Data products for PE-backed SaaS
Data products that move the multiple.
Elucidian works with PE-backed B2B SaaS companies to build, launch, and scale data products — creating new high-margin revenue streams, compounding NRR, and building the defensibility that commands a premium at exit.
Eight-figure
New data product ARR
Built on top of existing SaaS ARR at 80%+ gross margin
110% → 125%+
NRR trajectory
Expansion revenue through embedded data products and upsell motion
2x+
Multiple expansion
NRR improvement and data moat construction driving disproportionate exit valuation
The problem
Your portfolio company is sitting on a data asset it has never monetised.
Years of proprietary transactional, behavioural, and operational data powers the core product. But it isn't generating a revenue line of its own — and it isn't creating the switching costs that make clients operationally irreplaceable.
That's a missed opportunity on three dimensions simultaneously: new ARR, NRR expansion, and exit multiple. Buyers increasingly underwrite AI and data capabilities at a significant premium. Companies that can demonstrate a compounding data moat are valued materially differently from those that can't.
The gap is almost never the data. It's the capability to identify, build, price, and commercialise products from it — end to end, without the handoffs that kill momentum.
What Elucidian does
Six value creation plays, measurable within 12 months.
New data product revenue
Launch analytics, benchmarking, and predictive products as premium tiers — priced and packaged for enterprise buyers.
10–30% incremental ARR at 80%+ gross margin
NRR acceleration
Embed data products into the expansion motion — driving upsell and cross-sell through demonstrated analytical value.
10–20pt NRR improvement → 2–4x multiple expansion
Pricing architecture
Restructure pricing around data and usage signals — capturing willingness to pay that flat seat-based models leave on the table.
15–30% ARPU uplift, zero incremental CAC
Switching cost creation
Build client-facing data workflows and dashboards that make the product a system of record — operationally irreplaceable.
Gross retention above 95%
Data moat construction
Build proprietary datasets with network effects — the defensibility that buyers underwrite at exit and competitors can't quickly replicate.
Differentiated exit narrative for M&A or IPO
Operational AI
Apply AI to internal processes — reducing COGS, improving gross margin, and freeing engineering capacity for product development.
200–500bps gross margin improvement
How it works
From data asset to revenue line — without the handoffs.
Most organisations spread the data products journey across multiple leaders. Every handoff — from data science to product to commercial — creates delay, misalignment, and dilution. Elucidian owns the full chain under a single accountable operator.
01
Find the pain
Identify the problems your clients will pay to resolve — through data, interviews, and commercial analysis.
02
Find the data
Map proprietary data assets to validated pain points. Identify what's buildable, not just what's desirable.
03
Build and pitch
MVP in weeks, not quarters. Validate with paying clients before scaling engineering investment.
04
Maximise uptake
GTM, pricing, sales enablement, and growth infrastructure built for a PE hold period — not a five-year roadmap.
Track record
Built and proven inside a PE-backed SaaS company. Now available to yours.
Elucidian is led by a data products operator with 20+ years in data science, ML, and AI. Most recently: designing and building a data products business unit from a greenfield brief at a PE-backed B2B SaaS company — leading data science, product management, and business intelligence as a single integrated function with full P&L accountability.
The result is a live, revenue-generating portfolio of predictive ML models and agentic AI systems — built against an eight-figure ARR target, with NRR expanding toward 125%+ and a material improvement in exit multiple trajectory.
This is not advisory. The operating model — ideation, build, pricing, phased launch, GTM, growth, revenue protection — was designed, built, and run in production. That's the difference between a roadmap and a revenue line.
Experience
20+ years
Data science, ML, AI, and data product commercialisation
Company scale
Nine-figure ARR
PE-backed B2B SaaS, 1,000+ enterprise clients
Clients served
Cross-sector
Financial services, professional services, logistics, tech
Engagement models
Structured for PE hold periods.
Assessment
Fixed fee
A structured 2–3 week diagnostic of your portfolio company's data assets, commercial opportunities, and fastest path to new revenue. Delivered as a board-ready value creation plan with specific ARR, NRR, and multiple targets.
Most common
Fractional operating partner
Monthly retainer
Embedded operating leadership across data science, product, and commercial — owning the data products P&L and reporting to the board. Structured for 12–24 month value creation programmes with measurable milestones.
Fund partnership
By arrangement
A dedicated data products capability across your portfolio — identifying, prioritising, and executing opportunities at the fund level, with playbook and knowledge transfer across portfolio companies.
Is your portfolio company sitting on an unmonetised data asset?
Most are. The conversation starts with a 30-minute call — no deck, no pitch. A direct assessment of whether there's a data products opportunity worth pursuing and what it would take to capture it within your hold period.
hello@elucidian.co.uk