Senior living organizations can greatly increase their profitability when the time needed to understand occupancy data, and share it within the organization, is reduced from days to minutes.
To achieve this, extracting data from sources such as Yardi, then transforming it for usage in visualization tools such as Tableau is critical for success. Organizations that are on the path to embrace data-driven cultures will achieve a supreme competitive advantage by reducing the time to identify insight.
Working side by side with many multi-property senior living organizations, we have developed best practices in this technique. Below I have outlined one of these best practices by providing a case that shows how an executive may use a Tableau dashboard that is powered by transformed Yardi data to increase occupancy and inform property managers of areas for improvement.
Set Your Occupancy Rate
To analyze occupancy data, it's a best practice as a first step to set your target occupancy rate. We used a feature in Tableau called "dynamic parameter" to make this easy. In this case we set the rate at 92%. After applying this, the metric will be applied to every community in the organization.
To set the occupancy percentage target, simply type the percentage you want in the top right corner of the dashboard—as you can see below.
Analyze Top-Level Vacancy Data
After setting your occupancy percentage target, you now have a top-level view of occupancy and vacancy revenue across all communities. The below screen shot shows there is $5.43 million dollars available across the organization. Our data visualization experts chose blue to showcase areas for improvement because it's an attention-grabbing color. Aggressive colors like red can indicate failure and lead to negative emotions.
Now that you have identified the total amount of available revenue, the next question you should ask is, "What communities do we need to focus our attention on to increase revenue?"
What Community Do We Need to Focus on to Increase Revenue?
The dashboard showcases a bar chart that contains each community's revenue. Each bar contains several data points. The total revenue a community has generated, a thin grey line shows the total amount of revenue that a community can generate, and a thick black line showcases the total revenue amount if a community's rent is set at the current market rate. Each community that is preforming under the occupancy target rate that was set in the first step appears in blue.
The right half of the dashboard makes it easy to identify the vacant potential revenue for each community. The longest blue bar in the right chart represents the community that has the highest opportunity to generate revenue.
After analyzing the dashboard, you can see that the community Las Vacas has the most vacant potential revenue compared to all the other communities. Per the data, Las Vacas has a vacant potential revenue total of $263,100.
This example shows how much time to insight you can save by using a well-built data analytics platform. In just a minute or two, an executive can look at the graphs and determine that out of all their communities, Las Vacas is the one that needs the most help in reaching its target occupancy percentage.
Now that this is identified, the next question to ask is, "How do I get Las Vacas Occupancy Revenue Above Par"
How do I get Las Vacas Occupancy Revenue Above Par?
Identifying what type of units have the most vacancy will allow a general manager to focus marketing and sales efforts. The dashboards used in this case are built using data extracted from the Yardi Senior Living Management platform. To deliver metrics in this granular level, the ETL (extract, transform, load) process needs to be flawless. Then, the data can fit into easy to identify visuals like the one below.
Clicking on a property from the bar chart will activate a tooltip that contains a visualization (a new Tableau 10.5 feature) showcasing the properties occupancy trends.
We included a blue link that reads, "Drill into Las Vacas Community" at the bottom of the tooltip. This will take you to a new dashboard that contains micro details on the Las Vacas Community, including occupancy and vacancy rates of each unit type.
(shown below)
It's a best practice to understand who is using each dashboard. In this case, executives and general managers have different roles within the organization. Therefore, to display data for each role requires two separate dashboards. Additionally, this will limit a GM's access to the entire organizations data. Executives require a macro view of the entire organization while a GM's sole focus is a specific community.
(Shown below)
Each unit, occupied or vacant, in the Las Vacas community is broken down in the bar charts below. In Las Vacas there are two types of care available: Assisted Living, Memory Care, and four-unit sizes: Studio, Companion Suite,1-bedroom, and 2-bedroom.
Our visualization experts carried the same metric mythology across to this dashboard. The bar chart on the left shows the amount of occupied revenue. The thin grey bars showcase the difference to the set occupancy target for the care and unit type. On the right, the bar chart visualizes this difference so it's easy to identify what unit type holds the most revenue potential.
In this case, you can see that the Memory Care Companion Suite unit has the most vacant potential revenue.
Conclusion
The main question that this post answers is, "How do I increase occupancy revenue?" Creating this dashboard, powered by data extracted from Yardi, resulted in the ability to analyze a senior living organization with 30 plus properties in just moments. An executive can find out what single unit from a specific property they need to focus on to increase occupancy revenue.
Without a solid data platform, drilling down to this level of insight would require the exportation of many excel sheets taking an exorbitant amount of time. Reducing the time needed to make decisions is the main benefit of data analytics and why senior living organizations should invest in furthering their journey to culture driven by data.
Posted by Gage Peake
