Category : Google Analytics
Littledata announces Google Analytics integration for BigCommerce
We are excited to announce that Littledata will soon be available to BigCommerce merchants. Like our popular Shopify app for Google Analytics, our BigCommerce connection for Google Analytics will ensure accurate sales and marketing data across the user journey. BigCommerce will be Littledata's first ecommerce platform integration outside of Shopify. With brands like Superdry, Skull Candy and 5-hour Energy now on BigCommerce, the timing couldn't be better. Read on to see what we've been working on, the benefits for ecommerce marketers and data scientists, and how to get early access. Why BigCommerce? Founded in 2009, BigCommerce has seen remarkable growth over the last couple of years, especially in North America. A year ago when they went public, BigCommerce was already powering over 60,000 online stores in 120 countries. They have focused on additional sales channels such as Amazon since early on, and recently announced a major partnership with Amazon for fulfillment, and another with Mercado Libre for extension into the Latin American market. Everyone needs accurate data to make data-driven decisions. We're excited to be extending our ecommerce data platform to work with BigCommerce. We chose BC because it's a great fit with our customer base which are typically successful DTC brands looking to scale faster and smarter. In addition, there's a growing amount of overlap with our technology partners and agency partners around the world. Advanced Google Analytics integration Accurate data is essential for ecommerce growth, but ecommerce tracking is notoriously difficult. As the top data platform specifically designed for ecommerce, our upcoming release in the BigCommerce app store will change the game. Littledata's advanced Google Analytics connector for BigCommerce stores will give you accurate data, automatically. Whether you are an ecommerce manager looking for accurate data to drive decision making, or a CTO or web developer looking for a seamless tracking solution, we're here to make your job easier. Benefits include: Complete sales dataAccurate marketing attributionCheckout funnel trackingOwn the data in Google Analytics As with all Littledata connections, Littledata's BigCommerce Google Analytics integration has the added benefit of enabling accurate data in any connected BI dashboard or reporting tool. What's more, the integration will work with custom themes and headless BigCommerce setups! Subscription analytics Tracking recurring orders is one of Littledata's key benefits and one of the most-cited features in our five-star reviews. We are especially excited about extending our subscription analytics to BC stores, enabling accurate data about recurring transactions and customer lifetime value (LTV). Our first subscription analytics integration will be with our longtime integration partner ReCharge, who also recently launched on BigCommerce. Sign up here for early access. How to get early access Are you doing at least $1M in annual online revenue? If so, you can apply for early access to Littledata's BigCommerce integration for Google Analytics. Early adopters will not only get access to our data connector - they will also have a key role to play in shaping additional features and integrations. Note: if you are interested in connecting BigCommerce with GA4 (the newest version of Google Analytics), reach out to us about our beta program.
How to add Google Analytics to Shopify
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Learn more about your ecommerce customers' behavior with advanced checkout funnel analysis [VIDEO]
Ecommerce analytics are tricky to begin with. Add tracking your subscription services on top of that and you’re dealing with a whole other animal! Do you use Google Analytics to report on your Shopify store’s one-off purchases AND recurring orders? Check out our video on Littledata’s advanced checkout funnel analysis to find out how we’ve made subscription analytics easy. https://www.youtube.com/watch?v=EU3Cj2Z6AII Traditional ecommerce stores typically track one checkout funnel per property. The benefit is that this makes it easy to analyze the drop-off at each point. But, if you have multiple checkouts to track one-time orders and subscription purchases, important insights might go unnoticed when GA aggregates your data. Littledata automatically differentiates between your checkout funnels to show whether they’re subscription or one-time purchases. That way you know exactly what each funnel’s checkout completion rate is for different order types. This gives you the power to tailor your remarketing strategies for specific checkouts or products, further increasing your ads’ relevance to possible customers. Note: Do you trust your subscription tracking? Get accurate subscription tracking with the ultimate ReCharge guide for Shopify Littledata integrates with the top subscription ecommerce apps—including ReCharge Payments, Bold Commerce, and Ordergroove—and automatically tracks both Shopify and subscription checkouts. “Littledata is a must-have if you’re running Recharge and Shopify; it helped us figure out what channels were getting us our future subscribers and what helped convert them.” —Better Way Health To access your checkout reports in Google Analytics, go to your ecommerce analysis reports. From there, you can view your checkout behavior reports to get a general understanding of when users are dropping off throughout the checkout process. Find