| FEATURE | VALUE | VERSION | FUNCTIONS |
|---|
| Compare the performance of different attribution models | - Compare the performance of last touch, first touch, linear touch, Markov model, Shapley value, and logistic regression in a traffic allocation problem based on real customer journeys
| Beta phase | compare_models |
| Hidden Touch Attribution model | - Perform attribution when customer journeys are not available overcoming the main limitations of a classical Media-mix model
| Beta phase | hta_model |
| Next best action with Markov model | - Guide customers along journeys to maximize the conversion probability
| ≥ 3.4 | next_best_action |
| Buget allocation with Markov model | - Improve your budget allocation when customer journeys are available maximizing your ROI
| ≥ 3.3 | markov_budget_allocation |
| Transaction-level attribution with heuristic models, Markov model and Shapley value | - Monitor ROI for each channel at path-level and for aggregation of paths at time intervals
| ≥ 3.0 | heuristic_models
markov_model
shapley
|
| Real-time attribution with Markov model and Shapley value | - Save computational time. Train the model on huge amount of customer journeys, store the model parameters and then use it for performing attribution on new customer-journeys
| ≥ 3.0 | markov_model
shapley
|
| Markov model and Shapley value with odds | - More accurate attribution at path-level
| ≥ 3.0 | markov_model
shapley
|
| Combine results from Media-mix Model and Multi-touch attribution models at path-level | - If you have previously estimated a Media-mix model on your data, you can combine its results, at path level, with those of one of our multi-touch model
| ≥ 3.0 | combine_mta_mmm
|
| Out-of-sample validation algorithm for choosing the best Markov model order | - More accurate attribution with Markov models
- Choose the best order also for highly inbalanced data using precision-recall curve instead of roc curve
| ≥ 3.0 | choose_order
|
| Simplified Shapley value formula | - Classical Shapley value formula limit the use of Shapley value to problems with less than 10 channels, while simplified Shapley value can be used also with thousands of channels
| ≥ 3.0 | shapley |
| Multiprocessing | - Faster execution of Markov model when huge amounts of customer journeys are elaborated
| ≥ 3.0 | markov_model
|
| Read customer journeys directly from CSV files | - Process huge amount of customer journeys avoiding out-of-memory issues
| ≥ 3.0 | heuristic_models
markov_model
shapley
|