Benefits of 3rd-Party Data
A number of statistics suggest that third-party data benefits companies by enhancing digital sales capabilities, improving fraud detection, enriching machine learning models, providing a better customer picture, facilitating programmatic advertising, supporting personalization efforts, providing data breadth, depth, and scale, and powering forecasting models and decision-making processes.
Third-Party Data Enhances Digital Sales Capabilities
- A report published by McKinsey & Company in February 2019 offers some information on the benefit that retail bankers derive from using third-party data.
- According to this report, a North American bank was able to triple its yearly online product sales in just a year, thanks to its use of both first-party and third-party data, a powerful marketing stack, and a flexible operating model.
- The bank uses third-party data such as geospatial data and browsing behavior data.
- Its marketing technology stack allows for omnichannel campaigns and a holistic view of the customer, while its adaptive operating model allows for marketing tactics such as cross-functional marketing war rooms.
- Though not the sole reason the bank was able to triple its online product sales, the bank’s use of third-party data was instrumental in achieving this increase in sales.
- This example provided by McKinsey & Company illustrates that the use of third-party data alongside other marketing tools can lead to significant increases in online product sales.
Third-Party Data Facilitates Programmatic Advertising
- An article published by the Interactive Advertising Bureau (IAB) in December 2018 shows that in 2018, companies in the United States were increasing their spending on third-party data, particularly third-party audience data.
- That year, despite the emergence of quality, privacy, and regulatory issues associated with third-party data, spending on third-party audience data was projected to increase by 17.7% from $10.14 billion n 2017 to $11.94 billion in 2018.
- Digital data, specialty or engagement data, and identity data were the categories of third-party audience data that were projected to grow the fastest in 2018.
- According to the IAB, the increase could be attributed in part to the rise of programmatic advertising. This information suggests that there is value to leveraging third-party data in programmatic advertising.
- According to Orchid Richardson, the vice president and managing director of the IAB Data Center of Excellence, third-party data has become increasingly vital, thanks to the role that programmatic advertising now plays in the marketing landscape.
- Brands use third-party data such as geolocation and interests to “deliver highly personalized messages to consumers and expand [their] audience.”
Third-Party Data Supports Personalization Efforts
- A report published by experience and personalization platform provider Monetate in March 2019 indicates that third-party data is used in personalization efforts, especially in the retail industry.
- Monetate polled 607 marketers from North America and Europe and found that 37.7% of marketers in the retail industry use third-party data sources to support their personalization efforts.
- This percentage was only 22.1% in the travel and hospitality industry and only 14.1% in the insurance industry. Based on Monetate’s survey, 78% of businesses in North America, 79% of businesses in the retail industry, 72% of businesses in the travel and hospitality industry, and 70% of businesses in the insurance industry get a positive return on their personalization efforts.
- It is important to note, though, that as far as personalization efforts are concerned, there are more marketers who use purchase history, email activity, mobile actions, and website behavioral data than marketers who use third-party data sources.
Third-Party Data Improves Synthetic Identity Fraud Detection
- Banks in the United States can use third-party data in improving their synthetic identity fraud detection processes. McKinsey & Company was able to demonstrate this through a research study it recently conducted.
- According to an article the company published in January 2019, the company undertook research that involved the use of 15,000 customer profiles from a marketing database and of nine external- or third-party data sources.
- These external data sources provided information such as email addresses, financial behavior, landline phone numbers, mobile phone numbers, property records, and social media accounts.
- With the help of these external data sources, McKinsey & Company was able to develop a system for measuring the depth and consistency of a customer profile. The accuracy of this system was enhanced by machine learning models.
- Through this system, McKinsey & Company was also able to determine that of the 15,000 customer profiles included in its study, only 5% could be considered suspicious.
- McKinsey & Company’s research illustrates that banks can use third-party data to develop a similar scoring system for immediately identifying high-risk customers and low-risk customers.
Third-Party Data Enriches Machine Learning Models
- An article published by McKinsey & Company in February 2020 shows that commercial banks in North America can use both first-party and third-party data in creating practical machine learning models that, in turn, can help them achieve incremental growth.
- According to this article, “while commercial banking data sets will probably always be small relative to retail, commercial banks can now achieve step changes in performance by applying machine learning to a range of internal and third-party data.”
- Based on this same article, commercial banks in the region can develop handy machine learning models using large data sets.
- Commercial banks can achieve 10% to 15% incremental growth by creating next-product-to-buy models that would improve cross-selling.
- They can also achieve 5% to 15% growth by creating granular microsegment pricing models and 15% growth by developing adaptive retention treatment models and early-warning systems.
Third-Party Data Helps Provide a Complete Customer Picture
- An article published by the Boston Consulting Group in February 2019 suggests that companies can use third-party data in building a complete view of the customer and in simplifying the customer journey.
- In the article, the group cited a finance company as an example of a company that has successfully used third-party data in streamlining the customer journey. This company is a subsidiary of a large retailer in Europe.
- By using both first-party data and third-party data on its 16 million loyalty program members and housing this data in one safe and connected platform, this subsidiary was able to significantly reduce the number of screens that a customer has to go through to apply for a credit card online.
- It was able to cut down the number of screens from eight to three, and it was able to provide differentiated products such as price-advantaged loans.
Third-Party Data Provides Breadth, Depth, and Scale
- A white paper published by Data Axle, a Texas-based third-party data provider, shows that even though third-party data could be more expensive and less accurate than other types of data, it provides breadth, depth, and scale that could not be matched by zero-party, fist-party, or second-party data.
