Top 5 Use Cases for Advanced Business Analytics


The use of advanced analytics has grown rapidly in recent years as technology keeps getting better, cheaper, and easier to use. And demand will grow exponentially over the next five years, according to a report from Frost & Sullivan.

Deviki Gupta, senior industry analyst at Frost & Sullivan, said in the report that advanced analytics is expected to grow significantly in the future as clients become more comfortable with overall analytics and customers. increasing technology use cases.

What is advanced analysis?

Advanced analytics is a category of analytical tools that use sophisticated techniques and tools to analyze data beyond that of traditional business intelligence platforms.

Advanced analysis uses machine learning, pattern matching, semantic analysis, network and cluster analysis, sentiment analysis and neural networks for tasks such as making predictions, l ” Identifying patterns, generating recommendations, and uncovering deep insights into the data. Predictive and prescriptive analytics make up a large portion of the advanced analytics market.

Here are some of the top use cases of advanced analytics for business.

Learn about some of the differences between advanced analytics and BI tools.

1. Customer service

Advanced analytics are used in a variety of ways to improve customer service, and businesses are reaping the benefits.

According to an insurance industry survey Released earlier this year by Willis Towers Watson, the world’s third largest insurance brokerage and advisory firm, around 77% of insurance companies want to use advanced analytics on customer data, and 54% are already analyzing that data.

And what is the main reason for applying advanced analytics? Accelerate Customer service.

And the positive results are already there. According to the survey, 68% of respondents already see a positive impact on overall performance due to their advanced use of analytics, and 86% see a positive impact on financial performance.

And the insurance industry is not alone.

According to a recent Forrester Research survey of analytics professionals, 47% of respondents have used analytics to attract new customers, 31% have used it to improve customer retention, 28% have used to increase customer lifetime value and 23% used it to improve customer experience.

The percentage of companies that use advanced analytics for customer acquisition is up 14% from a year ago, Forrester analyst Brandon Purcell said.

“But a lot of companies actually start with customer retention like [an advanced analytics] use cases, because companies typically have better data on their own customers,” he added.

Analysts examine historical data from customers who have left, feed it into a supervised machine learning model, and then use the model to calculate risk scores for existing customers.

“You can optimize your retention efforts,” he said. “The carriers are really good at it – Verizon has a less than 1% churn because of it. Their churn models are very robust. So are the banks.”

Non-subscription businesses such as retail businesses are also looking to use advanced analytics to improve retention by looking at historical data on customers who haven’t purchased there for some time.

2. Predictive maintenance

According to a report published last December by IoT Analytics, predictive maintenance is the largest industrial advanced analytics use case that would have accounted for over 24% of the total market in 2019. According to the Deloitte Analytics Institute, predictive maintenance based on advanced analytics can increase productivity, reduce downtime and lower overall maintenance costs.

Royal Dutch Shell, for example, develops and maintains thousands of models for support predictive maintenance applications on thousands of pumps and valves on offshore oil rigs, said Doug Henschen, vice president and senior analyst at Constellation Research.

“This is a high-value use case because a failure on an oil rig could mean millions of dollars lost or, worse, environmental damage,” he said.

3. Recommendations

Another high-value use case for advanced analytics is recommending products or services to customers. Amazon is the 900-pound gorilla here, but plenty of other companies are implementing recommendations to improve sales.

There are also startups specializing in narrow applications of this technology.

The Climate Corporation, for example, is a digital agriculture company that helps farmers figure out what to plant, where and when to plant it, Henschen said. The company’s seed advisory service collects historical data such as past crop yields and soil samples and combines it with other data sources – including weather data – then runs it across thousands of locations. different models, he said.

“It provides recommendations on which seeds to plant and when, how deep to plant the seeds and how far apart the rows are,” Henschen said.

He said the farmers who used this information were able to increase their yields more than nine bushels per acre.

4. System optimization

Businesses use advanced analytics to optimize everything from supply chains and drug research to data center operations.

According to Dan Simion, vice president of AI and analytics at Capgemini, one of the leading use cases for advanced analytics in businesses is using pattern recognition to analyze trends in the use of computing resources, such as servers and networks, and in forecasting application usage and bandwidth requirements.

“The goal here is not to have unhappy customers because their apps aren’t working,” he said. “One of our clients used AI-infused models to predict the number of licenses needed for different applications.”

This use case was particularly useful when the The COVID-19 pandemic has struck, he said.

“When they switched to working from home, it resulted in an increase in requests for video collaboration,” said Simion. “With the AI-infused models, they were able to ensure they had the bandwidth and licenses to use the tools.”

The healthcare sector has been hit the hardest by COVID-19, and many organizations are using advanced analytics to address new challenges.

“We see them using advanced analytics to analyze patient and hospital data, and visualize COVID-19 data from inside the organization, as well as researching external sources,” said Vijay Raman, vice-president. president of product management at Ibi, an analytics provider. which is currently in the process of being acquired by Tibco.

Ibi recently worked with St. Luke’s University Health Network in Pennsylvania to help them develop over a hundred self-service applications that use advanced analytics. Raman said the scans are used globally to track the impact of the virus and locally to support high-risk patients.

5. Product development

Advanced analytics are also used to help create new products and services. Purcell said this was the most exciting emerging advanced analytics use case.

“Use the results of the customer analysis to create a new phone or whatever customers may need,” he said.

Analysis can also be part of the actual product or service, he added.

“In the world of IoT and connected devices, for example, how do we create products that can better follow customers and improve engagement through the products themselves? he said.

The future of advanced analytics

As advanced analytics continue to get easier and cheaper, we can expect to see many more use cases as technology becomes. more economically feasible.

“Fifteen years ago, predictive analytics and data mining methods associated with advanced analytics were typically emerging in high value-added use cases,” Henschen said.

These high value-added use cases include loans; fraud and risk analysis; handling of insurance claims; and high-value, high-volume marketing use cases, such as direct mail targeting and customer churn risk analysis for mobile and cable companies.

“The model development methods at the time were slow and manual, and the required infrastructure costs were high,” Henschen said. “As a result, it has not been easy to widely disseminate the benefits of these techniques.”

Today, data science platforms and cloud computing have reduced the costs and effort involved in building and using models, he said. Advanced analytics are now applied much more widely across all verticals and for many new use cases within those industries.


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