Home Store Performance Dashboards

Store Performance Dashboards

By Vikas Bansal

• 4 articles

Using date range and comparison filters

Overview Every dashboard in Datadrew comes with date range and comparison filters at the top of the page. These filters let you control what time period you are analyzing and compare it against a previous period to spot trends, growth, or decline. Setting a date range The date picker lets you choose the exact start and end dates for your analysis. You can: - Select a custom range: Click the date picker and choose any start and end date. All dashboard metrics will update to show data only for that window. - Use preset ranges: Quick options like "Last 7 days," "Last 30 days," "This month," or "Last quarter" are available for fast selection. - Change at any time: Switching the date range instantly recalculates all metrics, charts, and tables on the page. Enabling comparison mode Comparison mode lets you see how the current period stacks up against a previous one. When enabled: - Each metric card shows a percentage change (green for improvement, red for decline). - Trend charts overlay the comparison period so you can visually compare patterns. - The comparison period is automatically calculated based on your selected range. For example, if you select "Last 7 days," the comparison period is the 7 days before that. To enable comparison, toggle the comparison option in the date picker. You can also select a custom comparison period if the automatic one does not fit your needs. Group By filter The Group By filter controls how data points are aggregated on trend charts: | | | | | --- | --- | --- | | Option | What it does | Best for | | Day | Each data point represents one day | Short-term analysis (last 7-14 days). Spot daily fluctuations and day-of-week patterns. | | Week | Each data point represents one week | Medium-term analysis (last 30-90 days). Smooths out daily noise while showing weekly trends. | | Month | Each data point represents one calendar month | Long-term analysis (last 3-12 months). Best for seeing seasonal patterns and month-over-month growth. | | Quarter | Each data point represents one calendar quarter | Strategic analysis (last 1-2 years). Useful for board-level reporting and annual planning. | How date ranges apply across dashboards The date range and Group By settings you select carry over as you navigate between different dashboards in the same session. This means: - Set your date range once on the Store Performance page, and it stays active when you switch to the Ads or Customer dashboards. - Each dashboard may show slightly different date behavior based on data availability. For example, ad platform data may have a 1-2 day lag compared to Shopify order data. Data time limits by plan Depending on your Datadrew plan, you may have limits on how far back you can look: - Free plan: Last 3 months of data. - Starter plan: Last 12 months of data. - Pro plan and above: Unlimited historical data. If you try to select a date range beyond your plan's limit, the date picker will show a notice explaining the restriction. You can upgrade your plan to unlock more historical data. Tips for effective date analysis - Always use comparison mode when reviewing performance. Absolute numbers without context can be misleading — a revenue of $50,000 means different things depending on whether the previous period was $40,000 or $60,000. - Match the Group By to your date range. Using daily grouping on a 12-month range creates too many data points and makes charts hard to read. Use weekly or monthly instead. - Account for seasonality. When comparing periods, consider whether holidays, sales events, or seasonal patterns might explain changes. Compare year-over-year when possible for seasonal businesses. - Check data freshness. Datadrew syncs your Shopify data daily. Today's data may not be fully available until the next sync cycle. Ad platform data (Meta, Google) may have an additional 1-2 day delay due to reporting windows. Need help? If you have questions about date ranges or comparison filters, reach out to us at support@datadrew.io or use the in-app chat widget. Related articles - Store Performance dashboard overview - Exporting data to CSV or Excel - Customizing table columns in dashboards - Setting up global filters

