We are off to the races in 2023 already with Shopify officially launching Commerce Components by Shopify (CCS), an improved offering for large retailers. CCS allows enterprise retailers to access Shopify's foundational, high-performing components, such as its checkout, along with flexible APIs to build dynamic customer experiences that integrate seamlessly with a retailer's preferred existing services. But larger brands don't just want composable commerce. They also want -- actually, need -- complete, accurate, actionable data. Have Shopify's new Commerce Components been designed with the modern data stack in mind? There are lots of good things to say about Commerce Components. Enterprise retailers can take the components they need and leave those they do not, and developers are “free to build with any front-end framework they choose”, says Shopify. CCS uses Shopify's global scale infrastructure, which has over 275 network edge points to enable fast storefronts and checkouts no matter where customers are located -- and in a year where consumers are savvier than ever and demand a great experience. While we are excited about how this will attract larger brands to the Shopify ecosystem, we feel the Data Analytics component is underwhelming -- and won’t allow enterprise brands to track full server-side event data for building marketing attribution, product recommendation, or personalization data models. This component uses ShopifyQL, launched in mid-2022, as a neat query language for charting. But data analysts using ShopifyQL to query Shopify’s own data tables can only query the current state of the customer or order, and not understand the customer journey that led to that order. Popular reports such as marketing attribution by campaign or channel are just not possible from this data set. Furthermore, most enterprise brands we talk to want to own their own data warehouse and have the flexibility to use best-in-class tools like BigQuery, Looker, and dbt to store and analyze the data. Littledata provides a raw event data feed, directly sourced from Shopify’s servers to power just such a modern data stack -- and gives analysts the flexibility to build their own data models. Littledata is excited to work with brands using Commence Components (including headless stores), but we think Shopify will need to lean on its partner network to provide the breadth of functionality, especially in data analysis, that enterprise brands require. For now brands on our Littledata Plus plans are skeptical about the initial release of Commerce Components, just as they have been about Shopify's new Web Pixel and overall Shopify Theme changes.

Have you mapped out a data plan for 2023 yet? If you’re selling on a major DTC platform like Shopify or BigCommerce, GA4 is probably on your mind. With the sunsetting of Universal Analytics (GA3 or the “old version” of Google Analytics) on the horizon, it’s time to get going with event-based tracking. Many brands have been procrastinating about setting up GA4 – or, worse, only setting it up halfway so that browsing behavior is tracked but revenue and conversions are missing. But can you blame them? Shopify isn’t planning to release native GA4 integration until March 2023 at the earliest (and nobody’s expecting it to work well for serious DTC brands) BigCommerce released a beta version of their GA4 integration in November, but it’s extremely minimal, tracking only begin_checkout and purchase events Manual setup is costly and confusing (and has to be maintained every time you change your site or checkout flow) GA4 revenue tracking should be your top priority, but there’s a lot of confusion around GA4, made worse by Shopify apps that claim to offer GA4 integration but only offer client-side tracking. It shouldn’t be so complicated. At Littledata we’ve already fixed GA4 tracking for hundreds of top DTC brands. In this post I’ll show you how to check if you’ve set up GA4 correctly to capture orders and revenue, and how to start tracking ecommerce conversions today in the most secure and reliable way possible. Follow this guide to GA4 and you’ll be on your way to ecommerce data tracking in no time. We’ll look at how to get from this: To this: How to check if you’re tracking GA4 revenue and conversions After creating a new GA4 property and following the setup assistant to create a new data stream, you might have noticed that you’re instructed to copy and paste the Google tag (gtag.js) script on every page of your ecommerce site. Once you’ve added the Google tag to your site and linked your GA4 property, everything