[Ian Investor]

Hello — I manage a small newsletter and I’m curious whether artificial intelligence can help with two practical things: when to send and how often to send so more people open and click.

Specifically, I’m wondering:

• Can AI reliably identify the best send times for different subscribers or segments?
• Can it suggest the right sending frequency to increase opens/clicks without driving unsubscribes?
• What are the simple, non-technical tools or services that do this well?
• How much data is needed and what privacy or testing concerns should I watch for?

If you’ve tried AI-driven scheduling or frequency testing, I’d love to hear what worked, what didn’t, and any tool recommendations or quick tests a non-technical creator can run. Thanks!

[aaron]

Good question — focusing on both send time and frequency is exactly the right framing. You don’t want a theoretical fix; you want measurable lifts in opens, clicks and revenue.

The problem: Most teams pick a single ‘best hour’ or increase cadence and hope for the best. That ignores recipient behavior, time zones, and diminishing returns.

Why it matters: Small shifts in timing and frequency compound. A 5–10% lift in click-through rate scales to significant revenue when applied consistently across your list.

What I’ve learned: Use segmented, data-driven experiments — then let an AI model recommend per-segment or per-recipient timing. It’s faster and safer than guessing.

Step-by-step plan (what you’ll need, how to do it, what to expect)

1. What you’ll need: historical email log (send timestamp, recipient timezone, opens, clicks, conversions, revenue), your ESP’s A/B testing capability, and a simple spreadsheet or BI tool.
2. Prep: Export 90 days of data, normalize to recipient local time, and flag segments (region, seniority, product interest).
3. Analyze: Create a heatmap of open and click rates by local hour and day for each segment. Identify top 3 windows and low-activity periods.
4. Experiment: Run a 3×3 A/B test per segment — 3 send times × 3 frequencies (e.g., weekly, twice-weekly, monthly) with statistically meaningful sample sizes.
5. Automate: If tests show consistent winners, move to per-segment send-time rules or use an AI-based send-time optimizer for per-recipient timing.
6. Expect: Initial gains in opens/CTR within 1–2 weeks; conversion/revenue impacts may take 2–4 weeks to stabilize.

Metrics to track

• Open rate (by hour/day/segment)
• Click-through rate (CTR)
• Conversion rate and revenue per email
• Unsubscribe and complaint rates
• Deliverability (bounce rates, spam complaints)
• Engagement decay over repeated sends

Common mistakes & fixes

• Mistake: Changing timing and frequency at once. Fix: Isolate variables — test time and frequency separately.
• Mistake: Using overall averages instead of segments. Fix: Segment by behavior and timezone.
• Mistake: Optimizing for opens only. Fix: Optimize for clicks and revenue.
• Mistake: Small sample sizes. Fix: Calculate required sample sizes before testing.

AI prompt you can copy-paste

Act as an email marketing analyst. I will provide a CSV with columns: recipient_id, recipient_timezone, local_send_time (HH:MM), subject_line, open_timestamp, click_timestamp, conversion_timestamp, conversion_value. Analyze the data and:

• Return the top 3 local send-hour windows by segment (segment definitions: region, product_interest, and VIP status).
• Recommend optimal send frequency per segment (weekly, biweekly, monthly) with expected uplifts (open%, CTR%, revenue%) and confidence intervals.
• Provide suggested A/B test designs (variants, sample sizes, success metrics) and a rollout plan for winners.

1-week action plan

1. Day 1: Export 90-day email data and map recipient local timezones.
2. Day 2: Generate hour-by-segment heatmaps; pick 3 candidate send windows per segment.
3. Day 3: Define frequency cohorts and calculate sample sizes for tests.
4. Day 4: Launch A/B tests (time and frequency isolated).
5. Day 5: Monitor deliverability and early engagement signals (opens/CTR).
6. Day 6: Let tests run; watch conversion signals and unsubscribe rates.
7. Day 7: Analyze results; deploy winners to 10–25% rollout and monitor for 2 more weeks.

