Astronaut loading animation Circular loading bar

Try : Insurtech, Application Development

AgriTech(1)

Augmented Reality(20)

Clean Tech(5)

Customer Journey(12)

Design(36)

Solar Industry(6)

User Experience(56)

Edtech(10)

Events(34)

HR Tech(2)

Interviews(10)

Life@mantra(11)

Logistics(5)

Strategy(17)

Testing(9)

Android(47)

Backend(30)

Dev Ops(7)

Enterprise Solution(27)

Technology Modernization(2)

Frontend(28)

iOS(43)

Javascript(15)

AI in Insurance(35)

Insurtech(63)

Product Innovation(49)

Solutions(19)

E-health(10)

HealthTech(22)

mHealth(5)

Telehealth Care(4)

Telemedicine(5)

Artificial Intelligence(132)

Bitcoin(8)

Blockchain(19)

Cognitive Computing(7)

Computer Vision(8)

Data Science(17)

FinTech(50)

Banking(7)

Intelligent Automation(26)

Machine Learning(47)

Natural Language Processing(14)

expand Menu Filters

Insurance Chatbot & the Automated Insurance Agent

What is it that comes to your mind when you think of a “Chatbot”? For me, it always reminds me of Siri, Alexa who can chat with us just like real humans. So, a chatbot is an automated system that is designed to interact with humans to the extent that they do not even realize that they are talking to a computer program. Most of the industry verticals have adopted chatbots for automating their processes and Insurance sector is one of them.

The insurance sector has always been a laggard when it comes to adapting to new technologies, but AI backed technology and RPA for insurance is nothing less than a boon for this sector. Insurance industry primarily revolves around in-depth analysis and information processing which makes it ripe for AI intervention.

The rise of the Automated Insurance Agent and RPA:

Is chatbot a winner for the insurance sector or it is still struggling to find its place? As per the TCS survey report, the Insurance sector has invested an average of $124million on AI and related processes, and this value is projected to rise exponentially as more investment on diverse applications is on the immediate horizon. The automation of several processes like broking, low-level claims processing, standardized underwriting is already implemented, and more automation is expected to follow.

RPA for insurance has also helped to mechanize the repetitive tasks that once needed a dedicated workforce.

A change in the customer’s perspective:

Another factor that is playing a catalyst in pushing Insurance companies to digitize their operations is the customer. Customers are not shying away from the automated insurance agents rather they are embracing it full-heartedly. With the advent of extreme digitalization verbal communication has been replaced by written communication and people are accustomed to typing and texting. 77% of insurance customers are entirely okay with chatbots if it means alleviating the wait times that they often face with real-time customer representatives. Also, one out of every four insurance customers is comfortable with interacting with a chatbot which further implicates that automated insurance agents do not have a grim future and they are here to stay.

Machine learning applications for data:

The next step in the insurance industry involves leveraging the benefits of AI to analyze and collate the available data from various channels like the social media, emails, and online postings and provide customers with more specific and sophisticated insurance products. Such systems can help insurance companies to grow, improve sales, reduce costs and make well-informed decisions. It also helps to improve customer experience as they no more have to wait for getting their queries processed or obtaining information about their claims.

Implementing machine learning tools for making accurate predictions based on available data patterns is also a crucial part of the insurance industry. For instance, if one has available data for online insurance purchases, then it can help to narrow down the customer preferences based on the demographics which in return help with more lead conversion. The claims department can also analyze the data patterns for inconsistency and detect any fraudulent activities.

Jobs Creation:

The rise in the automated insurance agent may replace the conventional agent workforce, but there is a growing possibility of new job positions. As more and more companies will start deploying new technologies for their operations the need of digital analysts, online marketers and developers will subsequently rise. The companies will need technically proficient individuals with knowledge in machine learning, analytics and automation programs to manage their web-based sales.

Insurance companies are already feeling the pressure and the importance of automation. The rapid technological advancement and a paradigm shift in the consumer’s buying behaviour are requiring companies to adopt new technologies. Tech pundits have predicted that there is a wealth of information to explore when it comes to Artificial Intelligence for Insurance.   

Cancel

Knowledge thats worth delivered in your inbox

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.

Cancel

Knowledge thats worth delivered in your inbox

Loading More Posts ...
Go Top
ml floating chatbot