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4 minute read
Brendan McLoughlin, President of e123, is participating in an executive education course at the Massachusetts Institute of Technology on Artificial Intelligence (AI) and its implications for business strategy. This is the eleventh in a series of blog posts where he shares the insights he is gaining and how they apply to health insurance distribution.
I spend much of my time talking to health insurance executives who are ultimately responsible for generating revenue. This seems like a straightforward task, simply sell more than the year before and growth follows. But the most strategic and cutting-edge insurance marketers have realized that the kind of revenue they generate – and the amount it costs to generate it – is far more important than how much revenue they generate. And while generating the right kind of revenue is clearly a challenge, today’s advancements in artificial intelligence (AI) can be a major accelerator of this strategic goal.
Revenue Does Not Necessarily Equal Value
Insurance executives should be focused on creating a more valuable organization, but measuring value likely varies from company to company. For instance, a publicly traded firm might look at market capitalization or total shareholder return (TSR) as a measure of value, while privately held companies may focus on EBITDA and multiples. Regardless of the metric used, a Boston Consulting Group study concluded that profitable growth is the most important contributor to insurance company value.
What’s the Strategy, Kenneth?
The big strategic question then is, how can insurance executives on the distribution side of the business best contribute to profitable growth? It is evident that boosting sales volume at all costs does not drive long term value. In fact, the news in recent years is rife with examples of health insurance marketers being punished by investors for delivering growth that is neither profitable nor sustainable.
In a recent industry panel on the future of insurance distribution, a group of health insurance executives concluded that optimizing the ratio of Lifetime Value over Customer Acquisition Costs (LTV/CAC) is a simple, direct way to achieve profitable growth. And this makes great sense – as the LTV of a policy increases, and the cost to acquire that policy decreases, sustainable, profitable growth is achieved. While I agree with this straightforward assessment, I believe that AI can be the missing piece that makes LTV/CAC optimization to achieve the strongest profitable growth possible.
To Maximize or to Optimize, That is the Question
So, if optimizing LTV/CAC is the correct strategic objective, where should you begin? First off, optimizing doesn’t necessarily mean maximizing. Maximizing sales means selling as much as possible, but what constitutes “optimal” LTV/CAC? Those that study recurring consumer business suggest the “Rule of 3+”, that an LTV/CAC of 3 or more is likely profitable in the long run. As an example, if a health insurance policy generates $150 profit per month and has an average persistency of 24 months, the lifetime value of that policy is $3,600. The rule of 3+ suggests that a total customer acquisition cost of $1,200 would result in a LTV/CAC of 3.0 (3,600/1,200), and selling this policy would generate profitable growth.
But what about a higher LTV/CAC? If 3 is good, wouldn’t 6 be better? Not necessarily so. In fact, a LTV/CAC higher than 4 suggests that revenue, and therefore profitable growth, is being left on the table. In our scenario above, if a policy with a lifetime value of $3,600 could be acquired for just $600 (a 6.0 LTV/CAC), it suggests that more could be spent on marketing to sell more policies. Generally, marketing tends to exhibit diminishing returns - that is to say marketing activities become less efficient the more spending increases. So an effective goal would be to spend to the point where total LTV/CAC is between 3.0 and 4.0.
This is a Job for AI!
The kind of complex decision making we are describing is a perfect starting point for exploring potential AI applications. In a previous post, we discussed how Rule #1 for AI implementations is to align AI applications with business objectives. Here, achieving the objective of creating a more valuable organization can be accelerated by thoughtful application of AI tools.
Insurance executives in charge of generating revenue can start by assessing how each player in the distribution value chain can get “smarter” by optimizing LTV/CAC. This entails detailed analysis down to individual product lines, local markets, target customers and downlines. Going through this disciplined process will uncover areas where it makes sense to explore AI. For example:
- Sales Footprint Optimization - Machine learning (ML) algorithms can help identify where to add new agents by region, product line and time of year. Adding agents to areas of the business that are underserved will generally lead to lower CAC since new agents will benefit from low-hanging fruit. Every insurance distribution executive should be able to answer the question, “where should we add the next 1,000 agents?” AI can help answer that question, not just today, but continuously into the future.
- Commission Plan Testing - What is the optimal commission structure to maximize sales at the lowest possible cost? AI can help answer this question, enabling insurance marketers to create commission plans that motivate agents to sell more yet do not over-reward them. The result would be smart commissions - customized by local market, product line and time of year to reduce CAC and increase LTV.
- Marketing Mix Allocation - Once again, ML algorithms can give great insight into which local markets, products, customer segments and times of year to focus marketing efforts. Driving extra marketing into regions where there is potential to grow, and focusing those efforts on the products and customer segments that generate the highest LTV will dramatically increase the effectiveness of any organization's marketing spend.
- Product Bundling - The predictive power of AI can be harnessed to identify which combinations of products will be easier to sell and which can increase likelihood of renewal. Selling the right bundles to the right customer segment can drive both an increase in LTV and a decrease in CAC.
These are just a handful of examples how AI tools can help health insurance distribution organizations drive increased lifetime value, reduce costs to acquire, and ultimately win the battle for local market share. A disciplined process of assessing every stage of the sales process is bound to uncover dozens of such opportunities.
Conclusion
AI has the potential to significantly enhance the value of health insurance organizations by enabling strategic and data-driven decision-making. By focusing on optimizing Lifetime Value divided by Customer Acquisition Costs (LTV/CAC), health insurance executives can leverage AI to improve profitability and sustainable growth. AI applications such as sales footprint optimization, commission plan testing, marketing mix allocation, and product bundling are just a few powerful tools that can address the strategic goals and complexities of health insurance distribution organizations. By aligning AI initiatives with business objectives, health insurance companies can generate sustainable, profitable growth and create a more valuable and competitive organization.
Want to learn more about the future of AI in insurance distribution? Get in touch here. For prior posts in this series, click here or below:
