Home Drew AI — Use Cases

Drew AI — Use Cases

Vikas Bansal
By Vikas Bansal
• 8 articles

25 example questions to ask Drew AI

Not sure what to ask Drew AI? Here are 25 practical questions organized by category. Copy and paste any of these directly into Drew AI to get started. Ad performance 1. "Which Meta campaigns have the best ROAS this month?" 2. "Show me my Google Ads spend and conversions trend for the last 30 days." 3. "Are any of my ads experiencing creative fatigue? Which ones should I refresh?" 4. "Compare my Meta Ads vs Google Ads performance — where should I shift budget?" 5. "What should my next ad creative look like based on what is working?" Cross-channel and blended metrics 1. "Give me a full picture of last 7 days marketing performance across all platforms." 2. "Show me blended ROAS and CAC across all platforms." 3. "What is my Marketing Efficiency Ratio this month vs. last month?" 4. "Generate a weekly marketing report for last week." 5. "Which channel is bringing in the most new customers at the lowest cost?" Customers and retention 1. "Show me our customer segments — who are our champions vs. at-risk?" 2. "Who are our high-value customers at risk of churning?" 3. "What is our customer retention rate? How many customers make a second purchase within 90 days?" 4. "Show me LTV by acquisition cohort for the last 12 months." 5. "How long does it take the average customer to make a repeat purchase?" Products 1. "Which products are our heroes? Which are wasting ad spend?" 2. "Which products drive the most repeat purchases?" 3. "What products are frequently bought together?" 4. "Which first-order products lead to the highest customer LTV?" 5. "Show me product performance — revenue, orders, and ROAS by product." Website and email 1. "Where is my website traffic coming from? Show me a source/medium breakdown." 2. "Where are visitors dropping off? Analyze my conversion funnel." 3. "How are my Klaviyo email campaigns performing this month?" 4. "Which automated flows are generating the most revenue?" Strategy and benchmarks 1. "How does my ROAS compare to industry averages for my category? What should I focus on improving?" Tips for customizing these questions - Add time ranges: Append "for the last 30 days" or "this month vs. last month" to any question. - Add specifics: Replace generic terms with your actual product names, campaign names, or platforms. - Ask for visualizations: Add "show me a chart" or "visualize this as a trend" to get automatic chart generation. - Request exports: Add "export this as a CSV" to download the data. Need help? Reach out to support@datadrew.io or use the in-app chat. Related articles - What is Drew AI and how does it work? - Asking your first question to Drew AI - Tips for getting better answers from Drew AI - Understanding Drew AI visualizations Need help? If you have questions or run into issues, reach out to us at support@datadrew.io or use the in-app chat. We're happy to help.

Last updated on Jul 07, 2026

Industry benchmarks and competitive context

Drew AI does not just analyze your store in isolation — it can provide industry benchmarks and competitive context to help you understand how your performance compares to similar businesses. Two types of benchmarks Internal benchmarks (Datadrew data) Datadrew maintains aggregated, anonymized benchmark data from Shopify merchants on the platform. Drew AI can compare your store's metrics against these benchmarks, including: - Average order value (AOV) by industry - Repeat purchase rates - Customer retention benchmarks - Revenue growth trends These benchmarks come from your Datadrew data warehouse, so they are based on actual Shopify merchant data. External benchmarks (web search) For industry-wide benchmarks that go beyond Datadrew's data, Drew AI uses web search to find current, relevant context. This includes: - Average ROAS by industry and platform (e.g., "What is a good Meta Ads ROAS for DTC fashion?") - Email marketing benchmarks (open rates, click rates by industry) - Google Ads CPC and conversion rate benchmarks - Seasonal trends and market shifts - Platform updates and algorithm changes that might affect performance Example questions to ask - "How does my ROAS compare to industry averages for my category?" - "What is a good email open rate for e-commerce?" - "Is my customer retention rate above or below average?" - "What are typical Google Ads CPCs for DTC brands right now?" - "How do my Meta Ads metrics compare to industry benchmarks?" Competitive context While Drew AI cannot access your competitors' private data, it can provide competitive context through: - Google Ads auction insights: If you have Google Ads connected, Drew AI can pull auction insights data showing how you rank against other advertisers. - Market trends: Drew AI can search for recent market developments, consumer behavior shifts, and platform changes relevant to your business. - Best practices: Drew AI can look up current best practices for ad creative, email marketing, and conversion optimization. How to get the most out of benchmarks The best approach is to combine your store data with benchmarks in a single question: - "My Meta ROAS is 3.2x — how does that compare to industry benchmarks for health and wellness DTC?" - "Show me my email metrics alongside industry averages." - "Our AOV is $85 — is that good for our category?" Drew AI will pull your actual data and pair it with relevant benchmark context, giving you a complete picture of where you stand. Need help? Reach out to support@datadrew.io or use the in-app chat. Related articles - Industry Benchmarks — compare to peers - Cross-channel marketing analysis - Analyzing ad campaign performance - LTV and cohort analysis with Drew AI Need help? If you have questions or run into issues, reach out to us at support@datadrew.io or use the in-app chat. We're happy to help.

