footprints in the sand, but you can’t measure it directly. All the click data in the world won’t get you there because clicks, as measured, don’t capture intent – an unintentional click on the wrong image counts the same a premeditated click on the right one. Sure, you can count clicks, but if you can’t count the why’s, you don’t have causation. And, sure, you can measure customer satisfaction with an online survey, but the closest you can get is correlation and that’s not good enough. It’s causation or bust. You’ve got to figure out WHY they like your services. (Hint – it’s the people who interface directly with your customers and the latitude you give them to advocate on the customers’ behalf.)
Where services are difficult to innovate, the business model is almost impossible. No one is quite sure what the business model actually is an in-the-trenches-way, but they know it’s been responsible for the success of the company, and they don’t want to change it. Ultimately, if you want to innovate on the business model, you’ve got to know what it is, but before you spend the time and energy to define it, it’s best to figure out if it needs changing. The question – what does it look like when the business model is out of gas?
If you do what you did last time and you get less in return, the business model is out of gas.
Successful models are limiting. Just like with the Prime Directive, where Captain Kirk could do anything he wanted as long as he didn’t interfere with the internal development of alien civilizations, do anything you want with the business model as long as you don’t change it. And that’s why you need external help to formally define the business model and experiment with it. The resource should understand your business first hand, yet be outside the chain of command so they can say the sacrilegious things that violate the Prime Directive without being fired. For good candidates, look to trusted customers and suppliers.
To define the business model, use a simple block diagram (one page) where blocks are labelled with simple nouns and arrows are labelled with simple verbs. Start with a single block on the right of the page labelled “Customer” and draw a single arrow pointing to the block and label it. Continue until you’ve defined the business model. (Note – maximum number of blocks is 12.) You’ll be surprised with the difficulty of the process.
After there’s consensus on the business model, the next step is to figure out how the environment changed around it and to identify and test the preferred evolutionary paths. But that’s for another time.
Image credit – Steven Depolo
It’s not enough to sell things to customers, because selling things is transactional and, over time, transactional selling deteriorates into selling on price. And selling on price is a race to the bottom.
Sales must move from transactional to relational, where people in the sales organization become trusted advisers and then something altogether deeper. At this deeper level of development, the sales people know the business as well as the customer, know where the customer wants to go and provide unique perspective and thoughtful insight. That’s quite a thing for sales, but it’s not enough. Sales must become the conduit that brings the entire company closer to the customer and their their work.
When the customer is trying to figure out what’s next, sales brings in a team of marketing, R&D and manufacturing to triangulate on the future. The objective is to develop deep understanding of the customer’s world. The understanding must go deeper than the what’s. The learning must scrape bottom and get right down to the bedrock why’s.
To get to bedrock, marketing leads learning sessions with the customer. And it all starts by understanding the work. What does the customer do? Why is it done that way? What are the most important processes? How did they evolve? Why do they flow the way they do? These aren’t high-level questions, they are low-level, specific questions, done in front of the actual work.
The mantra – Go to the work.
When the learning sessions are done well, marketing includes experts in manufacturing and R&D. Manufacturing brings their expertise in understanding process and R&D brings their expertise in products and technologies. And to understand the work the deepest way, the tool of choice is the Value Stream Map (VSM).
Cross-organization teams are formed (customer, sales, marketing, manufacturing, and R&D) and are sent out to create Value Steam Maps of the most important processes. (Each team is supported by a VSM expert.) Once the maps are made, all the teams come back together to review the them and identify the fundamental constraints and how to overcome them. The solutions are not limited to new product offerings, rather the solutions could be training, process changes, policy changes, organizational changes or business model changes.
Not all the problems are solved in the moment. After the low hanging fruit is picked, the real work begins. After returning home, marketing and R&D work together to formulate emergent needs and create new ways to meet them. The tool of choice is the IBE (Innovation Burst Event).
To prepare for the IBE, marketing and R&D formalize emergent needs and create Design Challenges to focus the IBE teams. Solving the Design Challenges breaks the conflicts creates novel solutions that meet the unmet needs. In this way, the IBE is a pull process – customer needs create the pull for a solution.
