Making decisions in business quite often is based on assumptions about the future. There is a steady aspiration of companies to develop and deploy an effective process to understand trends and relationships in their activity in order to gain forward-looking insight to drive business decisions and actions. This is called predictive analytics. We can define the predictive analytics as the process that uses data and a set of sophisticated analytic tools to develop models and estimations of the environment behavior in the future.
In predictive analysis, the first step is to collect data. Depending on your target, varied sources are using, such as web archives, transaction data, CRM data, customer service data, digital marketing and advertising data, demographic data, machine-generated data (for example, telemetric data or data from sensors), geographical data, and others. It is important to have true and up to date information. Most of the time, you will have information from multiple sources and quite often in a raw state. Some of it will be structured in tables, other will be semi-structured or even unstructured, like social media comments.
Then it is necessary to clean and organize the data (it is called data preprocessing). Preprocessing usually takes 80% of the time and efforts of all analysis. After that, we produce a model using already existing tools for predictive analytics. It is important to note that we use collected data to validate the model. Such an approach is based on the main assumption of predictive analytics, which claims that the patterns in the future will be similar to the ones in the past.
You must ensure that your model makes business sense and deploy the analytics results into your production system, software programs or devices, web apps, and so on. The model can be valid for a certain period since reality is not static and environment can change significantly. For example, the preferences of the customers may change so fast that previous expectations become outdated. So, it is important to monitor model periodically.
There are plenty of applications for business based on predictive analytics. On completion of the article we will briefly consider some of them.
Fraud and Risk Detection.
Historically, this is one of the first applications of data science in business. Available data is used to identify non-obvious fraud or risk patterns, then business operations are monitored to detect such patterns. For example, banking companies use big data methodologies for predictive fraud propensity models and use those to create alerts that help ensure timely responses when unusual data is recognized. Airline companies use predictive analytics to predict flight delays.
Client’s behavior can be used for a better management. For example, you can define how many people do you need to put on staff at any time period to improve customer service. Some public hospitals in Paris use data to predict the daily and hourly number of patients at each hospital.
Times Series Forecasting.
Time series forecasting is a technique that is used to predict future values based on previously observed values. It is widely used in finance, in supply chain management, in production and inventory planning. For example, companies can accurately predict a product demand by looking at different types of factors: prior history, seasonality, market-moving events. Such analysis is used for a realistic planning of sales.
Refining Marketing Strategy.
Customer data analysis helps companies to understand how consumers are engaging with and responding to their marketing campaigns. Thus, companies can find new target market that they can capitalize on. In this regard, the use of predictive analytics gives insight how to sell a product to those who need it at a reasonable cost, at the right time and using the right channel.