Last updated on Jul 07, 2026

New vs. returning customers dashboard

What this dashboard shows The New vs. Returning Customers dashboard breaks down your store's performance by customer type — separating first-time buyers from repeat purchasers. This distinction is one of the most important in e-commerce analytics, because the economics of acquiring a new customer are very different from retaining an existing one. You can access this dashboard from the main Store Performance page (where it appears as a summary section) or navigate to the full report at Customers > New vs. Returning in the sidebar. Key metrics compared Each metric is shown side-by-side for new and returning customers, making it easy to compare: | | | | | --- | --- | --- | | Metric | New Customers | Returning Customers | | Revenue | Total revenue from first-time buyers | Total revenue from repeat buyers | | Orders | Number of orders from first-time buyers | Number of orders from repeat buyers | | Customer Count | Number of unique first-time buyers | Number of unique repeat buyers | | AOV | Average order value for new customers | Average order value for returning customers | | ARPU | Revenue per new customer | Revenue per returning customer | Percentage splits are also shown for revenue, orders, and customer count — so you can see what proportion of your business comes from each group. How to read the trend charts The dashboard includes trend charts that plot new and returning metrics over time. Here is what to look for: - Healthy growth: Both new and returning customer revenue are increasing, with the returning share gradually growing as your customer base matures. - Acquisition-dependent: If new customer revenue dominates (over 70-80%), your business relies heavily on continuous ad spend. Focus on improving retention. - Retention-strong: If returning customer revenue is a large share, your retention is working well. Consider investing more in acquisition to grow faster. - Warning sign: Returning customer revenue or count is declining period over period. This may indicate product quality issues, poor post-purchase experience, or increased competition. How to use this dashboard 1. Benchmark your split: Check the revenue split between new and returning customers. Most healthy DTC brands aim for 30-50% of revenue from returning customers, though this varies by product category and business stage. 2. Compare AOV: Returning customers typically have a higher AOV than new customers. If not, consider whether your product recommendations, loyalty programs, or email flows need improvement. 3. Track over time: Use the date comparison feature to see if your returning customer percentage is trending upward month over month. This is a strong indicator of improving customer loyalty. 4. Validate acquisition campaigns: After a major ad campaign or sale, check whether the new customer count spiked. Then monitor those customers in subsequent periods to see if they return. Tips and best practices - A first-time buyer is defined based on their entire Shopify order history — not just the selected date range. Even if you select the last 7 days, Datadrew checks whether the customer has any prior orders in your store's history. - Use this dashboard alongside the RFM Segmentation report for even deeper customer insights. RFM tells you which returning customers are champions versus which ones are at risk. - If your new customer ARPU is close to your customer acquisition cost (CAC), you are breaking even on first purchase. That is normal for many DTC brands — profitability comes from repeat purchases. - Export the data table to CSV for further analysis or to share with your team. Need help? If you have questions about the New vs. Returning Customers dashboard, reach out to us at support@datadrew.io or use the in-app chat widget. Related articles - Store Performance dashboard overview - RFM Segmentation - Understanding key store metrics - LTV Cohort Analysis

Last updated on Jul 07, 2026

Understanding key store metrics

Core store metrics The Datadrew Store Performance dashboard tracks several key metrics pulled directly from your Shopify store data. Understanding what each metric measures — and why it matters — helps you make better decisions about your store's growth. Revenue and order metrics | | | | | --- | --- | --- | | Metric | Definition | Why it matters | | Total Revenue | The total value of all orders placed during the selected period, in your store's currency. | Your top-line indicator. Track this daily to understand overall business health and growth trajectory. | | Order Count | The total number of orders placed during the selected period. | Helps you understand purchase volume independently of order size. A drop in orders with stable revenue may mean higher AOV but fewer buyers. | | Average Order Value (AOV) | Total Revenue divided by Order Count. Calculated as: Revenue / Orders. | Shows how much each customer spends per transaction. Improving AOV is often easier than acquiring new customers. Use it to evaluate upsell and cross-sell strategies. | | Average Revenue Per User (ARPU) | Total Revenue divided by the number of unique customers. Calculated as: Revenue / Unique Customers. | Unlike AOV, ARPU accounts for repeat purchases. A customer who orders twice contributes to higher ARPU even if each order is small. This metric reflects true customer value. | Customer metrics | | | | | --- | --- | --- | | Metric | Definition | Why it matters | | Customer Count | The number of unique customers who placed at least one order during the selected period. | Measures your active buyer base. Growth in customer count signals healthy acquisition. | | New Customer Count | Customers who placed their first-ever order during the selected period. | Directly measures the effectiveness of your acquisition efforts — ads, SEO, referrals, and other top-of-funnel activities. | | Returning Customer Count | Customers who had previously placed at least one order before the selected period and ordered again. | Reflects retention and loyalty. A growing returning customer count means your product and experience are driving repeat purchases. | Revenue breakdown by customer type | | | | | --- | --- | --- | | Metric | Definition | Why it matters | | New Customer Revenue | Total revenue generated by first-time buyers. | Shows how much of your revenue depends on new acquisition. High dependency on new customer revenue can be risky if ad costs rise. | | Returning Customer Revenue | Total revenue generated by repeat buyers. | Returning customer revenue is typically more profitable since you have already paid to acquire those customers. Growing this metric improves your margins. | | New Customer AOV | Average order value for first-time buyers only. | Compare this to returning customer AOV. First-time buyers often spend less, but a strong first purchase can indicate long-term potential. | | Returning Customer AOV | Average order value for repeat buyers only. | Returning customers often spend more per order. If this metric is declining, consider improving your loyalty or product recommendation strategies. | | New Customer ARPU | Revenue per unique new customer. | Measures the initial value of each acquired customer. Compare this to your customer acquisition cost (CAC) to understand acquisition profitability. | | Returning Customer ARPU | Revenue per unique returning customer. | Shows the ongoing value of retained customers. Higher returning ARPU means your retention strategies are working. | How these metrics connect These metrics work together to tell a complete story: - Revenue = Orders x AOV. If revenue drops, check whether it is because you are getting fewer orders or each order is smaller. - ARPU vs. AOV. If ARPU is much higher than AOV, it means customers are placing multiple orders — a sign of good retention. If they are similar, most customers are buying only once. - New vs. Returning split. A healthy store typically sees returning customer revenue grow over time as a percentage of total revenue. If new customer revenue dominates, your growth depends heavily on continued ad spend. Need help? If you have questions about any of these metrics or how to interpret them for your store, reach out to us at support@datadrew.io or use the in-app chat widget. Related articles - Store Performance dashboard overview - Understanding metrics and KPI definitions - Understanding blended metrics - New vs. returning customers dashboard

