Brainstorming, Going Forth, Grand Canyon, Ideation, Innovation, Lenses, Prototyping, Systems, Tool

The Donkey Stuck in the Status Quo

Once upon a time, there was a donkey. This donkey, with all other conditions being the same, would eat from the hay closest to him. Kind of an easy win strategy. Well one day the donkey was walking down the road and his hunger grew immensely. There was a bail of hay up ahead, and an equal sized bail of hay, the exact same distance behind him. With neither being closer, the donkey stood still, not choosing one nor the other until he died.

The end.

This heartwarming tale is called Buridan’s donkey and it is a paradox about free will. Often, we become the donkey. There are disruptive ideas out there to follow, but we sit in the middle, too afraid to give up one for the other.

Nassim Taleb, in his book Antifragile, says that “when some systems become stuck in a dangerous impasse, randomness and only randomness can unlock them and set them free.” The donkey just needs a little push; just one fly to land on its ear and nudge it towards one hay pile.

Organizations, teams, and people need that dose of random, unexpected, and different to get ideas moving.gonowhere

These little nudges can feel scary, but there are ways to minimize the risk and fear. Start by breaking down the problem you want to solve into its core pieces; boil it down to its base essence. Then start looking for small things to move the needle. If you were to solve this problem, what’s the first thing you need to figure out? Find a way to prototype and test that thing. Prototyping is great for keeping cost low and risks at a minimum (especially when it is with paper).

“Prototyping is one of the most effective ways to both jump-start our thinking and to guide, inspire, and discipline an experimental approach.” – Peter Sims, Little Bets

Regularly we will need to unstick ourselves. Each idea we naturally think of is a byproduct of your point of view, past experiences, skill set, and what you had for lunch. That’s why I am going to give you a tool to help, a tool forged in process-driven chaos. It’s called…

Donkey Dice

The rules here are very simple. In fact, there’s only three:

  • CARD: On a notecard, write down six lenses and number them.
    • Things like “How would WordPress do it?”, “How would I never solve this?”, or use a random word. (Random words should be generated before each use of Donkey Dice.)
  • ROLL: Roll 1 six-sided die and identify lens selected
  • THINK: Generate ideas with lens

It’s simple, but effective. As you get good at Donkey Dice, expand your card up to 12 lenses and use two six-sided dice. You can unlock the extreme level and list 20 lenses and roll a twenty-sided die. Soon your donkey will be making his way towards relief, instead of stuck in the muck of status quo.

“I wondered about the explorers who’d sailed their ships to the end of the world. How terrified they must have been when they risked falling over the edge; how amazed to discover, instead, places they had seen only in their dreams.” -Jodi Picoult, Handle With Care

 

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Diffusion of Innovation, Going Forth, Grand Canyon, Innovation, Micro-Patterns, Persona, Understanding the Customer

Where did I leave that Grand Canyon?

My great friend, Joseph Greaser, posted an excellent write up about understanding your audience. Especially when it comes to innovation. You can read all about his case study and data that illustrate this point clearly. Read his post, follow his blog, and then come right back.

Flat, average desert
The Grand Canyon after averaging out with the rest of the land.

The next time you are stuck needing some small talk, here’s a little trivia for you. The average elevation for the state of Arizona is roughly 4,100 feet. Yet that fact covers up a big hole in the data. Actually, one of the biggest holes. The Grand Canyon is also in Arizona. Its elevation bottoms out at only 70 feet in the canyon. Now you’re saying “Of course it glosses over the Grand Canyon. That’s what an average does. It smoothes over the really high peaks and really low valleys.”

So then why do we use the average when trying to understand your customer?

The whole point of understanding your customer is to understand their pain points, their usage, and what innovations they would gain from. But if we keep just looking at average data to “get a feel” for how the whole group is using your prototype, then you could be missing grand canyons of opportunity.

Let’s change the lens for this. Imagine you are a teacher and you are looking at the grades for your students at the end of the year. Half of your class ended with a grade of 100%, the other half ended with a grade of 50%. Would you just average them out and say that your class earned a 75%? You wouldn’t unless you wanted to be looking for a different line of work in the fall. No, you would see two distinct “user-groups” in your classroom. Regardless of grade, both user-groups need your attention.

You would work hard to understand why it wasn’t working for your 50-percenters. You would try new things, different strategies, and observe to identify their pain points. Even your 100-percenters need you. You need to observe them as well to find what is working, try to push them to new territory, and give them some challenges. You would be doing so much to understand your students.

Similarly in innovation, we must journey to the bottom of data canyon to understand our customers.

Let’s look at what the average leads us to. Based on analytics, a website was tracking an average of 5 minutes per session duration. Remembering what we can about averages from freshman year, we imagine the graph to look like this beauty.

averageExpected

Yet, through the magic of mathematics, this graph is also equally likely and just as valid.

averageUnexpected

Whoa! Talk about two completely different use cases for this website. Let’s put the graphs together to get the whole picture.

averageCombined

Only in some cases would innovating for the average actually provide some benefit to our customers. There are other valid cases where innovating for the average wouldn’t benefit the customers at all. Talk about wasted time, development, and effort.

Public Domain Image Rogers Everett - Based on Rogers, E. (1962) Diffusion of innovations. Free Press, London, NY, USA.
Public Domain Image
Rogers Everett – Based on Rogers, E. (1962) Diffusion of innovations. Free Press, London, NY, USA.

Another great example of this is to look at the Diffusion of Innovations curve. Ignore the extremes for now and look at what aiming for the average would get you. You would be nestled between the Early Majority and the Late Majority of customers. Your innovation would be targeting a user-group that is torn between being scared and skeptical of change. They would be wanting to get something out of your innovation, but at the same time they just don’t want to get left behind.

So what is a good innovator to do? One thing that we are always fond of is looking for micro-patterns instead of macro-solutions. Macro-solutions are the golden bullet, “this will work for everyone” type of product. These have their benefit in some instances but not when you are trying to understand your customers. Remember that averages smooth out the mountains and canyons that customers experience. Macro-solutions need averages to survive.

Micro-patterns help shed light on your customer personas. Go back to the classroom scenario, even though it is simplistic. Just looking at the grade data beyond the average shows us that their are at least two distinct customer personas in the class. It is ok to have multiple personas as long as you understand that they have different needs and desires from your product. The students with 100% grades have different demands from the classroom than the students with 50% grades. Your job as the innovator is to decide which group to innovate for.

The more complex your data is, the more micro-patterns there may be… and this is ok. Complex is neither good nor bad, it just is. As an innovator you aren’t here to judge numbers, you’re here to listen to their stories. And stories can be as deep as the Grand Canyon sometimes, but you have to make the trek to the bottom via mule to truly understand the customers there. Don’t let the shiny averages distract you with their homogenous targets for innovation.

Challenge:
Take a look at some data for your innovation.

  • What are some of the shiny, yet deceiving, averages that exist?
  • Dive deeper into your data. Are your averages glossing over some of the customer stories?
  • Try to identify the different, distinct user-groups for your innovation. List their pain points.