Last updated on Jul 07, 2026

Email marketing analysis (Klaviyo)

If you have Klaviyo connected to Datadrew, Drew AI can analyze your email and SMS marketing performance in detail. It connects to 18 Klaviyo API tools to give you real-time insights into campaigns, flows, subscriber lists, and engagement metrics. What Drew AI can analyze Drew AI has comprehensive access to your Klaviyo account, including: - Campaigns: Performance metrics for email and SMS campaigns — open rates, click rates, revenue, and more. - Flows: Automated flow performance, including welcome series, abandoned cart, post-purchase, and win-back flows. - Profiles: Subscriber information and engagement history. - Segments: Klaviyo segment sizes, definitions, and membership. - Lists: Subscriber list details and growth. - Metrics and Events: Detailed event data like opens, clicks, purchases attributed to email. - Templates: Email template details and usage. Example questions to ask - "How are my Klaviyo email campaigns performing this month?" - "Which automated flows are generating the most revenue?" - "What is the open rate and click rate for my last 5 email campaigns?" - "How is my abandoned cart flow performing? What is the recovery rate?" - "Show me my subscriber growth over the last 90 days." Combining email data with other sources Drew AI can combine Klaviyo data with your other analytics for richer insights. For example: - Compare email-attributed revenue with overall Shopify revenue to understand email's contribution. - Cross-reference your RFM segments with email engagement to see if your Champions are opening your emails. - Analyze how email campaigns affect overall site traffic by combining Klaviyo data with Google Analytics. Flow optimization Drew AI can review your automated flow performance and provide recommendations. It can identify: - Flows with declining performance that may need content refreshes - Revenue opportunities from flows that are performing above average - Gaps in your automation strategy (e.g., missing win-back or post-purchase flows) Getting started To use Klaviyo analysis, connect your Klaviyo account on the Integrations page in Datadrew. Once connected, Drew AI will automatically have access to your Klaviyo data and you can start asking questions immediately. Need help? Reach out to support@datadrew.io or use the in-app chat. Related articles - Connecting your Klaviyo account - Understanding Klaviyo metrics in Datadrew - Syncing RFM segments to Klaviyo - Customer segmentation and RFM analysis Need help? If you have questions or run into issues, reach out to us at support@datadrew.io or use the in-app chat. We're happy to help.

