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Anthrobyte

Pricing Intelligence

How one industrial distributor stopped pricing by memory across hundreds of local companies

Outcome

Margin improvement

3–5%

Decision time

45%Reduced

Manual analysis

90%less

01

The Problem

  • Nobody could tell whether a quote was strong or weak until well after it was sent.

  • Quotes lost on price were not recorded at all, so the same decisions were made again the following month with no more information than before.

  • That left pricing without a feedback loop: plenty of history on what sold, almost nothing on what margin was left behind.

  • Pricing was decided branch by branch and rep by rep.

  • The same product went out to comparable customers at prices that varied widely, and each one was defensible on its own, so nothing looked wrong.

  • Across hundreds of branches margin was being set by judgment and habit rather than by what an account would actually support.

02

What We Understood First

01

Pricing Was Already Highly Local

Hundreds of companies, different markets, and different pricing realities. A single national price would not reflect how customers actually buy.

02

Sales Data Was Only Part of the Demand Picture

Historical sales showed what customers bought, but not what they would have bought at another price — making elasticity the missing piece.

03

Market Context, Localized

Competitor prices across 5 marketplaces, adjusted to each company's own market, not one national number.

04

The Model Needed Business Guardrails

A good price could not simply maximize margin. Pricing had to respect margin floors, customer terms, pricing limits, and controlled price movement.

05

Adoption Would Determine the Real Impact

Recommendations only mattered if sales teams actually used them. The approach had to be tested and measured through quote adoption and conversion.

03

Solution

01

Re-engineered product-level elasticity with ML

Historical pricing, demand signals into product-level elasticity estimates.

02

Generated two actionable price points: Optimal and Aggressive

Gives reps two clear choices.

03

Added business rule constraints protecting margins, terms, and pricing limits

Keeps recommendations inside the commercial rules the business will actually accept.

04

Tracked adoption, not just accuracy

Measured whether reps actually used the recommendations and what happened after they did.

Guided price recommendation · SKU 44-118

live

$1,940

Optimal · 27% margin

floor $1,480Optimal $1,940Aggressive $2,320Ceiling $2,600

$2,320

Aggressive · 40% margin

Why this price

Last 6 quotes, this account
$1.2k-$1.7k
Expected win rate
74%
Margin
20%- 50%
04

What We Didn’t Automate

The price stayed a human call. We automated the homework behind it.

The system shows cost and market, then two pricing options, an optimal one built to win the sale and a higher aggressive one. The rep still decides.

Optimal

built to win

$1,940

Aggressive

margin upside

$2,320

Rep chooses · always

05

Recovering margin without losing trust

25%

Minimum margin floor

5

Marketplaces benchmarked live

“3–5% margin recovered, the way trust usually pays: quietly, and after the fact.”

Enterprise-scale distribution network · Hundreds of local companies

06

Where It Landed

Measured across 40 pilot branches, six months after rollout.

Gross margin

BeforeAfter
rolloutQ1Q2Q3Q4Q1Q2

+4.1%

blended margin

6.5h → 40m

weekly analysis per rep

1,900+

price drifts caught

07

Tensions Worth Naming

Tension 01

The full picture wasn't available.

Pricing was being set without customer-level elasticity, competitor prices were sometimes matched to the wrong products, and stock gaps meant sales velocity had to be used as an imperfect measure of demand.

Tension 02

What sold wasn't the whole demand picture.

The data showed what customers had bought, but not what they might have bought at a different price. That left teams with limited visibility into missed demand, margin opportunity, and true customer price sensitivity.

08

If you are facing this too

The Principle

Talk to us

Automate the information, not the decision.

Give people the market and the margin room. Let them decide.

Market → automatedDecision → human
09

What’s next?

PHASE 01IN FLIGHT

Deeper rollout

From pilot branches out across the network.

PHASE 02NEXT

More of the catalog

Beyond core SKUs into the long tail.

PHASE 03PLANNED

Wired into quoting & BOM

The right number, the moment a quote is built.

If you're navigating layered challenges and want a thinking partner, let's think together.

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