Every sales manager knows the margin conversation. Revenue is up; margin is flat. Individual deals look fine; the aggregate tells a different story. Post-quarter reviews identify the usual suspects: discounting pressure, raw material costs, a few large accounts with thin pricing.
What rarely comes up: the quoting process itself is making margin decisions on every BoQ, without anyone realizing it.
Not through discounting. Through product selection.
The decision point
A BoQ position arrives:
BoQ position
DN50 ball valve, PN16, stainless steel body, full bore, ISO-5211 mounting pad.
Your inside sales rep opens the product catalog. Three products match the specification:
| Product | Description | Margin |
|---|---|---|
| Product A | A branded valve from a preferred partner | 18% |
| Product B | A house brand equivalent meeting the same spec | 31% |
| Product C | A mid-tier option that marginally exceeds the spec | 24% |
| All three meet the tender requirement. The customer has specified a performance standard, not a brand. Any of them can go into the quote. | ||
Which one does your rep choose?
In most manual quoting workflows, the answer is determined by what appears first in the catalog search, what the rep has quoted before for similar positions, which product they remember, or what was in the last quote for this customer. Not by a deliberate margin optimization decision.
Across a 200-position BoQ with dozens of multi-match positions, these incidental choices accumulate. The difference between consistently proposing the 18% product and consistently proposing the 31% product, on a job worth 80,000 EUR in materials, is a margin gap that never shows up in a deal review because no individual decision looked wrong.
The structural problem
The margin loss from suboptimal product selection is invisible by design, because the products proposed are all technically valid. Nothing went wrong. The customer got a compliant quote. The job was won.
The structural reasons
The margin loss from suboptimal product selection in BoQ quoting is not visible at the line-item level. It is invisible by design, because the products proposed are all technically valid. Nothing went wrong. The customer got a compliant quote. The job was won.
The problem is what was left on the table, and the compounding effect across every BoQ your team processes in a year.
Manual quoting has structural reasons for defaulting to the first or most familiar match.
Speed
A BoQ with 300 positions and a submission deadline in 48 hours does not give the inside sales team time to evaluate margin on every multi-match line. They find a match, move on, and come back to difficult positions later. The default is what comes to mind fastest.
Absence of margin information at the moment of decision
The catalog search returns a list of products, typically ordered by SKU, alphabetically, or by sales frequency. Not by margin. The rep sees the products but not the margin implication of choosing between them.
Inconsistency across the team
Different reps make different choices on the same positions. One defaults to the branded option; another quotes the house brand. Neither is wrong individually. Together they create margin variance that is difficult to diagnose because it looks like normal quoting variation.
Accumulated habit
Over time, reps develop defaults for common product types: "we always quote Manufacturer X for this valve type." Those defaults may have been margin-optimal when established and may have drifted as product costs changed. Nobody reviews them because they're invisible.
Selection logic
When a BoQ position matches multiple internal products, an AI-driven quoting system can apply a selection logic that a human working under time pressure cannot reliably apply.
The simplest configuration is margin optimization: among products that meet the specification, select the one with the highest current margin. This is the default for standard tender positions where the customer has specified performance requirements, not brand. The system reads current purchase prices and pricing rules from the ERP at the time of matching, not from the rep's memory of what the margin used to be.
Win rate can be layered in
Among products at a similar margin level, the system can prefer the product with the highest historical acceptance rate with this customer or in this tender category. A technically equivalent product this contractor has accepted before is a better choice than one they've never seen, holding margin equal.
Customer brand preference is a third modifier
Some customers want consistency across a building installation and will specify a manufacturer even when the tender text doesn't. The system applies customer-specific preference rules that override margin optimization for defined segments, while still optimizing for the rest of the portfolio.
Stock availability matters too
Among equally valid matches, the system can prefer products with confirmed availability within the project delivery window. Proposing something that turns out to have a 12-week lead time after the tender is won creates a fulfillment problem that erodes both the margin and the relationship.
Finally, specification fit
Among products where one exceeds the specification and one meets it exactly, the system can be configured to propose the fit match by default, flagging the exceed match for human review in cases where over-specifying might be commercially desirable.
None of these decisions are impossible for an experienced inside sales rep to make. All of them are unreliable when applied manually across hundreds of positions under time pressure, without visibility into current margin data at the point of decision.
The compounding effect
The margin optimization benefit is largest not on the high-visibility products, but on the catalog long tail, where margin variance is wider and intuition is weaker.
The long tail
The margin optimization benefit is largest not on the high-visibility products -- the items your team quotes frequently and knows well -- but on the catalog long tail.
Every distributor or manufacturer has a long tail of products that are technically in the catalog but rarely quoted. Customer inquiries for these products arrive in BoQs for complex, multi-trade projects where the spec calls for something outside the team's daily experience. The rep finds a match, proposes it, and moves on.
What happens manually
In the long tail, margin on similar products varies more widely than on core lines. Pricing for rarely-moved items is less frequently reviewed, purchase prices may not reflect current supplier negotiations, and the rep's intuition about what's a good margin on an obscure fitting is less reliable.
What the system does
System requirements
Margin-aware product selection at the BoQ matching stage requires a few specific things from the system simultaneously.
Requirement 01
Current margin data is the foundation
not historical margin from a product master last updated six months ago, but current purchase prices, active supplier agreements, and current list prices, read from the ERP at the time of matching.
Requirement 02
The system needs to return ranked candidates rather than a single match
For every position with multiple valid products, the system presents all of them ranked by the configured optimization criterion. The inside sales team sees the top recommendation with the rationale, and can view alternatives if they want to override.
Requirement 03
The selection rules need to be configurable
per customer, per product category, and per tender type, not a single global setting applied to everything. Margin optimization is the default; customer preference, stock availability, and specification fit are modifiers that apply where configured.
Requirement 04
And the rep needs to see the reasoning
"Recommended: Product B. Meets specification. Current margin 31%. Two prior acceptances by this contractor. In stock." That transparency is what makes the recommendation trustworthy rather than a black box, and what allows the rep to override confidently when the situation warrants it.
What changes
Margin optimization at the quoting stage changes what the post-quarter review looks like.
Instead of reviewing margin by account and trying to explain why Account X came in below target, which usually leads to a discussion of pricing pressure and difficult customers, the review can look at the quoting stage directly: what was the average margin on multi-match positions this quarter, and how does it compare to the margin on single-match positions? Where is the team overriding the system recommendation, and in which direction?
This visibility does not exist in a manual quoting workflow because the decisions are not recorded. A rep proposes Product A instead of Product B and there is no log of the alternative that was available. An AI-driven system creates that log as a byproduct of the matching process.
For a sales manager accountable for both revenue and margin, the quoting stage is where margin is built: before the customer negotiates, before the discount conversation, before the post-mortem. The product selection decision on a multi-match position is a margin decision. Making it deliberately, every time, at scale, is the difference between a quoting process that protects margin and one that erodes it by default.
BoQ use case
See how turian's BoQ automation handles product matching and selection
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