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"]

Rule-based audiences are customer groups or segments derived by customer activities. It sounds simple, but rule-based audiences can be a game changer -- and too many DTC brands miss out on the basics of this powerful type of customer segmentation. In ecommerce, rule-based audiences can be made using transactional activities (checkout date, coupon applied, etc.), marketing actions (email opened, promotion entered, etc.) or even product details (eg. type of product, color or type purchased). Ecommerce companies use the intersection of these events to group customers for the purpose of reporting, remarketing, targeting, and other customer enrichment activities. But one size doesn't fit all. Let's take a look at the top rule-based audiences and how they are used in ecommerce marketing. Benefits of rules based segmentation There are a number of benefits to deploying rules based audience recipes in your business. Whether you are a small-to-medium sized business, fast-growing startup, or have been around the block for some time like Littledata customers Dr Squatch and Rothy's, audience recipes are the building blocks for broader, innovative ways to segment your customers. Rule-based audiences can help you increase customer retention while improving product visibility in the crowded ecommerce marketplace Powerful tools like the Adobe Experience Cloud have highlighted rules-based personalization and audience building as a core part of their feature set. As they put it, "With rules-based personalization, you’re in the optimization driver’s seat." We agree, but with traditional enterprise tools that type of personalization can get really expensive. The good news is that brands using a modern data stack don't necessarily need to shell out for Adobe. Rule-based audiences can now be used by any ecommerce store, no matter how big or small. Here are some of the key benefits: Increase personalization through tailor-made product marketing Improve existing products and/or services Increase upgrades and product upselling Enhance profitability through the targeting of high-value customers Increase retention with automation and buyer stage recognition Further marketing reach of customer types for remarketing, targeting and look-a-like audiences Enhanced visibility and reporting of customer cohorts for tracking new acquisition and customer lifetime value Rule-based segmentation results in a hyper-personalized approach to directly influencing your customers’ experience. The ability to be attentive during each stage of the customer’s lifecycle allows for a better understanding of what drives good and bad experiences.  Recipes for the top 7 rule-based audiences There are tons of different audiences you can build, but 7 always come up for successful DTC brands. In our case, we call them recipes, as they are the right number of ingredients to profile your customer base. X and Y in these examples will depend on your particular business: what you sell, how you sell it, and how often it makes sense for an ideal customer to come back and make a purchase or referral. Audience NameRecipe⭐️ First Time PurchasersCustomers who have made their first purchase in the last [X] number of days⭐️ Repeat PurchasersCustomers who have made at least 2+ purchases in the last [X] number of days⭐️ High SpendersCustomers who have made a purchase with order value greater than [$Y] in the last [X] number of daysAbandoned CheckoutsSite visitors that have added items to their shopping cart, but have not purchased in the last [X] number of daysBargain HuntersSegment of customers that have applied a promotional code on more than 1 purchase in the last [X] number of daysRecent BuyersCustomers who have made a purchase in the last [X] number of days⭐️ Inactive CustomersCustomers who have not made a purchase in the last [X] number of months*Additional segments include Loyal, Cancelled Customers, Location-based, Personalization (age, gender, preferences, income) Three audiences you should build today, with downstream activation examples All of these types of segmentation are potentially useful, even transformational, to your business. So where should you start? Today I will focus on the four most common and effective audience recipes that can generate immediate value to your store’s ability to identify, engage and enrich your customers’ experience. As highlighted above, those are: First-time purchasers Inactive customers High spenders Repeat buyers To make things even clearer, we'll even combine High spenders and Repeat buyers into a high-LTV segment: your best possible customers, big spenders who are also loyal to your brand. 1. First-time purchasers First Time Purchasers are a good starting point for audience segments. The ability to identify these customers early will pay big dividends into maturing their relationship with your brand and products. Also, first-time customers are always the most likely to engage with your content (for example, opening welcome emails or sharing on social media), which ends up increasing the return on your investment and the potential for longer life cycles.  How to Create a Welcome Email Template via Omnisend How to identify? Utilizing Littledata's order event tracking from your Shopify store, you can identify Order Completed in the last [X] number of days with a Customer Created event in the same time frame.  