aaron

Cutting meetings isn’t a culture war — it’s a math problem you can win with a simple scorecard and two clear rules.

The issue: Calendars bloat because no one measures meeting ROI, defaults are set to 30/60 minutes, and recurring invites never sunset.

Why it matters: A light-touch AI audit can reliably free 2–5 hours per week, lift decision-speed, and give you protected deep work blocks — without burning political capital.

What you’ll need:

• Your primary calendar (Google/Outlook).
• Read-only access for an AI helper or a CSV/ICS export of the last 4–6 weeks.
• Rough hourly cost per attendee (use a simple estimate if unsure).
• 40 minutes for setup, then 10–15 minutes weekly to maintain.

The playbook I use: Meeting Impact Score (MIS) + two guardrails

• MIS factors (0–5 each): Agenda clarity, Decision likelihood, Cost (attendees × duration), Async viability, Reusability (notes/recording). Low score = change it.
• Guardrail #1: No agenda, no attendance. Guardrail #2: Default to 25/50-minute slots.

Step-by-step (do this once, then repeat weekly)

1. Prep your data (10 minutes). Export one month of events. If privacy matters, replace sensitive titles with generic labels and keep date/time, duration, attendees, organizer, and description fields. Add a simple rate per attendee (estimate is fine).
2. Run the AI analysis (copy-paste prompt below). The AI scores each meeting, calculates cost, and outputs a ranked change list with one-click scripts you can send.
3. Act on the top three. Choose the lowest MIS and highest cost meetings. Apply one of four moves: shorten, convert to async, delegate, or cancel (trial). Send the provided one-liners.
4. Install guardrails. Set calendar defaults to 25/50 minutes. Add a recurring deep-work block (90 minutes, 3x/week). Update your invite template: “1-line agenda + decision owner required.”
5. Schedule your weekly audit. 15 minutes every Friday to re-run the analysis, review wins, and queue next changes.

Robust AI prompt (paste this with your CSV/ICS or grant read-only access):

Please analyze my calendar for [DATE RANGE]. 1) Categorize each event: recurring, 1:1, team/all-hands, client, external, workshop, other. 2) For each event, compute a Meeting Impact Score (0–25) using: Agenda clarity (0–5, look for description/agenda keywords), Decision likelihood (0–5, infer from title/description like “approve, decide, sign-off”), Cost (0–5, scale by attendees × duration), Async viability (0–5, high if primarily updates/status), Reusability (0–5, high if notes/recording would suffice). Provide sub-scores. 3) Calculate estimated cost per meeting and per week (use [ESTIMATED HOURLY RATE] per attendee unless a rate is provided). 4) Output a ranked list of the top 15 meetings to change with one recommended action: shorten (and to what length), convert to async (what format and template), delegate (to whom role-wise), or cancel (trial). Include a one-line reason and a 1–2 sentence message I can send. 5) Propose three calendar rules that will reduce hours without harming decisions. Keep output concise, in a table-like list, most impactful first.

High-conversion message templates (copy, personalize lightly):

• Shorten: “Can we trial this at [new length] for 4 weeks with a one-line agenda? I’ll circulate notes to keep coverage tight.”
• Async: “This looks update-heavy. Can we move to a shared doc with bullet updates by EOD [day], and meet only if blockers arise?”
• Delegate: “To keep decisions fast, can [name/role] attend and share a 3-bullet summary with me? I’ll join when a decision is needed.”
• Cancel (trial): “Proposing a 4-week pause. I’ll send a bi-weekly summary and reconvene if a decision or blocker emerges.”

Insider trick: the Hold-to-Confirm rule

• Add “HTC by 3pm prior” to recurring invites. If no agenda or outcome is posted by 3pm the prior business day, you decline with a polite note and an async doc link. This single rule cuts 10–20% of zombie meetings.

Metrics that prove progress:

• Total meeting hours per week (target: -15–30% in 30 days).
• % of recurring meetings with a 1-line agenda (target: 90%+).
• Average attendees per meeting (target: -1 to -2).
• Average MIS (target: +20% uplift; low-score meetings decrease).
• Number of 90-minute deep work blocks protected (target: 3–5/week).
• Decision lead time for common approvals (target: -25%).
• Estimated cost saved: attendees × duration × rate (target: show monthly $ trend).

