Prepare your data

Event Propensity Scoring needs identifiable, timestamped, partitioned event history with enough positive and negative examples to evaluate the requested target.

Technical prerequisites

• Event Propensity Scoring enabled in the workspace.

• A source supported by the intelligence, with the User model and every Event model on the same connection.

• Read access to the input models and permission to create or update scoring outputs. Check the source setup requirements using the credentials configured in DinMo.

• Raw Event models with usable physical partition metadata, stable primary keys, and a one-to-many (HAS_MANY) relationship from the selected User model.

Do not substitute pre-aggregated profile totals for raw events: DinMo needs event times to reconstruct what was known at historical observation dates.

Required models and mappings

Model	Requirement	Purpose
User	Required	Current population to score and optional fields to copy for activation.
Behavioral Event	One stream required in generic mode; optional in subscription mode	Usage, navigation, purchases, communications, or other tracked behavior.
Lifecycle Event	Required in subscription mode	Subscription state, movements, terms, and target labels.

Use the same stable profile key across these models. The current User model does not restrict the historical subjects used for training.

Every Event stream needs:

Mapping	Requirement
Event ID	Stable, non-null identity for deterministic deduplication.
Profile ID	Key linked to the User model.
Event timestamp	Business occurrence time, not ingestion time.
Event name	Categorical field identifying the business event.
Partition column and granularity	Physical partition field with hour, day, or month granularity. It can be the event timestamp field.

Subscription lifecycle data

Subscription mode also needs a stable Subscription ID and mappings for subscription_started, cancellation_requested, and subscription_ended. Source event names can differ: map their values or rules to these canonical movements.

Retain all earlier lifecycle history, including previous cycles and reversals, so each subscription's state can be reconstructed. Scoring is at (profile_id, subscription_id) grain: one person can have several scored subscriptions.

Optional subscription attributes include offer family, effective price, commitment end, billing frequency, and channel. These must be historized. Do not copy today's price, offer, or cancellation status onto earlier events if it was not known then.

Select meaningful model inputs

You choose which compatible Event fields DinMo may use. Useful inputs include device, content category, channel, quantity, duration, engagement value, and historized subscription terms.

Do not select free text, opaque IDs, join keys, or values that reveal the future target. Structural mappings are already part of the event contract and do not need to be selected again. User fields copied into outputs are activation data, not a substitute for historical Event inputs.

Input	Guidance and limit
Behavioral event names	Around 10 recommended; at most 50 selected values per stream.
Lifecycle movements	At most 10 canonical movements.
Additional model inputs	10 or fewer recommended; at most 30 per stream.
Categorical values	Keep meaningful categories below 50; sparse or high-cardinality fields can fail readiness checks.

Eligibility and minimum data requirements

These requirements determine whether training can proceed, not whether the model will deliver useful business lift.

Blocking requirement	Minimum
Prediction horizon	3 to 90 days.
Common stable history	At least six times the horizon.
Usable event values	At least 2.
Positive subjects	At least 100 in each training and evaluation period.
Negative subjects	At least 100 in each training and evaluation period.
Event integrity	Stable identities, valid business timestamps, and deterministic deduplication.
Source continuity	No unexplained gaps in the period declared complete.
Event-table partitioning	Valid physical partition column and granularity; required for production execution.
Subscription reconstruction	Subscription identity and required lifecycle movements available in subscription mode.

A positive subject performs the target within the future horizon; a negative subject does not. Missing partitioning blocks production execution. Recommended levels are at least 5 usable event values, 500 deduplicated target episodes, and 1,000 historical subjects. Falling below a recommendation produces a warning rather than blocking training.

History required by horizon

Horizon	Minimum common stable history
7 days	42 days
14 days	84 days
30 days	180 days
60 days	360 days
90 days	540 days

With multiple streams, history is measured over their common reliable period. Earlier lifecycle events still describe previous cycles, but do not extend that common period when behavioral data is unavailable. The target need not occur every day if tracking is continuous and every period has sufficient class counts.

Check before training

Check key consistency, duplicates, time zones, source freshness, partition continuity, and any changes in event meaning. Declare when each source became complete: missing history must not be interpreted as inactivity. Exclude future-dated events and incorrectly historized inputs.

Next: Configure and refresh.