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The 5 hidden problems for Insurtech

For Insurance giants, the marketplace is changing. For young insurtechs trying to displace these giants and keen on disrupting the landscape altogether; the next big market is becoming plain and obvious: Millenials and the generations that will follow them.

A new wave of AI-driven technologies is making subtle changes to the way young people are re-thinking the whole “Why do I need insurance again?” decision.

Millennials —  are most likely to purchase insurance through an app with a few taps on their smartphones — are driving less frequently than previous generations — thereby creating a market for lower cost, pay-per-mile auto insurance. 

Yet, despite the proclivity of this demographic to stay away from ownership (and, with that, the need for coverage), they do own assets that they want insured. Insurtech is well poised above all else, to satisfy their unique coverage needs.

A majority of the World’s insurance purchases are done physically (in-person), while only a small portion of sales comes from either the web or mobile – yes, even in 2019 and for the foreseeable future, that remains true.

The Hidden Problems of Insurtech


The ‘Insurtech’ model can be broken down into — those that operate at the broker-level, those that offer insurance services/products or product-level, and those that have a hybrid approach (such as peer-to-peer insurtech) that has an insurance product with a strongly linked brokerage aspect to it. Here is a look at the challenges that surround young companies operating in these models.

#1 Partnerships are stark & sparse


For existing incumbents, the advantage is obvious — seize on the hype created by insurtech upstarts, who are capturing previously untapped audiences towards new & innovative products. 

Also, read – Top Innovative Insurance Products of 2019

Large insurers will even venture into setting up their own start-ups; or invest in new technologies within their own business.  However, despite the mutual benefit-for-all reasoning behind partnerships, these are spread thin across most regions.

Without the support of a large insurer or two, insurtechs will find it hard to manage the unit economics of the policies they sell; which brings to question the sustainability of this model for scaling.

#2 Innovation beyond downstream distribution


Insurtechs that have either chosen not to partner/ not managed to attract the right partnership with large insurers — arguably face greater challenges. Most of the insurtech-startup funding pool has moved into distribution, and rightfully so.

Distribution has brought about long-awaited changes to delivering new products and customer experiences — aspects of the business that Insurance giants consistently struggle to produce in.

Insurance, however, has four fundamental units: the underwriting of insurance, claims servicing, regulatory overhead, and distribution (actual selling).

As these insurtechs grow, the looming question remains: how will they manage the other parts of insurance, if all the money has gone into refining one stream?. For example, are they sufficiently capable of handling claims and underwriting as the business scales? These questions are yet to be answered, and the models are yet to be proven.

#3 Frequent changes to the legal & regulatory framework


“Not all insurtech businesses qualify as insurance companies” since they depend on the type and extent of the services provided. A regulatory distinction is essential to separate them — without which a reliable guarantee cannot be given to customers in the event of a loss.

Legal and regulatory commitments change with region and country, hence insurtechs are typically unsuitable for covering potentially large losses. 

#4 Attitudes of the next generation


Younger generations are less likely than previous ones to pay heed to the importance of insurance. They simply do not see it as an important financial instrument. These challenges have plagued the industry for several decades, and insurtechs will have to assume this challenge for themselves as well. At its core, insurance is a hard product to sell, no matter how good the package looks.

Technology in insurance and advancements to customer experiences are making the furthest inroads, the industry has ever seen. Yet, low insurance penetration levels are still an indicator of how difficult it is for insurtechs to find adoption among the masses.

#5 Intelligent Customer-Experiences


Thanks to Big Tech (like Google, Amazon, Apple, etc.) — customer experience has evolved rapidly. Digital products and services are now highly customisable and can be delivered at a high quality consistently. Yet, it has taken until now for the same to slowly seep into insurance. Sensing a huge opportunity, Big Tech has started moving into the insurance on-demand space, which has forced the larger insurers to adapt quickly. 

Insurtechs, who are by-default product- and tech- first, tend to fare better than their much larger counterparts. Yet challenges with data will persist. Just how well insurtechs are using data, remains to be seen. 

Will technology in insurance have to face a test of time?

The use of exceptional data and advanced analytics can help link the behavioural characteristics of customers and their spending habits – true fodder for machine learning models. How will insurtechs leverage useful insights to tackle age-old insurance selling challenges, such as intention to abandon, the propensity to purchase, or the right communication channel — will be the true test of competitive advantage.

Mantra Labs is a deep-tech advisor & consultant for young Insurtechs helping them create a strategic vision and an agile evolution road-map that addresses challenges from scaling to delivery. To learn more, reach out to us at hello@mantralabsglobal.com.

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Insurance consumers around the globe are seeking convenience and expecting better customer experience. From millennials to Gen Z, with the agile connectivity, irrespective of the industry has numerous options to choose from. As the competition intensifies the insurance industry has to jump into the bandwagon of technovation in order to provide improved accuracy, cost-saving and excellent customer experience. 

Here is a list of the marketing trends in insurance that will prove to be a game-changer in the year 2020.

1. Robo Financial Advisors

According to a Business Insider Intelligence forecast, by the year 2020 Robo-advisers will manage investment products worth $1 trillion, which will spike up to $4.6 trillion by as early as 2022.

