Struggling With Data Overload? This Fixes Your Ecom Analytics — Outi Karppanen | Winning Data-led Strategies for Ecom Growth, Why Prioritize the Right Data for Ecommerce, Common Marketing Data Mistakes, Strategies for Data-driven Marketing (#329)
For ecommerce operators drowning in disparate data, this episode provides a clear framework for data-led growth. Outi Karppanen of Supermetrics outlines how to overcome data overload by prioritizing relevant metrics, centralizing data for a "single source of truth," and leveraging attribution models beyond last-click to drive incremental sales and optimize marketing spend.
Key takeaways
Stop collecting 'everything' or 'nothing' and strategically identify the core data sources (e.g., Google Analytics, Meta, Google Ads, Amazon, CRM) most critical to your specific marketing and sales channels.
Define conversions and metrics consistently across all platforms to ensure you are comparing "apples to apples" and achieve a unified data truth.
Move beyond last-click attribution to data-driven attribution models, and combine them with Marketing Mix Modeling (MMM) or incrementality experiments to understand true incremental sales and optimize campaign performance effectively.
Utilize tools that transform and centralize data from various platforms, mitigating the manual effort of reconciling discrepancies in how different channels define metrics like impressions or campaign names.
In this episode of the eCommerce Coffee Break podcast, we dive into winning data strategies for e-commerce growth. Our guest is Outi Karppanen, the lead marketing analytics strategist at Supermetrics.com. Outi shares her expertise on navigating the increasingly complex marketing analytics landscape, discussing how to make smart choices with data, handle privacy concerns, and optimize marketing efforts. Topics discussed in this episode: Why has the marketing analytics landscape evolve...
Frequently asked about this episode
What does this episode say about attribution modeling?
Stop collecting 'everything' or 'nothing' and strategically identify the core data sources (e.g., Google Analytics, Meta, Google Ads, Amazon, CRM) most critical to your specific marketing and sales channels.
What does this episode say about data strategy?
Define conversions and metrics consistently across all platforms to ensure you are comparing "apples to apples" and achieve a unified data truth.
What does this episode say about marketing analytics?
Move beyond last-click attribution to data-driven attribution models, and combine them with Marketing Mix Modeling (MMM) or incrementality experiments to understand true incremental sales and optimize campaign performance effectively.
What does this episode say about attribution modeling?
Utilize tools that transform and centralize data from various platforms, mitigating the manual effort of reconciling discrepancies in how different channels define metrics like impressions or campaign names.
