Sales StrategyThat Drives Results
It's more than a number.
SBI is your GTM strategy partner that understands what your new reality is and what you need to do to make your number now and in the future.
The New Reality
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AI-guided buyersAttention is won by relevance, not volume.
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Volatile budgetsPlans shift overnight.
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Aggressive competitorsThey outspend and out-hire.
What You Need
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StrategyBoardroom-ready forecasts that hold.
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OrganizationMore growth per rep without headcount creep.
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PerformanceFaster adaptation that turns uncertainty into advantage.
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GrowthInvestor confidence and sustainable enterprise value.
Explore the Levers that Drive Growth
Sales Strategy
What It Is
Why It Matters
AI-Era POV
Key Components
- Segmentation and prioritization of accounts based on revenue potential and strategic fit
- Coverage models that optimize seller-to-opportunity ratios across segments and channels
- Territory design grounded in revenue potential, competitive dynamics, and relationship mapping
- Quotas tied to addressable market opportunity and validated through bottom-up analysis
- Compensation that aligns with shareholder value, customer outcomes, and team collaboration
- Enablement and sales process that drive execution consistency and continuous improvement
- Change management frameworks that minimize disruption during strategic transitions
- Performance measurement systems that provide early warning signals for course correction
Frequently Asked Questions
How often should a sales strategy be updated?
What role does AI play in modern sales strategy?
When should we redesign our sales strategy vs. adjust our existing approach?
How do we implement strategic changes without disrupting current performance?
Account Segmentation
What It Is
Account segmentation is how sales leaders decide which customers and prospects deserve the most time, resources, and attention. The best models go beyond simple revenue tiers and factor in revenue potential, buying behavior, decision-making complexity, and growth opportunities. Done right, segmentation creates a clear playbook for where to invest and how to win.
Account segmentation is how sales leaders decide which customers and prospects deserve the most time, resources, and attention. The best models go beyond simple revenue tiers and factor in revenue potential, buying behavior, decision-making complexity, and growth opportunities. Done right, segmentation creates a clear playbook for where to invest and how to win.
Why It Matters
When segmentation is off, sales teams chase low-value deals while high-potential accounts get overlooked. That wastes seller capacity and slows growth. Companies with strong account segmentation see faster revenue growth and higher profitability because they know exactly which accounts to prioritize and how to cover them. In a world where sales resources are expensive and buyer attention is scarce, precision here is a competitive advantage.
When segmentation is off, sales teams chase low-value deals while high-potential accounts get overlooked. That wastes seller capacity and slows growth. Companies with strong account segmentation see faster revenue growth and higher profitability because they know exactly which accounts to prioritize and how to cover them. In a world where sales resources are expensive and buyer attention is scarce, precision here is a competitive advantage.
AI-Era POV
Old models relied on static reviews and revenue tiers. In the AI era, segmentation is dynamic. Machine learning analyzes real-time signals-such as engagement patterns, intent data, and competitive activity-to constantly refresh account priorities. For example, an account that spikes in product demo requests or competitor searches can be flagged and re-tiered instantly, keeping sellers focused on the best.
Old models relied on static reviews and revenue tiers. In the AI era, segmentation is dynamic. Machine learning analyzes real-time signals-such as engagement patterns, intent data, and competitive activity-to constantly refresh account priorities. For example, an account that spikes in product demo requests or competitor searches can be flagged and re-tiered instantly, keeping sellers focused on the best.
Key Components
- Scoring models that include revenue potential, strategic fit, and buying readiness
- Dynamic tiers that update automatically based on new data
- Coverage models that align seller capacity with account value
- Automated alerts when accounts show buying signals or shift tiers
- Seamless CRM and sales tool integration
- Ongoing validation using win/loss analysis
Frequently Asked Questions
How often should account segmentation be updated?
In the AI era, segmentation should be dynamic and update continuously based on new signals. At minimum, conduct quarterly reviews of tier assignments and model performance. The most sophisticated organizations implement real-time segmentation that automatically adjusts account tiers based on engagement patterns, buying signals, and competitive activity. This ensures sales resources are always focused on the highest-value opportunities. Legacy approaches that relied on annual segmentation reviews leave money on the table as high-potential accounts go unnoticed and low-value accounts consume disproportionate seller time.
What data sources are most important for segmentation?
Blend firmographics (company size, industry, location), technographics (technology stack, digital maturity), engagement data (website visits, content downloads, event attendance), intent signals (search behavior, competitor research), and competitive intelligence (incumbent relationships, budget cycles). The richest segmentation models incorporate both internal data from your CRM and marketing automation systems plus external data from intent providers, news feeds, and market intelligence platforms. Quality matters more than quantity-ensure data hygiene and integration before adding more sources.
How do you handle accounts that don't fit standard tiers?
Create exception paths for strategic or emerging accounts while keeping discipline across the portfolio. Establish clear criteria for exceptions (e.g., strategic partnership potential, market lighthouse accounts, category creators) and require executive approval for non-standard coverage. Without governance, exceptions erode segmentation discipline and create coverage inefficiencies. Review exceptions quarterly to determine if they should become permanent tier adjustments or return to standard coverage models.
What's the difference between account segmentation and buyer personas?
Account segmentation groups companies by value and priority to optimize resource allocation. Buyer personas define the individuals within those accounts who influence purchasing decisions. Both are essential-segmentation tells you which accounts to pursue, personas tell you how to engage within those accounts. Advanced organizations create segment-specific personas, recognizing that decision-making patterns vary by company size, industry, and maturity stage.
How do you measure the effectiveness of your segmentation model?
Track win rates by segment, revenue per account by tier, sales cycle length by segment, and seller productivity across coverage models. Effective segmentation should show higher win rates and faster sales cycles in top-tier accounts compared to lower tiers. If performance is flat across tiers, your segmentation criteria may not reflect true account value or buying readiness. Continuously test and refine your scoring model based on actual outcomes.
Ideal Customer Profile
What It Is
An Ideal Customer Profile (ICP) is a detailed description of the type of company that derives maximum value from your solution and generates the highest lifetime value for your business. It goes beyond basic firmographics to include behavioral characteristics, buying patterns, organizational maturity, and value alignment. A strong ICP serves as the North Star for all go-to-market activities, guiding everything from marketing campaigns to sales prospecting to product development priorities.