The IBE is a one or two-day event where teams solve the Design Challenges by building conceptual prototypes (thinking prototypes). Then, they vote on the most interesting concepts and create a build plan (who, what, when). The objective of the build plan is to create a Diabolically Simple Prototype (DSP), a functional prototype that demonstrates the new functionality. What makes it diabolical is quick build time. At the end of the IBE is a report out of the build plan to the leader who can allocate the resources to execute it.
In a closed-loop way, once the DSP is built, sales arranges another visit to the customer to demonstrate the new solution. And because the prototype designed to fulfill the validated customer need, by definition, the prototype will be valuable to the customer.
This full circle process has several novel elements, but the magic is in the framework that brings everyone together. With the process, two companies can work together effectively to achieve shared business objectives. And, because the process brings together multiple functions and their unique perspectives, the solutions are well-thought-out and grounded in the diversity of the collective.
Image credit – Gerry Machen
The decision has many facets and drives many questions, for example: Does it fit with core competence? Does it fit with the brand? How many will we sell? What will the market look like after it’s launched? Do we have what it takes to pull it off?
These questions then explode into a series of complex financial analyses like – return on investment, return on capital, return on net assets (and all its flavors) and all sorts of yet-to-be created return on this’s and that’s. This return business is all about the golden ratio – how much will we make relative to how much it costs. All the calculations, regardless of their name, are variations on this theme. And all suffer the same fundamental flaw – they are based on an artificial system of financial accounting.
To me, especially when working in new territory, we must transcend the self-made biases and limitations of GAAP and ask the bedrock question – Is it worth it?
In the house of cards of our financial accounting, worth equals dollars. Nothing more, nothing less. And this simplistic, formulaic characterization has devastating consequence. Worth is broader than profit, it’s nuanced, it’s philosophical, it’s about people, it’s about planet. Yet we let our accounting systems lead us around by the nose as if people don’t matter, like the planet doesn’t matter, like what we stand for doesn’t matter. Simply put, worth is not dollars.
The single-most troubling artifact of our accounting systems is its unnatural bias toward immediacy. How much will we make next year? How about next quarter? What will we spend next month? If we push out the expense by a month how much will we save? What will it do to this quarter’s stock price? It’s like the work has no validity unless the return on investment isn’t measured in days, weeks or months. It seems the only work that makes it through the financial analysis gauntlet is work that costs nothing and returns almost nothing. Under the thumb of financial accounting, projects are small in scope, smaller in resource demands and predictable in time. This is a recipe for minimalist improvement and incrementalism.
What about the people doing the work? Why aren’t we concerned they can’t pay their mortgages? Why do we think it’s okay to demand they work weekends? Why don’t we hold their insurance co-pays at reasonable levels? Why do we think it’s okay to slash our investment in their development? What about their self-worth? Just because we can’t measure it in a financial sense, don’t we think it’s a liability to foster disenchantment and disengagement? If we considered our people an asset in a financial accounting sense, wouldn’t we invest in them to protect their output? Why do we preventive maintenance on our machines but not our people?
When doing innovative work, our financial accounting systems fail us. These systems were designed in an era when it was best to increase the maturity of immature systems. But now that our systems are mature, and our objective is to obsolete them, our ancient financial accounting systems hinder more than help. The domains of reinvention and disruption are dominated by judgement, not rigid accounting rules. Innovation is the domain of incomplete data and uncertain outcomes and not the domain of debits and credits.
Profit is important, but profit is a result. Financial accounting doesn’t create profit, people create profit. And the currency of people are thoughts, feelings and judgement.
With innovation, it’s better to create the conditions so people believe in the project and are fully engaged in their work. With creativity, it’s better to have empowered people who will move mountains to do what must be done. With work that’s new, it’s better to trust people and empower them to use their best judgement.
Image credit – Jeremy Tarling
But if it can’t be done, how can you imagine it?
No one is buying a product like the one you imagined. There’s no market.
No one can buy an imaginative product that doesn’t yet exist. There may be a market.
Imagine things are good, just as they are.
Imagine an upstart competitor will obsolete your best product.
Let’s fix what is.
Let’s imagine what isn’t, and build it.
