Tag: analytics

Insurers Can Unlock $874 Billion in Untapped Potential by Investing in Data and Analytics—Everest Group | Press Release

Everest Group estimates the massive economic value insurance enterprises can generate by tapping their vast data pools to achieve operational efficiency and drive premium growth.

By investing in becoming data-centric organizations, insurance companies can unlock a collective economic value of US$874 billion, according to Everest Group. This potential lies in the impact data and analytics can have in helping organizations increase operational efficiency (US$306 billion), reduce claims leakage and fraud (US$117 billion), and achieve premium growth in the Property & Casualty (US$285 billion) and Life & Annuity (US$166 billion) sectors.

“Insurers today are challenged by an environment of high uncertainty that includes natural disasters, geopolitical and macroeconomic risks, the repercussions of a global pandemic, inflation and the looming threat of a recession,” said Ronak Doshi, partner at Everest Group. “Nevertheless, they can unlock immense value and come out on top by investing in data and analytics. Everest Group has documented how insurers can use data and analytics to achieve cost savings from increased operational efficiency, reduce claims leakage and fraud expenses, and drive premium growth by unlocking higher sales efficiencies and rapid product innovation. We estimate that collectively the industry could tap up to $874 billion in value by investing in a data-driven approach.”

Currently, insurers’ data and analytics spend is growing at an accelerated rate of over 25% annually. The spend is being directed toward digital transformation of business operations. Key priorities include:

  • Modernizing the data estate (especially, eliminating data silos and storing customer data in a governed data lake), enabling a single view of customers across ecosystems.
  • Redesigning operations to infuse data across workflows to enable enterprise-level, data-driven decision-making capabilities
  • Leveraging cloud-based analytics and AI/ML (artificial intelligence/machine learning) tools to process large data volumes for more effective risk assessment, building hyper-personalized products, and improving agility to respond to customer demands.
  • Building focused solutions to extract value from existing data, especially to build value-added products and services and minimize loss ratios.

These improvements will lead to value creation across five key areas:

  1. Product and experience innovation
  2. Minimization of claims payout
  3. Enhancement of operational efficiency
  4. Reduction in fraud abuse and claims leakage
  5. Revenue growth

These findings and more are detailed in Everest Group’s recently published report, “Insurance IT Services – State of the Market 2022: Unveiling the Economic Value of Data and the Road to Actualization.” In this report, Everest Group showcases the need for a platform-based operating model with a collapsed data supply chain that can enable insurers to transition into data-driven organizations. In addition, the report examines the global insurance IT services outsourcing market and lists various services and technology providers across the data and analytics ecosystem that can help insurers maximize the value from their data and analytics initiatives.

***Download a complimentary abstract of “Insurance IT Services – State of the Market 2022: Unveiling the Economic Value of Data and the Road to Actualization”***

About Everest Group
Everest Group is a leading research firm helping business leaders make confident decisions. We guide clients through today’s market challenges and strengthen their strategies by applying contextualized problem-solving to their unique situations. This drives maximized operational and financial performance and transformative experiences. Our deep expertise and tenacious research focused on technology, business processes, and engineering through the lenses of talent, sustainability, and sourcing delivers precise and action-oriented guidance. Find further details and in-depth content at http://www.everestgrp.com.

Analytics and AI Services Specialists PEAK Matrix® Assessment 2022

Top Analytics and AI Services Specialists

Enterprises looking to adopt Artificial Intelligence (AI) initiatives are finding it difficult to implement them at scale due to data-related challenges, inability to acquire skilled talent, advanced IP, and lack of AI and cloud capabilities. Hence, they are turning to analytics and AI services specialists to serve their needs. In turn, these providers are improving their capabilities through investments in talent, products and platforms, partnerships, industry expertise, and AI-based solutions designed to serve specific client needs.

In this report, we present an assessment and detailed profiles of 22 analytics and AI services specialists featured on the analytics and AI services specialists PEAK Matrix®. Each provider profile presents a comprehensive picture of its service focus, key Intellectual Property (IP) / solutions, domain investments, and case studies. The assessment is based on Everest Group’s annual RFI process for the 2021 and 2022 calendar year H1 (January-June), interactions with leading analytics and AI services specialists, client reference checks, and an ongoing analysis of the analytics and AI services market.

DOWNLOAD THE FULL REPORT Analytics and AI Services Specialists PEAK Matrix® Assessment 2022

What is in this PEAK Matrix® Report

This report provides a detailed analysis of 22 analytics and AI services specialists and includes:

  • Everest Group’s PEAK Matrix® evaluation of analytics and AI service providers and their categorization into Leaders, Major Contenders, and Aspirants
  • An overview of enterprise analytics and AI priorities and key challenges in scaling AI
  • Key analytics and AI services trends
  • A detailed assessment of the strengths and limitations of the providers in terms of their market impact and vision and capability

Scope:

  • Industry: data and analytics
  • Geography: global

LEARN MORE ABOUT Analytics and AI Services Specialists PEAK Matrix® Assessment 2022

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The Rising Role of Customer Data Platforms in Data-driven Personalization | Blog

By bringing together disparate data to gain a single customer view, Customer Data Platforms (CDPs) are becoming increasingly important to help brands drive personalized marketing efforts while maintaining trust and privacy. Learn more about the benefits of Customer Data Platforms and why they matter in this blog.

