Introducing: The Ensemble Model for Clicks
Gone are the days of using only one model to show you how your click channels are performing - click-based attribution at Fospha is now being measured using an Ensemble model, providing the most accurate attribution possible for click-based channels - and an even more accurate input for our impressions modelling!
The algorithm will continue to use a foundation of Google path and position based modelling from GA4, with multiple Fospha machine-learning models to give you an (even) more accurate view of the impact of click-based paid media throughout the funnel.
What are the benefits?
Though there's no such thing as the perfect model, some models are very valuable - especially when working together. An Ensemble model takes advantage of this fact, by running multiple valuable attribution models simultaneously, combining the results of each model together for a more robust and accurate evaluation of your marketing performance.
Think of it like taking 'wisdom from the crowds' - you're consolidating opinions from many experts before taking a decision rather than one.
Which models are included?
Last Click Attribution - Position based attribution which credits 100% of the sale to the last click in the customer journey that is not Direct - GA's standard model
Data-Driven Attribution - Click Frequency based attribution that looks back over the last 4 touchpoints in the customer journey, and assigns credit based on how important each click was in the estimated overall decision to purchase
Tree-Based Machine-Learning attribution that uses clicks to predict subsequent sales. Predictions are continuously improved as it learns from errors until it reaches the most likely scenario to explain the observed sales
Regularized Regression Machine-Learning attribution determines the value of a click by identifying patterns and assigning a weighting to each channel based on their impact on sales, whilst balancing the potential for over-weighting due to outliers
For each of the Machine-Learning models, we're modelling engaged sessions instead of overall sessions, which leads to a more accurate output as there are no bounce sessions being modelled, unlike other attribution providers.
What does an example model output look like?
Each model outputs a different importance score for the clicks for each channel, and we take an average of the outputs to result in the final click-based attributed value - see an example below:
As a reminder, we use the Ensemble model results as the foundational step to establish where click-media should get credit - and where converting clicks have been influenced by prior impressions so that we can re-allocate the credit to the influencing channels such as for any Paid Social, Display, Discovery, and Youtube.