So I've been doing this D2 Demand Solutions thing since I left Holiday Retirement a bit more than 7 months ago. I counted that I've now worked with an even dozen clients [ok a) there's no such thing as an odd dozen and b) why is it that I'm compelled to count things? Have been since I was a kid no 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 what's really going on in our business. And I've 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, they're really what our owner cares about it's 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 number BUT 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 rents if I sold 10 2BRs last month and 5 1BRs and this month I do the reverse and lease 5 2BRs and 10 1BRs my MOM new rents look way down. But it's just a temporary sales mix thing, not poor sales performance.
The thing is that these metrics still matter if 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 DON'T 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 won't 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 won't 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 won't tie to financials because amenity upcharges are real, but this metric won't have volatility simply due to changes in sales mix of highly amenitized vs base units being rented.