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English Predictive marketing analytics
Most marketing analysis looks backward. Predictive marketing analytics looks forward: it forecasts revenue and return so you can act before the quarter is over.
Predictive marketing analytics is the application of statistical models to marketing and revenue data to forecast future outcomes, such as expected revenue, channel contribution and the effect of budget decisions.
From reporting to forecasting
Classical marketing analysis tells you what happened. How many clicks, which campaign performed, what revenue was last month. Useful, but it is always a retrospective.
Predictive marketing analytics uses that same historical data to estimate the future. What will revenue be next quarter with this budget? What happens if I scale TV up and reduce display?
The difference is room to act. A retrospective lets you observe after the fact. A forecast lets you decide in advance.
What predictive analytics rests on
A predictive model combines several building blocks.
Historical data
Spend, revenue and external factors across multiple years form the basis of every estimate.
A statistical model
Marketing Mix Modeling establishes the relationship between media activity and revenue, including delayed effects.
Scenarios
The model calculates budget, market and pricing decisions through to an expected outcome per scenario.
Validation
Predictions are tested against actual results so that accuracy is demonstrable.
What it delivers for the marketer and the board
For the marketer, forecasting means budget decisions are substantiated. You know what a shift is expected to do before you make it.
For leadership it means predictability. A revenue forecast that is tested and updated gives the board a figure to plan against. Read also about marketing budget forecasting.
Frequently asked questions
What is the difference between predictive analytics and reporting?
Reporting describes what has happened. Predictive analytics estimates what will happen and what the effect is of decisions you have yet to make.
How accurate is a revenue forecast?
It varies by organisation and depends on data quality and history. More important than a percentage is whether the model tests its predictions against reality and explains deviations.
Do I need AI for predictive marketing analytics?
You mainly need a solid statistical model and reliable data. The value is not in the AI label, but in a model that is transparent and can be validated.
What data do I need?
Historical data on media spend per channel, revenue and external factors such as season and price. The more complete and consistent that data, the more reliable the forecast.
Forecast your revenue, plan with confidence.
Book a demo and see how Datafy forecasts revenue and calculates scenarios.