Conversion Optimization AI Prompts

Analyze checkout abandonment and design a recovery strategy. Checkout data: {{checkout_data}} CartAbandonment rate: {{abandonment_rate}} Average order value: {{aov}} 1. Abandonment by checkout step: Which steps lose the most customers? - Step 1 (Enter email/sign up): high drop here = account creation friction - Step 2 (Shipping details): drop here = unexpected shipping cost or complexity - Step 3 (Payment details): drop here = trust or payment method issue - Step 4 (Order review): drop here = final price concern or last-minute doubt Map drop-off rate at each step. 2. Abandonment reasons (from exit surveys or user research): - Unexpected shipping costs (primary reason cited in ~50% of cases) - Forced account creation - Complicated checkout process - Payment security concerns - Just browsing / not ready to buy - Found a better price elsewhere Which of these apply based on the data signals? 3. Immediate fix opportunities (no A/B test required): - Show shipping cost early: display on product page and cart, before checkout - Enable guest checkout: remove account requirement or make it optional after purchase - Add trust signals: SSL badge, money-back guarantee, security certifications near payment field - Reduce form fields: remove any non-required fields - Save progress: if user leaves, preserve their cart and form data 4. Cart abandonment email sequence: Email 1 (1 hour after abandonment): - Subject: 'You left something behind' - Content: items in cart, strong CTA, no discount yet - Goal: recapture the 30% who abandoned due to distractions Email 2 (24 hours): - Subject: 'Your cart is waiting - and here's 10% off' - Content: discount offer with urgency (expires in 48 hours) - Goal: recapture price-sensitive abandoners Email 3 (72 hours): - Subject: 'Last chance - your cart expires soon' - Content: urgency reminder, social proof on the products 5. Recovery revenue estimate: - Current recovered revenue from email sequence (if any) - Expected recovery rate with optimized sequence: industry benchmark 10-15% of abandoned carts - Annual revenue opportunity: abandonment volume x AOV x expected recovery rate Return: step-by-step abandonment analysis, root cause hypothesis, immediate fixes, email sequence design, and revenue recovery estimate.

Audit the e-commerce conversion funnel from landing to purchase and identify the highest-priority optimization opportunities. Funnel data: {{funnel_data}} Platform: {{platform}} (Shopify, WooCommerce, Magento, custom) Traffic: {{monthly_sessions}} monthly sessions 1. Funnel stages and current conversion rates: - Session to Product View rate: sessions that viewed at least one product page - Product View to Add-to-Cart rate - Add-to-Cart to Checkout Initiation rate - Checkout Initiation to Purchase rate (checkout completion) - Overall session-to-purchase conversion rate Benchmark comparison by device: - Desktop: typical e-commerce CVR 2-4% - Mobile: typical e-commerce CVR 1-2% - How does this store compare? 2. Cart abandonment analysis: - Cart abandonment rate: (add-to-cart - purchases) / add-to-cart - Industry average: ~70%. If > 80%, significant optimization opportunity. - Checkout abandonment rate: where in the checkout flow do users leave? - Most common exit pages during checkout 3. Device and traffic source breakdown: - Conversion rate by device (desktop, mobile, tablet) - Conversion rate by traffic source (organic, paid, email, direct, social) - Which combination (source x device) has the highest and lowest CVR? - Mobile CVR vs desktop CVR gap: if > 50% lower on mobile, mobile UX is the priority 4. Product page conversion rate: - Add-to-cart rate by product category - Products with high traffic but low add-to-cart rate (< 5%): page or product issue? - Products with high add-to-cart but low purchase rate: cart abandonment issue for specific products 5. Checkout friction analysis: - How many steps in the checkout? (Target: 1-2 pages) - Is guest checkout available? (Forced account creation kills mobile conversions) - Payment options: does the store offer the top payment methods for its audience? - Form fields: any unnecessary fields that slow completion? 6. Priority recommendations: - Top 3 interventions by expected revenue impact - Expected lift in CVR for each intervention - Estimated annual revenue gain from each 0.1% CVR improvement at current traffic Return: funnel metrics table, abandonment analysis, device breakdown, product page analysis, checkout friction assessment, and prioritized recommendations.