Robo advisors have been around for quite some time. In the year 2008, during the financial crisis, Jon Stein, a 30-year old entrepreneur launched “Betterment”, the first Robo-advisor. In recent years due to its low investment rates and data input based research results, it has increased in popularity. 

It is basically designed for the people who want to manage their finances with low management cost. Based on respective data inputs, the Robo-advisors offer any advisory services. 

The main purpose behind the making of the Robo-advisor is to bring the financial services to the wide range of population with lower investment cost as compared to the traditional human advisors. Upwardly.com, 5Paisa.com and Goalwise.com are some applications of Robo-advisors.

Behind the scenes of the software of Robo-advisors are actual human beings who track the market regularly and adjust the algorithms based on the current market condition. Robo-advisors are a boon to the end-users as they can invest in direct plans of mutual funds without shelling any commission. However lack of personalization and one-size-fits-all products are the areas of improvement.

2. Data Integration: The Future of Marketing

IDC estimates that, by the year 2020, the digital cosmos will reach 44 zettabytes, further complicating the lives of marketing professionals.

Integrating data sources is vital for any company, whether B2B or B2C to successfully meet Customer Experience expectations thereby drive accelerated sales revenue.

With an integrated source of information, retailers can administer and optimise marketing through KPI’s, metrics and dimensions that would not have been possible with the separate source system. In order to upscale marketing operations, a connected viewpoint is essential to evaluate the campaigns, audiences, events and channels, and drive the strategic goals.

From an operational viewpoint, CRM solution provides the organization with new business and the ERP system allows to manage and drive businesses around obstacles. A good place to start with the data integration is by Integrating these two systems shall provide marketers and the organizational sales-force with vital information, that can be shared with the stakeholders.

3. AI-driven Copywriting

Artificial intelligence can create cancer combating drugs, control self-driving cars, defeat the best brains at incredibly complex board games, but one realm it can’t perform flawlessly is communicating.

To help solve the issue, Google has been feeding it’s AI with more than 11,000 unpublished books, including 3,000 steamy romance titles. 

Autoencoder, a type of AI network, uses a data set to reproduce a result (in this case copywriting) using fewer steps. Insurers can harness this AI capability to create sentences and suggest the best-optimised language to approach the customers.

AI copywriting is evolving to a whole new level. Google granted  €706,000 (£621,000) to the Press Association, to run a news service with computers writing localised news stories. AI with the help of human journalists can write up to 30000 news stories a month and scale up the volume of the stories that would otherwise be impossible to produce manually.  

“Skilled human journalists will still be vital in the process, but Radar allows us to harness artificial intelligence to scale up to a volume of local stories that would be impossible to provide manually. It is a fantastic step forward for PA.”

  • PA’s editor-in-chief, Peter Clifton 

4. Gamification of Insurance

At the nexus of marketing trends ranging from social networking to the IoT to behavioural science and wearable tech;  gamification is a powerful lever for insurers and insurance agents. It creates an enriching digital experience and customer-centric business model.

Gamification offers great potential value to the insurance business process in the realm of consumer engagement and customer experience. From millennials to Gen Z, it has emerged as a useful practice and effective means to target early technology adopters by:

  • Transforming mundane tasks into interesting and fun experiences that keep users returning.
  • Increases brand awareness, brand penetration and affinity.
  • Increase sales by educating customers about product suitability and guide them to buying the product.
  • Motivating people to act in areas of healthcare and wellness, safe driving, financial planning and sustainability.

Ingress and AXA redefined the world of gaming and advertisement. December 5th, 2014, Niantic Labs the creator of ‘Ingress’ partnered with AXA. In the game, AXA Shield was initially only obtainable from AXA Portals, leading you to AXA business locations in person.

5. Advanced AI Capabilities in Insurance

Innovation and technology are the next frontiers in the insurance industry. While automation and IoT are already a reality for insurance, with the advent of AI there has been a holistic approach to Insurance automation. With insurance leveraging AI, it has expanded its reach to more ecosystems than ever before. Deploying AI capabilities in insurance can help make smarter underwriting decisions, fraud detections, risk assessment and create a better customer experience.

AI is driving significant change in business with insurance being no exception. It has the potential to enhance the insurance business model by-

  1. Improving the speed of the workflow: AI and RPA in insurance reduce redundancy of task. Automation of day to day tasks would reduce cost and time consumption thereby increasing accuracy, quality and competency.
  1. Customizing the services for better customer experience: One size no longer fits all, and the same goes for the insurance industry. With focus on individual markets, insurers can create niche usage-based products to sell the packages in a variety of ways.

Parag Sharma, CEO, Manta Labs and AI thought leader is going to speak about the Internet of Intelligent Experiences™: CX for the Digital Insurer at India Insurance Summit and Awards 2020 on March 12, 2020. Catch him live at IISA 2020.

Details

  1. Providing new insights: Insurance is no guessing game. Data in silos is the biggest drawback for any industry. AI in insurance can integrate this data and provide analytics to help actuaries have a better insight while making a decision about a product.

