Here's a few ways you could address attribution discrepancies and to maintain client trust: 1. Openly discuss the discrepancies and their potential causes. 2. Use multiple models to show how different approaches yield varied insights. 3. Highlight consistent patterns across models and provide context using benchmarks or historical data. 4. Recommend a holistic approach. Emphasize the importance of considering multiple data points or validating results with MMM or experiment like lift test. Remember: Trust is built on honesty and expertise. By addressing discrepancies head-on, you'll strengthen client relationships, improve your understanding of their business, and showcase your analytical prowess w.r.t to their needs.

Start with transparency: Openly acknowledge the discrepancies and explain that they're actually common in attribution modeling due to: Different tracking methodologies across platforms Varying attribution windows and rules Cross-device tracking limitations Data sampling differences Present data comparisons systematically: Show a clear comparison of: Results across different models Historical trends and patterns The size and nature of discrepancies Root causes when identifiable Provide business context: Connect findings to specific business KPIs Demonstrate how even with discrepancies, the insights can inform strategy Share case studies where similar discrepancies led to valuable insights Establish consistent reporting processes:

Such discrepancies are majorly because : 1. Intuition 2. Bias Attribution models are challenged when a certain intution/bias are compared to affix the model outputs. While this should be other way round. These should be used as validations of data backed model output. Further deepdive to understand the gap.