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InsurTalks Podcast with Dr. Robin Kiera: Nurturing Sales in the Global Shutdown

9 minutes, 45 seconds read

Today, we are facing a crisis unlike ever before. The outbreak of COVID-19 has shaken the very foundation of the world economy. Social distancing and lockdowns have disrupted supply chains and international trade. 

We are seeing an unprecedented surge in lay-offs since the last economic recession in 2008. Amongst other industries that have been affected, Insurance and InsurTech are also finding it difficult to deal with the crisis. To get deeper insights on the new strategy for Insurance Sales, we interviewed Dr. Robin Kiera, founder of  Digitalscouting and renowned Insurtech & Fintech Influencer.

Digitalscouting is a platform with over 70,000 followers for thought leaders, entrepreneurs, and senior managers in finance and insurance to share best practices, lessons learned, and up-to-date views on tech and business trends around the world. 

Dr. Robin is a Co-author of The InsurTECH Book which offers essential updates, critical thinking, and actionable insight globally from startups, incumbents, investors, tech companies, advisors, and other partners in this evolving ecosystem, in one volume. He also advises and mentors many start-ups and corporations in various industries. His other areas of expertise include Brand & Marketing, Public Relations, Business, and Management. 

Connect with Dr. Robin Keira- LinkedIn

Complete podcast:

Excerpt from the interview-

Impact of COVID-19 on Insurance in Europe

Europe’s Insurance markets are facing a severe crisis? How are carriers, agencies, and insurance professionals in Europe developing measures to survive the global shutdown?

Dr. Robin: We have several live shows that we do in English and German where we interview several CXOs in the insurance industry. Mr. Christopher Lohmann from Gothaer Insurance said that only 10% of sales are doing well. 

So the question is, what do they do well? 

The answer to that is, they talk to clients, change their value propositions and product portfolios. 

One agent from Allianz said he is doing well because in the initial weeks of the lockdown he reached out to small and mid-sized businesses and helped them cope with the situation — displaying humility on their part. One of them helped his client in restaurant business sign-up for an online delivery service during lockdown. It wasn’t his job, however, if his client does good business, he will remain his client. Some agents and brokers took this opportunity to invest time into their client’s businesses and flourishing them. 

Other areas of distribution such as bank insurance i.e. selling insurance via banks are facing losses as most retail banks are closed and a lot of them have not found ways to effectively communicate with their clients.  

On the claims side, we haven’t seen any disastrous news yet. Also, it is interesting to see from the Life Insurance segment which did not see a surge in claims for term life or whole life insurance. However, we saw general insurance lines such as car insurance going down as not many people are driving. It’s a mixed picture. While in US insurers are paying the claims, in Germany insurers have decided to wait till the end of the year to assess the state of claims. 

The holding company of AXA announced it’s revenue numbers for the 1st quarter which was (-)10%. The negative figure means that new businesses are slaughtered. On the other hand, there’s been a boost for agencies. Our clients ask us how they can help their sales team to sell insurance policies since they cannot meet during this time. There’s been a dramatic change in the way insurance sales works.

The Reinforced Insurance Sales Strategy

How can Insurers equip their agents to generate sales in this pandemic?

Dr. Robin: It depends on the philosophy of an insurance company. Many insurance companies here are very old and go as far back as the 19th century. They have a very authoritarian culture and the agent-insurer relationship is very complex. There are some trust issues due to the unethical practices. 

For example, one of the big insurers promised young people to get on board and provide them with customer portfolios. But on the day of business, they see that only half of the portfolios were provided. If there is such a toxic relationship in an organization, then it gets difficult to deliver good output. 

However, small and mid-sized insurance companies have a very nice working culture where they willingly help their sales people. They have a very effective way of communicating with each other. 

Insurance Sales in The New Normal 

What are some Attention hacking lessons for Insurers operating in ‘The New Normal’?

Dr. Robin: Let’s divide and focus on two groups. 

First is the End Customer. For example, we have a client which is a big insurer with a distribution model to banking i.e bank insurance. We looked at every single touch-point in the process and noticed that they were not present in any. So we started placing them in every touch-point. Therefore, when a user logs into his bank account, the insurer can extract their data with the demographics, individualize and present relevant content/product.

[Related: How Technology is Transforming Insurance Distribution Channels]

Second is Sales. It doesn’t make sense to spend millions of euros and dollars in advertising with celebrities. Instead, they should produce content with people they know in the insurance community such as bank managers, insurance agents, etc. However, what’s more important is how insurers communicate and manage their sales against brokers, retail employees, etc. 

One of the most unestimated channels of communication is the messenger system. For example, WhatsApp, which is very popular in the west. We started experimenting with WhatsApp two years ago and the response was amazing. 

Sales managers use this distribution channel to share content with their agents. It brings 10x or sometimes 50x better engagement than a newsletter coming from headquarters. It is very necessary to focus on the attention of the end customer but also of the sales team. There are many simple ways to engage with sales guys such as interviewing top 2-3 performers with a simple smartphone. The key is to produce massive content and share with customers and sales that they are zoomed into the company. 

[Product: Lead Generation Chatbot]

Prevailing Technological Challenges

What are some of the technological challenges faced by Insurers operating in the New Normal?

