Your Company-Level GP% Is Hiding Everything That Matters
If you can only build one report, build the margin-by-rep report. It will show you more about your pricing health in 10 minutes than a month of deal reviews. But it’s not the only lens you need — it’s the first of five, and together they turn a useless company-level average into an operational playbook.
You pull up the P&L. Gross profit margin: 9.2%. Same as last quarter. Roughly the same as last year. Leadership nods. Finance files it. Everyone moves on.
That 9.2% is real. It’s also nearly useless as a diagnostic tool. It’s the equivalent of a doctor telling you “your average body temperature across all organs is 98.6°F” — technically accurate, completely uninformative about the tumor in your liver or the infection in your kidney.
Company-level GP% is a blended average that smashes together every deal type, every OEM, every rep, every customer, and every competitive dynamic into a single number. A $5M sole-source Cisco ELA renewal at 16% margin gets averaged with a $3M competitive Dell server bid at 4% margin, and the result — some number in the middle — tells you nothing about why either deal was priced the way it was, whether either price was optimal, or what you should do differently tomorrow.
Here’s what company-level GP% can’t tell you: Which reps are consistently leaving 3 points on the table? Which OEM lines are structurally margin-poor and dragging down the blend? Which accounts accept 14% without blinking while others grind you to 5%? Which deal types carry twice the margin of others but represent a shrinking share of your pipeline?
The answers to those questions are sitting in your Salesforce instance right now. You have the data. What you’re missing is the framework for slicing it — knowing which dimensions to analyze, what patterns to look for, and what the numbers mean when you find them.
This guide covers five dimensions of margin analysis, starting with the one that delivers the fastest ROI — margin by rep — and building toward the most sophisticated. Each one reveals something different. Together, they give you the full picture.
Dimension 1: Margin by Rep
What you’re looking for: Which reps consistently achieve higher margins than their peers on comparable deals, and how wide the gap is.
The Salesforce setup: Build a report on closed-won opportunities from the last 12 months, grouped by Opportunity Owner. You need these fields: Opportunity Name, Amount, Gross Profit $ (custom field), and Gross Profit % (formula: GP$ / Amount). For each rep, calculate: total revenue, total GP$, average GP%, median GP%, deal count, and average deal size. You want at least 20 deals per rep to make the comparison meaningful — reps with fewer deals should be flagged but not compared directly.
What to build: A bar chart or table showing average GP% by rep, sorted from highest to lowest. Overlay the org-wide average as a reference line. If you want to get more precise, also show the median GP% — this strips out the effect of a single outlier deal that might skew a rep’s average.
What patterns to look for:
At most mid-market VARs, the spread between the highest-margin rep and the lowest-margin rep is 4–8 points. Your top rep averages 12.5% on won deals. Your bottom rep averages 5.8%. Both carry roughly similar deal sizes and cover comparable accounts. That 6.7-point gap is not random noise — it’s a difference in pricing behavior.
The first question is whether the gap is driven by deal mix or pricing discipline. A rep who handles more sole-source deals or more services-heavy deals will naturally show higher margin. To control for this, filter the comparison to a single deal type (competitive hardware, for example) or a single OEM. If the gap persists within a controlled segment — and it almost always does — you’ve isolated a coaching opportunity.
A common finding: your top-quartile reps (by margin, not revenue) aren’t your highest-revenue reps. They’re the ones who win deals at 11% instead of 8%, which means fewer GP dollars get left on the table per deal but their total revenue might be moderate. Conversely, your highest-revenue reps sometimes achieve that volume by pricing low to accelerate close rates. This isn’t necessarily wrong, but it should be a conscious strategic choice, not an unexamined habit.
The most valuable output of this analysis is the specific, actionable coaching conversation it enables. Instead of telling a rep “you need to improve your margins,” you can say: “On competitive Cisco deals in financial services accounts, your average GP is 7.1%. Three other reps selling comparable Cisco deals into comparable accounts average 10.8%. Let’s look at five of your recent deals and see where the gap is.” That’s a conversation that leads somewhere.
If you can only build one report this week, build this one. Margin by rep immediately identifies your best pricing performers and your biggest coaching opportunities. It takes 30 minutes to build in Salesforce, requires no custom fields beyond GP$ and GP%, and the output is directly actionable in your next deal review or one-on-one.