- Data Axle, for example, boasts that its database contains over 16 billion data points from an audience of over 320 million consumers and over 15 million businesses.
- According to Data Axle, third-party data helps in augmenting existing data and filling in gaps in other types of data.
Third-Party Data Powers Forecasting Models and Decision-Making Processes
- An article published by McKinsey & Company in October 2019 suggests that third-party data powers forecasting models and decision-making processes. In this article, McKinsey & Company cited the chemical sector as an example.
- According to the management consulting firm, players in the chemical sector can now create more accurate price forecasting and decision-making models by leveraging both in-house data such as stock levels and order intake and third-party data such as commodity price indices, trade statistics, and stock levels of distributors and manufacturers.
- A global fertilizer manufacturer that leveraged third-party data sources for general market or discrete event information such as political interventions and force majeure events was able to develop a more sophisticated price forecasting model.
- This new model is capable of forecasting both short-term and medium-term price fluctuations apart from the usual long-term price fluctuations. Thanks to this new model, the company is now better positioned to decrease its commodity spending by 2% to 3%.
Benefits of 3rd-Party Data For The Insurance Industry
Hard data or statistics that illustrate how third-party data benefits the insurance industry are in short supply. Only a few related statistics could be found on the subject. These few related statistics are supplemented by qualitative insights.
Third-Party Data Helps Insurers Improve the Customer Experience and Their Pricing, Claims, and Fraud Detection Processes
- An article published by Digital Insurance in July 2020 indicates that research and advisory firm Celent recently polled property and casualty insurers in North America.
- Based on this article, 68% of property and casualty insurers in the region utilize third-party data in their analytics.
- For property and casualty insurers in the region, the priority areas for analytics are underwriting (94%), pricing (81%), claims (81%), fraud (69%), and customer experience (66%).
- Third-party prefilled data is the insurance data source that property and casualty insurers in the region find most valuable. In the survey, respondents gave third-party data sources a rating of 4 out of 5.
- Third-party prefilled data is followed by images from devices such as cameras and smartphones (3.7), aerial/satellite data (3), geospatial data (2.,8), social network data (2.7), and smart home or property data (2.5).
Third-Party Data Helps Insurance Brokers Provide a Seamless and Personalized Service
- An article published by Accenture in July 2018 shows that thanks to the wide availability of third-party data, insurance brokers are now better equipped to provide their customers with a seamless and personalized service.
- According to this article, “the culmination of data storage and third-party data access enables brokers to better use analytics for enhanced business insights.”
- There are numerous third-party data sources that provide brokers valuable information such as buying behavior, claims data, demographics, exposure data, and prior coverages. Among these third-party data sources are Advisen, Capital IQ, Core Logic, Dun & Bradstreet, and ShareThis.
Third-Party Data Helps Insurers Streamline the Customer Journey
- A report published by Accenture in October 2018 also that third-party data can be used to reduce the amount information small business owners need to provide when researching insurance plans and gathering quotes.
- Based on this same report, third-party data, together with artificial intelligence, can also be used to create automated and personalized updates that will address the changing insurance needs of small business owners.
Third-Party Data Supports Insurers’ Preventative Communications
- An article published by KPMG in January 2020 shows that connected devices and third-party data can be used to develop not only personalized risk coverage but preventative communications as well.
- According to this article, insurers can leverage hyperlocal weather data to develop a push notification system that will alert customers to a severe weather event.
Third-Party Data Helps Insurers Reshape Their Risk Paradigms
- An article published by McKinsey & Company in July 2020 shows that insurers are now using external or third-party data such as prescription histories and credit scores to improve their underwriting processes and reshape their risk paradigms.
- According to this article, over 90% of life insurance policies are underwritten in a manner that takes prescription histories into account.
- Credit-based insurance scores, which are provided by credit agencies and validated by reinsurers, are widely used as well, as they have been proven to accurately predict policy lapses and mortality.
- One example of a credit-based insurance score is the TrueRisk score offered by TransUnion and verified by the Reinsurance Group of America.
- Third-party data on charitable giving, fitness behavior, pet ownership, and telemedicine could potentially inform mortality prediction models.
Third-Party Data Helps Enrich Insurers’ First-Party Data
- An article published by Accenture in June 2018 also shows that third-party data helps providers of small commercial insurance enrich their first-party data and strengthen their underwriting system.
- According to this article, 15% to 20% of data collected from insurance agents and customers is incorrect or inaccurate. This first-party data can be enriched by third-party data, but since third-party data can also be inaccurate, insurers must have intelligent data hubs that can synthesize and continuously evaluate the quality of this data.
- These intelligent data hubs should involve multiple data vendors and be enabled by artificial intelligence.
Third-Party Data Helps Insurers Speed Up the Underwriting Process
- An article published by Accenture in January 2020 shows that by leveraging third-party data sources (e.g., electronic health records), advanced rules engines, and intelligent automation, insurers are now able to underwrite policies with a lower face value, say $250,000 or less, without any need for human intervention.
- Policies with a lower face value can now be processed through straight through processing or STP, and an underwriting decision can be made within just a few minutes instead of the usual days or weeks.
Third-Party Data Helps Insurers Provide Usage-Based Insurance
- An article published by Ford in November 2020 indicates that third-party mobility data can be used to develop usage-based insurance.