uam

uam combines aggregated attribution and customer journey attribution in a single workflow.

If df_paths is not provided, uam returns the selected aggregated attribution baseline. If df_paths is provided, uam combines the selected aggregated baseline with Markov conversion-rate information from customer journeys.

By default, uam now uses reward_model as its aggregated-data baseline. The previous auto_mmm baseline behavior can still be selected explicitly with baseline_model = "auto_mmm".

Input structure

df_aggr should contain:

• a timestamp column
• one target or conversions column, passed separately through the target parameter
• one or more aggregated signal columns

D_variables should be a table/data frame with one row per non-target signal variable and these columns:

COLUMN	DESCRIPTION
variable	Name of the signal column in df_aggr.
channel	Final attribution channel associated with the signal. Multiple variables can map to the same channel.
measure	Measure type for the signal, such as impressions, clicks, cost, direct_searches, platform_signal, or another supported measure label.

The old D_measures parameter is no longer used. Measure information must be provided inside D_variables.

df_paths is optional. When supplied, it can contain either:

• already aggregated customer journey paths, with columns such as path, total_conversions, and optionally total_null; or
• event-level customer journeys, with columns such as id_path, timestamp, and touchpoint.

For event-level journeys, the current API expects the channel/touchpoint column to be named touchpoint. The old event-level column name channel is no longer supported for UAM paths.

Parameters

PARAMETER	TYPE	DEFAULT	DESCRIPTION
df_aggr	data.frame		Aggregated dataset with timestamp, target/conversions, and input signal columns.
D_variables	data.frame / table		Table with variable, channel, and measure columns.
target	str	"conversions"	Name of the target column in df_aggr.
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 aggregated paths are provided.
var_conv	str	"total_conversions"	Column containing conversions associated with each aggregated path.
var_null	str	"total_null"	Column containing null-path counts when available.
channel_conv	str	"((CONV))"	Conversion marker used in event-level customer journey data.
baseline_model	str	"reward_model"	Aggregated baseline model used by UAM. Allowed values are "reward_model" and "auto_mmm".
max_p	int	12	Maximum lag length considered in the aggregated baseline model.
nsim	int	1000	Number of simulations used when baseline_model = "auto_mmm". Ignored by reward_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.

Baseline selection

The default behavior is:

baseline_model = "reward_model"

To use the previous auto_mmm baseline behavior, set:

baseline_model = "auto_mmm"

When df_paths = NULL / None, uam returns the attribution from the selected baseline model.

When df_paths is provided, uam uses the selected baseline attribution and adjusts it with the Markov layer from the customer journey paths.

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.
baseline_model	str	Baseline model used by UAM: "reward_model" or "auto_mmm".
parameters_baseline	list / dict	Diagnostics from the selected aggregated baseline model.
parameters_mmm	list / dict	Backward-compatible alias for baseline diagnostics.
parameters_mta_adjusted	list / dict	Adjusted parameters used by the path-based Markov component.
mode	str	Execution mode metadata, such as reward_model_baseline_only or reward_model_baseline_markov_limited_adjustment.
raw_score_formula	str / object	Diagnostic information about the raw score formulation.
final_adjustment_formula	str / object	Diagnostic information about the final adjustment formulation.
row_sum_check	list / dict	Diagnostics checking that row sums remain consistent after adjustment.