Shipulski On Design

Posts Tagged ‘Competitiveness’

Today’s commercial environment is fierce.  All companies have aggressive growth objectives that must be achieved at all costs.  But there’s a problem – within any industry, when the growth goals are summed across competitors, there are simply too few customers to support everyone’s growth goals.  Said another way, there are too many competitors trying to eat the same pie.  In most industries it’s fierce hand-to-hand combat for single-point market share gains, and it’s a zero sum game – my gain comes at your loss.   Companies surge against each other and bloody skirmishes break out over small slivers of the same pie.

The apex of this glorious battle is reached when companies no longer have points of differentiation and resort to competing on price.  This is akin to attrition warfare where heavy casualties are taken on both sides until the loser closes its doors and the winner emerges victorious and emaciated.  This race to the bottom can only end one way – badly for everyone.

Trench warfare is no way for a company to succeed, and it’s time for a better way.  Instead of competing head-to-head, it’s time to compete with no one.

To start, define the operating envelope (range of inputs and outputs) for all the products in the market of interest.  Once defined, this operating envelope is off limits and the new product must operate outside the established design space.  By definition, because the new product will operate with input conditions that no one else’s can and generate outputs no one else can, the product will compete with no one.

In a no-to-yes way, where everyone’s product says no, yours is reinvented to say yes.  You sell to customers no one else can; you sell into applications no one else can; you sell functions no one else can.  And in a wicked googly way, you say no to functions that no one else would dare.  You define the boundary and operate outside it like no one else can.

Competing against no one is a great place to be – it’s as good as trench warfare is bad – but no one goes there.  It’s straightforward to define the operating windows of products, and, once define it’s straightforward to get the engineers to design outside the window.  The hard part is the market/customer part.  For products that operate outside the conventional window, the sales figures are the lowest they can be (zero) and there are just as many customers (none).  This generates extreme stress within the organization.  The knee-jerk reaction is to assign the wrong root cause to the non-existent sales.  The mistake – “No one sells products like that today, so there’s no market there.”  The truth – “No one sells products like that today because no one on the planet makes a product like that today.”

Once that Gordian knot is unwound, it’s time for the marketing community to put their careers on the line.  It’s time to push the organization toward the scary abyss of what could be very large new market, a market where the only competition would be no one.  And this is the real hard part – balancing the risk of a non-existent market with the reward of a whole new market which you’d call your own.

If slugging it out with tenacious competitors is getting old, maybe it’s time to compete with no one.  It’s a different battle with different rules.  With the old slug-it-out war of attrition, there’s certainty in how things will go – it’s certain the herd will be thinned and it’s certain there’ll be heavy casualties on all fronts.  With new compete-with-no-one there’s uncertainty at every turn, and excitement. It’s a conflict governed by flexibility, adaptability, maneuverability and rapid learning.  Small teams work in a loosely coordinated way to test and probe through customer-technology learning loops using rough prototypes and good judgement.

It’s not practical to stop altogether with the traditional market share campaign – it pays the bills – but it is practical to make small bets on smart people who believe new markets are out there.  If you’re lucky enough to have folks willing to put their careers on the line, competing with no one is a great way to create new markets and secure growth for future generations.

Image credit – mae noelle

Everyone says they want innovation, but they don’t – they want the results of innovation.

Innovation is about bringing to life things that are novel, useful and successful. Novel and useful are nice, but successful pays the bills.  Novel means new, and new means fear; useful means customers must find value in the newness we create, and that’s scary. No one likes fear, and, if possible, we’d skip novel and useful altogether, but we cannot.  Success isn’t a thing in itself, success is a result of something, and that something is novelty and usefulness.

Companies want success and they want it with as little work and risk as possible, and they do that with a focus on efficiency – do more with less and stock price increases.  With efficiency it’s all about getting more out of what you have – don’t buy new machines or tools, get more out of what you have.  And to reduce risk it’s all about reducing newness – do more of what you did, and do it more efficiently.  We’ve unnaturally mapped success with the same old tricks done in the same old way to do more of the same. And that’s a problem because, eventually, sameness runs out of gas.