With the explosion of data on consumers’ spending and online behavior available from a multitude of sources today, gleaning valuable insights from the massive amounts of information is a top priority for brands.

But unifying the various disconnected touch points clearly and comprehensively to make sense of all the data is one of the biggest challenges facing marketers.

Customer Data Platforms (CDPs) have emerged as an important software solution that can help businesses get closer to consumers and achieve their organizational goals. Let’s explore what is driving the rise of CDPs.

Shift to data-driven marketing

Third-party cookies stored within users’ browsers have historically been a key marketing technology to track visitor behavior activity, improve the user experience, and collect metadata.

But today’s new consumer priorities, data privacy laws, and evolving technologies are leading to the third-party cookie’s demise. Following Safari’s lead in 2016, the world’s three main browsers eliminated (or will eliminate) the use of third-party cookies.

In addition to the demise of third-party cookies, other developments are limiting the use of consumer data for marketers. On the mobile/tablet devices side, Apple’s iOS 14 now requires explicit consent for any mobile identification collection.

The General Data Protection Regulation (GDPR) in Europe and other similar regulations are impacting consumer data collection and processing. In addition, 71% of countries have data protection and privacy regulations and 9% have draft legislations, according to the United Nations Conference on Trade and Development (UNCTAD.)

These developments significantly impact the consumer targeting capabilities of advertisers who often depend on third-party data. The vast majority of advertisers use or have used retargeting and old-generation Data Management Platforms (DMPs) that rely heavily on segments fed by third-party data.

Along with targeting, measurement is also significantly hindered. With more stringent consent collection requirements, collecting the consumer identifications needed to track impressions, clicks, or views and reconstruct complete customer journeys is more difficult.

These changes represent a major shift in data-driven marketing – leading to greater reliance on first- and second-party data to meet the challenges of an increasingly privacy-focused world.

But the unfortunate reality is that most organizations simply aren’t ready to adapt to these trends.

In our report Emergence of CDPs: Charting the Path to Data-driven Personalization, we estimate that even though 90% of businesses agree that data-driven marketing is the future, only 20% consider themselves highly mature enterprises, citing the high cost of data acquisition, limited automation, and data fragmentation as some of the top challenges.

How can enterprises prepare?

With this imminent shift from third-party to first- and second-party data, the changing regulatory environment, and evolving customer expectations for omnichannel and hyper-personalized experiences, enterprises are actively investigating new ways of collecting and activating customer data to drive personalization while fostering customer trust.

This is where CDPs become increasingly important and act as a central repository for the marketing stack.

A CDP allows enterprises to capture and store user data to link with all the users’ interactions, including Customer Relationship Management (CRM) and eCommerce platforms, social media, websites, and apps. Having data from multiple different systems improves the likelihood of identifying an individual. See the customer data platform framework below:

Customer data platform framework

Screenshot 2022 11 10 093829

 

By gaining a single customer view, brands can better understand customer requirements and up-to-date communications preferences, personalize individual brand experiences based on past behavior, and create personalized recommendations for customer segments. All of this can be achieved using unique, relevant, and accurate information that a person has willingly shared with the brand.

CDPs do not replace existing data systems. Instead, their role is to enhance current tools’ capabilities, mitigate risk from the third-party cookie demise, and power marketing teams with near real-time best-in-class audience selection. CDPs bring together existing customer data, anonymous floating attributes, and digital behavior across channels, devices, and tools.

Customer data platform landscape

Adoption is on the rise with, CDPs being viewed by enterprises as one of the most viable, future-proof solutions for managing the overwhelmingly disparate data streams that today’s brands gather and generate about their consumers and prospects.

Enterprises have many choices in this rapidly expanding market, including:

  • Large enterprises like Adobe, Oracle, and Salesforce that are investing hundreds of millions of dollars in the space and offer CDP as part of a greater MarTech (marketing technology) package
  • Pure play CDP players like Celebrus, mParticle, and Treasure Data that are purpose-built to support and address CDP use cases first that don’t need to be integrated into a larger system

Along with the growth in new entrants, the CDP space has seen a flurry of merger and acquisition activity in recent years of players that have showcased their unique data, campaign, analytics, and delivery capabilities, as illustrated below to see merger and acquisition activity in the customer data platform landscape.

Merger and acquisition activity in the customer data platform landscape

Screenshot 2022 11 10 094205

The road ahead for customer data platforms

As the data management landscape continues to rapidly evolve, CDPs will play an important role in the marketing tech stack and enable marketers to achieve true 1:1 personalization.

