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Insurance as a service

4 minutes, 26 seconds read

The past years have seen strong traction in “as a Service” business model across several industries. The insurance industry is no different. 

The idea behind XaaS, or “as a Service” is that one can buy services from vendors on a subscription-basis – depending on their needs and requirements. It is especially beneficial to reduce time to benefit, installation costs, ensure scalability and swift upgrades. XaaS often corresponds to the availability of service on the cloud.

[Read More: Everything as a Service]

Now, 

What is Insurance as a Service?

Insurance as a Service implies that individuals or companies can buy pre-built elements of Insurance services on subscription-basis as per their needs and requirements.

How is Insurance-as-a-Service different from Sandbox?

The Sandbox approach emphasizes on experimenting and learning before finally adopting technology or systems to reduce the impact of failure. Whereas Insurance as a Service is a platform built after testing done on a wide user base and is available for users on a subscription basis. Insurers use a sandbox approach to test product-market fit before the actual release. Individuals, corporates, and even insurance companies can benefit from Insurance as a Service.
Details – Sandbox Approach in Insurance

What makes Insurance as a Service model impressive?

Insurance as a Service model requires only a little to no capital expenditure. The service infrastructure, owned by the provider, distributes the cost across users. 

After studying business cases, primarily for incumbent processes, corporates and stakeholders can test a particular service before actually investing in it. Businesses need not overhaul their core functions for integrations. A small-scale trial can be enough to adopt a specific model. In many such ways, Insurance as a service is an excellent option for incumbents, entrepreneurs, and startups.

Prerequisites

XaaS products are, in general, scalable and can be integrated across a variety of platforms without compromising customization and customer experiences. Their infrastructure relies heavily on data, analytics and contextual tools. The fundamental requirements from Insurance as a Service infrastructure are:

1. Customer analytics

Why: Advanced analytical technologies are great to get an insight about customer psychology and implement them to create related products. 

How: NLP-powered chatbots can create a transparent platform for communication with customers and dive into the functional requirements of the product.

[Related:The State of AI Chatbots in Insurance Report]

2. Personalized data

Why: This is a high-time to humanize conversations with customers and establish a real-time personalized relationship.

How: Through the omnichannel approach, it is possible to gather and unify customer data collected from various sources like social media, website, communication with agents, to name some.

3. Contextual tools

Why: To formulate products that can match customer expectations, offer convenience and empathy-based experiences.

How: Leveraging analytics, emotion AI and NLP-based technologies to analyze customers’ intent and perceptions about your brand from multiple sources (e.g. social media, forums, etc.)

How are start-ups developing models for Insurance as a Service?

As per recent InsurTech developments, start-ups are pursuing the following 3 Insurance as a Service model:

1. Full-stack

It involves an end-to-end infrastructure to deploy digital insurance. Here, a technology company can develop a platform for Insurance processes as well as licensed white-label backend. For example, Swiss startup Stonestep provides Micro-insurance as a Service by partnering with mobile network operators, retailers, and vendors who already have an existing distribution presence. 

Working with partners helps them to save infrastructure costs and helps them to make insurance available for even the most remote geographical locations.

[Related: Four New Consumer-centric Business Models in Insurance]

2. Digitizing Process Assistance

Most of the incumbents still rely on legacy systems and processes for underwriting, policy distribution, claims, and agent onboarding. The Insurance-as-a-Service model also assists companies to digitize and channelize insurance operations in a single system and then connect them to their engine. Mantra Labs is a leading provider of InsurTech services and offers plug and play products for digital insurers such as:

Insurance Chatbot: An NLP-powered that works on a self-learning model and is updated from time to time based on the interactions between agents and customers. It brings unparalleled benefits in terms of ROI saving licensing and agent salaries costs.

Paper to digital document parser: Mantra Labs’ Intelligent Character Recognizer allows users to convert and store paper-based or handwritten documents into a digital format. 

Today we need situation-dependent personal risk management products. Insurers can remodel their offerings based on real-time scenarios which will not only urge the customer to invest in the insurance policies but also work towards improving their customers’ health and welfare. For instance, you may not have comprehensive auto insurance. But, how good it will be if your insurer provided theft insurance whenever you enter a theft-prone area? It is a win-win situation for both — the policyholder as well as provider.

3. Digitizing Core Services

Some startups offer their services in a specific field of insurance. For instance, Mantra Labs focuses on customer engagement, new revenue streams, and security features. Some companies like Riskpossible help with underwriting, RightIndem for claims, and others for customer data management and fraud detection. 

Because these companies focus on specific insurance domains they are much more efficient in making Insurance services a winner.

[Related: Visual AI Platform for Insurer Workflows]


Mantra Labs is an InsurTech100 firm specializing in AI-first products and solutions for the new-age digital Insurers. For your specific requirements, please feel free to drop a line at hello@mantralabsglobal.com.


Further Reading:

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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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