will just start tracking automatically, right? Wrong. With the basic script all you get are engagement events such as page_view, session_start, view_search_result, and click. Obviously these “automatic events” are super important, but they don’t tell you what happens post-click. Here’s how to check if your GA4 ecommerce setup is working or not. 1. Check your Acquisition reporting in GA4 There are two places to look to see if you’re capturing ecommerce conversions. First, the Acquisition reports. You’ll see user and traffic engagement details grouped by channel, but no conversion or revenue data exists. You’re seeing which organic or paid channels are bringing visitors to your store, but you can’t tell if you’re generating any revenue from these visitors. GA4 revenue reporting not showing is one of the most asked questions by merchants and performance marketers. 2. Check your Engagement and Monetization reporting in GA4Taking a step further, check your Engagement and Monetization reports. Do you see GA4 reporting data about cart updates, interactions with the checkout flow, or any purchase or revenue data? If revenue is missing in GA4’s monetization overview, you need to start tracking ecommerce activity ASAP. Otherwise, you’ll end up with a lot of data points that lead nowhere and you will not have an accurate understanding of your ecommerce store’s performance.  [tip] Use our complementary instant order checker for GA4 to check your property [/tip] How to track ecommerce conversions and revenue in GA4 After landing on your store, online shoppers interact with collections and products before adding items to their carts and going through the checkout process. These web interactions must be captured as events and linked with customers and marketing data in GA4 to get a complete picture of your business. We have looked at what data can be missing from your GA4 events and which enhanced ecommerce events you should track. But how can you get all these ecommerce events in GA4?  Google Tag Manager (GTM) has always been the most common tracking method for Universal Analytics, and the setup process can be carried over to GA4. However, for a lean team, the setup process can be quite time-consuming and complex, having to create a Data Layer In Shopify, and then for each event, you must create: Firing Triggers in GTM  Data Layer Variables in GTM Ecommerce Tags in GTM Needless to say, there are quite a few maintenance pitfalls if you're going down this route. Setup is just the beginning. To make matters worse, Shopify is removing GTM from the checkout for Shopify Plus stores (standard Shopify stores never had access). So even if you take the time to add all your own events to tracking visitors before they make a purchase, you’ll no longer be able to track checkout steps (add-to-cart, etc) with GTM. If you want to save time and money while still having confidence in the accuracy of your GA4 data, Littledata is the perfect solution for you. Our proven app is used by over 1500+ brands and can help you track your ecommerce conversions with ease, giving you the reliable data you need to make informed decisions about your business. Littledata’s data layer uses a unique combination of client-side and server-side tracking to ensure accurate, complete ecommerce data in GA4 and any connected data warehouse or reporting destination. Littledata captures complete ecommerce data automatically in GA4 for Shopify and BigCommerce stores. We can break down those events into seven general categories: Marketing channels Browsing behavior Checkout steps Conversions Revenue Recurring orders Upsells Of course, each reporting category has useful data, but brands that really want to scale link it all together to look at revenue and LTV by channel, splitting out first-time purchases from repeat purchases or recurring orders (subscription analytics). As I mentioned earlier, Acquisition reports are some of the most valuable sets of data GA4 offers. They show which of your team’s marketing efforts bring the most results, from traffic through engagement and conversions. The difference between having accurate or questionable ROI data in these reports rests on how the purchase event is tracked. It is useful to have the engagement metrics grouped by channel, but the difference between having accurate or questionable ROI data in these reports rests on how the purchase event is tracked.  Get started with Littledata today so you will have the data you need to scale faster the smart way. We recommend tracking in UA and GA4 “in parallel” as soon as possible.

