What is Prescriptive, Predictive and Descriptive modelling and how can they help your business
Predictive, prescriptive and descriptive modelling branches from advanced analytics and is used to anticipate and act upon unknown future events with the goal of achieving the best possible outcomes.
Patterns found within historical and transactional data are used to identify future risks and opportunities. Predictive analytics models, therefore, capture these relationships amongst many other factors to assess the risks involved within a particular set of conditions, assigning a score or weightage to the various levels of risk. Businesses will now be able to effectively interpret big data for their benefit with the successful application of these models.
Breaking down Predictive, Prescriptive and Descriptive Analytics
The word ‘analytics’ in ‘predictive analytics’ is a bit of a misnomer. This is because predictive analytics is not considered as a branch of traditional analytics such as reporting or statistical analysis as often mistaken. Predictive analytics derives from finding predictive models which firms deploy to anticipate future business outcomes and/or consumer behaviour. It works by compounding and analysing data mining, statistics, artificial intelligence, machine learning and data modelling.
Descriptive analytics involves capturing things which occur and can be applied to any portion of the business landscape. Descriptive analytics is the foundation on which an algorithm may be developed. Although these metrics are simple and not particularly advanced, they are often too voluminous to manage without proper analytics tools.
Predictive analytics is typically used within dashboards and data reporting in organisations. Although sophisticated, these tools often lack the link between business decisions, process optimisation, customer experience or any other action for that matter. The model is unrivalled in producing insights, although, they are unable to produce explicit instructions on what to do with them. This is where prescriptive analytics is implemented to work alongside the predictive and descriptive analytics. Prescriptive analytics is where the developed insights meet their actions. Essentially, prescriptive analytics calculates the probability of an outcome and what can be done to trigger an influence in the direction which benefits the company; whether that be to make a sale more likely or contributing to the prevention of customer churn.
Our Predictive Analytics Capabilities
Statistical Analysis and Visualisation
We cover the analytical spectrum - planning, data collection, analysis, reporting and deployment.
Predictive Modelling & Data Mining
Industry-leading model-building, automation and evaluative capabilities.
Decision Management & Deployment
Advanced model management and analytical decision management makes our analytics come to life.
Big Data Analytics
Gain predictive insights to build competitive business strategies.
How does the process work?
Why companies are using these models and why should you too
Predictive analytics is allowing both SMEs and large organisations to become proactive and forward-looking through the anticipation of outcomes and behaviours, based on the collation of data. Prescriptive analytics can be taken one step further. Based on predictive analytics, prescriptive analytics conducts the optimal decision-making to provide suggestions for businesses to capitalise from their predictions or protect themselves from any arising implications. These tools help you gain a competitive advantage by discovering patterns in data and going beyond knowing what has happened, to anticipating what is likely to happen next.
Where Data Modelling is being used
Customer Relationship Management (CRM)
Predictive analysis applications are used to achieve the CRM objectives which drive marketing campaigns, sales targets and customer services. This analytical customer relationship can be applied throughout customer life cycles, right from customer acquisition, relationship growth, retention, re-targeting and win-back strategies.
Within the healthcare service segment, predictive modelling can determine whether or not patients are at the risk of developing certain conditions such as diabetes, asthma and other life-threatening illnesses - supporting medical decision-making and future research.
By implementing predictive modelling software, businesses have the ability to automatically determine risk within any given scenario. For example, in finance, the model predicts the best portfolios to maximise the return on investment (ROI) and deliver probabilistic risk assessments with the aim of yielding accurate financial forecasts.
Knowing what your customers want is essential in marketing. With the incorporation of predictive modelling, it allows companies to help identify the most effective combination of product versions, marketing communication channels, timing and suitable content that should resonate and be released towards the appropriate target audience.
By making accurate predictions on future risks and behaviours, predictive modelling can streamline the process of customer acquisition. This can be applied to a variety of industries which handle application level data such as in the medical industry (detecting illness) and financial sector (predicting and detecting bankruptcy).
The model works by analysing consumer spending, usage and behaviours; leading to efficient cross-sales or selling additional products to current customers for an organisation with various product offerings.