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4 Ways Data Science Impacts the Senior Living Resident Journey

Could you imagine being able to detect and prevent a health scare with a resident before the event occurs? Alternatively, imagine responding to shifts in occupancy with a high degree of agility.

Data Science can create operational efficiencies, improve resident’s health, drive staffing decisions, or any number of initiatives. This is the power of data science when applied to marketing and the resident acquisition process.

Command your community’s data and continue reading to learn:

• How to impact the four major milestones in the senior living sales cycle
• Questions and KPIs within each respective milestone data science continually informs to improve occupancy levels

Resident Acquisition Milestone #1: Initial Inquiry

With accurate inquiry KPI tracking, your senior living marketing team can adjust its strategy to increase inquiries for a specific service line with a cost-effective advertising channel. Applying data science to marketing during this event answers the following questions:

1.) Where are we generating quality inquiries?

2.) Is our team fielding all inquiries appropriately?

3.) Is our community reaching our desired audience?

During the initial inquiry, a prospective resident and/or ACI’s interest was piqued and they’re now aware of your community’s offerings.

The next goal becomes encouraging the prospective resident or ACI to visit your community’s campus. During this stage in the resident sales life cycle, data science can provide insight into the following KPIs:

• Average Cost Per Inquiry – The promotional cost of marketing divided by the total promotional cost of total number of inquiries generated
• Average Inquiry Quality – The amount of inquiries reflecting individual inquiries and the quality of inquiries over time:
  • Individual Inquiries: The quality of a specific inquiry based on the nature of the inquiry (whether phone lines are ringing with interested residents or are fielding spam phone calls)
  • Inquiries over time: The quality of aggregate inquiries over time by dividing all inquiries by high quality inquiries (defined by additional criteria like comparing historic data)
• Average Missed Call Rate – The percentage of inquiries that the staff misses in a given time period

Resident Acquisition Milestone #2: Campus Tours

Senior living marketing and sales teams have approximately 30 days to entice a prospective resident after a tour to move into a community. After that, the likelihood sales teams will convince the prospect to move in drastically diminishes. Given that reality, your team can use a data science model to gain clarity into the following questions:

1.) Is our team being proactive in arranging visits?

2.) Is our team engaging with high-value inquiries quickly enough?

3.) How can our community improve its conversion rate?

With the initial inquiry crossed off, your sales team is one step closer to impacting identified gaps in occupancy levels. Using data science for marketing, your community can stay in step with the resident journey by actively adjusting to changes in campus tour KPIs:

• Average Inquiry-to-Visit Rate – The amount of inquiries that attended a tour divided by the amount of total inquiries
• Average Inquiry-to-Visit Time – The amount of time it took from the initial inquiry to the first tour or visit
• Average Expected Monthly Fee – What a community can expect month-over-month based on the levels of care it provides

Resident Acquisition Milestone #3: Resident Move-Ins

Your community can identify where efficiencies can be made after analyzing its data through the lens of the KPIs and questions above. Additionally, data science for marketing and sales will shed light on questions such as:

1.) Are tours driving conversions as expected?

2.) Has our community forecasted conversions accurately?

3.) Why are some prospective residents not converting?

A prospective resident became a move-in. Now, your senior living marketing team can assess the effectiveness of its cumulative efforts. An investment in data science for marketing purposes will provide feedback into these move-in KPIs:

• Average Cost Per Resident – The total cost to acquire each resident from the initial inquiry to move-in
• Average Inquiry-to-Resident Time – The total length of time it takes to earn a resident from the point of initial inquiry until move-in
• Average Monthly Fee – The fee your community can expect on a monthly basis after insurance reimbursements
• Average Census (Occupancy Levels) – The actual number of occupied rooms divided by total rooms

Resident Acquisition Milestone #4: Resident Move-Outs

With insights from the questions and KPIs above, your senior living marketing team will have data-driven clarity into the following move-out questions:

1.) Are residents switching service lines?

2.) How long do residents typically stay?

3.) What is our community’s resident average lifetime value?

To clarify, a “move-out” is defined as the untimely passing of one of your community’s residents, or the event where a resident elects an alternative living option. Resident retention is a challenge for providers across the continuum of care. Assisted by data science, your community can strategically plan for these occupancy shifts.

By deploying a data science model, your team can impact these move-out KPIs:

• Average Length of Stay (LOS) – Total length of stay (in months/years) of an individual resident prior to a move-out
• Average Total Revenue Per Resident – The revenue each resident brings a community over the lifetime of their stay

Contact Our Team to Learn About Our Proprietary Data Science Model for Senior Living

Data science for marketing can be leveraged to narrow the timeframe from initial inquiry to move-in, while proactively preparing for the event of a move-out. Our team created a data science model exclusively for senior living providers to help them methodically influence their desired outcomes.

To learn more, contact our team. We’ll help your organization use its existing datasets to transform operations, marketing and occupancy levels.