Strategies for success: How small and medium-size insurers can compete more effectively in a soft market

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By Ravi Kumar, Matt Chamberlain | 27 September 2011

Insurers face a soft market that creates pricing challenges as companies are compelled to reduce prices in response to an overabundance of insurance capacity. This soft market will persist until either a catastrophe or gradual deterioration of underwriting standards remove enough capital from the market that insurers regain their pricing power. How long that will take is unknown, but the possibility exists that the soft market present today will persist for many years to come. The unforgiving competitive nature of the soft market requires all insurers to think strategically, but this applies particularly to small and medium sized insurers, who have less capital to absorb pricing errors. Our examples will focus on Homeowners, but the techniques presented apply equally as to other lines of business.

In recent years, larger carriers have rolled out sophisticated rating plans that make use of their vast data repositories to create powerful predictive models. This poses a threat not just to the profitability, but potentially to the survival of smaller players, who may believe that they lack the resources with which to construct cutting-edge pricing plans. However, the size that gives the larger insurers their power also can make them unwieldy. Smaller carriers can profit in niche markets that larger companies might find too small or too specialized to be worth pursuing. By focusing on customers in niches under-served by the largest carriers, smaller companies can thrive even during a soft market. But they need the right tools.

In a soft market, profitability cannot be achieved simply by raising rates; understanding and even anticipating customer needs becomes essential.

The right tools

Customer segmentation enables insurers to respond to the unique needs and behavior of different customer segments. Customers are typically first divided into groups by geography, gender, age, and other characteristics. The next step is to group customers based on customer behavior. The customer's propensity to retain and propensity to convert are two behaviors that insurers most likely would like to track. Understanding who the customers are and how they behave provides insight into the customers' life time value to the insurance company. This understanding is very useful in designing products, setting prices, and providing services to build long-term relationships with desired customers.

For example, customers who are older than 60 years are known to have very high retention rates. By definition, these customers are difficult to convert. If the goal is to target this market, it might be easier to target customers at 58 or even at 55, but well before they turn 60. After the customer has been acquired, it is important for the insurance company to design services that fit the needs of customers in this profile. If retention rates are low for Florida retirees except those retirees who are with one particular agent, a review of the successful agent's practices could reveal that the agent is calling customers 10 days before renewal to ask them to pay for their renewal over the phone via credit card. This practice could be extended to all Florida retirees renewing in all agencies to improve retention of this market segment. The goal is to transform the customer from a transaction buyer (who looks at just the price) to a relationship buyer who values the services provided by the insurance organization.

How can a small insurer take advantage of predictive modeling?

Predictive modeling is the use of data mining techniques to better understand strengths and weaknesses in a company's book of business and to predict the future loss experience of a given risk based on its relevant characteristics. It can be used to answer crucial questions, such as:

  • Which combinations of insured characteristics have been producing the best results for our business?
  • Which agencies have been the most profitable for us? What are their particular strengths?
  • Are there areas within territories that have been producing better results for us?

Predictive modeling can help a small insurer decide which niches within the larger market provide opportunities for optimizing its results.

Once a company has a market to target, the next step is to understand the competition. Price comparison analysis helps organizations understand whether their own products are priced low or high relative to those of competitors. Personal lines insurers sometimes neglect this vital area because of the difficulty of compiling and keeping up-to-date competitor rating engines. However, discovering these segments will be worth the investment, whether you are currently leaving money on the table by pricing below the competition or driving away business by pricing above the competition. Competitive analysis is not only a crucial step on its own, but will also give enhanced returns when combined with other tools.

These first steps help smaller insurers discover niches in which they can compete effectively. The next step is to design the right products for those niches. Effective product design aligns the coverage provided with the customer’s exposure to risk and prices the product accordingly. For example, homeowners are accustomed to receiving broad coverage on their houses, not just for named perils, but for all perils not specifically excluded. In the current economy they may want to rent out a house and maintain that broad coverage but with much lower coverage on the owners’ contents such as appliances, and not the renters’ property. Instead of offering a Dwelling Fire policy, a company aware of this market could offer an HO-3 policy with endorsements to make the product more suited for the target demographic.

