October 1, 2014
Is Price Optimization Really an Evil Idea?
No, because customers benefit, too. Most insurers should -- and can -- get to this next level of sophistication on pricing.
There seems to be a lot of misperception about what price optimization really is, largely driven by publicized assumptions that it will only serve the best interests of the company and hurt the consumer.
Basically, price optimization boils down to applying analytics to available information to develop more quantitative and targeted pricing policies and processes. Price optimization is currently used extensively in many industries. The benefits and rewards to both the companies and the customers are plenty, with the customer rewards being highly visible.
Through the use of price optimization, retailers are able to present highly personalized and appealing offers to their customers based on past shopping and buying patterns coupled with predictions of customer wants and needs. Retailers are able to keep their best customers informed of sales and special offers that are of real value to them. The travel industry uses price optimization to manage profitability and, equipped with insights that give them the ability to fine-tune the metrics, are able to offer very attractive options to travelers. Capacity that would otherwise have gone unused attracts happy customers and often brings them back.
For the insurance industry, it is important to understand that price optimization does not replace risk-based pricing; rather, optimization is the next level of sophistication for risk-based pricing. With price optimization, insurers are able to explore product options and then find an optimal balance point among all options and constraints within complicated rating orders and large sets of data. This makes it possible to construct and present more appealing, more targeted product and service offerings. Personalized offerings can be shaped to meet personalized needs. The laws of large numbers can be optimized for the individual situation.
Today, price optimization is being used most often by insurers in personal lines — in many cases, those that are trying to innovate and capitalize on the next wave of analytics. The goal is to improve the bottom line and increase market share by using newly available types of analytics, models, tools and methods. These insurers don’t see price optimization as an independent exercise; they view it as a key part of the business’s journey to the next level of maturity. Recognizing that rate changes and the resulting customer reactions have an immediate and very significant tie to new business and renewals, and understanding that informed consumers expect offers that meet their personalized requirements, insurers see optimization as a journey that is essential for profitable growth in personal lines.
It is only a matter of time before the principles involved are applied to commercial risk pricing, especially for smaller and middle markets. As the comfort level increases and experience with the insights and tools matures, price optimization will likely become a significant aspect of the collaboration and negotiation process for mid-market and even large, complex cases.
The business benefits of price optimization are undeniable. Improved insights give insurers greater ability to achieve specific financial objectives for growth and profit. Fortified with intelligence, including a better understanding of customer demand and buying behaviors by segment, insurers can make business decisions and tradeoffs based on agreed-upon metrics rather than emotion and historical understandings that sometimes morph over the years.
While the benefits are clear, the reality is that price optimization is a complex endeavor. It involves deep analytics, advanced business intelligence and ready access to complete and accurate data. Many companies are spending lots of time and resources building sophisticated models of loss cost, expenses and customer demand, incorporating competitive position and market data. Price optimization brings them all together, aligning to specific business goals and the regulatory framework, enabling companies to clearly understand the trade-offs between various pricing strategies.
The extent of the use of price optimization in the insurance industry is small in terms of the number of companies that have implemented optimization or are conducting pilots. It is, however, important to note that price optimization is being adopted by the largest insurance companies — those that have the most market share — so the portion of the industry that being affected is significant. It won’t be long before a very large percentage of the premiums being written will be based on rates developed by using advanced analytics capabilities that involve price optimization.
In many insurance companies, there are both real and perceived hurdles that impede progress in price optimization. Project capacity is limited, and price optimization does not always make the list of top-priority efforts. For some insurers, there is an inherent cultural resistance to change, particularly when today’s models have been delivering growth. Price optimization is complex; it requires special skills — deep experience in predictive modeling and advanced analytics. Price optimization involves a transformation of the entire pricing process.
But the insurers that are embracing and implementing price optimization are finding ways to overcome these challenges. Obviously, most national insurers have the volume of data that is necessary to get pricing optimization right, but they can also be burdened with an overwhelming amount of data that originates from multiple sources and isn’t always clean and consistent. In contrast, it is not unusual for regional insurers to think they don’t have enough data. The reality is that most insurers do have more than enough data to build and use customer demand models.
Price optimization will work for more insurers than one might expect. Now is the time to lay the ground work for competing effectively in the long run.