Campaign Analytics AI Prompts

Analyze this marketing A/B test and produce a decision-ready report. Test description: {{test_description}} Variants: Control: {{control}} | Treatment: {{treatment}} Primary metric: {{primary_metric}} Test data: {{results_data}} 1. Statistical analysis: - Control and treatment values for the primary metric - Absolute and relative difference - Two-proportion z-test (for conversion rates) or t-test (for continuous metrics) - p-value and 95% confidence interval - Is the result statistically significant at alpha = 0.05? - Statistical power: given the observed sample size and effect, what was the test's power? 2. Practical significance: - Effect size: Cohen's h (for proportions) or Cohen's d (for means) - Business impact: if this effect persists at scale, what is the annual revenue or cost impact? - Minimum detectable effect vs observed effect: did we detect what we expected to detect? 3. Secondary metric analysis: - Repeat for all secondary metrics - Did any guardrail metrics degrade significantly? 4. Segment analysis: - Break results by: device, geography, new vs returning, customer tier - Is the effect consistent across segments or driven by one? - Heterogeneous treatment effects: does the winner differ by segment? 5. Decision: - Implement / Do not implement / Run follow-up test - If implementing: rollout plan - If running follow-up: what specific question does the next test answer? 6. Learnings for future campaigns: - What does this test teach us about our audience or message? - Should this insight change any other running campaigns? Return: statistical analysis, effect size calculation, segment breakdown, decision, and learnings.

Generate a comprehensive campaign performance report for {{campaign_name}}. Campaign data: {{campaign_data}} Goal: {{campaign_goal}} (awareness, lead generation, conversion, retention) Time period: {{period}} 1. Performance summary: - Total spend: {{spend}} - Total impressions, clicks, conversions - Key derived metrics: - Click-through rate (CTR): Clicks / Impressions - Cost per click (CPC): Spend / Clicks - Conversion rate: Conversions / Clicks - Cost per acquisition (CPA): Spend / Conversions - Return on ad spend (ROAS): Revenue / Spend 2. Performance vs benchmarks: - Compare each metric to: campaign target, prior campaign, industry benchmark - Flag any metric more than 20% above or below benchmark 3. Channel breakdown: - Performance by channel (paid search, paid social, display, email, etc.) - Which channel delivered the lowest CPA? The highest ROAS? - Channel mix: what % of spend and conversions came from each channel? 4. Creative performance: - Top 3 and bottom 3 creative assets by CTR and conversion rate - What characteristics do the top performers share? (Format, message, visual style) - Recommend pausing the bottom performers 5. Audience performance: - Performance by audience segment (demographics, interests, remarketing vs prospecting) - Which audience delivered the best conversion rate and CPA? 6. Time performance: - Performance by day of week and hour of day - Are there peak and trough periods that suggest dayparting optimization? 7. Recommendations: - Top 3 optimization actions ranked by expected impact - Budget reallocation suggestion: which channels should get more / less? Return: performance summary table, channel breakdown, creative analysis, audience insights, and top recommendations.

Calculate the true ROI of this marketing campaign including all costs and revenue attribution. Campaign: {{campaign_name}} Spend data: {{spend_data}} Revenue attribution data: {{revenue_data}} Product margin: {{gross_margin_pct}} 1. Total cost of campaign: - Media spend (by channel) - Agency or creative fees - Technology platform costs - Internal labor cost (estimate: hours x fully-loaded cost per hour) - Total cost of campaign 2. Revenue attribution: - Direct response revenue: conversions directly attributed to the campaign - Assisted revenue: conversions where the campaign appeared in the path but was not the last touch - Attribution model used: last click, first click, linear, data-driven - Note the sensitivity: how much does attributed revenue change across attribution models? 3. ROI calculation: - Gross revenue attributed - Gross profit attributed (Revenue x Gross Margin %) - Net ROI = (Gross Profit - Total Cost) / Total Cost x 100% - ROAS = Gross Revenue / Media Spend (this overstates ROI; use gross profit ROI for real decisions) 4. Payback analysis: - For acquisition campaigns: CAC from this campaign - LTV of customers acquired: estimated LTV from this campaign's cohort - LTV / CAC ratio: is this campaign economically attractive? 5. Comparison to alternatives: - ROI vs other campaigns in the same period - ROI vs the cost of capital (hurdle rate) - Incremental ROI: what additional revenue vs a no-campaign baseline? 6. ROI by channel: - Compute net ROI for each channel in the campaign mix - Which channel delivered the highest gross profit ROI? - Where should budget shift in the next campaign based on this analysis? Return: total cost breakdown, attribution analysis, net ROI, LTV/CAC, and channel-level ROI comparison.

