Forecasts based on your actual historical data
Predictive models for revenue, churn, and campaign performance, validated against your own historical outcomes rather than a generic template.
Predictive analytics is using models trained on your own historical data to forecast outcomes like revenue, churn, or campaign performance, validated against what actually happened before being relied on.
What is Predictive Analytics?
We apply statistical and machine-learning models to your historical business data to forecast revenue, customer churn, and campaign performance, instead of relying on a trend line extrapolated by eye or a generic industry benchmark. The model is only as reliable as the data and validation behind it, which is why real time goes into data quality before any forecast gets trusted.
Skip the validation step and a model can look authoritative while being wrong - worse than a gut-feel forecast that at least gets flagged as uncertain - while a properly validated model surfaces relationships you wouldn't spot by eye, like which early account behaviours actually predict churn versus which merely correlate with it.
It's different from marketing intelligence and reporting, which describe what already happened; predictive analytics is specifically about forecasting what happens next, built and backtested against your own historical outcomes rather than a generic template.
Common problems we solve
Data has more gaps than anyone realises
Most businesses have more data gaps and inconsistencies than they think until someone actually audits it.
Past patterns don't always hold
A market shift, pricing change, or new competitor can break a model's assumptions without warning.
Overfitting to noise
A model that explains the past well can still predict the future badly.
Budget decisions ride on an eyeballed trend line
Forecasts driving real resourcing decisions deserve proper validation, not a spreadsheet trend line.
Retention spend applied evenly, not at risk
Without churn prediction, retention budget gets spread across accounts instead of targeted at the ones actually at risk.
Everything covered in this service
Data Quality & Preparation
Auditing historical data for gaps, inconsistencies, and definitional drift before committing to a model type.
Revenue & Pipeline Forecasting
Forecasting built on your actual sales cycle and conversion patterns, with scenario modelling.
Churn Prediction
Identifying which early behaviours genuinely predict churn, with risk scoring applied to the active base.
Campaign Performance Forecasting
Forecasting expected performance for planned campaigns, flagged when they differ structurally from training data.
Validation & Recalibration
Backtesting every model against known outcomes, with scheduled recalibration as conditions shift.
Our optimization framework
Audit Data
We assess what historical data actually exists, how clean it is, and whether it is sufficient to support a reliable model before committing to one.
Select Model
The model type is chosen based on what the data and the business question actually require, not a default template applied regardless of fit.
Backtest
Every model is validated against known historical outcomes before it gets used for a live forecast, and the accuracy is reported honestly, including where it underperforms.
Deploy
The validated model is put into use for the specific forecasting question it was built for - revenue, churn, or campaign performance.
Recalibrate
Models get revisited on a set schedule, since a model that was accurate six months ago can quietly drift as conditions change.
What actually changes
| Before | After |
|---|---|
| Trend line extended by eye | Model trained and validated on your data |
| Accuracy assumed, not checked | Backtested accuracy reported before use |
| Retention spend applied evenly | Risk scoring targets accounts actually at risk |
| Model built without a data audit | Data quality assessed before any model is built |
| Left unchanged until it breaks | Recalibrated on a set schedule |
What we report on
Backtested accuracy
Reported against known historical outcomes before a model is trusted for a live forecast.
Churn model precision & recall
Measured against actual churn events, not just plausible-looking scores.
Forecast variance over time
Tracked to catch model drift early, before it quietly becomes unreliable.
Tools & platforms
Industries we optimize for
Built for teams like yours
Why choose EASI7
We fix tracking before we touch results
Every engagement starts with the same audit we'd run on our own accounts, so the numbers you're optimizing against are actually true.
One accountable team, not siloed vendors
The same team that does this also owns the rest of your stack - no handoff, no blame-shifting between vendors.
Built for how AI search actually works
Everything we build is structured to be extractable by AI answer engines, not just ranked in a list of blue links.
Reporting tied to your own dashboard
Every number we report ties back to GA4/GTM events you can see directly - not a screenshot from a tool you can't independently verify.
The biggest source of bad forecasts isn't a weak model - it's skipping the data audit because everyone wants to get to the forecast faster. A model built on unaudited, inconsistent historical data produces a confident-looking number that's wrong in a way a gut estimate at least gets treated with appropriate suspicion.
