The Occupancy Equation: What It Really Takes to Build Stability

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Argentum 2016 Senior Living Executive Conference concurrent session Original session date: Tuesday, May 10, 2016, 1:30 - 2:45 PM Speakers: Andréa Catizone, COO, Senior Living SMART Chris Guay, President & CEO, Vitality Senior Living Deborah Howard, CEO, Senior Living SMART Carlene Motto, Executive VP of Sales, Belmont Village Senior Living Jayne Sallerson, COO, Sherpa John Shafaee, CEO, Medtelligent, Inc.

Transcript

Occupancy MattersDeborah Howard, Founder, Senior Living SmartCarlene Motto, Chief Marketing Officer, Belmont Village Senior Living

2

Trends affecting our business

Conventional approach to occupancy

Reframing the occupancy model

Exploring the drivers

Discussion

2

Red Ocean or Blue Ocean?

3

• Compete in existing market space

• Beat the competition

• Exploit existing demand

• Commodity/ Vanilla

• Operational model will not work

for next wave of residents/

families

• Increased lead acquisition costs/

3rd party

• Create uncontested market space

• Make the competition irrelevant

• Create and capture new demand

• Niche/ Specialty

• Personalization and choice of the

prospect/ resident journey

• Organic lead generation

Red Ocean Reality Blue Ocean Opportunity

Think different: think Blue

4

Understanding move-outs: by the numbers

Death is not the only reason for move-outs.

92% cite resident health as leading reason for move-out (up from 72%)

22 months average length of stay in assisted living (down from 36 months)

54% annual resident turnover pressured by rise in acuity (up from 41%)

$4,000 cost per resident turnover. $200,000 annual

community cost to replace residents from turnover (100 bed community)

Source: Illumination 2016 Analytics 10 year analysis

6

New construction: Competition with new buildings

Source: NIC MAP

7

Low penetration is everyone’s challenge

Source: The Scan Foundation, Data Breach

8

Pressure above and below the line

Shorter resident stays

Increased resident acquisition costs

Increased operating costs

Reduced top line revenue

Increased Risk and Liability

Reduced bottom line NOI

RevenueDOWN

ExpensesUP

NOI DOWN

10

Conventional Thinking: Sales = Occupancy

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Conventional Sales Team

Inside sales Outside sales

12

Conventional Work Flow

Inquiries Tours Closing Move-in

13

Conventional Occupancy Model

Sales Fills Building

Occupancy Declines

Sales Fills Building

Repeat

100%

14

15

Busting the myth: driving sales will not be enough

New challenges thwart performance

The Occupancy Gap

100%

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Blue Ocean Occupancy Equation

Move-Ins +/- Service Delivery +/- Retention

Stable Occupancy =

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From Sales Team …

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… to Occupancy Team

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From sales plan … to Occupancy Plan

Stable occupancy:

1. Is the common goal.

2. Requires participation of entire staff.

3. Is supported by coordinated strategies for clinical, operations, sales.

4. Is supported by a culture of service.

CLINICAL

OPERATIONS

SALES

SERVICE20

Exploring the Blue Ocean Drivers

SalesOperationsClinical

21

SALES

Drivers

Consumer

Website

24/7

Personalization

Sales Drivers

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“You must know your customer if you want to sell to them.” – Margaret Wylde, ProMatura Group

Recent study by the ProMatura Group suggests spending moretime on each senior living sales lead, as opposed to calling and giving tours to as many prospects as possible, could be the most successful way to convert leads into move-ins.

“Industry sales counselors generally focus on the product instead of the prospect,” Wylde said. “Then when confronted by resistance, which is predictable, most simply give up too soon. For prospects that ultimately closed and moved in, the salespeople on average invested almost 18 hours for independent living and approximately 10 hours for assisted living and memory care to learn about them, address their hesitation, and follow up with creative approaches that were personally relevant. If you get to know the user, you’re going to sell more product—period.”

