Behavioral Science for Data Scientists: Why Your Dashboard Users Think Differently Than You Do

library(bidux)
library(dplyr)

Introduction: The Gap Between Data Creators and Data Users

As data scientists and R developers, we’re trained to think systematically, explore data thoroughly, and validate our assumptions. But here’s a reality check: your dashboard users don’t think like you do.

Consider this scenario: - You see: A well-organized dashboard with 15 filters, 8 visualizations, and comprehensive data coverage - Your users see: An overwhelming interface where they can’t find what they need quickly

This isn’t because your users are less intelligent; it’s because they’re human, and humans have predictable cognitive patterns that affect how they process information.

The Core Problem: Cognitive Load vs. Analytical Depth

What You’re Optimized For (as a data scientist)

# Your typical approach to data exploration
data |>
  filter(multiple_conditions) |>
  group_by(several_dimensions) |>
  summarize(
    metric_1 = mean(value_1, na.rm = TRUE),
    metric_2 = sum(value_2),
    metric_3 = median(value_3),
    .groups = "drop"
  ) |>
  arrange(desc(metric_1))

What Your Users Need (cognitive efficiency)

• Quick orientation: “What am I looking at?”
• Immediate value: “What does this mean for my decision?”
• Clear next steps: “What should I do about it?”

Key Behavioral Science Concepts for Data People

1. Cognitive Load Theory: The Mental RAM Problem

Think of human attention like computer memory: limited and easily overloaded.

# Explore cognitive load concepts
cognitive_concepts <- bid_concepts("cognitive")

cognitive_concepts |>
  select(concept, description, implementation_tips) |>
  head(3)

For dashboards, this means:

• Users can only process 5-9 pieces of information simultaneously
• Every filter, chart, and button competes for mental resources
• Progressive disclosure beats comprehensive display

Practical example:

# Instead of showing all 12 KPIs at once:
# kpi_grid <- layout_columns(
#   value_box("Revenue", "$1.2M", icon = "currency-dollar"),
#   value_box("Customers", "15,432", icon = "people"),
#   # ... 10 more value boxes
# )

# Show key metrics first, details on demand:
kpi_summary <- layout_columns(
  col_widths = c(8, 4),
  card(
    card_header("Key Performance Summary"),
    value_box("Primary Goal", "$1.2M Revenue", icon = "target"),
    p("vs. $980K target (+22%)")
  ),
  card(
    card_header("Details"),
    actionButton(
      "show_details",
      "View All Metrics",
      class = "btn-outline-primary"
    )
  )
)

2. Anchoring Bias: The First Number Problem

Humans over-rely on the first piece of information they see. In data dashboards, this means the first number shown becomes a reference point for everything else.

# Learn about anchoring
bid_concept("anchoring") |>
  select(description, implementation_tips)

Dashboard implication:

If you show “Sales: $50K” first, users will judge all subsequent numbers relative to $50K, even if it’s not the most important metric.

Solution pattern:

# Provide context and reference points
sales_card <- card(
  card_header("Monthly Sales Performance"),
  layout_columns(
    value_box(
      title = "This Month",
      value = "$87K",
      showcase = bs_icon("graph-up"),
      theme = "success"
    ),
    div(
      p("Previous month: $65K", style = "color: #666; margin: 0;"),
      p("Target: $80K", style = "color: #666; margin: 0;"),
      p(strong("vs. Target: +9%"), style = "color: #28a745;")
    )
  )
)

3. Framing Effects: How You Say It Matters More Than What You Say

The same data can be interpreted completely differently based on how it’s presented.

# Explore framing concepts
bid_concept("framing") |>
  select(description, implementation_tips)

Example: Customer satisfaction of 73%

# Negative frame (emphasizes problems)
satisfaction_negative <- value_box(
  "Customer Issues",
  "27% Unsatisfied",
  icon = "exclamation-triangle",
  theme = "danger"
)

# Positive frame (emphasizes success)
satisfaction_positive <- value_box(
  "Customer Satisfaction",
  "73% Satisfied",
  icon = "heart-fill",
  theme = "success"
)

# Balanced frame (shows progress)
satisfaction_balanced <- card(
  card_header("Customer Satisfaction Progress"),
  value_box("Current Level", "73%"),
  p("Improvement needed: 17 percentage points to reach 90% target")
)

4. Choice Overload: Why More Options Create Worse Outcomes

Research shows that too many choices lead to decision paralysis and user dissatisfaction.

