Metrics that Matter #4: Length of Stay
Welcome to Metrics that Matter - our deep dive into the KPIs that power senior living. All of these KPIs are automatically calculated by Clarent and included in our KPI Library.
Today we're going to dig into a metric that impacts all areas of a community: length of stay!
KPI: Length of Stay (LoS)
What is Length of Stay?
Length of Stay refers to how long a resident is at a community or how long a unit is rented.
How do you calculate Length of Stay?
You can calculate LoS in 2 ways: measuring the number of days between move-in and move-out or by looking at the length of time that a unit is rented. These 2 methods are based on physical move-ins / move-outs or financial move-ins / move-outs and there may be a difference between the two measurements (for example, if a resident physically moved out of a unit but billing ends 15 days later, the financial length of stay will likely be different than the physical length of stay).
Many operators measure an "average LoS" by calculating the LoS for each resident who moves-out within a time period (say the prior quarter). Averages can be misleading however, as they can be skewed by extremes. If you have a few residents at the community who move out very quickly or stay for a very long time, this can skew the average and not be representative of the LoS for most residents.
Why is LoS an important KPI?
LoS is an essential metric because it impacts all areas of operations. LoS is part of your "business equation": prospects move-in, they stay as customers for a certain amount of time (the LoS), and pay revenue (care + rent). If you understand LoS you can fill in a key part of your business equation and start to understand and forecast your financial results.
You can use it to forecast future move-outs (a forward looking analysis), and you can also use it to understand what similarities (if any) exist amongst your "best" customers: the residents with the longest LoS. Did they all come from the same lead source? On the other hand, are there lead sources that consistently lead to earlier move-outs?
It's also possible to break out move-outs by reason (i.e. death, competitor, dissatisfaction, etc) to further segment LoS analysis. If you focus on "controllable" move-outs (such as dissatisfaction or moving to a competitor) you may find that certain communities have lower average LoS than others for controllable move-outs, suggesting that there is a customer retention issue at that community and residents are choosing to leave.
Why is this KPI hard to measure?
LoS is hard to calculate because you need to be very specific about how you define it: are you measuring the LoS of residents who are still in the community? Only those who have moved out in the last month / quarter / year? What about breaking out by care level? If you lump everything together, or are inaccurate in your measurement, you lose the value of the KPI.
How we do it at Clarent & suggested next steps
We measure LoS in several different ways. We typically measure LoS based on move-outs monthly (i.e. "what was the average LoS of all the residents who moved-out each month"), and we also count move-outs into a distribution of LoS: how many residents moved out after 0-6 months, 6-12 months, 12-18 months, etc. Using a distribution rather than an average allows us to avoid having our average by skewed by outliers who stay very long or very short (as noted above)
We also can break out LoS by care level, community, lead source, move-out reason, and more. One of our customers broke out LoS by lead source and realized that one of their lead sources had much shorter LoS than others - this led them to rebalance their sales & marketing budget.
Length of Stay impacts all areas of operations and is an essential part of the senior living "business equation"