out how to segment your data between Shopify and subscription checkouts to measure the exact drop-off rate at each stage of the checkout process for each checkout funnel in our latest learning video. Capture data at every turn In addition to tracking your checkout funnel completion rates for subscription checkouts, Littledata tracks crucial sales and marketing metrics, so you can: Get accurate marketing attribution data for subscription revenue, including first-time payments and recurring chargesUse custom dimensions to measure customer lifetime value (LTV)Track performance by payment source, subscription plan type, and product categoryView complete sales and marketing data with combined server-side and client-side trackingMake better, informed decisions for your Shopify store Resources Watch a quick demo video on how Littledata worksFind out how to calculate LTV with Google AnalyticsDownload the ultimate guide to subscription trackingCheck out our ReCharge FAQSubscribe to our YouTube Channel for more videos about analytics
Two ways to calculate customer lifetime value for ecommerce using Google Analytics data
Many of our customers come to us with a similar question: "how do I measure ecommerce lifetime value (LTV)?" The latest episode in our Learning Videos series shows you how to do just that for both your one-off purchasers and subscription customers. Our step-by-step tutorial covers two methods of calculating customer LTV using your Google Analytics (GA) data. You'll get to know Littledata’s custom dimensions in GA and learn how to visualize your calculations in Google Data Studio. https://www.youtube.com/watch?v=YOzHFN1ZjsA&t=21s During installation, Littledata automatically creates several custom dimensions in your connected Google Analytics property. These custom dimensions include: Lifetime Revenue, the sum total a customer has spent in your Shopify store (including one-time purchases and subscription orders)Shopify Customer ID, the unique identifier Shopify assigns to each customerLast Transaction DatePayment GatewayPurchase Count They offer better data to help you understand your customers' buying behavior, then calculate and visualize their LTV. To kick things off, you'll first need to export your data from GA to Google Sheets or another spreadsheet tool via CSV. Once you’ve enabled the GA add-on in Google Sheets, you're ready to get started. Method 1: Calculate LTV by Lifetime Revenue, Shopify Customer ID, and Transaction Count In the first method of calculating lifetime value, we’ll use Transactions as the metric. The dimensions we'll use—Shopify Customer ID and Lifetime Revenue—correspond with ga:dimension5 and ga:dimension3, respectively. Use the image below as a guide to set up your report: Next, set your Metrics Reference as Transactions and your Dimensions Reference as Custom Dimensions. After you run the report, Google Sheets should look something like this: Finally, use Google Sheets' built-in functions to calculate the average or median LTV of your customers. Method 2: Calculate LTV by Source/Medium, Transaction ID, Shopify Customer ID, and Transaction Revenue This second LTV calculation method helps you track which marketing channels bring in your most valuable customers: the ones who spend the most over time. In this method, use Transaction Revenue as the metric and Source/Medium, Transaction ID, and Shopify Customer ID as the metrics. These correspond with ga:sourceMedium, ga:transactionId, and ga:dimension1 respectively. This method requires the widest date range possible to capture the most transactional data possible—preferably since you started using Littledata. Before running the report, your Google Sheet should appear as follows: After exporting your data, your result will look like this—a list of transactions with source/medium and revenue data: Next, select all the data in your report to create a pivot table, aggregating by source/medium per customer. The result will reveal the total revenue per customer, per source. After completing the pivot table, you're ready to visualize your data in Data Studio. Build Reports in Google Data Studio Google Data Studio is one of our recommended reporting tools for ecommerce sites. Why? Because it's free, powerful, and works really well with Google Analytics. The first step in visualizing your data is to import your data into Google Data Studio by setting Google Sheets as your source. To do this, select your Google Sheets file followed by the pivot table you created in the previous method, and add it to your report in Google Data Studio. Change the data source by setting the aggregation to median so results yield the median lifetime revenue per traffic source. Your report dimension should be set to ga:sourceMedium and your metrics should be set to ga:transactionrevenue and ga:dimension1. Modify Shopify Customer ID from sum to count distinct to reveal the total unique customer IDs, which we'll use to sort our data. Sort by Shopify Customer ID to see the traffic source that brings the most customers to your site. The resulting report shows you the median lifetime revenue per traffic source, sorted by the total customers per source. References Quick Tips for Subscription Stores Using Custom Dimensions in GA 3 Deep Dives into Customer Lifetime Value for Ecommerce Sites LTV from GA vs LTV provided by Littledata How to Calculate Customer Lifetime Value in GA for Shopify Stores Custom Dimensions for Calculating Customer Lifetime Value Subscription Analytics Does Littledata work with my ecommerce reporting tool?
