The «one framework fits all» mistake
Most consulting frameworks are calibrated against one industry, usually the one the partner came from, and then sold to the others with the constants unchanged. The model looks the same on the slide, but underneath, the multipliers, the recovery rates, the dimension weights are wrong for three of the four verticals. The customer cannot see this. The output looks like an opinion from an expert. It is in fact a SaaS opinion applied to a DTC business, or a Fintech opinion applied to a ProfSvc business.
Caugia calibrates the same GRIP framework against four different industries. The structure stays the same: four dimensions (Guidance, Resources, Implementation, Performance), twelve pillars, deterministic scoring. The constants change. Here is why they must.
The physics differ. So the math must.
SaaS B2B Compounding through retention
A SaaS company at €30M ARR with 110% net-revenue retention doubles in roughly seven years without acquiring a single new customer. The math compounds. A 10-point gap in win-rate or expansion motion is not a 10% revenue gap, it is a 25-30% gap over five years, because the deficit compounds against the same retention curve. Performance dominates the model.
Below-median Rule-of-40 performers in the Bessemer Cloud Index attribute roughly 60% of their gap to identifiable GTM-system weakness, not to product-market fit. That is the calibration anchor for the drag sensitivity, and it lands at a meaningfully high level.
K_DRAG = 0.60 · P weight = 0.35 · P recovery = 0.70DTC Inventory turns, not retention curves
A DTC brand operates on a fundamentally faster clock. Cash-conversion cycle is measured in weeks. The half-life of a fix is roughly 4 months, against SaaS’s 9 months, Drivepoint’s cohort recovery work across 188 brands in the €5-15M range confirms this. The brand can correct course inside a single inventory turn, but the cost of being wrong is also more immediate: a bad Q1 starves Q2 of working capital.
Performance dominates even more than in SaaS, because repeat-rate, MER, and AOV are existential. Triple Whale’s State of DTC 2024 makes this explicit. But the drag sensitivity is lower than SaaS, because corrections happen inside a single quarter, not across multi-year retention curves.
K_DRAG = 0.55 · P weight = 0.45 · P recovery = 0.75Fintech B2B Regulatory inertia compounds
A Fintech B2B operator can have great product, great pricing, and great demand, and still lose 14 months to a compliance block. Acrew Capital’s State of Fintech 2024 puts the median time-to-recover from a regulatory event at roughly that, and the consequence cascades: integration partners freeze, prospects defer, the funnel restarts from the top.
The model has to reflect that. Resources (compliance headcount, sandbox-to-prod conversion, integration capability) and Implementation (regulatory operations) dominate. Performance is constrained because no Performance gain can outrun a regulatory drag. Recovery factors are systematically lower than SaaS or DTC because regulatory inertia is structural, not behavioural.
K_DRAG = 0.70 · R + I weight = 0.60 · P recovery = 0.60Professional Services Linear scaling, not exponential
A consulting practice or services firm scales linearly with headcount and utilisation. There is no compounding flywheel. A 10% utilisation gap creates roughly a 10% revenue gap, not 25% over five years, not a regulatory cascade, just a proportional shortfall. SPI’s PS Maturity Benchmark 2024 codifies this: ProfSvc is a people-business with bounded operational leverage, not a system-business with compounding leverage.
The drag sensitivity is the lowest of the four verticals. Guidance and Resources dominate (strategy, talent quality, practice leadership). Kennedy’s Pulse Survey confirms G + R as the practical centre of gravity in consulting outcomes.
K_DRAG = 0.50 · G + R weight = 0.60 · P recovery = 0.60The constant that changes the most: K_DRAG
K_DRAG is the framework’s sensitivity coefficient, how much revenue impact follows from a unit of system weakness. Across our four verticals it ranges from 0.50 (ProfSvc) to 0.70 (Fintech), a 40% spread. The Bessemer/Acrew/Drivepoint/SPI literature gives us the anchors. Applying SaaS’s 0.60 to ProfSvc would over-state drag by 20%; applying it to Fintech would under-state drag by 17%. Either error is fatal to the credibility of the output.
The constant that changes the least: GRIP itself
What does not change is the structural model. Every vertical we have studied still has four dimensions of GTM health (Guidance, Resources, Implementation, Performance). Every vertical still has twelve pillars underneath them. The structural taxonomy is invariant. It is the weights on the taxonomy that change.