Don’t waste time imagining radical new concepts. There’s no way to get there.
Use your imagination to create an unobtainable concept, then build a bridge to get there.
Imagine the future profits of our great recipe. Let’s replicate it.
Imagine our recipe has a half-life. Let’s disrupt it.
To be competitive, we’ve got to use our imagination to reduce the cost of our products.
To be competitive, we’ve got to use our imagination to obsolete our best work.
Put together a specification, a detailed Gannt chart and make it happen on time.
Imagine what could be, and make a prototype.
Let’s shore up our weaknesses and live to fight another day.
Let’s imagine our strength as a weakness and invent the future.
We are the best in the industry. Imagine how tough it is to be our competitor.
Imagine there’s a hungry start-up who will do whatever it takes to get the business.
We’ve got to protect our market share.
Imagine what we could create if we weren’t constrained by our success.
Imagine how productive we will be when we standardize the work.
Imagine how much fun we will have when we reinvent the industry.
Ask the customer what they want, built it and launch it.
Imagine what could be, build a prototype, show the customer, listen and refine.
Let’s follow the script. Imagine the profits.
Let’s burn the script and imagine a new one.
Image credit — Allegra Ricci
When doing work that’s new, sometimes it seems the whole world is working against you. And, most of the time, it is. The outside world is impossible to control, so the only way to deal with external resistance is to pretend you don’t hear it. Shut your ears, put your head down and pull with all your might. Define your dream and live it. And don’t look back. But what about internal resistance?
Where external resistance cannot be controlled and must be ignored, internal resistance, resistance created by you, can be actively managed. The best way to deal with internal resistance is to prevent its manufacture, but very few can do that. The second best way is to acknowledge resistance is self-made and acknowledge it will always be part of the innovation equation. Then, understand the traps that cause us to create self-inflicted resistance and learn how to work through them.
The first trap prevents starting. At the initial stage of a project, two unstated questions power the resistance – What if it doesn’t work? and What if it does work? If it doesn’t work, the fear is you’ll be judged as incompetent or crazy. The only thing to battle this fear is self-worth. If you feel worthy of the work, you’ll push through the resistance and start. If it does work, the fear is you won’t know how to navigate success. Again, if you think you’re worthy of the work (the work that comes with success), you and your self-esteem will power through the resistance and start.
Underpinning both questions is a fundamental of new work that is misunderstood – new work is different than standard work. Where standard work follows a well-worn walking path, new work slashes through an uncharted jungle where there are no maps and no GPS. With standard work, all the questions have been answered, the scope is well established and the sequence of events and timeline are dialed in. With standard work, everything is known up front. With new work, it’s the opposite. Never mind the answers, the questions are unknown. The scope is uncertain and the sequence of events is yet to be defined. And the timeline cannot be estimated.
But with so much standard work and so little new work, companies expect people to that do the highly creative work to have all the answers up front. And to break through the self-generated resistance, people doing new work must let go of self-imposed expectations that they must have all the answers before starting. With innovation, the only thing that can be known is how to figure out what’s next. Here’s a generic project plan for new work – do the first thing and then, based on the results, figure out what to do next, and repeat.
To break through the trap that prevents starting, don’t hold yourself accountable to know everything at the start. Instead, be accountable for figuring out what’s next.
The second trap prevents progress. And, like the first trap, resistance-based paralysis sets in because we expect ourselves to have an etched-in-stone project plan and expect we’ll have all the answers up front. And again, there’s no way to have the right answers when the first bit of work must be done to determine the right questions. If you think you’re worthy of the work, you’ll be able to push through the resistance with the figure out what’s next approach.
When in the middle of an innovation project, hold yourself accountable to figuring out what to do next. Nothing more, nothing less. When the standard work police demand a sequence of events and a timeline, don’t buckle. Tell them you will finish the current task then define the next one and you won’t stop until you’re done. And if they persist, tell them to create their own project plan and do the innovation work themselves.
With innovation, it depends. With innovation, hold onto your self-worth. With innovation, figure out what’s next.
