Cost to Hire Report

Top-Performer Uplift (Productivity F)
Hiring a “top” AE (top ~10–20% performer) can roughly double the sales output of a median performer. For example, at SMB-scale SaaS firms the top AEs earn ~2.0× the median on-target earnings (OTE) – implying they close about twice as much revenue (e.g. top performers ~$267K OTE vs. ~$130K median)repvue.com. Broader studies corroborate this gap: top-quartile sales reps produce ~2.05× the revenue of bottom-quartile reps on average (range up to 3.5× in some orgs). In practical terms, one “quota crusher” can equal 2+ average sellers in revenue contribution. We define “top performer” here as those consistently >120% of quota (upper decile/quintile).

Time-to-Fill (TTF)
Using internal recruiting, filling a field sales role takes ~45–60 days on averages. In small/mid organizations the median TTF for a sales specialist role is ~53 days (with an average ~56 days). Engaging a specialist sales recruiter can cut this significantly – often by 30–50%. Industry recruiters note that what might take an internal team 2 months can be done “in weeks” by a niche agencylinkedin.com. In practice, partnering with a focused sales recruiter can reduce TTF to around 30–35 days (vs. ~60 days internally), due to their ready talent.

Ramp to Productivity (Ramp-Up)
It takes ~5–6 months for a new AE to ramp to full productivity on average. Recent benchmarks in SaaS sales show an average AE ramp of ~5.7 months (up from ~5.3 in 2022). In other words, a typical AE hired today might only reach 100% quota capacity after ~170 days of onboarding, territory familiarity, and pipeline build. (Notably this ramp time has been growing, reflecting complex sales cycles.) Top performers tend to ramp somewhat faster – often hitting productivity milestones 1–2 months sooner than average peers – see RampΔ below.

Ramp Acceleration (RampΔ)
Top sales hires not only sell more – they ramp up faster. Data indicates high performers reach full productivity sooner: 54% of top-performing reps report being fully onboarded and productive within 3 months, versus only 30% of low. In effect, an elite AE might achieve quota productivity in ~3–4 months instead of ~5–6, getting to revenue generation ~30–50% faster. This accelerated ramp means less “ramp downtime” cost. We assume RampΔ of roughly 1.5–2 months saved for a top performer, based on the above stats and industry observations.

First-Year Compensation
Field AEs in SMB settings typically have a 50/50 base-variable split. Benchmarks (2024–25) show: Base salary around $70K (median), with a 50% target commission of ~$70K, yielding a $140K OTE median. Range: 25th percentile OTE ~$100K (e.g. base ~$50K) and 75th ~$180K (base ~$90K). For context, the median U.S. sales rep base is ~$63K (with top-quartile bases ~$93K). Mid-market SaaS AEs average ~$79K base and ~$154K OTE, aligning with the SMB range above. Top “rockstar” AEs can earn well above OTE via accelerators (often 1.5–2× commission rates beyond 120% quota), with top decile total comp reaching $250K+ in SMB. (We provide full comp percentile breakdowns in the data file.)

Vacancy Cost per Day
An open AE territory means foregone revenue. Each day of vacancy costs roughly $300–$600+ in lost revenue opportunity, depending on quota. A simple formula: if an AE’s annual quota is, say, $150K, that’s about $600 in revenue per workday that goes unrealized. Over a 60-day hiring cycle, that sums to ~$36K potential revenue loss. Industry averages: SHRM estimates an average cost of $4,100 for a 42-day vacancy across roles ($98/day), but revenue-generating roles run much higher – on the order of $7K–$10K per month. Indeed, research finds leaving key sales positions unfilled can cut a company’s revenue by 5% or more. We therefore use a conservative ~$500/day vacancy cost for an AE (approx. 20% of a $130K quota OTE per year), noting it could be higher for top territories. (Formula: daily cost ≈ Annual Gross Profit or Quota × (1/260).)