Quick KPI targets to aim for: +5–15% CTR, +3–7% conversion rate lift, <0.5% increase in unsubscribe rate. If you don’t hit those, revisit segments and sample sizes.

Short next step: export the 90-day CSV and run the AI prompt above against it, or tell me which ESP you use and I’ll outline exact steps for that platform.

— Aaron

Your move.

[Jeff Bullas]

Nice work, Aaron — solid, practical framework. I particularly like starting with segmented heatmaps; that’s where quick wins hide.

Here’s a complementary, action-first playbook you can run this week. Short, practical steps so you get measurable lifts fast.

What you’ll need

• 90-day email export: send timestamp (UTC), recipient timezone, opens, clicks, conversions, revenue.
• Your ESP with A/B testing or ability to create cohorts.
• Spreadsheet or BI tool (Google Sheets, Excel, or a simple dashboard).
• Access to an AI tool (ChatGPT or similar) for analysis prompts.

Step-by-step (do this, in order)

1. Normalize times: convert send timestamps to recipient local time and add weekday.
2. Segment: create 3–5 priority segments (e.g., region, VIP vs standard, product interest).
3. Heatmap: build hour-by-day open and click heatmaps per segment. Pick top 3 windows and a low window.
4. Design tests: isolate variables — first test send-time (3 windows), then frequency (2–3 cadence options).
5. Run A/B tests: use statistically meaningful samples (see example below). Let tests run 2–4 sends.
6. Analyze & roll out: deploy winners to 10–25% then scale while monitoring unsubscribe and deliverability.

Quick example (practical numbers)

• List: 100,000. Segment A: 20,000. For a detectable ~10% relative CTR lift, aim for ~3,000–6,000 recipients per variant.
• Test design: 3 send-time variants × control. Run across 2 consecutive sends to smooth timing noise.
• Expectation: early open/CTR signals in 7–10 days; revenue signal in 2–4 weeks.

Common mistakes & fixes

• Mistake: Testing time and frequency together. Fix: Run sequential experiments.
• Mistake: Small sample sizes. Fix: Calculate sample needs up front; pool segments only when similar behavior exists.
• Mistake: Chasing opens only. Fix: Prioritize CTR and revenue lift.

Copy-paste AI prompt (primary)

Act as an email marketing analyst. I will provide a CSV with columns: recipient_id, recipient_timezone, local_send_time (HH:MM), weekday, subject_line, open_timestamp, click_timestamp, conversion_timestamp, conversion_value. Analyze and return: 1) Top 3 local send-hour windows by segment (segments: region, product_interest, VIP). 2) Recommended send frequency per segment with expected uplifts (open%, CTR%, revenue%) and confidence levels. 3) Specific A/B test designs (variants, sample sizes, metrics to measure) and a 2-step rollout plan.

Prompt variants

• Per-recipient optimizer: Ask the AI to predict optimal hour per recipient using past open/click patterns and output a sample scheduling file for ESP import.
• Test validator: Ask the AI to review proposed test results and recommend whether to declare a winner or extend the test.

1-week action plan

1. Day 1: Export data and normalize local times.
2. Day 2: Generate heatmaps and pick windows.
3. Day 3: Use AI prompt to get suggested windows and sample sizes.
4. Day 4: Launch send-time A/B tests.
5. Days 5–7: Monitor opens/CTR and deliverability; let tests run two sends.

Small experiments, measured wins. Run one test this week and you’ll learn more than months of guessing. If you want, paste a sample of your CSV (anonymized) and I’ll help craft the exact AI prompt output.

— Jeff

[Rick Retirement Planner]

Good call — segmented heatmaps are where quick wins hide. I’d add one practical lens that often gets overlooked: engagement decay from increasing frequency. You can find a great send-time, but if you start emailing people more often without measuring tolerance, the initial lift can vanish as opens and clicks per send drop and unsubscribes creep up.