Source: Senior Housing News, May 3, 2016, reporting on analysis of lead and sales data from 502 senior living salespeople using Sherpa during period Jan. 1, 2015 to Dec. 31, 2015, conducted by the ProMatura Group

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Time spent with prospects pays off

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126

9794

53

112

98

79

62

Quartile 1(Top Performers)

Quartile 2 Quartile 3 Quartile 4(Worst Performers)

Quartiles of Conversion Ratios

Average Time in Minutes per Prospect Worked in 2015 by Quartiles of 2015 Conversion Ratios and Level of Care

ILAL/MC

Sales and Marketing Drivers

Red Ocean Strategy

Transactional

Measuring tasks & activities

More leads, more leads!

Focus on “Hot Leads”/ Urgent needs

Website is the “all-about-me show”

No effective after hours sales plan

Blue Ocean Strategy

Relational

Measuring relationship building

Work fewer leads deeper

Focus on earlier stage leads

Website is resource rich and focused on consumer

24/7/365 plan to manage inquiries and tours

25

Action

Planning

Research

Awareness

Consumer – Stages of Readiness

What are the options? What do I need to know? How can I make the best choice?

Timeframe, Funding Solutions, Transition

Daily Life, Accommodations/ Amenities, Care, Cost, Floor Plans. Comparing other Options

Room Planners, Move-in Coordination

26

Consumer - Top of Mind Questions

02

01

03

04

Where Will I Live?

What Will I Do?

Can I Afford It?

Who Will Take Care of Me?

27

Your Website

87% of Prospects Research Online

Before Contacting a Community

45% of Your Website

Prospects Will Make a Decision

Within 12 Months

75% Will Buy From The 1st Person They Speak To

1.5 million Searches per Week

28

Your Website – Lead Generator

Live Chat

Interactive Surveys

Inbound Content

Videos

Tracking Behavior

29

24/7/365 Response Plan

MOD Live Chat Call CenterMarketing

Automation

30

Personalization

31

Move Ins

Conventional Industry Indicators

LAGGING

New Leads

LEADING

Call Outs Tours

?32

PCSSM Leading Performance Drivers

Time Per Lead PlanningTotal Time in

the Selling

ZoneSM

Creative Follow-Up Move Ins

LEADING LAGGING

33

Occupancy Optimization

Predicted Length of Stay

Likelihood of Conversion

Move-Out Risk

Prospects that OPTIMIZE ALOS

Ranking each prospect based on their projected LOS in the community

Ranking each prospect based on likelihood of

becoming a resident

Ranking each resident based on the risk of an unplanned move-out

34

Number of Call Outs/Tours - Correlation is not Causation

35

OperationalDrivers

Engagement

SurveysRecruitment

and Retention

Operational Drivers

36

Operational Drivers

Red Ocean Strategy

Yearly surveys

Hiring, firing and performance reviews

Holiday family celebrations

Set operational structure

Blue Ocean Strategy

Point in time surveys

Pre-emptive recruitment, value recognition, and development

Ongoing and proactive family communication and engagement

Choice

37

Clinical Drivers

Pre-Move-In

Pro-ActiveCare

Predictive Analytics

Clinical Drivers

38

Clinical Drivers

Red Ocean Strategy

Clinical intervention starts at move-in

Reactive approach to care

Care Plan

No “ownership” of residents in acute settings

Not utilizing data

Blue Ocean Strategy

Clinical intervention starts pre move-in

Pro-Active approach to care and wellness

Personalized Care Plan

Protocols for MLOA residents

Utilizing predictive analytics

39

Short Length of Stays: a Real Problem!

• Difficult to catch and monitor• Very costly to maintain and operate• Creates a vicious cycle…

1. Sales constantly trying to fill beds2. Focused on quantity of leads over best match3. Quantity and quick leases result in even shorter

length of stay

Move outs in 90 days

Familiar pattern with many clients

40

What’s the Solution?

Focus on…

Smaller number of qualified leads; identify the best candidate

Actively monitor and manage resident health and satisfaction

This is difficult to do and nearly impossible without tools:

Spans sales and clinical staff

Large data set, key info not captured or recorded on paper

ALIS and Life2 have teamed up to do just that. We have developed tools that…

Select prospects based on likelihood to move in and predict length of stay

Identify current residents at risk of move-out in the next 90 days

Suggest actions that staff can take to improve outcomes

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