You’re sitting in your aisle seat and at scheduled departure time the pilot gets on the intercom and says, “Due to a ground traffic control issue our flight is delayed. We estimate we’ll depart in 40 minutes.” You are less than thrilled and grouchily settle back in your seat. After 30 minutes the pilot gets back on the air and says, “Well, we got clearance a little earlier than we thought, prepare for departure.” Compare your state of mind here with the following more common situation.
At departure time the pilot gets on the intercom and says, “We have a delay. We estimate we’ll depart in 10 more minutes.” You quietly wait while 10 minutes becomes 13, 15 and then you hear, “We’ll depart in another 10 minutes.” And again 10 minutes goes to 12 goes to 15 before you hear, “Prepare for departure.”
In both cases the plane left 30 minutes late. Yet the odds are that you are more unhappy in the second situation than in the first—and the reason is due to anchoring.
We tend to anchor around the first number we hear when used with an unfamiliar topic. If the pilot says a 40-minute delay or a 10-minute delay, we establish whatever number is stated as a standard by which we’ll judge future action.
The reality is both flights were delayed 30 minutes. However, due to our anchoring bias in the first instance we feel as if we are leaving 10 minutes early. In the second, well, let’s just say we don’t feel that we left early. In addition, the first pilot gave a reason for the delay and the second one didn’t. As we become more familiar with an unfamiliar topic—we know now that flight delays can be caused by ground traffic control issues—we are more willing to accept problems along the way.
Some insurance agents will present the lowest possible premium quote, especially if they know they’re in competition with someone, and they may win the initial sale on that basis. However, often a quote assumes a level of good health that a real-life buyer doesn’t meet, or the assumptions on cash value growth are too optimistic. Thus, the agent must return to the buyer with a higher price, having created a low price anchor in the client’s mind. The result can be the buyer backing out of the sale.
It is better for agents to use the approach of the first pilot by explaining, “Mr./Ms. Consumer, you need to know that regardless of which insurance company you use, the ultimate premium you wind up paying is largely dependent on the state of your health and the assumptions made about future policy returns. If you have some health issues and we use very conservative assumptions, the premium might be as high as $250 a month; but if your health is good and we’re more optimistic, the premium could be as low as $130 a month.”
By establishing a confidence interval for premium, a consumer is less likely to not buy if the ultimate premium is higher than the initial one discussed.
As long as a competitor’s bid comes in higher than $130, the original agent will probably get the business. In addition, because the agent has explained how the premium price is determined, a low-ball bid from a competitor will now be viewed with suspicion. The agent has essentially given the client a confidence interval, which means the anchor is $130 to $250. If the final premium turns out to be $180 a month, the buyer is more likely to stick with the purchase because it is still less than $250.
Anchoring comes into play in a different way if we are familiar with a topic. For example, let’s say you’re a frequent flier on a certain airline and your experience has been that a particular flight always leaves 30 minutes late. In such a case, what the pilot says won’t affect your anchoring position.
The same anchoring holds true in the life insurance sales process. A consumer who owns life insurance or has already been shopping around may already have preconceived notions of what the premium should be and what assumptions should be used for future returns. The agent needs to determine whether these anchors have been established and whether they are realistic.
Anchoring and the Law of Small Numbers
The media is great at using this bias. They’ll run a story about, say, a person who gave a dollar to a homeless person, bought a lottery ticket with their last dollar, and then won $10,000. They’ll then search the archives and report a story from years earlier in which a person gave a buck to a beggar and then won the lottery. The idea they are trying to convey is first, this happens a lot, and second, since two people gave to beggars and were then rewarded, there must be cause and effect.
The reality is that there is no correlation between the two incidents and there is no causality, but people tend to believe in the “law of small numbers,” humorously named because people tend to attach as much decision weight to a small data sample as a large one.
The law of small numbers and a belief in causality can be used to improve the perceived value of life insurance. A reason for not buying insurance is not thinking you’ll need it—I’m not going to die in the near future. Building an obituary book, showing obituaries of real people who died in their 30s, 40s, 50s, 60s (whatever is near the age of the prospect) brings home the point that, “Yes, you too can die and thus you need life insurance.”
