Big data can help insurance agents better understand and predict their clients’ behavior. Here’s how.
Emerging technologies such as artificial intelligence (AI) are rapidly transforming the insurance space. Whether carriers are using these capabilities to inform big-picture decision-making or individual agencies are leveraging predictive analytics to reach out to the most promising prospects, big data’s effects are being felt at multiple levels. In fact, nearly 70% of insurers rely on predictive models to assess risk in the underwriting process, according to a recent Willis Towers Watson survey.
While many insurance agents may be skeptical that big data is anything other than a buzzword, it can have a valuable role in the day-to-day work of selling policies. An agency management system (AMS), for example, often features strong data analysis capabilities. For agents on the ground, these tools can help them employ big data in four important ways.
1. Lead Tracking
Agents spend a lot of time and effort trying to convert leads into revenue-generating clients. Pulling and analyzing data on every lead contacted, the number of contacts made, and whether the lead ultimately bought a policy can help uncover what lead sources typically generate the most impressive ROI.
Reviewing this information also highlights which types of leads an agency should go after for specific product offerings. Instead of chasing after unproductive leads, agents can pinpoint those that have generated the most business in the past; that way, they can generate similar business in the future.
2. Agent Performance
Using data analytics, agency owners can track each agent’s performance by sales, client retention, or whatever benchmark they choose. Insights into individual performance enables owners to help agents who may be struggling, as well as recognize those who are performing well. Additionally, sophisticated analytical tools can give them fresh sales and marketing techniques so they can target and convert those prospects that have been the most receptive.
3. Cross-Selling Opportunities
AMS programs often store valuable data that can help agents match clients with the most effective policies for their changing needs. These cross-selling opportunities tend to produce more revenue because long-term clients are more likely to buy from an agent they know and trust.
A study by Marketing Metrics found that the odds of selling to an existing client rise to 60-70% compared with a new prospect. Combining descriptive analysis (the customer’s background information) with predictive analysis (what policies they would be receptive to hearing about), agents will be better equipped to respond to clients’ unique situations.
4. Product Diversification
Big data can help agents determine what product markets they should pursue to expand their book of business. Are prospects seeking insurance solutions the agency doesn’t currently offer? Have carriers launched innovative products that would appeal to the agency’s client base? Guided by advanced analytical tools, agents can make informed decisions about adding new items to their product roster.
Incorporating big data analytics into everyday operations results in better overall performance for an agency and its agents. To better understand client behavior and improve their offerings, agents should follow the lead of large insurers and dive into big data — sooner rather than later.