SELF STORAGE PRICING AND REVENUE MANAGEMENT

Self storage pricing and revenue

management: Real world opportunities

by Warren Lieberman and Jim Mullin, Veritec Solutions

We often think we know more about our customers than we do. Common misperceptions include: l If I raise rents above the street rate or by more than X%, my customers will move out l My competitors are lowering their prices, so I need to lower mine l My prices are higher than my competitors so I can’t charge more for more conveniently located units

Although there is some truth to these expectations, the tendencies noted above are usually not ‘all encompassing’ as often anticipated. For example, when analyzing the impacts of rent increases, many operators discover that when raising rents above the current street rate, fewer customers move out than expected. What we remember, however, are the instances where customers complain, tell us the rent increase is too much, and move out. 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 specific cases (e.g., customer complaints about rent increases), 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 behavioral biases. In total, these biases often result in the perpetuation of various pricing myths. For those willing to invest time and energy in data analysis and make decisions informed by ‘what the data show,’ opportunities to increase revenues and profits emerge.

Let’s test this. Suppose you have two available units of the same size on the second floor of your self storage facility. Unit A is one hallway turn from the lift; Unit B is two hallway turns away. What percent of your customers would pay 10 percent more for the unit closer to the lift, a slightly more convenient unit?

Was your answer 10 percent? Perhaps 20 or 25 percent?

Our experience in the US and Australasia shows that 25 – 45 percent of new customers are willing to pay more for convenience. Many operators underestimate the importance of convenience to their customers.

With regards to rent increases, the 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. In a data-driven world, operators are better able to determine (i) whose rents should be “For those willing to invest time and energy in data analysis and make decisions informed by ‘what the data show,’ opportunities to increase revenues and profits emerge.

increased by a greater amount and (ii) 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 total revenue generated by rent increases while simultaneously reducing the number of move-outs due to rent increases because they can better identify which leases merit an aggressive rent increase and which do not.

It’s good to be reminded that sometimes, what we think we know isn’t an accurate depiction of how our customers behave. Capitalising on that knowledge through data analysis can lead to higher revenues and profits. l

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