uam

Python

This example shows how to use uam with aggregate marketing data and, optionally, customer journey paths. The aggregate baseline uses measure-level aggregation weights before computing channel attribution.

D_variables maps each raw input variable to a final channel and measure. It must contain only variable, channel, and measure.

D_measures is optional. When provided, the recommended user-facing structure is one row per measure with measure and aggregation_weight:

measure	aggregation_weight
direct_searches	0.45
clicks	0.45
impressions	0.10

Several variables can map to the same channel: for example, Facebook impressions and Facebook clicks are both assigned to facebook_ads. aggregation_weight is used to combine those variable-level attributions into the final channel-level attribution. The internal signal scaling is estimated automatically from the data.

import pandas as pd
from ChannelAttributionPro import uam, markov_model, combine_mta_mmm

token = "yourtoken"

# data_mmm_3.csv includes aggregate signals in addition to digital journey data.
df_aggr = pd.read_csv("https://app.channelattribution.io/data/data_mmm_3.csv")

D_variables = pd.DataFrame({
    "variable": [
        "direct_searches",
        "facebook_impressions",
        "facebook_clicks",
        "google_impressions",
        "google_clicks",
        "tv"
    ],
    "channel": [
        "direct",
        "facebook_ads",
        "facebook_ads",
        "google_ads",
        "google_ads",
        "tv"
    ],
    "measure": [
        "direct_searches",
        "impressions",
        "clicks",
        "impressions",
        "clicks",
        "impressions"
    ],
})

D_measures = pd.DataFrame({
    "measure": ["direct_searches", "clicks", "impressions"],
    "aggregation_weight": [0.45, 0.45, 0.10],
})

target = "conversions"

Aggregated-data attribution without customer journey paths

Use this mode when you only have aggregate time-series data. In this case, uam estimates channel attribution directly from the aggregate signals and the target variable.

res = uam(
    df_aggr=df_aggr,
    D_variables=D_variables,
    D_measures=D_measures,
    target=target,
    df_paths=None,
    baseline_model="linear",  # "reward", "copula", or "linear"
    max_p=12,
    nsim=1000,
    seed=1234567,
    verbose=1,
    password=token,
)

attribution = res["attribution"]
print(attribution.head())

UAM with already aggregated customer journey paths

Use this mode when customer journeys are already represented as one row per path, with the number of conversions and null outcomes associated with each path.

df_paths_agg = pd.read_csv("https://app.channelattribution.io/data/data_uam_paths_t.csv")

res = uam(
    df_aggr=df_aggr,
    D_variables=D_variables,
    D_measures=D_measures,
    target=target,
    df_paths=df_paths_agg,
    baseline_model="linear",  # "reward", "copula", or "linear"
    var_path="path",
    var_conv="total_conversions",
    var_null="total_null",
    order=1,
    sep=">",
    verbose=1,
    password=token,
)

attribution = res["attribution"]
print(attribution.head())

UAM with event-level customer journey paths

Use this mode when each row is a single touchpoint event. The conversion event is identified by channel_conv and the path is reconstructed internally.

df_paths_events = pd.read_csv("https://app.channelattribution.io/data/data_uam_paths.csv")

res = uam(
    df_aggr=df_aggr,
    D_variables=D_variables,
    D_measures=D_measures,
    target=target,
    df_paths=df_paths_events,
    baseline_model="linear",
    channel_conv="((CONV))",
    order=1,
    sep=">",
    verbose=1,
    password=token,
)

attribution = res["attribution"]
print(attribution.head())