Remaining Relevant: Integrating Non-traditional Data into Insurance Insights
Every day, a typical connected customer interacts with hundreds of touchpoints across brands and industries, each activity or click flowing into an ever-expanding mountain of data. Some of these brands are learning to use that data to understand their customers. And, they’re doing such a good job of delivering custom–tailored experiences, that customers have come to expect that level of service and are turned off by those that don’t provide it. Insurers need to look to what leaders in other industries are doing in order to catch up.
In the past, policyholders would fill out a survey and submit records at the beginning of their relationship with their insurer and only interact with them to file or dispute a claim, discuss billing, or change information. This level of data collection didn’t provide insurers with a holistic view of the customer, so they would fill in the blanks with broad demographic data and create pricing models and services based on that data. That model won’t cut it for today’s policyholder. Traditional means of collecting data are just a single piece of a multi-faceted data profile that makes up the full picture of each and every customer. Gathering data from multiple sources empowers insurers like you to serve better and sell more.
Making non-traditional data work for your business
So, what kinds of data should you be considering when building out a holistic customer profile? In many cases, you need to look outside of traditional insight systems. Some insurers are incentivizing customers with cost savings opportunities in exchange for more detailed personal data. For example, IoT devices can provide enhanced insight around policyholder movements, health, weather conditions, or motor vehicle use. These expanded data sources can have significant impact across an organization including insight into next-best action, improved underwriting and more accurate pricing. In addition, insight-based discounts allow for a better customer experience and increased loyalty.
Another source of insight comes from using cognitive services, like text-to-speech or conversational sentiment tools, to develop automated chat services in both customer and agent channels. This level of service not only provides efficiencies for your organization and faster customer support, but through machine learning, you can predict customer policy lapses or mitigate customer churn before it becomes an issue.
Today’s customer also interacts with brands in real time on social media channels. Insurers are not only interacting with customers on platforms like Facebook, Twitter, and Instagram, but they are also leveraging those channels to alert customers to important issues or requests. Applying AI and machine learning to these channels allows you to be even more proactive in engaging customers and prospects and provides a better view of how your products and services are being received by your audience and the public at large.
Creating a confluence of non-traditional datasets
While gathering information from non-traditional sources is valuable, the data by itself only goes so far. By pulling insights from non-traditional sources into the data analytics mix and applying machine learning algorithms, you have an opportunity to be more relevant than ever to your customers and achieve exponential gains in productivity and efficiency across your organization. At this time, each of these data sources within most insurance companies remains siloed, limiting the overall value to your company.
Think about all of the data your company might gather from its customers and prospects: contracts, point of sale, case studies, web, email, mobile, partners, events, loyalty, geolocation, social media. Imagine the insights you could achieve by integrating and analyzing all sources of data from every part of your organization. With a 360-degree view of each customer, their behavior and what motivates their transactions, you’d be able to create the right set of services to meet their evolving needs.
Leveraging the right analytics, you could better identify leads, create optimal competitive intelligence operations, and empower employees to provide proactive customer services. Proactive service leads to minimizing customer risk, mitigating issues before they escalate, and huge savings on customer damage payouts and disruption. With AI, machine learning, and data integration, you can develop a true omni-channel customer experience, freeing your customers to interact with you on their terms without you losing control of important information.
Leaders in the insurance industry are leveraging advanced technologies to stay competitive and, most of all, relevant to their customers. Using solutions like Microsoft Dynamics 365 Customer Insights allows them to not only integrate and analyze all of their data, but also to create and integrate custom applications to get the information they need to optimize services. With this level of integration, no interaction or data point gets lost in the ether, ensuring a full snapshot of every customer. Plus, with Microsoft’s trusted enterprise-level security and manageability, they can be sure customer information, and their trust, is secure.
Learn more about how you can liberate your data and stay competitive. Download our latest white paper, Delivering Differentiated Policyholder Experiences.
