You're navigating data attribution discrepancies in marketing channels. How do you ensure accurate reporting?

Sometimes there simply will be discrepancies, due to issues like blocking of tracking in different tools, different models for data and definition of metrics, different time for updating data. Of course, it's important to ensure as far as possible that each tool is collecting data accurately and completely. But since we know that often we're not seeing true numbers anyway (because we can't track all visitors), the important thing is to look for the trends and insights that the data can provide.

It should be expected that different attribution models will yield slightly different results. Regression model results will not align perfectly with machine learning decision trees because they use different concepts. But what I find fascinating is that while there might be some discrepancies, you will always find a lot of similarities / consistencies in results. That's why ensemble models (average of multiple models) are very popular. Focus your storytelling on consistencies across models. If all models tell you TV is not generating enough revenue to cover costs, focus on that.

When facing data attribution discrepancies in marketing channels, I focus on deploying robust cross-validation techniques and regular audits to maintain accuracy. My approach includes integrating advanced machine learning models that predict and reconcile discrepancies by learning from historical data. Additionally, ensuring that all data sources are properly tagged and consistently monitored helps in identifying and rectifying any discrepancies early. This proactive stance not only enhances reporting accuracy but also builds trust in the data-driven insights provided to stakeholders.