Last updated on Jul 07, 2026

Cross-channel marketing analysis

One of Drew AI's most valuable capabilities is analyzing marketing performance across all your platforms at once. Instead of logging into separate ad managers and trying to compare numbers in spreadsheets, you can ask Drew AI for a unified view. What is cross-channel analysis? Cross-channel marketing analysis combines data from all your advertising and marketing platforms into a single, blended view. Drew AI can pull together data from: - Meta/Facebook Ads (21 real-time API tools) - Google Ads (3 real-time API tools) - Google Analytics (7 real-time API tools) - Klaviyo (18 real-time API tools) - Google Search Console (4 real-time API tools) - Shopify (historical order and revenue data) Key blended metrics Drew AI can calculate and explain these cross-channel metrics: - Blended ROAS: Total revenue divided by total ad spend across all platforms. - Marketing Efficiency Ratio (MER): Total revenue divided by total marketing spend — a broader measure that includes all marketing costs. - Customer Acquisition Cost (CAC): Total ad spend divided by new customers acquired. - New Customer ROAS (ncROAS): Revenue from new customers divided by ad spend — isolates the efficiency of customer acquisition. - Channel Contribution: How much each platform contributes to total revenue. Example questions to ask - "Give me a full picture of last 7 days marketing performance." - "Where should I shift budget for better results?" - "Compare my ad platforms — where should I focus?" - "Show me blended ROAS and CAC across all platforms." - "What is my Marketing Efficiency Ratio this month vs. last month?" Weekly marketing reports Drew AI excels at generating comprehensive weekly or monthly marketing summaries. These typically include: 1. Executive summary with top 3 insights 2. Key metrics with week-over-week or month-over-month changes 3. Platform-by-platform performance breakdown 4. Budget allocation recommendations 5. Anomalies or issues that need attention Try asking: "Generate a weekly marketing report for last week" for a comprehensive summary. Budget optimization Drew AI can analyze your spend distribution across channels and recommend where to reallocate budget for better returns. It considers each platform's ROAS, conversion volume, and trends when making recommendations. Need help? Reach out to support@datadrew.io or use the in-app chat. Related articles - Understanding blended metrics - Analyzing ad campaign performance - Blended Ads Summary — unified ad performance - Email marketing analysis (Klaviyo) Need help? If you have questions or run into issues, reach out to us at support@datadrew.io or use the in-app chat. We're happy to help.

Last updated on Jul 07, 2026

LTV and cohort analysis with Drew AI

Drew AI can analyze customer lifetime value (LTV) and cohort behavior, helping you understand how much customers are worth over time and how different acquisition cohorts perform. What is cohort analysis? Cohort analysis groups customers by when they made their first purchase (their acquisition month) and then tracks their behavior over time. This reveals patterns like: - How much revenue each cohort generates at 30, 60, 90 days and beyond - Whether newer cohorts are more or less valuable than older ones - How long it takes for customers to make a repeat purchase - Which acquisition channels bring the highest-value customers LTV metrics Drew AI can calculate Drew AI accesses your full order history to calculate LTV at multiple time windows: - 30-day LTV: Revenue per customer within their first 30 days - 60-day LTV: Revenue per customer within their first 60 days - 90-day LTV: Revenue per customer within their first 90 days - 6-month LTV: Revenue per customer within their first 180 days - 1-year, 2-year, 3-year LTV: Long-term customer value These metrics are calculated from actual Shopify order data, not projections. Example questions to ask - "Show me LTV by acquisition cohort." - "What is our average customer LTV at 90 days?" - "Compare the LTV of customers acquired in Q4 vs. Q1." - "Which monthly cohort has the highest repeat purchase rate?" - "What is our customer retention rate month over month?" Types of cohort analysis Drew AI supports several ways to cohort your customers: - LTV Cohorts: Group customers by acquisition month. The most common analysis type. - Product Cohorts: Group by the first product a customer purchased. Useful for understanding which entry-point products lead to the highest LTV. - Location Cohorts: Group by shipping location. Helpful for geographic expansion decisions. - Custom Cohorts: Group by order tags or customer tags. Useful for analyzing specific campaigns or customer attributes. Retention analysis Beyond LTV, Drew AI can analyze customer retention — how many customers come back to make a second, third, or fourth purchase, and how long it takes. This is essential for understanding your repeat purchase rate and optimizing retention marketing. Example questions: - "What is our customer retention rate?" - "How long does it take the average customer to make a second purchase?" - "What percentage of customers make a repeat purchase within 90 days?" Visualizations Drew AI often presents cohort data as heatmaps showing LTV growth over time, making it easy to spot trends at a glance. It can also generate line charts showing retention curves or LTV growth by cohort. Need help? Reach out to support@datadrew.io or use the in-app chat. Related articles - LTV Cohort Analysis - Why cohort analysis is the best way to calculate LTV - Customer segmentation and RFM analysis - Purchase Frequency dashboard Need help? If you have questions or run into issues, reach out to us at support@datadrew.io or use the in-app chat. We're happy to help.

