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Decision Intelligence
A prediction is not a decision. A decision is not value until someone acts on it.
We build the layer that converts forecasts into chosen actions: what to do, for whom, through which channel, at what moment, at what cost — and what it was worth compared to doing nothing.
4.1M+
decisionsExecuted monthly across client systems
+23%
conversionTypical lift from next-best-action ranking
100%
attributedEvery decision measured against a control group
The accurate model that lost money.
A client arrived with a churn model that predicted attrition with 91% accuracy. It had been running for a year. Churn had not improved at all. The model was excellent and completely useless, because nothing had been decided on the back of it — the list went to a team that discounted everyone on it, including the customers who were never going to leave.
We kept the model and built the missing half: a decision policy. Who is worth saving given their margin. Which of them responds to contact at all. What the cheapest effective intervention is per segment. Which channel and hour that intervention should use. And crucially, a holdout group who got nothing, so the value was provable.
Churn fell 31% and retention spend fell as well, because the discounting stopped going to customers who were staying anyway. The accuracy never changed. The decisions did.
We had been paying customers to stay who had no intention of leaving. Golden found that in the first fortnight.
Prediction, policy and proof — the three parts most firms only half build.
We deliver all three. Without policy, predictions sit in a dashboard. Without measurement, nobody can defend the investment next budget cycle.
Propensity & risk models
Churn, conversion, default, upsell, cancellation, complaint and demand models trained on your history with point-in-time correct features.
Forecasting
Demand, revenue, cash, occupancy, generation and staffing forecasts at the granularity operational teams can actually plan against.
Next best action
A ranked action set per customer accounting for value, eligibility, fatigue, cost of contact and business constraints — not just the highest score.
Decision policies & constraints
Business rules, regulatory limits, budget caps and fairness constraints encoded so automated decisions stay inside what the business will defend.
Pricing & offer optimisation
Test and optimise price, discount depth and offer construction against margin rather than volume, with guardrails on brand and compliance.
Incrementality measurement
Holdouts, uplift modelling and pre-registered metrics so the value claimed is the value that would not have happened anyway.
What changes when decisions get systematic.
Each of these is written into the engagement as a number with an owner, a baseline and a review date.
OUTCOME 01
Money moves to where it works
Retention budget, sales attention and marketing spend get allocated by expected value instead of by habit, list order or the loudest internal voice.
OUTCOME 02
Speed becomes an advantage
Decisions that used to wait for a weekly meeting happen at the moment of interaction. In competitive markets responding first is frequently worth more than responding better.
OUTCOME 03
The debate ends
With holdouts in place, arguments about whether the programme works are replaced by a number. Several clients have used exactly this evidence to defend and grow their AI budget.
The complete decision loop.
- Predictive models with documented features, validation and reason codes
- A written decision policy your risk and compliance teams have approved
- Real-time decision API serving your channels and agents
- Experiment framework with holdouts and pre-registered metrics
- Monthly incremental value report signed off with your finance team
- Retraining, monitoring and drift alerting in production
The engineering underneath.
Numbers from work already in production.
Measured against the baseline agreed with the client before the engagement started.
31%
Churn reductionDecision policy on an existing churn model
+23%
Conversion liftNext-best-action ranking in lending
−19%
Retention spendDiscounting stopped for customers who would stay
What analytics and finance leaders ask.
- 01. We already have data scientists. What do you add?Usually the policy and measurement halves. Most in-house teams are strong at modelling and under-resourced on the decision logic, the integration into channels and the holdout discipline that proves value.
- 02. Why insist on a holdout? It costs us upside.It costs a small, deliberate amount of upside and buys you the ability to prove the programme works. Every client who has kept holdouts has found at least one initiative that was quietly destroying value.
- 03. How do you avoid unfair or non-compliant decisions?Fairness and eligibility constraints are part of the policy layer, tested before release and monitored after. Reason codes accompany every decision so an adverse outcome can be explained to a customer or a regulator.
- 04. Can this work on a small data estate?Often yes. Decision quality is limited more by whether anyone acts on the output than by data volume. We have delivered material value on estates of a few hundred thousand records.
Where to go next.
Bring us a model nobody acts on. We will turn it into a decision that pays.
The assessment reviews your existing models, identifies the decisions they should be driving and quantifies the value currently sitting unused in your dashboards.
