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5 Revolutionary AI-driven Marketing Trends in Insurance for 2020

5 minutes, 41 seconds read

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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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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