What is Predictive Modelling?
Predictive modelling is an artificial intelligence (AI) process that uses statistical analytics and data mining techniques to unleash powerful insights from historical data and gives our clients a glimpse into the future behaviours of their customers.
Predictive modelling is used to outline and predict specific outcomes using probability, to enable businesses to forecast situations and deliver appropriate solutions to deal with them. Our predictive modelling specialists enable strategic thinking by uncovering insights hidden with historic data assets.
Where more descriptive analytics approaches are used to summarise past experiences and look for a key single variable to explain behaviour, predictive modelling solutions use the historic insights and past performance to identify and forecast the probability of future trends and the relationship between vast variables.
By understanding what customers are most likely to do, businesses can optimise all areas of the business; from acquisition and onboarding, to upsell and retention.
Predictive Modelling and Forecasting Solutions
Trends change and customers can fall in and out of love with brands for many reasons. Staying ahead of customer preferences and understanding what drives their behaviour in relation to the brand, underpins a customer centricity strategy.
Furthermore predictive modelling can also be used internally to monitor key stakeholders within the business including to predict staff turnover and improve employee engagement.
Predictive modelling helps to understand what customers are most likely to do next and thus, by nature, enables businesses to plan for and execute actionable strategies that deliver on their growth or sales objectives.
By forecasting behaviour, our clients are able to adapt their strategies be it through launching sophisticated email sequences, online experiences or simply optimising their operations to ensure the business has everything in place when the customer walks through your door.
Our Approach to Predictive Analytics
We take a four stage approach to our predictive analytics and modelling solutions:
Scope definition and analysis - Phase one focuses on understanding our clients’ business objectives, to review and identify the most appropriate predictive modelling options and ensure that the solutions we develop will answer the business goals, without impacting the overall strategic operations of the business. This phase is about aligning and focusing the strategy to create significant impact.
Data collection and preparation - Working closely with our experienced data ops team, we review our clients’ data sources, manage the warehousing, cleansing and features development, to ensure the data is in the best position for effective analysis. This phase ensures that we are operating with best practice in mind.
Model design, build and test - Using our artificial intelligence expertise we test multiple models to determine the most effective output. Models can vary in complexity, and the most sophisticated models can predict future behaviour based on the statistical analysis of multiple historic features. The testing phase is important in ensuring all probabilities are considered.
Final model outputs - The final phase ensures that the predicted behavioural outputs identified by the model are assigned to customers based on their probability and the insights are communicated to the business to underpin more strategic thinking and responsive tactics.
Business objective review and identification of the most appropriate modelling options.
Review of your data sources, manage the warehousing, cleansing and features development.
Model design, build
Using Machine Learning run through multiple models to test, which provide the best result.
A model that predicts the key behaviours and assigns probability to individual customers.
Our Centre of Excellence and Data Science teams have a strong track record of developing and maintaining highly effective predictive models for many business requirements; from estimating share of wallet/potential value, next best product recommendations and predictive response models to power marketing and promotional campaigns.