An Ideal Customer Profile (ICP) is a detailed description of the type of company that derives maximum value from your solution and generates the highest lifetime value for your business. It goes beyond basic firmographics to include behavioral characteristics, buying patterns, organizational maturity, and value alignment. A strong ICP serves as the North Star for all go-to-market activities, guiding everything from marketing campaigns to sales prospecting to product development priorities.
Why It Matters
Companies without a clear ICP waste up to 40% of sales capacity pursuing prospects that will never convert or become unprofitable customers. Research shows that organizations with well-defined ICPs achieve 68% higher account win rates and 50% shorter sales cycles compared to those using broad targeting. A precise ICP also improves customer retention, expansion revenue, and referenceability-high-fit customers become advocates who fuel growth through referrals and case studies. In investor-backed companies, ICP discipline directly impacts efficiency metrics like CAC payback and customer lifetime value.
Companies without a clear ICP waste up to 40% of sales capacity pursuing prospects that will never convert or become unprofitable customers. Research shows that organizations with well-defined ICPs achieve 68% higher account win rates and 50% shorter sales cycles compared to those using broad targeting. A precise ICP also improves customer retention, expansion revenue, and referenceability-high-fit customers become advocates who fuel growth through referrals and case studies. In investor-backed companies, ICP discipline directly impacts efficiency metrics like CAC payback and customer lifetime value.
AI-Era POV
Traditional ICP development relied on gut instinct and basic firmographic analysis. AI-powered ICP definition analyzes thousands of data points across won and lost deals, customer profitability, product adoption patterns, and churn indicators to identify the characteristics that truly predict success. Machine learning can continuously refine your ICP based on new customer data, market signals, and competitive dynamics-ensuring your targeting stays aligned with evolving market realities. Advanced ICP models go beyond static attributes to include dynamic signals like technology adoption patterns, hiring trends, and funding events that indicate buying readiness.
Traditional ICP development relied on gut instinct and basic firmographic analysis. AI-powered ICP definition analyzes thousands of data points across won and lost deals, customer profitability, product adoption patterns, and churn indicators to identify the characteristics that truly predict success. Machine learning can continuously refine your ICP based on new customer data, market signals, and competitive dynamics-ensuring your targeting stays aligned with evolving market realities. Advanced ICP models go beyond static attributes to include dynamic signals like technology adoption patterns, hiring trends, and funding events that indicate buying readiness.
Key Components
- Firmographic criteria including company size, industry, geography, and growth stage
- Technographic profile of technology stack, digital maturity, and adoption patterns
- Behavioral indicators such as buying triggers, decision processes, and evaluation criteria
- Economic factors including budget availability, ROI requirements, and profitability potential
- Organizational characteristics like team structure, strategic priorities, and change readiness
- Continuous ICP refinement based on win/loss analysis and customer performance data
- Negative ICP definition-explicitly identifying poor-fit characteristics to avoid
- ICP scoring and grading systems to prioritize prospects by fit strength
Frequently Asked Questions
How is ICP different from buyer personas?
ICP defines the characteristics of target companies, while buyer personas describe the individual decision-makers within those companies. ICP answers 'which companies should we pursue,' while personas answer 'who do we talk to within those companies and how do we engage them.' Both are essential-ICP drives targeting and account selection, personas drive messaging and engagement strategy. The most effective go-to-market strategies develop personas for each ICP segment, recognizing that decision-making patterns vary by company type.
Should we have one ICP or multiple?
Most companies benefit from a primary ICP (60-70% of focus) plus 2-3 secondary ICPs for adjacent opportunities. Multiple ICPs work when you serve genuinely different markets with distinct needs, buying processes, or value propositions. Too many ICPs dilute focus and create operational complexity. If you have more than 4-5 ICPs, you likely lack focus or haven't identified the common patterns across segments. Start with one primary ICP, prove product-market fit, then expand to adjacent segments systematically rather than trying to serve everyone simultaneously.
How often should ICP be updated?
Review ICP quarterly using win/loss data, customer performance metrics, and market intelligence. Make adjustments when you see consistent patterns-for example, if you're consistently losing deals in a certain segment or winning unexpectedly in another. Major ICP revisions should happen annually as part of strategic planning. The key is balancing stability (which enables focused execution) with adaptability (which ensures targeting stays relevant). AI-powered ICP systems can flag when customer data suggests ICP criteria should be reconsidered, enabling data-driven updates rather than calendar-driven reviews.
What do you do when a prospect doesn't fit ICP but wants to buy?f
Establish clear exception criteria and approval processes. Not every out-of-ICP deal should be rejected-some represent strategic opportunities like lighthouse customers, market expansion tests, or high-value anomalies. However, exceptions should require executive approval and shouldn't exceed 10-15% of deals without triggering an ICP review. The biggest risk is allowing too many exceptions, which dilutes ICP discipline and creates operational complexity. Track exception performance separately to determine if they're truly outliers or signal an ICP evolution.
How do you measure ICP effectiveness?
Track win rates by ICP fit (high-fit vs. low-fit), sales cycle length by segment, customer lifetime value by ICP, churn rates by profile, expansion revenue by segment, and overall pipeline quality (percentage of qualified opportunities matching ICP). Effective ICPs show dramatically higher win rates (60-70%+) for high-fit prospects vs. low-fit (20-30%), faster sales cycles, better retention, and higher lifetime value. If performance is similar across fit levels, your ICP criteria don't actually predict success and need refinement.
Sales Org Design
What It Is
Sales organization design encompasses the strategic structuring of roles, reporting relationships, spans of control, and operational cadences that enable sales teams to execute effectively at scale. This includes defining specialized roles (hunters vs. farmers, inside vs. outside, vertical specialists), establishing appropriate manager-to-rep ratios, designing career progression paths, and creating governance structures that balance autonomy with accountability. Modern sales org design must also account for AI augmentation and hybrid work models.
Sales organization design encompasses the strategic structuring of roles, reporting relationships, spans of control, and operational cadences that enable sales teams to execute effectively at scale. This includes defining specialized roles (hunters vs. farmers, inside vs. outside, vertical specialists), establishing appropriate manager-to-rep ratios, designing career progression paths, and creating governance structures that balance autonomy with accountability. Modern sales org design must also account for AI augmentation and hybrid work models.
