Product Analytics

Creative Strategy — ad creative performance

What is the Creative Strategy Dashboard? The Creative Strategy feature in Datadrew combines product-level ad performance data with creative analysis to help you understand how your ad creatives perform at the product level. By connecting your Meta Ads and Google Ads accounts, you can see which creatives drive the best ROAS and which are wasting budget. Creative Strategy insights are surfaced through the Product Performance dashboard and through Drew AI, your AI analytics assistant. This is a Pro plan feature. How It Works When you connect Meta Ads and/or Google Ads to Datadrew, the platform automatically: 1. Pulls ad spend, impressions, clicks, CPC, CPM, and CTR data at the product level 2. Matches this data with your Shopify revenue by product 3. Calculates a Blended Catalog ROAS for each product — combining all ad platform spend against Shopify revenue 4. Categorizes products into Hero Products, Ad Spend Wasters, and Potential Products based on their ROAS relative to your store average Key Metrics for Creative Analysis | | | | | --- | --- | --- | | Metric | Source | What It Tells You | | Meta Ads Spend | Meta Ads | How much you spent on Meta ads for this product | | Meta Ads CPC | Meta Ads | Cost per click — lower is generally better | | Meta Ads CPM | Meta Ads | Cost per 1,000 impressions — indicates creative competitiveness | | Meta Ads CTR | Meta Ads | Click-through rate — higher CTR suggests more engaging creative | | Google Ads Spend | Google Ads | How much you spent on Google ads for this product | | Google Ads CPC | Google Ads | Cost per click on Google | | Google Ads CTR | Google Ads | Click-through rate on Google | | Google Ads Conversions | Google Ads | Number of conversions attributed to this product | | Blended Catalog ROAS | Calculated | Revenue / Total Ad Spend — the ultimate creative efficiency metric | Using the Product Performance Dashboard for Creative Strategy Open the Product Performance dashboard and ensure your Meta Ads and/or Google Ads are connected. You will see ad metrics columns appear in the product table. Identifying Creative Winners - Sort by Meta Ads CTR (descending) to find products with the most engaging Meta creatives - Sort by Blended Catalog ROAS (descending) to find your most efficient ad-to-revenue products - Use column filters to show only products with CTR above a threshold (e.g., greater than 2%) Identifying Creative Losers - Filter for products with high ad spend but low ROAS — these are your budget wasters - Products with low CTR and high CPM need creative refreshes — the platform is charging more because the creative is not resonating Using Drew AI for Creative Insights Ask Drew AI questions like: - "Which products have the highest CTR on Meta Ads?" - "Show me products where we are spending the most on Google Ads but getting low ROAS" - "Compare Meta and Google Ads performance for my top 10 products" - "What products should I pause ads on based on ROAS?" Drew AI can query your live ad platform data and provide instant, conversational answers with charts and tables. Actions to Take - Double down on creatives for Hero Products — increase budgets where ROAS is strong - Refresh or pause creatives for products with declining CTR and rising CPM - Test new creatives for Potential Products (high ROAS but low spend) — these have room to scale - Use the product-type breakdown to see which product categories perform best on each ad platform - Compare Meta vs. Google Ads metrics side by side to determine which platform is more efficient for each product Need help? Contact us at support@datadrew.io or use the in-app chat. Related articles - Facebook/Meta Ads dashboard - Google Ads dashboard - Campaign-level analysis and ROAS tracking - Product Performance 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.