Innovation starts with different, but past tense success locks us into future tense sameness.  And that’s the rub with success – success breeds sameness and sameness blocks innovation.  It’s a strange duality – success is the carrot for innovation and also its deterrent. To manage this strange duality, don’t limit success; limit how much it limits you.

The key to busting out of the shackles of your success is doing more things that are different, and the best way to do that is with no-to-yes.

If your product can’t do something then you change it so it can, that’s no-to-yes.  By definition, no-to-yes creates novelty, creates new design space and provides the means to enter (or create) new markets.  Here’s how to do it.

Scan all the products in your industry and identify the product that can operate with the smallest inputs.  (For example, the cell phone that can run on the smallest battery.)  Below this input level there are no products that can function – you’ve identified green field design space which you can have all to yourself.   Now, use the industry-low input to create a design constraint.  To do this, divide the input by two – this is the no-to-yes threshold.  Before you do you the work, your product cannot operate with this small input (no), but after your hard work, it can (yes).  By definition the new product will be novel.

Do the same thing for outputs.  Scan all the products in your industry to find the smallest output. (For example, the automobile with the smallest engine.)  Divide the output by two and this is your no-to-yes threshold.  Before you design the new car it does not have an engine smaller than the threshold (no), and after the hard work, it does (yes). By definition, the new car will be novel.

A strange thing happens when inputs and outputs are reduced – it becomes clear existing technologies don’t cut it, and new, smaller, lower cost technologies become viable.  The no-to-yes threshold (the constraint) breaks the shackles of success and guides thinking in a new directions.

Once the prototypes are built, the work shifts to finding a market the novel concept can satisfy.  The good news is you’re armed with prototypes that do things nothing else can do, and the bad news is your existing customers won’t like the prototypes so you’ll have to seek out new customers. (And, really, that’s not so bad because those new customers are the early adopters of the new market you just created.)

No-to-yes thinking is powerful, and though I described how it’s used with products, it’s equally powerful for services, business models and systems.

If you want innovation (and its results), use no-to-yes thinking to find the limits and work outside them.

When it’s time for a tough decision, it’s time to use data.  The idea is the data removes biases and opinions so the decision is grounded in the fundamentals.  But using the right data the right way takes a lot of disciple and care.

The most straightforward decision is a decision between two things – an either or – and here’s how it goes.

The first step is to agree on the test protocols and measure systems used to create the data.  To eliminate biases, this is done before any testing.  The test protocols are the actual procedural steps to run the tests and are revision controlled documents.  The measurement systems are also fully defined.  This includes the make and model of the machine/hardware, full definition of the fixtures and supporting equipment, and a measurement protocol (the steps to do the measurements).

The next step is to create the charts and graphs used to present the data. (Again, this is done before any testing.) The simplest and best is the bar chart – with one bar for A and one bar for B.  But for all formats, the axes are labeled (including units), the test protocol is referenced (with its document number and revision letter), and the title is created.  The title defines the type of test, important shared elements of the tested configurations and important input conditions.   The title helps make sure the tested configurations are the same in the ways they should be.  And to be doubly sure they’re the same, once the graph is populated with the actual test data, a small image of the tested configurations can be added next to each bar.

The configurations under test change over time, and it’s important to maintain linkage between the test data and the tested configuration.  This can be accomplished with descriptive titles and formal revision numbers of the test configurations.  When you choose design concept A over concept B but unknowingly use data from the wrong revisions it’s still a data-driven decision, it’s just wrong one.

But the most important problem to guard against is a mismatch between the tested configuration and the configuration used to create the cost estimate.  To increase profit, test results want to increase and costs wants to decrease, and this natural pressure can create divergence between the tested and costed configurations. Test results predict how the configuration under test will perform in the field.  The cost estimate predicts how much the costed configuration will cost.  Though there’s strong desire to have the performance of one configuration and the cost of another, things don’t work that way.  When you launch you’ll get the performance of AND cost of the configuration you launched.  You might as well choose the configuration to launch using performance data and cost as a matched pair.