Image credit — Jonathan Kos-Read
A prototype is a physical manifestation of an idea. Where ideas are ethereal, prototypes are practical. Where ideas are fuzzy and subject to interpretation, prototypes are a sledge hammer right between the eyes. There is no arguing with a prototype. It does what it does and that’s the end of that. You don’t have to like what a prototype stands for, but you can’t dismiss it. Where ideas aren’t worth a damn, prototypes are wholly worth every ounce of effort to create them.
If Camp A says it will work and Camp B says it won’t, a prototype will settle the disagreement pretty quickly. It will work or it won’t. And if it works, the idea behind it is valid. And if it doesn’t, the idea may be valid, but a workable solution is yet-to-be discovered. Either way, a prototype brings clarity.
Prototypes are not elegant. Prototypes are ugly. The best ones do one thing – demonstrate the novel idea that underpins them. The good ones are simple, and the best ones are diabolically simple. It is difficult to make diabolically simple prototypes (DSPs), but it’s a skill that can be learned. And it’s worth learning because DSPs come to life in record time. The approach with DSPs is to take the time up front to distill the concept down to its essence and then its all-hands-on-deck until it’s up and running in the lab.
But the real power of the DSP is that it drives rapid learning. When a new idea comes, it’s only a partially formed. The process of trying to make a DSP demands the holes are filled and blurry parts are brought into focus. The DSP process demands a half-baked idea matures into fully-baked physical embodiment. And it’s full-body learning. Your hands learn, your eyes learn and your torso learns.
If you find yourself in a disagreement of ideas, stop talking and start making a prototype. If the DSP works, the disagreement is over.
Diabolically simple prototypes end arguments. But, more importantly, they radically increase the pace of learning.
Image credit – snippets101
By definition, when the work is new there is uncertainty. And uncertainty can be stressful. But, instead of getting yourself all bound up, accept it. More than that, relish in it. Wear it as a badge of honor. Not everyone gets the chance to work on something new – only the best do. And, because you’ve been asked to do work with a strong tenor of uncertainty, someone thinks you’re the best.
But uncertainty is an unknown quantity, and our systems have been designed to reject it, not swim in it. When companies want to get serious they drive toward a culture of accountability and the new work gets the back seat. Accountability is mis-mapped to predictability, successful results and on time delivery. Accountability, as we’ve mapped it, is the mortal enemy of new work. When you’re working on a project with a strong element of uncertainty, the only certainty is the task you have in front of you. There’s no certainty on how the task will turn out, rather, there’s only the simple certainty of the task.
With work with low uncertainty there are three year plans, launch timelines and predictable sales figures. Task one is well-defined and there’s a linear flow of standard work right behind it – task two through twenty-two are dialed in. But when working with uncertainty, the task at hand is all there is. You don’t know the next task. When someone asks what’s next the only thing you can say is “it depends.” And that’s difficult in a culture of traditional accountability.
An “it depends” Gannt chart is an oxymoron, but with uncertainty step two is defined by step one. If A, then B. But if the wheels fall off, I’m not sure what we’ll do next. The only thing worse than an “it depends” Gantt chart is an “I’m not sure” Gannt chart. But with uncertainty, you can be sure you won’t be sure. With uncertainty, traditional project planning goes out the window, and “it depends” project planning is the only way.
With uncertainty, traditional project planning is replaced by a clear distillation of the problem that must be solved. Instead of a set of well-defined tasks, ask for a block diagram that defines the problem that must be solved. And when there’s clarity and agreement on the problem that must be solved, the supporting tasks can be well-defined. Step one – make a prototype like this and test it like that. Step two – it depends on how step one turns out. If it goes like this then we’ll do that. If it does that, we’ll do the other. And if it does neither, we’re not sure what we’ll do. You don’t have to like it, but that’s the way it is.
With uncertainty, the project plan isn’t the most important thing. What’s most important is relentless effort to define the system as it is. Here’s what the system is doing, here’s how we’d like it to behave and, based on our mechanism-based theory, here’s the prototype we’re going to build and here’s how we’re going to test it. What are we going to do next? It depends.
What’s next? It depends. What resources do you need? It depends. When will you be done? It depends.