Product Repurchase Rate analysis

What is Product Repurchase Rate Analysis? The Product Repurchase Rate dashboard shows how likely customers are to buy again after purchasing a specific product, and how much long-term value each product generates. It combines repurchase behavior with product-level LTV metrics to help you understand which products build lasting customer relationships. This dashboard is available under Product > Repurchase Rate in the sidebar. Product Repurchase Rate is a Pro plan feature. How It Works For each product (or product type), Datadrew calculates: - How many customers purchased that product - How many of those customers came back for another order (of any product, within the repurchase window) - The LTV of customers who first bought that product — at 90-day and 180-day windows This gives you a clear picture of which products are best at driving repeat business. Dashboard Components Scatter Plot A visual chart showing each product plotted by repurchase rate and LTV. Products in the top-right quadrant (high repurchase + high LTV) are your best gateway products. Product Table A detailed table showing each product with the following metrics: | | | | --- | --- | | Metric | Description | | Product Name | Product title (with image when available) | | Customer Count | Total customers who purchased this product | | Repurchase Rate | Percentage of customers who made at least one additional purchase after buying this product | | AOV | Average order value for orders containing this product | | LTV (90 day) | Average lifetime value of customers within 90 days of first purchasing this product | | LTV (180 day) | Average lifetime value of customers within 180 days of first purchasing this product | | Total LTV | Overall lifetime value across all time | Dashboard Controls - Time Period — Set the date range for analysis. This determines which first purchases are included. - Group By — Switch between grouping by individual Product or by Product Type to see category-level patterns. - Repurchase Window — Adjust the maximum number of days to look for a repeat purchase. Key Insights to Look For - High repurchase rate products — Products with repurchase rates above your store average are your best "gateway" products. Customers who buy these first are most likely to return. - High LTV products — Even if the repurchase rate is average, some products attract customers who spend more over time. These are worth promoting. - Low repurchase + High volume — Products that attract many customers but have low repurchase rates may be one-and-done products. Consider creating post-purchase follow-up sequences for these buyers. - Product Type trends — Switch to the Product Type view to see if certain categories naturally drive more repeat business than others. Actions to Take - Feature high-repurchase-rate products in your acquisition campaigns to attract customers who are likely to come back. - Create tailored post-purchase email sequences for buyers of low-repurchase products, recommending complementary items. - Use the 90-day vs. 180-day LTV comparison to understand how quickly value builds for each product. - Combine repurchase data with the Basket Analysis dashboard to find natural cross-sell paths from low-repurchase products to high-repurchase products. Need help? Contact us at support@datadrew.io or use the in-app chat. Related articles - Product Performance dashboard - Purchase Frequency dashboard - Basket Analysis — product bundles - Product performance and basket 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.

Basket Analysis — product bundles

What is Basket Analysis? Basket Analysis reveals which products are frequently bought together in the same order. By analyzing real purchase patterns, it helps you create effective product bundles, optimize cross-sell recommendations, and improve your merchandising strategy. This dashboard is available under Product > Basket Analysis in the sidebar. Basket Analysis is a Pro plan feature. How It Works Datadrew analyzes every multi-product order in your Shopify store and identifies product combinations that appear together most frequently. For example, if many customers buy Product A and Product B in the same order, that combination will rank highly. The analysis supports different combination sizes: - 2-way combinations — Pairs of products bought together (e.g., "T-Shirt + Hoodie") - 3-way combinations — Trios of products bought together (e.g., "T-Shirt + Hoodie + Cap") - 4-way and 5-way combinations — Larger product bundles Dashboard Controls - Time Period — Set the date range for analysis. - Breakdown By — Analyze combinations by Product Title, Product Type, Vendor, or SKU. - Combination Size — Choose to generate 2-way, 3-way, 4-way, or 5-way product combinations. - Basket Size — Choose between "X items or more" (any order with at least that many items) or "Exactly X items" (only orders with exactly that many items). - Filters — Refine results by product type, vendor, tags, and other attributes. Reading the Results The results table shows each product combination with the following metrics: | | | | --- | --- | | Column | Description | | Product Combination | The group of products bought together, displayed with product images | | Orders | Total number of orders containing this exact combination | | Total Sales | Total revenue from orders containing this combination | | Order % | Percentage of all orders (in the selected period) that contain this combination | | AOV | Average order value for orders with this combination | | New Orders | Number of orders from first-time customers containing this combination | | New Total Sales | Revenue from first-time customer orders with this combination | | Repeat Orders | Number of orders from returning customers containing this combination | | Repeat Total Sales | Revenue from returning customer orders with this combination | Key Insights to Look For - High-frequency pairs — Product pairs that appear in many orders are natural bundle candidates. Consider offering them as a discounted bundle. - High AOV combinations — Combinations with above-average AOV indicate premium cross-sell opportunities. - New vs. Repeat split — If a combination is popular with new customers, it is a good acquisition bundle. If popular with repeat customers, it signals a strong cross-sell path. - Product Type patterns — Switch to the Product Type breakdown to see category-level bundling patterns (e.g., "Accessories + Apparel" combos). Actions to Take - Create product bundles on your Shopify store based on top-performing combinations. - Set up "Frequently Bought Together" recommendations using your top 2-way combinations. - Build cross-sell email sequences that recommend Product B to customers who purchased Product A. - Use 3-way combinations to create premium bundle offers with a discount incentive. - Test running ads featuring your top product combinations rather than individual products. Exporting Data Click the Export CSV button to download the basket analysis data for further analysis or to share with your merchandising team. Need help? Contact us at support@datadrew.io or use the in-app chat. Related articles - Product Performance dashboard - Product performance and basket analysis - Product Repurchase Rate analysis - Product Cohorts 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.

