CHAPTER 4 DATA ANALYSIS - Institut Teknologi...

is by calculating the data, and analyze the data that has been gathered. We

calculate the data by implementing the steps that is in this research

3.5. Proposed Solution

In this step the writer will give a point of view towards the problem that is

happening by using theory that has been studied previously. If it is

possible, a solution will be given towards the company to decrease or to

solve the problem in the company.

3.6. Conclusion and Recommendation

From the data analyzing that we have received in the last step, we can then

take a couple conclusion that can answer the purpose of the research that

we want to gain. Besides that, from the end result of the data, we can

expect an advice that will be useful and will be given to the company.

CHAPTER 4

DATA ANALYSIS

4.1 Company Profile

The company’s data was received from results of the interview and direct

observation, which covered: the history of the company, the company's

vision and mission, and the working methodology in this company.

4.1.1 Company History

Grand Pasundan Hotel is a modern hotel with a new concept, which offers

a unique combination of comprehensive business services and resort

amenities and all guest rooms are beautifully appointed with rich fabrics

and dark wood furniture, called the convention hotel concept.

It was built in year 2002 under PT Graha Bandung Kencana management.

The top management consists of Mr. Wimpy Setiawan Ibrahim as the

CEO, Mr. Denny Muliaman, SE, MBA and Mrs. Feny Lusiana Ibrahim as

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the board of directors, and Mrs. Lena Herwati as the commissioner.

The Grand Opening Grand Pasundan Convention Hotel was held on first

of March 2003. The hotel legality aspect is based on SIUK (Surat Ijin

Usaha Kepariwisataan) No. 556/ Si. 332 HM SW Dispar 24th December

2002, and its classified as three stars hotel.

Located in the west of Bandung, Jl. Peta 147-149 Bandung 40233, only 15

minutes from Airport and 20 minutes from Railway Station. The hotel also

has easy access about 2 km from Pasirkoja Toll Gate.

Figure 4.1 Hotel Map

4.1.2 Vision, Mission, and Company Structure

Grand Pasundan Convention Hotel vision is “ To create Grand Pasundan

Convention Hotel as the best hotel for Meetings, Incentives, Convention,

Exhibitions (MICE) in Bandung.”

The mission is “Introducing Grand Pasundan Convention Hotel to people

in Bandung, and in Indonesia generally.”

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Figure 4.2 Organization Structure

Food & Beverage department organize and manage the multiple,

additional restaurant operations, 24-hour service, banquet operations, and a

wide range of other F&B alternatives. These include snack bar, coffee

break, and meal services as part of convention/meeting operations, take-

out, and off-site catering, and alcoholic beverage outlets in lounges, show

bars, and lobby areas.

House Keeping department in a hotel is responsible for the hotel’s

cleanliness. There are three primary areas within the hotel that are

responsibility of the housekeeping department: (1) public spaces such as

restrooms, exercise areas, pools, lobbies, and corridors, (2) guest rooms;

also calculate the guest supplies, and (3) the hotel’s laundry area.

Front Office department is responsible for: (1) establishing and the daily

monitoring of room rates, (2) managing the reservation process, (3)

providing for guest services including services related to guest registration,

guest information, and guest assistance, and (4) managing the front-office-

related accounting and data collection process.

Human Resource & Development department responsible with concerns

relating to recruitment, selection, orientation, training, performance

evaluation, compensation, labor relations, safety and health, legal, and a

wide range of other specialized tasks.

Sales & Marketing department responsible in: (1) planning the hotel’s

sales and marketing strategy, (2) preparing and issuing sales contracts in a

timely manner, (3) maintaining accurate sales records, forecasts, and

histories, (4) coordinating and communicating special client requests with

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the affected hotel departments, (5) hosting client during their stay, (6)

conducting site tours.