Innovation is, by definition, work that is new. And, innovation, by definition, is uncertain. And that’s why with innovation, it depends. And that’s why innovation is difficult.
And that’s why you’ve got to choose wisely when you choose the people that do your innovation work.
Image credit – Sara Biljana Gaon (off)
Everyone wants to do more innovation. But how? To figure out what’s going on with their innovation programs, companies spend a lot of time to put projects into buckets but this generates nothing but arguments about whether projects are disruptive, radical innovation, discontinuous, or not. Such a waste of energy and such a source of conflict. Truth is, labels don’t matter. The only thing that matters is if the projects, as a collection, meet corporate growth objectives. Sure, there should be a short-medium-long look at the projects, but, for the three time horizons the question is the same – Do the projects meet the company’s growth objectives?
To create the causes and conditions for innovation, start with a clear growth objective by geography. Innovation must be measured in dollars.
Good judgement is required to decide if a project is worthy of resources. The incremental sales estimates are easy to put together. The difficult parts are deciding if there’s enough sizzle to cause customers to buy and deciding if the company has the chops to do the work. The difficulty isn’t with the caliber of judgement, rather it’s insufficient information provided to the people that must use their good judgement. In shorth, there is poor clarity on what the projects are about. Any description of the projects blurry and done at a level of abstraction that’s too high. Good judgement can’t be used when the picture is snowy, nor can it be effective with a flyby made in the stratosphere.
To create the causes and conditions for innovation, demand clarity and bedrock-level understanding.
To guarantee clarity and depth, use the framework of novel, useful, successful. Give the teams a tight requirement for clarity and depth and demand they meet it. For each project, ask – What is the novelty? How is it useful? When the project is completed, how will everyone be successful?
A project must deliver novelty and the project leader must be able to define it on one page. The best way to do this is to create physical (functional) model of the state-of-the-art system and modify it with the newness created by the project (novelty called out in red). This model comes in the form of boxes that describe the system elements (simple nouns) and arrows that define the actions (simple verbs). Think hammer (box – simple noun) hits (arrow -simple verb) nail (box – simple noun) as the state-of-the-art system and the novelty in red – a thumb protector (box) that blocks (arrow) hammer (box). The project delivers a novel thumb protector that prevents a smashed thumb. The novelty delivered by the project is clear, but does it pass the usefulness test?
To create the causes and conditions for innovation, demand a one-page functional model that defines and distills down to bedrock level the novelty created by the project. And to help the project teams do it, hire a good teach teacher and give them the tools, time and training.
The novelty delivered by a project must be useful and the project leader must clearly define the usefulness on one page. The best way to do this is with a one page hand sketch showing the customer actively using the novelty. In a jobs-to-be-done way, the sketch must define where, when and how the customer will realize the usefulness. And to force distillation blinding, demand they use a fat, felt tip marker. With this clarity, leaders with good judgement can use their judgement effectively. Good questions flow freely. Does every user of a hammer need this? Can a left-handed customer use the thumb guard? How does it stay on? Doesn’t it get in the way? Where do they put it when they’re done? Do they wear it all the time? With this clarity, the questions are so good there is no escape. If there are holes they will be uncovered.
To create the causes and conditions for innovation, demand a one-page hand sketch of the customer demonstrating the useful novelty.
To be successful, the useful novelty must be sufficiently meaningful that customers pay money for it. The standard revenue projections are presented, but, because there is deep clarity on the novelty and usefulness, there is enough context for good judgement to be effective. What fraction of hammer users hit their thumbs? How often? Don’t they smash their fingers too? Why no finger protection? Because of the clarity, there is no escape.
To create the causes and conditions, use the deep clarity to push hard on buying decisions and revenue projections.
The novel, useful, successful framework is a straightforward way to decide if the project portfolio will meet growth objectives. It demands a clear understanding of the newness created by the project but, in return, provides context needed to use good judgement. In that way, because projects cannot start without passing the usefulness and successfulness tests, resources are not allocated to unworthy projects.
