Maximise RevPAR through in-depth
analysis of Rooms Segments and
Business Mix
EAME Project #84848
Project Sponsor – Sue Finlay, DoSM
Black Belt – Peter Cullen
Master Black Belt – Paul James
Project Justification
Ahead of setting our Rooms Revenue budgets for 2005, we feel there is
a key opportunity to fully analyse the impact and interaction of our
different market segments.
This exercise is to ensure that the budget we submit is both accurate,
realistic and challenging and that our proactive Sales activity is focused
on the highest yielding areas of our business.
Our aim is to hypothetically model a 'perfect' business mix to maximise
RevPAR and profitability and identify how this mix changes through the
year and then to come up tactical plans to achieve this ‘perfect’ mix
Project
Justification
Trend
Analysis
for RevPAR
Linear Trend Model
Yt = 72.4160 + 0.197315*t
Actual
110
Fits
RevPAR
2005 budget
100
RevPAR = £87.59
Forecasts
Actual
Fits
Forecasts
90
80
70
60
MAPE:
MAD:
MSD:
50
0
Jan 02
10
20
30
40
Jun 04Time
50
60
70
17.823
13.035
241.802
80
Jun 08
If RevPAR continues to grow at the same rate, we will have to wait until June 2008 to ‘naturally’ reach the
budgeted RevPAR target for 2005. Therefore, we need to do something different
Sigma Level of Daily RevPAR results
The Year End budgeted
RevPAR for 2005 is £87.59.
Process Capability Analysis for Revpar
LSL
Process Data
USL
Target
LSL
Mean
Sample N
StDev (Within)
StDev (Overall)
*
*
87.5900
75.1374
870
15.9317
28.0007
If we continue to generate
similar RevPAR figures as we
do today (and take every day
that we do not achieve this
thresholdWithin
as being a ‘defect’),
the resulting sigma level is likely
Overall
to be 1.05
(671,740 DPMO)
Potential (Within) Capability
Cp
*
CPU
*
CPL
-0.26
Cpk
-0.26
Cpm
*
Overall Capability
Pp
PPU
PPL
Ppk
*
*
-0.15
-0.15
0
50
Observed Performance
PPM < LSL
663218.39
PPM > USL
*
PPM Total
663218.39
100
Exp. "Within" Performance
PPM < LSL
782781.48
PPM > USL
*
PPM Total
782781.48
150
200
Exp. "Overall" Performance
PPM < LSL
671740.63
PPM > USL
*
PPM Total
671740.63
Cpk of less than 0 shows that our current RevPAR performance is not capable of hitting
our year end goal for 2005
Comparative
X-bar Chart
for RevparAnalysis
200
Sample Mean
1
1
1
1
1
11
1
1 1
1
1
1
1
1 11 1
11
1 11
11
11111
1
UCL=122.9
100
0
1 1 1
11
1
1
1
1
1
1
0
11
111 1
MINITAB automatically
Mean=75.14 gives us a list of the data
points that failed SPC test 1
(more than 3s away from
LCL=27.34 the mean)
100 200 300 400 500 600 700 800 900
X-bar Chart: Revpar
Running the daily RevPAR
data through an X-Bar
control chart quickly
showed the data points that
were particularly high and
those that were particularly
low.
Sample Number
These ‘failure points’ were
then loaded into Excel and
Lookup functions were
used to cross-reference the
business mix that led to
each of these results
TEST 1. One point more than 3.00 sigmas from center line.
Test Failed at points: 2 4 5 6 82 142 176 177 178 179 199 200 201 212 227
228 229 230 232 235 266 267 268 269 273 304 356 357 370 371 376 377 433
476 477 503 528 529 586 587 588 589 590 591 592 593 598 599 600 625 626
627 638 675 705 720 721 733 761 803 809
These ‘failure points’ were then loaded into Excel and Lookup
functions were used to bring-forward the rooms mix data
corresponding to those dates. This enabled us to cross-reference
the business mix that led to each of these particularly high or low
results
Pivot Tables were then used to format the data into graphical
summaries
Comparative Analysis
The rooms business mix from the dates that ‘failed’ the control chart, were flagged as either particularly
‘HIGH’ or particularly ‘LOW’. This flag was then used to stratify the business mix percentages as seen
below. Initially, we worked at a high level looking only at the general mix between Transients & Groups
100%
90%
80%
70%
60%
Data
Average of % TTL Trans
Average of % TTL Grp
50%
40%
30%
20%
10%
0%
High
Low
H/L
It can clearly be seen from this bar chart that days where
there is no Group business lead to particularly
low RevPAR results whereas a 55% : 45% split between Group & Transient rooms is the mix that has
historically led to particularly high RevPAR results.