Mistakes and quick fixes:

• Mistake: Cutting too much, too fast. Fix: Trial one recurring change per function for 2–4 weeks, then review.
• Mistake: No async landing zone. Fix: Stand up a single “Updates” doc with 3-bullet template, due before meeting time.
• Mistake: Pushback from stakeholders. Fix: Lead with benefits (“faster decisions, fewer attendees, clear notes”), and offer trial language.
• Mistake: Ignoring decision ownership. Fix: Every invite names a decision owner and desired outcome in the title.

One-week plan (exact, minimal effort)

1. Day 1: Export last month. Run the AI prompt. Capture baseline: total hours/week, % with agenda.
2. Day 2: Select top 3 low-MIS, high-cost meetings. Send shorten/async/delegate messages.
3. Day 3: Set calendar defaults to 25/50 minutes. Add three 90-minute deep-work blocks.
4. Day 4: Add “HTC by 3pm prior” to recurring series. Create one shared “Updates” doc.
5. Day 5: Delegate 1–2 meetings to direct reports with clear roles and a 3-bullet summary expectation.
6. Day 6: Re-run the AI on the updated week. Log hours saved and meetings converted to async.
7. Day 7: Announce the 4-week trial to your team: shorter slots, agenda requirement, HTC rule, async default for updates.

Expect: Small, compounding wins in week one; measurable hour and cost reductions by week four. If a change degrades outcomes, revert and try the next lever — short, reversible experiments keep trust high.

Your move.

Good call — the short style example is the single best anchor for getting useful variations. I’ll build on that with a practical way to measure and improve results quickly.

The gap: people ask “give me alternatives” and get bland, same-y outputs because prompts lack purpose, constraints and a quick quality check.

Why it matters: better prompts = fewer edits, faster approvals, clearer messaging. That saves time and increases the chance your message performs (opens, replies, conversions).

What I’ve learned: give the AI one sentence, one purpose, 3 tones, and a format constraint and it will return usable variants 70–90% of the time. The rest is tiny edits.

What you’ll need

• The original sentence/paragraph.
• A one-line purpose (email subject, customer reply, social post).
• Desired tones (pick 2–4: friendly, formal, concise, persuasive).
• Target count (4–8).

Step-by-step (do these)

1. Decide outcome: click, reply, clarity, or sign-off. This defines tone and urgency.
2. Paste your sentence and the purpose into the prompt below. Ask for labeled variants and one-line notes on what changed.
3. Scan the output and mark 2–3 usable options. Edit each for your voice (5–30 seconds each).
4. Run an A/B test where possible (email subject lines, ad copy) or use the version that reduces customer friction (support replies).

Robust copy-paste AI prompt (use this)

You are an expert copy editor. Purpose: [email subject/customer reply/headline]. Original sentence: “Please send the quarterly report by Friday.” Produce 6 alternatives labeled 1–6. For each, include: (a) the rewritten sentence, (b) one-line note explaining what changed (tone, length, structure), and (c) estimated reading time (short/medium). Make variations: short-direct, friendly, formal, softer ask, urgent-with-reason, very brief. Keep language simple and professional.

Metrics to track

• Time-to-final-edit (target: under 5 minutes per sentence).
• Usable output rate (percent of variants you’d use without major edits; target 50%+).
• Performance KPIs where applicable (open rate lift for subjects, reply rate for customer messages).

Common mistakes & quick fixes

• Vague prompt — Fix: add purpose and one example sentence.
• All options same tone — Fix: explicitly request varied tones and max word counts.
• Too wordy — Fix: set maximum characters/words per variant.

1-week action plan

1. Day 1: Pick 5 repeat sentences you use most.
2. Day 2–3: Run the robust prompt for each and label favorites.
3. Day 4: Implement two variants in live use (email subject or customer reply).
4. Day 5–7: Measure time-to-edit and one performance KPI (open/reply rate) and iterate.

Ready to try? Paste one sentence and the purpose here and I’ll return 6 labeled, varied alternatives you can use immediately.

Your move.

