To put it simply, predictive modelling is used to predict future events using historical data. Predictive modelling is useful because it gives accurate insight into any question and allows users to create forecasts. For businesses to maintain a competitive advantage, it is critical to have insight into future events and outcomes that challenge crucial assumptions.
Predictive modelling is mainly used to understand customer behaviour, build programs to retain customers, develop cross-selling strategies, product optimization and understanding target audiences.
At its core, predictive modelling significantly reduces a companies’ cost to forecast business outcomes, environmental factors, competitive intelligence, and market conditions. Here are a few of the ways that the use of predictive modelling can provide value:
- Demand forecasting
- Workforce planning and churn analysis
- Forecasting of external factors
- Analysis of competitors
Predictive models and technologies promise huge benefits, but that doesn’t mean these benefits come seamlessly. In fact, predictive modelling presents some challenges in practice. These challenges include:
- Sufficiently large and comprehensive datasets
- The adaptability of models to new problems
- Data organization and hygiene
- Data privacy and security
In our opinion, the benefits outweigh the challenges. As brands and large organizations hyper-focus on data to make smart decisions, predictive modelling can give them an edge to test the waters before a significant product or campaign launch, address gaps in their current operations and take the necessary actions that will support their success.