uam
uam combines aggregated attribution and customer journey attribution in a single workflow.
If df_paths is not provided, uam returns the aggregated attribution baseline. If df_paths is provided, uam combines the aggregated baseline with Markov conversion-rate information from customer journeys. The public UAM workflow currently uses auto_mmm as its aggregated-data baseline and applies the current default scoring logic internally.
Input structure
df_aggr should contain:
- a timestamp column
- one target or conversions column mapped to
"target"inD_variables - one or more aggregated signal columns
D_variables maps the target column to "target" and each signal column to its final channel name.
D_measures maps each non-target signal column to its measure type.
df_paths is optional. When supplied, it should contain customer journey paths with conversion counts and optional null-path counts, or another supported journey format compatible with var_path, var_conv, and var_null.
Parameters
| PARAMETER | TYPE | DEFAULT | DESCRIPTION |
|---|---|---|---|
| df_aggr | data.frame | Aggregated dataset with timestamp, target/conversions, and input signal columns. | |
| D_variables | dictionary / list | Mapping from each aggregated column to "target" or to its final channel name. | |
| D_measures | dictionary / list | Mapping from each non-target aggregated column to its measure type. | |
| df_paths | data.frame | NULL / None | Optional customer journey data used to blend aggregated and path-based attribution. |
| var_path | str | "path" | Column containing serialized customer journeys when df_paths is provided. |
| var_conv | str | "total_conversions" | Column containing conversions associated with each path. |
| var_null | str | "total_null" | Column containing null-path counts when available. |
| max_p | int | 12 | Maximum lag length considered in the aggregated baseline model. |
| nsim | int | 1000 | Number of simulations used in the aggregated baseline model. |
| seed | int | 1234567 | Random seed for reproducible results. |
| verbose | int / bool | 1 | Controls runtime logging. |
| order | int | 1 | Markov model order used for the path-based component. |
| sep | str | ">" | Path separator used inside serialized journey strings. |
| ncore | int | 1 | Number of cores used by the path-based computation. |
| server | str / list[str] | app.channelattribution.io | Server endpoint used by the hosted computation service. |
| password | str | NULL / None | Authentication token for the hosted service. |
| return_diagnostics | bool | FALSE / False | If enabled, returns attribution together with diagnostic objects for advanced inspection. |
Output
If return_diagnostics = FALSE, the function returns an attribution data frame.
If return_diagnostics = TRUE, the returned list/dict may include:
| OUTPUT | TYPE | DESCRIPTION |
|---|---|---|
| attribution | data.frame | Final channel attribution by time period. |
| parameters_mta_adjusted | list / dict | Adjusted parameters used by the path-based component. |
| baseline_diagnostics | list / dict | Diagnostics from the aggregated baseline model. |
| mode | str | Execution mode metadata. |
| raw_score_formula | str / object | Diagnostic information about the raw score formulation. |