Unprofitable Long-Tail Customer Agent (Reference)

Unprofitable Long Tail Customers Agent identifies and highlights long tail customers whose margin rates are significantly underperforming compared to the average. Ranks all customer based on revenue and then assess their margin rate and compare by customer segment.

List of Required Fields

The following table lists the set of transaction fields required for the Long Tail Customers with Too Low Margin 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.

Name	Label	Description
BaseQty	Base Qty	The quantity used as a reference for volume-based pricing or rebate calculations.
CalculatedRebate	Calculated Rebate	The rebate amount computed for a customer based on volume, agreements, or pricing rules. Reduces effective margin when not accounted for in the price.
CustomerId	Customer Id	A unique identifier for the customer account.
CustomerName	Customer Name	The display name of the customer account.
CustomerSegment	Customer Segment	A classification grouping customers by size, behavior, or strategic value (e.g., key account, mid-market, long tail).
GrossMargin	Gross Margin	The absolute margin in value terms (Revenue minus Cost of Goods Sold), before overhead allocation.
InvoicePrice	Invoice Price	The price actually charged to the customer on the invoice. The realized transaction price.
PricingDate	Pricing Date	The date on which the transaction or pricing event occurred.
ProductGroup	Product Group	A category grouping products by type, business line, or market segment.
Quantity	Quantity	The number of units sold or transacted in the given period.

Definition

Series 1 (Revenue and Margin by Customer)

Item	Value	Description
Data Label	Revenue and Margin by Customer	Sets a label (name) of the Series1 data series (data set). Data label can be freely customized and renamed according to your preferences.
Data Source	[DM] Transactions (Transactions)	Selected Transaction Datamart. For more information see Required Customer Data.
Currency	USD	Defines which currency the money data will be converted into.

Group By

Group By	Level
customerId	Level 1
Name	Level 2
Customer Segment	Level 3

The specific field names may vary depending on the set of fields in your source Transaction Datamart. The selected fields will be utilized to establish the grouping hierarchy in the Summary Table of the Series 1 Period. For additional information about grouping, see the Group section.

Measures

Measure	Label	Aggregation	Name	Description
Invoice Price	Revenue	∑	Revenue	The aggregated sum of invoice price for all included transactions.
Margin	Profit	∑	Profit	The total profit earned from a customer, calculated as the difference between the realized price and the cost for all transactions. It represents the absolute profit contribution.
Margin	Avg Margin %	{ }	AvgMarginPct	The customer’s average margin rate, computed as total margin divided by total revenue. For more information see Average Margin Percentage.
Customer Adj	Revenue Rank	{ }	RevenueRank	The customer’s position when customers are sorted by revenue contribution. For more information see Revenue Rank.
Margin %	Margin Rank	{ }	MarginRank	The customer’s position when customers are sorted by margin rate or margin contribution. For more information see Margin Rank.

The specific measures may vary depending on the set of fields in your source Transaction Datamart.

Filters

Filters are used to narrow the dataset to only relevant, clean transactions before computing revenue, margin, and segment-based comparisons.

Keeps only recent transactions defined as N months ago, so the analysis reflects current performance trends.
Excludes zero-priced items and credit-only rows.
Ensures margin is actually computed or available, preventing incomplete records.
Keeps only items with a defined categorization, enabling consistent grouping by product attributes.
Excludes returns, cancellations, or placeholder entries, ensuring volumes represent actual sold units.

Example

Invoice Date > custom N months ago 3 (03/07/2025) AND Invoice Price > 0 AND Margin ≠ null AND Product Group ≠ null AND Quantity > 0

Summary Table

customerId	Name	Customer Segment	Revenue	Profit	Avg Margin %	Revenue Rank	Margin Rank
D-0001	Mahle GmbH	Cooling	2899880.1	807520.534	0.27846687	0.34	0.34
D-0002	Denso Corp	Cooling	2943645.3	811835.274	0.275792492	0.36	0.36
D-0003	Hanon Systems	Cooling	2853057.4	779720.958	0.273293119	0.28	0.26

customer Id – Top-level grouping dimension identifying the customer.
Name – Second-level grouping dimension indicating the customer name.
Customer Segment – Third-level grouping dimension indicating the customer segment.
Revenue – The aggregated sum of invoice price for all included transactions.
Profit – The total profit earned from a customer, calculated as the difference between the realized price and the cost for all transactions.
Avg Margin % – The customer’s average margin rate.
Revenue Rank – The customer’s position when customers are sorted by revenue contribution.
Margin Rank – The customer’s position when customers are sorted by margin rate or margin contribution.

Series 2 (Avg Margin by Customer Segment)

Item	Value	Description
Data Label	Avg Margin by Customer Segment	Sets a label (name) of the Series2 data series (data set). Data label can be freely customized and renamed according to your preferences.
Data Source	[DM] Transactions (Transactions)	Selected Transaction Datamart. For more information see Required Customer Data.
Currency	USD	Defines which currency the money data will be converted into.

