Executive Summary
Quota-setting isn’t just a tactical exercise — it’s a strategic centerpiece for any high-performing go-to-market engine. When done right, it becomes the invisible architecture that connects bold company goals with the daily grind of sales execution. When done wrong? It quietly erodes morale, wrecks budgets, and drives your best people out the door.
The process typically starts with FP&A setting high-level revenue targets. These goals cascade down through business units, geographies, and ultimately land in the hands of individual reps. But this isn’t just a matter of math — it’s a people equation too. The way quotas are allocated impacts performance, pay, and your ability to build and keep a winning team.
Picture this: You just hired a rockstar AE with top-tier OTE. But thanks to a flawed quota model, they only see a fraction of their variable comp — not because they missed the mark, but because the mark was wildly unrealistic. That rep is gone by Q3.
Quota-setting is where strategy meets execution. Get it right, and you unlock performance, clarity, and trust. Get it wrong, and you’re fighting uphill from day one.
Understanding Quota Setting Fundamentals
Compensation Alignment
An established process that is integrated with and supports related organizational areas and technology. The process generally involves cross-functional leadership from sales, operations, and finance.
Figure 1 below illustrates how company goals are cascaded and divided among business units and regions and finally assigned at the individual rep level.
Quota-setting generally follows one of three approaches: top-down, bottom-up, or top-down and bottom-up. Your company’s methodology for setting the actual quota will vary depending on which approach it follows; however, five guiding principles should apply regardless of the methodology.
Quota Setting Guiding Principles
Every strong quota-setting process rests on five bedrock principles. Ignore them, and even the best strategy will unravel in execution.
Obtainable
When the company hits its plan, 50–60% of reps should achieve quota. Some organizations may aim higher (80–90%) based on talent strategy, but this requires tightly managed hiring, enablement, and expectations.
Quota Setting Approach
There’s no one-size-fits-all quota-setting approach. You’ve got three main plays in the playbook:
Pick the approach that matches your maturity, market complexity, and internal resources. For example: Top-down is great for startups or new products. Bottom-up shines when market conditions shift unexpectedly (hello, COVID). Hybrid fits most growth-stage or mature businesses that want scalable yet localized control.
The quota-setting strategy begins with the quota-setting process. An effective process starts with guiding principles to bridge cross-functional teams and establish common objectives. Next, the quota-setting approach determines how the company will set and distribute quotas.
The following section will explore the quota-setting methodology: how the nominal quotas are set.
Approach is how you build the model. Methodology is what math you use inside of it.
Let’s say two companies both use top-down approaches. Company A has beautiful, clean CRM data. Company B’s CRM is... let’s just say “aspirational.” Their methodologies will be wildly different.
Company A might use predictive modeling and historical trends to generate quotas. Company B might need to lean on sales leadership judgment and spreadsheet gymnastics.
Key takeaway? Your methodology should reflect your data reality, resource capacity, and business needs — not someone else’s best practice deck.
1. Opportunity Planning
Best for new markets or products. Heavy on judgment, lighter on historicals. Example: launching into a new vertical where you lean on rep or manager insight. This methodology is typically used with a small number of accounts with a high degree of individual account and market knowledge.
2. Fixed Rate Allocation
The classic “peanut butter spread.” Everyone gets 10% more than last year. Simple but blunt. Works best when data is messy and territory potential is similar. This methodology is typically used when data is fragmented, and the quota-setting process, approach, and methodologies are not defined. Commoditized products with homogenous territories typically follow this approach.
3. Base Rate & Growth
Start with last year’s number and layer on strategic growth based on known factors. Common in SaaS or recurring revenue orgs. Like Fixed Rate Allocation, the top-down and bottom-up approach work well with these methodologies.
4. Matrix Correlation
You build a model using dimensions like territory size and market potential to set quotas across a 2x2 or 3x3 matrix. Great for SMB/mid-market. These books of business are often made up of companies from different verticals, each with different marketing growth. Therefore, quotas can be assigned based on the level of market growth. For example, higher market growth would result in greater year-over-year quota increases.
