Surprisingly, 2018 marks the 40th year of selling and marketing in my career. My original on the job training focused on sales methods and techniques. Naturally, I was introduced to the concept of center of influence for life insurance production. So, at the risk of boring you already here is a quick list of those original audiences recommended to me: Family, friends CPA/Lawyer Doctor/Dentist Business owners or leaders Government, school, church officials I observed many of my enthusiastic fellow newbies start building their networks of contacts. I also monitored the level of failure to reach the minimum goals. Most of the new recruits did not succeed. Luckily, I was tasked with a national direct mail effort by the CEO within 30 days of my recruitment. And it opened my eyes to the ability to reach out to highly qualified contacts who I did not know. In my small world, I was always the number 1 salesperson due to this knowledge. I normally state that if I had to depend on my center of influence that I would have failed miserably. I grew up in a rural area with lower socioeconomic conditions and very small population. There were 8 students in my grade throughout my K-12 education. In a job of numbers, that was very "slim pickins” Utilization of data, technology and analytics afforded me an infinite universe of influence by comparison. After careful consideration of my firm’s product, my product knowledge and the economic concerns at that moment in time, I could select the audience most likely to: a. need my services b. to listen to my presentation c. buy the policy and d. refer other buyers. Let me pause for a couple of different examples Challenge Agent, 22 years old. New BBA in Finance. No family/friend network. Life Insurance firm—Good Brand recognition, excellent disability policy and cash value life products. Method Selected the Surgeons in the radius area by the year graduated from Medical School. Used direct mail to present a major business issue for them—disability would cause a disruption in their cash flow which would possibly lead to loan defaults. Outcome The mailer produced a 1 percent response, 10 doctors inquired. All 10 set up appointments and purchased disability policy. Then each doctor offered a referral to their fellow doctors in their practice. In this case that was an average of 5. The agent had an ever- expanding circle of strong income professionals who would need his cash value solutions after the original loans for their medical school and practice were paid off. Challenge Agent, 30 years old. BBA in Marketing, making a career change. Senior market insurance services—Medicare supplement and retirement products Method Targeted the turning 62,63, 64, 65,66, years old audience in the radius area. Used direct mail to invite them to a seminar on Medicare. Medicare choices are extremely confusing to most consumers compared to their use of group health policies for most of their lives. Outcome 20 to 40 attendees per session. 50 percent would request a follow up meeting by submitting a complete financial profile stating their primary concerns. The close rate for Medicare Supplement policies is very high. And the same consumer also seeks the alternatives for their retirement funds—money management, cash value policy or annuity. The agent has an ever- expanding circle of satisfied seniors with retirement funds who eagerly recommend his services. Today, I have many more resources at my disposal to create a. custom audience b. write a personalized message c. conduct an omnichannel campaign d. predict the premium production I recommend that you build your center of influence by using data driven marketing. Direct Mail is the foundation of that effort as legislation has restricted initial contacts by phone or email. In the current digital age some young marketers dismiss the use of direct mail. I smile as I know that effective direct mail drives my digital flow of inquiries and initiates my center of influence. Happy Hunting, as my original sale manager used to say!
Life insurance companies that utilize direct mail marketing have relied on traditional response models to more efficiently target consumers for whom their products are relevant, attractive, appropriate and affordable. The actuaries at life insurance companies have applied sophisticated data science for years to establish risk, premiums, marketing allowances and more. The actuaries at these companies learned a fundamental truth about consumers: people are different demographically, behaviorally and socio-economically. Women generally live longer than men. Smokers represent an extremely high risk. There are high risk activities that some consumers enjoy more than others. In terms of health outcomes heredity has a role, body mass index has a role, and overall lifestyle has a role. Of course, these factors can be unique to each consumer. Paradoxically, the same principles that insurers use to accurately underwrite or deny life insurance policies also apply to the marketing of those policies. Why? Both analytic processes are attempting to predict an outcome; in the case of direct mail marketing, the outcome is a positive response to a marketing campaign and ultimately a sale. Consumers’ responses to marketing are based on different factors. These factors reflect who they are, where they are, how they behave, their attitude toward the world around them, their family situation, and what comprises their overall lifestyles. This is why “one size fits all” or “out of the box” predictive analysis does not work well when it comes to predicting response behavior. There are methods that utilize multiple segment-based models to determine which models—or combination of models—yield the highest response for a given audience. This methodology is alternatively referred to as ensemble modeling, model stacking, or nested modeling. Each of these specific approaches has subtle differences that are meaningful to nerdy data scientists, but their outcomes are the same: build and combine models that yield the greatest lift across diverse, heterogeneous population by determining market segments or geographic segments and refine the modeling parameters within each of those. Today, we’re going to focus on a nested modeling methodology. Nested models are founded on the premise that different consumer characteristics—whether they be geographic, demographic, lifestyle, attitudinal or behavioral—result in different reasons why people respond or do not respond. The problem with this approach is that the more ingredients that get added to the model soup recipe, the more diluted the outcome of that recipe becomes. It’s like the old data science joke analogy about the fallacy of marketing to the average consumer: you’re standing in a bucket of ice water with your head stuck in a hot oven. On average, you feel just fine! Nested models use the same group of predictors, but instead reserve the most differentiating variables to effectively distribute the target audience into more homogeneous population cohorts. Each group is modeled independently, and then reassembled into a single target audience with optimum response propensity. As we said, variables with significant value differences across different groups make good candidates for nesting within the overall ensemble. In the example below, we see that consumers’ state of residence is a meaningful separating factor because of very different population attributes. In the example below, we are focusing on the 64+ population. Nesting is not necessarily limited to a single variable split; indeed, the modeler can further refine and tune the final model through multiple levels of nesting. This has become a far more feasible approach than in the recent past due to fast (usually cloud-based) computing resources and statistical software applications that facilitate builds like this. In our example, we find that we can leverage many of these demographic variables to optimize response for our life insurance direct marketing campaign. How does this approach work in reality? The effectiveness of response models is measured by lift, a statistic that demonstrates how much mailing efficiency the final model yields with actual response rates. In the chart below, “global model” is the application of a single, traditional response model. “Individual state models” refers to models constructed like the diagram above demonstrates, and “best model for each case” selects the higher of the two resulting propensity scores for each prospective consumer. The bottom line is that with a smart, thoughtful nested modeling approach, an insurance company would realize enormous savings in its direct marketing costs. Reach us to see how our approach can benefit your business.