Why doesn't Shopify analytics match Google Analytics? [ebook]
Shopify analytics is fine for what it is: a siloed data source that is good at tracking Shopify orders. But if you want to track the complete customer journey and get accurate marketing data, you need to look elsewhere. Because it's both free and flexible, Google Analytics has become a top choice for a "single source of truth" to supplement Shopify analytics and other tools you might be using. And GA4, the newest version of Google Analytics, promises to be even more powerful. In our experience with hundreds of customers at Littledata we've found that many merchants turn to overblown solutions outside of GA (eg. fancy dashboards and generic data connectors) and then come back around to wanting to just fix the data in Google Analytics. After all, what good is the data if you can't trust it? Free ebook on Shopify and Google Analytics It's well known that Shopify's own analytics connection misses out on key issues like product list views, repeat purchases and marketing attribution. But where exactly does the tracking go wrong? What's going on behind the scenes? This new ebook is an insider's guide to Shopify Analytics vs Google Analytics. You will learn: Why transactions go missing in Google AnalyticsCommon issues for Shopify storesThe difference between marketing tags and Google Tag ManagerHow to set up checkout funnel trackingAnd all of the main reasons why Shopify doesn't match GA Download the ebook >>> Top brands turn to GA for a single source of truth, but there are some common things that go wrong. Even if you don't have a custom setup, things go wrong quickly -- including the "basics" like tracking ecommerce orders. We built Littledata to fix these issues automatically, saving you time and money. (Here's a quick demo video and our complete spec). But before you get into the details of the solution, it's important to understand the problem and what might be going wrong for your store in particular, whether you're seeing a lot of traffic that appears to be "Direct" but is actually from marketing channels like Facebook Ads or Klaviyo email marketing, you're missing repeat purchasing data, or your checkout funnel tracking is somehow out of whack. Get the ebook today. How to add Google Analytics to Shopify You can set up Enhanced Ecommerce in Google Analytics and then add Google Analytics to Shopify, but Shopify's default GA integration misses many key elements. Tip: With Shopify's default Google Analytics integration, 12 orders go missing for every 100 in Shopify. We highly recommend using an advanced data connector instead! If you would rather just get accurate data automatically, check out Littledata's 30-day free trial. It's the easiest way to avoid all of the known issues with Shopify's default Google Analytics integration. Plus, you still own the data in GA, whether or not you continue using our advanced data connections.
Optimizing Littledata's Shopify tracking script for speed and accuracy
At Littledata we know that page load speeds are essential for ecommerce success, and we have made some major improvements to our Shopify apps this month to improve both page speed and data accuracy. Having benchmarked over 20,000 ecommerce sites, and worked closely with larger DTC brands on Littledata Plus plans, we are well aware that technical factors such as page load speed are major drivers of ecommerce conversion rates. We have always had a minimal, super-fast script and GTM data layer, but v9 brings this to a whole new level. What’s new in LittledataLayer v9? The need for speed is driving some of our customers to headless setups, but for many stores there are lots of optimizations to be had from their existing Shopify theme and apps. Littledata’s main advance in this area is our server-side tracking, which means that our app has zero impact on your add to cart, checkout or payment steps. So the changes in v9 are focused on the landing pages, product listing pages and product details pages. The latest update improves both page speed and the accuracy of the data we track Some of the major improvements in LittledataLayer v9 are: Tracking all product list impressions, on whatever pages they are displayedThe correct product variant is tracked, if the listing is for a specific variantProducts loaded after the initial page load (i.e. "lazy-loaded" products) will also be trackedListing pages of more than 50 products (e.g. infinite scroll pages) are tracked In addition we’ve improved how some types of checkout are tracked, to ensure the marketing attribution of the order is correct, for: Buy Now buttons leading to an accelerated checkout (e.g. Paypal, Google Pay)Headless stores leading to a Shopify checkoutCustom checkouts which do not reuse the same Shopify cart token See our help center for more details about how tracking product list views works as the user scrolls down the page. All these changes will be automatically added for current customers, unless you opt out and choose manual updates, in which case you will need to manually upgrade. Please contact your account manager if you are unsure which option to take. Note: Unless you opt for manual updates, we will now automatically update the snippet Littledata adds to your Shopify store How does v9 of the Littledata tracking script improve page load speed? To send accurate product list views, product list clicks and product detail views, our app builds a data layer containing all the products on the page. This is true for both our Segment app and our Google Analytics app in the Shopify app store. Building this data layer on Shopify’s servers took time before the page was ever seen by a user; in this improved version we get the product data after the user has interacted with the page. This results in almost no impact to page load speeds from adding Littledata’s app, as measured by PageSpeed Insights - improving the score from 62% to 70%. And yes, a score of 73 out of 100 is not very impressive...but for our test store we haven’t done all the good things you should be doing to optimize your store, like compression and lazy-loading of images. So whatever your page speed was before the improvements, it should be up to 10 percentage points higher now. Speed test So how did we make the latest snippet faster? To start, it’s no longer requiring the same liquid code. We can see the difference using Shopify’s speed profiler extension for Chrome. Before the changes Shopify is spending over 80ms (out of 155ms total) processing the LittledataLayer snippet - and this test store does not have a particularly complex list of products. After changing to v9, we see this has dropped to less than 1ms, because now all the product data is fetched asynchronously from Shopify’s APIs as the user interacts with the page. The good news is that this comes at no cost to data accuracy. Our script already tracked the product impressions after the page load - now we wait to get the product data until it is really needed. As a key part of the modern data stack for DTC brands, we are always investing in efficiency and accuracy at Littledata. Schedule a demo to learn more, and let us know if you have suggestions for further technical improvements!
