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How Analytics tools are shaping the growth story across industries

September 20, 2016 by · 3 Comments
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Today’s guest post is written by Mohammad Farooq

If there’s one thing that businesses across all industries have in common today, it’s in their increased adoption of data to shape business decisions. Below is a demonstration of how key industries use analytics tools and the benefits these tools have in solving challenges of data capture and use to shape growth.


Traditionally, insurers have relied on manual sampling of data to understand their customer base and address challenges to their operations. Not only is this process time-consuming and costly, it is also highly prone to errors.

Manual analysis also relies on historical data, making it impossible to respond to changes that are happening in real-time. This means that threats such as fraud cannot be prevented, as they are only detectable after the fact.


The association of British insurers estimates the amount of annual undetected fraud at roughly £1.9bn (€2.2bn), a loss that costs policy holders an approximate cost increase of £50 on their yearly premiums. Some of the highest instances of fraud, according to the 2015 insurance fraud survey, are noted in staged accidents (31%) and applications (12%).

Insurers can protect themselves against such threats using better analytical insights. Below are two examples from Insurance Nexus of insurers who have benefited from use of claims analytics:

  • Annual savings of up to £2 in auto claims by a Uk insurer
  • A Swiss insurance company reduced risks associated with non-compliance to less than 1%

Benefits of analytics in underwriting:

  • Accuracy in risk pricing
  • Identifying and retaining customers through better comparisons of costs and pricing.
  • Reductions of errors in claims.
  • Reduced decision making time where claims are concerned.
  • Reduced cases of fraud, through analysis of different web-based platforms.
  • Reduced costs associated with errors, bad claims, litigation and customer attrition, leading to more attractive margins and better customer satisfaction


Financial institutions differ in their motivations for investing in data analysis as shown in the survey results below conducted by IDC .


But whatever their motivations, the benefits of analytics in banking are clear. American Banker Research surveyed 170 bankers on the usage of customer analytics in banking. 28% of them cited share of wallet as the biggest benefit experienced by their institutions.   Another 18% cited reduction in loan-related losses as the key benefit.

Banks also recognize the importance of making optimum use of their online resources to retain customer loyalty. As shown in the image below, a European bank experienced 27% increase in click-through rates for their banners and a sales increase of 12%, by relying on their analytics tools.



There are a lot of dynamics surrounding the field of healthcare. To mention, but a few:

  • Healthcare is becoming more value-based as customers continue to demand quality services.
  • Physicians and nurses are always in short supply which means that hospitals have to figure out how to be efficient and productive with the staff they have on hand.
  • Cost dynamics are changing, thanks to reduced death rates and more reported cases of chronic diseases.
  • More investment in research has led to new medical approaches and cures.


All these issues create a lot of complexity for health care providers. With more utilization of customer-based insights for decision making, healthcare providers will find it easier to:

  • Detect disease patterns, prevent outbreaks and respond to medical emergencies with speed.
  • Track implementation of preventive remedies. For instance, they can track how many people in their data base have received flu vaccines.
  • Efficient allocation of limited hospital staff.
  • Use customer browsing history on hospital websites to anticipate individual needs or crises.
  • Reduce wastage. Estimates by McKinsey show that U.S healthcare can reduce waste and save more than $300 billion annually in clinical operations, R&D and public health.


im5Image from

Learning institutions have many data sources such as:

  • Admission/enrolment records, (which are usually a combination of socio-economic data, demographics, past performance, health issues, etcetera)
  • Laboratory, library, cafeteria and general purchase records,
  • Attendance, test scores and grade tracking,
  • Sports records.

However, this information is hardly used for improving the learning environment and to anticipate student needs.


Arizona state University is a good example of use of analytics by a learning institution to improve user experience.

Insights gathered from the website’s international page showed that most of the traffic to the website came from all over the world, a factor that prompted the university to offer the pages in different languages.


Standing out among the millions of websites that are competing to sell to the same audience is near impossible without the use of data to help a business understand its environment and audience.

Though ecommerce businesses use many metrics to measure website performance, ‘conversion’ is the key KPI used to show the rate of success. A website that is experiencing low conversions can dig deeper to understand the reasons behind this performance. There are some amazing web analytics tools out there that you could choose from, here’s a list I’ve compiled about Web Analytics Tools which can prove to be handy.

im7Data sourced from:

Lists compiled by wappalyzer show that most key websites use web analytics tools. for instance, uses KISSmetrics, uses crazy egg and uses woopra.


Governments hold a central role in the ramping up and use of ICT and though they embrace this role fully by investing in reporting tools, computer equipment and data warehouses, there’s still a challenge when it comes to moving from mere data collection and processing to qualitative data analysis.

The use of tools to not only mine data but to improve analysis helps to address these challenges as the more value is extracted from data, the more it can be used to better the lives of citizens.

For instance, in the example below from the US government site, it’s possible for government departments to see how many people visit the website over a period of time, which pages they visit and the documents they download. This gives a clear indication of the services that people need most.



The body of evidence that shows benefits in increased investment in data tools suggests that opportunities for growth in any sector lie in data insights. The market has readily available analysis tools which are budget friendly and don’t require intensive training to operate, meaning that companies don’t need to roll out sophisticated data capture and warehousing infrastructure to start making use of their data.

Mohammad Farooq works as an Analyst. When not working, he goes backpacking around India. He regularly blogs about Travel, Movies, Political Issues and a lot of other things on his blog “ReveringThoughts


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September 2, 2016 by · 7 Comments
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ds_handbookThe Data Science Handbook gathers 25 interviews of Data Scientists. Interviews are well done, most questions depending on the previous answer. This gives a nice feeling of discussion between the interviewer and the Data Scientist. On the content side, it provides interesting insights about the job of Data Scientist. The book is however biased towards pioneers in the field spending 14h… Continue reading... | 7 Comments


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August 22, 2016 by · 1 Comment
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EverydataGoing opposite direction to the current Big Data trend, Johnson and Gluck discuss the little data we consume everyday in their book Everydata. The book is a fresh and easy reading. Through several practical examples, authors covers topics such as sample selection bias, correlation vs. causation and graphics (e g. how to play with plot axes).

Everydata is full… Continue reading... | 1 Comment


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July 24, 2016 by · 4 Comments
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June 21, 2016 by · 4 Comments
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Gradient Boosting Machine – A Brief Introduction

June 1, 2016 by · 15 Comments
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treesOne of the Predictive Analytics projects I am working on at Expedia uses Gradient Boosting Machine (GBM). This is currently one of the state of the art algorithms in Machine Learning. This article provides insights on how to get started and advices for further readings.

I will now focus on the use of GBM for regression, based on decision trees… Continue reading... | 15 Comments


Data Science Book Review: An Introduction to Statistical Learning

May 12, 2016 by · 1 Comment
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ISLWithout any suspense, “An Introduction to Statistical Learning” (ISL) by James, Witten, Hastie and Tibshirani is a key book in the Data Science literature. I would summarize it as a book written by statisticians for non-statisticians. Indeed, while the book “The Elements of Statistical Learning” was heavy on theory and equations, ISL is the practical counterpart. The book is very clear… Continue reading... | 1 Comment


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