Why It Matters
Poorly designed sales organizations create bottlenecks, role confusion, and inefficient resource utilization that directly impact revenue performance. Studies show that companies with optimized sales org design achieve 25% higher quota attainment and 30% lower sales rep turnover. In the AI era, organizations that fail to redesign for human-AI collaboration will find themselves at a significant competitive disadvantage as augmented teams outperform traditional structures.
Poorly designed sales organizations create bottlenecks, role confusion, and inefficient resource utilization that directly impact revenue performance. Studies show that companies with optimized sales org design achieve 25% higher quota attainment and 30% lower sales rep turnover. In the AI era, organizations that fail to redesign for human-AI collaboration will find themselves at a significant competitive disadvantage as augmented teams outperform traditional structures.
AI-Era POV
Traditional org design focused on hierarchical structures and standardized roles. AI-era design emphasizes human-AI collaboration, with AI handling routine tasks while humans focus on relationship building and complex problem-solving. This requires new role definitions, updated skill requirements, revised productivity metrics, and governance models that account for AI assistance. Organizations must also design for continuous learning and adaptation as AI capabilities evolve.
Traditional org design focused on hierarchical structures and standardized roles. AI-era design emphasizes human-AI collaboration, with AI handling routine tasks while humans focus on relationship building and complex problem-solving. This requires new role definitions, updated skill requirements, revised productivity metrics, and governance models that account for AI assistance. Organizations must also design for continuous learning and adaptation as AI capabilities evolve.
Key Components
- Role specialization that maximizes human strengths while leveraging AI capabilities
- Optimal span of control ratios adjusted for AI-augmented productivity
- Clear career progression paths that account for evolving skill requirements
- Governance structures that balance autonomy with accountability
- Performance metrics that measure human-AI collaboration effectiveness
- Continuous learning and adaptation frameworks for evolving AI capabilities
Frequently Asked Questions
How does AI change optimal sales org structure?
AI enables flatter organizations with wider spans of control, more specialized roles, and greater focus on strategic activities rather than administrative tasks.
What's the ideal manager-to-rep ratio in AI-augmented teams?
AI-augmented teams can support ratios of 1:12-15 compared to traditional 1:8-10, as AI handles routine coaching and performance monitoring.
How do you measure productivity in human-AI teams?
Coverage Model
What It Is
Coverage model defines how sales resources are allocated across market segments, geographic territories, customer tiers, and sales channels to maximize revenue potential while optimizing cost efficiency. This includes determining the right mix of inside vs. outside sales, channel vs. direct sales, and specialized vs. generalist coverage. Effective coverage models balance market opportunity with sales capacity, ensuring adequate attention for high-value prospects while maintaining cost-effective coverage for the broader market.
Coverage model defines how sales resources are allocated across market segments, geographic territories, customer tiers, and sales channels to maximize revenue potential while optimizing cost efficiency. This includes determining the right mix of inside vs. outside sales, channel vs. direct sales, and specialized vs. generalist coverage. Effective coverage models balance market opportunity with sales capacity, ensuring adequate attention for high-value prospects while maintaining cost-effective coverage for the broader market.
Why It Matters
Suboptimal coverage models are one of the most common causes of missed revenue targets and inefficient sales spend. Over-coverage inflates costs and creates territory conflicts, while under-coverage leaves revenue on the table and creates competitive vulnerabilities. Research indicates that companies with optimized coverage models achieve 23% higher revenue per sales rep and 18% better territory performance. Poor coverage decisions compound over time, creating structural inefficiencies that become increasingly difficult to correct.
Suboptimal coverage models are one of the most common causes of missed revenue targets and inefficient sales spend. Over-coverage inflates costs and creates territory conflicts, while under-coverage leaves revenue on the table and creates competitive vulnerabilities. Research indicates that companies with optimized coverage models achieve 23% higher revenue per sales rep and 18% better territory performance. Poor coverage decisions compound over time, creating structural inefficiencies that become increasingly difficult to correct.
AI-Era POV
Traditional coverage models were based on static annual planning and geographic boundaries. AI-era coverage uses dynamic optimization algorithms that continuously analyze account potential, competitive threats, buying signals, and sales capacity to recommend real-time coverage adjustments. This enables organizations to respond quickly to market changes, reallocate resources to emerging opportunities, and maintain optimal coverage efficiency as business conditions evolve.
Traditional coverage models were based on static annual planning and geographic boundaries. AI-era coverage uses dynamic optimization algorithms that continuously analyze account potential, competitive threats, buying signals, and sales capacity to recommend real-time coverage adjustments. This enables organizations to respond quickly to market changes, reallocate resources to emerging opportunities, and maintain optimal coverage efficiency as business conditions evolve.
Key Components
- Dynamic territory optimization based on real-time market signals and account potential
- Multi-channel coverage strategies that optimize direct, partner, and inside sales allocation
- Capacity planning models that balance market opportunity with sales resource availability
- Performance monitoring systems that track coverage effectiveness and identify gaps
- Automated rebalancing recommendations based on changing market conditions
- Integration with CRM and sales tools for seamless coverage execution
Frequently Asked Questions
How do you determine optimal coverage levels for different segments?
Use data analysis of account potential, sales cycle complexity, and competitive dynamics to determine appropriate coverage intensity for each segment.
When should coverage models be adjusted?
Monitor coverage effectiveness quarterly and make adjustments when performance metrics indicate over/under-coverage or when market conditions change significantly.
How do you balance direct sales with channel coverage?
Analyze customer preferences, deal complexity, and economics to determine optimal channel mix, with clear rules for channel conflict resolution.
Territory Design
What It Is
Territory design is the systematic assignment of accounts, prospects, and geographic regions to sales representatives in a way that balances opportunity, workload, and travel requirements. Effective territory design considers factors including account potential, geographic proximity, industry expertise requirements, relationship history, and competitive dynamics. The goal is to create territories that are both fair to sales reps and optimal for revenue generation, while minimizing conflicts and maximizing coverage efficiency.
Territory design is the systematic assignment of accounts, prospects, and geographic regions to sales representatives in a way that balances opportunity, workload, and travel requirements. Effective territory design considers factors including account potential, geographic proximity, industry expertise requirements, relationship history, and competitive dynamics. The goal is to create territories that are both fair to sales reps and optimal for revenue generation, while minimizing conflicts and maximizing coverage efficiency.
