Discount Creep Agent (Reference)

The Discount Creep Agent detects gradual increases in discounts over time. It uses time-series trend analysis and period-over-period comparisons of discount changes to identify these incremental increases.

List of Required Fields

The following table lists the set of transaction fields required for the Discount Creep Agent to compute its metrics and detection rules. Ensure these fields or equivalent are available and consistently populated in your source Transaction Datamart before deploying the Agent. Those exact field names are not required, but a similar fields with that data is required.

Periods

• Series 1 (Current Period)

• Series 2 (Previous Period)

• Join Series (Join Series)

Definition

Series 1 (Current Period)

Group By

Measures

Filters

To ensure the inclusion of only valid transactions for the Current Period, we exclude records with a zero Invoice Price and zero Quantity. Additionally, records are filtered to include only those with an Invoice Date later than a configurable relative date threshold, specified as N months ago from a designated anchor date.

Example

(Invoice Price > 0) AND (Quantity > 0) AND (List Price > 0) AND (Invoice Date > custom N months ago 3)

Summary Table (Current Period)

• Customer Segment – The top-level grouping dimension selected in the Group By definition. Rows are organized by customer segment.

• customerId – The customer identifier used as the next grouping level within the table hierarchy.

• Name – The customer name level that further specifies each grouped row.

• Current Discount % – The calculated discount rate for the current period, based on the configured measure for discount.

• Current Revenue – The aggregated sum of invoice price for all included transactions in the current period.

• Current Margin % – The weighted gross margin percentage for the current period.

Series 2 (Previous Period)

Group By

Measures

Filters

To ensure the inclusion of only valid transactions for the Previous Period, we exclude records with a zero Invoice Price and zero Quantity. Additionally, records are filtered to include only those with an Invoice Date within a specific date threshold, specified as N months ago in a designated anchor date.

Example

(Invoice Price > 0) AND (Quantity > 0) AND (List Price > 0) AND (Invoice Date between custom N months ago 12 (13/10/2024) AND custom N months ago 9 (13/01/2025))

Summary Table (Previous Period)

• Customer Segment – The top-level grouping dimension selected in the Group By definition. Rows are organized by customer segment.

• customerId – The customer identifier used as the next grouping level within the table hierarchy.

• Name – The customer name level that further specifies each grouped row.

• Previous Discount % – The calculated discount rate for the current period, based on the configured measure for discount.

• Previous Margin % – The weighted gross margin percentage for the previous period.

• Previous Revenue – The aggregated sum of invoice price for all included transactions in the previous period.

Join Series (Join Series)

Definition

Set up the relationship between the series by mapping the matching data columns/dimensions that you would like to include in the definition.

Measures

Summary Table (Join Series)

• Customer Segment – Grouping dimension used to present results per segment, consistent with the series’ Group By configuration for alignment across periods.

• customerId – The customer identifier that aligns rows across periods for the joined output.

• Name – The customer name shown for readability within each grouped row.

• Discount % change – The computed period-over-period change in discount derived from joining the current and previous period series.

• Revenue impact – The quantified effect on revenue associated with the observed discount change for the joined row.

Detection Rules

Detection Rules defines the Agent’s alert conditions and includes scheduling. When conditions are met, actions are triggered during the next Agent run.

• Series selection and conditions – Select which series to evaluate and specify the conditions that constitute a detection. Use Preview Rules to validate the rows that match before proceeding.

• Tuning and iteration – Refine the rule definitions to fit your business context and the number of detected cases.

Default thresholds are flexible, adjust as necessary.

Example

Discount % change > 5

Schedule

Configure when and how often the Agent runs as part of this step so detections are evaluated on a cadence.

Example

Start Date – The date when the scheduled task will run for the first time.
Period – Period which represents the offset between each run.
Interval – Interval which represents the number of repetitions in a selected period. Allowed characters are 0-9. 0 means one-off run.

Action Definition

Example

Impact Calculation

You can define specific metrics to compute the foreseen impact of the actions. Please only use total absolute value (and not relative values) as those metrics will be aggregated.

Impact Definition

Summary

Review the setup of the Agent, here you can see all the set parameters in one place.

If you are happy with the setup, click Submit for Approval. Once the Agent is approved, it becomes active and starts monitoring your data based on the schedule.