Engineering & Maintenance department responsible in: (1) protecting

and enhancing the financial value of the building and grounds for the

hotel’s owners, (2) controlling maintenance and repair costs, (3)

controlling energy usage, (4) ensuring the safety of those working in and

visiting the hotel.

Accounting department responsible to oversee the accounting and

bookkeeping functions in the hotel. This includes the development of the

systems to collect and report financial information.

4.1.3 The Product of Company

The hotel offers 7 products which consists of:

• Various types of rooms: Studio, Deluxe, Superior, Suite, and Presidential suite

• Health Spa & Fitness

• Swimming Pool & Poolside Cafeteria

• Live Music Bar & Restaurant

• Convention hall & Meeting Rooms

• Business Center & Drugstore

• Bar Lounge

Room Description

They provide 77 comfortable guest rooms consisting of Studio, Deluxe,

Superior, Suite, and Presidential Suite. Each room is facilitated with star

hotel standard. All types of the suites have the most up to date equipments.

The hotel provides selected non-smoking guest rooms, available at each

floor.

Table 4.1 Types & Amount of Room

Studio 15 rooms

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Deluxe 50 rooms

Superior 9 rooms

Suite 2 rooms

Presidential Suite 1 room

Room Facilities:

• Coffee and Tea-making facilities

• Fully Stocked Mini-bars

• IDD-Telephone

• Luxurious terry-cloth Bathrobes

• Complimentary Toiletries

• 24 Hour Room service

• Daily maid service

• Newspapers

• Video, TV

Convention Hall

Grand Pasundan Hotel is the only hotel that has the largest banquet in

Bandung. Called The Malibu Dome, suitable for conferences, conventions

and weddings. It can cater up to two thousand visitors. An elegance hall

furnished with selected materials to provide comfort ness, it can be equally

part size for smaller needs.

Meeting Rooms

Grand Pasundan Convention Hotel provides two different size of meeting

rooms, Sangkuriang, Room that is able to accommodate up to 200

participants and Dayang Sumbi Room, which is available for 25 seats.

Dining

The Cozy Corner Restaurant offers Indonesian, Oriental and European

favorites with Lasagna Nicole and Hainan Chicken Rice as their

specialties. They also provide Live Music every Friday and Saturday starts

at 20.00 - 24.00 WIB

Leisure Facilities

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They also have places for leisure activities such as gym and fitness centre,

spa, swimming pool, warm water pool for children and poolside cafeteria.

Other Facilities

They also provide other facilities to help the customer such as:

• Airport transfer

• Business centre

• Pastry shop & Drug store

• Lounge bar

• Modern function rooms

• Complete convention facilities

• Wedding & Banquet planning services

4.2 Company Pricing Process

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Figure 4.3 Pricing Process

We can see the pricing process in Grand Pasundan Convention Hotel in

Figure 4.3. General Manager conducts the pricing process with the

cooperation between Marketing division, Front Office division, House

Keeping division, and Accounting division.

Table 4.2 Division Pricing Assignment

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Division Input Process Output

General Manager Company’s Achievement Data

Analyzing previous condition to set in the future condition

Company’s New Target, Action Plan (pricing strategies)

Room Division Department

Fixed & Variable cost, market analysis, price factors

Calculating room rates based on data anaysis on historical data

Room Rates

Marketing Division

Strength, Weakness, Threat, and Opportunities of the company

SWOT analysis matrix

Strategies to deal with the market demand

Accounting Division

Variable product from guest supplies

Pricing survey of the variable product

Cost of variable product

The first thing to do is to estimate the market demand from marketing

division by analyzing relevant factors such as:

• Historical administrative data, which includes numbers of room

being sold.

• Peak season, such as public and school holidays.

• Tourism growth in the area and its surroundings.

