Best Practices for Data Collection in Ecommerce

A $50M+ ecom client recently asked me what a "great data setup" looks like. My response: It depends. But, for these guys (omni-channel, multiple countries, high price point)... Data Collection & Storage - clean and accurate data layer - server-side tracking for all major platforms - critical conversions and engagements tracked (web/app/offline) Governance - standardized naming conventions (metrics, UTMs, campaigns) - accurate data relative to source systems (oms, erp) - centralized user opt-in Measurement - attribution modeling (in-channel) - testing culture across all channels - incrementality and experiment-led measurement (holdouts, MMT, MMM) - qualitative and csat data collected and funneled back to business (merchants, ops) Customer & First-Party Data - single customer record across systems (online/offline, loyalty, cs) - customer data secure and governed - id resolution (cdp/cdp-lite/identity-graph) - Customer-level metrics drive business (LTV, CAC) - centralized audiences and segments Data Storage & Enablement - all data stored in same place (eg. cloud warehouse) - automated data pipelines to blend and clean data - up-to-date data dictionaries and schemas BI/reporting - data available when you need it (daily, weekly, real-time) - specific data by team (exec, departments, analysts) - ad-hoc/query access for data teams - no unnecessary reports - warehouse and pipelines optimized for cost and performance Trust & Team - your team trusts the data without hesitation - your team uses the data (forecasting, planning, optimization) - your team understands the data - KPIs mapped to owners (teams) Not all of this applies to every business, especially smaller ones. What else would you add to this list? #ecommerceanalytics #measure #dataanalytics

Many marketing teams rely on batch-processed data and third-party updates that are often hours or even days old. This lag creates several pain points: ❌ Delayed Insights: Marketers can't react quickly to changing trends or customer behavior. ❌ Limited Personalization: Personalizing customer experiences in real-time is impossible with stale data. ❌ Inefficient Campaigns: Marketing campaigns can't be dynamically adjusted based on real-time performance. ❌ Reliance on Third-Party Tools: Many marketing platforms have their own data processing limitations and update schedules, restricting flexibility and control. For example, some platforms may only update campaign performance data once a day, preventing marketers from making timely adjustments to their campaigns. Or, they might have limited capabilities for segmenting audiences based on real-time behavior. Some third-party tools might also impose restrictions on the volume of data that can be processed or the frequency of updates, creating bottlenecks for marketing teams. Did you know that you can leverage Google's Dataflow for real-time marketing insights? How Dataflow Solves the Problem: 1. Real-Time Data Ingestion: Dataflow can collect data from diverse sources, including: - Website and App Analytics: Capture user interactions, clicks, page views, etc. - CRM Systems: Integrate with customer relationship management (CRM) systems to get up-to-date customer data. - Marketing Automation Platforms: Pull data from platforms like Marketo or HubSpot. - Social Media Feeds: Capture social media mentions, trends, and sentiment. - E-commerce Platforms: Capture purchase data, browsing behavior, and other e-commerce events. 2. Data Unification and Enrichment: Dataflow uses Apache Beam to process the ingested data: - Unification: Combine data from different sources into a unified view. - Enrichment: Add contextual information, such as demographics, purchase history, or website activity. 3. Real-Time Analysis and Activation: - Vertex AI Integration: Use Dataflow to send enriched data to Vertex AI for real-time ML inference. This allows you to build predictive models for things like customer churn, conversion probability, or personalized product recommendations. - Marketing Platform Integration: Route the transformed data to your marketing platforms for immediate action. This enables real-time campaign optimization, personalized messaging, and targeted advertising. By processing data in real time, Dataflow empowers marketing teams to move beyond the limitations of stale data and third-party tools, unlocking a new level of agility, personalization, and effectiveness. Do you use Dataflow? Let me know in the comments and be sure to follow me for more daily data content. -- ☁️👨💻 👍 Like 🔗 share 💬 comment 👉 follow #dataengineering #dataanalytics #GCP #GoogleCloud #GoogleCloudPlatform #Dataflow

I've worked with more than 750+ eComm brands on their data connection between Shopify and Meta/Facebook. There's tons of problems I've found, but these are the top 5 data & tracking issues brands have (and don't even realize). ❌ Landing Pages drop tracking code - there's a ton of excellent third-party landing page platforms out there. Most people don't realize that they drop tracking code and lead to data gaps. (You need custom code that properly passes tracking code from the Landing page to the Shopify Checkout) ❌ Click data missing from Checkout - lots of customers need multiple web sessions to go from ad click to product purchases. Most people don't realize this leads to dropped click data and purchases that look like direct traffic (but should be attributed to ad clicks). (You need code that matches sessions and stitches the data together to ensure click data is included with all Purchase events, when available) ❌ Over-counting from non-web orders - A lot of brands have Shop orders, subscription renewals, and offline/draft orders get processed through the Shopify checkout. A basic CAPI connection will send Purchase events for these orders, which leads to misattribution and over-counting. (You need code that is smart enough to see the order source and re-route non-web orders to separate events) ❌ Light payloads with low EMQ - Most brands and most developers don't realize just how much data you can send in any given payload. If your data payloads are missing external id, FBP, and phone info, it leads to low EMQ scores and limits the performance of your ads. (You need an advanced CAPI connection that sends the upper limit of all data, ensuring maximum data coverage) ❌ Data volume too low - Many brands fail to hit the minimum volume of 50 conversions per ad set per week. Under this threshold, Meta simply isn't getting enough data to exit the learning phase and will optimize to clicks instead of conversions. (You need to either increase spend or consolidate your campaigns to ensure you have 50+ weekly conversions) --- If you're using the free/native Shopify CAPI connection, you likely have 3 or more of these issues. Even brands using paid CAPI solutions usually have 1 or more of these issues. If you need help assessing and/or fixing your data and tracking setup, comment below or shoot me a DM.