Shipulski On Design

Archive for the ‘Seeing Things As They Are’ Category

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.

When the train is getting ready to pull out of the station, and you know in your heart the destination isn’t right, what do you do? If you still had time to talk to the conductor, would you? What would you say? If your railroad is so proud of getting to the destination on time it cannot not muster the courage to second guess the well-worn time table, is all hope lost?

The trouble with thinking the destination isn’t right is that it’s an opinion. Your opinion may be backed by years of experience, good intuition, and a kind heart, but it’s still an opinion. And the rule with opinions – if there’s one, there are others. And as such, there’s never consensus on the next destination.

But even as the coal is being shoveled into the firebox and the boiler pressure is almost there, there’s still time to take action. If the train hasn’t left the station, there’s still time. Don’t let the building momentum stop you from doing what must be done. Yes, there’s the sunk cost of lining everything up and getting ready to go, but, no, that doesn’t justify a journey down the wrong track. Find the conductor and bend her ear. Be clear, be truthful, and be passionate. Tell her you’re not sure it’s the wrong destination, but you’re sure enough to pull the pressure relieve valve and take some time to think more about what’s about to happen.

No one wants to step in front of a moving train. It’s no fun for anyone, and dangerous for the brave soul standing in the tracks. And it’s a failure of sorts if it comes to that. The best way to prevent a train from heading down the wrong track is candid discussions about the facts and clarity around why the journey should happen. But we need to do a better job at having those tough discussions earlier in the process.

Unfortunately in business today, the foul underbelly of alignment blocks the difficult decisions that should happen. We’ve mapped disagreement to foul play and amoral behavior, and our organizations make it clear that supporting the right answer, right from the get-go, is the right answer. The result is premature alignment and unwarranted alignment without thoughtful, effective debate on the merits. For some reason, it’s no longer okay to disagree.

Difficult discussions are difficult. And prolonging them only makes them more difficult. In fact, that’s sometimes a tactic – push off the tough conversations until the momentum rolls over all intensions to have them.

Hold onto the fact that your company wants the tough conversations to have them. In the short term, things are more stressful, but in the long term thing are more profitable. Remember, though sometimes bureaucracy makes it difficult, you are paid to add your thinking into the mix. And keep in mind you have a valuable perspective that deserves to be valued.

When the train is leaving the station, it’s the easiest time to recognize the tough discussions need to happen but it’s the most difficult time to have them. Earlier in the project it’s easier to have them and far more difficult to recognize they should happen.

Going forward, modify your existing processes to cut through inappropriate momentum building. And better still, use your knowledge of how your organization works to create mechanisms to trigger difficult conversations and prevent premature alignment.

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.

There are many flavors of innovation – incremental, disruptive, and seven flavors in between. And there is lots of argument about the level of innovation – mine’s radical and yours isn’t; that’s just improving what we already have; that’s too new – no one will ever buy it. We want to label the work in order to put it in the right bucket, to judge if we’re doing the right work. But the labels get in the way – they’re loaded with judgments, both purrs and snarls.

Truth is, innovation work falls on a continuum of newness and grouping them makes little sense. And, it’s not just newness that matters – it’s how the newness fits (or doesn’t) within the context of how things happen today and how customers think they should happen tomorrow. So what to do?

Customers notice the most meaningful innovations, and they notice the most meaningful ones before the less meaningful. Evaluate the time it takes a customer to notice the innovation and there may be hope to evaluate the importance of the innovation.

The technology reduces cost, and at the end of the month when the numbers are rolled up the accountants can see the improvement. This is real improvement, but there’s a significant lag and the people doing the work don’t see it as meaningful. This one’s a tough sell – buy this new thing, train on it, use it for three months, and if you keep good records and do some nifty statistics you’ll see an improvement.

The technology reduces scrap, and at the end of the week the scrap bin will be half full instead of fully full. Scrap is waste and waste reduction is real improvement. This is an easier sell – buy it and train on it and at the end of the week you’ll notice a reduction in scrap. This is important but only to those who are measured on scrap. And today the scrap is emptied every week, now we can empty it every other week. The time to notice is reduced, but the impact may not be there.