Why It Matters
Poor territory design is a leading cause of sales team dissatisfaction, uneven performance, and missed revenue targets. Unbalanced territories create unfair advantages for some reps while handicapping others, leading to morale issues, turnover, and inconsistent results. Studies show that well-designed territories can improve sales performance by 15-25% and reduce rep turnover by up to 40%. In competitive markets, territory inefficiencies also create vulnerabilities that competitors can exploit.
Poor territory design is a leading cause of sales team dissatisfaction, uneven performance, and missed revenue targets. Unbalanced territories create unfair advantages for some reps while handicapping others, leading to morale issues, turnover, and inconsistent results. Studies show that well-designed territories can improve sales performance by 15-25% and reduce rep turnover by up to 40%. In competitive markets, territory inefficiencies also create vulnerabilities that competitors can exploit.
AI-Era POV
Traditional territory design relied on annual planning cycles and simple geographic or alphabetical divisions. AI-powered territory design uses advanced algorithms to analyze multiple variables simultaneously-account potential, travel time, relationship strength, competitive presence, and market dynamics-to create optimal territory assignments. Machine learning can identify patterns in successful territories and continuously optimize assignments based on performance data and changing market conditions.
Traditional territory design relied on annual planning cycles and simple geographic or alphabetical divisions. AI-powered territory design uses advanced algorithms to analyze multiple variables simultaneously-account potential, travel time, relationship strength, competitive presence, and market dynamics-to create optimal territory assignments. Machine learning can identify patterns in successful territories and continuously optimize assignments based on performance data and changing market conditions.
Key Components
- Multi-factor optimization algorithms that balance opportunity, geography, and workload
- Account potential scoring based on firmographic, technographic, and behavioral data
- Travel time and cost optimization to maximize face-to-face selling time
- Relationship mapping to preserve existing customer connections
- Competitive analysis to ensure adequate coverage in contested markets
- Performance monitoring and continuous territory refinement processes
Frequently Asked Questions
How often should territories be redesigned?
Major redesigns annually, with quarterly adjustments for significant changes. AI-powered systems can make minor optimizations continuously. The key is balancing stability (which allows reps to build relationships and expertise) with adaptability (which ensures coverage matches evolving market opportunities). Annual redesigns aligned with planning cycles provide the foundation, quarterly reviews address market shifts or performance issues, and AI-driven micro-adjustments optimize coverage without disrupting relationships. Organizations that never redesign territories accumulate inefficiencies over time as markets evolve and rep performance varies.
What's the best way to handle territory changes with existing reps?
Communicate changes early with clear rationale, provide transition support, and consider compensation protection during adjustment periods. Best practice is to involve top performers in design discussions, share the data and logic behind changes, provide 60-90 days notice before implementation, and offer accelerated commission rates or quota relief during the transition. Mishandled territory changes are a leading cause of top performer attrition. Treat changes as strategic initiatives that require change management, not administrative updates announced via email.
How do you measure territory design effectiveness?
Track metrics including territory balance (variance in opportunity across territories), rep satisfaction scores, quota attainment distribution (ideally 60-80% hitting quota), revenue per territory over time, and seller turnover by territory. Effective territory design produces consistent performance across territories adjusted for market maturity, minimizes rep complaints about fairness, and enables predictable scaling as you add headcount. If top performers consistently hit 150%+ while others struggle to reach 70%, your territory design likely has structural imbalances that coaching can't fix.
Should territories be based on geography, accounts, or industry verticals?
The answer depends on your go-to-market motion. Geographic territories work well for field sales requiring in-person meetings, especially in industries with geographic buying patterns. Account-based territories suit enterprise sales with complex decision processes and long relationship cycles. Vertical specialization makes sense when industry expertise drives differentiation and buying processes vary significantly by sector. Many organizations use hybrid models-geographic regions for field coverage, vertical specialization for complex deals, account-based for strategic customers. Test different models in pilot regions before full deployment.
How do you handle territory conflicts between sales reps?
Establish clear rules of engagement upfront with specific criteria for account ownership, deal registration protocols, and conflict resolution processes. Common approaches include: first touch rules (whoever contacts first owns the account), deal registration systems (log opportunities within 48 hours), split credit arrangements for genuine co-selling, and executive arbitration for contested situations. Document all decisions to create precedent and consistency. Territory conflicts that reach management attention signal unclear rules or inadequate territory design-fix the root cause, not just the symptom.
Quota Setting
What It Is
Quota setting is the process of establishing revenue targets for individual sales representatives and teams that are both achievable and challenging. Effective quota setting considers historical performance, market opportunity, territory potential, competitive dynamics, and individual rep capabilities. The goal is to create targets that motivate peak performance while maintaining forecast accuracy and ensuring fair distribution of opportunity across the sales organization.
Quota setting is the process of establishing revenue targets for individual sales representatives and teams that are both achievable and challenging. Effective quota setting considers historical performance, market opportunity, territory potential, competitive dynamics, and individual rep capabilities. The goal is to create targets that motivate peak performance while maintaining forecast accuracy and ensuring fair distribution of opportunity across the sales organization.
Why It Matters
Poorly set quotas are one of the most destructive forces in sales organizations, leading to demotivated teams, inaccurate forecasts, and missed company targets. Research shows that when quotas are perceived as unfair or unattainable, sales performance drops by 25-40% and turnover increases significantly. Conversely, well-calibrated quotas drive 15-20% higher performance and improve forecast accuracy by up to 30%. In investor-backed companies, quota credibility directly impacts valuation and board confidence.
Poorly set quotas are one of the most destructive forces in sales organizations, leading to demotivated teams, inaccurate forecasts, and missed company targets. Research shows that when quotas are perceived as unfair or unattainable, sales performance drops by 25-40% and turnover increases significantly. Conversely, well-calibrated quotas drive 15-20% higher performance and improve forecast accuracy by up to 30%. In investor-backed companies, quota credibility directly impacts valuation and board confidence.
AI-Era POV
Traditional quota setting relied on top-down revenue targets divided by headcount, often ignoring territory differences and market realities. AI-enhanced quota setting combines historical performance data, territory analysis, market intelligence, and predictive modeling to create fair, achievable targets. Machine learning can identify patterns in quota attainment and adjust targets based on changing market conditions, competitive dynamics, and individual rep development trajectories.
