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You’re Closer than You Think: New Ways to Get Started with Personalized Insurance
Lucy Kadets
January 18, 2022
- Personalization
While insurance product personalization is not a new concept, many insurance companies still haven’t officially launched their first personalization program.
In many ways this is understandable. First, many insurers may be intimidated by the various technology buzzwords that seem to be required: machine learning, artificial intelligence, big data analytics, and more. Most insurance companies tend to rely on traditional technologies and may not be fast adopters of new innovations. As a result, they may believe they don’t have the right technology in place or there are too many barriers to entry.
They may also think that personalization will cost too much. This mindset is understandable, too, but we highly recommend starting now and gradually applying valuable lessons learned along the way to develop more sophisticated personalization strategies.
“It’s too complicated.” Many insurance companies perceive personalization as being an extremely complex process. This perspective usually comes up when internal teams start thinking about the different types of data they think they need and how challenging it could be to balance the customer’s needs with their own goals related to risk and profitability.
Yet if insurance companies don’t get started with some type of personalization, they may miss out on the opportunity to connect with consumers in the way they prefer. A recent Accenture study showed that 95% of new customers are willing to share their data in exchange for more personalized insurance services. In the future, they may choose to give their business to insurers capable of providing the right personalized experience.
Now, in this article, we take a closer look at ways to use two different types of data: the data you already have and even the data you don’t have (yet).
By utilizing existing data, insurers can leverage valuable insights about their customers and begin to develop a personalized offer. One example: focusing on location can help insurers understand if the customer is located in a rural or urban area and then tailor future messages and offers specific to their needs. Another example would be offering extended coverage or even complimentary insurance offers to drivers that often travel out of their city on the weekends.
The important takeaway is to establish a baseline, which is essentially the idea of starting small and planning to iterate and improve based on lessons learned from these early efforts. The concept is similar to testing a digital marketing strategy to see what offers or campaign elements an audience responds to and evolving from there. Yet with personalization tools that also include machine learning and artificial capabilities, this baseline approach will also help train the model going forward and help insurers use personalization to offer customers more effective add-ons, improve profit margins, or target other specific goals.
See how BavariaDirekt has achieved faster, smarter and more personalized Insurance with Earnix. Read Now.
In many ways this is understandable. First, many insurers may be intimidated by the various technology buzzwords that seem to be required: machine learning, artificial intelligence, big data analytics, and more. Most insurance companies tend to rely on traditional technologies and may not be fast adopters of new innovations. As a result, they may believe they don’t have the right technology in place or there are too many barriers to entry.
They may also think that personalization will cost too much. This mindset is understandable, too, but we highly recommend starting now and gradually applying valuable lessons learned along the way to develop more sophisticated personalization strategies.
“It’s too complicated.” Many insurance companies perceive personalization as being an extremely complex process. This perspective usually comes up when internal teams start thinking about the different types of data they think they need and how challenging it could be to balance the customer’s needs with their own goals related to risk and profitability.
Yet if insurance companies don’t get started with some type of personalization, they may miss out on the opportunity to connect with consumers in the way they prefer. A recent Accenture study showed that 95% of new customers are willing to share their data in exchange for more personalized insurance services. In the future, they may choose to give their business to insurers capable of providing the right personalized experience.
Proven personalization strategies
We are completely sympathetic to these viewpoints and have written other articles to help any insurance company get started with personalization: Lean Personalization.Now, in this article, we take a closer look at ways to use two different types of data: the data you already have and even the data you don’t have (yet).
Use the personalization data you have
Most insurance companies already have access to more than 50 different types of data and data categories. This information may have been collected in the past and is now used in traditional pricing or marketing communication campaigns. For example, this data includes information such as age, home address, vehicle details, and much more.By utilizing existing data, insurers can leverage valuable insights about their customers and begin to develop a personalized offer. One example: focusing on location can help insurers understand if the customer is located in a rural or urban area and then tailor future messages and offers specific to their needs. Another example would be offering extended coverage or even complimentary insurance offers to drivers that often travel out of their city on the weekends.
How to get started with data you don’t have yet
This may sound counterintuitive—how can insurance teams offer insurance personalization with data they don’t actually have. In this case, we recommend starting with basic personalization strategies and programs and A/B testing them on agile channels, such as websites. Then, insurance companies can compare them with flat, non-personalized offers to see the benefits.The important takeaway is to establish a baseline, which is essentially the idea of starting small and planning to iterate and improve based on lessons learned from these early efforts. The concept is similar to testing a digital marketing strategy to see what offers or campaign elements an audience responds to and evolving from there. Yet with personalization tools that also include machine learning and artificial capabilities, this baseline approach will also help train the model going forward and help insurers use personalization to offer customers more effective add-ons, improve profit margins, or target other specific goals.
Start capitalizing on personalized insurance now
Personalization is here, and here to stay. It is understandable that it may be intimidating, especially for smaller, budget-constrained companies, but it is important to get started now. By starting small, thinking creatively, and continuing to apply insights to new strategies, insurers can begin to reap the full benefits of personalization today.See how BavariaDirekt has achieved faster, smarter and more personalized Insurance with Earnix. Read Now.
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