The Power of Data - What About Move-Outs

The Power of Data: What About Customer Move-Outs?

In a previous blog, we talked about understanding new customer move-ins. But what about existing customer move-outs? This “flip side” key performance index (KPI) is equally important to understand, because it is about losing customers as they decide to move out.

Analyzing the same data as before, we are looking at a self-storage operator who has experienced relatively stable occupancy for the past 18 months. We highlighted several core KPI’s of especially strong interest based on revenue per square foot.

We talked about how revenue per square foot (when based on total square feet of a facility) is often a good, and easily understood metric for total revenue. It “normalizes” revenue, so it is often a more robust metric when used in trend analysis. For existing customer move-outs, we look at two aspects of revenue per square foot:

  1. Based only on occupied units.
  2. Based on existing customer move-outs.

Dashboard Graph Example Move-Outs

Analyzing Move-Outs in Isolation

If we strictly focus on the purple line, existing customer move-outs, we see that it bumps along with ups and downs depending on month:

  • Notice increases in move-out revenue (loss) on January, March, June, August, October, December of 2021, and February 2022.
  • Conversely, there were decreases move-outs in the month or months following.
  • There are no observable cyclicality in the data presented. There was an increase in move-outs in January 2021, followed by a dip in February. The opposite is true in 2022, where there was a dip in January, followed by an increase in February.
  • However, the overall move-out trend is in an upward trajectory. That means the losses of move-out revenue over time is increasing. In isolation, this would be an disconcerting trend. As we shall see, this may not be the case.

Analyzing Move-Outs Relative to Occupied Units.

If we look at move-out data compared to occupied data, the black line, a different perspective emerges:

  • Occupied revenues per square foot has steadily increased over time. That is a good thing, as the operator is able to gain more revenue per square foot over time, as long as the trend exceeds that of inflation.
  • Looking again back to move-out revenues (loss), it makes sense that those revenues will increase over time as well. Those higher revenue occupied units, when customers move out, will likewise result in a higher loss of revenues as well.

What if Move-Out and Occupied Revenues Trend in Opposite Directions?

We noted there were months where move-out revenues increased over the previous month. In this case, profitability will take a hit. Existing customers of units yielding higher revenue per square foot are moving out more. It is worthwhile to consider why.

  • Are rent increases too aggressive for those already higher-paying customers?
  • Or is it a delayed response to a previous, lower move-out month?
  • If there were observable cycles, that could explain increases (and decreases). However, based on the data, this is not the case.

We also noted there were months where move-out revenues decreased over the previous month. This is good for profits, and it’s worthwhile to identify factors to replicate this scenario.

  • What were the rent increases during that month?
  • Which particular type of unit were rent increases made, and which ones were the move-outs?
  • Could rents have been increased a bit more to further profitability?
  • What were the rent increase “tipping points” where, if exceeded, would push a increase in move-out revenues, the bad-for-profit scenario.

Looking at customer move-outs is a continuation of a data-drive approach. By truly understanding the factors driving the data, operators can significantly improve their overall revenues, and thereby profits, over time.

In a future blog, we will see the interplay between occupied unit revenues, new customer move-ins, and existing customer move-outs.