Last updated on Jul 07, 2026

Store Performance dashboard overview

What this dashboard shows The Store Performance dashboard is your central hub for understanding how your Shopify store is performing. It brings together your most important e-commerce metrics — revenue, orders, customers, and advertising data — into a single view that updates daily. When you open Datadrew, this is the first page you see. It is designed to give you a complete snapshot of your business at a glance, so you can quickly spot trends, identify issues, and find growth opportunities without jumping between multiple tools. Dashboard sections The Store Performance dashboard is organized into seven sections, each giving you a focused view of a different part of your business: 1. Blended Ads Summary — Combined advertising metrics across all connected ad platforms (Meta Ads + Google Ads). Shows total spend, impressions, clicks, blended ROAS, and customer acquisition cost in one place. 2. Shopify Metrics — Core store performance: total revenue, order count, average order value (AOV), average revenue per user (ARPU), and customer counts. 3. New vs. Returning Customers — Breaks down revenue, orders, and AOV by new customers versus returning customers, so you can see the health of both acquisition and retention. 4. LTV Growth — Lifetime value trends showing how customer value evolves over time through cohort analysis. 5. LTV Cohort Analysis — Detailed cohort view showing how groups of customers acquired in the same period behave over time. 6. RFM Segments — A treemap visualization of your customer base segmented by Recency, Frequency, and Monetary value — helping you identify your best customers and those at risk of churning. 7. Growth Benchmarks — Compare your store's growth rates against industry peers to understand where you stand. Filters and controls At the top of the dashboard, you will find two key controls: - Time Period — Use the date picker to select any date range. You can also enable comparison mode to see how the current period compares to a previous one (e.g., this month vs. last month). - Group By — Choose how your data is grouped over time: by day, week, month, or quarter. This controls how trend charts display data points. How to use this dashboard 1. Daily check-in: Start each morning by reviewing the Blended Ads and Shopify Metrics sections. Look for any significant changes in revenue, spend, or ROAS compared to the previous period. 2. Weekly review: Set the date range to the past 7 days with comparison to the prior 7 days. Check whether new customer acquisition is keeping pace and whether returning customer revenue is growing. 3. Monthly analysis: Switch to a 30-day view grouped by week. Look at the RFM segments and cohort analysis to understand longer-term customer health. Tips and best practices - Connect both Meta Ads and Google Ads to unlock the Blended Ads section. This gives you a unified view of your total advertising performance without switching between platforms. - Use comparison mode to quickly spot improvements or declines. A metric shown in green means it improved versus the comparison period; red means it declined. - Click on any section title to navigate to its full, detailed report page for deeper analysis. - If a section shows a "Connect" prompt, it means the required integration has not been set up yet. Go to the Integrations page to connect the data source. Need help? If you have questions about the Store Performance dashboard, reach out to us at support@datadrew.io or use the in-app chat widget. Related articles - Understanding key store metrics - New vs. returning customers dashboard - Using date range and comparison filters - Understanding the Datadrew dashboard overview

Last updated on Jul 07, 2026