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Features


Features available in ChannelAttribution Pro


FEATURE
DESCRIPTION
VERSION
FUNCTIONS
Transaction-level attribution with logistic regressionPerform attribution with logistic regression at transaction level
≥ 3.7

logistic_regression

Compare the performance of different attribution modelsCompare the performance of last touch, first touch, linear touch, Markov model, Shapley value, and logistic regression in a traffic allocation problem based on your customer journeys
≥ 3.6

Beta phase

compare_models

Next best action with Markov modelFor a partial customer journey, predict the next best action that maximizes the conversion probability of the user
≥ 3.4

next_best_action

Hidden Touch Attribution modelPerform attribution when customer journeys are not available overcoming the main limitations of a classical Media-mix model
≥ 3.3

Beta phase

hta_model

Buget allocation with Markov modelTrain a Markov model with your customer journeys and return the best budget allocation among your channels
≥ 3.3

markov_budget_allocation

Transaction-level attribution with heuristic models, Markov model and Shapley valuePerform attribution with heuristic models, Markov model and Shapley value at transaction level
≥ 3.1

heuristic_models


markov_model


shapley


Real-time attribution with Markov model and Shapley valueTrain a Markov or Shapley model on a huge amount of customer journeys, store the model parameters and then use it to perform attribution on new customer journeys and save computational time
≥ 3.1

new_paths_attribution


Markov model and Shapley value with oddsPerform a more accurate attribution at transaction-level using odds instead of removal effects
≥ 3.1

markov_model


shapley


Combine results from Media-mix Model and Multi-touch attribution models at path-levelIf you have previously estimated a Media-mix model on your data, you can combine its results, at transaction level, with those of one of our multi-touch attribution model
≥ 3.1

combine_mta_mmm


Out-of-sample validation algorithm for choosing the best Markov model orderChoose the best Markov model order using an out-of-sample validation methodology, also for highly inbalanced data using precision-recall curve instead of roc curve
≥ 3.1

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
≥ 3.1

shapley

Read customer journeys directly from CSV filesProcess huge amount of customer journeys avoiding out-of-memory issues
≥ 3.1

heuristic_models


markov_model


shapley


MultiprocessingFaster execution of Markov model when huge amounts of customer journeys are elaborated
≥ 3.0

markov_model