Impact Of Advanced Analytics On 9 Major Business Sectors

IMPACT OF ADVANCED ANALYTICS ON 9 MAJOR BUSINESS SECTORS

In the business world, the use of advanced analytics is increasingly common as an organizational capability to forecast and direct future behaviour, as well as to optimize operational and commercial resources.

Introduction

Advanced analytics is the newest technology that uses machine learning algorithms to analyse mammoth volumes of information and eventually discovers a patterns and thus predicts events in a company’s production processes. To do this, it uses mathematical knowledge that provides statistics on all areas of the company. In addition, it is characterized by being predictive (what is going to happen) and prescriptive (what do I have to do to make it happen), so it could be said that it acts in the present with data from the past to make calculations about the future. That is why it is so important that the information collected by a company is comprehensive and of quality (ordered, verified and aligned with the business objectives).

In terms of business strategy as well, advanced analytics bestows the industry with it’s expertise. For example, what promotion to launch to increase customer engagement or how to deal with market conditions at a specific time.

Smart manufacturing with the help of advanced analytics

The rapid progress of manufacturing industry is based on advanced analytics which is generating transcendental changes not only in the production but also on consumer behavior and the way of doing business as well as it allow companies to adapt to market changes. The conceptualization has been defined as physical machinery and devices with sensors and software that work in a network and allow better predicting, controlling and planning business and organizational results. This emphasizes the use of digital methods for the planning and validation of all manufacturing stages, from product development to planning of production and facilities; for which, it is supported by a set of technologies that not only facilitate the prior validation of products and manufacturing processes, but also allow the reduction of development times for new products, manufacturing costs and manufacturing batches. That is, they make manufacturing more flexible, improve product quality and speed up market response times.

Advance analytics saving lives in health sectors

Advanced analytics in the health sector has progressed by leaps and bounds in the last decade, though there are many challenges related to the incredible amount of data to be analysed and the multiple sources of the data obtained can create issues in analyzing, processing and applying the knowledge amassed through advanced analytics. Advanced analytics helps this particular sector with new machine learning tools, better computing infrastructure, collaboration in patient information in real time and overall in cost savings.

Advance analytics in predicting customer tendency in retail

Retailers have always been laser-focused on putting the right products in the hands of the right consumers. Predictive data analytics are now being used to not only offer to buy recommendations  such as the next best offer on a shopping site, but also to hyper-personalize the entire customer experience online.

Analytics is also used to optimize pricing. According to a study, 60% of consumers believe pricing is the most important factor in purchasing a product. To address this, giant retail chains have developed their analytics hubs that leverage a gigantic amount of customer data to understand buying trends at its stores on the fly. A sudden drop or surge in sales of a particular article can be identified by an algorithm.

Applications of advanced analytics in Education

Advanced analytics is used quite significantly in higher education. As for example a university with over 26000 students has deployed a Learning and Management System that tracks, among other things, when a student logs onto the system, how much time is spent on different pages in the system, as well as the overall progress of a student over time.

In a different use case of the use of Big Data in education, it is also used to measure teacher’s effectiveness to ensure a pleasant experience for both students and teachers. Teacher’s performance can be redefined and measured against student numbers, subject matter, student demographics, student aspirations, behavioral classification, and several other variables.

Insurance Industry getting benefits from advanced analytics

Advanced analytics has been used in the industry to provide customer insights for transparent and simpler products, by analyzing and predicting customer behavior through data derived from social media, GPS-enabled devices, and CCTV footage. It also allows for better customer retention from insurance companies.

When it comes to claims management, predictive analytics has been used to offer faster service since massive amounts of data can be analyzed mainly in the underwriting stage. Fraud detection has also been enhanced.

Through massive data from digital channels and social media, real-time monitoring of claims throughout the claims cycle has been used to provide insights.

Applications of advanced analytics in transportation Industry

Governments in different countries use advanced analytics for traffic control, route planning, intelligent transport systems, congestion management, by predicting traffic conditions. Private-sectors use the same in transport for revenue management, technological enhancements, and logistics and for competitive advantage by consolidating shipments and optimizing freight movement. Individual use of Big Data includes route planning to save on fuel and time, for travel arrangements in tourism, etc.

Communications, Media and Entertainment

Advanced analytics in this industry simultaneously analyze customer data along with behavioral data to create detailed customer profiles that can be used to create content for different target audiences, recommend content on demand and measure content performance as well.

Advance analytics helping the farmers in agriculture

Data analytics are now changing the way farmers grow and provide food in countries where agriculture is the largest industry, but much of the land is currently underutilized.  Research collaborations are building a data-driven platform to analyse risk-sharing to upgrade farming practices. Data science helps predict the value of advanced farming practices, such as different types of fertilizers or irrigation systems, to encourage lenders to provide lower-risk loans. By using data science and machine learning techniques, they can quantify the predicted value of added resources as well as the probability of success. Underperforming or non-performing farms, in particular, stand to gain the most from this kind of agriculture programs.

Advanced analytics in maximising the profits in banking sector

Much like retail, banks are learning to consolidate internal and external customer data to build a predictive profile of each banking consumer. Banks and other financial institutions are now using the acuteness of their knowledge and insight they are gathering day by day, to provide customers with more worthy and customized services which saves the time for both the parties, instead of mass marketing programs which treat all customers with same strategy.

As for example some European banks are trying to boost retention of inoperative customers turned to machine learning algorithms to predict which customers have tendency to lessen their activities with the bank. The data-driven program helped create a targeted marketing campaign that lowered customer churn by 15%.

One of the banks of USA has started using advanced analytics for their perusal to measure discounts private bankers were offering to customers, proving that often there are incident of unnecessary discounts offered to the customers. The problem was corrected and drove 8% higher revenue within a few months.

One of the notable banks in Asia has used a humongous amount of data to analyse customer information such as demographics, products purchased, transaction data, and payment tendencies. The discovery of data patterns has helped creating 15000 segments to target the most accurate customer base accordingly, and thus to increase the changes of sales manifold.

Conclusion

The benefits derived from advanced analytics are growing exponentially with the consolidation of the digital economy and the Fourth Industrial Revolution. The millions of data that are produced under these conditions have long since ceased to be wasted, to become the main source of a world full of inspiration. Analytics transformed information and with it we can now answer the questions that are changing the world. Today, analytics is dedicated to predicting, inspiring, optimizing, and preserving and forecasting, among other challenges imposed on industries such as finance, retail, telecommunications and government, among others. With all this exponential growth, there is also the need to humanize the application of this technology more than ever, since it is only through those who need the data and the orientation towards business objectives that advanced analytics can be utmost feasible.