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Systemic Shift to Direct Relationships in U.S. Auto Insurance: Implications for Insurers
By Frank Cacchione Arnab Dey

MARKET FORCES DRIVING THE SHIFT

The basis of this systemic shift has similar attributes to the previous systemic shift in dominance of the Direct Writers over Independent Agency Carriers - advanced pricing/underwriting models, broad focused advertising and branding, low cost distribution and improved customer persistency. Four key market forces have been driving Direct insurers' ascendancy:

Direct customer acquisition through mass advertising campaigns.
Emergence of sophisticated pricing and underwriting models.
Increased consumer acceptance of the direct response model.
Enhanced analytics to improve customer lifetime value.

Direct customer acquisition through mass advertising campaigns

Both direct-to-consumer and agent-centered insurers have been steadily increasing their level of investment in advertising. Allstate, the biggest spender, increased its advertising 33% annually between 2001 and 2003 while GEICO and Progressive grew ad spending by 7 - 8% annually.

The increase in advertising spend has led to more
focused brand building efforts – the whole focus has
shifted to getting more customers into the door and then
pricing them appropriately using more sophisticated
pricing models.

2.Emergence of sophisticated pricing and underwriting models
The key enabler of the shift to mass advertising has been the emergence of sophisticated pricing models. To understand this shift, we must trace the evolution of the auto insurance marketing process from the 1980s through today:

In the 1980’s,an Affinity Group model was
followed, in which policies were marketed and
priced according to common characteristics of
separate affinity groups (e.g. Government
employees, Pensioners). This phase saw insurers
making investments to store data in analyzable
forms.
The 1990’s saw the emergence of the Broad Market
Segmentation Model, in which there were massmailings
of unique offerings to separate, broad
market segments (e.g. Young professionals, Empty
nesters, Farmers). Targeted mailings allowed
insurers to cherry-pick groups that had both

attractive risk profiles and were most likely to
respond.

Today, leading direct-to-consumer insurers like
Progressive are experimenting with more
sophisticated pricing models that allow them to
appropriately price coverage for high risk segments
which were previously ‘off-limits’, primarily due to
lack of underwriting data.

Historically, insurers could not afford to implement aggressive mass marketing campaigns with an undifferentiated call-to-action message due to an adverse selection problem – those consumers, most likely to respond (shoppers) tended to belong to high-risk segments (low credit, adverse driving record, etc.) that the insurer could not price appropriately, leading to too many rejected applications or under-priced policies.
Today, thanks to more sophisticated pricing models, leading insurers can price coverage profitably for almost any consumer that comes through the virtual door in a matter of minutes. These insurers can therefore jettison the smaller-scale, targeted mailings in favor of more cost-effective, larger scale ad campaigns.

What allows leading insurers to develop pricing models for high risk segments other insurers pass on? There are three:

These insurers have focused on collecting relevant data in analyzable form - both internal (e.g. applications, claim history) and external (e.g. credit scores, demographics, vehicle records).
They have built highly skilled analytical teams to build robust models on these data sets.
Most importantly, they have followed the test and learn approach by consciously writing 'high risk policies' systematically on a test basis to gain more understanding around how to price them appropriately.

Case Study: Finding Riches in a Mine of Credit Data

Jim Rhoades, a securities broker from Erie, Colo., had recently slashed his annual auto premiums by $400 and his homeowner's premium by the same amount by switching from State Farm to AllState. In addition to considering Rhoades's ding-free record, Allstate took into account seemingly unrelated information from his credit report, such as whether he pays bills on time (he does) and whether there'd ever been a claim on his home (there hadn't).

Allstate uses credit data to enhance its pricing structure, allowing it to tackle high risk segments as well as offer more competitive prices to low-risk customers like Mr. Rhoades. Since 1999, when Allstate began instituting credit-derived premiums, its revenue has grown 26 percent to $33.9 billion and its stock is up a whopping 187 percent. (Overall, insurance stocks are up just 31 percent.). According to analysts, AllState has “perfected the science” and is now able to select the 10 most predictive variables for use in its pricing models from 300+ variables (e.g. timeliness of payments and ratio of account balances to credit limits). "Thanks, in part, to its risk management strategy," says Cliff Gallant, an analyst with equity research firm Keefe Bruyette & Woods, "Allstate is enjoying a period of unprecedented profitability."

Source: Matthew Maier, Business 2.0, September 21, 2005

Experienced underwriters are increasingly leveraging analytics to capture the most profitable segments while avoiding subsidies to low-profit segments. Customers are segmented based on their risk and projected cash flows, which allows the insurer to price each policy profitably.

Last but not the least, underwriting analytics can also help an insurer adjust quickly to changing market conditions, for example, adjusting the terms and conditions to ensure that price setting is within acceptable bands in a hardening market.

3. Increased consumer acceptance of the direct response model

Consumer acceptance of the direct response model and call-to-action advertising has fueled the entry of multiple carriers into the fast-growing segment. The industry is currently dominated by captive agency carriers (who replaced the independent agency system) and this new direct distribution model driven by lower costs and extremely sophisticated pricing/underwriting models. Customer acceptance of the direct response model is evidenced by the increased share of direct-response insurers such as GEICO, AIG and Progressive as shown above. This is very analogous to what is witnessed in other sectors where consumers are increasingly looking for low cost options and are willing to deal directly like internet shopping, online brokerage (increased dominance of Fidelity/Vanguard online brokerages versus the traditional brokerage channel).

4. Enhanced analytics to improve customer lifetime value

As discussed above, Direct-to-consumer insurers are making significant investments in data gathering and analysis to enable more sophisticated pricing models. These insurers are also now focusing on other areas of customer lifecycle analytics - from customer acquisition to retention.

Customer Acquisition
Captive agency companies no longer have an acquisition cost advantage over direct-to-consumer marketers. Direct insurers enjoy lower acquisition costs due to a combination of low front-end costs per policy and good customer retention.

Several opportunities exist for insurers to improve their customer acquisition process:

Expand Mailing Universe: Insurers should be exploring opportunities to expand the universe of prospects they mail out to. This can be achieved by using a combination of using select external data (e.g. Polk, TransUnion) and smart response models based on historical data.
Improve Efficiency:
- Direct Channels: Leverage sophisticated techniques to improve campaign efficiency - e.g. ensure pre-screening rules do not "screen" out prospects prematurely, ensure inadequate use of credit bureau data does not lead to low "fill rate" in variables which causes pre-mature screening out of prospects, if pricing is sub optimal in certain risk segments do not include the segment in campaign but focus on that segment purely for test purposes
- Indirect Channels: Improve decisions around sales and agent deployment, and analyze broker incentives and commission schedules - e.g. analyze which agents are most able to capture new business and retain existing business when premiums increase and tailor compensation and marketing support accordingly, provide agents with incentives to capture and retain most profitable customers.
Identifying geographies to improve acquisition coverage: For example, Focus pricing and marketing support on territories where agents have track record of growing profitable business.

b. Customer Retention
Customer retention is a critical tool which insurers leverage extensively to minimize overall customer acquisition costs. Segmentation analysis, logistic and linear regression models can be used to retain and crosssell the most profitable customer segments, e.g. more effectively determine which policy holders should be reunderwritten, redesign renewal questionnaire, preemptively contact by phone or mail high value customers most likely to attrite to avert attrition, assess economics of multi-product discounts for select highprofit- potential customer segments.

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