The technology increases throughput, and at the end of the shift the bins will be fuller than full. Here – try it for a shift and see what you think. If you like it, you can buy it. I’ll be back tomorrow with a quote. This is noticeable within eight hours. And at the end of eight hours there are more things that can be sold. That’s real money, and real money gets noticed.

The technology makes the product last two hours instead of one. Here – try it for a couple hours. I’ll go get a coffee and come back and see what you think. You won’t have to stop the machine nearly as often and you’ll put more parts into finished goods inventory. The technology gets noticed within two hours and the purchase order is signed in three.

Where the old technology was load, this is quiet. Don’t bother with ear protection, just give it a go. Pretty cool, isn’t it. Go get your boss and I’ll sell you a couple units right now. This one shows its benefits the end user right away – first try.

The most meaningful innovations get noticed instantly. Stop trying to label the innovation and simply measure how long it takes your customer to notice.

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.

Put yourself out there. Let it hang out. Give it a try. Just do it. The reality is few do it, and fewer do it often. But why?

In a word, fear. But it cuts much deeper than a word. Here’s a top down progression:

What will they think of your idea? If you summon the courage to say it out loud, your fear is they won’t like it, or they’ll think it’s stupid. But it goes deeper.

What with they think of you? If they think your idea is stupid, your fear is they’ll think you’re stupid. But so what?

How will it conflict with what you think of you? If they think you’re stupid, your fear is it will conflict with what you think of you. Now we’re on to it – full circle.

What do you think of you? It all comes down to your self-image – what you think is it and how you think it will stand up against the outside forces trying to pull it apart. The key is “what you think” and “how you think”. Like all cases, perception is reality; and when it comes to judging ourselves, we judge far too harshly. Our severe self-criticism deflates us far below the waterline of reality, and we see ourselves far shallower than our actions decree.

You’re stronger and more capable than you let yourself think. But no words can help with that; for that, only action will do. Summon the courage to act and take action. Just do it. And to calm yourself before you jump, hold onto this one fact – others’ criticism has never killed anyone. Stung, yes. Killed, no. Plain and simple, you won’t die if you put yourself out there. And even the worst bee stings subside with a little ice.

I’m not sure why we’re so willing to abdicate responsibility for what we think of ourselves, but we do. So where you may have abdicated responsibility in the past, in the now it’s time to take responsibility. It’s time to take responsibility and act on your own behalf.

Fear is real, and you should acknowledge it. But also acknowledge you give fear its power. Feel the fear, be afraid. But don’t succumb to the power you give it.

Put yourself out there. Do it tomorrow. You won’t die. And I bet you’ll surprise others.

But I’m sure you’ll surprise yourself more.

In school the problems you were given weren’t really problems at all. In school you opened the book to a specific page and there, right before you in paragraph form and numbered consecutively, was a neat row of “problems”. They were fully-defined, with known inputs, a formal equation that defined the system’s response, and one right answer. Nothing extra, nothing missing, nothing contradictory. Today’s problems are nothing like that.

Today’s problems don’t have a closed form solution; today’s problems don’t have a right answer. Three important factors come into play: companies and their systems are complex; the work, at some level, is always new; and people are always part of the equation.

It’s not that companies have a lot of moving parts (that makes them complicated); it’s that the parts can respond differently in different situations, can change over time (learn), and the parts can interact and change each others’ response (that’s complex). When you’re doing work you did last time, there’s a pretty good chance the system will perform like it did last time. But it’s a different story when the inputs are different, when the work is new.

When the work is new, there’s no precedent. The inputs are new and the response is newer. Perturb the system in a new way and you’re not sure how it will respond. New interactions between preciously unreactive parts make for exciting times. The seemingly unconnected parts ping each other through the ether, stiffen or slacken, and do their thing in a whole new way. Repeatability is out the window, and causal predictability is out of the question. New inputs (new work) slathers on layers of unknownness that must be handled differently.