Dimension 2: Margin by Deal
What you’re looking for: The shape of your margin distribution across all deals.
The Salesforce setup: Start with the same closed-won report from Dimension 1 — same fields, same 12-month window. Remove the rep grouping, add Close Date and Stage, and export to a spreadsheet or BI tool so you can build a histogram.
What to build: A frequency distribution of GP% across all won deals. The X-axis is GP% in 1-point buckets (0–1%, 1–2%, 2–3%, and so on up to 25%+). The Y-axis is the number of deals in each bucket. This is the single most revealing chart in margin analysis, and almost no VAR has ever built it.
What patterns to look for:
The most common shape is bimodal — two humps. One cluster of deals sits around 4–7% (your competitive hardware deals where reps priced defensively). Another cluster sits around 12–18% (your sole-source deals, services-heavy deals, and deals where a strong rep held margin). The valley between the two humps is where the money lives.
If your distribution is bimodal, the strategic question becomes: what would it take to shift deals from the left hump to the right hump? The answer is usually not “charge more” — it’s “identify which deals in the left hump were actually priced too low for their competitive context.” A sole-source deal sitting in the 5–7% cluster is a pricing failure. A competitive three-way bid sitting there might be priced correctly.
If your distribution is a single tight cluster — say, most deals falling between 7–10% — that’s a different signal. It means your reps are converging on a narrow range regardless of deal context. They’re applying a default markup rather than pricing each deal on its merits. The variance is too low, which sounds good until you realize it means you’re underpricing easy deals and possibly overpricing hard ones.
Also look at the tails. Deals below 3% should be rare and should each have a documented strategic justification. If you have 40 deals under 3% and most of them lack any notation about why, you have a floor enforcement problem. Deals above 20% are your best-case outcomes — study them to understand what made that margin possible and whether those conditions are replicable.
Dimension 3: Margin by OEM
What you’re looking for: Which vendor lines are structurally margin-rich and which are structurally margin-poor — and whether your overall margin trend is being driven by mix shift between them.
The Salesforce setup: You need a field that captures the primary OEM or vendor on each deal. Some VARs track this at the opportunity level (a “Primary Vendor” picklist), others track it at the line-item level on Opportunity Products. If you have line-item data, you can do this analysis at the SKU level, which is more precise. If you only have opportunity-level data, use whatever vendor field you have. Group by vendor. Calculate average GP%, total GP$, total revenue, and deal count per vendor.
What to build: A two-axis chart. X-axis is total revenue by OEM. Y-axis is average GP% by OEM. Each OEM is a bubble, sized by deal count. This gives you a portfolio view of your vendor economics in a single frame.
What patterns to look for:
You’ll typically see three clusters. High-volume, low-margin OEMs — these are your Dell and Lenovo hardware lines, where competitive pricing compresses margin into the 5–8% range but the revenue volume is significant. Low-volume, high-margin OEMs — these might be a specialty vendor like F5, Infoblox, or a cybersecurity player where you have deep technical differentiation and fewer competitors, allowing margins of 15–22%. And then there’s Cisco, which usually sits somewhere in the middle on both axes — high volume, moderate margin — because it spans so many product categories with different competitive dynamics.
The strategic insight is about mix shift. If your company-level GP% dropped from 9.5% to 8.8% this year, the cause might not be that reps are pricing worse. It might be that your revenue mix shifted toward Dell hardware (low margin) and away from Palo Alto (higher margin). The blended number went down even though pricing behavior on each OEM line stayed flat. Without the OEM-level view, you’d misdiagnose the problem and tell your reps to “price better” when the real issue is a pipeline and go-to-market question about which OEM lines you’re emphasizing.
Also compare your margin by OEM to the discount programs each OEM offers. If you’re averaging 7% on Palo Alto deals but Palo Alto’s partner program offers 12–15 points of base discount, something is wrong — your reps are passing too much of the OEM discount through to the customer instead of retaining it as margin. That’s a training issue, not a market issue.
Dimension 4: Margin by Account
What you’re looking for: Which customers consistently accept higher margins and which ones squeeze you to the floor — and whether the squeeze accounts are actually worth the effort.