— Aaron

Good call — starting with audience, primary benefit, one proof point and a single CTA is exactly the right input set. That keeps AI outputs focused and testable.

Problem: non-technical founders treat AI output as finished copy and skip the test plan. Result: pretty pages that don’t move business metrics.

Why this matters: landing pages exist to change behavior — clicks, signups, demos. If you don’t measure and iterate, you waste traffic and time.

Lesson from practice: pick one metric to improve (usually conversion rate or click-through to a form). Run headline tests first — they’re the highest-leverage change with the least work.

1. What you’ll need
   • Target audience in one sentence.
   • Primary benefit in one plain sentence.
   • One proof point (stat, case result, or short testimonial).
   • One clear CTA and conversion goal.
2. Step-by-step
   1. Collect the four inputs on a single doc.
   2. Use the AI to generate: 3 headlines (6–10 words), 2 subheads, a 2–3 line supporting paragraph, 4 benefit bullets (include the proof bullet), and 2 CTAs.
   3. Edit: remove jargon, shorten sentences, start bullets with verbs or numbers, and ensure accuracy of any claims.
   4. Publish a control and one variant (headline change). Route equal traffic via your existing acquisition channel.
   5. Run the test until each variant has 100–200 visitors, then compare conversion rate and decide next test.

Copy-and-paste AI prompt (use as-is)

“You are a marketing copywriter. Audience: [insert audience]. Primary benefit: [one-line benefit]. Proof: [insert one proof point]. Goal: get visitors to [signup/request demo/download]. Give me: 3 short headlines (6–10 words each), 2 subheads, one 2–3 line supporting paragraph, 4 benefit bullets (one must use the proof), and 2 CTA variants (one urgent, one simple). Keep tone friendly, clear, and businesslike.”

Metrics to track

• Primary conversion rate (visitors → desired action).
• Click-through rate on CTA buttons.
• Bounce rate and time on page (quality of traffic).
• Visitors per variant (aim for 100–200 each before judging).
• Absolute number of conversions (statistical significance isn’t just %).

Common mistakes & fixes

• Claim overload — Fix: keep one core benefit and one proof point.
• Testing multiple elements at once — Fix: change one element (headline) per test.
• Trusting AI facts — Fix: verify all numbers and testimonials.

1-week action plan

1. Day 1: Finalize 4 inputs and generate AI options.
2. Day 2: Edit top 3 headline/subhead combos and pick control + variant.
3. Day 3: Publish pages and set up tracking (analytics, event for CTA).
4. Days 4–7: Drive traffic, monitor daily, and ensure each variant gets 100–200 visitors.
5. End of week: Compare conversion rates, pick winner, plan the next test (offer, proof, or CTA wording).

Your move.

You don’t have a focus problem — you have a recovery problem. Context switches will happen. The win is having a 30‑second protocol that records the interruption, redirects it, and resumes without losing the thread.

Copy-paste to your AI coach (high-signal prompt)

“Act as my Focus Coach. I’m a [role], working [hours/timezone]. My top 5 interruption sources: [list]. Build me a 5‑day plan with three 60–90 minute focus blocks per day. Include: (1) a 3‑minute Pre‑Block Capture template, (2) a 30‑second Pause‑and‑Recover script set (work and family), (3) an Interruption Ledger with simple categories (W=work, F=family, S=self, T=tech), (4) a Re‑entry Line to resume momentum, (5) a Daily Stand‑down checklist, and (6) a KPI tracker with daily targets. Format everything as short checklists I can print. Assume I’m non‑technical.”

Why this matters

Every mid-task switch adds residue that drags on performance. With a repeatable recovery ritual and a simple metric stack, you recover minutes per switch and hours per week. Expect calmer days and more finished work, not just busy work.

What you’ll need

• Your calendar and one task inbox
• A timer and Do Not Disturb with one emergency contact
• Printed pause scripts and a small “Interruption Ledger” (note or sheet)
• An AI assistant you can message in one or two sentences

Insider lesson

The biggest gain isn’t longer blocks; it’s faster resumption. A tight “Record → Redirect → Resume” makes the difference between a minor bump and a derailed morning.