Last updated on Jul 07, 2026

Product performance and basket analysis

Drew AI can help you understand which products are driving your business, which ones customers come back for, and which products are frequently purchased together. This is essential for optimizing your product catalog, marketing spend, and merchandising strategy. Hero product identification Drew AI can analyze your products by combining Shopify sales data with ad spend from Meta and Google Ads to identify: - Hero Products: High revenue, high ad efficiency — these are your stars. - Potential Heroes: Strong organic sales but low or no ad spend — opportunities to scale with advertising. - Ad Spend Wasters: High ad spend but low returns — consider pausing or reworking creative for these products. Example questions: - "Which products are our heroes? Which are wasting ad spend?" - "Show me product performance — revenue vs. ad spend." - "Which products have the highest ROAS?" Repurchase rate analysis Understanding which products drive repeat purchases is key to building long-term customer value. Drew AI can analyze repurchase rates across different time windows: 1 month, 2 months, 3 months, 6 months, 1 year, and all-time. Example questions: - "Which products drive the most repeat purchases?" - "What is the repurchase rate for our top-selling products within 90 days?" - "Show me products with high first-order volume but low repurchase — these need retention focus." Basket analysis Basket analysis reveals which products are frequently bought together. Drew AI can identify product combinations (pairs, trios, and more) and show metrics like order count, total sales, and the split between new vs. repeat customers. Example questions: - "What products are frequently bought together?" - "Show me the top product bundles by revenue." - "Which product combinations have the highest average order value?" Product-level LTV Drew AI can also analyze customer lifetime value by first-order product, helping you understand which products attract the most valuable long-term customers. Example questions: - "Which first-order products lead to the highest customer LTV?" - "Compare the 6-month LTV of customers who first bought Product A vs. Product B." Breakdown options All product analyses can be broken down by product title, product type, vendor, or SKU — giving you flexibility to analyze at the level that makes sense for your business. Need help? Reach out to support@datadrew.io or use the in-app chat. Related articles - Product Performance dashboard - Basket Analysis — product bundles - Product Repurchase Rate analysis - Analyzing ad campaign performance Need help? If you have questions or run into issues, reach out to us at support@datadrew.io or use the in-app chat. We're happy to help.

