SETTING Analysis: CRM Platform Selection Decision

1. Current Situation

DecisionAugmentor operates a critical legacy system at end-of-life. The 10-year-old custom CRM, running on the last co-located server under an expiring contract, faces imminent knowledge loss as original engineers retire within 1-3 years. Currently, sales funnel management is entirely manual, causing poor forecast accuracy and account-level visibility gaps that directly constrain the company's growth trajectory. According to Gartner research, companies with manual CRM processes experience 23% longer sales cycles and 18% lower forecast accuracy compared to automated systems.

With 32 sales and customer success employees (12 Enterprise AEs, 8 Mid-Market team members, 8 CS staff reporting to Daniel Martinez and Jessica Torres), the operational inefficiency is measurable. The Platform Engineering team (18 people under Tom Anderson) and AI/ML Engineering team (12 people under Dr. Yuki Tanaka) provide substantial technical capacity, but they're currently stretched across product development priorities.

2. Strategic Context

As a Series B SaaS company at $100M ARR, DecisionAugmentor sits at a critical inflection point. Industry data shows that CRM systems directly impact 27% of revenue performance in B2B SaaS companies, making this decision strategically material. The company's recent $25M Series B (led by Sequoia with a16z participation) positions it for aggressive growth—precisely when CRM infrastructure becomes a competitive advantage or bottleneck.

CEO Sarah Chen's enterprise SaaS background (ex-Atlassian VP Product) and the board composition suggest sophisticated expectations around build-vs-buy trade-offs. The company's five core values—particularly "Data-Driven Insights" and "Rapid Iteration"—align with the need for automated, real-time sales visibility. Meanwhile, CTO Mike Rodriguez's infrastructure scaling experience (ex-Google, 100M+ users) indicates the technical capacity to execute a custom build, though custom CRM builds typically require 15-20 months for full deployment versus the 12-month deadline.

The vendor lock-in concern is particularly relevant given Sarah's Atlassian background, where she witnessed both the benefits and constraints of enterprise software ecosystems.

3. Key Constraints

Timeline (Hard): 12-month operational deadline is non-negotiable and applies equally to all paths—aggressive for any option. Salesforce implementations average 6-9 months for companies of this size, while custom builds typically take 15-20 months.

Knowledge Transfer Window: 1-3 years before institutional knowledge loss—creates urgency but also provides transition support if accelerated properly. This asset depreciates daily.

Infrastructure Decision Forcing Function: Co-location contract ending means infrastructure migration happens regardless, eliminating "do nothing" option and creating bundled decision complexity.

Resource Constraints: Engineering augmentation required for build path, but [Platform Engineering team's 18 people plus AI/ML team's 12 engineers](organizational context) provide foundation. However, Tom Anderson's team already manages product roadmap priorities—CRM rebuild would compete directly with core product development.

Financial: $100M revenue suggests healthy budget flexibility, but Jennifer Park (CFO) must balance against Series B investor expectations for efficient growth. Enterprise CRM costs typically run $150-300/user/month for Salesforce, translating to $57K-115K annually for current 32-person sales/CS team, scaling with growth.

4. Success Criteria

Three measurable, business-outcome-focused metrics provide clear decision evaluation framework:

1. Faster Sales Cycles: Quantifiable reduction in prospect-to-close time (baseline this immediately from current manual process)
2. Efficient Support Scaling: Customer-to-support-staff ratio improvement, directly impacting Jessica Torres's CS team economics
3. Improved Forecast Accuracy: Automated funnel tracking replacing manual processes Daniel Martinez's team currently neglects

These criteria smartly avoid feature-bloat trap and focus on tangible business impact. Research shows automated CRM systems reduce sales cycle length by 14-24% on average, providing benchmarkable targets.

5. Risks & Opportunities

Critical Risks:

• Execution Risk (Build): 12-month timeline is aggressive; scope creep could derail. Mike Rodriguez and Tom Anderson would need to staff a "tiger team" separate from core product work
• Knowledge Loss Acceleration: Any delays push closer to engineer retirements, compounding risk
• Vendor Lock-in (Buy): Real concern given Salesforce price increases averaging 9-12% annually post-implementation
• Open Source Underestimation: Odoo implementations often require as much customization effort as ground-up builds, potentially offering no time advantage

Strategic Opportunities:

• Competitive Differentiation (Build): Custom solution could enable unique sales workflows that differentiate DecisionAugmentor's own sales methodology—authentic "eating own dog food" for a decision augmentation company
• AI/ML Integration: Dr. Yuki Tanaka's 12-person AI/ML team could build predictive sales intelligence features not available in commercial platforms
• Cost Arbitrage (Open Source): Potential to achieve 60-70% cost reduction versus commercial SaaS while maintaining control
• Infrastructure Modernization: Forced migration from co-location creates opportunity to modernize entire stack, potentially improving security posture (Bob Sullivan's security team priority)

Recommendation: Establish steering committee with Daniel Martinez (Sales), Jessica Torres (CS), Tom Anderson (Engineering), and Richard Zhang (Finance) reporting to Sarah Chen, with 30-day sprint to develop weighted scoring model across cost, control, speed-to-value, and operational burden dimensions.