This suggests that a stronger presence of Group Rooms will lead to an increased RevPAR result.
However, we needed more detail
Comparison of business mixes that resulted in particularly high and
particularly low RevPAR
50
45
40
The same method was used again but
stratified to the next level of segment detail to
see, again, what business mix characterised
High and Low RevPAR days
Data
35
Average of % TCNC
Average of % TCC
Average of % TLNC
Average of % TL Othr
Average of % Grp Ass
Average of % Grp Corp
Average of % Grp Leis
Average of % Othr
Average of % Comp avlbl
Average of % Comp occpd
30
25
20
15
10
5
0
High
Low
Dates that had a particularly high RevPAR H/L
were characterised by a fairly even mix of business across all
the different segments.
By contrast, days with a particularly low RevPAR are characterised by very high concentrations of rooms
in the Transient Leisure segments and by almost-complete absence of any Retail and Group rooms.
Optimum mix to maximise RevPAR based on historical data
Segment
TCNC
TCC
TLNC
TL Other
Grp Assoc
Grp Corp
Grp Leis
Other
Optimum
Percentage of Total
ADR
195.00
109.50
147.40
80.00
131.40
88.90
111.75
65.00
11%
17%
4%
10%
20%
21%
12%
5%
Comparison of business mixes that resulted in particularly high and
particularly low RevPAR – split by Weekdays and Weekends
A similar pattern is seen when split
between Weekdays and Weekends.
40
35
30
The days with particularly low RevPAR
results are characterised by high
concentrations of Transient Leisure rooms
and an absence of premium rate Retail
rooms and Groups. By contrast, the High
RevPAR days show a more even spread of
business across all major segments
Data
Average of rn % tcnc
Average of rn % tcc
Average of rn % tlnc
Average of rn % tlo
Average of rn % grpass
Average of rn % gc
Average of rn % gl
Average of rn % other
Average of rn % comp ttl
25
20
15
10
5
0
Weekday
Weekend
Weekday
high
Weekend
Low
RevPAR HLM Day Type
Comparison of business mixes that resulted in particularly high RevPAR –
split by Weekdays and Weekends
Data
Weekday
Weekend
Average of rn % tcnc
9.61
18.83
Average of rn % tcc
20.04
15.20
Average of rn % tlnc
3.11
8.73
Average of rn % tlo
8.51
18.48
Average of rn % grpass
23.73
6.39
Average of rn % gc
19.74
18.22
Average of rn % gl
9.82
10.95
Average of rn % other
5.45
3.21
Summary –
Highest midweek RevPAR comes from roughly a 40:60 split between Transients & Groups
Highest weekend RevPAR comes from the opposite, a 60:40 split between Transients & Groups
Our analysis shows that these are the business mixes that have historically resulted in the highest
RevPAR results. If we can now replicate this business mix more often then, logically, we should enjoy an
improved overall RevPAR performance
Comparison of business mixes that resulted in particularly high –
Seasonality
Furthermore, our Sales & Marketing Dept wanted to know whether this mix changed with seasonality.