In plain English: engagement decay means each extra email usually brings less value than the one before, and beyond a point it can make overall performance worse. The trick is to measure that drop-off, pick the sweet spot for each segment (or recipient), and automate rules that respect individual behavior.

Step-by-step plan — what you’ll need, how to do it, what to expect

1. What you’ll need: a 90-day email export (send timestamp in UTC, recipient timezone, opens, clicks, conversions, unsubscribes), your ESP cohort/A/B tools, and a spreadsheet or BI tool.
2. Establish a baseline: calculate per-segment metrics — average opens, CTR, conversion rate, revenue per send, and unsubscribe rate. This is your reference for decay.
3. Create frequency cohorts: for each priority segment, build 3–4 cohorts (e.g., monthly, biweekly, weekly, twice-weekly). Keep cohorts large enough to detect a ~10% relative change (sample-size guidance from Jeff is a good starting point).
4. Run sequential tests: don’t change time and frequency simultaneously. First lock in the send window (using your heatmaps), then run the frequency cohorts for 2–4 sends so short-term noise smooths out.
5. Monitor the right signals: track CTR and revenue per send first, then opens, then unsubscribe rate and deliverability. Key thresholds to watch: a sustained drop in revenue per recipient, or an unsubscribe increase >0.3–0.7% depending on list maturity.
6. Decide and roll out: if a higher frequency increases revenue per recipient without materially worsening unsubscribes or deliverability, roll it to 10–25% then scale. If revenue per recipient falls or unsubscribes rise, revert and test a middle cadence.
7. Automate guardrails: implement rules that reduce cadence for recipients who show engagement decay (e.g., no opens in 90 days), and increase cadence only for clearly responsive users (repeat clicks/conversions).

What to expect: early signals on opens/CTR within 7–10 days, clearer revenue patterns in 2–4 weeks, and longer-term list health impacts over months. Aim for measurable revenue lift per recipient while keeping unsubscribes and spam complaints low — that balance is what builds confidence and sustainable gains.

If you want, tell me which metric worries you most (unsubs, deliverability, or revenue) and I’ll show the exact thresholds and guardrail rules I’d use for that concern.

[Jeff Bullas]

You’re right on decay — that’s where lists live or die. Here’s how to let AI choose both “when” and “how often” without burning trust: build a simple fatigue score, set a send-skip budget, and let a lightweight bandit allocate frequency within guardrails.

What you’ll set up

• A per-recipient fatigue score (updates weekly)
• Two-hour “quiet” and “prime” windows per recipient
• A frequency allocator (bandit) that tests cadences but respects fatigue
• Clear guardrails for deliverability and unsub risk

What you’ll need

• 90–180 days of logs: send timestamp (UTC), timezone, opens, clicks, conversions, revenue, unsubscribes, spam complaints, bounces
• Your ESP’s A/B or rules engine and the ability to upload a scheduling file
• A spreadsheet or BI tool; any AI assistant to run the prompt below

How to do it (clear steps)

1. Baseline and windows: Normalize to local time; build segment heatmaps to pick 2–3 strong send windows per segment. Also compute each recipient’s top two-hour window from their last 5–10 opens. If a recipient lacks data, fall back to the segment window.
2. Fatigue score (simple but powerful): Start everyone at 60. Each week:
   • -5 points for every marketing email received in the last 14 days (cap -20)
   • +10 for a click in the last 14 days, +5 for an open (cap +20)
   • +15 for a conversion in the last 30 days
   • -10 if no opens in 60 days; -20 if no opens in 90 days
   • -25 if a spam complaint in last 90 days (and move to low cadence)

   Map score to cadence:

