Event logs, billing records, session data, support tickets — the information is there. The gap is between what the data holds and what the team can actually see, trust, and act on quickly enough to matter.
The reality of modern infrastructure, unpacked.
SaaS businesses generate data continuously — every login, every feature click, every support interaction, every upgrade and cancellation. The volume is not the problem. The problem is that the people who need to act on this data — product managers, growth teams, customer success — are often working from dashboards that show them what happened last month, not what is happening right now or what is likely to happen next week.
Most SaaS companies have assembled analytics tools over time rather than by design. There is usually a product analytics tool, a billing platform, a CRM, and some form of business intelligence — each holding a fragment of the picture. The effort required to connect these fragments into a question-and-answer workflow is high enough that most teams stop asking the harder questions.
A customer success manager who finds out a customer has churned from the billing system, not from an early warning. A product team that ships a feature and waits two weeks for enough data to understand whether it is working. A founder presenting to the board with revenue projections assembled from three spreadsheets on a Sunday night. The data existed to avoid each of these situations — it just was not accessible in the right form at the right time.
We target specific workflows where manual effort meets its ceiling, delivering measurable, high-leverage outcomes.
Churn is usually diagnosed after it has happened. Billing cancellations are a lagging signal — by the time they appear, the decision to leave was made weeks earlier, and the window for intervention has already closed.
A significant share of new signups never reach the moment where the product's value becomes clear. Most teams know this is happening but cannot identify precisely where in the onboarding flow users are losing momentum.
Product teams ship features and measure success by usage counts. But usage counts do not distinguish between a feature that is genuinely valuable and one that users try once and abandon.
Expansion revenue — upsells, seat additions, plan upgrades — is often the largest growth lever for a mature SaaS business, but it is rarely tracked with the same rigour as new ARR. The behaviours that predict expansion are sitting in the product data, unconnected to the billing record.
Aggregate metrics like total MRR and overall churn rate mask the differences between customer segments. A cohort acquired through one channel may retain at twice the rate of another — but this is invisible in the top-line numbers.
Outcomes in the reader's language, focused on actual usage.
Event-level tracking across every product interaction — session patterns, feature usage, workflow completion — structured so that product and growth teams can query it without waiting for a data analyst.
A unified view of MRR, ARR, expansion revenue, contraction, and churn — connected to your billing system and updated continuously. Designed for founders, finance leads, and anyone who prepares board materials.
A machine learning model trained on your historical data that surfaces at-risk accounts to customer success teams, with the behavioural signals driving each risk score — not just the score itself.
Step-by-step conversion analysis from first visit through trial, activation, and paid conversion — with the ability to segment by acquisition channel, cohort, and user attribute to find where different groups diverge.
Retention and revenue cohort views that let teams compare performance across acquisition periods, plan types, company sizes, or any custom dimension — updated automatically as new data arrives.
A single view combining product engagement, revenue performance, and retention metrics — configurable by role so each team sees the signals relevant to their decisions without digging through data exports.
Predictive models are only as reliable as the data they are trained on. In the early months of a deployment — before the model has seen enough of your customer lifecycle — we are transparent about confidence levels and do not present early outputs as definitive signals. The model improves as it accumulates data, and we communicate that progression clearly. The concern we hear most often is whether AI-generated risk scores will be used to replace customer success judgment rather than inform it. We build these systems to give teams more context, not to automate decisions that benefit from human relationship and nuance. A churn risk score without the underlying behavioural signals is not something we would ship — the reasoning needs to be visible.
A model monitors login frequency, feature engagement depth, support ticket volume, and billing history for every active account. Accounts whose pattern resembles past churned customers are surfaced to the customer success team — typically two to four weeks before a cancellation would otherwise appear.
Rather than setting manual thresholds for every metric, an anomaly detection layer monitors your key signals continuously and flags deviations that warrant investigation — a sudden drop in daily active usage, an unexpected spike in failed payments, a cohort retaining significantly below baseline.
A model trained on historical expansion events learns which product usage patterns reliably precede upsells. Accounts matching that pattern are surfaced to sales or success teams with context — what they are doing in the product and why that suggests readiness for a conversation.
We start by understanding what decisions your teams are actually trying to make — and work backwards from there to the data and systems that would support those decisions well.