Top-Performer Uplift (Productivity F)
In Sales Development roles, top performers (top 20% who exceed meeting quotas) are ~1.5× as productive as the median rep, in terms of pipeline generated. For instance, salary data shows top SDRs earn ~$130K vs. ~$85K median OTE – reflecting higher commission from more meetings/opps set. Industry surveys likewise note that the best SDRs deliver ~150%+ of the results of an average SDR (e.g. top reps booking ~2× meetings per week vs. mid-level reps). In qualitative terms, a “quota crusher” SDR (often >120% quota) can do the work of ~1.5 SDRs. We define top SDRs as the upper quintile by SQLs/appointments generated with high quality.

Time-to-Fill (TTF)
~40– Fifty days is a typical time to fill an SDR role internally. SDRs are entry-level but still in high demand, so internal hiring can take about 6–7 weeks on average (median ~42 days). (The overall average time-to-hire across industries is ~44 days.) However, because SDR talent pools are broad, using a specialized recruiter can speed this up. Many firms report filling SDR openings in ~3–4 weeks with external recruiting help, versus ~6+ weeks internally. (No published stat specific to SDR agency fill time was found, so we assume ~50% acceleration similar to sales roles.) For our ROI model: TTFᵤ ≈ 45 days, TTFₚ ≈ 25 days for SDR.

Ramp to Productivity
SDRs ramp faster than AEs. ~3 months (90 days) is the average ramp-up for a new SDR to reach full productivity. The Bridge Group finds ~3.2 months is typical, consistent since 2007 (most SDRs fully effective by month 3). This includes learning the product, scripts, and cadence practice. Top SDR hires might ramp in closer to ~2 months if highly coachable, whereas underperformers can take 4+ months or never fully ramp before exiting. We will use ~90 days ramp as the baseline.

Ramp Acceleration (RampΔ)
Top SDRs tend to ramp a bit faster – often reaching targets several weeks sooner than average. One study notes high performers were ~80% more likely to be fully productive by 3 months than low performers. In practice, a standout SDR might hit the daily call/meeting quota in ~8 weeks versus ~12 for a typical new rep. For modeling, we assume top SDRs ramp ~1 month faster than median (e.g. 60 days vs 90 days to full productivity), based on the above onboarding survey and anecdotal reports of “quick-start” reps.

First-Year Compensation
SDRs in SMB/B2B tech have relatively standardized pay: Base ~ $55K and Variable ~ $30K at target, for about $80K–$85K OTE. Range: entry-level SDRs often start around $50K OTE in smaller markets, while top-end SDRs (75th pct or in high-cost markets) approach $100K OTE. For example, a survey finds SDR base salaries typically $50K–$60K (avg ~$56K) in SaaS firms. OTE ranges from ~$70K at small companies up to ~$110K in enterprise segments. Our model will use $55K base + $30K variable = $85K OTE (50–65% base) as the median. (Notably, SDR compensation is less variable than AE’s; even top SDRs might earn ~$130K with accelerators, whereas bottom-quartile may be closer to $60K if they miss quota.)

Vacancy Cost per Day
An empty SDR seat means fewer leads and pipeline for AEs. We can estimate cost via pipeline contribution: if an SDR generates, say, 10 qualified meetings/month and each has $X revenue potential, the daily value of an SDR can be back-calculated. As a rough rule: an SDR generating ~$500K in pipeline/year (which might translate to ~$100K won revenue) is “worth” ~$1,900 in pipeline per workday (or ~$400 in eventual revenue per day assuming 20% close rate). That said, to be conservative: each lost SDR day costs on the order of $100–$300 in revenue impact. Another approach: SHRM’s average vacancy cost ~$98/day, but SDRs have direct revenue impact so likely higher. Using the salary multiple method: one SDR ~$85K OTE might drive ~$170K value annually (2× multiple), equating to ~$650 value per workday. For ROI calculations, we’ll use $200/day as a modest estimate of lost revenue/opportunity per unfilled SDR (recognizing actual impact depends on lead conversion lags). The vacancy also burdens sales reps who must self-prospect in the interim, an inefficiency not easily quantified.

