Last updated: September 2025 | Reading time: 10 minutes | Level: Advanced
The problem: You suspect that marketing spend and sales performance are connected, but how strong is the relationship? And what other hidden correlations is everyone else missing? Traditional charts show only one dimension — you need a visualization for relationships.
The solution: Scatter Plots reveal correlations that remain invisible in other charts. In 10 minutes you’ll learn how to find hidden relationships and make data-driven decisions before the competition even knows what to look for.
What Will You Learn in This Qlik Sense Scatter Plot Tutorial?
- ✅ Correlation detection: Distinguish strong, weak, and false relationships
- ✅ Multi-variable analysis: Analyze 3-4 dimensions simultaneously
- ✅ Business insights: 5 practical use cases with immediate findings
- ✅ Statistical significance: Separate real trends from random patterns
- ✅ Advanced features: Clustering, forecasting, and outlier detection
How do scatter plots revolutionize data analysis?
How to uncover hidden correlations in Qlik Sense scatter plots?
Classic situation: You’re the Marketing Director and you see:
- Campaign A: $50K budget, 200 leads
- Campaign B: $80K budget, 180 leads
- Campaign C: $30K budget, 250 leads
Without Scatter Plot: You see only individual values and guess
With Scatter Plot: You instantly see that budget does NOT correlate linearly to leads and discover the sweet spot at $30-40K
What are the secrets of Qlik Sense scatter plots?
Scatter Plots can display 4 dimensions simultaneously:
- X-axis: Marketing Budget
- Y-axis: Generated Leads
- Bubble size: Lead Quality Score
- Color: Campaign Type
When Are Scatter Plots the Right Choice in Qlik Sense?
perfect_for:
- Marketing ROI vs. Lead Quality
- Employee performance vs. experience
- Product price vs. sales volume vs. customer satisfaction
- Website traffic vs. conversion vs. bounce rate
- Production output vs. quality vs. cost
not_suitable_for:
- Fewer than 20 data points (→ Table)
- Time series trends (→ Line Chart)
- Category comparisons (→ Bar Chart)
- Hierarchical data (→ Treemap)
How do I create a scatter plot in Qlik Sense?
Before configuring, skim the Qlik official scatter plot documentation to confirm which features are available in your Qlik Cloud version.
How to identify data relationships in Qlik Sense?
Minimum requirements:
- 2 numeric variables (X and Y axis)
- At least 20-30 data points
- Optional: 1-2 additional dimensions (size, color)
Example data structure:
Campaign | Budget | Leads | Quality_Score | Channel | Region
Campaign_A | 45000 | 220 | 8.2 | Social | North
Campaign_B | 32000 | 180 | 9.1 | Email | South
Campaign_C | 67000 | 290 | 7.8 | PPC | West
How do I configure a scatter plot in Qlik Sense?
- Add new object → “Scatter plot”
- Dimension (bubble identification):
Field: [Campaign]
Label: "Campaign"
- X-axis (first variable):
Expression: Sum([Budget])
Label: "Marketing Budget $"
Axis limit: Auto
- Y-axis (second variable):
Expression: Count([Leads])
Label: "Generated Leads"
Axis limit: 0 to Auto
How do you add a third dimension with bubble size?
Bubble size (optional third dimension):
expression: "Avg([Quality_Score])"
label: "Lead Quality"
size_range: "3 to 15"
How do you add a fourth dimension with color in Qlik Sense?
Color (optional fourth dimension):
expression: "[Channel]"
color_palette: "12-color distinct"
legend: "Right"
How can scatter plots reveal correlations from marketing to operations?
How can scatter plots optimize marketing ROI in Qlik Sense?
Problem: “Which campaigns deliver the best ROI with the highest lead quality?”
Setup:
dimension: "[CampaignID]"
x_axis:
expression: "Sum([MarketingSpend])"
title: "Marketing Budget $"
y_axis:
expression: "Sum([Revenue])"
title: "Generated Revenue $"
bubble_size:
expression: "Avg([LeadScore])"
title: "Avg Lead Quality"
color:
expression: "[Channel]"
values: ["Social", "Email", "PPC", "Content"]
trend_line: true
Business insight: You instantly see campaigns with high ROI AND high lead quality. The sweet spot between budget and results becomes visible.
How to analyze sales performance versus experience with scatter plots?
Problem: “Does sales performance correlate with years of experience?”
Setup:
dimension: "[SalesPersonID]"
x_axis:
expression: "[YearsExperience]"
title: "Years of Experience"
y_axis:
expression: "Sum([SalesRevenue])"
title: "Annual Revenue $"
bubble_size:
expression: "Count([CustomerRetention])"
title: "Customer Retention Rate"
color:
expression: "[SalesTeam]"
conditional_coloring: true
Advanced feature: Display the correlation coefficient:
-- Add as a text object:
Correl(Aggr(Sum([SalesRevenue]), [SalesPersonID]),
Aggr([YearsExperience], [SalesPersonID]))
How to find the product price sweet spot?