5. Pipelining Planning
You lean on weighted pipeline and conversion rates to back into quota. Data governance must be rock solid for this one to work. The pipeline planning method is often used with territory—or vertical-based customer segmentation models and with SMB and Mid-Market segments.
6. Individual Account Level Planning
Each rep forecasts account-by-account. Accurate but time-consuming. Best for high-value enterprise sales motions. Individual Account-Level Planning is best for Enterprise or Global-Level accounts, where sales reps have few accounts and are often part of their stakeholders’ strategic business planning process.
7. Cluster & RFM Analysis
You apply statistical clustering based on Revenue, Frequency, and Monetary value to forecast potential. Requires data science support but can be incredibly precise for large orgs. This methodology is often used with the top-down approach and applied to all customer segments except Strategic Accounts.
Quota-setting is a team sport — and without clear roles, it turns into chaos.
That’s where governance comes in. The best organizations build a Quota-Setting Governance Committee made up of senior leaders from Sales, Finance, and Operations. This committee isn’t just for show — they own the process design, timeline, methodology, and final signoff.
Think of it as the steering wheel that keeps quota-setting on track and aligned with the broader Sales Compensation Governance Program.
Good governance means:
- Clear decision rights (who decides, who informs, who executes)
- Transparent timelines and expectations
- A repeatable process, not a fire drill
- Built-in checks from cross-functional stakeholders
No governance = late quotas, misaligned expectations, and sales teams flying blind.
A strong committee = predictability, trust, and executional excellence.
Figure 2, Governance Model
A cross-functional sales, operations, and finance team determines and leads the best-in-class quota-setting process, approach, and methodology. Many organizations do not fully build and deploy governance models within quota-setting or sales incentive compensation programs.
The core concept is that quota setting requires a division of responsibilities with clear expectations of process, timeline, approach, and methodology. Lastly, one team should be designated as the final recommender, with sales, operations, and finance leadership as the final decision-makers. The Bain RAPID model is a helpful decision-making framework that can be leveraged.
Quota-Setting Design
and Implementation Framework
Let’s break the quota-setting process into five digestible phases. Each has a distinct purpose and timeline. If you start 4–6 months before the fiscal year, you’ll give yourself breathing room and dramatically reduce rollout chaos.
Finance or FP&A
Finance or FP&A generally leads the overall top-line global and regional revenue forecast.
Sales Operations, Revenue Operations, or GTM Operations
The Operations team is typically responsible for the data-gathering process, working closely with all other cross-functional teams to apply the methodology and recommend future state, regional, market, and individual quotas. The Operations team is generally the recommender in the decision-making framework.
Field Sales Leadership
Field Sales Leadership provides insights, input, and checks on sales operations recommendations for individual-level quotas. In a top-down approach, sales leadership is generally VP-level or above. Generally, first-line sales managers are involved in bottom-up or top-down and bottom-up.
Sales Compensation Design
The Sales Compensation Design team must be informed of changes in quota setting, including year-over-year growth, as this may impact accelerator rates.
The Sales Compensation Design team will also provide input on the previous fiscal year and period quota-setting, as the performance distribution will impact the actual pay analysis and could undermine the company’s overall talent strategy.
System & Tools
The Systems & Tools team may be involved if new commission changes or software requires changes in quota distribution from previous periods.
Human or People Resources
Human or People Resources may be involved if there are material changes in the company’s talent strategy.
Data Science or Data Insights
If the decided quota allocation process is top-down or a data model is used to categorize market opportunities, data science or insights teams will be involved.
The Communications & Enablement phase is a critical period during which the Program Manager ensures all materials are developed, socialized, and approved. Proper planning will result in a successful and smooth rollout.
It is essential to refer back to the quota-setting guiding principles when designing the quota implementation plan. The goal of quota-setting is to set accurate quotas that motivate and retain high performers while managing a financial budget. However, the process should also be simple for sales reps and leaders to understand, transparent to build trust, and relevant and timely in communication.