Traditional quota setting relied on top-down revenue targets divided by headcount, often ignoring territory differences and market realities. AI-enhanced quota setting combines historical performance data, territory analysis, market intelligence, and predictive modeling to create fair, achievable targets. Machine learning can identify patterns in quota attainment and adjust targets based on changing market conditions, competitive dynamics, and individual rep development trajectories.
Key Components
- Bottom-up territory analysis combined with top-down revenue requirements
- Historical performance trending adjusted for market and competitive changes
- Territory potential assessment using account scoring and market intelligence
- Individual rep capability evaluation and development trajectory analysis
- Scenario modeling for different market conditions and competitive responses
- Quarterly quota review and adjustment processes based on performance data
Frequently Asked Questions
What percentage of reps should hit quota in a well-designed system?
Typically 60-80% of reps should achieve quota, with top performers exceeding by 20-50%. If attainment is consistently above 90%, quotas may be too low and you're leaving growth on the table. If attainment is consistently below 50%, quotas are unrealistic and will destroy motivation and credibility. The distribution matters too-you want a bell curve, not a bimodal distribution where some reps crush quota while others badly miss. Bimodal distributions indicate territory imbalances or segmentation problems, not quota issues. Board and investor expectations also factor in-PE-backed companies often target 70-75% attainment to demonstrate achievable stretch goals.
How do you handle quota adjustments mid-year?
Establish clear criteria for adjustments (market changes, territory modifications, M&A activity, product delays) and communicate changes transparently with adequate notice. Mid-year quota changes should be rare and reserved for material shifts in territory opportunity or market conditions. When adjustments are necessary, provide 30-60 days notice, document the rationale with data, adjust compensation plans if needed, and apply changes consistently across affected reps. Frequent quota changes erode trust and signal poor planning. Organizations that adjust quotas quarterly usually have deeper issues with forecasting accuracy or strategic planning that need to be addressed.
Should quotas be the same for all reps in similar roles?
Quotas should reflect territory opportunity and individual capability. Similar roles may have different quotas based on territory potential and rep experience. A rep covering established accounts in a mature market should have higher quotas than one opening a new territory or market segment. New reps typically receive ramped quotas-50% of full quota in months 1-3, 75% in months 4-6, 100% by month 7. The key is perceived fairness backed by data. If quota variance can't be explained by objective territory analysis, reps will assume unfair treatment and performance will suffer.
How do you build bottoms-up quotas that roll up to company targets?
Start with territory analysis to determine realistic potential for each rep based on account value, market size, competitive position, and historical performance. Sum individual quotas to get total sales capacity, compare to company revenue targets, and identify the gap. Close gaps through territory optimization, headcount additions, pricing improvements, or product expansion-not by arbitrarily inflating individual quotas. If bottoms-up analysis suggests $80M is achievable but the board wants $100M, you need strategic initiatives to bridge the $20M gap, not heroic assumptions. Companies that force quotas to match targets without addressing capacity constraints set teams up for failure.
What's the right balance between individual and team quotas?
Most B2B sales organizations use primarily individual quotas (70-90% of variable compensation) with team or company components (10-30%) to encourage collaboration. Enterprise sales with complex team selling may shift more weight to team quotas. The risk of heavy team quota weighting is free-rider problems where low performers coast on team results. Individual quotas drive accountability and enable performance management, while team components reward cross-functional collaboration and prevent silo behavior. Adjust the balance based on how much genuine teamwork your sales motion requires.
Compensation
What It Is
Sales compensation design involves creating incentive structures that align individual rep behavior with company objectives while attracting and retaining top talent. Effective compensation plans balance base salary, variable pay, accelerators, and non-monetary incentives to drive desired behaviors including new logo acquisition, expansion revenue, margin protection, and customer satisfaction. Modern compensation must also account for team collaboration, AI tool adoption, and ecosystem selling in complex B2B environments.
Sales compensation design involves creating incentive structures that align individual rep behavior with company objectives while attracting and retaining top talent. Effective compensation plans balance base salary, variable pay, accelerators, and non-monetary incentives to drive desired behaviors including new logo acquisition, expansion revenue, margin protection, and customer satisfaction. Modern compensation must also account for team collaboration, AI tool adoption, and ecosystem selling in complex B2B environments.
Why It Matters
Misaligned compensation plans can destroy value faster than almost any other sales mistake, encouraging short-term thinking, margin erosion, and internal competition that damages customer relationships. Poor compensation design leads to 40% higher sales turnover and can reduce overall sales performance by 20-30%. In the AI era, compensation plans that don't evolve to reward new behaviors and capabilities will fail to drive adoption of productivity-enhancing tools and collaborative selling approaches.
Misaligned compensation plans can destroy value faster than almost any other sales mistake, encouraging short-term thinking, margin erosion, and internal competition that damages customer relationships. Poor compensation design leads to 40% higher sales turnover and can reduce overall sales performance by 20-30%. In the AI era, compensation plans that don't evolve to reward new behaviors and capabilities will fail to drive adoption of productivity-enhancing tools and collaborative selling approaches.
AI-Era POV
Traditional compensation focused primarily on individual bookings and revenue targets. AI-era compensation must reward broader value creation including customer lifetime value, ecosystem collaboration, AI tool proficiency, and data quality contributions. This requires more sophisticated measurement systems and may include team-based incentives, customer success metrics, and innovation bonuses for reps who effectively leverage AI tools to drive productivity gains.
Traditional compensation focused primarily on individual bookings and revenue targets. AI-era compensation must reward broader value creation including customer lifetime value, ecosystem collaboration, AI tool proficiency, and data quality contributions. This requires more sophisticated measurement systems and may include team-based incentives, customer success metrics, and innovation bonuses for reps who effectively leverage AI tools to drive productivity gains.
Key Components
- Balanced incentive structures that reward both individual and team performance
- Customer lifetime value and expansion revenue components beyond initial bookings
- AI adoption and productivity improvement incentives
- Margin protection and pricing discipline rewards
- Collaboration bonuses for cross-functional and ecosystem selling
- Non-monetary recognition programs that reinforce desired behaviors
Frequently Asked Questions
What's the optimal mix of base salary vs. variable compensation?
Typically 50-70% base for enterprise sales, 30-50% for transactional sales, adjusted based on sales cycle length and market predictability.
How do you compensate for team-based selling?
