10 reasons to switch to server-side tracking for ecommerce analytics

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 Yes, you read that right — reliance on an outdated tracking script on your thank you page means you might be missing visibility on $1 in every $5 spent on your store.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. 3. 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] 4. 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. 5. 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. 6. 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. 7. 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. 8. 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. 9. 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]

2022-07-23

The Ultimate Guide to connecting Segment to Redshift (and other powerful analytics tools)

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"]

2021-06-02

Try the top-rated Google Analytics app for Shopify stores

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