Influencing for Impact

Whole-Brain Thinking

Design Thinking:

Arts-based Dialogue

"Structuring the Fuzz"
The Design of Ideation for
New Product Ideas

By Arthur VanGundy PhD
(Continued from page 4)

Thinking and Knowing

"In this day and age, if you are not confused, you are not thinking clearly."
—Burt Nanus

"The utmost extent of man's knowledge, is to know that he knows nothing."
—Joseph Addison


This stage helps gather general background information (Thinking) and provides clarity about a specific problem (Knowing). Thinking immerses you in the general problem area (e.g., product category or business segment). Knowing guides you to achieve new perspectives and to select the "best" problem statement.

Thinking involves data mining (analyzing) and asking background questions to improve problem understanding. Knowing encourages sudden insight by testing assumptions which, in turn, can spark new perspectives, new problem statements. These statements can be within the original box or they can lead to new boxes.

Thinking: Data Mining. The first part of thinking involves gathering as much data as possible—total problem immersion. Data mining is the time to dig in and see what you can find. Learn as much as you can about the product category. This is a database for understanding your initial objective as well as suggesting potential avenues for new product concepts. Market research, trend analysis, scenario analysis, cultural anthropology, and empathetic design all can be used to understand a problem.

Not all breakthrough ideas come from these sources, however. Rely on intuition and what you experience as relevant. Assess the relevance of new technologies. Many breakthrough products and technology came about not because of market research, but because of the BtB thinking of an innovative individual or group. The telephone, submarine, and xerography—just to name three—never would have been invented had they relied on market-driven data.

Thinking: Questioning. In addition to the traditional data sources discussed above, valuable information also can be obtained by asking general background questions. They can help uncover information not gathered from data mining. Defer judgment and ask as many questions about the product as possible. Each question and answer provides triggers for potential solutions. Ask even obvious questions with obvious answers. Go into a "questioning frenzy," if you will.

To help trigger questions, use the results of your problem analysis and prompts such as "Who? What? Where? When? and How? For instance, here are some general back-ground questions:

What does our company do?
What do we want to do in the future?
What do we want to do differently?
Where are we positioned in the minds of our customers?
How would we like to be positioned?
How do our customers benefit from our products or services?
When would it be best for us to move to another box?
Who are our customers?
What values are associated with our brands?
How consistently do we transmit these values?
Who would we like to have as customers?
What markets would we like to segment?
What are the components of our brand equity?
What extensions would be best for us to explore?
What is our brand equity?
How do we know that?
What is our brand awareness?
Should we broaden or narrow our brand?
Where do we want to be in one, three, five years?
If anything were possible, what would we do?
Why do our customers like us?
When don’t our customers like us?
What do we like about our competition?
What do our competitors’ customers like about them?
What do our competitors’ customers like and dislike about us?
Who has achieved the positive results we want?
How are they doing that?
How can we do that?
Who is doing something well in our industry or another?
What can we borrow from them (e.g., learnings, tools, approaches)?

Answering these types of questions should reduce problem fuzz. In some cases, new definitions or even solutions may pop out. If so, you already are in the Knowing phase. Sudden clarity, no matter how slight, means you may see your problem with new eyes.

Knowing. To "know" a problem is to frame it in a box. The first box we create is the "given" or "presented" problem. When we become aware of this box, we can stay within it or move to another. Thus, knowing involves reframing a problem within an initial box (BiB thinking), moving to other boxes (BtB), or some combination.

Suppose the initial problem is: "How might we improve our lawnmower?" To stay within a box ask, "Who?" "What?" "Where?" "When?" "How?" Then, use the answers to suggest new statements within the box. For instance, "What is involved in using a lawnmower?" or "How is our lawnmower used?" These questions might be answered as follows: pushed, pulled, lifted, trim sidewalks, cut wet grass, pull start, gasoline and oil, etc. These answers might lead to such BiB reformulations as: "How might we make our lawnmowers easier to push?" "How might we prevent accidental starting?" "How might we make lawnmowers easier to start?"

To move to new boxes and create new growth platforms, ask "Why…?" or "What else can it do?" Thus, "Why do we want to improve our lawnmowers?" An answer to this question might be, "To cut grass better." This answer, in turn, could suggest a new box such as, "How might we remove grass?" Or, asking "What else can it do?" might result in a lawnmower that also functions as a motorized wheel chair.

After arriving in a new box, you can stay within it by repeating BiB questioning or move to a different box.


Thinking and Knowing steps can be summarized as follows:

1. State the problem

2. Thinking—Data Mining: Gather background information

3. Thinking—Questioning: Generate and answer general questions

4. Knowing—Box-in-Box: Restate the problem within the category (Ask, "Who? What? Where? When? How?")

5. Knowing—Box-to-Box: Move to new categories by asking "Why…?" of the original problem, or "What else can it/we do?"

6. If desired, repeat "Knowing—Box-in-Box."

7. If desired, repeat "Knowing—Box-to-Box."

Here's an example of these steps based on a recent client of mine in the consumer products business (To maintain confidentiality, I have altered the facts somewhat):

1. State the problem: "How might we improve smoke detectors?"

2. Thinking—Data Mining: Research indicated relatively high aided- and unaided-brand awareness across a variety of psychographics.

3. Thinking—Questioning: Where are we positioned in the minds of our customers? How would we like to be positioned? How do our customers benefit from our products or services?

4. Knowing—Box-in-Box: What could we improve? Where could it be improved? What could be combined? New problem statements: "How might our detectors sense smoke more reliably?" "How might we warn people of smoke more effectively?"

5. Knowing—Box-to-Box: Why do we want to improve smoke detectors? Answer: To make homes safer. Redefinition: "How might we make homes safer?" [Note that this broadens the original problem of improving smoke detectors and is framed to explore a broader range of product categories.] "What else could a smoke detector do?" (e.g., detect carbon monoxide)

6. Repeat Knowing—Box-in-Box: What could be safer in homes? When are homes unsafe? What causes homes to be unsafe? Who causes homes to be unsafe? How do people improvise to improve home safety? These questions might spark new statements or ideas such as a moisture detector in the basement or a warning sound when a child opens a basement door.

7. Repeat Knowing—Box-to-Box: Ask "Why?" of the new problem statement or ask, "What else might we do?" The client also wanted to expand into other potential markets, so they decided not to limit the project to just home safety. Instead, they targeted possible safety products in other areas. For instance: "How might we improve water and snow skiing safety?" "How might we improve bicycling safety?" "How might we improve automotive safety?"

"When you stop learning, stop listening, stop looking and asking questions,
always new questions, then it is time to die."
—Lillian Smith

Continued on page 5.

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Copyright 2000, Arthur B. VanGundy. All rights reserved

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