We provide predictive analytics services for Australian businesses that want to stop reacting and start anticipating. AI-powered lead scoring, churn prediction, revenue forecasting, and sales intelligence that tells you what’s coming before it happens.
The Challenge
Business decisions are still overwhelmingly reactive. Sales teams chase leads based on who came in most recently rather than who’s most likely to buy. Customer success teams discover churn after it happens. Revenue forecasts are built on hope and history rather than statistical models.
Look, the data to make better predictions already exists in most organisations. CRM records, transaction histories, engagement patterns, support tickets, website behaviour. These contain the signals that predict what’s going to happen next. But humans aren’t equipped to process thousands of data points and spot the subtle patterns that distinguish a customer about to churn from one about to expand.
According to Forrester, companies using predictive analytics are 2.9x more likely to report revenue growth above industry average. And yet most mid-market businesses are still making decisions the same way they did a decade ago.
The cost of reactive decision-making is invisible but enormous. Every lost customer who could have been saved. Every sales hour spent on a lead that was never going to convert. Every marketing dollar poured into a channel that’s declining. These add up to millions in wasted resources. So why aren’t more businesses doing something about it?
Probably because predictive analytics has historically required data science teams, expensive platforms, and months of setup. That’s not the case anymore.
Our Approach
We build predictive models specific to your business, trained on your data, and integrated directly into the tools your teams already use. This isn’t a standalone analytics platform that requires someone to log in and check. It’s intelligence delivered where decisions are made, whether that’s your CRM, Slack, email, or a custom dashboard.
Our process starts with defining the predictions that matter most. For a SaaS company, that might be churn probability and expansion likelihood. For a B2B services firm, it could be lead-to-close conversion probability and deal size prediction. For a healthcare organisation, patient no-show prediction and referral source analysis. We identify the highest-value prediction, build a model, prove it works, and then expand.
This connects directly with our data analytics capability. In most cases, we need a solid data foundation before building predictive models, and if that foundation doesn’t exist yet, we’ll build it. Our AI strategy process helps prioritise which predictions will deliver the most business impact.
Each model is built with transparency at its core. We don’t just tell you that a customer is likely to churn. We tell you why, based on the specific factors driving the prediction. Gartner reports that organisations with explainable AI see 30% higher adoption rates among business users. This makes the output actionable: your team knows exactly what to do with each prediction.
And when predictions need to feed into more sophisticated workflows, our custom AI model development capability handles the heavy lifting. (Thing is, most businesses don’t need complex custom models to get started. A well-built prediction model on clean data outperforms a fancy one on messy data every single time.)
What We Build
| Prediction Type | Business Impact | Typical Accuracy |
|---|---|---|
| Lead Scoring | Sales teams focus on high-probability deals | 78-88% |
| Churn Prediction | Retain at-risk customers before they leave | 80-90% |
| Revenue Forecasting | Accurate pipeline projections for planning | 75-85% |
| Demand Forecasting | Optimise inventory and resource allocation | 76-86% |
| Customer Lifetime Value | Prioritise acquisition by predicted value | 72-82% |
Every model includes clear documentation, explainability features, and integration with your existing tools. We’re not interested in building black boxes.