One of the worst mistakes an insurance broker can make is to refuse to adapt and change, especially in a world where companies are constantly trying to find ways to outperform their competitors in order to build their customer bases. However, what can be considered an even more unfortunate loss is failing to utilize data analytics to improve their markets and get more sales. As a result, they face a myriad of problems:
- They waste their time chasing uninterested or indifferent leads, either relying on their own intuition or on outdated client lists, causing immense frustration and making it extremely difficult to convince a lead to even start considering onboarding into a company.
- Their marketing campaigns and offers are much more generalized and bland, attempting to send a one-size-fits-all package into a massive, diverse audience. Clients who see these campaigns will feel disinterested, or even misunderstood, as many of these “generalized campaigns” can find themselves being displayed to wildly different audiences in different regions, where the promises in the campaign don’t even apply to them. Essentially, there is no personalization whatsoever.
- They will be likely to repeat mistakes and bring back failing campaign strategies, as they do not know how well their leads and potential clients are actually being convinced by the data given. Hence, insurance brokers are unable to plan ahead properly and only lead themselves down a rabbit hole of frustration as they struggle to understand what exactly is going wrong with their plans.
- And finally, simply misunderstanding risks due to a lack of data can lead to brokers either overpromising or underdelivering their policies and plans, or they may even send the wrong plan completely, which can not only lead to client dissatisfaction, but potential legal actions when clients are denied their claims for a plan. For instance, a broker could promise a client that a certain plan will allow them to protect themselves against earthquake damage, but it turns out that the plan actually supports flood damage and NOT earthquake damage, then the client, seeing their claim denied, could sue the broker for fraud and breach of contract.
But what happens when data analytics is applied?
Not only will the issues listed above be resolved completely, but utilizing data analytics can also bring about some extra benefits that can serve as a way to even gain an advantage over other companies that are not using it to their fullest potential. For instance:
- Data analysts are able to more easily identify the right customers and the right audiences, and tailor personalized plans and campaigns towards these audiences. This can be done by taking demographics, purchases and behaviors, and life events in order to identify the leads that are likely to consider onboarding into the company, ensuring that the insurance broker can spend their time focusing on the people willing to do business instead of uninterested, misunderstood leads that require significantly more effort.
- They can track sales performances, which allows them to see exactly how their business practices are affecting potential customers, or if their sales pitches and practices are even effective in the first place. Hence, they can more easily see where mistakes are being made, rather than simply relying on gut feeling, allowing them to quickly learn and adapt to changing environments, instead of repeating the same mistakes over and over again.
- Customers are more likely to stay with the company, as the insurance broker can detect when customers are more likely to leave the company, whether through general disinterest or an extremely decreased engagement in the company in general, and offer them potential loyalty offers or even do check-ins with the customer. Hence, customers tend to feel more satisfied with the company and helps to build long-standing relationships with their customer base.
- They can adjust prices and assess risks more comfortably, as knowing exactly what a customer will need can allow for insurance brokers to suggest the exact plan they will need. Rather than having to guess on whether a plan will support flood damage or not, or giving a client an extremely expensive and expansive plan for homeowners insurance, the insurance broker could just suggest that the customer should use a simple renter’s insurance plan, since they don’t actually own a home and live in an apartment, hence not really needing homeowners insurance.
Conclusion
Overall, it is exceptionally clear that data analytics aren’t just a “benefactor” for an insurance broker, but a necessity for a successful business. Not only will it be easier to onboard customers and retain them, but it will also reduce the amount of time and frustration by the broker themselves, ensuring that the brokerage runs smoothly and efficiently. Even then, brokerages who refuse to apply these data analytics are being outcompeted by companies across the globe, who are able to offer services that are more personalized and have customer bases that can trust and rely on them.