• Incrementality Analysis

  Introduction to Incrementality Analysis Country Level Metrics When analyzing incrementality data in Polaris, it is important to understand that the hierarchical media mix model (MMM) used to produce the results ensures that metric totals always match up with the app event data that was input into the model at the app level and at the country level, minus some very minor rounding errors. For example, if the app event data pulled from your MMP or uploaded as a custom import reflected a totFeatured

• Incrementality Actionability

  A New Reality: Multiple Sources of Truth When translating incrementality measurement data into action, it is important to consider the nature of decision making with two sources of truth, incrementality and last touch, and the natures of those differing methodologies. For the most part, incrementality metrics can be used exactly how last touch metrics are used both strategically and operationally. Polaris can display incrementality and last touch metrics side by side to make this process muchFeatured

• Hierarchical Blended Metrics

  Allow us to output incrementality metrics at the same granularity as the imported last touch data. Currently, we support 3 levels: channel, campaign, and site (which can be used for anything including ad set, publisher, sub-publisher, or creative). We'll be adding a 4th level dedicated to creative. Through our blending technique, further levels of granularity don’t decrease certainty in the model, which is the main problem with attempting to model deep granularity. The second iteration of BlendFew readers

• Privacy's Impact on Polaris

  Privacy concerns have led to changes in how data is collected and tracked. While these changes affect many advertisers, Polaris is not greatly affected due to its unique data approach. We are in that sense fortunate that none of these privacy changes have any impact on us since we don't rely on last-touch data. However, to achieve greater accuracy and extra granularity in our Blended metrics and to fine-tune our Incrementality data. The Role of Last Touch to Polaris We use last-touch data aFew readers

• Comparing Incrementality and Last Touch

  Comparing incrementality metrics to last touch metrics is an important type of analysis. In many cases, both strategic and operational decisions are still being made based on last touch attribution, so any discrepancy between last touch and incrementality represents a potential inefficiency, sometimes with drastic financial impacts. It is critical to identify these discrepancies and confirm those with the largest impacts through incrementality experiments. Basics To begin, there will very liFew readers