Analyze the B2B demand generation funnel from awareness to closed revenue. Funnel data: {{funnel_data}} (leads by stage, conversion rates, time in stage, revenue closed) Sales cycle: {{avg_sales_cycle}} days ACV: {{average_contract_value}} 1. Funnel stage definitions and metrics: - MQL (Marketing Qualified Lead): lead meeting the scoring threshold - SQL (Sales Qualified Lead): MQL accepted by sales - Opportunity: SQL with discovery meeting completed - Proposal: opportunity with proposal sent - Closed-Won: contracted revenue For each stage: volume, conversion rate to next stage, average days in stage 2. Conversion rate analysis: - MQL to SQL: what % of marketing leads are accepted by sales? Below 50% may indicate a lead quality problem - SQL to Opportunity: what % of accepted leads convert to active pipeline? - Opportunity to Close: win rate against proposals sent - Overall funnel conversion: leads to closed-won 3. Revenue forecast from current pipeline: - Pipeline by stage: weighted by stage probability - Expected revenue in next 90 days from current pipeline - Pipeline coverage ratio: pipeline / quota (target > 3x for 90-day quota) 4. Lead source contribution: - MQL volume by source (content/SEO, paid, events, outbound, referral) - Conversion rates by source: which sources produce the highest quality leads? - Revenue contribution by source: where does closed revenue actually come from? - Cost per MQL and cost per closed deal by source 5. Sales cycle and velocity: - Average days from MQL to close by source and segment - Deals stalling in specific stages: which stage has the longest dwell time? - Pipeline velocity: (Opportunities x Win Rate x ACV) / Sales Cycle Length 6. Marketing contribution to revenue: - Marketing-sourced revenue: deals where marketing generated the first touch - Marketing-influenced revenue: deals where marketing contributed at some point - Marketing's % contribution to total revenue Return: funnel conversion table, pipeline forecast, lead source ROI, sales velocity analysis, and marketing revenue attribution.

Design a comprehensive marketing performance dashboard for the CMO and marketing leadership team. Business model: {{business_model}} Marketing channels: {{channels}} Key business goals: {{goals}} Reporting cadence: weekly and monthly 1. Dashboard sections and metrics: SECTION 1 - Revenue and Pipeline Impact: - Marketing-sourced revenue (month and YTD vs target) - Marketing-influenced revenue - Total pipeline value from marketing-sourced leads - Pipeline coverage ratio (pipeline / quota) SECTION 2 - Demand Generation: - MQL volume (week, month, YTD vs target) - MQL-to-SQL conversion rate (trend) - Cost per MQL by channel - Funnel velocity (days MQL to close) SECTION 3 - Channel Performance: - Spend, conversions, CPA, ROAS by channel - Channel mix % (share of spend vs share of conversions) - MoM change in CPA by channel SECTION 4 - Brand and Organic: - Organic traffic (YoY trend) - Domain authority and backlink growth - Branded search volume trend - NPS score (monthly) SECTION 5 - Customer Marketing: - Email engagement rate (open rate, CTOR) - Churn rate trend - Expansion revenue from marketing programs - Renewal rate (if applicable) 2. Alert conditions for weekly review: - MQL volume > 20% below weekly target: investigate channel performance - CPA increase > 15% WoW in any channel: bid strategy or competition change - Organic traffic > 10% below baseline: potential SEO issue 3. Drill-down structure: - Each metric links to a supporting report - CMO can click from MQL count to breakdown by channel, source, and sales owner 4. Audience customization: - CMO view: pipeline impact + channel efficiency + NPS - Channel manager view: their channel only, deep detail - CEO view: revenue impact + efficiency ratio only Return: dashboard sections with metric definitions, alert conditions, drill-down structure, and audience views.

Optimize paid media budget allocation across channels to maximize return at a given spend level. Current budget: {{total_budget}} Current channel allocations: {{current_allocation}} Performance data by channel: {{performance_data}} (spend, conversions, revenue) 1. Current state analysis: For each channel: - Current spend and % of total budget - Conversions and revenue attributed - CPA and ROAS - Marginal ROAS: performance of the last incremental dollar spent (from spend vs performance curve) 2. Response curve estimation: For each channel, estimate the diminishing returns curve: - Collect historical data points: (spend level, performance outcome) per week - Fit a saturation curve: Revenue = a x (1 - e^(-b x Spend)) - Where is the inflection point? Where does marginal ROAS drop below 1? 3. Optimal allocation theory: - Budget is optimally allocated when marginal ROAS is equal across all channels - If Channel A marginal ROAS (1.8) > Channel B marginal ROAS (0.9): shift budget from B to A - Continue reallocation until marginal ROAS equalizes 4. Proposed reallocation: - For each channel: recommended new budget - Expected change in conversions and revenue vs current allocation - Total portfolio ROAS improvement from optimization 5. Constraints to consider: - Minimum effective budget per channel (below a threshold, channels stop working) - Channel caps from inventory limitations or audience saturation - Strategic channels that require funding beyond what pure ROI math suggests - Brand safety requirements 6. Testing plan: - Do not implement all reallocation at once: risk of performance disruption - Phased approach: 20% reallocation per month with measurement between each step Return: marginal ROAS by channel, response curve parameters, optimal allocation table, revenue uplift estimate, and phased implementation plan.