Run Charts of RevPAR over time clearly show a seasonal cycle of low and high periods…but did the
business mix change to reflect this at different times of year? Additionally, our Revenue Management
software system (TLPE) operates with ‘seasonal’ parameters. These same parameters were used
during this analysis
Data
August
Low
Average of rn % tcnc
15.93
Average of rn % tcc
17.73
Average of rn % tlnc
3.54
Average of rn % tlo
14.76
Average of rn % grpass
4.51
Average of rn % gc
31.81
Average of rn % gl
7.43
Average of rn % other
4.29
Peak
14.25
14.85
10.48
12.31
11.38
1.42
32.13
3.19
Summary – (figures below are approximated)
Low Season = 55 : 45 split between Transients and Groups
Shoulder Season = 55 : 45 split between Transients and Groups
Peak Season = 40 : 60 split between Transients and Groups
August = 55 : 45 split between Transients and Groups
7.33
18.61
2.90
6.76
38.47
11.46
8.70
5.77
Shoulder
11.89
22.91
8.81
11.36
16.94
11.60
11.64
4.85
Verification of Business Mix
Given the results seen so far, the Sales & Marketing dept thought that the influence of Standard Life
may have been effecting the figures.
Standard Life was our biggest volume corporate account up until 2002 when we made a strategic
decision to terminate the contract on the basis that the rate was too low and it was diluting our ADR
The Standard Life contract was terminated in April ’03 so we decided to run the same analysis only from
May ’03 onwards to see if there was any marked difference
Percentage Group Rooms May 03 - May 04
100
80
90
70
80
60
70
50
% Group
% Trans
Percentage Transient Rooms May 03 - May 04
60
50
40
30
40
20
30
10
20
0
HIGH
LOW
RevPAR High or Low
HIGH
LOW
RevPAR High or Low
The results stayed exactly the same. Minitab Boxplots were another way of displaying the same data.
These two boxplots clearly show that the High RevPAR days (green) occur when there is roughly 40%
Transient rooms and 60% Groups
Verification of Business Mix
Further verification was done using the monthly figures from the hotel’s P&L rather than the daily figures
from the Revenue Plan. This meant that we had much fewer individual data points but there were some
interesting patterns within this data that had not shown up in the daily figures.
Regression Plot
RevPAR = 58.8612 + 0.0718999 Grp Leis Occ
+ 0.0000108 Grp Leis Occ**2 - 0.0000001 Grp Leis Occ**3
S = 12.1000
R-Sq = 48.1 %
R-Sq(adj) = 42.2 %
Analysis of this segment shows a strong curvilinear
relationship with the highest R-sq value achieved using a
cubic regression model.
120
110
100
This model shows that RevPAR increases along with
Group Leisure rooms up to a threshold of roughly 700
rooms a month. Beyond this, the lower rates in this
segment begins to dilute the overall RevPAR and pull it
back down
90
RevPAR
For instance, the Group Leisure segment showed a strong
correlation (p=0.001 and r = 0.68).
80
70
60
Average
number of occupied rms per month over the
Regression
same data
collection period was 5859.
95% CI
50
40
30
0
100
200
300
400
500
600
Grp Leis Occ
700
800
900
PI
So, an 95%
optimum
of 700 Group Leisure rooms out of 5859
in total = 12% (which verifies exactly the % mix for this
segment from the comparative analysis – see slide 16)
Up to a threshold of 700 rooms per month (12% of total mix) the Group Leisure
segment contributes positively to overall RevPAR. Beyond this threshold, an
increasing level of transient leisure rooms begins to dilute the overall yield
Further Verification of Business Mix
The analysis so far had been based on percentages of occupied rooms and did not take into account
variation in the actual number of occupied rooms. To filter out these effects, the data was filtered to use
only dates that had exactly the same number of occupied rooms.
The highest frequency was found with 254 occupied rooms, for which we had 21 separate dates. These
21 data points, each with 254 occupied rooms were then subjected to the same analysis as before
100%
90%
80%
70%
Data
Average of rn % other
Average of rn % gl
Average of rn % gc
Average of rn % grpass
Average of rn % tlo
Average of rn % tlnc
Average of rn % tcc
Average of rn % tcnc
60%
50%
40%
30%
20%
10%
0%
Medium
high
RevPAR HLM
Owing to the much smaller sample size, there
were actually no ‘Low’ days within this dataset (254
rooms = 97% occupancy so this is not surprising). Again, the main difference that characterises the
‘High’ days is the higher percentage of Group rooms (esp Grp Assoc.) and reduced volumes of
Transient (esp Trans Corp Contracted)
Effect of Business Mix on Rooms Profitability
One key concern was whether all this work on increasing RevPAR would ultimately make it’s way to the
bottom line. In order to test this, we ran regression plots of RevPAR against Rooms Division profit
figures from the hotel’s P&L
Correlation between RevPAR and Rooms
Division Profit shows an extremely strong
relationship with 98.4% of the variation in profit
being explained by the changes in RevPAR.
