Occupancy is optimised at the expense of revenue
Discounting to fill is easy to measure and easy to celebrate, and the revenue given away to achieve it usually is not measured at all.
Storage solutions
Rate, occupancy and churn as one picture rather than three reports
Pricing, occupancy and customer churn joined per site and unit type, so a rate change is judged on what it did to revenue rather than on what it did to fill.
Self-storage economics are a continuous trade between rate and occupancy, and most operators can see each separately and neither together. Existing-customer rate increases, move-in discounts and churn interact in ways a monthly occupancy report cannot show. We build the joined view so pricing decisions are tested against revenue per available square foot rather than against the fill rate alone.
Discuss your operationWhat we usually find
The problems that come up in almost every one of these businesses.
Churn is reported late and in aggregate
Move-outs are counted monthly and in total, so the relationship between a rate increase and the customers it drove away stays invisible.
Unit mix is set by history
The mix of unit sizes reflects how the site was built rather than what the local demand and pricing data now say it should be.
Where we start
What the first engagement usually covers.
Scope depends on the state of your systems. These are the pieces that recur in this sector.
Revenue per available square foot
The operative metric, built per site and unit type, so rate and occupancy are judged together rather than traded blindly.
Rate change and churn
Existing-customer rate increases tracked against the move-outs that followed, by cohort, so the next increase is priced on evidence.
Customer lifetime value
Discount, tenure and churn joined, so the true cost of a move-in offer is known rather than assumed.
Unit mix and demand
Enquiry and conversion data against the unit mix actually available, showing where the site is turning away demand it could serve.
What changes
What is different afterwards.
You own all of it: the code, the written definitions and documentation aimed at whoever maintains this after us.
- Revenue per available square foot, by site and unit type
- Rate increases tested against the churn they cause
- Lifetime value net of move-in discounting
- Unit mix judged against local demand
The builds behind it
Sector knowledge decides the order. The builds are the same four.
Single source of truth
Your finance, customer and operational records joined into one dataset, with each figure defined once and traceable back to the system it came from.
Analytics layer
Reporting built on top of the joined data, aimed at the few revenue and cost drivers that actually change the result.
Workflows
One defined task, automated inside a process that already exists, measured against whatever it replaced.
Financial modelling
Driver-based models built on the same definitions as your reporting, so the forecast and the actuals stop disagreeing.
Worth asking
Questions this tends to answer.
- Is a rate increase gaining or losing us revenue?
- What does a move-in discount actually cost over the tenancy?
- Which unit sizes are we short of?
- Which sites are full at the wrong price?