Six years ago we started Littledata to help brands get more complete data on their customer lifecycle — pushing the advantages of server-side tracking to generate accurate analytics. We’d seen how custom-coded server-side tracking for bigger brands like MADE.com had brought greater trust in marketing attribution and analysis, and we wanted to bring that technological leap to many more direct-to-consumer brands to enable them to be more data-driven. Fast forward six years and many others now believe server-side tracking’s time has come. Leading brands such as Dockers, Grind Coffee and Rothy’s are using Littledata to power their reporting and analysis — and your brand should be too. Here are the top ten reasons why you should swap out your Google Tag Manager (GTM) client-side tracking for a robust server-side tracking implementation. 1. Accurate revenue and orders Revenue and purchase volumes are paramount for any ecommerce store. However, tracking orders via the thank you page is increasingly unreliable. Getting the revenue in your analytics reports to match what your company is actually selling (as recorded in your financial results) is the #1 reason to switch to server-side tracking. Last year I wrote a longer article on why 12% of client-side orders don’t get tracked. In the year that followed, the release of iOS 14 (and beyond), the rise of ad-blockers, and web visitors increasingly opting out of tracking have all helped increase that number of lost revenue tracking to nearly 20% for some stores. Yes, you read that right — reliance on an outdated tracking script on your thank you page means you might be missing visibility on $1 in every $5 spent on your store. How would plugging that hole change your whole company’s attitude to analytics? 2. Data security Can you take the risk that important revenue and customer data is leaking out via the end user’s browser? Yes, data sent to the user’s browser and tracking pings back to Google Analytics are both encrypted. But data on the web page and the network requests can be intercepted by other scripts running via Google Tag Manager or a Chrome extension. In other words, data in the browser is inherently insecure. By contrast, if the checkout steps and orders are tracked server-side—directly from your ecommerce platform to your analytics platform—there is no risk of this kind of snooping. You can even add commercially-sensitive stock levels or gross margins to order events, increasing the usefulness of analysis without compromising data security. [tip]Related reading: Do you need to process customer data in-house to be truly data secure?[/tip] 3. Consistent data for reporting Have you ever dived into your website data and been utterly confused by which event names you need to report on? Things like typos, random capitalization (is it “Add To Cart” or “add TO Cart”?), and missing events caused by whoever set up Google Tag Manager. These make reporting a lot harder than it should be. Littledata’s server-side tracking follows a dependable and consistent data schema; you can be sure add_to_cart is always add_to_cart. This is especially important when you are working across multiple country stores (or multiple brands) and want to be sure you are comparing apples with apples when it comes to ecommerce conversion rate, checkout completion rate, or other key metrics. 4. Custom dimensions on your buyers Your ecommerce platform already gives you a fair amount of information about potential customers (more than you can safely expose on the web page as we just found out in the last point). With server-side tracking, however, you can enrich event data to get even more dimensions for reporting. These help you paint an even better picture of your customer, while still maintaining their privacy Littledata sends a range of user-scoped custom dimensions with checkout and order events. These reporting dimensions can be used to build cohorts based on when the customer last ordered, analyze customer lifetime value, or link a customer’s web activity in Google Analytics with their customer history in Shopify. That information goes a long way when you’re determining your best buyers and creating marketing campaigns to reach them. [tip]Learn how to use custom dimensions in Google Analytics for subscription ecommerce.[/tip] 5. Better sales attribution back to a specific Facebook or Instagram user Facebook Ads’ ability to attribute your sales to their mobile ads has been so eroded by iOS 14 that they strongly encourage all brands to use their new server-side Facebook Conversions API (CAPI). CAPI allows you to not only link an order to a browser session but also a range of customer identifiers. These identifiers improve your Event Match Quality score and better attribute your campaign spend—even between devices and browser sessions. Brands using CAPI have seen up to 30% improvement in their Return On Advertising Spend— so even just one use case justifies any cost in switching to server-side tracking. [tip] You can now get started with Facebook Ads via Conversions API by connecting your store using Littledata [/tip] 6. Post-purchase events—refunds, cancellations, and fulfillment Some events in a customer’s journey from discovery to delivery don’t happen in the customer’s internet browser. So, they can’t be tracked at all without server-side events. A few good examples of this are order refunds and order cancellations (say due to fraud or delivery stock problems). If you exclude these from your analytics, you may have an overly rosy view of some acquisition channels with a high return or fraud rate. Your marketing team might also want to trigger email notifications or other campaigns when a customer’s order is out for delivery or delivered. Server-side fulfillment events can achieve this too, triggered by any shipping app that integrates with your ecommerce platform. 