Advanced analytics has been growing in the past decade owing to better analytics directly affecting decision-making accuracy and others. According to a report from Frost and Sullivan, it is expected to grow exponentially in the future. Enterprise analytics uses ML models, pattern matching, network analysis, cluster analysis, recognition of patterns in data for accurate and real-time inferences that can help the companies make informed business decisions.
Some of the top use cases for enterprise analytics are:
Improving customer service experience
According to a Forrester Research survey, it was found that 47% of the participants use analytics for acquiring new customers, 31% of them use it for customer retention, and 28% use it to increase the customers’ lifetime value, and 23% use it for improving customer experience. With a better and advanced analysis of customer data, you can have a 360-degree view of your customers and know the specific motivators guiding their buying process. Not only that, it can be used to deduce out the reason for leaks in your sales funnel and mend it.
Process and System optimisation
According to Dan Simion, VP of AI and analytics at Capgemini, top analytics use case in the organization uses pattern recognition to analyze trends in allocation and utilization of resources, such as servers, bandwidth requirements, networks, predicting usage of applications, and so on. This helps minimize the downtime, identify peak periods for scaling, have a better allocation of resources, and optimizes the entire process.
Better Product Development
With better insights from production data, innovative approaches can be employed for better product development. The insights from data help verify the product concepts, help identify the stages in production obstructing the product quality, helps in modifying the processes with injunctions of better components and better engineering set to improve the overall product quality.
Leveraging Big Data to the Fullest
Big data is gold if it can be used for generating in-depth insights for organizations. All the channel or sources of data like IoT devices, social channels, different databases, customer transaction data produced every day helps you get an in-depth view of how your product, processes, people and systems are performing if advanced analytics is used. This helps in building your data into a competitive edge for the company.
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Prevention of Churn
Churn means ceasing of customer relationship with the organization. With predictive analytics, you can deduce that the customers have a greater chance of moving off from the organization and stopping them.
According to the Deloitte Analytics Institute, predictive maintenance helps in increasing overall productivity. Predictive maintenance enabled through advanced analytics helps identify the faults or the possible failures that can happen in the future. The root cause of the faults or incidents often lies buried in the organization’s data like a year of production, model, warranty, standard operating procedures for the equipment, the optimal availability expected, the stress the system can hold, and so on. Better and in-depth inferences from such data can help improve the equipment up-time, increase overall productivity, and lower maintenance costs. Not only that, analytics on network traffic can help prevent breaches, make applications secure, and more. The analytics on data storage can help frame better databases architecture that allows real-time access and control of the data.
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