Problem: “Is there an optimal price for maximum sales volume?”
Setup:
dimension: "[ProductID]"
x_axis:
expression: "Avg([Price])"
title: "Average Price $"
y_axis:
expression: "Sum([UnitsSold])"
title: "Units Sold"
bubble_size:
expression: "Sum([Price] * [UnitsSold])"
title: "Total Revenue"
color:
expression: "[ProductCategory]"
reference_lines:
x_line:
value: "Avg(TOTAL [Price])"
label: "Market Average Price"
y_line:
value: "Avg(TOTAL [UnitsSold])"
label: "Average Sales"
How to analyze website performance with scatter plots?
Problem: “How are traffic, bounce rate, and conversions connected?”
Setup:
dimension: "[PageURL]"
x_axis:
expression: "Sum([PageViews])"
title: "Page Views"
y_axis:
expression: "Sum([Conversions]) / Sum([Sessions])"
title: "Conversion Rate %"
bubble_size:
expression: "Avg([TimeOnPage])"
title: "Time on Page"
color:
expression: "If([BounceRate] > 0.7, 'High', If([BounceRate] > 0.4, 'Medium', 'Low'))"
title: "Bounce Rate Category"
How to Balance Production Efficiency and Quality?
Problem: “Does higher production speed lead to worse quality?”
Setup:
dimension: "[ProductionLineID]"
x_axis:
expression: "Sum([UnitsPerHour])"
title: "Units per Hour"
y_axis:
expression: "Avg([QualityScore])"
title: "Quality Score (1-10)"
bubble_size:
expression: "Sum([ProductionCosts])"
title: "Production Costs"
color:
expression: "[Shift]"
values: ["Morning", "Afternoon", "Night"]
cluster_analysis: true
How to use advanced features for pro-level scatter plots?
The scatter plot properties reference documents every setting available in the properties panel, including clustering and regression options covered below.
How to Use Automatic Clustering in Qlik Sense?
clustering:
enabled: true
number_of_clusters: 4
method: "K-Means"
color_by_cluster: true
show_centroids: true
Business value: Automatic grouping of similar data points (e.g., High/Medium/Low performers).
How to add regression lines and R² in Qlik Sense scatter plots?
trend_analysis:
linear_regression:
enabled: true
show_r_squared: true
confidence_interval: 95%
polynomial_regression:
degree: 2
for_curved_data: true
For detailed data exploration beyond visual patterns, use tables for detailed data exploration to drill into the individual data points behind your scatter plot clusters.
How to detect outliers in Qlik Sense scatter plots?
outlier_detection:
method: "Standard Deviation"
threshold: 2.5
highlight: true
color: "#FF0000"
automatic_labels: true
How to add dynamic reference lines in Qlik Sense?
reference_lines:
performance_benchmarks:
x_line: "Percentile(TOTAL [XValue], 0.75)" # Top 25%
y_line: "Percentile(TOTAL [YValue], 0.75)" # Top 25%
quadrant_labels:
q1: "High X, High Y"
q2: "Low X, High Y"
q3: "Low X, Low Y"
q4: "High X, Low Y"
How do you interpret correlation in Qlik Sense scatter plots?
When your scatter plot reveals time-based patterns in your data, switch to a line chart for trend visualization over periods — it shows the same story more clearly when sequence matters more than correlation.
How to Recognize Correlation Strength in Scatter Plots?
Strong positive correlation (r > 0.7):
- Points form a clear ascending line
- Example: Marketing budget → Leads (well-planned campaigns)
Weak correlation (0.3 < r < 0.7):
- Points show a trend, but with scatter
- Example: Experience → Performance (other factors matter)
No correlation (r ≈ 0):
- Points randomly distributed
- Example: Employee age → Sales performance
Negative correlation (r < -0.3):
- Points form a descending line
- Example: Product price → Sales volume
How to Distinguish Correlation from Causation?
correlation_detected: "Budget correlates with Leads"
possible_causes:
- "Higher budget leads to more leads"
- "Successful teams get more budget"
- "Seasonal effects influence both"
- "Third variable (team quality) influences both"
caution: "Correlation ≠ Causation"
How to optimize performance for large datasets in Qlik Sense?
Clean expression optimization for chart calculations matters especially here — poorly written aggregations in scatter plot axes can multiply render times on larger datasets.
Why Is My Scatter Plot Slow in Qlik Sense?
Performance indicators:
- More than 2,500 bubbles
- Load time > 5 seconds
- Browser lag during interaction
How to optimize Qlik Sense scatter plots?
