Summary
Third-party data can boost a business's leads, traffic, and sales. It's often more reliable than internal data, and integrating it into a CRM or sales system can bring in new customers. But to make the most of it, businesses should have a clear plan: identify the problem they're trying to solve, calculate project costs, and determine which data sets to use.
Players
Definition
Data enrichment gives datasets a power-up by adding new information from various sources or enhancing existing data. You can create complete customer profiles by merging details from sales calls and online interactions. Enriched data enables accurate data-driven decisions and unlocks hidden patterns and insights that might have gone unnoticed. However, adding poor-quality data can damage the overall accuracy and value of your dataset, so it is essential to be cautious.
To improve the quality and value of existing datasets, businesses can use versatile data enrichment techniques such as incorporating new external data, standardizing and cleaning existing data, and adding new variables to an existing dataset. Data enrichment offers the power to uncover hidden insights that fuel growth, innovation, and success, achieving business objectives like Operational Excellence, New Product Development, Business Transformation, or Customer 360. By unlocking these insights, businesses can stay ahead of the curve and revolutionize their operations.
Advantages
Insurance companies may leverage third-party data to improve customer segmentation, target new markets, or reduce fraud. Additionally, they may want to use third-party data to supplement their own data to create a complete picture of their customers. Below are a few other reasons.
Managing limited data and risks
Insurance and insurtech companies may face challenges in accessing sufficient, accurate, and complete data on their customers. To address this, they can turn to third-party data providers who have amassed large databases of consumer information. Third-party data can help supplement or verify a company's existing data, improve accuracy of underwriting and pricing decisions, mitigate risks, detect and prevent fraud, and enhance customer segmentation and personalization.
Some companies may have siloed data that is inaccessible to the people who need it. Third-party data can help fill in these gaps and provide a more complete picture of customers. Third-party data can also be used to improve risk assessment models and predict the likely damage of severe weather events, enabling insurers to develop strategies to mitigate risk and process claims more effectively.
Fraud detection
Third-party data can be particularly useful in detecting fraud, both before and after a claim has occurred. Data enrichment can aid in identifying potential customers and tailoring marketing campaigns to their needs, as well as segmenting customers more effectively. Additionally, third-party data can help enhance personalization, creating a more customer-centric approach that improves customer satisfaction and loyalty.
However, companies must be careful when using third-party data to ensure that they do not engage in unfair pricing practices or misuse consumer data. With proper oversight and responsible use, third-party data can provide significant benefits to insurance and insurtech companies.
Types
GIS - Geographic Information System (address verification, geospatial data)
This system captures, stores, manipulates, analyzes, manages, and presents all types of geographically referenced data. It is often used for mapping and location-based applications. Insurance companies may use this type of data to verify the address of customers and policyholders.
Telematics Data - Vehicle Data
Data collected from sensors and other devices that are installed in vehicles. It can be used for various purposes, such as tracking the location of a vehicle, monitoring driving habits, and diagnosing mechanical problems. Insurance companies may use this type of data to help assess the risk of insuring individuals and groups of people.
IOT Data (Internet Of Things)
Data collected from devices that are connected to the internet. These devices can be used for various purposes, such as monitoring the environment, tracking inventory, and controlling systems. Insurance companies may use this type of data to help assess the risk of insuring individuals and groups of people.
Wildfire Data
Data collected about wildfires used for purposes such as predicting a fire's spread, monitoring its progress, and coordinating resources. Insurance companies may use this type of data to help assess the risk of insuring individuals and groups of people.
Workplace Safety Data
When it comes to business insurance, data on workplace safety can be very helpful in assessing risk. This data can be used to identify trends and hazards so that companies can take steps to mitigate the risks. Workplace safety data can be collected from various sources, such as government agencies, private companies, and research organizations.
Climate Data
This is data that is related to the climate. It can be used for various purposes, such as predicting weather patterns, monitoring environmental conditions, and planning for emergencies. Insurance companies may use this type of data to help assess the risk of insuring individuals and groups.
Costs
The cost of integrating or enriching data can vary widely, depending on the project's specific needs. The cost may just be a few dollars per API call for a simple integration or enrichment job. However, for more complex jobs that require a lot of data, the cost can be much higher, potentially reaching into the thousands of dollars per year for premium data sets. The cost of data is often a major consideration when businesses plan their data strategy.
Third-party data can be a valuable asset for businesses, but it is important to consider the cost before making any decisions. Integrating or enriching data can be costly, and the price of premium data sets can be prohibitive for some companies. However, the benefits of using third-party data can be significant, so weighing the costs and benefits carefully before making any decisions is important.
Best practices
To develop a successful data enrichment/integration strategy, follow these best practices:
- Clearly define the business problem you are trying to solve.
- Use longer forms when gathering information from leads to ensure accuracy and completeness.
- Evaluate your data to determine which data sets are most valuable.
- Determine the cost of data enrichment/integration before making any decisions.
- Establish necessary infrastructure such as data storage and processing.
- Consider a data management platform (DMP) for managing and integrating data.
- Work with a trusted partner and check references and reviews.
Businesses can confidently develop successful data enrichment/integration strategies by following these best practices. To achieve this, they need to evaluate the collected data to determine its value and best utilization, weigh the costs against potential benefits, and establish the necessary infrastructure. They can process and analyze data effectively using simple API calls or a data management platform. Ultimately, this allows businesses to make informed decisions and achieve success.