• SWOT analysis on marketing:

Internal – Weakness:

• Low room capacity

• Hotel location which is quite far from city and shopping center

Internal – Strength:

• Large capacity of Convention Hall

• Situated nearly the industrial area

• Flexible room rates

• Good room quality & services

External – Threat:

• Possibility of inflation (5% according to BPS)

• Four stars hotels offers a competitive price

• Company which has large scale of building offers the place

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for meeting & wedding with a competitive price

External – Opportunities:

• Tourism growth in Bandung predicted to be increase until 11% in 2008

• Increasing demand in high season, & public holidays.

• Strategic transportation access (Pasirkoja Toll gate)

Table 4.3 SWOT Analysis Matrix

SWOT/TOWS Analysis

Threat:

• Inflation 5%

• Competitive price from 4 stars hotels

• Price War

Opportunities:

• Tourism growth (11%)

• Increase demand in high season

• Strategic transportation access

Weakness:

• Low room capacity

• Located far from city

1. Increase promotion2. Setting Dynamic

Pricing

1. Increase the promotion especially near the toll gate

2. Increase price in high season

Strength:

• Large Convention Hall

• Around industrial area

• Flexible room rate

• Good room quality & services

1. Create attractive meeting packages, and discounted room rates

1. Increase the promotion2. Increase the average room

rate, and setting the pricing dynamic

3. Approach customer with promotion for business gathering, wedding, and meetings

o T – W Strategy: Increasing the promotion and setting dynamic pricing

is useful to increase the demand which leads to increase in revenue,

setting an attractive two prices level could also increase our

competitiveness among hotels in Bandung. This strategy is to establish

a defensive plan to prevent the firm’s weaknesses from making it

highly susceptible to external threats.

o O – W Strategy: The Grand Pasundan Hotel located near the toll gate

but far from the city. The other opportunity is increase in tourism

growth and increase demand in high season. The strategy is to

overcome our weaknesses to pursue the opportunity.

o T – S Strategy: This strategy identifies ways of the Grand Pasundan

Hotel to use the company strength to reduce its vulnerability to

external threats.

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o O – S Strategy: This strategy is to pursue the opportunities that are

good fit to the company’s strengths. Increasing the promotion is to

increase the demand and deal with the weaknesses, threat, and the

market opportunities. Setting the dynamic pricing strategy to attract

customer and deal with the competitor is a proper strategy to reach the

opportunity by using the company’s strength.

The conclusion of this SWOT matrix analysis is that the author chose to

set the dynamic pricing strategy which is a good fit strategy to pursue the

opportunities with the company’s strength in order to maximizing the

revenue.

Figure 4.4 Pricing Step

After estimating the demand, Room Division Department should estimate

the price factor, both internal factors and external factors. Internal factors

consist of human resources and hotel facilities. External factors cover

challenges posed by competitors, government tax, minimum award wages

(UMR), peak seasons, and the possibility of inflation.

In my case study on the Grand Pasundan Hotel, the Room Division

Department, a liaison between the Front Office and the House Keeping, is

in charge of estimating appropriate room rates. This department collects

the data about operating fixed price and variable products (products which

is not always used) of each room including guest supplies (such as

toiletries, sewing kits, etc.), manpower, electricity and water usage along

with repair & maintenance.

This data is transferred to the Accounting Division. This division

calculates the operating price, using this data and the variable costs based

on a pricing survey of variable products.

The Marketing Division of the hotel then conducts a SWOT analysis on

the market demand in regards to room rates.

The final decision on setting the price lies with the General Manager. The

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Estimate Demand

Price Factor

Setting the ADR

Published as a coherent

with action plan

GM reviews the estimation put forth by the Room Division Department

before setting the final rates. If he finds the estimation to be insufficient in

any way, the process would start again in pursuit to find a rate that is

compatible with the company’s target. The target is set by the GM, which

includes expected room revenue and expected occupancy rate. The room

rate factors are explained below in Table 4.3.