Product Performance dashboard

What is the Product Performance Dashboard? The Product Performance dashboard gives you a comprehensive view of how every product in your catalog is performing — combining Shopify sales data with ad spend data from Meta Ads and Google Ads. It helps you identify your hero products, spot budget wasters, and find untapped potential. This dashboard is available under Product > Product Performance in the sidebar. Product Performance is a Pro plan feature. How It Works Datadrew blends data from multiple sources to give you a unified view: - Shopify — Orders, revenue, units sold, customers, and average selling price per product - Meta Ads — Ad spend, CPC, CPM, CTR, clicks, and impressions attributed to each product (when connected) - Google Ads — Ad spend, CPC, CPM, CTR, clicks, impressions, conversions, and conversion value per product (when connected) - Blended Catalog ROAS — A unified return on ad spend metric that combines revenue from Shopify with blended ad spend from all connected platforms Dashboard Components Scatter Plot A visual chart plotting each product by Revenue (Y-axis) vs. Blended Catalog Ad Spend (X-axis). This makes it easy to spot products in different quadrants: - Top-right — High revenue and high ad spend (potential heroes or wasters depending on ROAS) - Top-left — High revenue with low ad spend (organic winners) - Bottom-right — Low revenue with high ad spend (budget wasters) - Bottom-left — Low revenue and low ad spend (potential products to test) Product Table A detailed table with every product, showing all available metrics. The table supports: - Search — Find products by name or product ID - Column visibility — Show or hide columns to focus on the metrics that matter to you - Column filters — Filter rows by metric thresholds (e.g., show only products with ROAS greater than 3x) - Sorting — Sort by any column to rank products - Pagination — Navigate through large catalogs Catalog Summary Below the table, a summary row shows aggregate totals for your entire catalog — total revenue, total ad spend, and blended ROAS across all products. Dashboard Controls - Time Period — Set the date range for analysis. - Breakdown By — View data by Product Title, Product Type, or Vendor. - Filters — Refine results by product type, vendor, tags, and more. - Column Preferences — Your column visibility choices are saved automatically and persist across sessions. Key Metrics Explained | | | | --- | --- | | Metric | Description | | Blended Catalog ROAS | Product revenue divided by total ad spend (Meta + Google). Higher is better. "Non-ad-driven" means the product generated revenue without any attributed ad spend. | | Blended Catalog Spend | Total combined ad spend from Meta and Google Ads for this product. | | Net Revenue | Total revenue from Shopify orders for this product. | | Orders | Total number of orders containing this product. | | Units Sold | Total quantity of this product sold. | | Avg Selling Price | Average price per unit of this product. | Product Insights (Requires Ad Connections) When you have Meta Ads and/or Google Ads connected, Datadrew automatically categorizes your products into three groups: - Hero Products — High revenue and high ROAS. These are your best performers — scale ad spend on these. - Ad Spend Wasters — High ad spend but low ROAS. Review and optimize or pause campaigns for these products. - Potential Products — Low ad spend but high ROAS. These are underinvested — consider increasing budget allocation. Exporting Data Click the export button to download your product performance data as CSV or XLSX. The export respects your current column visibility and applied filters. Need help? Contact us at support@datadrew.io or use the in-app chat. Related articles - Basket Analysis — product bundles - Product Repurchase Rate analysis - Product performance and basket analysis - Creative Strategy — ad creative 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.

