When it comes to understanding or anticipating customer needs, we often assume we know more than we do. Common misperceptions in the self-storage industry include:
- If rents are raised above the street rate or by more than a certain percentage, my customers will move out.
- All my competitors are lowering their prices, so I need to lower mine.
- My prices are higher than my competitors so I can’t charge more for a more conveniently located unit in a unit group.
Base Rate Bias.
While there is certainly some truth to all of the above, the tendencies noted above are not as “all encompassing” as we often anticipate. For example, when carrying out a detailed analysis of rentals and rent increases, many operators discover that far fewer customers move out than they anticipate when rents are raised to levels above the current street rate.
What we remember, however, are the instances where customers complain and let us know that the rent increase was too much. In fact, the tendency to ignore general information (e.g., many customers continue to rent despite receiving a rent increase) and focus on information relevant to a specific case (e.g., a customer complains about a rent increase), even when the general information is more important has a name: base rate bias.
Base rate bias often operates in conjunction with other aspects of reasoning errors, such as confirmation bias (looking for and interpreting information in a manner that confirms preconceptions), the tendency to avoid revising beliefs even when presented with new evidence, as well as other biases. In total, these biases often result in the perpetuation of various pricing myths. For those who are willing to invest the time and energy in data analysis and adopt data-driven decision making, many opportunities to increase revenues and profits emerge.
How can decision-making biases affect you?
Let’s put this to the test. What percent of your customers do you think would pay 10 percent more to rent a unit that is slightly more convenient? For example, suppose you have two available units of the same size on the second floor of your self-storage facility. One unit is only one hallway turn from the elevator and the other is two hallway turns away. (Assume all the units closer to the elevator are occupied). Take a moment to think about this before reading the next paragraph. What percent of your customers do you think would pay 10 percent more for the unit closer to the elevator?
Was your answer 10 percent? Perhaps 20 percent or even 25 percent?
In fact, our experience in the United States, Canada, South Africa and Australia shows that 25 – 45 percent of new customers will generally be willing to pay more for a more convenient available unit. Even at single-story, all drive-up facilities, 15 – 20 percent of customers, and frequently more than that, are prepared to pay more for a unit they consider to be better located. Many operators underestimate the willingness of customers to pay more for a better located unit.
Focus on the base rate.
With regards to rent increases, the types of biases noted above often result in operators being more cautious than they need to be. That does not mean everyone’s rent should be increased by a greater amount. Rather, in a data-driven world, the question is refocused into:
- Whose rents should be increased by a greater amount and,
- Whose rents should be increased by a lesser amount (or not at all)?
By refocusing the rent increase process and decisions, operators can increase the overall amount of rent increases while simultaneously reducing the number of rentals that terminate due to receiving a rent increase. While that may seem counter-intuitive, it is possible because data-driven techniques are better able to identify the leases that are more likely to move out due to a rent increase, as well as those leases, where if they do move out, would lead to greater revenues from new move-ins paying a higher rate.
It’s often good to be reminded that sometimes what we think we know, isn’t an accurate depiction of how our customers behave. Capitalizing on that knowledge can lead to higher revenues and profits.