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 2×2 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.
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.
