Analysis of Accommodation Price Differentiation at CHL Hospitality

This case study analyzes the accommodation pricing strategy of CHL Hospitality, examining price differences across different brands (Resort, Cottage, Classic, and Comfort), states (NSW, QLD, VIC), and locations (Metropolitan and Regional cities). The study uses descriptive statistics, ANOVA, and t...Read More

Action plan
Action Objective Hypothesis Data
organization
Analysis method
Issue one To determine current
average pricing for
accommodation by
brands, states and
locations
_ Data will be
organized using
a Pivot table
Descriptive analysis
will be applied to
derive the average
mean prices
Issue two To determine whether
price differentiation exist
among the
accommodation brands
H0: The
accommodation
price is uniform
among
accommodation
brands.
H1: Price
differentiation
exist among the
accommodation
brands.
The prices will
be organized
into three
accommodation
brands i.e.
Resort, Cottage
and Classic
using filter and
placed in a
separate excel
sheet.
Analysis of variance
technique will be to
test the hypothesis by
comparing the p value
of the F ratio with
default value at 95%
level of significance.
Bar graph and table
will be used for
presentation.
Issue three To determine whether
price differentiation exist
among the states
H0: The
accommodation
price is uniform
among the states.
H1: Price
differentiation
exist among the
states.
The prices will
be organized
into three states
i.e. NSW, QLD
and VIC
using filter and
placed in a
separate excel
sheet.
One-way analysis of
variance from excel
add in analysis tool
Pak.
Bar graph and table for
presentation
Issue four To determine whether
price differentiation exist
between the locations
H0: The
accommodation
price is uniform
between the
locations.
H1: Price
differentiation
exist between the
locations.
The prices will
be organized
into two
locations i.e.
Metropolitan
cities and
Regional cities
using filter and
placed in a
separate excel
sheet.
Two independent t test
to compare prices of
the two locations.
Tables and bar graph
will be used to
represent the findings.
Issue five Determine whether
introduction of the
comfort brand has
increased competition
among the
accommodation brands
- Pivot table will
be used to
organize the
data.
Descriptive statistics

Report
Results and findings
Average accommodation prices by Accommodation brands
Brands
Average of
Price
1 - Resort 200.2704167
2 - Cottage 202.65625
3 - Classic 201.4627083
Grand Total 201.463125
Average accommodation prices by States
State Average Price
1 - NSW 201.02375
2 - QLD 202.196875
3 - VIC 201.16875
Grand
Total 201.463125
Average accommodation prices by Locations
Location
Average of
Price
1 - Metropolitan
Cities 201.5591667
2 - Regional
Cities 201.3670833
Grand Total 201.463125
Comparison of accommodation prices by accommodation brands
ANOVA
Source of
Variation SS df MS F P-value F crit
Accommodati
on 136.6128292 2
68.3064
1
7.19034
6
0.00106
3
3.06029
2
Error 1339.463265 141
9.49973
9
Total 1476.076094 143

Resort Cottage Classic
199
199.5
200
200.5
201
201.5
202
202.5
203
200.2704167
202.65625
201.4627083
Graphical representation of average prices
against accommodation brand
Accommodatoion brand
Accomodation price
Comparison of accommodation prices by accommodation states
SUMMARY
State Count Sum Average
Varianc
e
NSW 48
9649.1
4 201.0238
9.70880
3
QLD 48
9705.4
5 202.1969
11.9640
2
VIC 48 9656.1 201.1688
8.89754
7
ANOVA
Source of
Variation SS df MS F P-value F crit
State 39.2686125 2 19.63431
1.92679
8
0.14942
8
3.06029
2
Error 1436.807481 141 10.19012
Total 1476.076094 143

NSW QLD VIC
200.4
200.6
200.8
201
201.2
201.4
201.6
201.8
202
202.2
202.4
201.02375
202.196875
201.16875
Graphical representation of average prices
against different states
States
Average price
Comparison of accommodation prices by accommodation Locations
t-Test: Two-Sample Assuming Equal Variances
Metropolitan cities Regional cities
Mean 201.5591667 201.3670833
Variance 11.25641901 9.514677289
Observations 72 72
Pooled Variance 10.38554815
Hypothesized Mean Difference 0
df 142
t Stat 0.357623642
P(T<=t) one-tail 0.360577987
t Critical one-tail 1.655655173
P(T<=t) two-tail 0.721155975
t Critical two-tail 1.976810994

48; 50%48; 50%
Pie chart representation of comfort
Yes No
Discussions
It was found that average accommodation prices of resort, cottage and classic were 200.27,
2002.65 and 2001.46 respectively as shown in the tables above. The accommodation prices in
terms of states were 201.02, 2002.20,201.46 for NSW, QLD and VIC respectively. The current
accommodation prices in terms locations that were establish were 201.56 and 201.37 for
Metropolitan and Regional areas respectively. NSW resort was found to be slightly cheaper than
the rest of the accommodation brands.
In comparing the accommodation prices among the accommodation brands, the p value obtained
from ANOVA table was 0.00106, while was less than 0.05 at 95% level of significance. We
therefore rejected null hypothesis and concluded that there was enough evidence to conclude that
statistically significant price differentiation existed among the accommodation brands. The
accommodation brand that had the highest prices was cottage, followed by classic then resort as
shown by the graph above.
In comparison of the prices among the states, the p value obtained was 0.1494 as shown in the
ANOVA table above, which was greater than 0.05. Thus, we failed to reject null hypothesis at
95% level of significance. We therefore concluded that there was no enough evidence to support
existence of price differentiation among the states. Even though QLD was found to have high
prices as compared to NSW and VIC as shown in the graphs above, the difference was not
statistically significant. The customer has no choices to choose from if is to decide the state in
which the cost is lower but rather choose any state because the accommodation prices do not
differ significantly.
In comparing accommodation prices by locations, Metropolitan cities were found to have
slightly higher prices (201.25) than regional cities (201.08) as shown in the t test table above.

The p value of the t statistic was 0.845 which was higher than 0.05 at 95% level of significance.
Null hypothesis was accepted meaning that there was no enough evidence to support price
differences between the locations, therefore, the accommodation prices in the two locations were
similar meaning customers have no choices to choose from.
The number of respondents who reported that the comfort has increased competition and those
who denied were equal (48) as shown in the graph above. This means that the introduction of
comfort brand has not increased the competition among the other accommodation brands.
References
Ito, P. K. (1980). 7 robustness of anova and manova test procedures. Handbook of statistics, 1,
199-236.
Fujikoshi, Y., Ohmae, M., & Yanagihara, H. (1999). Asymptotic approximations of the null
distribution of the one-way ANOVA test statistic under nonnormality. Journal of the Japan
Statistical Society, 29(2), 147-161.
Kock, Ned. "Using WarpPLS in e-collaboration studies: Descriptive statistics, settings, and key
analysis results." International Journal of e-Collaboration (IJeC) 7, no. 2 (2011): 1-18.
Yamane, T. (1973). Statistics: An introductory analysis.
Heiberger, R. M., & Neuwirth, E. (2009). R through Excel: A spreadsheet interface for statistics,
data analysis, and graphics (pp. 323-330). New York: Springer.