Use overlay compensation, team bonuses, or split credit arrangements that reward collaboration while maintaining individual accountability.
Should AI productivity gains affect compensation?
Consider productivity bonuses for effective AI adoption while adjusting quotas to reflect enhanced capabilities over time.
Sales Process
What It Is
Sales process defines the systematic methodology that guides prospects through the buying journey from initial contact to closed deal. A well-designed process includes defined stages, exit criteria, required activities, and decision points that ensure consistent execution across the sales team. Modern sales processes must be flexible enough to accommodate different buyer types and deal complexities while maintaining enough structure to enable coaching, forecasting, and performance management.
Sales process defines the systematic methodology that guides prospects through the buying journey from initial contact to closed deal. A well-designed process includes defined stages, exit criteria, required activities, and decision points that ensure consistent execution across the sales team. Modern sales processes must be flexible enough to accommodate different buyer types and deal complexities while maintaining enough structure to enable coaching, forecasting, and performance management.
Why It Matters
Without a consistent sales process, organizations experience wide performance variations, unpredictable forecasts, and difficulty scaling successful behaviors. Companies with well-defined sales processes achieve 18% higher revenue growth and 12% better forecast accuracy. In complex B2B sales, process consistency becomes even more critical as deal sizes increase and sales cycles lengthen. Poor process discipline also makes it impossible to identify and replicate best practices across the team.
Without a consistent sales process, organizations experience wide performance variations, unpredictable forecasts, and difficulty scaling successful behaviors. Companies with well-defined sales processes achieve 18% higher revenue growth and 12% better forecast accuracy. In complex B2B sales, process consistency becomes even more critical as deal sizes increase and sales cycles lengthen. Poor process discipline also makes it impossible to identify and replicate best practices across the team.
AI-Era POV
Traditional sales processes were static methodologies documented in training materials. AI-enhanced processes provide real-time guidance, next-best-action recommendations, and risk alerts directly within the sales workflow. Machine learning analyzes successful deal patterns to suggest optimal activities for each stage, while predictive analytics identify deals at risk and recommend intervention strategies. This creates a dynamic, learning process that improves over time.
Traditional sales processes were static methodologies documented in training materials. AI-enhanced processes provide real-time guidance, next-best-action recommendations, and risk alerts directly within the sales workflow. Machine learning analyzes successful deal patterns to suggest optimal activities for each stage, while predictive analytics identify deals at risk and recommend intervention strategies. This creates a dynamic, learning process that improves over time.
Key Components
- Clearly defined sales stages with specific entry and exit criteria
- Required activities and deliverables for each stage of the process
- AI-powered next-best-action recommendations based on deal characteristics
- Risk scoring and early warning systems for deals in jeopardy
- Integration with CRM systems for seamless process execution
- Continuous process optimization based on win/loss analysis and performance data
Frequently Asked Questions
How many stages should a sales process have?
Typically 5-7 stages for complex B2B sales, fewer for transactional sales. The key is having meaningful distinctions that guide behavior and enable accurate forecasting. Each stage should represent a genuine progression in buyer commitment, not just seller activities. Common stages include: Initial Contact, Discovery/Qualification, Solution Design, Proposal/Evaluation, Negotiation, Closed Won/Lost. Too few stages (3-4) lack granularity for accurate forecasting, too many stages (10+) create administrative burden without adding insight. Test your stage definitions-if deals routinely skip stages or if stage progression doesn't correlate with close probability, your stages aren't aligned with how buyers actually purchase.
How do you ensure sales process adoption?
Make the process valuable to reps through embedded tools and insights that help them win deals, not just administrative requirements. Show how following process improves their personal results-higher win rates, faster closes, better forecasting. Tie process compliance to compensation and performance reviews but pair enforcement with enablement. Provide ongoing coaching and reinforcement through regular pipeline reviews where managers reinforce process discipline. The biggest adoption killer is processes that exist for reporting purposes but don't help reps sell. Build processes that make reps more effective, then adoption becomes natural rather than forced.
Should the sales process be the same for all deal types?
Have a core process with variations for different deal types (new logo vs. expansion, enterprise vs. SMB) while maintaining consistent principles and measurement. The buyer's journey differs between a $50K new logo deal and a $2M enterprise expansion, so forcing the same process creates friction. However, maintain common stage definitions and exit criteria so you can aggregate forecasts and compare performance across deal types. Most organizations use a primary process for their core motion with documented variations for other scenarios. Document when to use which process and ensure reps understand the differences.
What are exit criteria and why do they matter?
Exit criteria are the specific conditions that must be met before an opportunity can advance to the next stage. For example, moving from Discovery to Proposal might require: confirmed budget, identified decision makers, documented business case, technical fit validated, and timeline established. Exit criteria prevent premature stage advancement that inflates forecasts and creates false confidence. Without clear exit criteria, stage progression becomes subjective-reps advance deals based on hope rather than buyer commitment. Rigorous exit criteria improve forecast accuracy by 20-30% because only deals meeting objective standards get forecasted at high confidence.
How do you update a sales process without disrupting the team?
Introduce changes incrementally, starting with pilot teams who provide feedback before broader rollout. Document the rationale for changes-connect updates to win/loss data, competitive intelligence, or buyer feedback so reps understand why. Provide comprehensive enablement including training, updated playbooks, and coaching support. Give advance notice (60-90 days) and allow parallel processes during transition. Track adoption and performance metrics to ensure changes drive improvement. The biggest mistakes are surprise process changes rolled out via email or changes that aren't connected to clear performance problems. Treat process updates like product launches that require planning, communication, and support.
Prospecting & New Logo
What It Is
Prospecting and new logo acquisition encompasses all activities focused on identifying, engaging, and converting net-new customers who have never purchased from your company. This includes market research, lead generation, outbound campaigns, social selling, referral programs, and first-meeting conversion strategies. Effective prospecting combines systematic approaches with personalized outreach to build pipeline and drive new customer acquisition at scale.
Prospecting and new logo acquisition encompasses all activities focused on identifying, engaging, and converting net-new customers who have never purchased from your company. This includes market research, lead generation, outbound campaigns, social selling, referral programs, and first-meeting conversion strategies. Effective prospecting combines systematic approaches with personalized outreach to build pipeline and drive new customer acquisition at scale.