For every 100 orders in Shopify, 12 go missing
If you’re using Shopify’s default Google Analytics tracking you might have noticed that the revenue in Google Analytics never matches what you see in the Shopify dashboard. This is a big problem: missing orders means orders that can’t be attributed to marketing campaigns, whether those channels are paid or organic. Littledata’s improved Google Analytics app for Shopify increases order throughput to Google Analytics from worse than 90% to better than 99.9% -- and it works automatically in the data layer. How big is the problem? We sampled a set of larger DTC brands on Shopify, together processing 50,000 orders a month through a standard Shopify checkout. Looking at a month of traffic, we compared the paid orders for these Shopify stores with the Thank You pages tracked in Google Analytics. Remarkably, only 88% of orders were tracked on average, ranging from 78% tracked in the worst store setup to 96% in the best store. That is a big loss. For every 10,000 orders processed, 1,200 were going missing. Assuming each of those customers cost $50 to acquire, that is $60,000 of marketing spend which can't be attributed to sales. Whatever the CAC for your ecommerce brand, you can’t afford to miss significant data like this about transactions, not to mention the marketing campaigns that led to those sales in the first place! For every 10,000 orders processed, 1,200 went missing Revenue aside, what about those 1,200 customers who are likely still being retargeted by abandoned checkout campaigns, even though they did complete the checkout process? There is a good case to be made for remarketing to your best customers for upsells, cross-sells, referrals and more. But remarketing to your new customer base as if they never made a purchase is certainly bad business. And it gets worse. When I looked at 10 stores that have non-standard checkout setups, using apps such as ReCharge or CartHook, the percentage of orders tracked (excluding recurring orders) ranged from a pathetic 9% up to a disappointing 70%. Shopify’s order tracking relies on customers seeing the Shopify Thank You page, and many other checkouts do not immediately redirect there. What are the main reasons for missing transactions in Google Analytics? Littledata has had five years of experience debugging GA tracking, so at this point we’ve pretty much seen it all. In fact, it's the most common question our sales team hears: why doesn't my data in Shopify match my data in Google Analytics? The ecommerce ecosystem is constantly evolving, including headless setups and subscriptions in the Shopify checkout. But some things remain the same. The most common problem areas for disappearing orders are: 1. Users not waiting for the Thank You page to load Many tech-savvy buyers know that your store will email them an order confirmation, so if they’re in a hurry - and the thank you page takes a few seconds - why should they wait? This is especially true with payment gateways like PayPal, which have their own payment confirmation page. 2. Thank you page overloaded with marketing tags Most order tracking relies on a script to fire on the thank you page, and if your store has lots of these scripts then it could take 10+ seconds before the crucial Google Analytics script is run. Customers won’t wait 10 seconds to see a page which has no value for them. 3. Draft orders paid at a later date Does your store create draft orders? This is more common for B2B stores, and means the order is completed well after the customer web session finishes. That means no thank you page, so no way to track the orders in a standard GA implementation. 4. Third-party checkouts That Thank You page on Shopify may never appear at all if your store uses third-party checkouts. 5. Recurring orders Like paid draft orders, recurring orders are payments that happen outside of the customer’s web session. The user never goes through a checkout or sees the thank you page. 6. Duplicate tracking Refreshing the order confirmation page, or clicking through on an order confirmation email to view the page again, might cause another transaction event to be fired from the page. [tip]Get the free ebook about why Shopify doesn't match Google Analytics[/tip] How is Littledata’s tracking different? Littledata offers server-side order tracking, hooking into the order creation in Shopify after the payment has been made. That allows us to track draft orders paid after the event, recurring orders, and orders through channels like Amazon that don’t use the Shopify Checkout. It also allows us to add refunds back in real-time, so you can track net sales against marketing channels. Littledata de-duplicates all orders, so an order is only ever reported once - giving a 100% match with what is in Shopify admin. Server-side tracking ensures complete analytics If you want to compare like-for-like, as I did for this article, our app also sends a ‘Thank you page’ event (in the same way the order tracking done in Shopify’s standard setup). This event can also be used to trigger Google Tag Manager tags, using the built-in GTM data layer. Interested in improving your Google Analytics setup? You might be interested in 6 common reasons why GA is not accurate and how Littledata’s Google Analytics app works. [subscribe]
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