Table 4.4 Room Rate Factors

Factors Components Remarks

Estimating Demand

• Historical data

• Seasons

• Tourism growth

• SWOT analysis

Room sold data, achievement analysisPublic and school holidaysE.g: New mall, business opportunitiesStrategies to anticipate the market demand

Price Factor Internal• Human resources• Hotel Facilities

External• Competitor• Government Tax• UMR• Seasons• Possibility of inflation

Effectiveness of the employeesThe facilities that hotel offers

Price war from competitorE.g: PPh, PPn, PBB, etc.Minimum award wagesPublic and school holidaysInflation rate 5% (BPS 2007)

Operating Cost The average operating cost consist of:

• Guest Supplies• Manpower• Room type• Electricity and water

usage• Repair & maintenance

The average daily occupied room cost per type:

• Studio = Rp 100,000

• Deluxe = Rp 136,000

• Superior = Rp 150,000

• Suite = Rp 200,000

• P. Suite = Rp 300,000

Setting Room rate Based on all above factors and the company’s target. Company will create action plan every month to achieve the company’s target.

The published rate

• Studio = Rp 380,000

• Deluxe = Rp 600,000

• Superior = Rp 950,000

• Suite = Rp 1,200,000

• P. Suite = Rp 1,900,000

4.2.1 The Pricing Strategies 2007

This strategies set by the General Manager with a consideration from the

owner’s expectation. They published the rate a month before the season

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comes. The strategies are generally same for each year, but it moves under

some circumstances such as Ramadhan and Lebaran season. The

maximum discount given is 35-50% based on the company policy. The

average discount rate given in this hotel is 35% from the published rate,

they give the discount rate based on advanced booking reservation and

other condition. This strategy is often used by a mere guess by the room

division department, rather than an exact formulation.

• January, discounted 35% from the published rate; the discount rate

is given based on owner’s expectation. The reason is because on

the first month the market demand usually drop based on historical

data, and market analysis. They set the low rate to attract customer

for everyday in this month.

• February – March, they set the business package; it is a package of

rooms on the published rate with the benefits includes meeting

rooms, laundry, breakfast, lunch and dinner, which is attracting

company to use the hotel facilities such as meeting rooms,

convention hall, and also room with benefits for example, breakfast

and dinner. The result is on February the revenue increases because

the revenue from corporate MICE in Grand Pasundan Hotel

increases.

• April, discounted 30% from the published rate; it is given based on

owner’s expectation. The reason is because there are no public

holiday this month, the management set the low rate to attract

customer for everyday in this month.

• May – June – July – August, The management set the school

holiday package; it is a set of packages for family (4 pax) includes

breakfast and dinner for 4 persons, which is attracting the

customer. The price used is the published rate, with extra benefits

laundry and mini bar.

• September, Ramadhan package, they use different price on Sunday

and Monday, which is 50% discount from the published rate at all

types of rooms. The other day of the week use the same as

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published rate. The reason is because based on experienced, the

market demand on Ramadhan usually decrease on Sunday and

Monday. They set the price to attract customer.

• October, it is the Lebaran season. They set the same price as the

published rate, but different price only for studio type. The

management increases the studio rate from Rp 380,000 to Rp

500,000 because it’s a high season.

• November, The management set an attractive price for deluxe

room, they reduce its price from Rp 600,000 to Rp 399,000 with

breakfast and mini bar. The other type still uses the same published

rate. The reason is because the deluxe room has the biggest

capacity (number of room available).

• December, in this high season, the hotel management increases its

price up to 30% for every class excluding the presidential suite.

The rate is increased as the demand increase on this month. They

also put extra benefits for breakfast and dinner.

From the Table 4.5 and Table 4.6 below we can see there are:

• No. of Rooms : Total number of rooms available each day of

month, the total room of Grand Pasundan Hotel is 77 rooms, there

are such factor like in house use and room out of order which affect

in total number room available.

• No. of Days: Total number of days of month, by calendar.

• Room Night Available: Total number of room available in month;

it is equal to No. of Room x No. of Day.

• Room Night Occupied: Total number of room being sold each

month.