How Predictive Analytics connects with AI search
Predictive Analytics ├── Marketing Intelligence ├── Customer Insights ├── Automated Insights └── AI-Powered Reporting
Marketing Intelligence
Turns the same historical data into competitive and market signal, not just an internal forecast.
Customer Insights
Explains the behavioural patterns behind a churn or conversion prediction, not just the number itself.
Automated Insights
Surfaces what a forecast means in plain language, rather than leaving the model output for someone else to interpret.
AI-Powered Reporting
Puts forecasts and their accuracy into the same dashboard stakeholders already check, instead of a separate one-off model output.
Related AI Analytics services
Reports That Explain the Number, Not Just Show It
AI-generated narrative summaries layered on your dashboards, so reporting time goes to decisions instead of the write-up.
AI AnalyticsCatch Cross-Channel Patterns Before Your Next Review
We aggregate every channel and surface what's actually shifting in your marketing performance, before it shows up in a quarterly review.
Find the Segments Hiding in Data You Already Have
AI-assisted analysis that turns the data you already collect into segments and decisions, not a bigger spreadsheet.
AI AnalyticsGet The Recommendation, Not Just The Report
We detect the change, surface the likely cause, and deliver a recommended action wherever your team already works.
How to engage us for this
Project-based
A defined outcome with a start and end date - an audit, a migration, a campaign build, a tracking overhaul. Fixed scope, fixed price, agreed upfront.
Ongoing retainer
Continuous management and optimization once the initial build is live - campaigns, SEO, reporting, and iteration run every month under one accountable team.
Advisory
Strategy and oversight without full delivery - we review what's already running, unblock decisions, and point an in-house or existing team in the right direction.
Common questions
It depends entirely on the data and the specific question being forecast - there's no universal accuracy figure we'd stand behind across every business. We backtest every model against your own historical outcomes and report that accuracy honestly before it's used for a live decision, including where it's weaker.
No - no honest forecasting engagement can guarantee that. Markets shift, and a model's assumptions can break without warning. What we guarantee is a model properly validated against your historical data, with accuracy reported honestly and a recalibration schedule to catch drift as conditions change.
It varies by model type, but as a rough floor, churn and campaign forecasting need at least twelve to eighteen months of consistent historical data to identify a reliable pattern. Less than that, and we'll say so rather than building a model on too thin a base.
We tell you during the data audit, before building anything on top of it. Sometimes that means a data cleanup phase first, and sometimes it means starting with a simpler model that's honest about its limitations rather than a sophisticated one built on unreliable inputs.
Typically quarterly at minimum, more often for fast-moving businesses or after a material change - a pricing shift, a new competitor, a market disruption. A model left untouched for a year is likely drifting even if nobody's noticed yet.
To a degree - some models expose which factors are driving a prediction, which gets you partway to a 'why'. But a correlation surfaced by a model still needs human interpretation to confirm it's actually causal rather than coincidental in your specific dataset.
Not sure if your historical data can actually support a forecast?
We'll audit what you have before recommending a model - or telling you honestly that it's not ready yet.
Ready to get started?
We usually reply within 24 hours.
Predictive Analytics, in detail
Scope, area by area
| Area | What we deliver |
|---|---|
| Data Audit | A report on data quality and readiness before any model is built |
| Forecasting Models | Revenue, churn, or campaign models built and validated against your own historical outcomes |
| Validation Report | Backtested accuracy against known outcomes, stated honestly including where the model is weaker |
| Recalibration Plan | A schedule for revisiting and retraining the model as new data accumulates |
How this compares
| Predictive Analytics | Trend Extrapolation |
|---|---|
| Model trained and validated against your actual historical data | A trend line extended by eye or in a spreadsheet |
| Backtested accuracy reported before being relied on | Accuracy assumed rather than checked |
| Recalibrated on a schedule as conditions change | Left unchanged until it obviously stops matching reality |
A well-validated trend extrapolation can be perfectly adequate for a stable, simple pattern - the model only earns its cost when the underlying relationships are complex enough that eyeballing them stops working.
What we measure this against
- Backtested forecast accuracy against known historical outcomes
- Churn model precision and recall against actual churn events
- Forecast variance over time, tracked to catch model drift early
Where this applies
- A SaaS company wants to forecast pipeline conversion for the next two quarters based on actual historical deal patterns
- A subscription business wants to identify which accounts are genuinely at churn risk before renewal, not just which ones look busy
- A marketing team wants to forecast how a planned campaign is likely to perform based on comparable past campaigns