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5 Best Tips for Adopting Predictive Analytics in Insurance

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Insurance companies collect large volumes of data on a day-to-day basis. However, what if this data could be restructured and used for accurately predicting the purchase patterns of the business’ most loyal clients? And what if it could offer you insights on how the right offers and discounts at the right time could compel the customers to renew their services?

Predictive analytics in insurance can make it possible. 

Chances are that when you first started your insurance business you may or may not have paid sufficient attention to the powerful tools and technologies that would be available to you in the coming years. In the wake of the global pandemic, several insurance companies were forced to do away with outdated analytics models and turn to a data-driven framework to extract real-time forecasting.

If you are looking for tips on how you should go about incorporating predictive modeling in insurance, here’s what will help:

Conduct Market Research

Market research and viability testing form a crucial component of initiating any digital project. You can start out by listing the pros and cons of the implementation and visualize the effect that predictive analytics in insurance will have on your business. You can even test it to collect sample data and find areas where the application of predictive analysis will bring a significant boost. For example, predictive analytics in insurance underwriting could be a lucrative venture. Conversely, you may identify the problems that you wish to solve and see how you can use predictive analytics to solve them. With such experiments, you can perform a weighted analysis as to whether or not you should proceed with the initiative.

Use it to Power Marketing

Predictive analytics can drive behavior-based marketing to obtain the desired results. It is low-hanging fruit and can feature on top of your predictive analytics in insurance actualization goals. Plus, it is highly effective in delivering instant results.

For instance, you can initiate a re-targeting campaign if you notice that your customer is mildly disengaged or shifting towards a separate vendor. Similarly, it can scan lead profiles to shortlist the ones who are more likely to purchase your products so that you can offer personalization. In both cases, you can forecast possible actions to nudge the customer forward or intervene to prevent churn.

Collect and Scrub the Existing Data

Given that data powers the predictive analytics model, finding the right data can lend you the right direction. Hence, the first line of action involves capturing as much data as possible from all sources, your CRM software, MarTech stack, etc. Apart from the quantity, data quality also plays a vital role in determining the accuracy of predictive modeling in insurance. Thus, the next course should be to cleanse the data and make it readily available to the predictive analytics model.

Establish KPIs and Measure Them

As with any digital solution, you must define the measurable KPIs and set benchmarks to conduct performance analysis at different stages of implementation and execution. Start by capturing data on your current model and the projected uptick after introducing predictive analytics in insurance. Keep the former as a baseline and the latter as the benchmarks. Additionally, these KPIs must also correspond to milestones to mark the key points in the journey towards predictive analytics.

Continue Evaluating the Predictive Model

Once you have set up your predictive modeling framework, you should continue testing and validating its performance throughout its lifecycle. This path of continuous improvement allows you to discover issues and bottlenecks in the system and offers insights into how you can streamline it. You may even feed it anomalous situations to train it to be more prepared for unexpected scenarios.

Are You Ready to Predict the Future?

Predictive analytics in insurance underwriting or risk assessment eliminates all the guesswork that goes into it. Use the above tips to make it an integral part of your insurance business and touch new heights of growth and development!

Image Credit: https://www.damcogroup.com/Insurance/

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Source: https://datafloq.com/read/5-best-tips-adopting-predictive-analytics-insurance/15359

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