Weekly Marketing Reporting Framework: From Data to Decisions

Why a weekly reporting framework matters

Weekly marketing reporting is a decision workflow, not a slide deck. The goal is to turn last week’s performance into a small set of choices for this week: where to invest, what to pause, what to fix in tracking, and what to investigate. A repeatable cadence prevents two common failure modes: (1) reacting to random noise and (2) letting real problems linger because nobody owns the next step.

This chapter gives you a five-part cadence you can run every week in 30–60 minutes with the same structure, the same tables, and a clear “owner + due date” for follow-ups.

The five-part weekly cadence (from data to decisions)

Part 1) KPI snapshot (what happened)

Purpose: Establish the headline outcomes and whether you are on/off plan. Keep it short: 6–10 numbers max.

What to include:

• Time range: Last 7 days vs prior 7 days (and optionally vs same week last year if seasonality matters).
• Spend (total and by major channel).
• Conversions (the primary conversion you manage weekly).
• Revenue or value (if available weekly).
• Efficiency signals: CAC and ROAS at minimum.
• Context flags: launches, promos, tracking incidents, creative swaps, landing page changes.

How to run it (step-by-step):

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1. Lock the comparison windows (e.g., Mon–Sun vs prior Mon–Sun) so the team stops debating date ranges.
2. Pull totals first (all channels combined), then the top 2–4 channels that drive most spend or conversions.
3. Write one sentence that describes the week in plain language (e.g., “Spend up, conversions flat, efficiency down”).

Tip: If you can’t summarize the week in one sentence, you’re not at the snapshot yet—you’re already diagnosing.

Part 2) Driver analysis (why it happened)

Purpose: Explain the KPI changes using a consistent set of breakdowns so you don’t chase random cuts of data each week.

Use three lenses:

• Channel: Which channels moved the totals?
• Funnel stage: Did the change come from click volume, conversion rate, or downstream quality?
• Segment: Which audience, geo, device, product line, or customer type drove the shift?

Practical workflow (step-by-step):

1. Start with contribution: Identify which channel(s) explain most of the change in spend and conversions. Use a simple “delta table” (this week minus last week).
2. Then isolate the funnel lever: For the top changing channel, check whether the change is primarily from (a) traffic/clicks, (b) conversion rate, or (c) value per conversion.
3. Finally segment the driver: Pick one segmentation that is stable and actionable (e.g., new vs returning, geo tiers, device). Look for a concentrated change rather than a uniform shift.

Questions to answer:

• Did performance change because we bought more traffic, paid more per click, converted worse, or attracted lower-quality users?
• Is the change isolated to one campaign/ad set/keyword group, or broad across the channel?
• Is the change concentrated in a segment we can target or exclude?

Part 3) Efficiency check (CAC/ROAS/LTV signals)

Purpose: Confirm whether the week’s results are sustainable and aligned with unit economics. This is where you prevent “growth at any cost” or “over-optimizing short-term ROAS.”

What to check weekly:

• CAC trend: This week vs prior week and vs target.
• ROAS trend: Same comparisons, plus note any attribution/reporting lag that could understate recent ROAS.
• LTV signals (leading indicators): If full LTV isn’t available weekly, use proxies you already trust (e.g., trial-to-paid rate, first-week retention, average order value, repeat purchase rate in 7 days). Use the same proxy every week.
• Mix shift: Did performance change because spend shifted toward inherently higher-CAC segments (e.g., new customers) or lower-intent placements?

Efficiency “sanity checks” (quick tests):

• Spend up + ROAS down: Is it a scaling effect (diminishing returns) or a tracking/attribution issue? Look for simultaneous drops across many campaigns (tracking) vs isolated drops (true performance).
• CAC up but LTV proxy up: Might be acceptable if you’re buying better customers; flag for follow-up rather than immediate cuts.
• ROAS up but conversions down: You may be under-spending or overly constrained by bidding/targeting; check impression share/auction metrics if available.

Part 4) Actions (what we will change and expected impact)

Purpose: Convert analysis into a small number of controlled changes with an owner, a timeline, and an expected directional impact. This is the “decision” part of reporting.

Rules for good weekly actions:

• Limit to 3–5 actions so the team can execute and attribute outcomes.
• Make actions specific: “Reallocate $2,000/day from Campaign A to Campaign B” beats “optimize paid social.”
• State the hypothesis: Why you believe the change will help.
• Define the success metric: What will move (CAC, ROAS, conversions) and by how much (even a rough range).
• Set guardrails: A stop condition (e.g., “pause if CAC exceeds $X for 3 consecutive days”).

Action format you can reuse:

Part 5) Follow-ups (open questions and data fixes)

Purpose: Capture uncertainties and measurement issues so they don’t get lost. Weekly reporting should produce a short backlog of investigations and data hygiene tasks.