Last updated on Jul 07, 2026

Customer segmentation and RFM analysis

Drew AI can analyze your customer base using RFM (Recency, Frequency, Monetary) segmentation — a proven framework that groups customers based on how recently they bought, how often they buy, and how much they spend. This is one of the most powerful features for understanding your customer health. How RFM segmentation works Datadrew automatically computes RFM scores for every customer in your Shopify store. Each customer is assigned to one of nine segments: - Champions: Your best customers who buy often and spend the most. Reward them and turn them into brand ambassadors. - Loyal: High frequency and spend with regular engagement. Great candidates for upselling and product reviews. - Promising: Recent purchasers with moderate engagement. Nurture them with loyalty programs and personalized recommendations. - New Customers: Bought recently but only once. Focus on welcome series and education about your brand. - Warm Leads: Single recent purchase. Nurture them toward a second purchase with targeted incentives. - Cold Leads: Single purchase some time ago. Time for a re-engagement campaign. - Need Attention: Previously above-average customers who have gone quiet. Win them back with limited-time offers. - Should Not Lose: High-value customers who have not purchased in a while. Prioritize personal outreach and win-back campaigns. - Sleepers: Below-average activity but they do have purchase history. Try reactivation campaigns to bring them back. - Lost: Lowest recency and engagement. Consider aggressive win-back campaigns or accept natural churn. What you can ask Drew AI Example questions: - "Show me our customer segments — who are our champions vs. at-risk?" - "Who are our high-value customers at risk of churning?" - "What percentage of revenue comes from each customer segment?" - "How many customers are in the Champions segment vs. last year?" - "Which customers in the Should Not Lose segment have spent the most?" All-time vs. last-year analysis Drew AI can analyze RFM segments using two different time windows: - All Time: Uses the customer's entire purchase history since they first ordered. - Last 1 Year: Only considers purchases from the past 12 months, which is useful for spotting recent changes in customer behavior. Actionable insights Drew AI does not just show you the segments — it provides actionable recommendations. For example, it might suggest targeting your "Need Attention" segment with a re-engagement email campaign, or tagging your Champions in Klaviyo for a VIP promotion. If you have Klaviyo connected, Drew AI can also help you understand how your email marketing aligns with your customer segments. Need help? Reach out to support@datadrew.io or use the in-app chat. Related articles - RFM Segmentation - Syncing RFM segments to Klaviyo - Email marketing analysis (Klaviyo) - LTV and cohort analysis with Drew AI Need help? If you have questions or run into issues, reach out to us at support@datadrew.io or use the in-app chat. We're happy to help.

Last updated on Jul 07, 2026

Analyzing ad campaign performance

Drew AI is a powerful tool for understanding how your advertising campaigns are performing across Meta (Facebook/Instagram) and Google Ads. It pulls real-time data directly from your ad platforms, so you are always looking at current numbers. What Drew AI can analyze When it comes to ad campaigns, Drew AI can: - Show campaign-level performance metrics (spend, impressions, clicks, conversions, ROAS) - Identify your best and worst performing campaigns - Detect creative fatigue — ads that were once performing well but are now declining - Analyze ad set and individual ad performance - Compare performance across time periods (week-over-week, month-over-month) - Track budget utilization and pacing Meta/Facebook Ads Drew AI connects to 21 Meta Ads API tools to give you comprehensive coverage of your Facebook and Instagram advertising. You can ask about campaigns, ad sets, individual ads, and creative performance. Example questions: - "Which Meta campaigns have the best ROAS this month?" - "Show me which ads are winning and which I should pause." - "Are any of my ads experiencing creative fatigue?" - "What should my next ads look like based on what is working?" - "Compare my Meta campaign performance — last 7 days vs previous 7 days." Google Ads Drew AI connects to Google Ads to analyze your Search, Shopping, Performance Max, and Display campaigns. It can run GAQL queries behind the scenes to pull exactly the data you need. Example questions: - "How are my Google Ads campaigns doing this week?" - "Which keywords are driving conversions vs. wasting budget?" - "How are my Shopping campaigns performing? Any optimization opportunities?" - "Show me my Google Ads spend and conversions trend for the last 30 days." - "How am I doing vs. competitors in auction insights?" Cross-platform comparison One of Drew AI's strengths is comparing performance across platforms. Instead of switching between ad managers, ask Drew AI to give you one unified view. Example questions: - "Compare my Meta Ads vs Google Ads performance — where should I focus?" - "Show me blended ROAS and CAC across all platforms." - "Where should I shift budget for better results?" What you need connected To analyze ad campaigns, make sure you have connected your Meta Ads and/or Google Ads accounts on the Integrations page in Datadrew. Drew AI will automatically detect which ad accounts are available and use the correct account IDs when pulling data. Need help? Reach out to support@datadrew.io or use the in-app chat. Related articles - Blended Ads Summary — unified ad performance - Campaign-level analysis and ROAS tracking - Cross-channel marketing analysis - Facebook/Meta Ads dashboard Need help? If you have questions or run into issues, reach out to us at support@datadrew.io or use the in-app chat. We're happy to help.

Last updated on Jul 07, 2026