A property and casualty insurer used this technique to develop an effective pricing strategy.
As the economic slowdown continues, insurance companies continue to worry about how to retain their most profitable customers.
Customers who continue their business relationships with the insurer are more than revenue generators. Satisfied customers are, indeed, a company's brand ambassadors. They spread the word about positive experiences they have had with their insurer to others they come in contact with. This word-of-mouth marketing is valuable for a company, especially in these tough times.
As companies evaluate how to retain their best customers, many consider implementing price optimization. For most customers, price connotes real value. Understanding price and what it means to a customer is an important aspect of understanding the person's lifetime value to a company. In fact, according to one estimate, 30 percent of insurance buyers are price-motivated or ‘serial shoppers’, without brand loyalty.
Price optimization is a strategy with which a company, after getting to know how sensitive its existing clients are to changes in product prices, will arrive at how much business it can obtain within defined profitability levels. Optimal pricing is necessary if a company wants to link its business volume with profits and more importantly, if it wants to increase profits by keeping the same levels of customer retention.
A U.K.-based insurer, for example, opted to implement price optimization. The insurer, with an estimated USD 637 billion in assets, implemented pricing optimization through advanced analytics in its underwriting division. The analytics helped the insurance giant identify customer segments with a higher probability of cancellation. The company then prepared a plan of action to retain these clients.
Price optimization has become increasingly important because sales of personal lines of business have become very competitive. Many insurers are also looking at launching new products, some of which are in niche customer segments. In this context, getting the price right is all the more imperative or a company may lose valuable clientele to its competitors.
Price optimization is an important component of overall price management which is crucial to profitability. In fact, it is the next frontier of insurance policy management.
Understanding pricing at a finite level is a prerequisite to understanding the sensitivity of price changes. It is one of many variables that can be used to estimate the elasticity of demand for each life insurance policyholder's risk profile. When all these variables are analyzed, it is possible to identify the cluster of policyholders that are more price-elastic.
So what are the components of an effective price optimization model require?
WNS used price optimization analytics to help a property and casualty insurer. The company, with both personal and commercial lines of business, had based its policy for premiums only on low-cost models. When competition intensified, the company was keen to develop demand models as an input to an optimization engine to help it develop a better pricing strategy.
WNS worked closely with the client's actuaries and underwriters to understand the insurer's business needs. The data architecture was comprehensively analyzed in order to extract relevant data from multiple legacy systems. WNS then developed demand models using inputs such as product information, distribution channel, customer demographics, claim information, competitor prices, tenure with the company and seasonal factors. It then calculated competitor prices using a third-party tool to use as an input to the demand models.
WNS created demand models for several states in U.S. in a very short period of time in a cost-effective manner to enable the running of optimized pricing pilots. By deploying these models, the client was able to improve its retention levels by maintaining the same level of profitability using an optimized pricing strategy.
Price optimization is not a one-time event but rather a key part of a revenue strategy. Ideally, this should be a continuous process, allowing a company to refine its assumptions as new data becomes available.
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