Data-Driven Policy Renewal Programs

In every industry, the cost of customer acquisition is high and insurance is no different, if not even higher. Agency owners understand that decreases in customer retention need to be overcome by adding new customers. Building a strategy to increase customer retention while continuing to drive new business customer acquisition enables an agency to achieve an increase in organic growth and profitability. An increase in retention helps build more customer loyalty and provides an opportunity to expand your business. This growth can be driven more easily and consistently if the retention numbers remain steadily higher than industry averages.

Agencies run regular programs/campaigns to achieve their retention objectives, which can be made smarter and more effective by enabling them with data-driven inputs. The simplest method is to run reports of when policies are due for renewal and when to contact those clients. With the advent of advanced predictive models, agencies can now predict if a client is likely to renew or not, and how to support or alter that outcome.

In this article, we will discuss using basic data, along with some descriptive analytics to create four additional strategies for identifying targeted clients to contact for more educated and thoughtful renewal discussions.

Strategy A: Simple Client Expiration Reports

This strategy is employed by most agencies using reports from their Agency Management Systems, tabulating the active policies due for renewal in the current book of business with the following information at a minimum:

  1. Client Name
  2. Policy Number
  3. Current Premium
  4. Renewal Date
  5. Total Count of Policies
  6. Total Premium Across All Policies
  7. Renewal Premium for Policy (if available)

These reports are typically run by department or line of business with policies due for renewal within a specified period of time. They can be created at regular intervals for targeted communications. Within these reports, records can be sorted using specific parameters like “Total premium across all policies” and “Total counts of policies” to enable prioritization for reaching out. The renewal time window for communications can be specific to a line of business, e.g., commercial lines renewal window could be three months, while personal lines could be one. Using data will help you target the right clients with the right message at the right point in time.

Strategy B: Client Segments and Special Treatment

Basic analytics can be used for a book of business to identify average premium per policy for personal and commercial lines clients. You can then use these average values as a threshold to segment clients into VIP and regular clients. This process provides four client segments:

  • Personal – VIP
  • Personal – Regular
  • Commercial – VIP
  • Commercial – Regular

An engagement strategy for VIP clients should be more intensive and proactive, while the engagement strategy for regular customers could be less intensive. A metric other than average premium such as segmenting your top 20% or 30% of your clients could be used to make the VIP club more exclusive. This client segment, along with the report-based approach mentioned in Strategy A, can be used in combination as a starting point to begin to use your client data to help increase retention and ultimately help your agency become more profitable and grow.

Strategy C: Policy Attributes

In addition to the data discussed in Strategies A and B, policy attributes also provide important levers for discussing policy renewals with clients. For example, suggesting additional coverages, varying deductibles to fit premium to their budget, etc. are simple but valuable parameters to influence retention. Alerting your staff to this information while they are speaking to the client will make the conversation more meaningful. In fact, a consistent agency-wide strategy can be executed with clear tactical indicators by using policy attributes from the book of business.

If this is done along with data parameters described for Strategies A and B, the retention campaigns will become highly personalized and provide an improved experience for the client.

Strategy D: Interaction and Predisposition Indicators

Along with the book of business attributes, if one has engagement variables such as the count and outcome of interactions or the status of the last interaction, it can act as an important lever to create multiple targeted communications to a single client.

For example, a client near the top of the list for retention had reached out to check if a specific coverage is included in his policy, and the result was not affirmative. That client now has a higher chance of having a less positive outlook towards renewing that policy. A tactical recommendation would be to reach out to the client well ahead of the renewal date with a positive message. And, contrary to this example, if the last interaction was fruitful, then the retention call can be enhanced with a cross-sell recommendation.

 

Conclusion

With the advent of analytics, retention strategies can be built around predictive models. Just as a businessperson sees patterns in operations and results and uses those to maximize the success of his endeavors, analytics uses machine learning to detect micro-patterns to predict behavior. This prediction can be used for intervention and correction or positive confirmation.

A key strategy to consider in the retention program is quality. There is business that is not worth renewing for various reasons like insufficient revenue, risk profile mismatch, and loss experience attributes. A filter for these and similar attributes can be built into Strategies B, C, and D to improve the quality of business by not pursuing renewals with negative attributes.

Strategy A provides the foundation by helping your agency identity whom to contact. Progressively, Strategies B, C, and D help determine the when and how of retention interactions. In all four, data can be used to make the retention and renewal activities in an agency sharper at multiple levels. Even the most basic strategy can be augmented with data analytics in a short period of time, and your agency can then look forward to graduating to the next level of advanced data-driven strategy.

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