Using Data to Predict Insurance Sales Trends
History tells us the insurance market fluctuates between hard and soft market cycles, yet that alone may not reflect insurance sales trends.
Reliable data can help explain insurance sales trends and predict with reasonable accuracy what may happen in the future. How does it work? Data informs insurance sales agents and brokers by analyzing historical data, recognizing patterns, and highlighting potential sales opportunities.
We’ll explore the role of data in insurance sales and how it can be used to predict sales trends. We’ll also provide an overview of techniques for analyzing data, highlight some case studies, and review some of the challenges in using data to predict sales trends.
Shortcuts:
The Connection Between Data and the Hard and Soft Markets
Key Data Sources for Predicting Insurance Sales Trends
Techniques for Analyzing Data to Predict Trends
Case Studies: How Data Predicts Insurance Sales Trends
Challenges in Using Data for Predicting Sales Trends
Data Paves the Way for Your Success
The Connection Between Data and the Hard and Soft Markets
It’s possible to roughly predict the potential for insurance sales by studying the cycle of hard and soft markets.
Before technology became so advanced, predictable cycles were all the information brokers and agents had to rely on when devising marketing strategies. The best they could do was to endure the tough times and focus heavily on sales during better times.
Thanks to advanced technology, we have access to lots of accurate data to help brokers and agents understand where the market has been and where it’s going.
Understanding the Hard and Soft Insurance Markets
In the soft market, rates are low, and it’s easier to get insurance. As the soft market cycles into a hard market, premiums increase and insurance companies tighten their guidelines.
The hard market is never good news for insureds. Your prospects and customers appreciate the soft market because it gives them an opportunity for attractive rates. Everyone likes to save money, making it easier to sell in a soft market.
The soft market also gives customers more options. Underwriting isn’t as stringent in the soft market, meaning customers may qualify for better insurance plans than were available to them in the hard market.
Overall, people are more receptive to insurance during the soft market, making it far easier to close sales.
On the other hand, the hard market presents a very challenging period for brokers and agents. Customers complain about rates. They’re annoyed and incensed if underwriting won’t qualify them for the best plans or the most discounts.
The best news about the hard market is that it will eventually complete its cycle, and customers will once again be receptive to sales agents.
Data-Driven Decision-Making In Insurance Sales
Data-driven decision-making in insurance sales is a methodical way for brokers and agents to make decisions to find target markets, assess risks, and guide customers toward the most appropriate insurance products.
Agents and brokers may rely on a variety of data types, including:
- Historical sales data
- Customer demographics
- A review of the soft and hard markets
- Health data
- Average policies per client
- Sales quotes versus closed sales
- Top sales agents
Accurate sales forecasts are important for brokers and agents because predictions can help them prepare for heavy times of quoting and closing.
Before the busy soft market begins, you may need to evaluate the effectiveness of your software programs, such as your CRM, email platform, and screen-sharing tool. It’s also an excellent time to stock up on brochures and office supplies and employ extra staff to ensure you can close every possible sale.
Key Data Sources for Predicting Insurance Sales Trends
Think about data sources such as internal data, external data, and third-party data. Each classification is meaningful in predicting insurance sales trends. Let’s take a deeper look at each of them.
Internal Data
Internal data refers to CRM data, sales data, historical data, and performance data.
Your CRM holds a wealth of data you can use in your marketing efforts.
Here are some types of data you can store in your CRM;
- Contact information (name, address, phone number, email address, best times to contact them, and their preferred method of contact)
- Demographics (education, gender, work history, marital status, whether they have children)
- Lead management (past and future appointments, notes on interactions, tasks, reminders)
- Policies and effective dates
- Financial (premiums, commissions, etc.)
- Analytics (sales targets, sales growth, sales funnel, conversions)
External Data
External data will help you make informed decisions about which customers to contact and the best times to contact them.
Here is a list of external data that will lend could help shape your marketing activities:
- Weather patterns
- Economic indicators
- Regulatory changes
- Infectious disease outbreaks
This type of data will give you insight into market demand for a particular product, customer preferences, and competitor strategies, enabling you to tailor your products and marketing efforts more precisely.
Third-Party Data
Third-party data brings the full scope of insurance marketing into view. Here are some types of third-party data to look for:
- Competitor data
- Industry reports
- Surveys
- Market research
Collectively, internal, external, and third-party data will bring forth trends and data over time, allowing you to better understand buying triggers and forecast future demand. By sharing data with other brokers and agents, they can replicate your success, adding to the success of your agency.
Techniques for Analyzing Data to Predict Trends
There are four techniques for analyzing data to predict insurance sales trends.
1. Time Series Analysis
Much as it sounds, a time series analysis requires collecting data points over time and then analyzing and interpreting them. It is a way of predicting future trends based on past performance.
Your CRM should be of great help in doing this type of analysis.
2. Predictive Modeling
Predictive modeling is a technique that involves using historical data to create models that forecast future behavior or outcomes.