Product Basket Analysis with Datadrew

Retail giants like Amazon, Walmart rely heavily on this commerce strategy of showing the frequently bought together products to cross-sell and upsell their products. It helps figure out the shopping trends, patterns across different channels not limited to online but offline also. Benefits of basket analysis or cart analysis or Frequently bought together products - - Create New Product Bundles: Identify items often purchased together to create attractive product combos. - Enhance Product Recommendations: Display complementary products on product pages to increase cart size and boost “add to cart” rates. - Upsell on the Thank You Page: Suggest additional products after purchase to encourage immediate repeat sales. - Optimize Cross-Selling in Marketing: Use insights for targeted promotions via email, WhatsApp, SMS, and ads, showcasing other items commonly bought together by similar customers. - Compare Combos for Revenue Growth: Compare different product combinations for new sales vs repeat sales. How to use Basket analysis in Datadrew platform - By default it gives you the product baskets sorted by highest numbers of orders. You can choose whether you want to see baskets with minimum 2, 3 or 4 products. You can also customise the report as per your needs with following options like timeperiod, breakdown by product or SKU or vendor or product type. KPIs for frequently bought together products - - #Orders: Number of orders which included these products bundles - Total sales: Total revenue from these product bundles - %Orders: The percentage of orders that included these products - AOV: Total sales of all orders including these products / number of all orders including these products - New orders: Number of new orders which include these products - New sales: Sales of new orders which include these products - Returning orders: Number of returning orders which include these products - Returning sales: Sales of returning orders which include these products Filters You can further slice and dice this data with the filters. - Order and Customer Tags - - Exclude or include orders with certain order tags or customer tags - Location - Country - State - City - Product - Tags (e.g., new season vs. old season) - SKU - Vendor Share with team and put it to action - You can export the csv and share with your team. You can also share access of the platform from settings so your team can come up with other impactful insights and collaborate with you.

Product Repurchase Analysis

It's hard for a brand owner to know which products to promote to retain the acquired new customers For a sustainable business, it's important that you keep and promote the products that not only sell well in the first place but also turns your customers into repeat buyers Understanding the analysis The tabular view has the following key variables - - First-order product - Which product was there in the first order of a new customer - New customers - How many new customers were acquired on a particular product - Re-purchasers - How many of those new customers made at least a 2nd purchase - % age of re-purchasers who purchased anything from the website - % age of new customers purchased the same product again The scatter plot helps you quickly identify the products as per two variables - new customers and Repeat Buyers %. ‍ Some Use cases Promote the products with a higher repeat rate The products with high percentage of repurchasers signify that the product is being liked by the new customers and is converting new customers into repeat buyers. Hence, promote these products more to increase the customer lifetime value Kill products with low value and low repeat rate / Improve Product quality We recommend reducing advertising spends(or efforts) on products that have low AOV (average order value) and have less percentage of repeat buyers as these products lowers the customer lifetime value and profitability. A product repurchase report can also help store owners identify product quality issues. If a product has a low repurchase rate, it may be an indication that customers are not satisfied with the product quality or performance. Identify opportunities for cross-selling By analysing the repurchase report of different products, We can identify opportunities for cross-selling. For example, if a customer frequently repurchases a certain product, store owners can recommend other products that complement that product. This can increase the average order value and drive more sales. Optimise product offerings By looking at which product types breakdown, We can identify which product types have a high repurchase rate and focus on offering more products within that category. Additionally, we can identify product types that have a low repurchase rate and either improve them or remove them from their product catalogue. Determine customer loyalty By analysing how many customers repurchase the same product or any product, We can identify customers who are loyal to their brand and those who are not. This information can be used to create targeted marketing campaigns to retain loyal customers and win back those who are not as loyal. Have any further questions? You can book a call here.