   • 80–100: up to 2/week
   • 60–79: 1/week
   • 40–59: 1/2 weeks
   • 0–39: monthly or pause 30 days
3. Send-skip budget: Give each recipient a weekly budget (e.g., 2 “credits”). A standard newsletter costs 1 credit; promos cost 2. If budget is used up, skip until next week. High-fatigue recipients start with 1 credit; high-engagement get 3.
4. Bandit for frequency: After you lock in the send-time window, use a multi-armed bandit across 2–3 cadences (e.g., weekly vs twice-weekly) within the above limits. Reward = weekly revenue per recipient minus a penalty for unsub/complaint (e.g., subtract $5 per unsub, $20 per complaint). The bandit shifts traffic to the winning cadence while your guardrails prevent over-sending.
5. Guardrails that auto-revert:
   • Unsubs >0.5% for any cohort over 2 sends: step down one cadence level
   • Spam complaints >0.08% or soft bounces >2%: revert to prior settings and review content/list hygiene
   • Revenue per recipient drops >8% week-over-week for 2 weeks: reduce frequency by one level
6. Holdout for true lift: Keep a 5–10% random holdout on your pre-test cadence to measure net impact. No optimization is complete without this.
7. Scheduling file: Generate a weekly file with columns: recipient_id, next_send_date_local, local_send_hour, cadence_level, credits, reason_code (e.g., “high_engagement”, “cooldown_90d”). Upload to your ESP or rules engine.

Example (round numbers)

• List 100,000. Baseline: 3% CTR, $0.24 revenue per recipient per week, unsub 0.25%.
• Top quartile (fatigue 80–100) moves to 2/week in their prime window; middle stays at weekly; low-fatigue cohort drops to biweekly.
• After 3 weeks: top quartile +22% revenue/recipient, unsub +0.12%; middle +6%, unsub flat; low-fatigue +2%, unsub down 0.05%.
• Holdout shows net +9–12% revenue/recipient with healthy list metrics.

Copy-paste AI prompt

Act as my email optimization analyst. I will provide a CSV with: recipient_id, recipient_timezone, send_timestamp_utc, open_timestamp, click_timestamp, conversion_timestamp, conversion_value, unsubscribe_flag, spam_complaint_flag, bounce_flag. Do the following and return CSV outputs and a plain-language summary:

• Compute per-recipient fatigue score using these rules: start 60; -5 per marketing send in last 14 days (min -20); +10 click (14d), +5 open (14d) cap +20; +15 conversion (30d); -10 if no opens 60d, -20 if no opens 90d; -25 for spam complaint (90d). Clamp 0–100.
• Assign cadence: 80–100=2/week; 60–79=1/week; 40–59=1/2 weeks; 0–39=monthly or pause 30d. Include a weekly credit count (2 for high, 1 for low).
• Derive each recipient’s top two-hour local window from last 10 opens; if insufficient data, use segment window (region×product_interest×VIP) and include the chosen window.
• Simulate a 3-week bandit across cadences within guardrails, using reward = weekly revenue per recipient minus $5 per unsub and $20 per complaint. Return recommended cadence share by segment and expected lift with 80% confidence intervals.
• Output a schedule file with: recipient_id, next_send_date_local, local_send_hour, cadence_level, credits, reason_code.

Common mistakes and quick fixes

• Mistake: Optimizing for opens. Fix: Use revenue/recipient and penalize unsubs/complaints in the objective.
• Mistake: No holdout. Fix: Always reserve 5–10% control to verify net lift.
• Mistake: Pushing frequency during poor inbox placement. Fix: If bounce or complaint spikes, slow down and fix list hygiene/content first.
• Mistake: One-size-fits-all cadences. Fix: Tie cadence to fatigue bands and adjust weekly.

7-day plan

1. Export 90–180 days of data; normalize to local time; add weekday.
2. Build segment heatmaps; pick 2–3 prime windows per segment.
3. Run the AI prompt to compute fatigue scores and initial cadence bands.
4. Create the first scheduling file for 25% of the list; include a 10% holdout.
5. Launch with guardrails; monitor unsub, complaints, bounces daily.
6. Let the bandit reallocate for two sends; review weekly revenue/recipient.
7. Scale to 60–100% if lift holds and guardrails stay green.

Keep it simple: start with fatigue bands, add send-skip budgets, then let the bandit fine-tune. That’s how you get lift fast without burning your best subscribers.

— Jeff

[Author]

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