Dr. Robin: The insurance industry needs to change culturally. They need to be a part of the daily lives of its customers and agents. They need to win their hearts, minds, and home screens. Insurers need to be on the apps, on platforms, and help them provide value. For that, we need technology. 

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

Technology helps in faster claims and application processes which is a given but there are still many companies in the world that do not have these standard processes automated. I believe that the true game-changer will be AI, data science, data analytics in claims or underwriting departments. 

[Report: The State of AI in Insurance 2020]

Insurance is ripe for AI. Still, why are some Insurers still hesitant to invest in AI?

Dr. Robin: Traditional Insurers overlook the capabilities of AI and other technologies. Having people both — from inside and outside of the industry can give a broader perspective on applications of technology to bring innovative products and solutions. 

Product Innovations in Insurance

What product innovations in insurance are going to take place for short-term, mid-term, and long-term?

Dr. Robin:  In the short-term, adaptation of certain payment methods like pausing the payments and lowering risks could be done. 

In the mid-term, you will see more flexible products, more lean underwriting, or more lean claim management. What we need to think is about our role in society. Our mind-set should not be of pushing insurance products down the throats of the customers. What our role is to pool risks and stand by those who have been hit by these risks. We should not just help them during claims but before as well. 

For example, why should we have to replace a car if we could send them a push notification intimating them of the hailstorm and advising to put their car inside the garage? 

Another example is, why to sponsor super expensive healthcare when we can help the people to live healthier lives. Having customers call you portrays that the insurer is the solution. And that is what I believe the long-term goal should be. 

COVID-19: An Impetus to Digital Processes?

Insurers are taking the distribution process online. How are the Insurers adjusting to this new model? Is the ongoing pandemic, going to be the impetus for insurance to move completely digital? 

Dr. Robin:  Having come so far to 2020, I think it is very rude to do time-consuming tasks. What is needed here is to educate the people that their manual tasks can be done by machines. Today, there is so much knowledge available out there. One of my clients asked me how much we should invest in educating employees. To which I said, zero. You don’t have to hire so many consultants to educate them, just send them relevant YouTube videos. 

Way Forward for InsurTech

InsurTech largely facilitates technology in terms of scale, distribution, and market fit. How will New Normal InsurTech create market attractiveness?

Dr. Robin: The first thing that InsurTech or any other company needs to understand that all the plans made earlier have now become irrelevant. It’s time to research and build value propositions for existing clients and prospects. This would include a new pricing and pilot structure as well. 

For example, we can create a small package of services which an insurer can easily download and try out. I don’t think they should aim for $100-$200 million deals but rather give this small package demo to insurers trying to get them hooked on it. This will pave the way for getting more business. I have seen some software companies slashing prices to 70%-80% in some cases. It’s not always the question of pricing, but when InsurTechs say that they’re not able to sell, it ‘s time to re-evaluate their pricing models.   

Road to Recovery for General Insurance

Many General Insurance lines are hit- Travel, Motor, Home – what will be the road to recovery for these Insurance lines?

Dr. Robin: It’s going to be difficult. I was talking to Chris Skinner, an author in the finance space where he said that it’ll take two years to recover. This is a significant paradigm shift in Insurance Sales. Maybe we will never go back to normal. I used to travel a lot for business. Right now, I am very happy to do remote workshops. It is unpredictable how it’s going to pan out.  

The Expert Advice

First, it is time to bypass the moment of shock. Most insurers have done that. They are past the crisis mode and are addressing the issues at hand. 

Second, avoid watching too much news about the coverage on COVID-19. It’ll just bring the morale down.

Third, Stay Zero! and re-evaluate everything- what kind of products we should make, what do clients need, which clients have money to invest, what additional products can we launch, what should be the pricing model, what kind of value proposition we should provide and

Fourth, is to take action rather than Netflix. There will be massive layoffs, so it’s important to ensure you bring value to the company. 

Last but most important, is to focus on the attention of the end customer and provide value to them. Make your customer come to you.


AI is going to be essential for Insurers to gain that competitive edge in the post-pandemic world. Check out Hitee — an Insurance specific chatbot for driving customer engagement. For your specific requirements, please feel free to write to us at hello@mantralabsglobal.com.

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Lake, Lakehouse, or Warehouse? Picking the Perfect Data Playground

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In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?

This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.

Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes 

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

FeatureData WarehouseData LakeData Lakehouse
Data TypeStructuredStructured, Semi-Structured, UnstructuredBoth
Schema ApproachSchema-on-WriteSchema-on-ReadBoth
Query PerformanceOptimized for BISlower; requires specialized toolsHigh performance for both BI and AI
AccessibilityEasy for analysts with SQL toolsRequires technical expertiseAccessible to both analysts and data scientists
Cost EfficiencyHighLowModerate
ScalabilityLimitedHighHigh
GovernanceStrongWeakStrong
Use CasesBI, ComplianceAI/ML, Data ExplorationReal-Time Analytics, Unified Workloads
Best Fit ForFinance, HealthcareMedia, IoT, ResearchRetail, E-commerce, Multi-Industry
Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.

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