Do I double down on this idea or kill it before I waste another year?

The core insight from this deliberation is simple: you need to distinguish between a broken business model and poor execution. A broken model (kill signal) means even your best customers don't generate enough revenue to justify the cost of acquiring them. Poor execution (fixable) means your unit economics work for some customers, but you're not finding enough of them or converting them efficiently.

Here's why the metrics approach works: After one year in market, you have enough customer data to calculate CAC payback and LTV:CAC accurately. If you can't calculate these numbers within 2-4 weeks, that's a red flag—it means your tracking is so broken that you lack basic operational visibility. That's a kill signal by itself, because it suggests you lack the operational discipline to execute even if the business model works.

The thresholds themselves are grounded in real industry data. An 18-month CAC payback period is the upper acceptable limit for enterprise SaaS businesses. Anything beyond that means you're waiting too long to recover the money you spent acquiring customers, which creates cash flow pressure and limits your ability to scale. A 3:1 LTV:CAC ratio is the minimum viability floor—it means you generate $3 in lifetime revenue for every $1 spent acquiring a customer. Below that, your unit economics don't work.

The critical move is isolating your best customers' metrics, not averaging across everyone. James's insight here is crucial: if your successfully-converted customers (those who stayed 6+ months) hit 3:1 LTV:CAC, but your overall ratio is 2:1, you have a targeting or early-stage churn problem. You're acquiring the wrong customers or losing them before they become profitable. That's an execution problem—fixable by improving your sales qualification, onboarding, or product. If even your best customers fail the 3:1 test, no amount of optimization fixes the fundamental unit economics.

The 2-4 week timeline balances speed with accuracy. Priya's financial analysis showed that a 4-week diagnostic sprint preserves runway while establishing clean baseline metrics. James pushed back on longer diagnostics because they consume runway without generating decision signals. Aisha's concern about data quality is valid—most founders lack clean tracking—but 2-4 weeks is enough time to either find the data or instrument tracking. If you can't do it in that timeframe, that's information too: your operational systems are too weak to support this business.

The monthly review and 90-day kill switch address the risk of false positives. If metrics show "double down" signal today but deteriorate within 90 days, you need to exit quickly rather than hoping things improve. This prevents the trap of throwing good money after bad.

Critical success factors for executing this decision framework:

1. Data access (Weeks 1-2 if tracking exists, or Weeks 1-4 if instrumentation needed):

• Can you isolate customer acquisition cost (CAC)? You
  need to include fully-loaded costs: ad spend, sales salaries, marketing tools, and allocated overhead divided by number of new customers acquired.
• Can you calculate lifetime value (LTV)? You need average revenue per customer × gross margin × average customer lifetime in months.
• Can you segment converting customers (those who stayed 6+ months) separately from churned customers? This is critical—if your best customers show healthy 3:1 LTV:CAC but overall metrics fail, you have a targeting problem (fixable), not a broken model.

2. Timeline decision (choose one based on your current state):

• Clean tracking exists (2-week path): If you already have 3+ months of accurate CAC and LTV data, use James's 2-week sprint. Allocate 10-15 hours to pull reports and segment converting customers. This is only viable if your accounting/finance system is already tracking these metrics.
• Data exists but accuracy unclear (4-week path): If you have data but aren't confident in its accuracy, use Priya's 4-week sprint. Week 1-2 verify data sources and fill gaps; Week 3-4 calculate metrics. This is the most common scenario.
• No clean tracking (6-week path): If you lack basic CAC or LTV tracking, use Aisha's 6-week sprint. Weeks 1-2 instrument tracking (set up formulas, define cost allocation, create customer cohort reports); Weeks 3-4 collect clean data; Weeks 5-6 calculate and decide. If instrumentation can't be completed in 2 weeks, kill immediately—this signals execution failure at the operational level.

3. Kill trigger execution (non-negotiable):

• Calculate CAC payback period: Total customer acquisition cost ÷ monthly revenue per customer = months to recover CAC. If >18 months, kill.
• Calculate LTV:CAC ratio for converting customers only: (Average monthly revenue × gross margin × average customer lifetime in months) ÷ fully-loaded CAC. If <3:1, kill.
• If you cannot calculate both metrics within your chosen timeline (2, 4, or 6 weeks), kill immediately. Inability to measure basic unit economics after one year in market is a kill signal itself.

4. If metrics pass thresholds (double down scenario):

• Execution improvements are the focus, not pivoting the model. If CAC payback is 15-18 months and LTV:CAC is 3:1-3.5:1, your unit economics work—the problem is execution efficiency.
• Prioritize: (a) Sales process optimization (reduce sales cycle, improve close rate), (b) Customer onboarding (improve time-to-value, reduce early churn), (c) Targeting refinement (focus acquisition on customer segments with best LTV:CAC ratios).
• Set monthly review checkpoints. If metrics deteriorate below thresholds within 90 days of doubling down, kill switch activates.