The data scientist instinct:

“Let’s give them 20 different chart types and 15 filters so they can explore any question!”

The user reality:

1. Use defaults and ignore customization options
2. Get overwhelmed and abandon the task
3. Make suboptimal choices due to cognitive fatigue

Better approach:

# Instead of 15 filters visible at once
ui_complex <- div(
  selectInput("region", "Region", choices = regions),
  selectInput("product", "Product", choices = products),
  selectInput("channel", "Channel", choices = channels),
  # ... 12 more filters
)

# Use progressive disclosure
ui_simple <- div(
  # Show only the most common filters first
  selectInput("time_period", "Time Period", choices = time_options),
  selectInput("metric", "Primary Metric", choices = key_metrics),

  # Advanced filters behind a toggle
  accordion(
    accordion_panel(
      "Advanced Filters",
      icon = bs_icon("sliders"),
      selectInput("region", "Region", choices = regions),
      selectInput("product", "Product", choices = products)
      # Additional filters here
    )
  )
)

Translating Data Science Skills to UX Thinking

Your A/B Testing Mindset → UX Validation

You already know how to test hypotheses with data. Apply the same rigor to UX decisions:

# Your typical A/B test
results <- t.test(
  treatment_group$conversion_rate,
  control_group$conversion_rate
)

# UX equivalent: Test interface variations
dashboard_test <- list(
  control = "Current 5-chart overview page",
  treatment = "Redesigned with progressive disclosure"
)

# Measure: task completion time, user satisfaction, error rates
# Analyze: same statistical rigor you'd apply to any experiment

Your Data Validation Process → User Mental Model Validation

Just as you validate data quality, validate that your interface matches user expectations:

# Document user mental models like you document data assumptions
user_assumptions <- bid_interpret(
  central_question = "How do sales managers think about performance?",
  data_story = list(
    hook = "Sales managers need quick performance insights",
    context = "They have 15 minutes between meetings",
    tension = "Current reports take too long to interpret",
    resolution = "Provide immediate visual context with drill-down capability"
  )
)

# Validate these assumptions like you'd validate data quality
summary(user_assumptions)

The BID Framework: A Systematic Approach for Data People

The BID framework gives you a systematic way to apply behavioral science, similar to how you approach data analysis:

Stage 1: Interpret (Like defining your research question)

• What specific question does this dashboard answer?
• Who are the users and what are their constraints?

Stage 2: Notice (Like identifying data quality issues)

• Where do users get stuck or confused?
• What cognitive bottlenecks exist?

Stage 3: Anticipate (Like checking for statistical biases)

• What cognitive biases might affect interpretation?
• How can design mitigate these biases?

Stage 4: Structure (Like choosing the right visualization)

• What layout best supports the user’s mental model?
• How should information be hierarchically organized?

Stage 5: Validate (Like testing your model)

• Does the user leave with clear insights?
• Can they take action on what they’ve learned?

Practical Next Steps

1. Start small: Pick one dashboard and apply the BID framework
2. Measure: Track user behavior metrics (time to insight, task completion)
3. Iterate: Use the same experimental mindset you apply to model development
4. Learn progressively: Use bid_concepts() to explore one behavioral principle at a time

# Explore available concepts by category
all_concepts <- bid_concepts()
table(all_concepts$category)

# Start with these fundamental concepts for data dashboards
starter_concepts <- c(
  "Cognitive Load Theory",
  "Anchoring Effect",
  "Processing Fluency",
  "Progressive Disclosure"
)

for (concept in starter_concepts) {
  cat("\n### ", concept, "\n")
  info <- bid_concept(concept)
  cat(info$description[1], "\n")
  cat("Implementation:", info$implementation_tips[1], "\n")
}

Why This Matters for You

Understanding user cognition makes you a more complete data professional:

• Better adoption: Your analyses get used instead of ignored
• Clearer communication: Stakeholders understand and act on your insights
• Strategic thinking: You solve business problems, not just technical problems
• Competitive advantage: Most data scientists don’t think about user experience

Remember: The best analysis in the world is worthless if users can’t understand and act on it. Behavioral science helps bridge that gap.

Resources for Further Learning

• Use bid_concepts() to explore all available behavioral science concepts
• Try the getting-started vignette for hands-on BID framework practice
• Check out the telemetry-integration vignette to measure the impact of your UX improvements

The goal isn’t to become a UX expert; it’s to apply your analytical thinking to user experience and create more effective data products.