Marketing Trends in Insurance: The Bottom Line

Today, at the core of marketing in Insurance, lies AI, Machine Learning and advanced data analytics to foster better experiences for the end-user. We’ve listed 5 most important trends that have the potential to shape marketing business models for Insurance and InsurTech firms. Be it Robo financial advisors or gamification, impressing customers remains the prime goal for Insurers.

Have thoughts and queries regarding upcoming marketing trends in Insurance? Please feel free to drop us a word at hello@mantralabsglobal.com.

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Data Science is enormous. It brings forth a scientific approach to gather a massive amount of useful data from raw & disordered information (often collected from open sources). According to recent research, over 2.5 million terabytes of data appear daily. In 2020 every person produces 1.7 MB of data per second. Scientists, Analysts, and numerous other specialists use this data to derive decision-ready insights.

Using data science, marketers can get a clearer picture of their target audience. With this knowledge, any organization’s marketing department can formulate strategies to target customers who portray higher chances of conversion. Also, by delivering values, organizations can eventually maximize revenues. Going with the traditional methodologies, data processing can be a daunting task. Data Science offers a cost-effective solution to businesses seeking data-driven insights.

Let’s delve deeper into 5 most profitable and practical use cases of data science in marketing.

1. Budget Optimization

The primary goal of any marketer is to achieve the highest possible ROI from the allocated budget. This objective is undoubtedly difficult and time-consuming. On top of which, because of changing market dynamics and user preferences, strategies often go off the track leading to unanticipated outcomes.

Data science can be a saviour here. By analyzing the marketing department’s spending and acquisition ratio, organizations can build a model to distribute the budget in the smartest way possible. A clear picture will help marketers to invest money in the most relevant and surplus channels, thus optimizing key metrics.

2. Defining Audience Persona

While every marketer is familiar with the process of building the target audience portrait, determining the exact persona of the potential customer can still be a challenge. The lack of proper data insights might lead to ineffective advertiser decisions leading to a waste of resources.

Data science methods help marketers to understand the user persona and their preferred communication channels with data-driven insights. This means that the marketing budget will be spent on the right channels of influence, ignoring the irrelevant media, which a normal human being will think of covering for “just in case”. Such adjustment will inevitably increase the ROI and optimize the entire advertisement campaign. This will also retain brand relevance to the customers.

[Related: Your shopping cart just got a lot smarter!]

3. Brand New Social Media Marketing Strategy

Social media trends change faster than a human can track it. Facebook, LinkedIn, and Twitter define what is popular, and a marketer has to catch up with the trends.

Data science can keep you on track with the changing trends. Using the logic of Data Science in Marketing, one can get a bigger picture of what type of content people like interacting with. Data science allows us to gather and analyze data about people’s online behaviour. It provides the key metrics to adjust the SMM (Social Media Marketing) goals, which include – the time of posting, content type, amount, etc. These simple adjustments using data science insights can help increase the marketing ROI drastically.

4. Clearer Content Strategy

One of the biggest gaps between planning and execution that marketers face is knowing which channels will be affected and what kind of people will interact with their content and with what sentiment. Will be potential customers? Are interactors content gatherers? Are they the competition? Do they intend to ruin your reputation?

Knowing all this information will help streamline your content strategies.

As long as you know who your customers are; what are their perceptions about your brand; what information can attract/repel your customers; what social channels they are mostly active on; what are their sentiments with your content; what they usually do when they like or dislike a content; you’ll know what type of content you should produce.

For instance, some people hate emails, while others adore reading them. Some people want to resolve their queries publicly on social media, which some care about their online image. Data science can help achieve personalization to some extent, which can help humanize the conversations with your followers.

Let’s take another example of how data science in marketing can help stakeholders. It gives marketers insights about what phrases a customer would use while searching for a product/services online. Marketers can utilize this insight and prepare a content strategy that embeds these terms more often in your posts and articles.

Therefore, we can say that data science brings a variety of actionable insights about customer acquisition channels, their preferences, and engagement style, which can help plan content strategy accordingly.

5. Increasing Customer Loyalty

Your best customers are the ones who will not just purchase your product once but also will repeat buying and bring their friends and relatives to your store. Organizations realize that customer retention is easier than acquiring new customers.

But consolidating loyalty may be tricky. Data science can provide the marketing department with all the necessary information that can help boost customer loyalty. Based on purchase history and current search queries, analysts can predict their customer’s inclination towards a product. Accordingly, brands can create the most relevant offers for their customers. With personalized offers, existing customers feel special and will return to your brand and not go to the competitors.

The Essence of Data Science in Marketing

Using data science in marketing may ease the work of employees and uplift your strategies to new heights. We have to admit that the more structured information marketing teams have, the more effective their strategies become. At the core of any marketing efforts, data science can optimize cost for data processing and result in overwhelming conversion rates.

[Related: 5 Deep Learning Use Cases in Insurance]


About the Author: Marie Barnes is a writer for Bestforacar and an enthusiastic blogger interested in writing about technology, social media, work, travel, lifestyle, and current affairs. She shares her insights with the world through blogging. You can follow her on Medium.

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