Now for the real complexity culprit – people. Companies are nothing more than people systems in the shape of a company. And the work, well, that’s done by people. And people are well known to be complex. In a bad mood, we respond one way; confident and secure we respond in another. And people have memory. If something bad happened last time, next time we respond differently. And interactions among people are super complex – group think, seniority, trust, and social media.

Our problems swim with us in a hierarchical sea of complexity. That’s just how it is. Keep that in mind next time you put together your Gantt chart and next time you’re asked to guarantee the outcome of an innovation project.

Complexity is real, and there are real ways to handle it. But that’s for another time. Until then, I suggest you bone up on Dave Snowden’s work. When it comes to complexity, he’s the real deal.

Image credit – miguelb.

Planning is important work, but it’s non-value added work. Short and sweet – planning is waste.

Lean has taught us waste should be reduced, and the best way to reduce waste from planning is to spend less time planning. (I feel silly writing that.) Lean has taught us to reduce batch size, and the best way to reduce massive batch size of the annual planning marathon is to break it into smaller sessions. (I feel silly writing that too.)

Unreasonable time constraints increase creativity. To create next year’s plan, allocate just one for the whole thing. (Use a countdown timer.) And, because batch size must be reduced, repeat the process monthly. Twelve hours of the most productive planning ever, and countless planning hours converted into value added work.

Defining the future state and closing the gap is not the way to go. The way to go is to define the current state (where you are today) and define how to move forward. Use these two simple rules to guide you:

• Do more of what worked.
• Do less of what didn’t.

Here’s an example process:

The constraint – no new hires. (It’s most likely the case, so start there.)

Make a list of all the projects you’re working on. Decide which to stop right now (the STOP projects) and which you’ll finish by the end of the month (the COMPLETED projects). The remaining projects are the CONTINUE projects, and, since they’re aptly named, you should continue them next month. Then, count the number of STOP and COMPLETED projects – that’s the number of START projects you can start next month.

If the sum of STOP and COMPLETED is zero, ask if you can hire anyone this month. If the answer is no, see you next month.

If the sum is one, figure out what worked well, figure out how to build on it, and define the START project. Resources for the START project should be the same as the STOP or COMPLETED project.

If the sum is two, repeat.

Now ask if you can hire anyone this month. If the answer is no, you’re done. If the answer is yes, define how many you can hire.

With your number in hand, and building on what worked well, figure out the right START project. Resources must be limited by the number of new hires, and the project can’t start until the new hire is hired. (I feel silly writing that, but it must be written.) Or, if a START project can’t be started, use the new resource to pile on to an important CONTINUE project.

You’re done for the month, so send your updated plan to your boss and get back to work.

Next month, repeat.

The process will evolve nicely since you’ll refine it twelve times per year.

Ultimately, planning comes down to using your judgment to choose the next project based on the resources you’re given. The annual planning process is truly that simple, it’s just doesn’t look that way because it’s spread over so many months. So, if the company tells its leaders how many resources they have, and trusts them to use good judgment, yearly planning can be accomplished in twelve hours per year (literally). And since the plan is updated monthly, there’s no opportunity for emergency re-planning, and it will always be in line with reality.

Less waste and improved quality – isn’t that what lean taught us?

I’ve had some great teachers in my life, and I’m grateful for them.  They taught me their hard-earned secrets, their simple secrets.  Though each had their own special gifts, they all gave them in the same way – they asked the simplest questions.

Today’s world is complex – everything interacts with everything else; and today’s pace is blistering – it’s tough to make time to understand what’s really going on.  To battle the complexity and pace, force yourself to come up with the simplest questions.  Here are some of my favorites:

For new products:

• Who will buy it?
• What must it do?
• What should it cost?

For new technologies:

• What problem are you trying to solve?
• How will you know you solved it?
• What work hasn’t been done before?

For new business models:

• Why are you holding onto your decrepit business model?

For problems:

• Can you draw a picture of it on one page?
• Can you make it come and go?