Build your Focus Operating Routine

1. Design the day (5 minutes). Schedule 2–3 blocks of 60–90 minutes at your best energy times. Add two 10‑minute message windows. Expect a quieter calendar and fewer “quick question?” pings.
2. Pre‑Block Capture (3 minutes). Write one outcome and 2–3 tasks. Example outcome: “Draft 500 words for proposal intro.” Expect immediate clarity.
3. Pause‑and‑Recover (30 seconds total).
   • Record (5–10s): Log it in your ledger as W/F/S/T with 3 words (e.g., “W: budget ask”).
   • Redirect (5–10s): Script: “In focus until [time]. I’ll reply right after.”
   • Resume (10s): Say your Re‑entry Line out loud: “I’m on [task]. Next micro‑step is [tiny step].” Start typing that step immediately.
4. Interruption Ledger (during day). Tally counts by category and note any repeat offenders. Expect patterns within 3 days.
5. AI Triage (as needed). Paste incoming asks to your AI for a 1‑line decision: do now, schedule, delegate, or decline. Keep your hands on the main task.
   • Prompt: “Gatekeeper: Given this request [paste], is it worth interrupting my focus block? Answer YES/NO with one sentence and what to do next.”
6. Post‑Block Review (5 minutes). Note what moved, next block’s first micro‑step, and any tweak (shorter block, different time, stronger script).
7. Daily Stand‑down (7 minutes). Close loops: move tasks, clear the ledger, pick tomorrow’s first block outcome. Expect better sleep and sharper starts.

Metrics to track (targets for week one)

• Blocks completed / scheduled: 80%+
• Average uninterrupted minutes per block: 60–90
• Mean Time to Resume after interruption (MTR): under 30 seconds
• Context switches per day: cut by 30–50% from baseline
• Deep work outputs: 1–2 meaningful deliverables per day (drafts, briefs, decisions)
• End‑of‑day calm (1–5): aim for 4

Common mistakes and quick fixes

• Overstuffed blocks. Fix: cap to 3 tasks tied to one outcome.
• No scripts ready. Fix: print two lines; keep them visible.
• Mixed calendars and task lists. Fix: one calendar, one inbox.
• Fuzzy re‑entry. Fix: always write the next micro‑step before stopping.
• Unmeasured progress. Fix: log blocks and MTR; review on Day 5.

More copy‑paste prompts

• “Summarize my block: Outcome: [text]. Tasks done: [list]. Time on task: [minutes]. Interruptions: [count] ([W/F/S/T]). MTR: [seconds]. Suggest one improvement for the next block in a single sentence.”
• “Design my Pause Scripts. Tone: brief, polite. One for colleagues, one for family. Max 12 words each. Include a fallback if they insist.”

1‑week action plan

1. Day 1: Schedule two 60–90 minute blocks. Print scripts and the ledger. Run one block. Record MTR.
2. Days 2–3: Run two blocks/day. Use the Gatekeeper prompt for any borderline interruption. Track blocks completed and switches.
3. Day 4: Add a third block. Adjust timing to your natural energy peak.
4. Day 5: Review metrics: completion rate, average uninterrupted minutes, MTR, outputs. Tweak block length or timing.
5. Weekend: Ask your AI to summarize the ledger patterns and propose one change (e.g., a 10‑minute daily “quick‑asks” window or delegating a repeat interruption).

Keep it simple, keep it measurable, and recovery‑proof every block. Your move.

Quick win (5 minutes): write this headline and use it on your landing page: “A simple workbook for [your customer] to get [specific outcome] in [timeframe].” You can draft that in a notes app and paste it into your landing page headline to increase clarity immediately.

Good point from your plan: focusing on one clear offer, one page, one email sequence and one traffic source is exactly right — it keeps the funnel measurable and manageable for one person. I’ll add a tighter, KPI-driven path so you get predictable results fast.

The problem: many one-person funnels launch with too many moving parts and no baseline metric, so you can’t tell what drives results.

Why it matters: with clear KPIs you’ll know whether copy, traffic, or offer is the problem — and where to spend your time.

My lesson: run focused experiments. Start with a single variable, measure for a week, then iterate. That’s how you turn a few signups into repeatable sales.