Group By

Group By	Level
Customer Segment	Level 1

Measures

Measure	Label	Aggregation	Name	Description
Margin	Avg Margin %	{ }	AvgMarginPct	The customer’s margin rate computed from transaction‑level data and aggregated per customer. For more information see Average Margin Percentage.

The specific measures may vary depending on the set of fields in your source Transaction Datamart.

Filters

Filters are used to narrow the dataset to only relevant, clean transactions before computing revenue, margin, and segment-based comparisons.

Keeps only recent transactions defined as N months ago, so the analysis reflects current performance trends.
Excludes zero-priced items and credit-only rows.
Ensures margin is actually computed or available, preventing incomplete records.
Keeps only items with a defined categorization, enabling consistent grouping by product attributes.
Excludes returns, cancellations, or placeholder entries, ensuring volumes represent actual sold units.

Example

Invoice Date > custom N months ago 3 (03/07/2025) AND Invoice Price > 0 AND Margin ≠ null AND Product Group ≠ null AND Quantity > 0

Summary Table

Customer Segment	Avg Margin %
Cooling	0.275038107
Electric Vehicles	0.276062197
Powertrain	0.276370726

Avg Margin % – The customer’s average margin rate.

Join Series (Join Series)

Item	Value	Description
Data Label	Join Series	Sets a label (name) of the Join Series data (data set). Data label can be freely customized and renamed according to your preferences.

Definition

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

Series	Dimension	Series	Dimension
Revenue and Margin by Customer	Customer Segment	Avg Margin by Customer Segment	Customer Segment

Measures

Label	Description
Revenue Rank	The customer’s position when customers are sorted by revenue contribution. For more information see Revenue Rank.
Margin Rank	The customer’s position when customers are sorted by margin rate or margin contribution. For more information see Margin Rank.
Margin Variance	The difference between a customer’s margin rate and the relevant benchmark. For more information see Margin Variance.
Margin Lift Potential	The potential profit uplift (percentage) per customer if their margin rate were raised to the segment average. For more information see Margin Lift Potential.
Margin Lift Potential Pct	The potential profit uplift (percentage) per customer if their margin rate were raised to the segment average. For more information see Margin Lift Potential Percentage.
Revenue Uplift	Estimated additional revenue that could be realized if the identified underperforming customers’ pricing/margins are improved toward average levels. For more information see Revenue Uplift.
Current Margin %	The customer’s current average margin rate, calculated as the total margin divided by total revenue for all included transactions. This value reflects the actual margin performance for each customer over the analysis period. For more information see Current Margin Rate.
Segment Margin %	The average margin rate for the customer’s assigned segment, computed as the aggregated average margin across all customers within the same segment. This serves as a benchmark for comparing individual customer performance to their segment peers. For more information see Segment Margin Rate.
Suggested Margin %	A target margin rate proposed for the customer, typically set to the segment average or another benchmark value. This measure provides an actionable goal for margin improvement, highlighting the potential uplift if the customer’s margin is raised to the suggested level. For more information see Suggested Margin Rate.

Summary Table

customerId [Revenue and Margin by Customer]	Name [Revenue and Margin by Customer]	Customer Segment [Revenue and Margin by Customer]	RevenueRank	MarginRank	MarginVariance	MarginLiftPotential	MarginLiftPotentialPct	Revenue Uplift	Current Margin %	Segment Margin %	Suggested Margin %
D-0001	Mahle GmbH	Cooling	0.34	0.34	-0.003103716	-9000.405273	-0.011145729	-9000.405273	0.27846687	0.275363154	0.276915012
D-0002	Denso Corp	Cooling	0.36	0.36	-0.000429338	-1263.819734	-0.001556744	-1263.819734	0.275792492	0.275363154	0.275577823
D-0003	Hanon Systems	Cooling	0.28	0.26	0.002070034	5905.926635	0.007574411	5905.926635	0.273293119	0.275363154	0.274328137

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.

This specific rule flags customers in lower revenue ranks whose average margin underperforms their segment average beyond a configurable threshold, using the joined output and measures such as Revenue Rank, Margin Rank, and Margin Variance to operationalize that logic. Optionally, indicators such as Margin Lift Potential and Revenue Uplift can be used to prioritize detected cases for follow‑up actions.

info Default thresholds are flexible, adjust as necessary.

Example

Series	Rules
Join Series (Join Series)	`RevenueRank < 0.2 AND MarginRank < 0.1 AND MarginLiftPotential > 1,000 AND MarginVariance < -0.05`

Schedule

Set the preferred start date and frequency that you want the Agent to run.

Example

Start Date	Period	Interval
10/11/2025 11:01	Month	1

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

Notifications assigned to specific users (The assignment must be made to a genuine system user). For more information see Action Definition.

Example

Summary	Description	Due Date	Assign to
Long Tail Customers with too low margin	The list below shows long tail customers with low profit and margin lift potential	in 7 days	`First Name` `Last Name`

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

Measure	Impact Type	Realization Rate (%)	Order
Revenue Uplift	Revenue	50	1
MarginLiftPotential	Profit	40	2

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.

Review the final results in the Summary step.