Predictive Analytics enables individuals, companies, public institutions and many other organizations to gain valuable insights from their vast collections of data to improve their operations and bring benefits to its customers.
Predictive analytics gains insights from big data by using methodologies from a wide and diverse array of fields, including:
- data mining,
- statistical modelling,
- machine learning,
- deep learning,
- other artificial intelligence (AI) methods
The main goal of predictive analytics consultants is to employ a wide array of methods on real-time and historical data to find trends, make predictions and forecasts about its customers or future events. Although the predictive analytics is often implemented by team of data scientists, data engineers and other persons from technical departments it often involves other levels of organizations.
Success in data science product implementations often requires deep understanding of the business processes in the organization, so it is imperative that upper layers of management are also involved in the early planning and later monitoring of the predictive analytics projects.
Examples of predictive analytics in marketing
Predictive analytics has been adopted in the last decade by almost all layers of organizations. One of the early adopters of predictive analytics were the marketing and sales departments, which use them for a wide array of purposes in their day to day operations:
- predictions of customer lifetime value (CLV)
- churn prevention
- lead scoring
- optimization of sales funnels (leads flow)
- customer retention
- identification of up-selling/cross-selling opportunities
- customer segmentation
- fraud transactions (anomaly detection)
- sales and demand forecasting
On technical level, predictive analytics works by calculating a quantitative score for entities, which can be persons such as clients or employees. It can be aggregations of people, e.g. organizations or products and services. With scoring, the company attaches a certain probability to an entity, e.g. client, with respect to certain action, e.g. purchase of given product. It is the mechanism which enables organizations to make a wide array of different predictions:
- what is the credit risk of a client applying for a loan
- what is the probability that a given high value employee may leave the company
- what is the probability that a critical piece of component may break down
- what is the probability that a given flagged transaction is a potential fraud
- what is the probability that a customer, given the recent browsing and historical purchases will be interested in selected products
- what is the probability that a given sensor is malfunctioning
Predictive scoring has been integrated in business operations of many industries – manufacturing, finance, healthcare, marketing, oil and gas companies, retailers, sports teams, social media, e-commerce and others. Other organizations employing predictive analytics includes government and other public organizations.
Predictive analytics models
One of the tasks facing data scientists assigned a predictive analytics project is to select the appropriate approach for the project at hand. Data science has a wide array of potential methods available for the predictive analytics modelling and not every approach may be suitable for the given task.
Generally though, some of the models that are especially suitable for predictive analytics are:
- Logistic regression
- Decision trees
- Random forests
- Support Vector Machines
- K-means clustering
- Neural nets
Open Source Tools for predictive analytics
To implement predictive analytics solutions, one can use a wide array of languages and libraries dedicated to this type of tasks.
One can use both lower level approach, e.g. implementation in python programming language using scikit-learn, numpy libraries. Or can use other software such as:
- R Software
- Apache Spark
An alternative to developing predictive analytics solution in-house is to employ data science consulting companies, specializing in this field.