How to activate? A great opportunity is through personalized welcome emails. By connecting to your ESP (eg. Klaviyo, MailChimp, Iterable) and building a customized message to all first time customers can be the first step to long-standing customer relationships.  2. Inactive customers Inactive Customers are a great win-back opportunity to gain customers back that have been inactive (or not purchasing) in a particular period of time. When a customer has been deemed inactive it’s too late to start formulating a strategy on returning them to your active customer pool. Instead building a strategy to identify, entice, and track appropriately is a must in any customer-focused business.  Drive Repeat Purchases To Your Shopify Store With Automated Emails via Privy How to identify? Utilizing Littledata's order event tracking from your Shopify store, you can identify customers who have (at one-point) had an Order Completed event and with no purchase activities in the last [X] number of days. How to activate? Winback or revive email campaigns catered towards time-sensitive discounts, hyper-personalization (reference specific product categories a customer engaged or purchased in the past), summaries of product improvements, and membership benefits are effective strategies. Utilizing your current ESP, SMS, or retargeting platform alongside these customer groups can push once-active customers to return. 3. Repeat buyers & high spenders Repeat Buyers & High Spenders are the backbone of your business. As the tenured marketing freelancer would attest: “It’s easier to keep a happy customer than to find a new one”. Building customer loyalty requires a business to deliver on what is promised and to do so with their highest-value customers in the right channels and messaging.  How to identify? Utilizing Littledata's order event tracking from your Shopify store, you can identify customers that have completed Order Completed events and total purchase count, purchase total, and revenue collected, during a [x] period of time and [x] number of times. Google Analytics users can also export data based on specific custom dimensions for LTV: Littledata – Lifetime Revenue Littledata – Purchase Count Littledata – Shopify Customer ID How to activate? There are several options here, including email and SMS (texting). SMS is a great tool to continuously engage with your customers. Invitations for users to sign-up for a loyalty program to provide exclusive offers or to release product updates can come simply through a users’ most desirable medium - their phone. With SMS boasting a +95% open rate, it's the most effective way to have a two-way connection with your customer and showcase value-added services.  For Littledata's Shopify Plus customers, the most popular platforms for this type of engagement are Yotpo and Loyalty Lion. Technology for activating rules based segmentation Leveraging modern technology furthers the ability to do so repeatedly and with best-in-class platforms. Here are two examples of leaders in that space: Segment (sometimes called Segment.com) and Hightouch. Hightouch Hightouch syncs the data from your data warehouse to the tools your business relies on. It’s called operational analytics and it allows customers to leverage their existing technology (ie. your data warehouse) to pipe customer data to downstream platforms for activation, engagement, and other business activities. Since Littledata's no-code event collection is captured downstream in your Google Analytics platform, customers can leverage that same data when it is stored in their data warehouse. Modeled inside the platform with out-of-box SQL logic, segments can be then pushed automatically (and scheduled) to deliver on the intended goals.  In fact, that's one of Hightouch's taglines: No scripts. No APIs. Just SQL. Segment Segment is a customer data platform (CDP) that integrates cohesively with Littledata's no-code event collection. Segment allows customers to integrate data from a catalog of sources (including the Shopify source, maintained by Littledata) and activate to destinations for customer engagement, activation and reporting. Inside the platform there are features that allow customers to create personas or audience segments, deploy functions, and build out layers of automation to seamlessly leverage their platforms’ source data. [tip]See what's new in Littledata's Shopify source for Segment, including more consistent product properties and enhanced Personas matching [/tip] Littledata Littledata is designed for the modern stack, whether you're using just a couple of tools such as Google Analytics and Data Studio or a whole modern data pipeline (eg. Segment, Fivetran and Redshift). If you're using a Shopify or BigCommerce checkout, you can use Littledata's analytics connectors to capture complete sales and marketing data and send it downstream. It's the easiest way to ingest the data you need to create enriched audience personas, and the only way to get 100% accurate ecommerce data automatically with extensive, ongoing development efforts. Not sure which tools you need? Book a demo with our data experts to discuss your analytics plan.