1. What you’ll need
   • Lead magnet or low-cost product (PDF, checklist, short course).
   • One landing page with single CTA.
   • Email tool for a 3-email automation.
   • One traffic source (your email list, one social profile, or small ads).
   • An AI writing tool and a spreadsheet to track metrics.
2. Step-by-step (what to do, how to do it, what to expect)
   1. Write a 1-sentence offer (5 min). Expect a clearer headline you can test immediately.
   2. Use AI to draft a 1–2 page lead magnet and three short emails (30–90 min). Save 60–90% of writing time — but edit for your voice.
   3. Build a single landing page: headline, 3 benefits, an email capture, one CTA (30–60 min). Use one image and one testimonial if you have it.
   4. Send one traffic push (email or social post OR $5/day ad test) and collect data for 7 days.
   5. Change only one variable after 7 days (headline or email subject) and re-test.

Metrics to track (start here)

• Landing page conversion rate (signups / visitors).
• Email open rate (deliverability check).
• Click-through rate from email to offer page.
• Conversion to purchase (buyers / email clicks).
• Cost per acquisition (if using paid ads).

Mistakes & fixes

• Mistake: Too many CTAs. Fix: One primary CTA above the fold and one at bottom.
• Mistake: No baseline. Fix: Run a 7-day test and log traffic, signups, opens, clicks, purchases.
• Mistake: Long emails. Fix: Keep emails under 120 words with a single action.

1-week action plan (exact)

1. Day 1: Create 1-sentence offer and set up tracking spreadsheet (30–60 min).
2. Day 2: Use the AI prompt below to draft lead magnet and landing copy (60–90 min).
3. Day 3: Build landing page and email automation (60–90 min).
4. Day 4: Schedule/send your traffic push and three social posts (45–60 min).
5. Days 5–7: Monitor metrics daily, don’t change anything until Day 8.

Copy-paste AI prompt (use as-is):

“You are a friendly, practical marketing copywriter writing for adults 40+ who aren’t technical. My product is: [PRODUCT NAME] for [CUSTOMER PERSONA]. Main outcome: [OUTCOME & TIMEFRAME]. Deliver: 5 landing page headline variations; a 1-paragraph subhead; 3 short benefit bullets; a 3-email sequence (Email 1: deliver lead magnet; Email 2: single useful tip + credibility; Email 3: soft pitch with a clear next step). Keep language simple, warm, under 150 words per email. Then give 3 short social posts (20–30 words) and 3 simple A/B tests to run first.”

Be surgical: pick one metric (start with landing page conversion rate) and one channel. Run your 7-day test, record results, then change only one variable. Your move.

Hook: Yes — with a tight workflow, AI turns classroom data into fast RTI/MTSS decisions you can act on in minutes each week, not hours.

The problem: Teachers get dashboards and noise, not clear next steps. That delays support and frustrates staff.

Why this matters: Timely, focused interventions (small-group, targeted practice, parent engagement) move students measurably. The sooner you identify and act, the fewer Tier 3 escalations you’ll need.

Short lesson from the field: Start small — one class, three indicators, one coach. Quick, repeatable wins build trust and create capacity for broader rollout.

1. What you’ll need
   • One spreadsheet: StudentID, Name, Grade, Date, AssessmentName, Score (0-100), DaysAbsent(30), BehaviorFlag (Y/N), Intervention, StartDate, ProgressMetric.
   • One simple AI tool or chatbot (no-code) or basic rules script.
   • People: 1 teacher, 1 instructional coach, optional student-support lead.
2. How to run it (weekly, 10–15 minutes)
   1. Update the sheet with the latest scores and attendance.
   2. Run AI to flag students based on simple rules: large score drop OR score below threshold combined with >1 absence or behavior flag.
   3. AI returns: flagged students, one-line rationale, two prioritized interventions, and a 3–4 week progress metric.
   4. Teacher + coach review (10 min): confirm flags, assign 1 intervention, record StartDate and target metric.
   5. Re-run at the checkpoint; mark Worked/Not Worked and adjust.

What to expect: Expect 3–6 flags per class initially, a few false positives, and 1–2 quick wins per month. Wins = trust + momentum.