System Development
Territory Transition Management
Implementation requires careful handling of territory transitions that may result from new quota assignments. Sales operations must coordinate with sales leadership to execute account transitions according to established protocols. This includes transferring account ownership in CRM systems, updating territory assignment rules, and properly handling in-flight opportunities. Critical attention must be paid to maintaining customer relationships during these transitions.
Performance Tracking
The implementation team must activate new performance-tracking mechanisms across multiple levels of the organization. This includes enabling dashboard visibility for front-line managers, activating roll-up reports for directors and executives, and implementing any new tracking metrics associated with the new quota assignments. Special attention should be given to ensuring proper year-over-year comparison capabilities for performance analysis.
Exception Management
During implementation, sales operations must actively manage any approved exceptions to standard quota assignments. This includes processing pre-approved adjustments, implementing special territory arrangements, and documenting all non-standard situations for future reference. The implementation team should maintain a detailed log of all exceptions and their resolutions.
Sales Manager Support
The success heavily depends on providing adequate support to sales managers as they begin working with new quotas. This includes ensuring managers have access to proper tools and reports, tracking performance against new targets, and handling common questions from their teams. Organizations should establish a dedicated support channel for managers during the initial implementation period.
Data Validation
Organizations must conduct ongoing data validation throughout implementation to ensure accuracy across all systems. This includes regular reconciliation of quota numbers between systems, validation of territory assignments, and verification of proper crediting rules implementation. The validation process should include specific checkpoints at key stages of the implementation.
You launched your quotas — now what? Time to measure effectiveness.
Here are four lenses to evaluate whether your quotas are doing their job:
These four indicators will assess the quota-setting program’s strategic and operational effectiveness. Additionally, they provide insight into the organization’s financial budgeting goals, operational effectiveness, pipeline management, and talent strategy.
The Participation Rate or the Percentage of all Eligible Participants That Meet or Exceed 100% of Their Assigned Quota
Are at least 50–60% of reps hitting quota when the company hits its target? That’s the sweet spot. Too high? Your quotas are soft. Too low? You’re crushing morale.
The table on the next page provides general guidelines for participation rate at three points of corporate plan achievement.
The idea is straightforward: if the company underachieves, the participation rate will be lower than if the company overachieves.
If the company achieves 110% of the plan but only 30% of eligible plan participants meet or exceed the quota, this can mean your pay curves are too restrictive, i.e., the threshold at which pay begins is too high, or the quota-setting for certain territories or regions is ineffective.
Why This Matters for Talent Strategy & Sales Compensation Design
If company attainment is greater than median attainment, this undermines the company’s retention strategy. This is exacerbated if quota-setting ineffectiveness is due to market, territory, or regional differences because it means that your high performers will not be in the 30% to 40% that achieve or exceed quota. Instead, the correlation between quota and performance is due to where employees sit instead of their skillset and expertise.
Why This Matters for Finance & Budget
This outcome can also happen when pay curve thresholds, i.e., those rates paid under 100% of quota attainment, are too high and/or the rates paid are too low. Pay curve thresholds are vital in designing a compensation plan since they impact employee and company cash flow. If pay curve thresholds are set too high, bookings or revenue will come in, but no associated cash flow will be attached.
The Quota Distribution Shape
Are most reps clustered around the middle or wildly spread out? Healthy distributions have a modest right skew. Extreme skews (in either direction) signal design issues.
Figure 6 below outlines an optimal quota distribution. The distribution is slightly right-skewed, with approximately 40% to 45% achieving 80% to 120% of the quota and 50% to 60% achieving 100% or more.
Figure 7 below shows three distributions: where the rep’s quotas are set too low, too high, and then optimally. The distribution shape has a material impact on talent strategy and financial goals. If quotas are set too low, then the cost of sales will be considerably higher than budgeted. If quotas are too high, top-performing talent will leave as incentive pay lags the market.
Pipeline and Forecasting Accuracy
Good quotas align with pipeline reality. You should see consistent coverage ratios and conversion rates across like-for-like territories.