The Salesforce setup: Group your closed-won report by Account Name. For each account with at least three deals in the last 12 months, calculate average GP%, total GP$, total revenue, and deal count. Accounts with fewer than three deals don’t give you a reliable pattern — flag them for future tracking but don’t draw conclusions.
What to build: A scatter plot with total revenue on the X-axis and average GP% on the Y-axis. Each dot is an account. This creates four quadrants that tell you exactly where your account portfolio stands.
What patterns to look for:
Top-right quadrant: high revenue, high margin. These are your best accounts. They buy a lot and they don’t grind you on price. Protect these relationships at all costs. Assign your best reps. Over-invest in services and support. Do not let a competitor get a foothold.
Bottom-right quadrant: high revenue, low margin. These are your “busy but broke” accounts. You’re doing $3M a year with them but making 4% because their procurement team runs everything through a reverse auction or because your rep has been trained by years of negotiation to pre-discount every quote. These accounts need a strategic conversation: can you improve margin by shifting the product mix toward services, or by differentiating on value rather than competing on price? If the margin can’t be improved, the harder question is whether the revenue is worth the cost to serve.
Top-left quadrant: low revenue, high margin. These are your hidden gems — smaller accounts that accept healthy margins, probably because competition is limited or because they value the relationship over the price. The play here is to grow wallet share. If they’re paying 15% on a $200K annual spend, what would it take to make that $500K?
Bottom-left quadrant: low revenue, low margin. These accounts are actively destroying value. They don’t buy much and what they buy, they buy cheap. Some of them are worth developing if there’s a clear growth path. Most of them are not, and your reps’ time would be better spent on accounts in the other three quadrants.
The account-level view also surfaces a critical coaching input: customer-specific price sensitivity. When a rep is preparing a quote for an account that shows up in the top-right quadrant with a historical average of 13.5%, there’s no reason to price at 8%. The data says this customer accepts higher margins. Use that data.
Dimension 5: Margin by Deal Type
What you’re looking for: How margin differs across the types of deals you sell — and whether your reps are calibrating their pricing to deal type or applying a flat markup regardless.
The Salesforce setup: This one requires a “Deal Type” field on the Opportunity object, which most VARs don’t have out of the box. If you don’t have it, create a picklist with these values: Hardware Refresh, Net-New / Greenfield, Competitive Displacement, ELA / License Renewal, Professional Services, and Managed Services. You’ll need to backfill the last 12 months of data, which takes effort but only has to be done once. Going forward, make it a required field at opportunity creation.
What to build: A table or bar chart showing average GP%, median GP%, deal count, and total GP$ by deal type. Simple, but powerful.
What patterns to look for:
If your pricing is well-calibrated to deal type, you should see a clear margin hierarchy. Managed services contracts should carry the highest margins (30%+), followed by professional services engagements (20–30%), ELA renewals (10–15%), hardware refreshes (7–12%), and competitive displacements and net-new builds at the bottom (5–10%). The exact numbers vary by VAR, but the rank order should be consistent.
If you don’t see this hierarchy — if hardware refreshes and ELA renewals show similar margins, or if managed services deals aren’t meaningfully higher than hardware deals — it means your reps aren’t differentiating their pricing by deal type. They’re applying a default range regardless of context. That’s a structural problem, because deal type is one of the strongest predictors of how much pricing power you have.
The most actionable finding from this analysis is usually around sole-source ELA renewals. These deals should carry premium margins because the customer is committed to the platform and you’re the reseller of record. But reps often price renewals like competitive bids — partly out of habit, partly because they worry the customer will benchmark them, partly because nobody ever told them the ceiling was higher. If your ELA renewal margin is within 2 points of your competitive hardware margin, you’re underpricing renewals and it’s probably the single easiest margin improvement available to your org. For a step-by-step framework your reps can use at the point of quoting, see our VAR Pricing Decision Tree.
Where to Start: The Priority Stack
Five reports, five weeks. Here’s the build order that delivers the most value fastest.
These five reports take 5 weeks to build and minutes per week to maintain. The variance they reveal — between your best and worst reps, between your highest and lowest margin OEMs, between accounts you’re defending and accounts you’re losing — is the roadmap for every pricing conversation your team has from here forward. Once you have the data, the next step is calibrating it against what’s normal: see how your numbers compare in the 2026 VAR Margin Benchmark Report.
From the team at MarginArc — margin intelligence for the IT channel.