As marketers, we now have access to an unprecedented wealth of data. The amount of structured data, such as web events and customer insights, is rapidly increasing, alongside the growing challenge and intrigue of managing unstructured data.
Businesses want—and need—to better understand the insights this data can provide. By employing strong analysis, experimentation, and a robust modeling process, we can develop models with a good fit and effective predictive capabilities.
A trained model can serve a wide range of purposes, including:
- Anomaly detection in the case of big data analysis, such as analytics event monitoring.
- Predicting customer attention and engagement in the case of creative ads, websites and app screens.
- Media spend analysis in the cases of media measurement.
- Marketing mix and spend forecasts in the case of marketing mix modelling and forecasting.
- Predicting purchase intent from website events, in the case of conversion rate optimisation and ads audiences for remarketing.
- Per-customer lifetime value predictions and churn estimations in the case of CLV analysis.
- Product recommendations and content personalisation in the case of improving digital experience.