All this detail may feel like overkill, but it’s not because the consequences of getting it wrong can decimate profitability. Here’s why:

Profit = (price – cost) x volume.

Test results predict goodness, and goodness defines what the customer will pay (price) and how many they’ll buy (volume).  And cost is cost.  And when it comes to profit, if you make the right decision with the wrong data, the wheels fall off.

Image credit – alabaster crow photographic

If you want to run a brainstorming session to generate a long list of ideas, I’m out. Brainstorming takes the edge off, rounds off the interesting corners and rubs off any texture. If you want me to go away for a while and come back with an idea that can dismantle our business model, I’m in.

If you can use words to explain it, don’t bother – anything worth its salt can’t be explained with PowerPoint. If you need to make a prototype so others can understand, you’ve got my attention.

If you have to ask my permission before you test out an idea that could really make a difference, I don’t want you on my team. If you show me a pile of rubble that was your experiment and explain how, if it actually worked, it could change the game, I’ll run air cover, break the rules, and jump in front of the bullets so you can run your next experiments, whatever they are.

If you load me up to with so many projects I can’t do several I want, you’ll get fewer of yours. If you give me some discretion and a little slack to use it, you’ll get magic.

If, before the first iteration is even drawn up, you ask me how much it will cost, I will tell you what you want to hear. If, after it’s running in the lab and we agree you’ll launch it if I build it, I won’t stop working until it meets your cost target.

If there’s total agreement it’s a great idea, it’s not a great idea, and I’m out. If the idea is squashed because it threatens our largest, most profitable business, I’m in going to make it happen before our competitors do.

If twice you tell me no, yet don’t give me a good reason, I’ll try twice as hard to make a functional prototype and show your boss.

To do innovation, real no-kidding innovation, requires a different mindset both to do the in-the-trenches work and to lead it. Innovation isn’t about following the process and fitting in, it’s about following your instincts and letting it hang out. It’s about connecting the un-connectable using the most divergent thinking.  And contrary to belief, it’s not in-the-head work, it’s a full body adventure.

Innovation isn’t about the mainstream, it’s about the fringes. And it’s the same for the people that do the work.  But to be clear, it’s not what it may look like at the surface. It’s not divergence for divergence’s sake and it’s not wasting time by investigating the unjustified and the unreasonable. It’s about unique people generating value in unique ways. And at the core it’s all guided by their deep intention to build a resilient, lasting business.

image credit: Chris Martin.

There’s a lot of stress in the working world these days, and to me, it all comes down to our blatant disrespect of uncertainty.

In today’s reality, we ask for plans then demand strict adherence to the deliverables – on time, on budget, or else. We treat plans like they’re chiseled in granite, when really it should be more like dry erase markers and a whiteboard. Our markets are uncertain; customers’ behaviors are uncertain; competitors’ actions are uncertain; supply chains are uncertain, yet our plans are plans don’t reflect that reality. And when we expect absolute predictability and accountability, we create stress and anxiety and our people don’t want to try new things because that adds another level of uncertainty.

With a flexible, rubbery plan the first step informs the second, and this is the basis for the logical shift from robust plans to resilient ones. Plans should be less about forcing adherence and more about recognizing deviation. Today’s plans demand early recognition of something that did follow the plan and today’s teams must have the authority to respond quickly. However, after years of denying the powerful force of uncertainty and shooting the messenger, we’ve trained our people to hide the deviations. And, with our culture of control and accountability, our teams require our approval before any type of change, so their response time is, well, not timely.

At our core, we know uncertainty is a founding principle in our universe, and now it’s time to behave that way. It’s time to look inside and decide to embrace uncertainty. Accept it or not, acknowledge it or not, uncertainty is here to stay. Here are some words to guide your journey:

• Resilient not robust.
• Early detection, fast response.
• Many small plans, done in parallel.
• Do more of what works, and less of what doesn’t.
• Plans are meant to be re-planned.

