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Cognitive Approach VS Digital Approach to Insurance

Digital transformation has gone from talk to action, with a momentum that shows no signs of slowing down. As cognitive capabilities have penetrated process, people, technology, things, augmented intelligence and decision making; the cognitive approach to insurance business is no longer considered a back-office ‘efficiency play’. 

A cognitive computing system replicates human intelligence and comes up with solutions for largely ambiguous and complex situations. Implementing this cognitive capability in Insurance enhances customer insights and deduce customer feel through interaction insights, sentiments and connectedness. 

In Insurance, where companies are constantly tweaking business models to improve profitability, the digital approach to insurance is falling short of industry expectations. The ‘Cognitive’ approach is a step ahead of the ‘Digital ‘approach to insurance, and Data is the key ingredient to going cognitive.

Cognitive Insurance a step ahead of Digital Insurance.

The word cognitive is often used interchangeably with the term Artificial Intelligence. However, there are subtle differences between the two, in terms of their purpose and application. Cognitive computing is a process used to describe AI systems that aim at implementing human thought processes such as real-time analysis of the environment, context and intent analysis; and the ability to solve problems. Where AI relies on algorithms to solve a problem, cognitive computing systems have higher goals of creating algorithms that mimic the human brain’s reasoning process to solve a number of problems with changing data and problems.

The purpose of going cognitive in insurance was created solely with the purpose of reducing human effort and refining the existing process across various insurance verticals. 

Examples of cognitive insurance use cases.

  • Traveller’s Insurance Group had sent a fleet of 65 drone surveillance operating-agents to Houston in order to assess the damage from Hurricane Harvey -the costliest tropical cyclone in recorded history
  • USAA had rolled out an Intelligent Personal Assistant, using Amazon Alexa and Clinc that has insurance industry-specific deep vocabulary and knowledge, that goes beyond the capabilities of traditional chatbots or digital solutions. 
  • Liberty Mutual introduced a new app to help drivers involved in car accidents, to quickly assess the damage to their car in real-time using a smartphone camera. The app provides damage-specific repair cost estimates. 
  • AXA Insurance implemented a Google Tensor Flow-based application by using deep analysis of customer profiles. The application can optimize pricing by predicting traffic accidents with nearly 78% accuracy. 
  • Fokoku Mutual, a large Japanese Insurance company, has replaced it’s 34 strong claims assessment workforce with an implementation of IBM Watson Explorer AI solution. The solution can analyze and interpret claim data including unstructured text, images, audio and video to decide policy payouts. 

In the past, insurance industry professionals made decisions based on experiences and historical data. A cognitive approach, to insurance business solutions, is at the helm of a new wave bringing innovation and transformation to insurance. These cognitive capabilities enable insurers to make strategic decisions based on a set of data which continuously updates in real-time, thereby leveraging AI to bring automated efficiency to insurers while delivering the best possible experience to the insured user.
  

 

 

References: 

https://www.mantralabsglobal.com/blogs/cognitive-automation-and-its-importance/ 

Use cases:
https://www.linkedin.com/pulse/cognitive-use-cases-insurance-sushil-pramanick-fca-pmp/  

https://www.lntinfotech.com/wp-content/uploads/2018/02/Moving-from-a-Digital-Insurance-Business-to-a-Cognitive-Insurance-Business.pdf  

https://searchenterpriseai.techtarget.com/definition/cognitive-computing

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10 Analytics Tools to Guide Data-Driven Design

Analytics are essential for informing website redesigns since they offer insightful data on user behavior, website performance, and areas that may be improved. Here is a list of frequently used analytics tools to guide data-driven design that can be applied at different stages of the website redesign process. 

Analytics Tools to Guide Data-Driven Design

1. Google Analytics:

Use case scenario: Website Audit, Research, Analysis, and Technical Assessment
Usage: Find popular sites, entry/exit points, and metrics related to user engagement by analyzing traffic sources, user demographics, and behavior flow. Recognize regions of friction or pain points by understanding user journeys. Evaluate the performance of your website, taking note of conversion rates, bounce rates, and page load times.

2. Hotjar:

Use case scenario: Research, Analysis, Heat Maps, User Experience Evaluation
Usage: Use session recordings, user surveys, and heatmaps to learn more about how people interact with the website. Determine the high and low engagement regions and any usability problems, including unclear navigation or form abandonment. Utilizing behavior analysis and feedback, ascertain the intentions and preferences of users.

3. Crazy Egg:
Use case scenario: Website Audit, Research, Analysis
Usage: Like Hotjar, with Crazy Egg, you can create heatmaps, scrollmaps, and clickmaps to show how users interact with the various website elements. Determine trends, patterns, and areas of interest in user behaviour. To evaluate various design aspects and gauge their effect on user engagement and conversions, utilize A/B testing functionalities.

4. SEMrush:

Use case scenario: Research, Analysis, SEO Optimization
Usage: Conduct keyword research to identify relevant search terms and phrases related to the website’s content and industry. Analyze competitor websites to understand their SEO strategies and identify opportunities for improvement. Monitor website rankings, backlinks, and organic traffic to track the effectiveness of SEO efforts.

5. Similarweb:
Use case
scenario: Research, Website Traffic, and Demography, Competitor Analysis
Usage: By offering insights into the traffic sources, audience demographics, and engagement metrics of competitors, Similarweb facilitates website redesigns. It influences marketing tactics, SEO optimization, content development, and decision-making processes by pointing out areas for growth and providing guidance. During the research and analysis stage, use Similarweb data to benchmark against competitors and guide design decisions.

6. Moz:
Use case scenario: Research, Analysis, SEO Optimization
Usage: Conduct website audits in order to find technical SEO problems like missing meta tags, duplicate content, and broken links. Keep an eye on a website’s indexability and crawlability to make sure search engines can access and comprehend its material. To find and reject backlinks that are spammy or of poor quality, use link analysis tools.

7. Ahrefs:
Use case scenario:
Research, Analysis, SEO Optimization

Usage: Examine the backlink profiles of your rivals to find any gaps in your own backlink portfolio and possible prospects for link-building. Examine the performance of your content to find the most popular pages and subjects that appeal to your target market. Track social media activity and brand mentions to gain insight into your online reputation and presence.

8. Google Search Console:

Use case scenario: Technical Assessment, SEO Optimization
Usage: Monitor website indexing status, crawl errors, and security issues reported by Google. Submit XML sitemaps and individual URLs for indexing. Identify and fix mobile usability issues, structured data errors, and manual actions that may affect search engine visibility.

9. Adobe Analytics:
Use case scenario:
Website Audit, Research, Analysis,
Usage: Track user interactions across multiple channels and touchpoints, including websites, mobile apps, and offline interactions. Segment users based on demographics, behavior, and lifecycle stage to personalize marketing efforts and improve user experience. Utilize advanced analytics features such as path analysis, cohort analysis, and predictive analytics to uncover actionable insights.

10. Google Trends:

Use case scenario: Content Strategy, Keyword Research, User Intent Analysis
Usage: For competitor analysis, user intent analysis, and keyword research, Google Trends is used in website redesigns. It helps in content strategy, seasonal planning, SEO optimization, and strategic decision-making. It directs the production of user-centric content, increasing traffic and engagement, by spotting trends and insights.

About the Author:

Vijendra is currently working as a Sr. UX Designer at Mantra Labs. He is passionate about UXR and Product Design.

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