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Power BI · Retail & Distribution

Cost to serve and the customer portfolio

Every distributor can rank customers by revenue and margin. Almost none can rank them by what it costs to serve them, which is the ranking commercial decisions should use.

PublishedApril 19, 2026
Length13 pages · 14 min read
SectorRetail & Distribution
PlatformPower BI
Service areaData, AI & Integration
Abstract

Cost to serve and the customer portfolio

Summary

Cost to serve is everything between the order and the cash that is not the cost of the goods: order handling, picking complexity, freight, returns, payment terms and account management time.

This paper argues that the reason it is rarely calculated is not analytical difficulty but organizational — the data sits in five places and nobody has been asked to join it up — and that the model's value depends more on the commercial team accepting the allocation basis than on the sophistication of the allocation itself.

Key findings

Four things this paper argues

If you read nothing else, read these. The analysis that follows sets out the evidence for each.

01

A defensible approximation the commercial team trusts beats a precise model they dispute

Activity drivers people can count and verify produce a ranking that changes behaviour. A sophisticated allocation nobody can follow produces an argument.

02

Freight is usually the largest differentiator and the most commonly omitted

It is omitted because the data is awkward, and a model without it ranks customers wrongly and confidently.

03

Account management time is the least evenly distributed cost

Even a crude tiering by account class beats a flat allocation, which systematically flatters the most demanding accounts.

04

The productive responses are commercial, not a firing list

Ordering minimums, delivery consolidation, terms changes and self-service channels. Most expensive customers do not know they are expensive.

Analysis

The argument in full

What the model has to include

Order handling, where small orders cost nearly as much to process as large ones. Picking and packing complexity including special handling. Freight, particularly where a customer requires split deliveries or expedited shipping. Returns and credits, which concentrate heavily in a small number of accounts. Payment terms and collection effort, which are a real cost of capital. And account management time, which is the hardest to allocate and frequently the largest.

The technical build is not the difficult part. Getting the commercial team to accept the allocation basis is.

  • Order processing driven by order count or line count where entry effort varies
  • Picking and packing weighted for special handling and custom labelling
  • Freight at consignment level where invoiced, driver-based where not
  • Returns as line count plus a fixed handling cost per return event
  • Account management tiered by account class rather than allocated flat

Designing for acceptance

Our advice is to be conservative and transparent. Use activity drivers people recognise — number of order lines, number of deliveries, number of returns — rather than a sophisticated model nobody can follow.

Publish the drivers alongside the result so anybody can see why an account scored as it did. An account manager who can see the reasoning will argue about the driver, which is a productive conversation. One who cannot will reject the number entirely, and rejection is difficult to reverse.

Then sense-check the extremes with people who know the accounts. The most and least profitable customers in the model should be recognisable to the commercial team; if they are not, something in the allocation is wrong and it is better to find that before publication.

What the model usually reveals

Two things, reliably. A group of accounts that look healthy on gross margin and consume disproportionate resource. And a group of small accounts that are quietly excellent because they order predictably and never call.

Neither is actionable in isolation. The value is that the commercial conversation shifts from 'grow revenue' to 'grow the right revenue', which is a different and considerably more useful discussion.

It is worth being explicit that the productive responses are rarely to exit customers. Ordering minimums, delivery consolidation, a change to payment terms, a self-service channel for routine orders, or a straightforward price adjustment with the reasoning explained. Most customers who are expensive to serve do not know they are, and a proportion will change behaviour when asked.

Framework

Something you can apply without us

Every paper in this series ends with a framework you can run internally. We would rather you used it and reached your own conclusion than took ours on trust.

Framework

Building a model that gets used

Five stages. The first and last are commercial rather than technical.

1

Agree

Cost pools and drivers agreed with commercial and operations before any engineering.

2

Include

Freight and returns, even where the data is awkward. Omitting them ranks customers wrongly.

3

Reconcile

Total pooled cost in equals total allocated out. A non-zero residual is a finding.

4

Explain

Publish drivers alongside results so an account manager can see the reasoning.

5

Sense-check

Walk the extremes with people who know the accounts, before publication.

Implications

What this means, depending on your seat

The same argument lands differently across an executive team. These are the three versions worth separating.

For the commercial director

For the finance director

For the CIO

References

Where to check this for yourself

Microsoft's own documentation for the product behaviour described above. We would rather you verified the basis than accepted our summary of it.

01
Power BI semantic models
02
Row-level security with Power BI
03
Direct Lake overview
04
What is OneLake?
05
Dynamics 365 Business Central warehouse management

On these references: each entry names a Microsoft Learn article or documentation area by title, because deep links change while titles are stable. Searching the title on learn.microsoft.com will reach the current version. Where we have cited a figure or a product behaviour, it is Microsoft's statement rather than ours; where we have given a number of our own it is labelled as such in the text.

Recognise the situation?

We will build a cost-to-serve model against one customer segment using data you already have, and walk your commercial team through the extremes before anything is published.

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