This technique involves data mining, applying machine learning algorithms, and other statistical methods to identify trends and patterns within a data set.
You may need the help of an advanced marketer to help with this technique.
3. Customer Segmentation
Customer segmentation is a process of dividing groups of customers or prospects into groups that have similar characteristics. By understanding the unique needs of each group, you can:
- Tailor your messaging more precisely
- Prioritize your efforts
- Focus on high-value clients
- Optimize sales strategies
Customer segmentation leads to higher conversion rates and retention, and greater customer satisfaction.
You may be able to implement this technique using your CRM.
4. Correlation Analysis
Correlation analysis is a method you can use to measure the strength and direction of two or more variables. (e.g., considering how the real estate market affects Gen-Z homebuyers, which impacts how many people of that generation will need homeowners insurance).
This technique will require some extra research on your part, but it will help you make decisions according to how people live in today’s world. Universities are good places to start for generational data.
What you can glean from techniques for analyzing data is that while insurance may not change very much, socioeconomic factors are constantly evolving. Data will point the way as to how to sell insurance at any given time.
Case Studies: How Data Predicts Insurance Sales Trends
Moody’s says the risk landscape and the state of the insurance industry are “not business as usual.” The data they collect will likely impact the products they offer. In turn, their decisions will impact your customer base and potential sales.
Some of the current trends that insurance companies are researching are:
- Cyberattacks
- Climate change
- Net Zero
- Unprecedented Economic Shocks
- Catastrophes
- Global supply chain issues
- Geopolitical issues (wars, terrorists, etc.)
- Crumbling infrastructures
- Long-tail claims (settling claims after the policy expired)
- Longevity and mortality
Let’s do a deeper dive into the first two issues on the list – cyberattacks and climate change.
Cyberattacks
Cybercrime is now ubiquitous. It’s a daily occurrence, and attacks occur around the clock. What’s more, people, businesses, and governments rely heavily on the internet every day.
People, businesses, and governments rely on insurance companies to protect them against the most invasive cyberattacks.
Data informs insurance companies on how to manage cyber risks better and what they can do to minimize the potential for cyber risk claims.
Climate Change
One hundred ninety-five parties signed onto the Paris Agreement, which is a binding international treaty on climate change. All member countries agreed to keep warming temperatures at 1.5 degrees Celsius above pre-industrial temperatures.
Climate change could increase risks for car insurance, homeowners insurance, commercial insurance, and life insurance.
The faster climate change accelerates, the less time insurance companies have to mitigate risks and recalculate actuarial data.
You can go down the list and draw a direct correlation to every bullet we presented and its impact on the insurance industry. Issues that affect the insurance industry affect your sales goals.
While you don’t need to conduct your own research in these areas, some of your clients may be intrigued about how much you know about the backend of the insurance industry and how current and future trends could impact their policies. For that reason, it’s a great idea to keep up with the news in the insurance industry.
Challenges in Using Data for Predicting Sales Trends
While data science is a science, it’s not an exact science. With that in mind, we’ve put together a list of challenges related to using data for predicting sales trends.
- It’s time-consuming – Insurance agents need to find the balance between spending their time on data collection and analyzing tasks and spending time on sales activities.
- It can be costly – Hiring a data analyst can be costly. You may not have the skills and resources to do it yourself.
- The insurance industry is continually evolving – Just when you think you have a few things figured out, the landscape changes again, and you have to go back to the drawing board.
- Data privacy concerns – Some data may not be readily accessible due to data privacy laws.
- Lack of availability of skilled data analysts – There is a shortage of skilled data analysts, and the demand for them is growing.
- Getting good quality data – While we have many tools for collecting data, there are challenges with receiving inaccurate data, duplicate data, inconsistent data, fragmented data, and outdated data.
Nonetheless, there are ways to overcome these challenges, such as investing in data training, implementing machine learning, and using AI to clean up data.
Using data to predict insurance sales trends may feel like an insurmountable venture, yet you don’t have to do it alone.
Insurance companies may be willing to partner with you in your efforts to find timely, cost-effective ways of gleaning information from data to help you increase your sales.
Data Paves the Way for Your Success
Today, you no longer have to rely so heavily on following the hard and soft markets in insurance sales. You have access to meaningful data that you can obtain quickly.
Your CRM is a good starting point for making sense of internal data. External and third-party data combined with internal data to give you the scope of the landscape before you create an overall sales plan.
We’ve shown you different techniques for utilizing data – some of which you can do on your own, and others will require the help of a quality data analyst.
Challenges will undoubtedly come your way, yet we’ve given you some ideas on how to overcome them.
While collecting and analyzing data is complicated, technology enables you to perform these tasks with little effort.
As an insurance agent or broker, you don’t need a degree in data analysis, but it is a field you should be familiar with. As the insurance industry continues to evolve, predictive data analytics will enable you to understand the landscape as it changes, perhaps even in real time.
Having the ability to predict policyholder buying trends may make the difference between gaining a competitive edge or giving it away to another agent or broker.