And if you’re into innovation, this applies doubly.

Image credit – dfbphotos.

Big Data is powerful – measure what people do on the web, summarize the patterns, and use the information for good. These data sets are so powerful because they’re bigger than big; there’s little bias since the data collection is automatic; and the analysis is automated. There’s huge potential in the knowledge of what people click, what pages they land on, and what place the jump from.

It’s magical to think about what can be accomplished with the landing pages and click-through rates for any demographic you choose. Here are some examples:

• This is the type of content our demographic of value (DOV) lands on, and if we create more content like this we’ll get more from them to land where we want.
• These are the pages our DOV jump from, and if we advertise there more of our DOV will see our products.
• This is the geographic location of our DOV when they land on our website, and if we build out our sales capacity in these locations we’ll sell more.
• This is the time slot when our DOV is most active on their smart phones, and if we tweet more during that time we’ll reach more of them.

But just as there’s immense power knowing the actions of your DOV (what they click on), there are huge assumptions on what it all means. Here are two big ones:

1. All clicks are created equal.
2. When more see our content, more will do what we want.

Here is an example of three members (A, B, C) of your demographic of interest who take the same measurable action but with different meaning behind it:

Member A, after four drinks, speeds home recklessly; loses control of the car; crashes into your house; and parks the car in your living room.

Member B, after grocery shopping, drives home at the speed limit; the front wheel falls off due to a mechanical problem; loses control of the car; crashes into your house; and parks the car in your living room.

Member C, after volunteering at a well-respected non-profit agency, drives home in a torrential rain 15 miles per hour below the speed limit; a child on a bicycle bolts into the lane without warning and C swerves to miss the child; loses control of the car; crashes into your house; and parks the car in your living room.

All three did the same thing – crashed into your house – but the intent, the why, is different. Same click, but not equal. And when you put your content in front of them, regardless of what you want them to do, A, B, and C will respond differently. Same DOV, but different intentions behind their actions.

Big Data, with its focus on the whats, is powerful, but can be made stereoscopic with the addition of a second lens that can see the whys. Problem is, the whys aren’t captured in a clean, binary way – not transactional but conversational – and are subject to interpretation biases where the integers of the whats are not.

With people, action is preceded by intent, and intent is set by thoughts, feelings, history, and context. And the best way to understand all that is through their stories. If you collect and analyze customer stories you’ll better understand their predispositions and can better hypothesize and test how they’ll respond.

In the Big Data sense, Nirvana for stories is a huge sample size collected quickly with little effort, analysis without biases, and direct access to the stories themselves.

New data streams are needed to collect the whys in a low overhead way, and new methods are needed to analyze them quickly and without biases. And a new perspective is needed to see not only the amazing power of Big Data (the whats), but the immense potential of seeing the what’s with one eye and the whys with the other.

Keep counting the whats with traditional Big Data work – there’s real value there. But also keep one eye on the horizon for new ways to collect and analyze the whys (customer stories) in a Big Data way.

Collection and analysis of customer stories, if the sample size is big enough and biases small enough, is the best way I know to look through the fog and understand emerging customer needs and emerging markets.

If you can figure out how to do it, it will definitely be worth the effort.

The Holy Grail of marketing is to identify emerging customer needs before anyone else and satisfy them to create new markets. It has been a long and fruitless slog as emerging needs have proven themselves elusive. And once candidates are identified, it’s a challenge to agree which are the game-changers and which are the ghosts. There are too many opinions and too few facts. But there’s treasure at the end of the rainbow and the quest continues.

Emerging things are just coming to be, just starting, so they appy to just a small subset of customers; and emerging things are new and different, so they’re unfamiliar. Unfamiliar plus small same size equals elusive.

I don’t believe in emerging customer needs, I believe in emergent customer behavior.

Emergent behavior is based on actions taken (past tense) and is objectively verifiable. Yes or no, did the customer use the product in a new way? Yes or no, did the customer make the product do something it wasn’t supposed to? Did they use it in a new industry? Did they modify the product on their own? Did they combine it with something altogether unrelated? No argument.

