README
This is a Python implementation based on the ChannelAttribution package in R developed by Davide Altomare and David Loris.
https://cran.r-project.org/web/packages/ChannelAttribution/ChannelAttribution.pdf
Please Note: The authors of the original R library now also provide a python implementation of ChannelAttribution which you should probably use instead of my implmentation since theirs has additional features not available in this package.
Installation
pip install pychattr
Markov Model
import pandas as pd
from pychattr.channel_attribution import MarkovModel
data = {
"path": [
"A >>> B >>> A >>> B >>> B >>> A",
"A >>> B >>> B >>> A >>> A",
"A >>> A"
],
"conversions": [1, 1, 1],
"revenue": [1, 1, 1],
"cost": [1, 1, 1]
}
df = pd.DataFrame(data)
path_feature="path"
conversion_feature="conversions"
null_feature=None
revenue_feature="revenue"
cost_feature="cost"
separator=">>>"
k_order=1
n_simulations=10000
max_steps=None
return_transition_probs=True
random_state=26
# instantiate the model
mm = MarkovModel(path_feature=path_feature,
conversion_feature=conversion_feature,
null_feature=null_feature,
revenue_feature=revenue_feature,
cost_feature=cost_feature,
separator=separator,
k_order=k_order,
n_simulations=n_simulations,
max_steps=max_steps,
return_transition_probs=return_transition_probs,
random_state=random_state)
# fit the model
mm.fit(df)
# view the simulation results
print(mm.attribution_model_)
channel_name total_conversions
0 A 1.991106
1 B 1.008894
# view the transition matrix
print(mm.transition_matrix_)
channel_from channel_to transition_probability
0 (start) A 1.0
1 A B 0.5
2 A (conversion) 0.5
3 B A 1.0
# view the removal effects
print(mm.removal_effects_)
channel_name removal_effect
0 A 1.0000
1 B 0.5067
Heuristic Model
import pandas as pd
from pychattr.channel_attribution import HeuristicModel
data = {
"path": [
"A >>> B >>> A >>> B >>> B >>> A",
"A >>> B >>> B >>> A >>> A",
"A >>> A"
],
"conversions": [1, 1, 1],
"revenue": [1, 1, 1],
"cost": [1, 1, 1]
}
df = pd.DataFrame(data)
path_feature="path"
conversion_feature="conversions"
null_feature=None
revenue_feature="revenue"
cost_feature="cost"
separator=">>>"
first_touch=True
last_touch=True
linear_touch=True
ensemble_results=True
# instantiate the model
hm = HeuristicModel(path_feature=path_feature,
conversion_feature=conversion_feature,
null_feature=null_feature,
revenue_feature=revenue_feature,
cost_feature=cost_feature,
separator=separator,
first_touch=first_touch,
last_touch=last_touch,
linear_touch=linear_touch,
ensemble_results=ensemble_results)
# fit the model
hm.fit(df)
# view the heuristic results
print(hm.attribution_model_)
channel first_touch_conversions ... ensemble_revenue ensemble_cost
0 A 3.0 ... 8.1 5.1
1 B 0.0 ... 0.9 0.9
[2 rows x 13 columns]