Shopify Analytics vs BigQuery – It’s common for founders to open the Shopify dashboard and feel like something is missing. You can see total sales and top‑selling products, but what about lifetime value by product variant, SKU‑level profitability or multi‑touch attribution? Data‑driven leaders quickly realise that Shopify’s built‑in analytics, while great for beginners, only scratch the surface. As your store grows and marketing channels diversify, questions like Which ads drive my highest‑value customers? or How many repeat purchases come from a specific variant? become nearly impossible to answer with native reports.
This post compares Shopify’s native analytics with building a custom data warehouse on Google BigQuery. We’ll explore where Shopify’s analytics fall short, the hidden costs of third‑party plug‑ins, and how owning your data in BigQuery unlocks deeper insights, better attribution and actionable dashboards. At the end, you can download our Setup Guide: Syncing Shopify to Looker Studio, which walks through a practical integration.
The limitations of Shopify Analytics
Shopify Analytics provides basic insights into sales, traffic and customer behaviour. However, the depth of data varies by plan, with advanced reports and custom filters only available on higher tiers. Even on the Plus plan, there are several gaps:
Lack of multi‑channel attribution and customization: Shopify’s native reports don’t integrate with ad platforms or CRM data by default. There is no way to attribute sales across channels (Google, Meta, email) or build cohort analyses.
Siloed data: The dashboard only shows Shopify store activity. You can’t easily combine store data with marketing spend, CRM events or inventory levels from other systems.
Missing metrics: A LinkedIn article on moving beyond Shopify notes that the native analytics lack cost‑of‑goods sold, lifetime value (LTV), profit by SKU, subscription revenue breakdown and customer segmentation. Shopify also doesn’t provide purchase behaviour analysis (e.g., RFM scores), churn prediction or cross‑store profitability.
No unified view for multiple stores: Merchants with several Shopify stores can’t get a consolidated report out of the box.
Because Shopify’s UI and its APIs aggregate data differently, numbers in the dashboard rarely match what you get when exporting data via the API. Weld’s blog notes three key sources of discrepancy: attribution models (e.g., last‑click vs. first‑click), event aggregation (UI counts orders earlier in the lifecycle while the API only exposes completed transactions) and time zones. These differences make reconciliation difficult and erode trust in the numbers.
Why plug‑ins and dashboards aren’t enough
Many merchants install analytics plug‑ins or SaaS dashboards to overcome Shopify’s gaps. While these tools offer quick installation and e‑commerce‑specific metrics, they come with trade‑offs. According to 173tech’s article on moving beyond Shopify analytics, plug‑ins often create data silos, since each app tracks only part of the picture. They also become expensive as order volume grows (a few hundred dollars per month at 1 k orders can balloon to thousands per month at 10 k orders) and still may not provide full flexibility. Because they rely on Shopify data, any inaccuracies or missing fields in the core platform propagate downstream.
In short, plug‑ins solve surface‑level reporting but cannot answer advanced questions like: What is my customer lifetime value by product variant? How profitable is each SKU after discounts, returns and shipping costs? Which marketing cohort yields the highest repeat purchase rate? To answer these, you need to control the underlying data model and combine multiple sources.
Enter BigQuery: building a custom data warehouse
Google BigQuery is a serverless data warehouse designed for fast SQL queries across massive datasets. Instead of relying on pre‑built reports, you define your own metrics, join Shopify data with ad platforms, CRM, inventory and finance systems, and generate dashboards using Looker Studio, Power BI or Tableau. The benefits of syncing Shopify to BigQuery include:
Real‑time analytics and scalability: BigQuery can ingest high volumes of data and run complex queries in seconds. An Estuary article points out that syncing Shopify to BigQuery enables real‑time order tracking, inventory intelligence and custom dashboards with lower latency than batch ETL.
Unified customer & sales analytics: By combining Shopify data with ad platforms, CRMs and email tools in BigQuery, you can build a complete customer journey and understand which channels drive the most valuable customers.