But while clarity and this level of depth is a good start, it’s not enough. It’s time for a deeper dive. The project must distill the novelty into a conflict diagram, another one-pager like the others, but deeper. Like problem definition on steroids, a conflict must be defined in space – between two things (thumb and face of hammer head) – and time (just as the hammer hits thumb). With that, leaders can ask before-during-after questions. Why not break the conflict before it happens by making a holding mechanism that keeps the thumb out of the strike zone? Are you sure you want to solve it during the conflict time (when the hammer hits thumb)? Why not solve it after the fact by selling ice packs for their swollen thumbs?
But, more on the conflict domain at another time.
For now, use novel, useful, successful to stop bad projects and start good ones.
Image credit – Natashi Jay
No need to wait for new hires, just move resources from one project to another. Stop project A and start project B. Simple, right? Not so much. Emotional attachment causes project A to defend their resources and project B to complain the resources haven’t moved. Resources will be slow to flow.
No need to take the time to develop new capability, just reassign capable resources from business 1 to business 2 and watch progress unfold. No problem, right? Wrong. There’s immense organizational drama from prioritizing one business over another. Again, the pace of resource flow will be glacial.
And with innovation, the drama is doubled. It’s threatening when resources flow from mainstream projects with tangible (but small) returns to more speculative projects with highly uncertain returns. But that’s what must happen.
If there’s a mismatch between the words and resource allocation, believe resource allocation.
If the innovation banners are plastered on all the walls and everyone has the tee shirt, yet the resources don’t flow to the innovation work, it’s an innovation farce. Run away. Here’s what the four HOWs of innovation look like through the lens of resource allocation.
How To Start. Define the yearly funding level for innovation resources that is independent of the yearly planning process. In short, create an innovation tax at a fixed percentage of revenue. This gets funded before anything else. It’s the pay-yourself-first approach to innovation. And when the money is allocated and the resources flow, there’s no need for banners and tee shirts. Alignment comes with the money.
Next, choose a leader to put in place standing processes to continuously funnel project ideas into a common hopper. One pile for all ideas – university research, mergers and acquisitions, voice of the technology, voice of the customer (direct observation and listening), patents and YouTube videos of purposeful misuse of your product.
How To Choose. Define funding levels across the various flavors of projects in the portfolio and set up a standing meeting for senior leaders to choose the best projects. This selection process is light on analysis and heavy on judgment, so allocate leaders who are not afraid to use good judgement. And set up a standing meeting with the CEO to pace the selection work (make sure senior leaders allocate their time.)
How To Execute. Internal, external, or partner, the work defines the right way to allocate resources. Based on the work, choose the right organization and the best leader and fully staff the project before considering a second project. The most popular failure mode is running too many projects in parallel and getting none done. The second popular failure mode forgetting to fund the support resources needed for innovation. Allocate money for tools, time, training and a teacher. Establish a standing meeting where senior leaders review the projects. This must be outside the review process normal projects.
How To Improve. No one ever allocates time to do this. To get the work done, trick the system and include the work as a standing agenda item in the How To Execute review meetings. Find a problem, fix a problem. Improve as you go.
Allocate the best resources to the best projects and make sure senior leaders allocate time to the innovation work. The best predictors of successful innovation are the character of the fully-staffed, fully-funded projects and the character of people that run them.
Image credit – conorwithonen
If you want to gain ground on your competition you’ve first got to know where things stand. Where are their advantages? Where are your advantages? Where is there parity? To quickly understand the situations there are three tricks: stay at a high level, represent the situation in a clear way and, where possible, use public information from their website.
A side-by-side comparison of the two companies’ products is the way to start. Create a common set of axes with price running south to north and performance (or output) running west to east. Make two copies and position them side-by-side on the page – yours on the left and theirs directly opposite on the right. Go to their website (and yours) and make a list of every product, its price and its output. (For prices of their products you may have to engage your sales team and your customers.) For each of your products place a symbol (the company logo) on your performance-price landscape and do the same for their products on their landscape. It’s now clear who has the most products, where their portfolio outflanks yours and where you outflank them. The clarity and simplicity will help everyone see things as they are – there may be angst but there will be no confusion and no disagreement. The picture is clear. But it’s static.
