What is the Prescriptive Analysis?
Descriptive analytics is the first stage in business analytics that helps us understand what happened in the past. For example, in the case of product sales, with the help of tools like Power BI, descriptive analytics can tell us which products are selling more and which ones are not.
Predictive analytics, on the other hand, uses machine learning to predict what may happen in the future. With the integration of R scripts into Power BI, we can create forecasting models. Continuing the previous example, with predictive analytics, we can understand how product sales might be affected by trends in customer buying patterns, seasonality, etc.
The prescriptive analysis is the third stage in business analytics, after descriptive and predictive analytics. After analyzing the current business data and using it to make decisions based on predicted trends, with the help of machine learning, prescriptive analysis is used to get insights from the data to determine why certain decisions should be made. Analysts and data scientists use mathematical and computational models to analyze possible outcomes and evaluate them to make the best decisions for their companies. For example, prescriptive analytics will tell you what steps you could take to increase product sales.
How does prescriptive analysis improve company performance?
Nowadays, data is everywhere. Companies depend heavily on data to analyze customer behavior and a single modification made to a website or pricing package may have a huge impact on customer conversion rate, churn rate, and total profit.
For Software as a Service (SaaS) companies especially, all customer data should be recorded and analyzed through various analytics tools and techniques. It is very important for companies to be able to understand their past behavior and know what changes can have positive results. Also, based on the market dynamics and competitors, companies can also predict financial and customer trends for the next few years.
According to Gartner, the number of companies using prescriptive analytics tools may increase from ~10% in 2016 to over 35% by the end of 2020. Beyond that, prescriptive analysis allows companies to simulate and optimize potential strategies to better understand their options before making decisions. By using prescriptive analysis and changing their strategies accordingly, businesses can increase efficiency, better address company goals, increase their conversion rates and lower their churn rates.
iLink’s Data Analytics practice consists of experts in Data Strategy, Data Lakes, Data Warehouse, Business Intelligence, Machine Learning, and Data Science. Our deep functional expertise enables us to understand the underlying business challenges, customer needs and industry dynamics to connect the dots and deliver real world insights. To learn more about how we can empower you with the right tools and knowledge required to gain a competitive edge, talk to an expert now.