• Room Occupied per Day: Average number of room being sold per

Day.

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• Guest Night Occupied: Total number of guest arrived in month.

• Occupancy %: Percentage of room being sold in month; it is equal

to (RNO / RNA) x 100%.

• Double Occupancy %: Percentage of guest who stay for two person

for each room; it is equal to ((GNO-RNO) / RNO) x 100%

• Average Room Rate: Average price of room being sold for each

month; it is equal to Room Revenue each month divided with the

RNO.

• Room Revenue: The total room revenue in month.

As we can see from the Table 4.5 there is a hotel target for the year 2007,

they have a different strategy every month. They set the pricing strategy

based on the forecast demand and their published rate. So, they put a

different price every month based on their action plan.

Compared to the company’s target, Table 4.6 shows that the Grand

Pasundan Hotel achieved it’s target but in certain month, they did not

achieve the target because in some month they could not estimate the

demand correctly. The pricing formula is not good enough with the current

strategy.

From the data in Table 4.7, we can say that the high season is in school

holiday which is May, June, July, August, and in the end of the year which

is November and December.

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Table 4.5 Grand Pasundan Hotel Target 2007

Description January February March April May June July August September October November December JAN - DEC

2007

No of Rooms 75 75 75 75 75 75 75 76 76 76 76 76 905

No Of Days 31 28 31 30 31 30 31 31 30 31 30 31 365

Room Night Available

2,325 2,100 2,325 2,250 2,325 2,250 2,325 2,356 2,280 2,263 2,186 2,263 27,248

Room Night Occupied

1,423 1,203 1,478 1,476 1,546 1,456 1,695 1,531 1,026 1,490 1,230 1,395 16,949

Room Occupied Perday

46 43 48 49 50 49 55 49 34 48 41 45 46

Guest Night Occupied

2,523 2,032 2,418 2,515 2,713 2,531 3,237 3,035 1,816 2,682 2,214 2,511 30,227

Occupancy % 61.20 57.29 63.57 65.60 66.49 64.71 72.90 64.98 45.00 65.84 56.27 61.64 62.20

Dbl Occupancy %

77.30 68.91 63.60 70.39 75.49 73.83 90.97 98.24 77.00 80.00 80.00 80.00 78.34

Average Room Rate

245,500 246,185 245,956 244,241 254,500 250,343 255,162 295,000 305,000 335,000 295,000 335,000 274,582

Room Revenue 349,346,500 296,160,555 363,523,500 360,500,250 393,457,000 364,500,000 432,500,000 451,645,000 312,929,999 499,150,000 362,850,000 467,325,000 4,653,887,804

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Table 4.6 Grand Pasundan Hotel Achievement 2007

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Description January February March April May June July August September October November December JAN - DEC

2007

No of Rooms 76 76 76 76 76 76 76 76 76 76 76 76 912

No Of Days 31 28 31 30 31 30 31 31 30 31 30 31 365

Room Night Available

2,325 2,100 2,325 2,278 2,356 2,275 2,356 2,353 2,217 2,262 2,177 2,259 27,283

Room Night Occupied

1,383 1,519 1,268 1,082 1,362 1,671 1,577 1,682 1,159 1,246 1,633 1,553 17,135

Room Occupied Perday

45 49 41 35 44 54 51 54 37 40 53 50 47

Guest Night Occupied

2,529 2,857.00 2,254 1,776 2,207 3,319 2,940 3,035 2,115 2,257 2,875 2,692 30,856

Occupancy % 59.48 72.33 54.54 47.50 57.81 73.45 66.94 71.48 52.28 55.08 75.01 68.75 62.80

Dbl Occupancy %

82.86 88.08 77.76 64.14 62.04 98.62 86.43 80.44 82.48 81.14 76.06 73.34 80.08

Average Room Rate

249,861 252,306 279,625 286,085 278,533 295,691 308,972 288,457 292,253 340,092 284,100 355,970 292,854