Why It Matters
New logo acquisition is the lifeblood of sustainable growth, as even companies with excellent retention eventually face market saturation without new customer acquisition. Companies that excel at new logo acquisition grow 40% faster than those focused primarily on existing customer expansion. In investor-backed companies, new logo metrics directly impact valuation multiples and growth sustainability. Poor prospecting capabilities also make companies vulnerable to competitive threats and market disruption.
New logo acquisition is the lifeblood of sustainable growth, as even companies with excellent retention eventually face market saturation without new customer acquisition. Companies that excel at new logo acquisition grow 40% faster than those focused primarily on existing customer expansion. In investor-backed companies, new logo metrics directly impact valuation multiples and growth sustainability. Poor prospecting capabilities also make companies vulnerable to competitive threats and market disruption.
AI-Era POV
Traditional prospecting relied heavily on manual research, cold calling, and generic email campaigns. AI-powered prospecting uses machine learning to identify ideal customer profiles, predict buying intent, and personalize outreach at scale. AI agents can handle initial research, list building, and first-touch personalization, allowing sales reps to focus on building relationships and conducting meaningful conversations with qualified prospects.
Traditional prospecting relied heavily on manual research, cold calling, and generic email campaigns. AI-powered prospecting uses machine learning to identify ideal customer profiles, predict buying intent, and personalize outreach at scale. AI agents can handle initial research, list building, and first-touch personalization, allowing sales reps to focus on building relationships and conducting meaningful conversations with qualified prospects.
Key Components
- AI-powered ideal customer profile identification and lookalike modeling
- Automated prospect research and contact information enrichment
- Personalized outreach campaigns based on prospect behavior and interests
- Multi-channel engagement strategies across email, social, phone, and events
- Lead scoring and qualification frameworks to prioritize follow-up activities
- Conversion optimization for first meetings and discovery conversations
Frequently Asked Questions
What's a good new logo acquisition rate for B2B companies?
Varies by industry and deal size, but typically 20-40% of revenue should come from new logos acquired in the current year.
How do you balance prospecting time with existing account management?
Allocate 20-30% of rep time to prospecting for account managers, 60-80% for hunters. Use AI tools to make prospecting more efficient.
What's the best way to measure prospecting effectiveness?
Track metrics including qualified meetings generated, conversion rates by channel, time from first touch to opportunity, and cost per acquired customer.
Pipeline & Forecasting
What It Is
Pipeline and forecasting involves the systematic management of sales opportunities and the prediction of future revenue outcomes based on current pipeline health, historical patterns, and market conditions. This includes opportunity qualification, stage progression tracking, probability assessment, and revenue prediction across multiple time horizons. Effective forecasting provides leadership with reliable visibility into future performance and enables proactive resource allocation and strategic decision-making.
Pipeline and forecasting involves the systematic management of sales opportunities and the prediction of future revenue outcomes based on current pipeline health, historical patterns, and market conditions. This includes opportunity qualification, stage progression tracking, probability assessment, and revenue prediction across multiple time horizons. Effective forecasting provides leadership with reliable visibility into future performance and enables proactive resource allocation and strategic decision-making.
Why It Matters
Forecast accuracy is critical for business planning, investor confidence, and operational efficiency. Poor forecasting leads to missed targets, resource misallocation, and loss of credibility with boards and investors. Companies with accurate forecasting (within 5% of targets) achieve 15% higher valuations and 25% better operational efficiency. In public companies, forecast misses can result in significant stock price volatility and loss of investor confidence.
Forecast accuracy is critical for business planning, investor confidence, and operational efficiency. Poor forecasting leads to missed targets, resource misallocation, and loss of credibility with boards and investors. Companies with accurate forecasting (within 5% of targets) achieve 15% higher valuations and 25% better operational efficiency. In public companies, forecast misses can result in significant stock price volatility and loss of investor confidence.
AI-Era POV
Traditional forecasting relied on sales rep intuition and simple probability percentages by stage. AI-driven forecasting analyzes hundreds of variables including deal characteristics, buyer behavior, competitive dynamics, and historical patterns to generate more accurate probability assessments. Machine learning models can identify early warning signals for deal risk and provide recommendations for improving deal outcomes, creating a more predictive and actionable forecasting process.
Traditional forecasting relied on sales rep intuition and simple probability percentages by stage. AI-driven forecasting analyzes hundreds of variables including deal characteristics, buyer behavior, competitive dynamics, and historical patterns to generate more accurate probability assessments. Machine learning models can identify early warning signals for deal risk and provide recommendations for improving deal outcomes, creating a more predictive and actionable forecasting process.
Key Components
- AI-powered deal scoring based on multiple variables and historical patterns
- Real-time pipeline health monitoring with automated risk alerts
- Multi-horizon forecasting (30/60/90 day rolling predictions)
- Scenario modeling for different market conditions and competitive responses
- Transparent methodology and model governance for forecast credibility
- Integration with CRM and sales tools for seamless data collection and analysis
Frequently Asked Questions
What level of forecast accuracy should companies target?
How do you improve forecast accuracy over time?
Should individual reps or managers own the forecast?
What pipeline coverage ratio should we maintain?
How do you prevent sandbagging in forecasts?
Sales Enablement
What It Is
Sales enablement encompasses all activities that equip sales teams with the knowledge, skills, tools, and content needed to engage buyers effectively and close deals successfully. This includes onboarding programs, ongoing training, sales content creation and management, competitive intelligence, objection handling frameworks, and performance coaching. Modern enablement must be personalized, just-in-time, and continuously updated to reflect changing market conditions and buyer expectations.
Sales enablement encompasses all activities that equip sales teams with the knowledge, skills, tools, and content needed to engage buyers effectively and close deals successfully. This includes onboarding programs, ongoing training, sales content creation and management, competitive intelligence, objection handling frameworks, and performance coaching. Modern enablement must be personalized, just-in-time, and continuously updated to reflect changing market conditions and buyer expectations.
Why It Matters
AI-Era POV
Key Components
- Personalized learning paths based on role, experience, and performance gaps
- AI-curated content recommendations based on deal context and buyer characteristics
- Real-time coaching and guidance integrated into daily sales workflows
- Competitive intelligence and battle cards updated based on market changes
- Performance analytics that identify enablement effectiveness and improvement opportunities
- Continuous content optimization based on usage analytics and deal outcomes
Frequently Asked Questions
How do you measure sales enablement ROI?