5. Runway protection:

• If you have <4 months of runway remaining, use the 2-week path only. A 6-week diagnostic consumes 33% of runway, leaving minimal execution buffer.
• If you have 4-6 months of runway, 4-week path is appropriate.
• If you have 6+ months of runway, 6-week path is viable if tracking is completely absent.
• Build in a 1-month buffer after decision: if you kill, you need runway to wind down responsibly or pivot; if you double down, you need runway to execute improvements before next review checkpoint.

6. Decision documentation:

• Record your metrics, thresholds, and decision rationale in writing. This prevents rationalization later ("metrics were borderline, but I felt good about it").
• If you're at a borderline case (CAC payback 17-18 months, LTV:CAC 2.8-3.1), document why you chose to double down or kill. The benchmark is the anchor, but execution quality and team confidence matter in edge cases.
• Share decision with your board/investors if you have them. This prevents "surprise" pivot later and builds accountability.

• James Park (Execution & Strategy): "Calculate CAC payback and LTV:CAC for converting customers within 2 weeks. Kill if CAC payback > 18 months OR LTV:CAC < 3:1. If metrics pass, you have an execution gap (fixable), not a broken model (kill signal)." High confidence (0.60).
  Key reasoning: The real question isn't whether to double down or kill—it's whether the unit economics work for any customer segment. Benchmarks alone create ambiguity, but isolating your best customers' LTV:CAC ratio gives you a clean execution vs. economics distinction.

• Priya Desai (Financial Analysis): "Execute a 4-week diagnostic sprint to establish clean metrics, then apply hard benchmarks: CAC payback > 18 months OR LTV:CAC < 3:1 = kill immediately. If both metrics pass, allocate remaining runway to execution improvements with monthly metric reviews." Very high confidence (0.85).
  Key reasoning: Unit economics benchmarks—not sunk costs or subjective feel—must drive this decision. The research validates clear thresholds: 18-month CAC payback is the upper acceptable bound, and 3:1 LTV:CAC is the minimum viability ratio. The 4-week timeline preserves execution capacity while establishing quantitative foundation for a confident decision.

• Aisha Thompson (Operational Rigor): "Execute a 6-week diagnostic sprint. Week 1-2: Audit tracking—can you calculate CAC payback and LTV:CAC accurately? If no, instrument immediately. Week 3-4: Collect clean data. Week 5-6: Calculate and apply kill triggers. If metrics pass all tests, double down with focused execution plan." High confidence (0.85).
  Key reasoning: Most founders at the "double down/kill" juncture lack accurate metrics. The 6-week sprint balances speed with data quality—if you can't calculate basic benchmarks, that's a kill signal. The kill triggers are unambiguous: CAC payback > 18 months OR LTV:CAC < 3:1 for converting customers = immediate shutdown.

The three experts disagreed on timeline (James recommended 2 weeks, Priya 4 weeks, Aisha 6 weeks) and on whether data quality issues should block the decision immediately or trigger instrumentation first.

James vs. Priya/Aisha on timeline: James argued that after one year in market, you should have enough data to calculate basic metrics within 2 weeks—if not, that's itself a kill signal. Priya countered that 2 weeks is too aggressive for founders with incomplete tracking; 4 weeks allows both data quality verification and clean calculation. Aisha pushed for 6 weeks to include data collection time, arguing that most founders lack 3-6 months of historical burn data required for reliable calculations.

Resolution: The board synthesized these positions. If your tracking is already clean (data exists and is accurate), 2 weeks is sufficient—this is James's scenario. If you have data but need to verify accuracy, 4 weeks is appropriate—this is Priya's scenario. If you lack tracking entirely and need to instrument it, 6 weeks is realistic—this is Aisha's scenario. The key is that all three experts agreed on the kill thresholds (18-month CAC payback, 3:1 LTV:CAC) and the principle that inability to calculate these metrics within your available timeframe is itself a kill signal.

James vs. Priya on benchmarks: James argued that "acceptable range" benchmarks (12-18 months for CAC payback) create gray zones where founders can't decide. Priya countered that 18 months is not a gray zone—it's the upper bound, and anything beyond it is a clear kill signal. The board sided with Priya: 18 months is the threshold, not the middle of an acceptable range. However, James's insight about isolating converting customers' metrics was incorporated into both Priya's and Aisha's final recommendations, addressing his concern about distinguishing execution gaps from broken models.