This is what makes the framework portable. We do not invent a new GTM theory for each industry. We re-calibrate the same theory against industry-specific anchors. Same engine, four sets of constants, full transparency on which constant comes from which source.
What this means for how you read benchmark data
The market is full of «benchmark» reports that publish a single number for «the median» GTM team. Without a per-vertical decomposition, those numbers are noise. A 110% NRR median across «the industry» is a SaaS-weighted statistic that has zero meaning for a DTC operator (where NRR is not even the right primary metric) and active harm for a ProfSvc operator (whose retention dynamics are entirely different from subscription software).
The Caugia approach: every constant we publish has a vertical scope attached. K_DRAG = 0.60 is a SaaS B2B constant, not a Caugia constant. The Phase 2 cohort backtest in Q3 2026 will tighten the per-vertical numbers further against observed customer outcomes, but the principle holds: numbers without a vertical scope are misleading.
Implications for cross-vertical advisors and VCs
If you advise companies across more than one of these four verticals, or if you sit on the board of a SaaS company and a DTC company simultaneously, the per-vertical calibration matters operationally. The same red flag (low Performance score) does not mean the same thing in each portfolio company. In SaaS it usually points at the retention engine. In DTC it points at MER or repeat-rate. In Fintech it might be downstream of an upstream Implementation problem you have not surfaced yet. In ProfSvc it points at utilisation and engagement margin.
One framework. Four lenses. Same math, different constants. The discipline is the same in all four cases: identify constraint location, quantify revenue leakage, sequence the intervention. The numbers behind each step depend on which industry you are inside.
Frequently Asked Questions
Why can a single GTM framework not use the same constants across SaaS, DTC, Fintech, and Professional Services?
Because the physics of each business differ. SaaS B2B compounds through net-revenue retention, DTC turns inventory into cash inside a quarter, Fintech B2B can lose over a year to a single compliance block, and Professional Services scales linearly with utilisation. The same drag sensitivity and recovery factors cannot be true for all four. Caugia keeps one GRIP framework with four dimensions and twelve pillars, but re-calibrates the constants per vertical. You can see the calibration for your own company with the free GTM diagnostic.
What is K_DRAG and why does it change by vertical?
K_DRAG is the framework's sensitivity coefficient: how much revenue impact follows from one unit of GTM-system weakness. It ranges from 0.50 in Professional Services to 0.70 in Fintech B2B, a 40 percent spread anchored to the Bessemer, Acrew, Drivepoint, and SPI literature. Applying the SaaS value of 0.60 to Professional Services would over-state drag by about 20 percent; applying it to Fintech would under-state it by about 17 percent. Either error breaks the credibility of the number. The free GTM diagnostic auto-selects the right K_DRAG for your vertical.
Does the GRIP framework itself change between industries?
No. The structural model is invariant. Every vertical still has the same four dimensions of GTM health (Guidance, Resources, Implementation, Performance) and the same twelve pillars underneath them. What changes is the weights and constants on that taxonomy, not the taxonomy. That is what makes the framework portable: one deterministic engine, four sets of constants, full transparency on which constant comes from which source.
Why are generic GTM benchmark numbers misleading for my company?
Because a single median across the whole market is usually a SaaS-weighted statistic with a vertical hidden inside it. A 110 percent NRR median has no meaning for a DTC operator, where NRR is not even the right primary metric, and is actively harmful for a Professional Services firm with entirely different retention dynamics. Every constant Caugia publishes carries a vertical scope: K_DRAG = 0.60 is a SaaS B2B constant, not a universal one. A diagnostic that names your single binding constraint and quantifies the revenue leakage in euros is worth more than any market-wide median.
How do I find out which calibration applies to my own company?
Run the free GTM diagnostic. It costs EUR 0, needs no card, and auto-selects the per-vertical constants from your setup, then names your single binding constraint and quantifies the revenue leakage in euros. For a deeper read, Pulse is EUR 249 and the full Report is EUR 750, while GRIP OS, the operating system with the Sophie copilot, is available on a contact-for-pricing basis. The free diagnostic also surfaces your AI Answer Market visibility across the six engines Caugia tracks: ChatGPT, Claude, Gemini, Perplexity, Grok, and Mistral.
See the calibration in your vertical
The simulator and the assessment both auto-select the per-vertical constants based on your workspace setup. Run a quick analysis on your own company to see which constants apply and what they imply for your number.