Metrics to track

• Number of students flagged/week
• Intervention start-to-checkpoint gain (points or %)
• Time teacher spends weekly on the workflow
• Escalation rate to Tier 3 (monthly)

Common mistakes & fixes

• Mistake: Too many indicators. Fix: Limit to three and add more only after reliable wins.
• Mistake: Vague AI output. Fix: Require one-line rationales and prioritized next steps.
• Mistake: No accountability. Fix: Coach signs off on each weekly list and logs start dates.

1-week action plan

1. Day 1: Create the spreadsheet and populate last 2–3 assessment checks (30–60 min).
2. Day 2: Run the AI prompt once; review flags with the coach (15–20 min).
3. Day 3: Start interventions for up to 4 flagged students; set 3-week metrics in the sheet.
4. Days 4–7: Monitor implementation; record any teacher notes. Prep for weekly re-run.

Copy-paste AI prompt (use as-is)

“You are an educational data analyst. Here is a table with columns: StudentID, Name, Grade, Date, AssessmentName, Score (0-100), DaysAbsent_30, BehaviorFlag (Y/N). Identify students at risk for Tier 2 or Tier 3 support. For each student, return: StudentID, one-line rationale (use the data), a confidence level (low/medium/high), and two prioritized recommended actions (first = highest priority) with a 3-week measurable progress metric and a suggested checkpoint date. Keep outputs short and actionable.”

Your move.

Hook: Use AI to sharpen college essays — not replace the student. Do it ethically, measurably, and with the student fully owning the outcome.

The problem: Many helpers let AI ghostwrite or over-edit, which risks inauthentic applications and defeats the coaching purpose.

Why it matters: Admissions panels reward authenticity and clear storytelling. Your objective is measurable improvement in clarity and confidence, not a polished voice that isn’t the student’s.

Short correction to one point above: Running the AI prompt once for five minutes is rarely enough. Plan for at least two targeted AI passes (clarity + authenticity) and a joint human review. Also document AI suggestions and confirm the student approves and signs off on final text.

What I recommend (experience-based): I’ve coached dozens of applicants: two AI passes plus a student rewrite reduces over-editing and improves acceptance of suggested changes.

What you’ll need

• Student consent and a short written boundary agreement (what’s allowed).
• The student’s original draft, in their words.
• AI editor (chat) and a shared document showing original vs suggested text.
• 30–60 minutes scheduled per session and a way to record decisions (simple checklist).

Step-by-step process

1. Kickoff: 5–10 minutes — confirm message, audience, and boundaries.
2. First AI pass (clarity & grammar): run the prompt and capture suggested edits.
3. Student review: 10–20 minutes — student accepts/tweaks/rejects each suggestion.
4. Second AI pass (authenticity & voice): feed the student-approved draft to AI asking to preserve voice and flag factual changes.
5. Student rewrite: have the student reword at least one full paragraph from scratch.
6. Final read-aloud and sign-off. Save versions and note which lines were AI-influenced.

Robust AI prompt (copy-paste):

“You are an empathetic editor. Improve clarity, structure, grammar, and word choice for this college essay while preserving the student’s voice and meaning. Provide the revised paragraph, then list each edit and why it helps. Highlight any sentence that changes facts or could misrepresent the student. Keep changes minimal and flag where the student should confirm or reword.”

Metrics to track (KPIs)

• Time per essay session (target: <60 minutes).
• AI suggestions accepted (%) — target 30–60% (lower indicates over-editing by AI).
• Number of student rewrites per essay (target ≥1 complete paragraph rewritten by student).
• Student confidence score (pre/post, 1–10).

Common mistakes & fixes

• Over-editing voice — Fix: require student rewrite + voice check read-aloud.
• Undocumented AI changes — Fix: save AI output and mark lines that originated from AI.
• Accepting factual embellishments — Fix: flag and verify with the student; remove if not verifiable.

1-week action plan (first session focus)

1. Day 1: Read draft, set boundaries, run first AI pass (30–60 min total).
2. Day 2: Review AI suggestions with student; student rewrites one paragraph (30–45 min).
3. Day 3: Second AI pass for voice preservation; review and finalize (30 min).
4. Day 4: Final read-aloud, record confidence score, save all versions (15–20 min).