What's the best way to keep enablement content current?
How much time should reps spend on enablement activities?
Should enablement be centralized or distributed across teams?
What's the difference between sales training and sales enablement?
Sales Operations
What It Is
Why It Matters
Without strong sales operations, organizations lack the data visibility and process discipline needed to scale effectively or make informed strategic decisions. Companies with mature sales ops functions achieve 25% higher sales productivity and 20% better forecast accuracy. Poor sales ops leads to data silos, manual processes, inconsistent reporting, and inability to identify and replicate successful behaviors across the team.
Without strong sales operations, organizations lack the data visibility and process discipline needed to scale effectively or make informed strategic decisions. Companies with mature sales ops functions achieve 25% higher sales productivity and 20% better forecast accuracy. Poor sales ops leads to data silos, manual processes, inconsistent reporting, and inability to identify and replicate successful behaviors across the team.
AI-Era POV
Key Components
- Integrated data management across sales, marketing, and customer success systems
- Automated reporting and dashboard creation with real-time performance visibility
- Process optimization and standardization across the revenue organization
- Technology stack management and integration for seamless operations
- Performance analytics and benchmarking to identify improvement opportunities
- Strategic analysis and recommendations for leadership decision-making
Frequently Asked Questions
When should a company invest in dedicated sales operations?
What's the difference between sales ops and revenue ops?
How do you measure sales operations effectiveness?
Track metrics including data quality, report accuracy, process compliance, technology adoption, and overall sales team productivity improvements.
Channel Ecosystem
What It Is
Channel ecosystem management involves building and optimizing networks of partners, resellers, distributors, and other third-party organizations that extend market reach and sales capacity. This includes partner recruitment, onboarding, enablement, performance management, and conflict resolution. Effective channel management creates win-win relationships that expand market coverage while maintaining margin discipline and brand consistency across all customer touchpoints.
Channel ecosystem management involves building and optimizing networks of partners, resellers, distributors, and other third-party organizations that extend market reach and sales capacity. This includes partner recruitment, onboarding, enablement, performance management, and conflict resolution. Effective channel management creates win-win relationships that expand market coverage while maintaining margin discipline and brand consistency across all customer touchpoints.
Why It Matters
AI-Era POV
Key Components
- Strategic partner selection and recruitment based on market fit and capability assessment
- Comprehensive partner onboarding and enablement programs
- Performance monitoring and optimization systems for partner productivity
- Channel conflict resolution processes and clear rules of engagement
- Joint go-to-market planning and execution with key strategic partners
- AI-powered partner matching and opportunity routing for optimal outcomes
Frequently Asked Questions
What percentage of revenue should come through channels?
How do you prevent channel conflict?
What makes a good channel partner?
Partner Strategy
What It Is
Partner strategy defines the strategic approach to building and managing business partnerships that accelerate growth, expand market reach, and enhance competitive positioning. This includes identifying potential partners, evaluating partnership opportunities, structuring partnership agreements, and developing joint go-to-market strategies. Effective partner strategy creates mutually beneficial relationships that generate incremental revenue and strategic value for all parties involved.
Partner strategy defines the strategic approach to building and managing business partnerships that accelerate growth, expand market reach, and enhance competitive positioning. This includes identifying potential partners, evaluating partnership opportunities, structuring partnership agreements, and developing joint go-to-market strategies. Effective partner strategy creates mutually beneficial relationships that generate incremental revenue and strategic value for all parties involved.
Why It Matters
Strategic partnerships can accelerate growth by 50-100% when executed effectively, providing access to new markets, complementary capabilities, and enhanced credibility. However, poorly chosen or managed partnerships can drain resources, create conflicts, and damage market position. In the AI era, partnership ecosystems are becoming increasingly important as no single company can deliver complete solutions, making partnership strategy a critical competitive differentiator.
Strategic partnerships can accelerate growth by 50-100% when executed effectively, providing access to new markets, complementary capabilities, and enhanced credibility. However, poorly chosen or managed partnerships can drain resources, create conflicts, and damage market position. In the AI era, partnership ecosystems are becoming increasingly important as no single company can deliver complete solutions, making partnership strategy a critical competitive differentiator.
AI-Era POV
Traditional partnership strategy relied on relationship-based deal-making and manual partner management. AI-enhanced partnership strategy uses data analytics to identify optimal partners, predict partnership success, and optimize joint go-to-market activities. Machine learning can analyze partner performance patterns, recommend partnership opportunities, and help orchestrate complex ecosystem plays that maximize value for all participants.
Traditional partnership strategy relied on relationship-based deal-making and manual partner management. AI-enhanced partnership strategy uses data analytics to identify optimal partners, predict partnership success, and optimize joint go-to-market activities. Machine learning can analyze partner performance patterns, recommend partnership opportunities, and help orchestrate complex ecosystem plays that maximize value for all participants.
Key Components
- Strategic partnership identification based on market analysis and capability gaps
- Partnership evaluation frameworks that assess strategic fit and revenue potential
- Joint go-to-market planning and execution with clear success metrics
- Partnership performance monitoring and optimization systems
- Governance structures that protect strategic interests while enabling collaboration
- AI-powered partnership matching and opportunity identification
Frequently Asked Questions
How do you identify the right strategic partners?
Look for complementary capabilities, shared target markets, cultural alignment, and mutual value creation opportunities rather than just revenue potential.
What's the difference between channel partners and strategic partners?
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AI in Sales
At SBI, we are clear-eyed about where AI creates value and where it does not. AI is only as powerful as the strategy behind it. Without clear priorities, disciplined execution models, and the right operating decisions, AI simply accelerates the wrong outcomes. SBI ensures AI is applied on top of a sound sales strategy, not layered on for its own sake, so technology strengthens judgment instead of replacing it.
Predictive Lead Scoring
AI algorithms that identify high-value prospects and prioritize sales efforts for maximum conversion rates.
Intelligent Forecasting
Machine learning models that provide accurate revenue forecasts and identify pipeline risks early.
Personalized Outreach
AI-powered personalization that creates relevant, compelling messages for each prospect.
Related Resources
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Success Stories
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Sales Strategy FAQs
From the Experts
Mike Hoffman
CEO, SBI Growth Advisory
Scott Gruher
President, SBI Growth Advisory
Isaac Silverman
CTO, SBI Growth Advisory