Top-Performer Uplift (Productivity F)
In Customer Support, top performers (top 20% by tickets resolved and customer satisfaction) can handle significantly more volume and quality. It’s common to see a ~1.5×–2× gap in ticket throughput between a top support agent and an average agent, while maintaining higher CSAT. For instance, industry data shows that in helpdesks the top 5% of agents have resolution times ~5× faster than average (82 hours vs 17 hours) – illustrating the outlier impact of star performers. A more conservative view: various productivity studies find high performers ~400% more productive than peers on average across jobs. For less complex roles like support, the gap may be smaller but still substantial (e.g. one support “ace” doing the work of ~2 reps). We define “top” support reps as those with outstanding metrics: e.g. highest tickets closed per day with >95% customer satisfaction, or top 10% in internal rankings.

Time-to-Fill (TTF)
Support agents typically ramp up in ~1–2 months. Training in product knowledge, ticketing systems, and protocols usually takes a few weeks, and agents often reach full handle capacity by ~60 days in. Some metrics: call centers report ~60–90 days for new agents to hit stride. For simpler support roles, 1 month might suffice for basic proficiency, but for technical support or complex products, up to 3 months is observed. We will assume an average ~60-day ramp for a support rep to be fully productive in an SMB context (where support issues are moderately complex).

Ramp Acceleration (RampΔ)
Top-performing support hires can ramp notably faster – often by 2–4 weeks – due to sharper learning curves and proactive skill uptake. There is less published data here, but by analogy: top performers in other fields were 5× less likely to still feel unprepared after one years. In support, a quick-learner might become independent in, say, 4 weeks vs. the usual 8. We assume a ~30% faster ramp for a top-tier support hire (e.g. reaching full ticket load in ~40 days instead of 60). This means less time in training mode and quicker ROI on that hire.

First-Year Compensation
Customer Support roles are typically salaried (with little or no variable pay for front-line agents). Nationally, median base salary ~ $40K for general Customer Service Reps. However, in SMB tech companies, support specialists often earn higher: ~$50K–$55K/year median in North America. We’ll use $50K base as a representative figure (no formal bonus, aside from possible small performance bonuses). Range: entry-level support in low cost areas might be ~$30–35K (25th pct), whereas experienced Tier-2 support or high COL areas see ~$60K+ (75th pct). For instance, the median support salary in the U.S. is reported at $55K, and BLS data shows middle 50% roughly $35K–$55K. We will present $40K (25th), $50K (median), $60K (75th) as an approximate range for a SMB support rep’s annual pay. (These roles don’t have an “OTE” concept since variable pay is minimal – compensation is essentially base pay.)

Vacancy Cost per Day
An unfilled support seat primarily affects customer experience and team workload, which can indirectly hit revenue via poor service or client churn. To quantify: consider metrics like average tickets per agent per day. If an agent handles 10 tickets/day with an average customer value of $Y at risk per ticket, one can estimate cost. Another approach: lost productivity – e.g., if a support team is short one member, remaining agents may let service levels slip (longer response times, lower CSAT). For modeling, one can use the salary proxy: each support rep (at $50K/year) provides value in keeping customers satisfied; dividing ~$50K by 260 workdays gives ~$192/day of “value”. Additionally, consider churn: studies show a single bad service incident can cause ~$243 in lost business on average (per dissatisfied customer). If a vacant role means 5 customers didn’t get timely help in a day, the risked revenue could be >$1K. We will use a modest $150/day vacancy cost for support in our ROI (recognizing that the true cost may surface in customer retention metrics over time). This accounts for productivity loss and the potential revenue impact of degraded service. (Notably, this cost can spike if the support function is critical – e.g. in SLA-bound B2B support, an unfilled role could incur SLA penalties or lost contracts.)