The areal differences define the gaps to close and the advantages to exploit. Now it’s time to define the momentum and trajectories of the portfolios to add a dynamic element. For your most recent product launch add a one next to its logo, for the second most recent add a two and for the third add three. These three regions of your portfolio are your most recent focus areas. This is your trajectory and this is where you have momentum. Extend and arrow in the direction of your trajectory. If you stay the course, this is where your portfolio will add mass. Do the same for your competitor and compare arrows. You know have a glimpse into the future. Are your arrows pointing in the same directions as theirs? Are they located in the same regions? How would feel if both companies continued on their trajectories? With this addition you have glimpse into the stay-the-course future. But will they stay the course? For that you need to look at the patent landscape.
Do a patent search on their patents and applications over the previous year and represent each with its most descriptive figure. Write a short thematic description for each, group like themes and draw a circle around them. Mark the circle with a one to denote last year’s patents. Repeat the process for two years ago and three years ago and mark each circle accordingly. Now you have objective evidence of the future. You know where they have been working and you know where they want to go. You have more than a glimpse into the future. You know their preferred trajectories. Reconcile their preferred trajectories with their price-performance landscapes and arrows 1, 2 and 3. If their preferred trajectories line up with their product momentum, it’s business as usual for them. If they contradict, they are playing a different game. And because it takes several years for patent applications to publish, they’ve been playing a new game for a while now.
Repeat the process for your patent landscape and flop it onto your performance-price landscape. I’m not sure what you’ll see, but you’ll know it when you see it. Then, compare yours with theirs and you’ll know what the competitive landscape will look like in three years. You may like what you see, or not. But, the picture will be clear. There may be discomfort, but there can be no arguments.
This process can also be used in the acquisition process to get a clear picture a company’s future state. In that way you can get a calibrated view three years into the future and use your crystal ball to adjust your offer price accordingly.
Image credit – Rob Ellis
When doing new things there is no predictability. There’s speculation, extrapolation and frustration, but no prediction. And the labels don’t matter. Whether it’s called creativity, innovation, discontinuous improvement or disruption there’s no prediction.
The trick in the domain complexity is to make progress without prediction.
The first step is to try to define the learning objective. The learning objective is what you want to learn. And its format is – We want to learn that [fill in the learning objective here]. It’s fastest to tackle one learning objective at a time because small learning objectives are achieved quickly with small experiments. But, it will be a struggle to figure out what to learn. There will be too many learning objectives and none will be defined narrowly. At this stage the fastest thing to do is stop and take a step back.
There’s nothing worse than learning about the wrong thing. And it’s slow. (The fastest learning experiments are the ones that don’t have to be run.) Before learning for the sake of learning, take the necessary time to figure out what to learn. Ask some questions: If it worked could it reinvent your industry? Could it obsolete your best product? Could it cause competitors to throw in the towel? If the answer is no, stop the project and choose one where the answer is yes. Choose a meaningful project, or don’t bother.
First learning objective – We want to learn that, when customers love the new concept, the company will assign appropriate resources to commercialize it. If there’s no committment up front, stop. If you get committment, keep going. (Without upfront buy-in the project relies on speculation, the wicked couple of prediction and wishful thinking.)
Second learning objective – We want to learn that customers love the new concept. This is not “I think customers will love it.” or “Customers may love it.” In the standard learning objective format – We want to learn that [customers love the new concept]. Next comes the learning plan.
What will you build for customers to help them understand the useful novelty of the revolutionary concept? For speed’s sake, build a non-functional prototype that stands for the concept. It’s a thin skin wrapped around an empty box that conveys the essence of the novelty. No skeleton, just skin. And for speed’s sake, show it to fewer customers than you think reasonable. And define the criteria to decide they love it. There’s no trick here. Ask “Do they love it?” and use your best judgement. At this early stage, the answer will be no. But they’ll tell you why they don’t love it, and that’s just the learning you’re looking for.
Use customer input to reformulate the learning objective and build a new prototype and repeat. The key here is to build fast, test fast, learn fast and repeat fast. The art becomes defining the simplest learning objectives, building the simplest prototypes and making decisions with data from the fewest customers.
With complexity and newness prediction isn’t possible. But learning is.
And learning doesn’t have to take a lot of time.
Image credit — John William Waterhouse