For press, analysts, and operators citing the 100,000-stack benchmark.
Methodology v1.1.0 Generated Jun 2, 2026 · Seed 42
81.66% of modeled B2B SaaS GTM stacks contain at least one redundant tool pair.
Median team is sitting on $93,240/yr in modeled recoverable spend. We modeled 100,000 stacks across 12 operator archetypes. Open methodology, reproducible from one command.
81.66%
of modeled stacks contain at least one redundant tool pair
$93,240
median annual recoverable spend per stack
100%
of stacks containing Outreach are flagged for replacement
96.05%
of stacks containing Mailchimp flag it as redundant
$33,000
median annual overlap if Marketo and Pardot share a stack
Methodology in three sentences
100,000 synthetic GTM stacks are generated by sampling tool combinations from 12 operator archetypes (early-stage B2B SaaS, mid-market sales-led, enterprise RevOps, AI-native modern team, and 8 others) weighted by realistic population prevalence. Each stack runs through scanStack() — the same pure scoring function that powers the public StackScan tool — producing per-tool verdicts, overlap detections, and modeled recovery. Same seed + same engine = bit-identical aggregates; the dataset is committed in the open repo and runs in ~1784 seconds.
Why synthetic, not customer data
StackSwap is pre-revenue. Citing customer scans we don't have would be misleading. Synthetic stacks let us span the full operator distribution — including patterns a real customer base would underweight — and the methodology is fully reproducible. The numbers should be read as “modeled directional truth,” not empirical fact, and the limitations are documented at /methodology.
Frequently asked about the data brief
The StackSwap data brief is a press-ready summary of the 100,000-stack GTM simulation: five citation-grade headline statistics, the methodology disclosure that backs each one, a pre-written attribution quote, and links journalists can use to verify the underlying data.
The simulation generates synthetic-but-realistic B2B SaaS GTM stacks across 12 archetypes (early-stage founder-led, mid-market sales-led, PLG, AI-native, enterprise outbound, etc.) using a deterministic seeded engine. Tool assignments per archetype come from real B2B SaaS prevalence data. Full methodology, archetype weights, and reproducibility command live on /methodology.
Yes. Every stat on the data brief is open-license for editorial use. Cite as "StackSwap (2026), 100,000-stack GTM simulation" with a link to /methodology for the methodology disclosure. Direct quotes from Nick French (Founder, StackSwap) are also pre-cleared on the brief.
The simulation reruns when archetype weights or tool prevalence shift materially — typically quarterly. Each rerun produces a new dated dataset; the data brief always reflects the latest run. The methodology version increments with each substantial change so historical citations remain traceable.