Before proposing anything, we map what data you are collecting, where it lives, how clean it is, and what your teams are currently doing to answer analytical questions. Many SaaS companies have more useful data than they realise — and some have significant gaps. We need to know which situation you are in before we can recommend anything.
A dashboard built around a question is useful. A dashboard built around available data is usually ignored. We work with the teams who will use each part of the system to understand what they are trying to decide, what information would change that decision, and what format would make it easy to act on.
Churn prediction models and revenue forecasts are tested against historical outcomes before they are used in any operational workflow. We are specific about what confidence level the model is operating at, and we do not present early-stage models as production-ready until the validation results justify it.
Analytics infrastructure that requires a specialist to update is a liability. Every system we build includes documentation, training for whoever will own it internally, and a clear path for your team to extend it without coming back to us for every change.
Measured by operational outcomes, not just technical uptime.
~0%
Reduction in churn
from 8% to ~4% monthly over six months
0% → 7%
Free-to-paid conversion
following funnel friction identification
~0%
Expansion revenue growth
through product-signal-driven outreach
Quiet, honest, and specific results.
A B2B SaaS company had a monthly churn rate of around 8% and no reliable way to identify which accounts were at risk until they had already cancelled. Their customer success team was operating reactively, spending most of their time on cancellation conversations rather than retention.
Monthly churn decreased from roughly 8% to around 4% over six months. Customer success time shifted from cancellation calls to proactive outreach. The team found that they could work the same number of accounts with meaningfully better outcomes because they were spending time on the right ones.
A consumer SaaS product with a freemium model was converting around 2% of free users to paid plans. The team had theories about why conversion was low but no data to test them against — their analytics could show them that users were dropping off, but not where or why.
Free-to-paid conversion improved from 2% to approximately 7% over three months following targeted changes to the two identified friction points. Onboarding completion rates increased by roughly 65% in the same period.
An enterprise SaaS company knew that expansion revenue was important to their growth model but had no systematic way of identifying which accounts were ready for an upsell conversation. Account executives were working from gut feeling and tenure rather than product signals.
Expansion revenue increased by approximately 50% over the following two quarters. The sales cycle on expansion deals shortened by around 30% because conversations were initiated at the moment of genuine readiness rather than on a quarterly cadence.
Multi-seat account analytics, contract renewal tracking, expansion signal detection, and user hierarchy mapping across organisations. Supports both self-serve and sales-assisted growth motions.
A two-week review of your current data collection, tooling, and the questions your teams are trying to answer. We produce a clear picture of gaps, quick wins, and a sequenced roadmap — with honest estimates of effort and dependency.
An 8–12 week build covering the core analytics infrastructure — event tracking, data integration, revenue metrics, and the initial dashboard layer. Delivered with documentation and team training.
A 4–6 week engagement to build and validate a specific predictive model — churn prediction, expansion signals, or growth forecasting — trained on your historical data and integrated into your existing workflows.
Continued involvement after launch — model retraining as your data grows, new metric development, dashboard iteration, and support as your team's analytical questions become more sophisticated.
Enterprise-grade security embedded at the core.
Enterprise-grade controls, rigorous compliance baselines, and delivery discipline woven into the architecture from day zero.
All personally identifiable information is handled in compliance with GDPR and CCPA. Data minimisation, consent management, and the right to deletion are built into the data architecture — not applied as a layer on top of it.
All data in transit is encrypted. Role-based access controls and full audit logging are standard across every system we build. Storage and processing infrastructure meets enterprise security standards.
Retention policies are configured to match your contractual obligations and regulatory requirements. Data residency options are available for customers with geographic storage requirements.
Adhering to the highest standards of security and regulatory compliance.
Our foundational technology stack is designed around principles of immutability, deterministic performance, and zero-trust security. We deploy modern, enterprise-grade tooling to ensure every architecture we deliver is robust and extensible.
Data processing and modelling infrastructure for high-volume SaaS event and revenue data
Everything you need to know about partnering with us and our engineering standards.
Every SaaS company is at a different stage — different data maturity, different team structure, different questions they are trying to answer. If something on this page reflected a problem you recognise, we would be glad to hear where you are. No presentation. Just a conversation about what you are working through and whether we are a useful fit.