101 Design Methods: A Structured Approach for Driving Innovation in Your Organization

by Vijay Kumar

4.1 Observations to Insights Learning from what is observed in research by revealing nonobvious inner meanings

This step could be the foundation of the next following stages of research. It's also crucial to make sure that the insight is not biased or partial. The truness of the insights might be ensured through discussion of the team. But the time limitation is another obstacle.

STEP 1: Gather observations and describe them.

No interpretations or judgments

STEP 2: Ask why and find an agreed-upon rationale.

happening. Find out people’s reasoning behind their actions and behaviors.

STEP 3: Describe the Insights.

insight. Insights should be written as a general statement since it represents a higher-level learning from a specific observation.

STEP 4: Organize the insights.

spreadsheet.

STEP 5: Discuss and refine.

A collection of structured insight statements that can be traced back to corresponding observations

4.2 Insights Sorting

Manually sorting insights from research to find clusters and hierarchies

manageable number of insights—

STEP 1: Gather insight statements.

STEP 2: Do a sample sort and reach alignment on clustering logic.

STEP 3: Cluster and recluster insight statements.

STEP 4: Define the clusters.

STEP 5: Discuss next steps.

Reveals patterns Reveals relationships Structures existing knowledge Facilitates discussion

Clusters of insights showing patterns and relationships

4.14 Semantic Profile Making profiles of entities based on a set of semantic scales and comparing those profiles

Frequently, Semantic Profiles are used to compare how different user groups think and feel about entities.

STEP 1: Select entities to compare.

STEP 2: Define key attribute scales.

STEP 3: Create a Semantic Profile diagram.

STEP 4: Create entity profiles.

STEP 5: Analyze patterns.

STEP 6: Capture insights and share them.

This method could also be used to generate persona or mode. By analysing patterns of the attribute, people's behavior pattern could be identified. It might also be better to use computers when there is a larege data set. Question: how big the data set sould be to build a reliable pattern?

Facilitates comparison Facilitates discussion Identifies opportunities Visualizes information

A visual comparison of a set of entities against multiple attribute scales A visual comparison of different users or user groups against multiple attribute scales

4.15 User Groups Definition Defining the different kinds of users present in a context

STEP 1: List user activities and user types.

STEP 2: Identify attribute scales.

that several attributes may be equally important, and if this is the case, you can create multiple user definition maps.

STEP 3: Create a 2 × 2 map and plot user types.

STEP 4: Define user groups.

STEP 5: Describe common characteristics of user groups.

Builds higher-level systems Facilitates comparison Keeps grounded in research Structures existing knowledge

4.16 Compelling Experience Map Mapping the entire user experience with five stages—attraction, entry, engagement, exit, and extension

after. The framework divides any experience into five stages: Attraction, Entry, Engagement, Exit, and Extension.

Defined—Can you describe it? Is it bounded?

Fresh—Is it novel? Does it startle, amuse, amaze? Immersive—Can you feel it? Can you lose yourself in it? Accessible—Can you try it? Can you get it to do what you want? Significant—Does it make sense? Does it make you remember, connect, think, grow? Transformative—Do you feel different? Do you have something to show for it?

STEP 1: Select an experience to analyze and create a worksheet for analysis.

STEP 2: Describe the Attract stage.

STEP 3: Describe the Enter stage.

STEP 4: Describe the Engage stage.

STEP 5: Describe the Exit stage.

STEP 6: Describe the Extend stage.