Step 1: Revenue attribution audit - review the attribution model in use. Identify if it is accurately crediting channels, including the impact of dark social and direct traffic. Recommend attribution improvements. Step 2: Conversion funnel audit - map the full conversion funnel from session to purchase. Identify the top 3 drop-off points. Segment the funnel by device, traffic source, and new vs returning. Step 3: Customer segmentation - build RFM segments for the full customer base. Size each segment, compute revenue contribution, and define the marketing action for each tier. Identify the At-Risk and Can't Lose Them segments as top priority. Step 4: Product analytics - apply the product performance matrix (traffic vs conversion). Identify the top 10 Hidden Gems to promote and the top 5 high-traffic, low-conversion pages to fix. Step 5: Pricing and promotion review - compute the revenue and margin impact of the last 3 promotions. Test whether any products are candidates for a price increase based on elasticity signals. Review discount addiction indicators. Step 6: Retention strategy - compute the second-purchase conversion rate and the average time between orders by category. Design a post-purchase email flow with specific timing and content for the top 3 product categories. Step 7: 90-day growth plan - synthesize findings into a prioritized growth plan: top 3 conversion improvements, top 3 retention actions, and top 2 acquisition efficiency improvements. For each: expected revenue impact, required investment, and measurement plan.

Identify and prioritize personalization opportunities to improve conversion and customer experience. Customer data: {{customer_data}} Behavioral data: {{behavioral_data}} Current personalization capabilities: {{current_capabilities}} 1. Personalization opportunity inventory: Where can personalization improve the experience? - Homepage hero: show category or product relevant to the visitor's history - Product recommendations: 'Recommended for you' based on browse/purchase history - Search results: personalized ranking based on category preferences - Email content: dynamically insert products based on browse history - Pricing/offer: segment-specific offers (first-time buyer discount vs loyalty reward) - Category landing: reorder products based on the visitor's likely preferences 2. Personalization value estimation: For each opportunity: - Baseline performance of the non-personalized version - Expected lift from personalization (cite studies or similar implementations) - Traffic volume affected - Estimated incremental revenue = Traffic x Baseline CVR x Expected Lift x AOV 3. Data requirements per opportunity: - What data is needed? (Purchase history, browse history, declared preferences, segment membership) - Is this data available and accessible in real-time? - Any privacy or consent requirements? (GDPR, CCPA) 4. Quick wins (no ML required): - New vs returning: show a 'welcome back' message and recently viewed products for returning users - Geo-based: show regionally relevant products or shipping estimates based on IP location - Cart-based: upsell products based on cart contents (highest affinity pair from market basket analysis) - Email click-based: if user clicked category X in last email, show category X products next time 5. Advanced personalization (requires ML or CDP): - Collaborative filtering: 'customers like you also bought' - Propensity scoring: predict likelihood to buy each product and rank recommendations accordingly - Churn prediction-based offers: detect at-risk customers and trigger a personalized retention offer 6. Measurement plan: For each personalization implementation: - Control group: show non-personalized version to 10-20% of users - Measure: CVR, AOV, and revenue per visitor - Minimum duration: 2 weeks, minimum conversions per variant: 100 Return: personalization opportunity map, value estimates, data requirements, quick win implementations, and measurement framework.

Analyze product page performance and identify conversion optimization opportunities. Product page data: {{product_page_data}} (traffic, add-to-cart rate, purchase rate, heatmap data) Top products to analyze: {{product_list}} 1. Product page conversion hierarchy: - Views to Add-to-Cart rate by product (benchmark: 5-15% for most categories) - Add-to-Cart to Purchase rate (measures checkout conversion for this specific product) - Page exit rate before any engagement 2. Above-the-fold analysis: - Do the primary product images load quickly? (LCP < 2.5 seconds) - Is the price visible without scrolling? - Is the primary CTA (Add to Cart) visible without scrolling? - Is the product name and key value proposition immediately clear? - What does the heatmap show for click and scroll distribution? 3. Product image quality signals: - Number of images: research shows > 3 images increases conversion - Image types: do they include: hero shot, lifestyle image, detail/zoom, 360/video? - Mobile image display: do images load quickly and display correctly on mobile? 4. Product description analysis: - Is the primary benefit stated in the first sentence? - Are technical specifications separated from benefits? - Is social proof (review count, rating) prominently displayed near the CTA? - Is shipping information and return policy visible on the product page? 5. Social proof signals: - Review count and average rating visible near Add-to-Cart - User-generated content (UGC) or customer photos - 'X people are viewing this' or 'Only 3 left in stock' urgency signals - Are these signals accurate and trustworthy? (Fake scarcity damages trust) 6. Low-performing product deep dive: For each product with add-to-cart rate < 3%: - Is the price competitive vs comparable products? - Is the primary concern a product quality issue or a page presentation issue? - What do the top 10 reviews say? Any consistent complaints? 7. A/B test backlog for product pages: - 5 specific tests ranked by expected impact on add-to-cart rate Return: product page metrics table, above-the-fold assessment, image and content analysis, social proof audit, and A/B test backlog.