For enterprises to reach the desired business outcomes and mitigate risks in their personalization journeys, they should follow a comprehensive roadmap with the following four steps for data-driven 1:1 personalization:

Roadmap for data-driven 1:1 personalization

Screenshot 2022 11 10 094416

 

For more, see our report, Emergence of CDPs: Charting the Path to Data-driven Personalization, or view our webinar, Hyper-personalization Using Customer Data Platforms (CDPs). To discuss Customer Data Platforms further, please reach out to Sandeep P at [email protected].

Discover  how CX leaders can meet the expectations of their digitally enabled customers in our webinar, How are Leading Organizations Delivering Exceptional Customer Experience?

Unleashing the Potential of Data in Insurance – The Road Ahead | Blog

Leading insurance organizations seek to be more data-driven in their business decisions by harnessing the full potential of the data that resides within their enterprise boundaries. With the evolving technology landscape, real-time experience management, and explosion of data types, insurers are increasingly leveraging real-time insights to improve customer experience. In this blog, we will explore the potential benefits for carriers of unlocking data in the insurance value chain.

Insurance enterprises are facing a tough business environment marred by macroeconomic challenges, heightened natural catastrophes, and unfavorable interest rates. This is creating an urgency to re-evaluate underwriting and pricing models by taking data-driven approaches.

Data can help insurers unleash the next growth wave, enable targeted cross and up-selling generated through higher customer engagement levels, and provide a 360-degree view of their customer needs. For example, embedding data and analytics and Artificial Intelligence and Machine Learning (AI/ML) models within the claims workflow can enable zero-touch insurance claims transactions. The digital interaction process can flow seamlessly from intaking all filed claims consistently across channels, validating and assigning complexity scoring to each claim, segmenting and routing the claims based on complexity, to finally settling them as quickly as possible.

Infusing intelligence across insurance operations while investing in data and analytics capabilities can generate a surplus economic value of US$ 874 billion, according to Everest Group research, as illustrated below.

Exhibit 1

Picture1
Source, Everest Group

However, the industry faces challenges to effectively unlock the full potential of data in insurance, including:

  • Siloed and scattered data: Insurers face a high data spread across disparate systems, business lines, functional areas, and channels preventing them from gaining a 360-degree customer view, resulting in high integration costs
  • Inadequate enterprise-wide data strategy: Insurers need to foresee the entire insurance lifecycle to democratize enterprise-level data and analytics objectives and define how they can manage data as an asset and drive critical business decisions
  • Attraction and retention of skilled talent: Employees with technical expertise and domain-specific skills are scarce

The changing road ahead

Insurers are not only striving to make data-driven decisions but also beginning to explore new business models by combining available big data with advanced AI and ML capabilities.

Insurers are shifting from being risk mitigators to playing more of a risk avoidance role with data, cloud, and platforms being their foundational components. Digitization of the value chain, new business models, and underwriting transformation are helping insurers expand their roles from underwriters to risk decision partners who predict unforeseeable risks and ensure protection.

Data from connected devices is becoming a prominent source to assess and prevent risks. To illustrate, in the auto insurance industry, sensors, blind-spot assist, collision avoidance tools, and other safety systems have already been pre-built into vehicles using behavioral data to help improve safety.

Vast data stores are opening up opportunities to price risk more accurately and offer personalized product structures. For instance, utilizing climate and other third-party data empowers insurers to assess geographical areas that present greater catastrophic risk and charge higher premiums instead of measuring these types of risk through traditional approaches.

Deploying AI and other latest technologies not only assists with ingesting unstructured data but also helps generate actionable insights that previously were unavailable to underwriting and claims teams. Insurance data and analytics spend is growing at an accelerated rate of over 25% annually as insurers look to transition to being data-driven enterprises.

Leveraging data from different types of sources such as wearables, internet of things (IoT) sensors, and telematics through clients’ lifestyles and behavior, insurers are embarking on a new age digitized underwriting process. Smart loss capture and IoT sensors are expected to bridge the gap between the traditional claims processing mechanism to zero-touch claims transactions.

How will the insurance industry progress toward a data-driven approach?

Insurers need to actively engage with the ecosystem of data generated by the insurance enterprises as well as information coming in from external sources such as InsurTechs, and services and technology partners. By doing this, insurers can create and implement strategies that will lead to unmatched automated decision-making support that they can leverage to drive growth and efficiency and extract maximum value.

Exhibit 2

Picture2

Source, Everest Group

Data will be a central driving force to strengthen competitiveness in the industry moving forward – allowing carriers to leave behind their traditional approach of solely being risk protectors and move them toward being risk preventers.

As insurers look to become data-driven, data centers and cloud services can enable companies to respond to evolving customer needs, improve resiliency, instill agility, and drive enhanced operational efficiency. Similarly, leveraging AI/ML models and predictive analytics offer a major solution to the challenge of providing real-time actionable insights. Insurers that can create true differentiation and impact using internal and external data will be able to future-proof their business and be seen as leaders in times to come.

To learn more, check out our State of the Market Report 2022 – Unveiling the Economic Value of Data and the Road to Actualization. To discuss more on these topics and share your perspectives with our analyst team, contact [email protected], [email protected], [email protected], and [email protected].

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