So Ive been doing this D2 Demand Solutions thing since I left Holiday Retirement a bit more than 7 months ago. I counted that Ive now worked with an even dozen clients [oka) theres no such thing as an odd dozen and b) why is it that Im compelled to count things? Have been since I was a kidno wonder I got into metric-driven analytics]
Anyway, the point I want to make is that across virtually all of this client base, I have found an almost never-ending pursuit for the perfect metric (or metrics)that simple set of KPIs that will do everything we need to know whats really going on in our business. And Ive realized that part of why this is a virtually impossible quest is that there are really two VERY DIFFERENT purposes for a metric.
As the name suggests, these metrics deal with financial results. For public companies, they give us advance insight into what the EOQ numbers are going to look like; for private companies, theyre really what our owner cares aboutits all about the cash in bank.
The trouble with financial metrics is that they can show skewed data that leads to incorrect assumptions about how operations/sales is performing. For example, if I forecast 95% occupancy for the community and I hit that numberBUT my 1BR occupancy is 97% and my 2BR occupancy is 92%, my financials will be below budget (and vice versa if the 2BRs are the higher occupied unit) just because of the sales mix. This is particularly an issue if you look at a metric like month-over-month (MOM) new rentsif I sold 10 2BRs last month and 5 1BRsand this month I do the reverse and lease 5 2BRs and 10 1BRsmy MOM new rents look way down. But its just a temporary sales mix thing, not poor sales performance.
The thing is that these metrics still matterif I do sell more 1BRs than 2BRs, my cash in bank and my reportable revenue is truly lower, so I need to know that. But I DONT WANT TO GIG OPS FOR JUST A SHORT-TERM SALES MIX ISSUE.
Business behavioral metrics/dashboards/reports
Enter the behavioral metric which is great for operational dashboards and reports. With these metrics, I normalize for the sales mix. For example, I can calculate a new rent at the unit type level and then aggregate up to a community-level new rent by using a UT-count weighted average of the two. This fixed ratio gives me a number indicative of the community-level rent, but it wont tie to financials because I normalized away the sales mix. It will, however, show me whether my underlying rent trend is up or down because sales mix variances wont affect this metric.
I can do the same thing for unit-level amenities. I can strip away the unit amenities and track base rent movements. Again, these wont tie to financials because amenity upcharges are real, but this metric wont have volatility simply due to changes in sales mix of highly amenitized vs base units being rented.