The relationship is a positive linear one showing
that the greater RevPAR which can be
achieved, the higher the profit.
Regression Plot
rooms div pr = -199554 + 14335.9 RevPAR
- 138.919 RevPAR**2 + 0.770159 RevPAR**3
S = 15663.4
R-Sq = 98.4 %
R-Sq(adj) = 98.2 %
800000
rooms div pr
700000
This shows clearly that our efforts in increasing
RevPAR will translate into increased profits
600000
Interestingly, using a cubic regression model,
the relationship shows a slight upward curve as
the RevPAR increases. This suggests that as
RevPAR increases beyond about £90, the profit
increases
more-than-proportionately.
Regression
500000
400000
300000
95% CI
200000
50
60
70
80
RevPAR
90
100
110
Given the
fixed cost-base of
95% relatively
PI
occupying a room, this makes perfect sense!
As, once those fixed costs are covered, any
additional RevPAR will flowthrough to the profit
line at a higher and higher percentage
Effect of Increased Levels of Group Business on Revenues
Putting a financial value to this project centred around to main areas.
1)
The improved ADR that we get from Group segments will displace and outweigh any
impact on transient business
2)
As Group Rooms usually bring other revenue generating elements with them (eg banqueting,
room hire etc), there proved to be a significant differential between incremental spend of Transient
and Group rooms
Rolled-up Monthly ADR figures for Groups &
Transients show that Groups (£113)
command a significantly higher ADR than
Transients (£104).
Increasing our proportion of Group Rooms
should improve our overall ADR and RevPAR
140
130
Group Incremental Rev per Room Night = £115.78
120
ADR
113.003
110
Transient Incr Revenue per Room Night = £42.27
104.185
100
90
80
70
group
trans
segment
negative
Process Improvements
How do we sell more Group Rooms?
Sales Team Strategy
•
Property Sales Team move to spend 70% of their sales time concentrating on accounts that will
generate Group Rooms rather than the current situation where they spend 70% account
managing transient corporate clients
•
Sales Team to take enquiries through to contracting stage themselves to improve conversion.
Currently the sales team hand-off all enquiries to Events as soon as an enquiry ariives
•
Sales Team to have regular reviews with local DMC’s, Tourist Board, Edinburgh Convention
Bureau and Edinburgh International Conference Centre to ensure that we are aware of future
city-wide events and forecasted residential demand
Events Team Strategy
•
Re-structure of Events team from multi-skilled all-rounders into specialist ‘sellers’ and ‘planners’
to, again, improve conversion. ‘Sellers’ report to Events Manager with dotted line reporting to
Sales Manager. ‘Planners’ report only to Events Manager
•
Weekly meeting between Sales & Events ‘Sellers’ to review business and focus on converting
existing tentative groups
•
Implementation of an MS Access Group & Event Enquiry Log to track the source, size and type
of bookings that are coming in. Data from this tool is then used to actively call back turned-down
business, track rate sensitivity, geographical source (which is then linked into planning of sales
trips etc). While Opera S&C system can capture this data, slow speed issues meant that
operators were not recording this data rather than wait for the system to catch-up
Process Improvements
Pricing
•
Increase commissions payable to booking agents from an uncompetitive 8% to a local industry
standard 10%
•
Use TLGO system to develop an at-a-glance price banding tool in order to offer a Best Available
Group Rate on any given day. Price banding is based on existing business and forecasted
demand
•
Development of a Group forecasting and ‘Pace’ report
Linked F&B Strategy
•
Develop a ‘social calendar’ of local events that are currently being held at competitors that we
can actively target and ‘steal’