When you ask a customer how to improve your product, their answers aren’t all that important to them. But when a customer takes initiative and action, when they do something new and different with your product, it’s important to them. And even when just a few rouge customers take similar action, it’s worth understanding why they did it – there’s a good chance there’s treasure at the end of that rainbow.

With traditional VOC methods, it has been cost prohibitive to visit enough customers to learn about a handful at the fringes doing the same crazy new thing with your product. Also, with traditional VOCs, these “outliers” are thrown out because they’re, well, they’re outliers. But emergent behavior comes from these very outliers. New information streams and new ways to visualize them are needed to meet these challenges.

For these new information streams, think VOC without the travel; VOC without leading the witness; VOC where the cost of capturing their stories is so low there are so many stories captured that it’s possible to collect a handful of outliers doing what could be the seed for the next new market.

To reduce the cost of acquisition, stories are entered using an app on a smart phone; to let emergent themes emerge, customers code their own stories with a common, non-biasing set of attributes; and to see patterns and outliers, the coded stories are displayed visually.

In the past, the mechanisms to collect and process these information streams did not exist. But they do now.

I hope you haven’t given up on the possibility of understanding what your customers will want in the near future, because it’s now possible.

I urge you to check out SenseMaker.

It’s pretty clear that innovation is the way to go. There’s endless creation of new technologies, new materials, and new processes so innovation can create new things to sell. And there are multiple toolsets and philosophies to get it done, but it’s difficult.

When doing new there’s no experience, no predictions, no certainty. But innovation is no dummy and has come up with a way to overcome the uncertainty. It builds knowledge of systems through testing – build it, test it, measure it, fix it. Not easy, but doable. And what makes it all possible is the repeatable response of things like steel, motors, pumps, software, hard drives. Push on them repeatably and their response is repeatable; stress them in a predictable way and their response is predictable; break them in a controlled way and the failure mode can be exercised.

Once there’s a coherent hypothesis that has the potential to make magic, innovation builds it in the lab, creates a measurement system to evaluate goodness, and tests it. After the good idea, innovation is about converting the idea into a hypothesis – a prediction of what will happen and why – and testing them early and often. And once they work every-day-all-day and make into production, the factory measures them relentlessly to make sure the goodness is shipped with every unit, and the data is religiously plotted with control charts.

The next evolution of innovation will come from systematically improving people systems. There are some roadblocks but they can be overcome. In reality, they already have been overcome it’s just that no one realizes it.

People systems are more difficult because their responses are not repeatable – where steel bends repeatably for a given stress, people do not. Give a last minute deliverable to someone in a good mood, and the work gets done; give that same deliverable to the same person on a bad day, and you get a lot of yelling. And because bad moods beget bad moods, people modify each other’s behavior. And when that non-repeatable, one-person-modifying-another response scales up to the team level, business unit, company, and supply chain, you have a complex adaptive system – a system that cannot be predicted. But just as innovation of airliners and automobiles uses testing to build knowledge out of uncertainty, testing can do the same for people systems.

To start, assumptions about how people systems would respond to new input must be hardened into formal hypotheses. And for the killer hypotheses that hang together, an experiment is defined; a small target population is identified; a measurement system created; a baseline measurement is taken; and the experiment is run. Data is then collected, statistical analyses are made, and it’s clear if the hypothesis is validated or not. If validated, the solution is rolled out and the people system is improved. And in a control chart sense, the measurement system is transferred to the whole system and is left to run continuously to make sure the goodness doesn’t go away. If it’s invalidated, another hypothesis is generated and the process is repeated. (It’s actually better to test multiple hypotheses in parallel.)

In the past, this approach was impossible because the measurement system did not exist. What was needed was a simple, mobile data acquisition system for “people data”, a method to automatically index the data, and a method to quickly process and display the results. The experimental methods were clear, but there was no response for the experiments. Now there is.

