Choosing Charts For Marketing Decisions

Why this matters

Marketing decisions move money. Clear charts help teams act: pause a weak channel, scale a winning campaign, fix a leaky funnel, or re-target a segment. As a Marketing Analyst, you’ll often be asked: “What does this mean, and what should we do?” The right chart makes the answer obvious.

• Weekly performance reviews: show ROAS trends by channel.
• A/B tests: compare variant lift by audience segment.
• Funnel analysis: locate biggest drop-offs to prioritize fixes.
• Budget planning: display spend share and efficiency shifts.

Concept explained simply

Different questions need different visuals. Choose the chart that matches the decision you want to drive.

Mental model: Question → Chart family

• Compare categories (one point in time): sorted bars or lollipops.
• See change over time: line chart; small multiples if many lines.
• Part-to-whole at a point in time: 100% stacked bars (limited categories).
• Distribution of a metric: histogram or box plot (by group).
• Relationship between two metrics: scatter plot (bubble adds a third).
• Composition over time: stacked area or 100% stacked area.
• Funnel stages: horizontal bar funnel (descending bars) with drop-off labels.
• Cohorts/retention: heatmap (time vs cohort).
• Geography matters: choropleth or symbol map.
• Uncertainty or variability: error bars or confidence bands.

Pro tips to reduce chart overload

• Max 6–8 categories per view. Beyond that, split into small multiples or focus on top items.
• Direct-label lines and bars where possible; minimize legends.
• Start axes at zero for bars; lines can start at a non-zero baseline if clearly labeled and justified.

A quick chooser checklist

• What decision is needed? (Pause/scale, allocate budget, fix funnel, target segment)
• Does time order matter? If yes → line/small multiples. If no → bar/rank.
• Do we compare parts to a whole? If yes → 100% stacked bar; avoid pies with many slices.
• Is spread/variance key? If yes → distribution plot (box/histogram).
• Are we showing relationships? If yes → scatter/bubble with trend and labels.
• Will the viewer act on outliers or thresholds? Add reference lines/bands.

Worked examples

Example 1: Weekly ROAS by channel (last 12 weeks)

Decision: Scale or cut channels based on efficiency trend.

• Best chart: Line chart with one line per channel. Highlight current week; add target ROAS reference line.
• Why: Time order matters; trends and trend changes are clearer with lines.

Alternative designs

• Small multiples of single-channel lines if 5+ channels to reduce clutter.
• Monthly aggregation lines if weekly volatility distracts from signal.

Example 2: Conversion funnel (Impressions → Clicks → Add to Cart → Purchase)

Decision: Which step to fix first.

• Best chart: Horizontal bar funnel with bars sized by absolute counts, plus labels for step conversion and drop-off percentage.
• Why: The largest drop-off is visually obvious; action focuses on the biggest loss.

Alternative designs

• Stacked bars by stage for multiple campaigns (small multiples across campaigns).
• For A/B funnels, side-by-side funnels with consistent scales.

Example 3: Order value by device (Mobile vs Desktop)

Decision: Tailor promotions by device based on customer value.

• Best chart: Box plots for AOV by device.
• Why: Medians, spread, and outliers show if differences are meaningful beyond averages.

Alternative designs

• Overlaid histograms (transparent) if you need the shape; ensure bins align.
• Dot plots of means with error bars if stakeholders prefer simpler visuals.

How to choose (step-by-step)

1. Name the decision: What will change if the chart convinces the team?
2. Pick the core question: compare, trend, part-to-whole, distribution, relationship, or funnel?
3. Limit the view: show the minimum to decide; move extra detail to a second panel.
4. Add context: reference lines, targets, or benchmarks.
5. Label clearly: metric definition, time period, currency, and whether values are absolute or rates.

Exercises

Do these before checking solutions. Aim for one sentence of reasoning per choice.

1. Exercise 1 (ex1): You ran an email A/B test (Subject A vs B) across 5 segments (New, Returning, High LTV, Dormant, VIP). You must recommend which subject to roll out. Choose the chart type and what to label.
2. Exercise 2 (ex2): Q1 budget review: 4 channels (Search, Social, Display, Affiliates). You need to show current spend share and how CPC changed over the last 8 weeks to argue for reallocation. Choose the chart(s) and key annotations.

• Checklist:
  • Chart directly answers the decision question.
  • Categories are limited or split into small multiples.
  • Axes and units are labeled; rates vs absolutes are clear.
  • Reference lines or targets included when relevant.

Tips if you’re stuck

• Time changes? Choose a line chart.
• Comparing variants across segments? Clustered bars or small multiples of bars.
• Budget share? 100% stacked bar (limit categories).
• Two related metrics? Scatter plot with quadrant guides.

Common mistakes and self-check

• Pie overload: Many slices hide differences. Prefer 100% stacked bars or ranked bars.
• Too many lines: 6+ lines become spaghetti. Use small multiples or focus on top 3.
• Mismatched scales: Dual axes can mislead. Use panels or index to a baseline instead.
• No context: Missing targets or time ranges leads to misinterpretation. Add reference lines and period labels.
• Bars not starting at zero: Magnifies small differences. Keep bar axes at zero.

Self-check

• Can a non-analyst state the decision after 5 seconds? If not, simplify.
• Does the chart emphasize signal (trend, ranking) more than decoration? Remove clutter.
• Would a different chart change the conclusion? If yes, pick the more honest one.

Practical projects

• Create a weekly marketing performance dashboard: lines for ROAS and CPA by channel; 100% stacked bar for spend share.
• Build an A/B testing board: clustered bars for lift by segment; funnel comparison for Variant A vs B.
• Retention heatmap: cohorts by signup month with week-on-week retention shading.

Who this is for, prerequisites, and path

Who this is for

• Marketing Analysts and anyone presenting performance to stakeholders.

Prerequisites

• Basic understanding of marketing metrics (CTR, CVR, CPA, ROAS, AOV).
• Comfort with spreadsheets or BI tools to create charts.

Learning path

• Start: Choosing charts (this lesson).
• Next: Designing readable dashboards (layout, color, labeling).
• Then: Statistical thinking for experiments (uncertainty, CIs, power).

Mini challenge

PM asks: “Did our onboarding redesign help mobile purchases?” You have weekly Purchase Rate by device (Mobile, Desktop) for 10 weeks before and 10 weeks after launch.

• Pick a chart and list 2 annotations you would add to support a decision to continue iterating or roll back.

One strong approach

Two-panel small multiples line charts (one per device) with a vertical launch marker and a 4-week moving average. Add a target band if one exists.

Next steps

• Apply the checklist to one of your current reports.
• Replace any unclear pie or busy line chart with a clearer alternative.
• Share with a teammate and ask: “What decision would you make from this?”

Quick test

Available to everyone for free. Only logged-in users will have their progress saved.