Predictive Analytics (PA) encompasses a variety of statistical techniques from Data Mining, Predictive Modelling, Machine Learning, and Deep Learning algorithms.
The focal point of PA is on capturing interactions between the explanatory and the predicted variables from past episodes, using them to foretell the unknown results. However, the precision and effectiveness of the results are significantly related to the level of data analysis and the quality of the statements.
Predictive Analytics features and benefits:
Predictive models make use of patterns found in historical and transactional data to identify threats and opportunities.
Models depict relationships among many factors to allow evaluation of risk or capability linked with a particular set of circumstances.
PA makes available a predictive result (probability) for each (customer, employee, component, organisational unit, healthcare patient, product SKU, vehicle.)
Regulate, inform, or influence corporate practices to apply across large numbers of individuals, such as in Marketing, credit risk measurement, fraud uncovering, manufacturing, healthcare, and government operations including law implementation.
Where to apply Predictive Analytics:
PA is used in actuarial science, marketing, financial services, insurance, telecommunications, retail, travel, mobility, healthcare, child protection, pharmaceuticals, capacity planning, social networking, and in many other fields. One of the best uses is in customer's credit history for loan applications, customer data, to rank individuals by their possibility of making future credit payments on time, and in numerous financial processes.
Soonfuture, the value of PA will be to predict and prevent potential issues from accomplishing almost-zero break-downs and towards decision optimisation. Furthermore, the translated data can be utilised for product-life cycle improvement.
Predictive Analytics Process:
1. Define project outcomes, the extent of the work, business goals, classify the data sets to be used.
2. Data mining for PA prepares data from multiple sources for analysis and provides a complete view of customer interactions.
3. Data analysis inspects, cleans and models data to the discovering of useful information that turns up to a conclusion.
4. Statistics analysis validates the assumptions, hypothesis, and tests them by using standard statistical models.
5. Predictive modelling provides automatically the ability to generate accurate predictive models about the future.
6. Predictive model deployment provides the option to implement the analytical results in the everyday decision-making process.
7. Model monitoring can review the model performance to ensure that it is providing the results expected.
Applications of Predictive Analytics
1. Customer relationship management (CRM) objectives such as marketing campaigns, sales, and customer services, applied throughout the customers' lifecycle, right from the acquisition, relationship growth, retention, and win back.
2. Health Care will have the possibility to reveal the patients who are at risk of certain conditions such as lifetime illnesses, diabetes, asthma, as well as supporting medical decision right away.
3. Collection Analytics optimises the allocation of resources by identifying the collection agencies, contact strategies, legal actions if any, and to strengthen the recovery whilst reducing costs.
4. PA analyses customers spending, usage and other behaviours, leading to efficient cross sales, or selling additional products to current customers for an organisation that offers multiple products.
5. Fraud detection. It can find inaccurate credit applications, fraudulent transactions both done offline and online, identity thefts and false insurance claims.
6. PA predicts the best portfolio to maximise return in the capital asset pricing model and probabilistic risk assessment to yield accurate forecasts.
7.PA helps to identify the most effective combination of products, marketing material, communication channels, and timing to target a given consumer.
8.PA can help with insurance policies predicting the chances of illness, default, bankruptcy, and follow the process of customer acquisition by predicting the outcome risk behaviour of a customer using application-level data.
SUMMING UP: To define it, PA is a technology that learns from experience (data) to predict the future behaviour of individuals to drive better decisions.
Predictive Analytics is useful in insurance, banking, marketing, financial services, telecommunications, retail, travel, healthcare, pharmaceuticals, oil and gas and other industries. Many are the uses, so take advantage of the many benefits you will have when deploying a Predictive Analytics tool.
Dave Food
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