Marketing Automation with Quarto

Reflection on Dashboards, Automation, and Reproducible Reporting

Lucynda Young

Cal Poly Pomona

2026-05-11

Marketing Automation with Quarto

Reflection Presentation

Audience-centered reflection on dashboards, automation, dynamic reporting, and reproducible analytics.

Presentation Roadmap

1. Quarto-based interactive dashboards

2. Parameterized reporting

3. Avoiding dashboard fatigue

4. Challenges learning the tools

5. Quarto, reproducibility, and AI

1 · Interactive Dashboards

Prompt

In Step 1, Dr. Albert Rapp showed you how to create an interactive dashboard with Quarto. What did you like about the Quarto-based interactive dashboard compared with other options introduced in this class previously?

What I Liked Most

Quarto turns a dashboard from a collection of charts into a reproducible communication product.

It combines code, visuals, explanation, and publishing in one workflow.

That makes it stronger than a dashboard that only displays metrics without context.

Quarto vs. Traditional Dashboards

Traditional Dashboard

  • Good for monitoring
  • Often KPI-heavy
  • Can feel disconnected from the “why”
  • Usually requires the viewer to interpret the story

Quarto Dashboard

  • Good for explaining
  • Combines narrative with analysis
  • Easier to reproduce and update
  • Helps the viewer understand the business meaning

Why It Matters in Marketing

Marketing data changes constantly: campaigns, channels, customers, costs, conversions, and engagement.

A Quarto dashboard can be re-rendered when the data changes, instead of manually rebuilt.

That supports better decision-making because the report is both current and traceable.

Example: KPI Workflow

library(tidyverse)

marketing_kpis <- tibble(
  channel = c("Paid Search", "Email", "Organic Social", "Referral"),
  conversions = c(420, 310, 185, 125),
  cost = c(12500, 3200, 2100, 900)
) |>
  mutate(cpa = round(cost / conversions, 2))

marketing_kpis
# A tibble: 4 × 4
  channel        conversions  cost   cpa
  <chr>                <dbl> <dbl> <dbl>
1 Paid Search            420 12500  29.8
2 Email                  310  3200  10.3
3 Organic Social         185  2100  11.4
4 Referral               125   900   7.2

2 · Parameterized Reporting

Prompt

In Step 2, you learned how to set parameterized reporting in Quarto. Explain what parameterized reporting is and how it works. Do you see the application of it in your workflow in your current or past jobs, or future jobs?

What Parameterized Reporting Means

Parameterized reporting means one report template can generate many customized reports.

Instead of copying the same report over and over, the analyst changes inputs such as:

Region · Channel · Campaign · Date range · Product category · Marketplace

How It Works

Report Template

  • Fixed structure
  • Reusable code
  • Consistent visual design
  • Standard business questions

Parameter Values

  • Filter the data
  • Update charts
  • Change labels
  • Customize the final message

Example Parameter Logic

params <- list(
  region = "West",
  channel = "Email"
)

campaign_data <- tibble(
  region = c("West", "West", "East", "East"),
  channel = c("Email", "Paid Search", "Email", "Paid Search"),
  conversions = c(310, 420, 260, 390)
)

campaign_data |>
  filter(region == params$region, channel == params$channel)
# A tibble: 1 × 3
  region channel conversions
  <chr>  <chr>         <dbl>
1 West   Email           310

Workflow Application

For a digital marketing or eCommerce workflow, parameterized reporting could support recurring reports across:

Amazon

Walmart

eBay

Houzz

Wayfair

Brand website

Why It Saves Time

A single report template can produce multiple versions for different audiences.

This reduces manual editing and improves consistency.

It also lowers the risk of mistakes caused by copying, pasting, and reformatting old reports.

3 · Avoiding Dashboard Fatigue

Prompt

In Step 3, Sean Nguyen talks about how to avoid dashboard fatigue for users in his talk, “Beyond Dashboards: Dynamic Data Storytelling with Python, R, and Quarto Emails.” What are his suggestions for ways in which you can make your dashboard effective and help your colleagues pay attention to your report?

The Problem

Dashboard fatigue happens when users see too much data and not enough meaning.

Too many charts can make people stop paying attention.

The audience should not have to hunt for the insight.

Better Approach: Dynamic Data Storytelling

Less Effective

  • Many metrics at once
  • No clear priority
  • Charts without interpretation
  • Users must search for meaning

More Effective

  • Clear headline insight
  • Focused visuals
  • Explanation of what changed
  • Suggested action or next step

How to Help Colleagues Pay Attention

Lead with the most important takeaway.

Use plain business language, not just technical metrics.

Deliver focused updates instead of expecting people to check dashboards constantly.

Use automation to send the right information at the right time.

Dashboard Design Rule

A good dashboard does not simply answer, “What happened?”
It helps the audience decide, “What should we do next?”

4 · Challenges Learning the Tools

Prompt

Programming is all about increasing efficiency and reducing repetition. Share some of the challenges you might have in learning the various tools in the Module.

Main Challenge

The tools are powerful because they connect many skills, but that also makes the learning curve steep.

Quarto requires attention to YAML, markdown, R code, file structure, rendering, HTML output, and design.

Specific Challenges

YAML indentation and formatting errors

Package installation and library conflicts

Working directory and file path issues

Choosing the right output format

Balancing code, design, and written explanation

How I Can Improve

Technical Habits

  • Use organized project folders
  • Render early and often
  • Save reusable templates
  • Comment code clearly

Communication Habits

  • Start with the audience
  • Reduce clutter
  • Explain the “so what”
  • Match format to purpose

5 · Reproducibility and AI

Prompt

Share your thoughts about the learning experience you have had about Quarto, literate programming, and reproducible reports. Also, share your plan, if any, for using the knowledge in the era of AI.

What I Learned

Literate programming connects the analysis and the explanation.

This makes the report easier to understand, check, and update.

For analytics work, reproducibility builds trust.

Why Reproducible Reports Matter

If a report cannot be recreated, it is harder to trust.

Quarto documents the path from data to decision.

That is valuable when teams need to explain results to managers, clients, or stakeholders.

Quarto in the Era of AI

AI Can Help With

  • Drafting code
  • Explaining errors
  • Summarizing results
  • Improving wording

Quarto Helps With

  • Verification
  • Transparency
  • Reproducibility
  • Professional delivery

My Plan

Use Quarto templates for recurring marketing reports.

Use AI for support, but verify outputs through reproducible workflows.

Build reports that connect data, insight, and action.

Final Reflection

Key Takeaways

Quarto

Combines code, visuals, and narrative in one professional workflow.

Automation

Reduces repetition through reusable templates and parameterized reports.

Storytelling

Helps people understand what changed and what action should come next.

Closing Statement

Quarto helps turn raw data into communication that is easier to update, easier to trust, and easier to act on.

References

  • Rapp, A. Interactive dashboard instruction in the Quarto module.
  • Nguyen, S. Beyond Dashboards: Dynamic Data Storytelling with Python, R, and Quarto Emails.
  • Course module materials: Marketing Automation with Quarto.