CRM and Email Analytics AI Prompts

Analyze the effectiveness of lifecycle email sequences and identify gaps in the program. Lifecycle data: {{lifecycle_data}} (email type, trigger event, send date, open, click, conversion) Customer journey stages: {{stages}} (onboarding, activation, engagement, expansion, retention, win-back) 1. Lifecycle email inventory: Map every automated email to the customer journey stage: - Trigger: what event sends this email? - Goal: what action should the recipient take? - Metric: how is success measured? 2. Coverage gaps: - Which stages have no automated email coverage? - Are there high-value moments in the customer journey with no triggered email? - Common gaps: post-onboarding engagement, pre-renewal reminder, post-cancellation win-back 3. Sequence performance: For each lifecycle sequence: - Open rate by email position (Email 1, 2, 3...): how quickly does engagement decay? - Click rate by position - Completion rate: what % of recipients receive all emails in the sequence? - Conversion rate: what % take the desired action? 4. Time-to-convert analysis: - From lifecycle email send to conversion: how long does it take? - Is there an optimal timing window? (Some emails may be sent too early or too late) 5. Optimization opportunities: - Sequences with lowest conversion rate: content or timing issue? - High open but low click sequences: strong subject line but weak email body - Low open sequences: timing, subject line, or sender name issue 6. Recommended additions: Based on the gap analysis, propose 3 new lifecycle automations: - Trigger event - Goal - Message approach - Expected impact on activation/retention/revenue metric Return: lifecycle email inventory, coverage gap analysis, sequence performance table, and 3 recommended new automations.

Calculate Customer Lifetime Value (LTV) using multiple methods and apply it to marketing decisions. Customer data: {{customer_data}} (cohort, revenue history, churn events) Business model: {{business_model}} Discount rate: {{discount_rate}} (cost of capital, typically 10-15%) 1. Simple LTV (for early-stage / approximate use): LTV = Average Purchase Value x Purchase Frequency x Customer Lifespan - Average Purchase Value: total revenue / total orders - Purchase Frequency: total orders / total unique customers per period - Customer Lifespan: 1 / Monthly Churn Rate (in months) - Gross Profit LTV: multiply by gross margin % 2. Cohort-based LTV (most accurate for historical data): - For each acquisition cohort: cumulative revenue per customer through each month of life - Plot the cumulative LTV curve: how does LTV grow as cohort ages? - LTV at 12 months, 24 months, and steady state - Are newer cohorts trending above or below older cohorts? (Improving or declining customer quality) 3. Discounted LTV (for financial decisions): Discounted LTV = sum over t: (Expected Cash Flow_t / (1 + r)^t) - Where r = monthly discount rate = (1 + annual rate)^(1/12) - 1 - Cash flow_t = monthly gross profit from the cohort in month t - Captures the time value of money: a dollar of LTV received in year 3 is worth less than in year 1 4. LTV by segment: - LTV for different acquisition channels, customer segments, product categories, geographies - Which segments have 2x or higher LTV than average? - This should drive differential CAC targets by segment 5. LTV / CAC framework for marketing decisions: - Healthy: LTV / CAC > 3 - Acceptable: LTV / CAC 1-3 (with path to improvement) - Unsustainable: LTV / CAC < 1 - Maximum CAC by segment = LTV x maximum acceptable CAC ratio 6. LTV improvement levers: - Increase average order value (cross-sell, upsell) - Increase purchase frequency (engagement, reminder programs) - Reduce churn (retention programs) - For each lever: estimated impact on LTV Return: LTV calculation by method, cohort LTV curves, segment LTV comparison, LTV/CAC framework, and LTV improvement lever analysis.

Analyze the performance of this email campaign and identify optimization opportunities. Email data: {{email_data}} Campaign type: {{type}} (newsletter, promotional, lifecycle, transactional) Audience: {{audience_size}} recipients 1. Deliverability metrics: - Delivery rate: delivered / sent (target > 98%) - Bounce rate: hard + soft bounces / sent - Spam complaint rate: spam reports / delivered (target < 0.1%) - List health: what % of the list is engaged (opened at least once in 90 days)? 2. Engagement metrics: - Open rate: unique opens / delivered (benchmark varies by industry, typically 20-40%) - Click-to-open rate (CTOR): clicks / opens (measures email content quality, target > 10%) - Click rate: clicks / delivered - Unsubscribe rate: unsubscribes / delivered (target < 0.2%) 3. Conversion metrics: - Conversion rate: desired action completions / delivered - Revenue per email: total revenue attributed / emails delivered - Compare to target and prior campaigns 4. Timing analysis: - What day of week and time of day were emails sent? - Compare open rate and click rate at different send times (if A/B tested) - Industry best practice send times for this audience segment 5. Subject line analysis: - If A/B tested: winning subject line and the open rate lift - Characteristics of high-performing subject lines: length, personalization, urgency, question vs statement - Recommendation for next campaign 6. Segment performance: - Break engagement metrics by: customer segment, tenure, geography, prior engagement level - Which segment has the highest CTOR? Should receive more targeted sends. - Which segment has the lowest open rate? Review frequency, relevance, send time. 7. Optimization recommendations: - Top 3 actions to improve performance in the next send Return: deliverability metrics, engagement analysis, conversion results, segment breakdown, and optimization recommendations.

Audit the health of this email list and build a re-engagement and list hygiene plan. List data: {{list_data}} (subscriber_id, subscribe_date, last_open_date, last_click_date, email_type) List size: {{list_size}} Current engagement rate: {{engagement_rate}} 1. Engagement segmentation: Classify every subscriber into engagement tiers: - Active: opened or clicked in the last 90 days - Warming: opened or clicked 90-180 days ago - At-risk: last engaged 180-365 days ago - Inactive: no engagement in > 365 days - Never engaged: subscribed but never opened a single email Size and % of list in each tier. 2. List decay rate: - How quickly is the active tier shrinking as a % of total? - Monthly new subscribers vs monthly subscribers moving to inactive - If inactive subscribers > 30% of list: email deliverability is at risk 3. Impact on deliverability: - High inactive rates signal to ISPs that your emails are unwanted - Gmail, Outlook, and Apple Mail track engagement heavily - Estimated deliverability risk: at current inactive %, what is the projected impact on inbox placement? 4. Re-engagement campaign design: For the 'At-risk' tier: - Trigger: 180 days since last open - Sequence: 3-email re-engagement series - Email 1: 'We miss you' + best recent content - Email 2: Incentive offer or value reminder - Email 3: 'Last chance' + explicit opt-down/unsubscribe option - Success criteria: any open or click = move back to 'Warming' tier 5. Sunset policy: - After the re-engagement sequence: move non-responders to suppression list - Do NOT delete: keep suppressed for compliance (unsubscribe proof) - Expected list size reduction and engagement rate improvement from sunset 6. List growth quality: - Which acquisition sources are producing the highest-engagement subscribers? - Which sources produce low-engagement (likely purchased or low-intent) subscribers? - Stop acquiring from low-quality sources even if it slows list growth Return: engagement tier breakdown, list decay analysis, re-engagement sequence, sunset policy, and acquisition source quality assessment.