People systems are governed by what people think and feel, and the stories they tell are the surrogates for their thoughts and feelings. When an experiment is conducted on a people system, the stories are the “people data” that is collected, quantified, and analyzed. The stories are the response to the experiment.

It is now possible to run an experiment where a sample population uses a smart phone and an app to collect stories (text, voice, pictures), index them, and automatically send them to a server where some software groups the stories and displays them in a way to see patterns (groups of commonly indexed stories). All this is done in real time. And, by clicking on a data point, the program brings up the story associated with that data point.

Here’s how it works. The app is loaded, people tell their stories on their phone, and a baseline is established (a baseline story pattern). Inputs or constraints are changed for the target population and new stories are collected. If the patterns change in a desirable way (statistical analysis is possible), the new inputs and constraints are rolled out. If the stories change in an undesirable way, the target population reverts back to standard conditions and the next hypothesis is tested.

Unbiased, real time, continuous information streams to make sense of your people systems is now possible. Real time, direct connection to your employees and your customers is a reality, and the implications are staggering.

Thank you Dave Snowden.

From the outside it’s unclear how things happen; but from the inside it’s clear as day. No, it’s not your bulletproof processes; it’s not your top down strategy; and it’s not your operating plans. It’s your people.

At some level everything happens like this:

An idea comes to you that makes little sense, so you drop it. But it comes again, and then again. It visits regularly over the months and each time reveals a bit of its true self. But still, it’s incomplete. So you walk around with it and it eats at you; like a parasite, it gets stronger at your expense. Then, it matures and grows its voice – and it talks to you. It talks all the time; it won’t let you sleep; it pollutes you; it gets in the way; it colors you; and finally you become the human embodiment of the idea.

And then it tips you. With one last push, it creates enough discomfort to roll over the fear of acknowledging its existence, and you set up the meeting.

You call the band and let them know it’s time again to tour. You’ve been through it before and you all know deal. You know your instruments and you know how to harmonize. You know what they can do (because they’ve done it before) and you trust them. You sing them the song of your idea and they listen. Then you ask them to improvise and sing it back, and you listen. The mutual listening moves the idea forward, and you agree to take a run at it.

You ask how it should go. The lead vocalist tells you how it should be sung; the lead guitar works out the fingering; the drummer beats out the rhythm; and the keyboardist grins and says this will be fun. You all know the sheet music and you head back to your silos to make it happen.
In record time, the work gets done and you get back together to review the results. As a group you decide if the track is good enough play in public. If it is, you set up the meeting with a broader audience to let them hear your new music. If it’s not, you head back to the recording studio to amplify what worked and dampen what didn’t. You keep re-recording until your symphony is ready for the critics.

Things happen because artists who want to make a difference band together and make a difference. With no complicated Gantt chart, no master plan, no request for approval, and no additional resources, they make beautiful music where there had been none. As if from thin air, they create something from nothing. But it’s not from thin air; it’s from passion, dedication, trust, and mutual respect.

The business books over-complicate it. Things happen because people make them happen – it’s that simple.

There’s a natural hierarchy to work.  Your job, if you choose to accept it is to climb the ladder of hierarchy rung-by-rung. Here’s how to go about it:

Level 1. Work you can say no to – Say no to it. Say no effectively as you can, but say it. Saying no to level 1 work frees you up for the higher levels.

Level 2. Work you can get someone else to do – Get someone else to do it. Give the work to someone who considers the work a good reach, or a growth opportunity. This isn’t about shirking responsibility, it’s about growing young talent. Maybe you can spend a little time mentoring and the freed up time doing higher level work. Make sure you give away the credit so next time others will ask you for the opportunity to do this type of work for you.

Level 3. Work you’ve done before, but can’t wiggle out of – Do it with flair, style, and efficiency; do it differently than last time, then run away before someone asks you to do it again. Or, do it badly so next time they ask someone else to do it. Depending on the circumstance, either way can work.

Level 4. Work you haven’t done before, but can’t wiggle out of – Come up with a new recipe for this type of work, and do it so well it’s unassailable. This time your contribution is the recipe; next time your contribution is to teach it to someone else. (See level 2.)

