How to build a rep ramp model from SMB to enterprise

Most ramp models are built backwards. A finance partner picks a number — 90 days, six months, "two quarters to full productivity" — and the sales org reverse-engineers a curve to match. That works when every rep is selling the same motion to the same buyer. It falls apart the moment you stack segments. An SMB AE closing $15K deals in 21 days and an enterprise AE working a $400K deal across seven stakeholders are not on the same learning curve, and pretending they are is how leaders end up firing reps who were actually on track.

A defensible ramp model bakes segment economics into the curve itself. Here's how to build one that holds up when your CFO challenges it.

Start with the deal, not the rep

Before you draw a single ramp line, document the deal physics of each segment. You need four numbers per segment, and you need them from your own CRM, not a benchmark deck:

• Median sales cycle length (closed-won, last four quarters)
• Median ACV
• Average number of opportunities a tenured rep carries
• Win rate from stage-2 to closed-won

Say your SMB segment runs a 28-day cycle at $18K ACV with reps carrying 22 open opps and winning 32% from qualified stage. Your mid-market is 74 days at $65K with 14 open opps and 26% win rate. Enterprise is 168 days at $310K, six to eight live opps, 19% win rate.

These four numbers determine the earliest possible moment a rep can show productivity. An SMB rep who started March 1 can theoretically close their first self-sourced deal by early April. An enterprise rep who started March 1 cannot mathematically close a self-sourced deal until late August, no matter how talented they are. Any ramp model that ignores this is fiction.

Define productivity in stages, not as a single milestone

"Fully ramped" is a lazy term. Replace it with three measurable thresholds, each tied to leading indicators appropriate to the segment.

Stage 1 — Activity competence. The rep is hitting the input metrics that correlate with pipeline creation in your segment. For SMB, that's often raw call/email volume and demo-set rate. For enterprise, it's account research depth, multi-threading per account, and executive meetings booked. This stage should close within 30–45 days regardless of segment, because it measures effort and skill, not outcome.

Stage 2 — Pipeline competence. The rep is generating qualified pipeline at the rate a tenured peer does. SMB reps should hit this around day 60. Mid-market around day 90. Enterprise around day 120–150, because building a real enterprise pipeline requires multiple account plans to mature.

Stage 3 — Bookings competence. The rep is converting that pipeline to revenue at the segment's expected win rate. SMB: day 90–120. Mid-market: day 180. Enterprise: day 270–365.

The trap most leaders fall into is judging an enterprise rep on stage-3 metrics at month four. The rep's pipeline might be perfectly healthy and their first deals six weeks from close, but the dashboard shows zero bookings and panic sets in. A staged model prevents that.

Build the curve from observed cohort data

Open your CRM and pull every AE hired in the last 24 months, grouped by segment. For each rep, plot monthly bookings from month one through month twelve, then take the median (not the mean — one whale skews everything).

The shape you'll typically see:

• SMB reps: near-zero for months 1–2, sharp climb in month 3, plateau near tenured-rep productivity by month 5.
• Mid-market: flat through month 3, gradual climb months 4–7, asymptote around month 9.
• Enterprise: essentially flat through month 5, first meaningful bookings in months 6–8, plateau around month 12, with high variance because one $500K deal swings the line.

That observed curve is your ramp model. Not a borrowed benchmark, not what your VP wishes were true. The median of your own cohort.

If you don't have enough hires per segment to make this statistically meaningful — say fewer than eight reps per segment over two years — use the deal-physics math from section one as your floor and add a 20% buffer for the learning curve. State explicitly that the model is provisional and will be re-baselined once you have cohort data.

Translate the curve into quota and comp

This is where most ramp models quietly fail. Leadership builds a beautiful productivity curve, then assigns the rep 100% of annual quota in their first year anyway because "we need the coverage."

Tie ramp quota directly to the curve. If your enterprise cohort data shows a new hire produces a cumulative 35% of tenured output in their first 12 months, ramp quota for year one is 35%, with monthly targets that mirror the curve's shape — close to zero in months 1–4, accelerating through months 8–12.

Comp follows the same logic. A ramp guarantee that pays full OTE for the first two quarters costs less than the alternative: losing a strong enterprise hire in month five because their commission check is zero and they assume they're failing. The math almost always favors the guarantee in segments with cycles longer than 90 days.

Stress-test the model against attrition signals

A ramp model isn't just a planning tool — it's an early-warning system. For each stage threshold, define what "off-track" looks like and what intervention triggers.

An SMB rep who hasn't hit activity competence by day 45 is a coaching problem or a hiring miss; decide which by day 60. An enterprise rep with no executive meetings booked by day 90 has either a territory problem or a prospecting problem; either way, intervene before month four, because by month six it's too late to course-correct inside the ramp window.

Teams that review ramp progress against the staged curve monthly catch underperformance roughly a quarter earlier than teams that wait for bookings data. That quarter is the difference between a save and a replacement.

The takeaway

• Pull your own four numbers — cycle, ACV, opp load, win rate — for each segment this week, and use them to set the mathematical floor for when productivity is even possible.
• Replace "fully ramped" with three staged thresholds (activity, pipeline, bookings) and assign each a segment-specific timeline so reps and managers can diagnose problems early.
• Re-baseline ramp quota against your observed cohort median, not aspirational targets, and pair longer-cycle segments with a ramp guarantee that survives the first two quarters.