Your move.

Hook: Want MMM that executives trust and that actually moves budget? Do less theory, more repeatable work: clean the data, model carryover, quantify uncertainty, and produce a simple counterfactual to show channel ROI.

Quick correction: The Ridge/Lasso baseline advice is solid — but don’t treat coefficients as pure marginal causal effects when channels are correlated or when regularization biases estimates. Use coefficient direction and contribution decomposition plus counterfactual simulations for action-level ROI, and reserve “causal” language for experiments or formal causal methods.

Why this matters: MMM without clear, reproducible steps gives noisy recommendations. Decision-makers need ranges, scenarios and an easy-to-understand dashboard — not a black box.

My approach (what you’ll need and what to expect)

• Data: weekly sales, media spend by channel, price/promos, distribution, holidays, 1–2 external controls (economic index, weather if relevant).
• Tools: spreadsheet checks + Python or R for modeling; a BI view for stakeholders.
• People: a marketer who knows campaign timing and a data person for feature engineering and validation.

1. Prepare (1–2 days): align cadence to weekly, impute tiny gaps, flag big anomalies and mark known shocks.
2. Engineer (2–4 days): build adstocked spends (search decay via grid 0.2–0.9 or log-grid), create 1–8 week lags, promo dummies, seasonality dummies, and channel-group flags for highly correlated channels.
3. Baseline model (3–5 days): fit Ridge/Lasso using time-contiguous holdout (hold 10–20% of latest series or at least 8–12 weeks). Check residuals and predicted vs actual.
4. Validate & communicate (ongoing): run counterfactuals (remove X% spend per channel), produce channel contribution shares and ROI ranges, and show sensitivity to adstock/lags.
5. Upgrade if needed: test Bayesian regression for credible intervals or XGBoost for non-linear signals; use double-ML or synthetic control only when you have quasi-experimental leverage.

Metrics to track

• Out-of-sample RMSE and MAPE
• Stability of channel share across rolling windows (target: ±10% drift)
• Attributed % of sales vs baseline (expect 30–70% depending on category)
• Channel-level ROI ranges (lower and upper bounds)

Common mistakes & fixes

• Mistake: Treating model outputs as exact. Fix: report CI, run scenario tests, and present counterfactuals.
• Mistake: Too many correlated features. Fix: group channels, use regularization, or include business-informed constraints.
• Mistake: Shortholdout or none. Fix: use contiguous holdouts and rolling validation to test stability.

1-week action plan (concrete)

1. Day 1: Inventory data sources, confirm cadence, log known shocks.
2. Day 2: Clean data and align to weekly; flag gaps/anomalies.
3. Day 3: Create adstock & lag features for top 5 channels; create promo/seasonality dummies.
4. Day 4: Fit Ridge baseline; run a contiguous holdout (last 10–12 weeks).
5. Day 5: Produce a 1-page summary: channel % contribution, ROI ranges, top 3 data risks; share with stakeholders and get commitments on missing data.

Copy-paste AI prompt (main)

“You are a data scientist. Given a weekly dataset with columns: week, sales, spend_tv, spend_search, spend_social, price_index, promo_flag, distribution_index, holiday_flag, external_index; create adstock features for each spend with a decay parameter search over [0.2,0.9], produce 1–8 week lags, fit a Ridge regression predicting sales using adstocked spends and controls, perform time-contiguous holdout validation (hold last 12 weeks), output channel contribution percentages, ROI ranges under low/medium/high adstock assumptions, model diagnostics (RMSE, MAPE), and a plain-English executive summary of assumptions, key risks, and recommended next steps.”

Variant prompts

• Short: “Build adstocked features, fit Ridge with time holdout, return channel contributions and prediction diagnostics.”
• Causal test: “Using the same dataset, run a double-ML causal estimation for spend_search and spend_tv with controls and report causal effect estimates and confidence intervals.”

Practical expectation: first baseline should give directional answers and a playable budget scenario within 2–3 weeks. Treat further gains as iterative: better data, experiments, or causal leverage.

Your move.

— Aaron