Level 5. Work that scares others – Figure out why it scares them; break it into small bites; and take the smallest first bite (so others can’t see the failure).  If it works, take a bigger bite; if it doesn’t, take a different smallest bite.  Repeat, as needed. Next time, since you’ve done it before, treat it like level 3 work. Better still, treat it like level 2.

Level 6. Work that scares you – Figure out why it scares you, then follow the steps for level 5.

Level 7. Work no one knows to ask you to do – You know your subject matter better than anyone, so figure out the right work and give it a try.  This flavor is difficult because it comes at the expense of work you’re already signed up to do and no one is asking you to do it. But you should have the time because you followed the guidance in the previous levels.

Level 8. Work that obsoletes the very thing that made your company successful – This is rarified air – no place for the novice. Ultimately, someone will do this work, and it might as well be you.  At least you’ll be able to manage the disruption on your own terms.

In the end, your task, if you choose to accept it, is to migrate toward the work that obsoletes yourself. For only then can you start back at level 1 on the ladder of your own making.

If you’re working in a company you like, and you want it to be around in the future, you want to know if it will grow.  If you’re looking to move to a new company, you want to know if it has legs – you want to know if it will grow. If you own stock, you want to know if the company will grow, and it’s the same if you want to buy stock.  And it’s certainly the case if you want to buy the whole company – if it can grow, it’s worth more.

To grow, a company has to differentiate itself from its competitors.  In the past, continuous improvement (CI) was a differentiator, but today CI is the minimum expectation, the cost of doing business.  The differentiator for growth is discontinuous improvement (DI).

With DI, there’s an unhealthy fascination with idea generation.  While idea generation is important, companies aren’t short on ideas, they’re short on execution.  But the one DI differentiator is the flavor of the ideas.  To do DI a company needs ideas that are radically different than the ones they’re selling now.  If the ideas are slightly twisted variants of today’s products and business models, that’s a sure sign continuous improvement has infiltrated and polluted the growth engine. The gears of the DI engine are gummed up and there’s no way the company can sustain growth.  For objective evidence the company has the chops to generate the right ideas, look for a process that forces their thinking from the familiar, something like Jeffrey Baumgartner’s Anticonventional Thinking (ACT).

For DI-driven growth, the ability to execute is most important.  With execution, the first differentiator is how the company investigates radically new ideas.  There are three differentiators – a focus on speed, a “market first” approach, and the use of minimum viable tests (MVTs).  With new ideas, it’s all about how fast you can learn, so speed should come through loud and clear.  Without a market, the best idea is worthless, so look for “market first” thinking.  Idea evaluation starts with a hypothesis that a specific market exists (the market is clearly defined in the hypothesis) which is evaluated with a minimum viable test (MVT) to prove or disprove the market’s existence.  MVTs should error on the side of speed – small, localized testing.  The more familiar minimum viable product (MVP) is often an important part of the market evaluation work.  It’s all about learning about the market as fast as possible.

Now, with a validated market, the differentiator is how fast company can rally around the radically new idea and start the technology and product work.  The companies that can’t execute slot the new project at the end of their queue and get to it when they get to it.  The ones that can execute stop an existing (lower value) project and start the new project yesterday.  This stop-to-start behavior is a huge differentiator.

The company’s that can’t execute take a ready-fire-aim approach – they just start.  The companies that differentiate themselves use systems thinking to identify gaps in resources and capabilities and close them. They do the tough work of prioritizing one project over another and fully staff the important ones at the expense of the lesser projects.  Rather than starting three projects and finishing none, the companies that know how to do DI start one, finish one, and repeat.  They know with DI, there’s no partial credit for a project that’s half done.

All companies have growth plans, and at the highest level they all hang together, but some growth plans are better than others.  To judge the goodness of the growth plan takes a deeper look, a look into the work itself.  And once you know about the work, the real differentiator is whether the company has the chops to execute it.

Image credit – John Leach.