Predictive analytics has revolutionized how companies mine data and extract actionable insights about customer behavior, but its full potential is only barely being realized in the insurance industry.
But why do insurance businesses need to use predictive analytics, and how can they use it to gain an advantage over the competition?
Firstly: what is predictive analytics?
Simply defined, predictive analytics is a big data discipline that leverages existing information to make predictions about the future.
It incorporates techniques from data mining, artificial intelligence (AI) and machine learning to make educated guesses about how customers may behave under a given set of circumstances.
In the context of insurance, this could help insurance professionals identify important clues to take business action, such as which customers are at risk of canceling their policies - and what action could be taken to prevent them jumping ship?
What insurance businesses need to get started
Implementing predictive analytics is a multi-step process involving data acquisition, analysis, modeling, and deployment. However, the fundamental groundwork is, of course, a steady inflow of data.
Predictive analytics software (whether standalone or as part of a business intelligence platform) runs almost exclusively on the cloud. So, insurance businesses currently working with 30-year old technology, like Excel spreadsheets, must upgrade their systems in order to have easy access to the most up-to-date customer data.
What's to be gained?
The next question is, of course, what are the benefits that insurance professionals can experience by implementing this analytics discipline?
From an insurance professional's perspective, there are 4 main benefits of predictive analytics:
Enhanced upselling and cross-selling abilities
A heightened customer experience
Improved retention rates
Overall improved customer satisfaction
In the current volatile market, conditions that characterize insurance, customer churn and insurance claim losses can easily overwhelm businesses, particularly those on the smaller end of the spectrum. It is therefore important to focus on these benefits when outlining a strategy for predictive analytics adoption.
Less acquisition equals more profit
As mentioned above, predictive analytics can help insurance professionals improve customer retention rates, which can be done by both identifying pain points before they arise and increasing account value by carefully targeted upselling and cross-selling strategies.
Acquiring new customers costs five times as much as retaining existing ones. It therefore makes proper business sense for insurance businesses to do everything in their power to ensure that existing customers both stay with the company and are kept as profitable as possible.
When it comes to cross-selling and upselling opportunities, predictive analytics algorithms can give insurance professionals intelligent clues as to which types of additional policies they have a strong chance of successfully selling to different customers. Such insights can be mined both from a company's own, historic sales data as well as (in some cases) industry trends.
Increased upselling and cross-selling has a positive effect on retention as well as a business's bottom line. The anecdotal experience of many in the industry indicates that customers that have a diverse range of policies with one insurance business are more invested in the relationship - and therefore less likely to leave.
Better customer experiences benefit both business and customer
Offering customers a superior customer experience is key to increasing their satisfaction and retention rates and boosts their lifetime value by 98%.
While over 54% of insurers are using predictive modeling to improve their management of key business concerns such as risk and claim management, predictive analytics' potential for delivering a heightened customer experience has traditionally been neglected by the industry.
It is time for that to change.
Delivering a streamlined technology offering to customers (such as the ability to administer policy details independently online) is a key differentiator between insurance businesses that are sinking or swimming in this era.
A few other ways that predictive analytics can improve the customer experience include:
Flag warning indicators of customer disengagement, allowing businesses to proactively reach out and offer solutions.
Identify usage patterns that allow for deeper segmentation, avoiding irrelevant customer contact and facilitating more targeted, meaningful engagement.
Detect and prevent insurance fraud, passing on savings to customers in the form of reduced premiums.
The bottom line
It is important for insurance professionals to understand that predictive analytics is a necessary and worthwhile investment for their businesses. It would be wise to start developing the strategy for implementing predictive analytics and choose the proper technology that, over time, will put them in the position to take full advantage of the benefits that predictive analytics has to offer, both to them and their customers.
Roi Agababa is Chairman and CEO of Novidea, provider of the first cloud-based platform for real-time business intelligence and agency/brokerage workflow management for the insurance distribution market. Under his leadership, Novidea has revolutionized how insurance professionals interact with data and their customers to enable unprecedented growth and create new opportunities at every touch-point.