Pipeline analysis begins with a coverage ratio assessment. The coverage ratio is defined as the relationship between the current pipeline value and the assigned quota. Most high-performing sales organizations require specific coverage ratios based on their sales model:
Transactional sales models typically require 3-4X pipeline coverage at the beginning of a period
Complex solution sales may require 5-6X coverage due to lower conversion rates
Recurring revenue models often target 2.5-3X coverage with higher win rates
Undercoverage across multiple territories strongly suggests that quotas may exceed market potential. When 70% or more of territories consistently show coverage below target levels, this indicates a potential structural issue with quota-setting rather than individual performance problems.
Conversion rate (the % of opportunities that progress from stage to stage in your company’s pipeline) consistency provides another critical indicator of quota appropriateness. When quotas align properly with market opportunity, conversion rates should remain relatively consistent across territories with similar characteristics. Organizations should analyze win rates across three dimensions:
Material deviations in these dimensions often signal misalignment between quota and opportunity. For example, if territories with the highest quota-to-potential ratios consistently show lower win rates than comparable territories, this suggests quota allocation issues rather than sales execution problems.
Sales cycle length provides complementary insights by measuring how quota pressure affects deal progression. Sales cycles compressed by more than 20% at period ends often indicate unhealthy discounting driven by excessive quota pressure. Organizations should establish expected cycle times by product type, deal size, and customer segment, then monitor variances correlating with quota levels.
Forecast accuracy represents the most sophisticated pipeline metric, measuring a sales rep’s ability to predict outcomes reliably. Quotas at appropriate levels typically generate consistent forecast accuracy, while misaligned quotas produce erratic forecasting behavior. Two key patterns indicate potential quota-setting issues:
Systems over-forecasting across multiple territories suggests that representatives are trying to close an unrealistic “quota gap.”
Declining forecast accuracy as the period progresses indicates pipeline quality issues driven by aggressive prospecting to meet unrealistic targets.
Implementing robust pipeline and forecasting metrics requires integration with CRM systems and regular review cadences. Organizations should establish:
Weekly pipeline reviews at the front-line manager level
Bi-weekly forecasting accuracy reviews at the director level
Monthly executive leadership reviews analyzing pipeline metrics against quota attainment patterns
Organizations can identify potential quota-effectiveness issues early enough to implement corrective actions by consistently monitoring these metrics. This transforms quota management from a retrospective exercise into a dynamic process that optimizes alignment between corporate objectives and field execution.
Operational Effectiveness Measures
Did you hit the release timeline? Is CRM data accurate? Are field leaders satisfied? These may feel tactical, but they’re core to a trusted process.
CRM Data Accuracy
CRM data accuracy serves as the foundation for quota operational effectiveness. Poor data quality drives both inefficiency and inaccuracy in quota setting. Organizations must systematically measure data quality across key fields:
• Account assignment accuracy
• Territory definition completeness
• Historical performance data availability
When data accuracy falls below acceptable thresholds, organizations should implement targeted data cleanup initiatives before proceeding with quota allocation. Many quota-setting failures can be traced to poor data quality rather than methodology or process issues.
Quota Timing Performance
Quota timing performance examines how well the organization adheres to its planned quota release schedule. Organizations should monitor three key timing indicators:
• Percentage of quotas assigned by start of period
• Average days between period start and quota assignment
• Variance between planned and actual quota release dates
When timing metrics consistently fall below targets, organizations should examine root causes such as approval bottlenecks, data availability issues, or insufficient staffing resources. Timing effectiveness directly impacts sales force confidence, early-period productivity, and attainment patterns.
Exception Management Efficiency
Exception management efficiency examines quota-related exceptions’ volume, type, and resolution time, as these directly impact field productivity and satisfaction. Organizations should track:
• Percentage of territories requiring non-standard quota assignments
• Average exception resolution time
• Exception recurrence rate by category and region
High exception volumes often signal underlying territory design, data quality, or quota methodology issues. For example, if more than 15% of territories require exceptions, this suggests a systematic problem with the quota-setting approach rather than isolated cases requiring adjustment.
