Modern businesses increasingly rely on data to succeed, taking on the “data-centric” tag. As large amounts of data allow companies and their products to evolve quickly efficiently to address the demands of customers, the trend will continue to strengthen. To surpass in the adoption of data analytics in the call centers, you need to have a complete grasp of all the types of data analytics.
A preparatory stage in data processing that summarizes data from past periods to provide insights and prepare the gathered data for future analysis.
For instance, a hotel chain would use descriptive analytics to determine the level of demand for new VIP suites in a hotel. Similarly, an insurance company can use descriptive data analytics to see what services are the most popular in a given season, while an online retailer can find out the least popular products from new arrivals.
A stage where the information gathered during descriptive analysis is compared against other metrics to find out why something happened.
With diagnostic analytics, a hotel chain would compare the demand for VIP suites in different regions or hotels in a region, while the insurance company would, for example, get insights into what age group uses dental treatment the most in the target area. Meanwhile, an online retail store might use diagnostic analytics to discern what regions ordered a particular product from new arrivals more.
A data analysis type that allows companies to forecast problems that might occur in future or a trend and how would be unfolding.
With this type of analysis, a hotel could predict how much a new promising guest service would bring in revenue in a given region. A retailer could use in-depth data on their customers and other metrics to forecast a reaction on a new store type.
A data analysis type that uses advanced technology heavily to find the best solution based on data provided from predictive analytics. Thus, prescriptive analytics would determine what a company could do with a problem or trend foreseen by predictive analytics. Like predictive analytics, prescriptive analysis needs its own business logic and algorithms. As for prescriptive analytics techniques, machine learning to avoid call center crime is one of the most common.
From one side, prescriptive analytics techniques can be used to gain highly rich insights into customer behavior across industries. On the other, Machine Learning algorithms can be trained to analyze stocks markets and automate human decision making by presenting decisions based on large amounts of internal and external data. In any case, prescriptive analytics is a costly investment: the investors need to be confident that the analysis yields substantial benefits.
Information is one of the most valuable business assets of today. Various types of data analytics allow businesses to improve their operations and customer experiences, providing insights and a clearer picture of the business in general. Relying on the extensive experience of top management and their employees, modern companies would mostly rely on descriptive and diagnostic analytics to aid human decision making in the following. Still, as the role of data only grows and the analysis tools evolve, companies that want to be ahead of the competition will also use predictive and prescriptive analytics to stay ahead and automate a number of operations.