When your expressions use set identifiers like {1} to compute market totals, check the set analysis for chart expressions guide to ensure your aggregation formulas evaluate correctly across selections.
Strategy 1: Smart Sampling
-- Only representative data points:
{<[RecordID] = {"=Mod(RecordNumber(), 5) = 0"}>}
Strategy 2: Increase Aggregation
-- Instead of individual transactions, use monthly aggregates:
Dimension: Month([Date])
X-axis: Sum([Revenue])
Y-axis: Sum([Costs])
Strategy 3: Calculation Condition
calculation_condition:
condition: "Count(DISTINCT [Dimension]) <= 1000"
message: "Too many data points. Please apply a filter."
What are the best practices for designing effective scatter plots?
How to optimize axes in Qlik Sense scatter plots?
X-axis guidelines:
zero_point: "Only when value range is meaningful"
scaling: "Linear (default) or Log (for exponential data)"
grid_lines: "Use sparingly"
title: "Clear with units"
Y-axis guidelines:
zero_point: "Usually start at 0"
auto_scaling: true
negative_values: "Mark clearly"
What are the bubble size guidelines for Qlik Sense scatter plots?
minimum_size: 3 # Ensure readability
maximum_size: 15 # Avoid overlap
scaling: "Proportional to bubble value"
null_values: "Display as minimum size"
How to apply color guidelines to scatter plots?
categorical_data:
palette: "Qualitative (distinct colors)"
maximum: 8 # More becomes unreadable
continuous_data:
palette: "Sequential (e.g., Blues)"
legend: "With min/max values"
performance_data:
palette: "Diverging (Red-Yellow-Green)"
center: "Benchmark/Target"
What are common mistakes and pro solutions for Qlik Sense scatter plots?
How to avoid too few data points in Qlik Sense scatter plots?
Problem: 8 bubbles in a scatter plot
Solution: Minimum 20-30 points, otherwise use a table
How to avoid unsuitable variables in Qlik Sense scatter plots?
Problem: Categorical data on continuous axes
Solution:
correct:
x: "Budget (numeric)"
y: "Leads (numeric)"
incorrect:
x: "Product category (categorical)"
y: "Leads (numeric)" # → Use a Bar Chart instead
How to fix meaningless bubble size in Qlik Sense scatter plots?
Problem: Bubble size is random/constant
Solution: Size must represent a meaningful third dimension
How to avoid overinterpreting correlations in Qlik Sense?
Problem: r=0.3 interpreted as “strong relationship”
Solution:
interpretation_guide:
r_0_3: "Weak relationship"
r_0_5: "Moderate relationship"
r_0_7: "Strong relationship"
r_0_9: "Very strong relationship"
What is the quality checklist for scatter plot go-live?
Before release, check these 12 points:
- ⬜ At least 20-30 data points
- ⬜ X/Y axes are numeric and meaningfully scaled
- ⬜ Bubble size has business meaning
- ⬜ Colors are meaningful and colorblind-friendly
- ⬜ Axis titles include units
- ⬜ Correlation coefficient displayed (if relevant)
- ⬜ Outliers are identified and explained
- ⬜ Reference lines for benchmarks (if useful)
- ⬜ Legend is complete
- ⬜ Chart loads in <5 seconds
- ⬜ Title explains the discovered correlation
- ⬜ Causation vs. correlation clarified
How do you create scatter plots in Qlik Sense?
What are common Qlik Sense scatter plot exam questions?
Question 1: When is a Scatter Plot more suitable than a Line Chart?
Answer: When analyzing correlations between two numeric variables, not for time series trends.
Question 2: How do you interpret the correlation coefficient?
Answer: r > 0.7 = strong, 0.3-0.7 = moderate, < 0.3 = weak. Negative = inverse relationship.
Question 3: What is the difference between correlation and causation?
Answer: Correlation shows a statistical relationship, causation proves a cause-and-effect connection.
How do I troubleshoot Qlik Sense scatter plots?
Why Do All Bubbles Have the Same Size?
Solution: Check the bubble size expression:
-- Wrong: Constant values
Avg([ConstantValue])
-- Correct: Variable values
Sum([Revenue]) / Count([Customers])
How do you identify correlations in scatter plots?
Solution:
- Remove outliers and re-examine
- Consider time lag (lagged correlation)
- Check for non-linear relationships (polynomial regression)
How to fix overlapping bubbles in Qlik Sense?
Solution:
- Add jitter for minimal random displacement
- Enable transparency (alpha blending)
- Aggregate data at a higher level
For an overview of all findings, pair your scatter plot with KPI objects for summary metrics — a single correlation coefficient displayed prominently tells executives more than a full chart grid.
Pro tip: The best Scatter Plot answers the question “Is there a relationship?” with statistical clarity. If you can’t explain the R², the chart isn’t ready yet.