Cross Functional Satisfaction
Cross-functional satisfaction metrics offer important qualitative insights into operational effectiveness. Regular surveys of key stakeholders, including finance teams, sales operations, field sales leadership, and IT/systems teams, can reveal process friction points that quantitative metrics might miss.
When satisfaction scores fall below established acceptable levels, organizations should conduct focused interviews to identify specific improvement opportunities. Given its impact across multiple business functions, cross-functional alignment is important for quota setting.
Resource Utilization
Resource utilization tracks the effort required to execute the quota-setting process:
• Total person-hours invested in quota-setting activities
• Distribution of effort across process phases
• Overtime or temporary staff requirements
• System utilization metrics
Excessive resource requirements often indicate process inefficiencies, inadequate system support, or overly complex methodologies. For example, if more than 70% of quota-setting effort is spent on manual data gathering and validation, this suggests an opportunity for process automation or data integration improvements.
Establishing an operational effectiveness dashboard that tracks these metrics over time enables organizations to implement a continuous improvement approach to quota management. The Quota Setting Governance Committee should review this dashboard quarterly, and specific action plans should be developed to address any metrics falling outside acceptable ranges.
The world of quota-setting is evolving fast — and the smartest orgs are getting ahead of the curve.
AI + Analytics Are Changing the Game
AI-driven territory and quota models aren’t a luxury anymore. Companies using advanced analytics see 7–10% better attainment and 20% less rep churn. Why? Because the quotas feel fair and data-driven.
Dynamic Quota > Static Quotas
Annual quota cycles are giving way to dynamic models. Companies are shifting to quarterly refreshes or mid-year rebalancing based on real-time market signals. It’s more work, but it’s more responsive.
Integrated Systems Are a Must
Quota, comp, CRM, and forecasting systems must be tightly integrated. This allows real-time impact analysis when anything changes — like a new hire, new product, or territory shift.
Governance Matures
More orgs are formalizing quota-setting into broader Sales Compensation Governance structures. This helps manage exceptions, disputes, and change requests with clarity.
Automation Unlocks Scale
Workflow tools are cutting time spent building and launching quotas by 40–60%. That frees up your ops teams to focus on strategy, not spreadsheet wrangling.
Let’s talk edge cases that often spark debate: over-assignment and quota bands.
Over Assignment
This is when the sum of all individual quotas exceeds the company’s revenue goal. Sounds wild? Actually, it’s smart — when used well.
Why do it?
Hedge against attrition and ramp time
Account for double crediting across teams
Bake in stretch targets
Offset portfolio or territory complexity
Finding the Right Balance
The key to effective over-assignment is finding the right level of over-assignment for your organization’s specific circumstances. Both insufficient and excessive over-assignment create significant challenges:
Implementation Best Practices
Successful quota over-assignment requires careful implementation following these key practices:
Conclusion
Quota over-assignment represents a sophisticated approach to addressing common challenges in sales organizations. When implemented thoughtfully and connected to the broader talent and compensation strategy, it creates appropriate buffers against attrition, ramp time, and structural complexities while maintaining sales force motivation and performance.
As with other quota-setting practices, over-assignment should align with your obtainability, alignment, consistency, transparency, and timeliness guiding principles. When these principles inform your over-assignment approach, this practice becomes important to your overall quota methodology.
Understanding Quota Size Impact on Performance
Quota size generally has the most significant impact on performance distribution. Analysis of performance data consistently shows that smaller quotas typically demonstrate greater percentage achievement and wider distribution variance compared to larger quotas. This phenomenon creates an inherent inequity when using the same compensation structures across all quota sizes.
The relationship between quota size and performance follows a predictable pattern: as quota size increases, the extreme attainment percentages (both high and low) tend to narrow. For example, an Account Executive with a $5 million quota might achieve 200% or more of their target, while an Account Executive with a $50 million quota rarely exceeds 140%. This compression occurs because larger territories naturally contain more diversity in accounts, market conditions, and competitive dynamics, creating a statistical averaging effect.
