Deep Dive Diagnostic Report

OVERALL SCORE

68

Needs Attention

COORDINATION INDEX

41

Critical

ARM SPREAD

47

High Variance

Arm 1: Sales Operations

Analysis: Pipeline management (35) is the Sales bottleneck. Low coverage ratios and inconsistent stage definitions mean forecast accuracy (52) suffers from bad inputs, not bad methodology. Reps are enabled (78) — they know how to sell — but the pipeline infrastructure they're working with is unreliable. Fix pipeline discipline first; forecasting will improve as a downstream effect.

Arm 2: Marketing Operations

Analysis: Strong demand generation and campaign execution, but lead management (65) reveals the coordination gap: leads are generated well but the handoff to sales is where quality degrades. This is a Sales-Marketing Alignment (R01) problem, not a marketing capability problem. Marketing creates value that gets lost in the transition.

SEVERITY: HIGH. Onboarding (22) and health scoring (28) are critical failures. Customers are not reaching time-to-value, and there is no early warning system for churn. Retention (48) is being maintained through reactive heroics by individual CSMs, not systematic process. Expansion (55) is happening opportunistically, not through structured playbooks. This entire arm is operating on individual effort, not operational infrastructure. It will not scale.

Arm 4: Order-to-Cash

Analysis: Billing is accurate (72) but the quote-to-order process (58) involves too much manual intervention. Low automation (42) means scaling deal volume will create bottlenecks. DSO of 55 indicates collections are slower than industry standard — likely related to unclear billing terms established during the sales process.

Arm 5: Pricing & Finance

Analysis: Strongest operational arm aside from Planning. Pricing strategy is sound, financial execution is disciplined, and unit economics are well-understood. This arm is a model for what other arms should aspire to in terms of process maturity. The slight gap in pricing strategy (78 vs 85 execution) suggests pricing decisions are good but could benefit from more systematic competitive analysis.

Analysis: PS is losing money on delivery (margin at 35). Projects are under-scoped during the sales process (PS-Sales coordination failure, R05), leading to scope creep and resource overruns. Service delivery (38) indicates inconsistent methodology — quality depends on which consultant is assigned, not on repeatable process. This directly feeds the CS problem: poor implementations lead to poor adoption which leads to churn.

Arm 7: Data & Reporting

Analysis: Tech stack is capable (70) but data quality (55) undermines everything built on top of it. KPI alignment at 58 confirms that teams define the same metrics differently — "pipeline" means different things to sales, marketing, and finance. Reporting exists (68) but reports based on inconsistent data create a false sense of measurement. Fix data quality and KPI definitions before investing in more dashboards.

Arm 8: Planning & Strategy

Analysis: Strongest arm. Territory design, quota methodology, capacity planning, and GTM planning are all mature and well-executed. This arm demonstrates what operational maturity looks like across the board — and it's the benchmark other arms should target. The gap between Planning (85) and execution arms (Sales 58, CS 38) confirms the core diagnosis: strategy is not the problem, execution and coordination are.

MQL definitions not shared. Marketing scores leads by engagement; sales qualifies by budget/authority. These are fundamentally different criteria producing different lists. Neither team trusts the other's pipeline numbers.

No structured transition process. CS learns about new customers from automated email notifications, not from sales briefings. Expectations set during the sales process are not documented or transferred. CS frequently discovers misaligned expectations 2-3 weeks post-close.

Each team defines pipeline differently. Revenue forecast uses three different numbers depending on which team presents. No single source of truth for core business metrics.

CRM data is accessible but marketing automation and CS platforms are siloed. Customer lifecycle data requires manual compilation from three different systems.

Some customer feedback reaches marketing, but it's anecdotal, not systematic. Marketing doesn't know which customer segments retain best or which features drive adoption. This information exists in CS data but isn't synthesized or shared.

CRM is connected to billing. Marketing automation has a basic CRM sync. CS platform is standalone. No bi-directional data flow between CS and any other system. Integration is present but incomplete.