There is no question that figuring out optimal pricing is a tough thing. If prices are too high you lose occupancy which means a lose of revenue. If prices are too low, you lose revenue and if significantly lower than they should be it could send a message to the marketplace that you are not offering a quality product.
There is no question that figuring out optimal pricing is tough. If prices are too high, you lose occupancy, which means a lose of revenue. If prices are too low, you lose revenue and, if significantly lower than they should be, it could send a message to the marketplace that you are not offering a quality product.
This past week I got a chance to visit with Sheila Donahoe, the CIO, and Shamim Wu, the executive VP of sales for Holiday Retirement about their move from Old School, primitive pricing to sophisticated big data driven pricing.
Holiday Retirement was founded in 1971 by a builder by the name of Bill Colson. He started with one community, grew initially to 9 communities all in the west. Over time the building pace picked up, peaking in 1988 and 1989 with 22 new properties added each of those years. In 2007 the company was sold to Fortress Investment Group and toward the end of that year Bill Colson passed away from colon cancer.
Today the company continues to operate out of the Portland area with more than 300 senior living communities that primarily serve independent living residents. Their portfolio does include a small number of assisted living communities.
Old School Pricing
As is true with many large senior living companies, Holiday Retirement had a pricing group that would, once a year, review and set pricing levels for each community. There was some science and some data behind the process, but it was very primitive. This meant that there were frequent requests for exceptions, which led to lots of sales time spent on negotiating pricing, first between local and corporate staff and then between local sales people and residents. It was hard for everyone and, ultimately, Holiday felt they were leaving revenue on the table.
Finding a Better Solution
The leadership at Holiday knew many other industry sectors, like multi-family housing, mini-storage, airlines, and hotels, were using big data to get to the right price every time. They also knew that senior living was not quite like those industries and went looking for a pricing company that would be willing to work with the unique needs of senior living.
After significant exploration they picked Prorize to be their pricing partner vendor (Prorize is also a Senior Housing Forum Partner). The initial thinking was that Prorize would be able to take one of their existing pricing models, do some fine tuning, and off they would go. After some initial investigation, it became clear that in order to come to the right solution Prorize would need to build a new pricing engine. This was primarily because the rate of turnover was so much less than in other industries.
They first rolled out the Prorize pricing tool in 2013 as an experiment that had to satisfy three significant needs:
- It had to improve revenue
- It had to make the lives of sales people and the selling process better and easier
- It had to not alienate residents and resident families.
The initial test used two small groups of communities that had similar market characteristics, allowing Holiday to do a comparison of the old and new pricing methods. The experiment was a success. Part Two of this article will be a deeper dive into the details.