For decisions:

• What is the minimum viable test?
• Why not test three or four options at the same time?

For people issues:

• Are you okay?
• How can I help you?

For most any situation:

• Why?

These questions are powerful because they cut through the noise, but their power couples them to fear and embarrassment – fear that if you ask you’ll embarrass someone.  These questions have the power to make it clear that all the activity and hype is nothing more than a big cloud of dust heading off in the wrong direction. And because of that, it’s scary to ask these questions.

It doesn’t matter if you steal these questions directly (you have my permission), twist them to make them your own, or come up with new ones altogether.  What matters is you spend the time to make them simple and you summon the courage to ask.

Image credit — Montecruz Foto.

It’s harder than ever to innovate, and getting harder.

The focus on growth can be empowering, but when coupled with signed-in-blood accountability, empowering turns to puckering.  It’s an unfair double-bind. Damned if you try something new and it doesn’t work, and damned if you stay the course and don’t hit the numbers.  The most popular approach seems to be to do more of what worked.  A good approach, but not as good as it’s made out to be.

Doing more of what worked is good, and it works.  But it can’t stand on its own.  With today’s unreasonable workloads, every resource is fully booked and before doing more of anything, you’ve got to do less of something else.  ‘More of what worked’ must walk hand-in-hand with ‘Stop what didn’t work.’  Without stopping, without freeing up resources, ‘more of what worked’ is insufficient and unsustainable.

But even the two together are insufficient, and there’s a much needed third leg to stabilize the stool – ‘starting new work.’  Resources freed by stopping are allocated to starting new work, and this work, also known as innovation, is the major source of growth.

‘More of what worked’ is all about productivity – doing more with the same resources; and so is ‘stopping what didn’t work’ – reclaiming and reallocating ineffective resources. Both are important, but more importantly – they’re not innovation.

As you’re well aware, the rules are changing faster than ever, and at some point what worked last year won’t work this year. The only way to stay ahead of a catastrophe is to make small bets in unproven areas.  If the bets are successful, they turn into profitable innovation and growth. But the real value is the resiliency that comes from the ritualistic testing/learning cycles.

Going all-in on what worked last year is one of the riskiest bets you can make.

Things are cyclic, but there seems to be no end to the crusade of continuous improvement. (Does anyone remember how the Crusades turned out?) If only to take the edge off, there needs to be an injection of absurdity.

There’s no pressure with absurdity – no one expects an absurd idea to work.  If you ask for an innovative idea, you’ll likely get no response because there’s pressure from the expectation the innovative idea must be successful. And if you do get a response, you’ll likely get served a plain burrito of incremental improvement garnished with sour cream and guacamole to trick your eye and doused in hot sauce to trick your palate. If you ask for an absurd idea, you get laughter and something you’ve never heard before.

When drowning in the sea of standard work, it takes powerful mojo to save your soul.  And the absurdity jetpack is the only thing I know with enough go to launch yourself to the uncharted oasis of new thinking.  Immense force is needed because continuous improvement has serious mass – black hole mass. Like with light, a new idea gets pulled over the event horizon into the darkness of incremental thinking.  But absurdity doesn’t care.  It’s so far from the center lean’s pull is no match.

But to understand absurdity’s superpower is to understand what makes things absurd.  Things are declared absurd when they cut against the grain of our success.  It’s too scary to look into the bright sun of our experiences, so instead of questioning their validity and applicability, the idea is deemed absurd.  But what if the rules have changed and the fundamentals of last year’s success no longer apply?  What if the absurd idea actually fits with the new normal?  In a strange Copernican switch, holding onto to what worked becomes absurd.

Absurd ideas sometimes don’t pan out.  But sometimes they do.  When someone laughs at your idea, take note – you may be on to something.  Consider the laughter an artifact of misunderstanding, and consider the misunderstanding a leading indicator of the opportunity to reset customer expectations.  And if someone calls your idea absurd, give them a big hug of thanks, and get busy figuring out how to build a new business around it.