The following is an opinion piece written by Jessica Dolezal, Sr. Data Scientist at Prevedere. The views expressed within the article are not necessarily reflective of those of Insurance Business.
In the modern insurance industry landscape, customer retention is paramount. Innovations in technology have reduced the switching costs, with consumers easily able to obtain quotes online and find other providers. In an industry with few consumer touch points, it can be a challenge to stay on top of customers’ minds.
To do that effectively, industry forecasters have learned to rely on external economic data to appropriately forecast customers’ behaviors and identify opportunities to engage with them. While this approach is proving to be sound, especially as more insurers have gained the ability to process large amounts of data quickly, we are learning that some indicators are not as useful as many forecasters have originally believed. Here are three misconceptions that have been debunked as the industry matures.
Myth #1: Housing starts is the best home insurance indicator
Traditionally, housing starts has been hailed as the preeminent indicator to dictate demand and growth for home insurance. While housing activity, specifically new builds, remains a valid indicator, we discovered that there is a much better indicator available: housing prices.
It turns out that there is more of a causal relationship between housing prices and home insurance packages. The value of a home is a critical factor in the type of policy a customer will buy. In other words, as housing prices rise, customers will spend more to insure those homes.
While home value is the leading indicator, it’s important to note that new builds can still play an important role within a forecast. Some indicators may be stronger than others, but it’s key to look at the bigger picture and identify how some of those indicators may interact. For example, if housing values in an area are rising and the builds in that same area are multiplying, you’d do well to heed those combined indicators.
Myth #2: The national average of miles traveled is a good auto indicator
Vehicle miles traveled is a commonly used indicator to predict claims frequency within auto insurance. Most forecasters look at the national indicator, but the regional indicators are much more useful. You could make the argument that that’s true of any industry, that the more granular the data the better, but that’s especially true of the miles traveled indicator in auto insurance.
The national number is easily obtained. You can Google it right now and find it. It’s a general macroeconomic indicator. Finding the state-level data is difficult, tedious work because there is no central repository of data. You can only collect that data in a conglomeration of independent sources, which is why many forecasters default to the national metric.
But driving habits vary drastically from region to region, as do miles traveled, rendering the correlation between miles traveled and claims inconsistent. Seasonal patterns affect northern states and, therefore, driving habits and miles traveled much more acutely than southern states. Employment trends vary greatly from region to region, as some regions are focused on particular industries. For example, if the domestic oil and gas industry were to take a hit, employment in Houston would be disproportionately affected, which would in turn reduce miles traveled in the region, as there would be fewer workers driving to and from work.
The bottom line is that to really get the benefit of the miles traveled indicator with regard to number of claims, you must drill down on the state level.
Myth #3: Life insurance is dependent on life events
Catering to your customers as they experience important life events is important, especially now that we can better anticipate these events through the power of AI - but a life event is not the best indicator for the life and annuities markets.
Life and annuities data actually looks more like retail data, which means that consumer sentiment may be the best leading indicator. Life events, such as a big purchase or inheritance, are important, but whether or not a customer buys a new policy has more to do with their level of financial confidence. If they’re pinching pennies, they’re not going to purchase the bigger package.
It’s an effective strategy, then, to target customers based on high consumer sentiment, so pay attention to rising housing values, low interest rates that are motivating consumers to purchase at high values, and wage growth. Once you’ve identified areas of positive consumer sentiment, find within that population the demographics that are relevant to your product, in this case an older demographic. Marrying the two with an anticipated life event is ideal.
Traditional indicators can be useful, but they need to be cross-referenced with other indicators, some of which may be counterintuitive, and considered with as much context as you can muster. If you take the time to look at all the indicators, are willing to challenge long-held notions about certain indicators, and do the heavy lifting of drilling down into more granular data, you will find yourself in the enviable position of reaching your customers at the right time with the right products -- every marketer’s ultimate goal.