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How Insurance industry is leveraging the Artificial Intelligence

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For more than 100 years Insurance industry has been functioning in very similar fashion but the recent developments in technology and its adoption by the people has made the insurance industry rethink about how it goes about its business.

A study by Mercer identified Technology and Big data as one of the top 6 challenges the industry is facing followed by Industry problems of Growth and Customer Focus issues.

This should not be worrying because the industry can start solving the issues of growth and customer focus using the new technology available now. A specific branch of FinTech has been carved out to cater to insurance. It is called the InsurTech. This class of technology is being specifically focused on Insurance industry use cases.

InsurTech is about leveraging the Artificial Intelligence capabilities that are evolving and working on Big Data available from various sources. One of the biggest use cases involves using Machine Learning algorithms to mine data to get better insights about consumers, their shopping patterns, lifestyle choices from huge data sets that are now available thanks to mobile and web adoption in the world. It can be safely said that innovation starts from looking and analyzing data, and the Insurance Industry is for sure to benefit from doing it.

The individual companies are transforming the way they handle selling insurance to processing and settling claims. Artificial intelligence is being used to completely handle insurance claims, making the whole process faster, in a recent announcement Lemonade Insurance demonstrated doing this in mere 3 seconds.

AI can not only help in claims processing, however, can also help in setting prices, modeling the risks associated with insurance, customer acquisition, distribution, and operations. It can solve problems across the whole value chain of the insurance industry. It does not take much to start as well. One recommended approach by Mantra Labs is to start with Digital Transformation and in the process start implementing the AI related improvements in the systems and processes.

Mobile Apps, Chatbots, improved Web interfaces are some key elements to improving the customer focus issues highlighted earlier and these can be assisted by AI to provide customised experience to individuals.

In Summary, we can say that AI is already transforming the insurance industry and it’s here to stay.

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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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