Two categories of follow-ups:

• Open questions (analysis): Things you need to validate (e.g., “Is the conversion drop concentrated in iOS?”).
• Data fixes (instrumentation/reporting): Things that make the numbers unreliable (e.g., “UTM missing on new email template,” “conversion event firing twice on checkout”).

Follow-up checklist (step-by-step):

1. Write each follow-up as a question or task with a clear deliverable.
2. Assign an owner and due date.
3. Specify what data/source will answer it.
4. Decide whether it blocks actions (if yes, reduce spend risk until resolved).

One-page weekly marketing reporting template

Use this as a single page you can paste into a doc, email, or dashboard note. Keep it consistent week to week.

WEEKLY MARKETING REPORT (ONE-PAGE TEMPLATE)  — Required Fields Only

1) Time Range
- Reporting window: __________________________
- Comparison window: _________________________
- Notes on seasonality/promos/incidents: ______

2) KPI Snapshot (Totals)
- Spend: __________
- Conversions: ____
- CAC: ___________
- ROAS: __________
- Revenue/Value (if used): __________

3) KPI Snapshot (By Channel)  [fill for top channels]
Channel | Spend | Conversions | CAC | ROAS | Notes
--------|-------|-------------|-----|------|------------------------------
_______ | _____ | ___________ | ___ | ____ | _____________________________
_______ | _____ | ___________ | ___ | ____ | _____________________________
_______ | _____ | ___________ | ___ | ____ | _____________________________

4) Driver Analysis (Why)
- Primary driver(s): __________________________
- Funnel lever (volume vs CVR vs value): ______
- Segment(s) impacted (geo/device/audience): __
- Confidence level (low/med/high) + why: ______

5) Efficiency Check
- CAC vs target: _____________________________
- ROAS vs target: ____________________________
- LTV proxy signal(s) this week: _____________
- Any mix shift affecting efficiency? _________

6) Decisions & Actions (What we will change)
Decision 1: __________________________________
- Owner: ______ Start: ______ Expected impact: ______ Metric: ______ Guardrail: ______
Decision 2: __________________________________
- Owner: ______ Start: ______ Expected impact: ______ Metric: ______ Guardrail: ______
Decision 3: __________________________________
- Owner: ______ Start: ______ Expected impact: ______ Metric: ______ Guardrail: ______

7) Follow-ups (Open Questions + Data Fixes)
- [ ] __________________________________ Owner: ______ Due: ______
- [ ] __________________________________ Owner: ______ Due: ______
- [ ] __________________________________ Owner: ______ Due: ______

Example: narrative that connects metrics to a decision

Scenario: You manage paid search and paid social for a subscription product. Last week you noticed efficiency changes and need to decide whether to reallocate budget.

1) KPI snapshot (what happened)

• Time range: Jan 6–12 vs Dec 30–Jan 5
• Total spend: $50,000 (up 11%)
• Total conversions: 1,000 (flat)
• CAC: $50 (up 11%)
• ROAS: 2.0 (down from 2.3)

One-sentence summary: We spent more but did not gain conversions, so efficiency worsened.

2) Driver analysis (why it happened)

Channel contribution: Paid social spend increased by $8,000 week-over-week and accounted for most of the CAC increase; paid search was stable.

Funnel lever: Paid social clicks increased, but conversion rate declined (traffic quality or landing experience issue rather than volume constraint).

Segment: The conversion rate drop was concentrated in a new prospecting audience segment and on mobile devices; retargeting performance was stable.

3) Efficiency check (CAC/ROAS/LTV signals)

• Paid social CAC moved above target, while paid search CAC remained within target.
• ROAS decline was isolated to prospecting campaigns; retargeting ROAS held steady.
• LTV proxy (trial-to-paid rate) did not improve for the new prospecting segment, suggesting the higher CAC is not buying better customers.

4) Actions (what we will change and expected impact)

5) Follow-ups (open questions and data fixes)

• Open question: Did the conversion rate drop start on a specific day that aligns with a creative or landing page change? Owner: Analyst. Due: Thu.
• Open question: Is the issue isolated to iOS Safari or all mobile? Owner: Analyst. Due: Thu.
• Data fix: Verify the conversion event is firing once per subscription on mobile (no duplicates, no missing fires). Owner: Analytics Engineer. Due: Fri.

How to run the weekly meeting in 45 minutes

Common pitfalls to avoid (and what to do instead)

• Pitfall: Reporting every metric you can pull. Instead: Keep the snapshot tight; push details into driver analysis tables.
• Pitfall: Changing breakdowns every week. Instead: Standardize channel + funnel lever + one segmentation, and only add a new cut when there’s a specific hypothesis.
• Pitfall: “Insights” without decisions. Instead: Require each insight to map to an action, a follow-up, or a deliberate “no change” decision.
• Pitfall: No accountability for data issues. Instead: Track data fixes like product bugs: owner, due date, and verification step.