Understanding Predictive Analytics
The data science discipline has gained huge popularity among corporations given its ability to decode insights from seemingly irrelevant pieces of daily information. Data analytics training is in high demand given the paucity of professionals in the field of data science. Predictive analytics can be understood as a highly advanced version of analytics that is being used to make predictions about unforeseen future events.
The predictive analytics process entails a range of statistical methods like data mining, machine learning, predictive modeling, etc. All these methods are applied to analyze historical and current data to make future predictions. Let’s jump over to some of the most prominent applications of the predictive analytics method in the field of business intelligence.
With the advent of advanced analytics methods like predictive analytics, the marketing game has changed. Audience targeting is all about the personalization of marketing communication with the customer. Here a customer base is segregated into groups based on extensive factors other than the commonly used age, gender, occupation. These factors might include interest, likes, spending habits, transaction history, etc. This helps companies to customize their messaging as per the audience profile and predict those who are more likely to purchase the goods or services.
The risk analysis process is a complex one and it plays a huge role in the success of any business venture. It helps to analyze and predict the problems which might occur for a business based on a complex understanding of the variables that affect the business. Predictive analytics is used in this context to help build decision support systems that can help determine the profitability of any business operation. A possible application of this technique is in the banking sector for analyzing the credit risk of borrowers. The variables related to borrowers are factored in to derive conclusions.
Sales forecasting is an important aspect of business intelligence. Any given corporation has to think about the revenue that it’ll generate in the near future. The sales forecast is a complex process involving a lot of variables that influence the sales figures. These variables might include seasonality, market events, macroeconomic factors, general industry trends, etc. Data mining techniques can help assess consumer preference and outlook after factoring in all these variables. The end goal is to predict the demand for a given product or service produced by a firm.
The cost of acquiring a new customer for any business is far greater than the cost of retaining an existing one. This is why churn prevention is important for enterprises. Churn prevention helps to analyse and predict when and why customers decide to switch to other brands and end their relationship with the company. The companies can maintain a proactive approach to retain their existing customer using predictive analytics by leveraging big customer data sets.
The main goal of financial modelling is to create a simplified model of the complex real-world financial landscape that will help to predict and assess the performance of various financial assets. These are mathematical models designed to represent the quantitative performance of financial assets in the near future. In simpler terms, it is all about converting the hypothesis and assumptions regarding the financial markets into numeric figures that represent performance.
The 21st-century businesses are all about understanding the needs and wants of customers and providing adequate solutions in terms of products and services. It’s far from the traditional business approach of forcing a product or a service using rigorous marketing. Understanding consumer needs requires conducting surveys. Market analysis using surveys helps businesses to understand their customers better, this results in increased profitability and high customer retention.