AI Marketing Analytics & Forecasting
As an AI marketing analytics agency, we build AI-assisted forecasting, anomaly detection, and reporting tied to real decisions and dashboards, not just content generation. We use AI where it genuinely improves decision speed and accuracy, and stay quiet about it everywhere else.
What’s Included
How We Actually Do This
AI-ready data: the checklist before any model gets built
Consistent event tracking across at least 6–12 months, clean product/SKU data (the same integrity issue that undermines SEO and ads undermines forecasting too), and a defined source of truth for revenue figures. "Garbage in, garbage out" isn't a cliché here — an AI model trained on inconsistent historical data produces confident-sounding forecasts that are simply wrong.
Where AI genuinely earns its place
Anomaly detection (flagging a campaign or inventory metric that's moved outside its normal range before a human would notice), demand forecasting for inventory and budget planning, and report summarisation that surfaces the three things that actually changed instead of forty metrics that didn't. These are narrow, well-defined use cases — not a general promise that "AI will understand your marketing."
Governance: privacy and human validation
Forecasts and anomaly flags are treated as decision support, not decisions — a human reviews and validates before budget or inventory calls get made on top of them. We're explicit about model limitations and confidence intervals in every report, rather than presenting AI output with false certainty.
What an executive report actually looks like
A short summary of what changed and why (not forty unranked metrics), a forecast range with a stated confidence level rather than a single misleadingly precise number, and any anomalies flagged with enough context to act on — not just an alert with no explanation. The goal is a report a founder can read in five minutes and know what to do next.
How It Works
Audit current data and reporting setup
Build forecasting/anomaly detection models on your historical data
Integrate into reporting workflows
Ongoing calibration
Best For
Brands with enough historical data (traffic, sales, ad spend) to make forecasting genuinely useful.
Not a Fit If
Very early-stage brands without enough historical data yet.
FAQs
How is AI used in marketing analytics?
Primarily for demand forecasting, anomaly detection across campaign and inventory metrics, and automated report summarisation — narrow, well-defined tasks where AI can process more data faster than manual review, with a human validating the output.
Can AI improve attribution?
It can help model more sophisticated multi-touch attribution than simple last-click, but it can't fix attribution that's broken at the tracking layer — data quality has to come first.
What data is needed for AI marketing analytics?
At least 6–12 months of consistent, clean historical data across traffic, sales, and ad spend — the same data hygiene standard that supports reliable reporting generally, just with more history required for forecasting to be meaningful.
How do you prevent AI from analysing bad data?
We run a data-quality audit before building any model, and flag known gaps or inconsistencies explicitly in reporting rather than letting a confident-looking forecast mask an underlying data problem.