7. Seamless tracking—even with a rapidly changing storefront Before founding Littledata I advised brands like MADE.com and Figleaves.com on analytics setup. Even these larger, well-staffed brands found it really hard to keep their client-side tagging in sync with their store theme updates. Every time a page layout was changed, inevitably one of the GTM triggers (which expected on-page buttons or images in a particular HTML format) would break. The interruption in tracking wouldn’t be noticed until someone went to check the reports at the end of the month—and the accuracy damage to the data was done. Server-side tracking removes this risk. Whatever changes you make to the layout of the page, the data layer is maintained on a server and cannot be tampered with. 8. Avoiding custom scripts on the checkout Tracking checkout steps is important as part of the customer journey. But adding custom JavaScript to the checkout is a security risk for any store (see point 2). It would allow a hacker to scrape credit card information from the customer or redirect them to a fake checkout where the payment is intercepted. With Shopify, it’s just not possible to add scripts to the checkout. Although Shopify Plus has a workaround with the ability to edit a checkout.liquid file, it’s likely at some point Shopify will close the loophole for the reputation of the platform. This is an area where again server-side tracking provides a better way. As the customer steps through the checkout steps, their progress can be shadowed on the server and the same checkout funnel can be visualized without any scripts on the checkout itself. 9. Faster page loading Many of the brands we work with support many data destinations. When using client-side tracking, this requires multiple tracking scripts to be loaded and multiple network requests to be made to track a single event. All of this extra browser activity slows down the user experience, especially on mobile devices. A slower page load, as measured by Google’s PageSpeed Insights tool, reduces the SEO of your landing pages. So, although the overall contribution of client-side tracking on your page load speed might be minimal (~5%), removing the tracking has a doubly positive impact on both traffic and conversion rate. The more reporting tools or marketing platforms your store is sharing data with, the greater the impact you’ll see from switching to server-side. 10. It’s the future! For all of these reasons, I believe in 5 years' time there will only be server-side tracking. The browser limitations, data security risks, and need for accuracy are only going to increase. Meta might be ahead of the curve with Facebook Conversions API but I would expect other marketing platforms to follow suit once they see its success. So why wait until your data is really broken? Get started with a no-code server-side implementation today. [subscribe]

Cloud data warehouses offer a way for ecommerce companies to scale as the size of their data increases, promoting unlimited storage space, cost optimization and analytics horsepower. But where do you start? Are there no-code solutions that are also best-in-class? Segment is an increasingly popular way to connect website data to a data warehouse such as AWS Redshift. In this guide we'll take a close look at exactly how this works, and the pros and cons for your longterm company data needs. Using Segment to connect Shopify to AWS Redshift What is Segment? Segment is a powerful Customer Data Platform (CDP) solution, but it's also much more than that. Segment provides businesses the ability to organize customer activity events from various platforms to a broad range of destinations, One of those destinations can be a data warehouse - an ecosystem that serves as the centralized source of data collection. This includes the big three: BigQuery, Redshift, and Snowflake. The technology focuses on the tasks of collection, storage, and management of business data - with the purpose of turning operational data into meaningful information. For any company looking to harness the value of the activities gathered inside their CDP, it’s a no-brainer that bringing a data warehouse into the mix is the next best step. Amazon Web Services (AWS) and its data warehouse offering, Redshift, remains the market leader in this space because of its compatibility with data integration pipelines and analytics tools. One of your Segment destinations can be a data warehouse such as AWS Redshift For ecommerce sites this can be difficult to implement manually (not to mention maintenance time, costs and complexity!), but Littledata's Shopify source for Segment does this automatically. With Littledata’s capabilities, you have the ability to direct, track, and identify custom events across all critical customer activities, including across your Shopify website, whether that's a simple Shopify instance, a headless Shopify setup or multiple country stores doing international sales. Coupling that with Segment’s unified CDP takes powerful data to activation, and the ability to direct platform data to marketing channels for increased engagement, conversion and retention. Whether you want to use a data warehouse for deep analysis, audience building or real-time recommendations, Littledata + Segment + Redshift is a proven solution for Shopify stores. Setting up your Redshift data warehouse Segment's documentation portal gives a step-by-step breakdown of provisioning a Redshift cluster, configuring a database user, securing data ingestion, and providing a path to data collection into your Redshift instance. Breaking the process down in digestible chunks, here are the necessary steps to go from data to data warehouse: Choose the best instance for your needs: Dense vs. Compute StorageProvision a new Redshift Cluster: 5 simple steps from start to finishCreate a database user: Creating a user to manage your instanceConnect Redshift to Segment: Select sources, credentials, and go Redshift allows users to start small and scale up on-demand as needs grow Collecting events in Segment Event tracking is a critical part of the data collection process. Creating a plan tracking plan associated with measurable business outcomes, such as acquiring new customers, increasing retention and activating new leads, and mapping those outcomes to business goals, is an important step in the data journey. Understanding this relationship will provide guidance to the relevant events or actions that must be configured to successfully track. With Littledata's automated solution, you can avoid the blocking-and-tackling of configuring the best-in-class event strategies surrounding (client side) device-mode and (server side) cloud-mode events:  Device-Mode events include Cart Viewed, Page Viewed, Product Clicked, Product Image Clicked, Product List Viewed, Product Shared, Product Viewed, Products Searched, Registration Viewed, Thank you Page Viewed Cloud-Mode events include Checkout Started, Checkout Step Completed, Coupon Applied, Customer Created, Customer Enabled, Fulfillment Created, Fulfillment Updated, Ordered Cancelled, Order Completed, Order Refunded, POS Order Placed, Payment Failure, Payment Info Entered, Product Added, Product Removed To streamline the process for ecommerce sites, Littledata's tracking script automatically sends events to Segment through its analytics.js library, making it easy to collect all the critical event activities associated with a customer’s store journey - from browsing behavior through the checkout funnel and repeat purchases (including recurring billing for stores selling by subscription). Additionally, from every event where this is an identifiable customer (from both device-mode and cloud-mode), Littledata will send an Identify call - the identification of a customer when the customer logs into your storefront, a last step of the checkout process, with the order, and also after a purchase with a customer update. With Littledata’s streamlined modeling, data can be accurately represented and pushed to downstream destinations, such as marketing activation channels and data warehouses.  [subscribe heading="Littledata connects Shopify to Segment and your data warehouse" button_text="Book a demo" button_link="https://www.littledata.io/app/enterprise"] Connecting Segment data to your data warehouse Now that your Redshift instance is up and running, the next step is to connect to Segment and start collecting data into your data warehouse. There are two ways to complete this step - one, through Segment’s native migration, and the other, utilizing no-code data pipeline tools (recommended). Whichever process you choose, you will have the opportunity to push data out of Segment into your data warehouse environment and start utilizing it across your business. Option 1: Segment’s native migration  As mentioned, Redshift data warehouse is one of the many destinations that Segment can send data to. You can directly connect to Redshift from within Segment to stream event data.  Segment’s catalog provides direct integration to best-in-class data warehouses Essentially, it’s as simple as: Login to your Segment App and proceed to the Catalog sectionIn the top menu, choose DestinationsSelect Redshift in the Storage Destinations list After configuring your user permissions and selecting the data sources you would like to sync, you’ll enter in your credentials and connect to your data warehouse. Voila! Data will now be continuously replicated into your Redshift instance based on your plan: Free: Data refreshed (synced) 1x per dayTeam: Data refreshed (synced) 2x per dayBusiness: Data refreshed (synced) as fast as hourly As for historical data, all plans will allow loading up to 2 months of your historical data, with the Business plan allowing for full historical backfills. Since Segment provides an environment to support many, it requires a premium plan to collect complete history and sync data real-time. Segment’s infrastructure is suitable for instantaneous data collection to downstream points Option 2: Leverage data pipeline services The second way to get data out of Segment into your data warehouse is through data pipeline platforms. Data pipeline or ETL (Extract, Transform, Load) platforms, provide prebuilt integrations to over 100+ enterprise software sources, and focus on a maintenance-free structure where replica data is automatically transformed, standardized, and normalized on collection. The automated adjustment to schema and API changes, allows business users to streamline developer tasks in a no-coding required environment. Companies like Stitchdata ("Stitch") and Fivetran, leaders in the space, provide frictionless, subscription-based memberships that allow integrating data to data warehouse destinations convenient for any business size. ETL platforms streamline data from end-to-end and require limited technical lift To set up, simply sign into your console, click on the Segment icon in the available integrations, and enable. You will automatically be pushed into the Segment tool to confirm authorization and (another voila!) data will begin replicating.  