Data-driven decisions · Participatory Methods

The activity Data-driven decisions, inspired by the Integrated Data Thinking™ framework developed by Sudden Compass®, is designed to help teams leverage both qualitative (thick data) and quantitative (big data) sources to address critical questions. This method fosters a shared and balanced understanding of how to approach research-related questions—whether to uncover new opportunities or to optimize existing processes.

Preparation

Define the purpose:
- Teach participants to classify questions as either discovery (new ideas) or optimization (improving what already exists).
- Encourage critical thinking to select appropriate research methods for each type of question.
- Promote a balanced approach between qualitative and quantitative data to make more informed decisions.
Prepare the materials:
- A 2x2 quadrant canvas or template with the axes: - X-axis: **Unknown (discovery)** on the left and **Known (optimization)** on the right. - Y-axis: **Qualitative data (thick data)** at the top and **Quantitative data (big data)** at the bottom.
- Sticky notes in different colors to classify questions.
- Markers or pens.
- Whiteboard, flip chart paper, or digital tools if done virtually.
Set up the space:
- Choose a space where participants can comfortably collaborate around the canvas.
To run the activity virtually:
- Use collaborative platforms that support shared whiteboards and digital sticky notes.

Step-by-step instructions

Introduce the methodology:
- Explain the key concepts:
  - Discovery questions: Explore the unknown to generate new ideas or markets.
  - Optimization questions: Improve existing processes based on clear and known data.
  - Thick data: Qualitative data that answers "how" or "why" questions.
  - Big data: Quantitative data that answers "how many" or "how often" questions.
Classify questions:
- Ask participants to write down questions relevant to their current project or problem on sticky notes.
- Guide them to place each question in the appropriate quadrant of the canvas by considering: - Is it a discovery or optimization question? - Is it qualitative or quantitative?
Define approaches:
- Review each question and where it’s placed on the quadrant. As a group, discuss the most suitable method to address it, for example: - Qualitative/Discovery: Ethnographic interviews. - Qualitative/Optimization: Focus groups. - Quantitative/Discovery: Exploratory data analysis. - Quantitative/Optimization: A/B testing.
Reflect and prioritize:
- Identify areas where critical questions are missing and discuss how to address them in the future.
- Prioritize the most relevant questions for the team or project.

Purpose

The purpose of Data-driven decisions is to help teams classify critical questions, select appropriate methods to address them, and develop a balanced perspective between discovery and optimization by using both qualitative and quantitative data.

Required materials

2x2 quadrant template.
Colored sticky notes.
Markers or pens.

Platforms

Practical recommendations

Provide clear examples of questions to guide participants in the classification process.
Use colors to easily distinguish between qualitative and quantitative questions.
Facilitate group discussion to encourage reflection on methods and approaches.

Inspiration

Sample questions for the "Data-driven decisions" activity Qualitative/Discovery (Thick Data/Unknown):

How do customers perceive our service compared to competitors?
What motivates customers to choose our brand over others?
How do customers’ personal values influence their purchasing decisions?
What emotions do users experience when interacting with our product?
How does customer experience vary depending on geographic location?
What expectations do customers have for our new product or service?
What stories do customers tell about their experience with our brand?
How does local culture affect consumer preferences?
What obstacles do customers encounter when using our product?
What emerging patterns are evident in customer preferences?

Qualitative/Optimization (Thick Data/Known):

Why do users leave our website after their first visit?
What factors contribute to customer satisfaction with our technical support?
Which product features do customers find most useful?
How can we improve the customer experience in our physical stores?
Which elements of our communication strategy best resonate with our audience?
Why are some customer segments more loyal than others?
Which parts of the purchase process require more support?
What content formats do users prefer to learn about our services?
How can we reduce friction points during the checkout process?
What do customers expect from our after-sales service?

Quantitative/Discovery (Big Data/Unknown):

What trends are emerging in our industry?
Which markets show the most potential for our next product launch?
What are the consumption patterns during high-demand periods?
What changes in customer behavior suggest new market opportunities?
Which demographic segment is showing the most interest in our products?
What data indicates opportunities for product diversification?
Which regions are showing unexpected sales growth?
What are the latest online shopping habits among our customers?
What usage patterns are linked to the adoption of new features?
How do customer preferences vary by season?

Quantitative/Optimization (Big Data/Known):

What is the most-used feature in our mobile app?
What percentage of customers complete a purchase after adding items to the cart?
Which marketing channel generates the most conversions?
What is the average time users spend on our homepage?
Which days of the week generate the most sales?
Which price range drives the highest volume of sales?
What percentage of users activate their account within the first 24 hours?
What is the customer churn rate after three months of use?
Which region delivers the highest profitability compared to operational costs?
What are the most searched product categories among returning customers?

Approaches and strategies

Qualitative/Discovery

Focus groups: Facilitate structured discussions among users to explore motivations and perceptions.
Ethnographic interviews: Observe and interview users in their everyday context to understand behaviors.
Empathy maps: Create visual representations of users’ emotions, needs, and goals.
User narratives: Analyze stories or experiences shared by users to identify hidden patterns.
Co-creation: Host collaborative workshops where participants design potential solutions.
Guided brainstorming: Facilitate creative sessions to explore unknown variables related to the problem.
Context analysis: Examine the social or cultural environment to uncover behavioral influences.
Visual collages: Use cutouts and graphics to express perceived aspirations and challenges.
Free association: Lead exercises where participants spontaneously link concepts and values.
Case exploration: Investigate specific examples to identify emerging opportunities.

Qualitative/Optimization

Test groups: Gather specific users to assess the effectiveness of new ideas.
Focus groups: Discuss improvements to existing products with key customer segments.
Targeted interviews: Ask about specific aspects of the user experience to optimize processes.
Feedback analysis: Review customer opinions and suggestions to improve key touchpoints.
User flow review: Observe specific interactions to identify bottlenecks.
Design evaluation: Test prototypes with users to validate experience changes.
Case study analysis: Explore real-world examples of successful or failed improvement efforts.
Process adjustments: Identify small changes to enhance system performance.
Journey mapping: Visualize the customer journey to pinpoint critical moments.
Hypothesis testing: Validate assumptions through direct interaction with key users.

Quantitative/Discovery

Exploratory analysis: Examine large datasets to uncover emerging trends.
Pattern identification: Detect unexpected relationships between variables in large databases.
Statistical forecasting: Predict future scenarios using predictive models.
Demographic segmentation: Group users by shared characteristics to identify opportunities.
Trend exploration: Analyze changes in behavior or preferences over time.
Cross-analysis: Identify correlations across different datasets.
Time-based analysis: Study how key metrics change over specific periods.
Market evaluation: Identify opportunities in underexplored regions or segments.
Scenario generation: Create potential future scenarios to assess strategic options.
Behavioral analysis: Examine how users interact with a product or system.

Quantitative/Optimization

A/B testing: Compare two versions of a solution to determine which is more effective.
Key metrics analysis: Monitor specific data such as conversion rates or time on site.
Multivariate optimization: Adjust multiple variables to maximize results.
Advanced segmentation: Divide users into targeted groups for personalized approaches.
Performance evaluation: Measure the effectiveness of campaigns, products, or processes.
Real-time monitoring: Track live data to instantly adjust strategies.
Option comparison: Analyze the results of different approaches to choose the most efficient one.
Historical trend analysis: Examine past changes to detect recurring patterns.
Simulations: Model different scenarios to predict the impact of decisions.
ROI measurement: Calculate return on investment to validate strategies.