Organizations applying uniform pay structures across different quota sizes inadvertently create significant inequities, often adversely impacting senior and high performers. Sales reps with smaller quotas can reach accelerator thresholds more easily, usually earning disproportionately higher compensation than those with larger quotas.
Sales reps managing larger territories generate more absolute revenue for the organization and face more significant challenges in achieving their quota, yet they will receive less overall compensation due to the difficulty of overachieving a larger quota.
The identification of quota bands begins with data analysis. By plotting quota attainment against quota size across the entire sales organization, patterns emerge that reveal where natural groupings occur. These patterns typically show wider variance in smaller quota sizes that narrow progressively as quota size increases. The points where significant narrowing occurs mark the boundaries between different quota bands.
What Are Quota Bands?
Quota bands represent a systematic approach to grouping quota ranges based on “decreasing dispersion” patterns. By examining the relationship between quota size and attainment percentages, organizations can identify natural break points—or “funnels”—where performance distribution patterns demonstrate significant changes.
Figure 9 below visually represents the expected relationship between quota size and attainment. Despite outliers, the “decreasing dispersion” pattern is evident as the quota size increases.
Why Use Quota Bands?
Because Quota Size has Highest Impact on Performance
The identification of quota bands begins with data analysis. By plotting quota attainment against quota size across the entire sales organization, patterns emerge that reveal where natural groupings occur. These patterns typically show wider variance in smaller quota sizes that narrow progressively as quota size increases. The points where significant narrowing occurs mark the boundaries between different quota bands.
In Figure 9 below, the example scatterplot reveals three likely quota bands based on performance distribution:
• Territories under $20 million showing high variance with top performers reaching 160% of quota
• Territories between $20-50 million showing moderate variance with top performers reaching 145% of quota
• Territories over $50 million showing narrower variance with top performers reaching 130% of quota
Approach to Setting Quota Bands
Step 1: Create Quota Correlation Scatterplot
These natural breakpoints then form the boundaries of the quota bands, which become the foundation for differentiated compensation structures.
Why Use Quota Bands
Sales organizations implement quota bands to ensure pay equality for top performers across different quota sizes. Without quota bands, organizations face several challenges:
By implementing quota bands, organizations can maintain comparable earnings opportunities for similar performance across all territory sizes. This approach ensures that top performers receive equitable compensation regardless of their territory assignment.
The Approach to Setting Quota Bands
Establishing quota bands is typically a function of the Sales Compensation Design cycle and is not required for quota-setting strategy and distribution.
Sales Compensation teams that need to set quota bands should follow four steps:
Step 1: Create Quota Correlation Scatterplot
The process begins by creating a scatterplot that maps quota size against performance achievement. This visualization identifies natural breakpoints where performance dispersion patterns change significantly. The scatterplot analysis should be at the role level.
For example, include all Account Executives regardless of geography or vertical. Starting with the entire population provides a framing for additional analyses, i.e., by geography, vertical, etc.
When examining these scatterplots, look for areas where the data points begin to funnel or narrow, indicating decreasing variability in performance outcomes. These visual indicators provide the first signal of where quota band boundaries might logically exist.
Step 2: Calculate Break Points
Once the scatterplot is created, the next step involves statistical analysis to confirm the visually identified break points. This typically involves:
Breaking quota data into increments for analysis. The increments will depend on the size of the quotas. For example, if the min-max distribution of quotas is $100K to $1 million, $50K or $100K increments may work. If the min-max distribution is $5 million to $250 million, $10 or $20 million increments will be best.
Calculating standard deviation of performance within each increment.
Identifying significant drops in standard deviation between adjacent increments
Looking for sustained patterns rather than isolated anomalies
The objective is to identify where standard deviation demonstrates sudden, sustained decreases, confirming the funneling effect observed visually in the scatterplot. These statistical breaks provide the quantitative foundation for quota band boundaries.