Another potential opportunity involves selling a policy that works in conjunction with an insured’s existing policy. For example, the insured might have an earthquake policy from the California Earthquake Authority with a 15% deductible and may want a deductible buy-down to 5%, or have a wind-only policy from a residual market plan and need a wind-excluded policy. The common theme is that the smaller insurer must find customers whose needs aren't being met and tailor its products for them.

A crucial aspect of product design is risk segmentation—aligning the premium charged with the varying profiles of different risks. For example, in Homeowners insurance it is convenient for the customer to purchase an insurance product that packages together the desired coverage for a single indivisible premium, but it should not be priced as though fire, water, wind, theft, and liability risk all move in sync. A burglar alarm does not reduce risk from fire losses, so applying a constant burglar alarm credit to the entire premium means that locations in areas prone to brush fires might be getting too large a discount. By pricing each peril separately, the premium charged can be most closely aligned with underlying risk without requiring the insured to buy different policies.

Another way to better segment risk is through territory design. Territories are usually relatively broad, following generally accepted geographical boundaries. However, this can result in inaccurate pricing. For example, in property insurance, coastal regions are often divided into a higher-priced coastal segment and a lower-priced inland segment. The price changes as one crosses the invisible line between these territories. However, the risk doesn’t change suddenly; it changes continuously as one gets farther from the coast. By increasing the number of territories, an insurer can achieve smaller price differentials between adjacent territories, resulting in more accurate pricing. Larger insurers may be constrained by the desire to avoid dislocating existing books of business, but territory redesign enables an insurer who refines its rating system to skim the cream. When the insurer’s own data is insufficient, it can be supplemented with other data, such as catastrophe model output for catastrophe risks or demographic data for non-catastrophe risk.

Another way in which smaller insurers can compete is by increasing the homogeneity of their risks through underwriting rules. The more homogenous the risk, the less data is needed to be able to price it properly. To do this, it is essential to understand unusual exposures, such as insureds with potentially dangerous animals or houses constructed with unusual materials or techniques. Although all of these risks may be insurable, they are not similar. Grouping them together makes losses less predictable.

All of these techniques will help the smaller insurer gain an edge. Further gains are possible by leveraging agent knowledge. Agency segmentation helps the smaller insurer identify and focus on the agents that will bring in the most coveted business. Predictive modeling can measure agents against the performance expected for the sort of risks they write and the insurer can structure commissions to reward agents for writing the more profitable risks. Smaller insurers can also target agency marketing efforts to specific geographical and demographic segments based on the knowledge gained from other analyses.

Traditional rating procedures estimate the expected losses from a group of potential customers with specific characteristics. However, the profitability of different lines depends as much on customer demand as it does on their expected losses. It is common knowledge that renewal business tends to continue renewing, as well as have a lower loss ratio. Demand modeling analyzes the probability that a quote will convert into a policy, or that an existing customer will renew. In its more sophisticated forms, it estimates the expected policy lifetime, which enables the insurer to amortize the cost of customer acquisition, instead of loading it entirely into the first term. By understanding which insureds will convert at a higher rate and renew for longer, the insurer can offer these customers a lower price and win their business away from less sophisticated competitors. The ultimate goal should be price optimization – combining a sophisticated pricing model and demand model to yield the maximum profit subject to management specified constraints.

Conclusion: Being small can be a competitive advantage

Traditional pricing methodologies are not sufficient to stay competitive in a soft market, especially for a smaller insurer whose reservoir of experience may not be deep enough to provide the strategic insights that matter.

A holistic pricing strategy, in contrast, takes into account customers, competitors, and the carrier’s strategic goals. By discovering which segments competitors are pricing inefficiently, or which variables they are failing to consider, insurers can identify high-quality, underserved customers, and take steps to bring them into their portfolio.

Smaller carriers need to find the niche markets where they add the most value. Their small size helps them be nimble in the face of changing market conditions. Using just a few of the analytical approaches described above can give them an edge in finding the right niche and pricing products appropriately. Combining these approaches can enable a smaller company to thrive in a soft market.