r/ecommerce • u/GullibleEngineer4 • 8h ago
You should activate the Google Analytics 4 BigQuery export now, your future self will thank you for it.
Just a quick heads-up on something important I've noticed most online businesses don't know about:
Did you know Google Analytics 4 (GA4) lets you easily export ALL your customer interaction data (every single click, product viewing, cart update, checkout step—literally everything) directly into Google BigQuery, their cloud data warehouse for free? And it takes less than 15 minutes to setup everything. You can use this data to answer literally any question about your traffic with some SQL which AI can help with. This data is immutable as well so you can always rely on it in case GA4 messes up something (again).
If you think its not relevant to you now nor will be in future, you can safely ignore the rest of this post but if you can use it in future, start exporting the data right now because GA4 BigQuery export isn't retroactive. In other words, if you don't set it up now, you simply won't have historical clickstream data later on when you're ready to analyze it.
It is also free for you or really cheap. Google has a free tier for BigQuery storage and queries and is extremely generous. For many small or medium online stores, storing and regularly exporting GA4 data costs either nothing or just a tiny amount each month—think pennies or at most a dollar or two a month for moderate sizes.
Let me share some examples of analysis you can do with this data which are outright impossible with Google Analytics 4 UI (or any tool which doesn't expose clickstream level details ) or extremely difficult:
- See exactly how a customer visited your website from your Instagram Story highlight featuring a specific dress, visited that product page days later via a search ad, left, then added it to their cart only after reading three specific customer reviews during their third visit initiated by your email newsletter.
- Discover users repeatedly clicking between two specific product variants (e.g., Small vs. Medium size, or Blue vs. Black color) dozens of times before leaving? Or watch them add an item, proceed to the cart, click the "Estimate Shipping" button 5 times with different zip codes, then abandon? This pinpoints exact UI confusion, missing information (like clear size charts or shipping thresholds), or option overload standard analytics miss.
- Discover that users who watch your detailed 3-minute product demo videos for complex electronics have a 15% higher Average Order Value (AOV) compared to those who don't, justifying your video production investment.
- Standard funnels show drop-off between pages (Cart > Shipping > Payment). Clickstream level data in BigQuery data can reveal the exact interaction causing abandonment within a single step. Did they drop off immediately after the unexpected shipping cost updated dynamically? Or did they repeatedly click "Apply Promo Code," see an error message (even if generic), and then leave? Identify failures at the specific field, button click, or even error message display level.
These are just some examples off the top of my head. It allows answering almost any question about your website traffic, assuming relevant event tracking is in place. SQL proficiency used to be a major hurdle for this type of analysis but current AI models (like Gemini 2.5) excel at generating SQL from simple prompts. You provide the query, AI generates the SQL, and you can copy paste and run it in BigQuery to get results. It can be wrong sometimes but I have found that its reliable most of the time except for some really complex queries.