Custom, flexible reporting: BigQuery lets you define cohort analyses, LTV calculations and SKU‑level profitability, which are not available in Shopify’s built‑in reports. You can query data using standard SQL and build dynamic dashboards in Looker Studio without exporting CSVs.
Predictive analytics: Because BigQuery supports machine learning and integrates with tools like Vertex AI, you can forecast demand, churn or customer lifetime value—capabilities that Shopify lacks.
Cost efficiency at scale: While there is an upfront cost to set up a warehouse and ETL pipelines, the per‑query cost of BigQuery can be lower than paying recurring fees for multiple analytics plug‑ins.
Challenges and integration options
Building a Shopify‑to‑BigQuery pipeline isn’t trivial. Shopify’s GraphQL API has strict rate limits and returns deeply nested data structures. Traditional batch ETL tools may only update data hourly or daily, leading to stale dashboards. To overcome these challenges, consider:
Managed ELT tools like Fivetran, Stitch or Airbyte. They handle API authentication, rate limits and schema changes, streaming Shopify data into BigQuery with minimal setup. However, they charge per million rows processed.
Real‑time streaming platforms such as Estuary Flow or RudderStack. Estuary uses a streaming‑first architecture to capture Shopify data via bulk GraphQL queries and write it into BigQuery with low latency. This approach ensures near real‑time dashboards and minimal operational overhead.
Custom pipelines built with Google Cloud Functions, Cloud Run or Cloud Workflows. For technical teams with engineering resources, writing your own ETL gives full control over transformation logic. Windsor.ai notes that integrating Shopify with BigQuery allows running complex queries, combining data from multiple channels and predictive analytics, but warns that API limits and schema complexity can make development time-consuming.
Building your own dashboards
Once your Shopify data lives in BigQuery, you can build dashboards tailored to your business. Here’s a high‑level roadmap:
Define metrics and KPIs: Identify the questions you can’t answer with Shopify alone (e.g., LTV by product variant, cohort retention, gross margin by product line). Map out the tables and joins needed in BigQuery.
Model the data: Use SQL or dbt to transform raw Shopify tables into analytics‑ready models (orders, customers, products, events). Include dimension tables for marketing channels, product categories and time periods.
Create dashboards: Connect Looker Studio or another BI tool to BigQuery. Design dashboards for revenue, marketing, operations and retention. For example:
LTV by variant dashboard: Visualize average order value, frequency and lifetime value per product variant.
Attribution dashboard: Evaluate marketing spend vs. revenue by channel and campaign. Use BigQuery’s support for multi‑touch attribution models rather than Shopify’s last‑click default.
Inventory & fulfillment dashboard: Track stock levels, shipping times and forecast low‑stock items.
Automate updates: Schedule your ELT tool or pipeline to refresh data continuously. Use incremental queries to reduce load and ensure dashboards reflect the latest orders and returns.
Integrate with performance marketing: Once you have a central warehouse, you can feed data back into advertising platforms to create look‑alike audiences or to automate campaigns. For guidance on aligning marketing with data, see our post on performance marketing for lead generation.
Extend to ERP/CRM: To truly own your data, integrate BigQuery with your ERP or CRM. Check out our guide on Shopify ERP/CRM integration for steps to sync orders and customers into a unified system.
Conclusion
Shopify’s built‑in analytics offer a good starting point, but they can’t answer the questions that sophisticated e‑commerce businesses need. Key metrics such as SKU‑level profitability, lifetime value by variant, and multi‑store comparison are missing. Third‑party plug‑ins fill some gaps but come with high costs and fragmented data. Migrating your data to BigQuery empowers you to own your data and build real dashboards that drive strategic decisions.
By centralising Shopify data alongside marketing, CRM, and inventory sources, you gain a 360‑degree view of your business. BigQuery’s scalability, real‑time processing and SQL flexibility allow you to compute custom metrics, run predictive models and visualise insights in Looker Studio. Ready to get started? Download our Setup Guide: Syncing Shopify to Looker Studio to see step‑by‑step instructions for connecting your store to BigQuery and building your first dashboards.