In Figure 9 below, the standard deviation falls around $20 to $25M. Other data points are often incorporated, including mean and median attainment within the quota increments, min-max attainment, etc.
This step can be done in statistical software packages like R or KNIME, but Microsoft Excel’s functionality suits most businesses.
Approach to Setting Quota Bands
Step 2: Confirm Quota Bands
Confirm funneling by looking for sudden, sustained drops in Standard Deviation across Increments.
Step 3: Determine the Number of Quota Bands
With break points identified, organizations must decide how many quota bands to implement. This decision balances equitability with operational complexity. While more bands create greater equity, they also increase administrative complexity and may introduce unnecessary segmentation when differences are minimal.
For each potential quota band, calculate:
• Standard deviation of performance within the band
• The 90th percentile attainment
• Population percentage falling within the band
Figure 9 below shows the consolidated quota increments into quota bands. In this example, the 90th percentile achievement drops significantly as the quota increases.
Approach to Setting Quota Bands
Step 3: Determine the Number of Quota Bands
Most organizations typically implement between two and four quota bands based on specific performance patterns. The optimal structure depends on the distribution of territories across different size ranges and the magnitude of performance variation between them.
Step 4: Assess Goodness of Quota Bands
The final step involves validating the effectiveness of the proposed quota bands using several criteria:
Linking Quota Bands to Accelerator Rates
Once quota bands are established, organizations must develop appropriate accelerator structures for each band to create equitable earning opportunities across all territory sizes. This means sales reps will have different accelerator rates based on their quota size.
How Pay Curves Change by Quota Size
Pay curves must be designed to normalize compensation opportunity across different quota sizes. This typically means implementing progressively higher accelerator rates as quota size increases. The relationship follows a simple principle: the lower the 90th percentile (or the larger quota), the higher the accelerator rate needed to create comparable earning opportunities.
For example:
• Small quota band ($20M). 90th percentile of 160%, Accelerator rate of 2.5x
• Medium quota band ($20-50M). 90th percentile point of 145%, Accelerator rate of 3.3x
• Large quota band (>$50M). 90th percentile of 130%, Accelerator rate of 6.7x
This progressive structure ensures that the representative with a $50 million quota who achieves 130% of the target (representing exceptional performance for that territory size) can earn comparable incentive compensation to the representative with a $10 million quota who achieves 160% of their target quota.
The Accelerator Rate Formula
The accelerator rate for each quota band is calculated using the following formula:
Leverage represents the target incentive multiplier at the 90th percentile of attainment within the quota band. Note that leverage points can be obtained from benchmarks but are generally strongly, positively correlated to pay mix. Account Executives typically have 2.5 to 3.5x leverage, whereas Relationship Managers typically have 1.5 to 2.5x leverage.
This formula ensures that representatives reaching the 90th percentile, or in other words, the high performers for their respective quota band, will earn the same percentage of target incentive, creating equity across different territory sizes.
Implementation Considerations
When implementing quota bands and differentiated accelerator rates, organizations must consider several operational factors:
Complexity vs. Fairness Tradeoff
Organizations must balance the equitability benefits of multiple quota bands against the complexity they introduce. The implementation spectrum ranges from:
• Single quota band/accelerator structure (simplest but least fair)
• Few quota bands at the corporate level (moderate complexity and fairness)
• Differentiated bands by job role and geography (more complex but more equitable)
• Individual-level customization (most fair but prohibitively complex)
Most organizations find optimal balance with three to four quota bands differentiated by role and, in rare cases, by market or region.
Communication Strategy
Successful implementation requires clear communication of the rationale behind quota bands. Incumbents impacted by quota bands, including first and second-line managers, must understand the following core principles:
• Why do different quota sizes require different accelerator structures
• How the approach creates more equitable earning opportunities
• Why the structure is fair despite apparent differences in rates
Communication should focus on the outcomes—that top performers have similar earnings potential regardless of territory size—rather than dwelling on the mechanical differences in accelerator rates.