Stitchdata’s user-friendly interface for connecting platforms to destinations The benefits of cloud-ETL platforms, not only include their out-of-box integrations, but the list of features included to help visualize, maintain, and support ongoing data integration tasks: Over 100+ database and SaaS platform integrationsIn-app support including email alert monitoring and support SLAs14-day free trial to kick-off and vet the platform prior to fully onboardingSOC2 security compliant, encrypted communication and an AWS cloud backed environment Ecommerce data With the appropriate event tracking configured at data collection by Littledata, your data can be properly analyzed for ecommerce store performance. The downstream output can be properly displayed by: Customer behavior before, during and after purchaseOrder performance relative to average order value, add-to-carts, average order size, and cart abandonmentShopper engagement including product views and purchasesCoupon and discounting activitiesCustomer checkout funnel and stage of drop-offConversion rate and lifetime value With the emphasis on accuracy completed at the inception data collection stage, the ability to produce the above areas of performance becomes that much more straightforward. This means spending more time analyzing and visualizing data, then transforming and modeling data for analytical use.  Empowering your data Once your data is available in your data warehouse, replicating frequently, and building history, it’s time to utilize it. That can come in a number of various opportunities, depending on your business needs. Most notably, companies will focus on transforming data into actionable blocks and pushing into business intelligence (BI) tools.  Transformation To properly stitch event data together - say in the case to tie all interactions by a site visitor to achieve multi-channel attribution - companies can leverage existing packages that transform, marry and enrich data points. These packages - or prebuilt libraries - produce powerful results that end up restructuring data from their raw state to analysis-ready. Fishtown Analytics’ product dbt does just that, performing user-stitching, simplifying data structures, and speeding up data modeling to use instantly within reporting, analytics, or machine learning applications.  Leveraging transformation can streamline data modeling and enrich data for analytical-use BI Tools Companies usually begin the conversation here, “I’d like to see a dashboard like X” or “Can we get a report showing Y?”. In fact, what they are looking for is a way to properly view data in digestible, actionable views. BI (Business Intelligence) tools do just that - whether it’s through data visualizations (dashboards), self-service analytics, or prebuilt reporting. Enterprise BI and SaaS tools like Looker and Tableau (like outlined in the table below) create the speedy path to data viewing. They can be simply connected to a data warehouse and publish dynamic views for instant performance tracking. Data can be presented in dashboards across many dynamic charts, tables, and graphs BI Tools Breakdown CategoryVendorsBreakdownMarket LeadersTableau, Looker, PowerBI, Mode, DatabricksEnterprise tier platforms with extended featuresRisersDomo, Klipfolio, Kissmetrics, SigmaSaaS-oriented products with cost on user and dashboard usePrebuiltGlew, Daasity, Dashthis, Rubix3Ecommerce focused with prebuilt visualsOpenDataStudio, MetabaseOpen-source/no-cost platforms So a straightforward reporting and visualization solution with the setup we've described in this article, would be to connect Shopify to Segment, then Segment to Redshift, then Redshift to Tableau. Learn more about how to connect BI tools to your Shopify data in Segment, whether as a Segment destination using alias calls or a dynamic view pulling from data in your warehouse. Another option is connecting reporting tools directly to Google Analytics data in parallel with your Redshift setup (for example, use Tableau on top of GA for marketing analysis and Looker on top of Redshift for deeper analysis and predictive analytics). Building for the future Companies that put an emphasis on building the foundational components of data ingestion, management and analytics early on see many benefits. Primarily, you are able to increase your ability to measure and understand your business properly. Data warehouses provide an opportunity to collect all of your store, site, customer, marketing, other relational data - all in one place. This creates a centralized view of your business and gives an upper hand to companies looking to take a data-driven approach to growth. Cloud tools and no-code options remove the need for technical resources, freeing up dollars that can go elsewhere without sacrificing the ability to use and analyze data. No matter the size of your business, taking data seriously is the first step to empowering your business for the future. Data warehouses are no longer the property of only mega enterprises. Want to build a modern ecommerce data stack but not sure where to start? Get in touch for a free consultation. [subscribe heading="Littledata connects Shopify to Segment and your data warehouse" button_text="Book a demo" button_link="https://www.littledata.io/app/enterprise"]