Mark Giblin - London, England, United Kingdom | Professional Profile | LinkedIn (original) (raw)

Ecom Brands: 10 Common Data Issues We Come Across 1/ Attribution Missteps Relying on outdated MTA tools or last/first touch attribution instead of contribution and incrementality models. This can lead to wasted ad spend. Start with simple geolift (MMT) and hold-out tests to measure incrementality. Understand that not all channels can be tracked w/ click-based tools. 2/ Pixel Problems Incorrect, duplicative, or missing ad pixels lead to inaccurate data collection. Audit your pixels monthly for accuracy. GA, Meta, Google, TikTok, Affiliate platforms, etc. 3/ Server-side Tracking Not using server-side tracking, resulting in signal loss and hindering ad optimization. You can be losing 20-30% of your conversions w/o proper SS setup. 4/ Excel Hell & Reporting Inefficiencies Manual and infrequent updating of reports, often trapped in spreadsheets. Automate common reports. Even basic KPIs updated daily for the team will be a big win. 5/ Product Catalogs are Stale Bloated or incorrect product feeds with outdated information and mismatched pixel data. Audit your catalogs frequently. Segment your feeds. Setup alerts. Use a centralized feed source or vendor to streamline updates. 6/ UTM Inconsistencies Poor campaign and UTM naming standards make performance breakdowns difficult. Create a standards document and ensure that all your marketers and agencies use it religiously. 7/ Customer Data Fragmentation Lack of a single source of customer truth, with customer and 1P data spread across various systems. Start to ingest your 1P data into a data warehouse. You should own and know how to govern your customer data. 8/ KPI Blindness Limited understanding of critical metrics like New Customers, LTV, MER, Contribution Margin, CAC... Knowing which to use and when. Understanding driver trees and ensuring your team knows the key metrics for their channel. 9/ Inaccurate Forecast Failures Poorly constructed forecasts or a complete lack of forward-looking projections. Even a basic forecast, shared across the team will help. 10/ Data Silos Isolated data and poor transparency across different teams and departments. Centralize datasets, KPIs and reports where possible. Better communication and data access. The good news is that fixing these issues is 25% technical cleanup and 75% education/process improvement. What other data challenges do you face in your e-commerce operations? #ecommerceanalytics #dtcmarketing #measure #ecommerce