Room Revenue 345,557,763 383,252,814 354,564,500 309,543,970 379,361,946 494,099,661 487,248,844 485,183,918 338,720,653 423,754,546 463,936,115 552,821,492 5,018,046,222

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Table 4.7 Room Sold by Type of Room

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Type of Room January February March April May June July August September October November December Jan - Dec 2007

Studio 298 265 288 289 313 411 331 318 258 230 331 313 3,645

Deluxe 966 1,079 846 650 931 1,111 1,083 1,228 787 829 1,116 1,001 11,627

Suite Room 15 19 14 14 11 12 15 22 11 21 22 34 210

Superior 102 153 114 127 102 135 145 110 101 135 134 174 1,532

Presidential Suite 2 3 6 2 5 2 3 4 2 31 30 31 121

Total Room Night Sold (RNO)

1,383 1,519 1,268 1,082 1,362 1,671 1,577 1,682 1,159 1,246 1,633 1,553 17,135

4.2.2 Weakness Point from the Existing System:

The Grand Pasundan Convention Hotel use a seasonal strategy which makes the demand analysis also seasonal. The demand

forecast that the management applied include:

• Historical data, an administrative data which includes number of room being sold, the data will be forecasted to get the

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approximate demand in the next period;

• Seasons, a period of the year characterized by a particular climatic feature or marked by a particular activity or event,

the season directly affect the demand and it has to be yet considered;

• Tourism growth, the process of increasing in places for vacation and places of interest, the growth number represent the

number of possibility of increasing demand and will be considered for the demand forecast;

• SWOT analysis, it is a demand forecast factor which analyse the strategy to adapt with the company strength,

weakness, opportunity, and threat.

They set a same rate in everyday of the month. From the historical data, it is apparent that low seasons require different tariff to

achieve the target revenue. Compared to the company’s target, Table 4.6 shows that the Grand Pasundan Hotel achieved it’s

target, but not in certain month. The pricing formula is not good enough with the current strategy. They could increase revenue

by using dynamic pricing. The reason why they set it monthly based because they picture the demand as in seasons only, not

day by day. Their daily target is set by dividing the monthly target with the number of days. The current strategy ignore the

opportunity of daily demand; an opportunity to attract more customer by setting different tariff, which leads to increase in

revenue. The weakness in this strategy is they did not have the exact formula to set the discount rate and how many rooms that

they should protect for high fare.. They should revaluate their strategy and consider the dynamic pricing with protection level

and booking limit.

4.3 Data Analysis

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From the data that was obtain from Grand Pasundan Convention Hotel, the company has a problem in pricing strategy:

Table 4.8 Company’s Pricing Problem

Problem Reason

Pricing Strategies They ignore the daily demand differences; an opportunity to attract more customer by setting a different tariff, which leads to increase in revenue. They did not have the exact formula to set the discount rate and how many rooms that they should protect for high fare. They should revaluate their strategy.

To solve the problem, the author proposed a strategy using dynamic pricing with protection level and booking limit. This

revenue management method is to set the protection level and booking limit by using dynamic pricing set for everyday of the

year, the reason is that the current strategy is using flat rate and for seasonal strategy they set a discount rate, which is given to

every customers including an advance room booking. The benefit of this method is to maximize the revenue by setting two

prices level and set the number of rooms to protect with the high rate and the number of booking limit with low rate.

Monday RNO in 2007

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60

Week

RN

O

Monday

Figure 4.5 Monday Room Number Occupied in 2007

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Tuesday RNO in 2007

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60

Week

RN

O

Tuesday

Figure 4.6 Tuesday Room Number Occupied in 2007

Wednesday RNO in 2007

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50 60

Week

RN

O

Wednesday

Figure 4.7 Wednesday Room Number Occupied in 2007

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Thursday RNO in 2007

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60

Week

RN

OThursday

Figure 4.8 Thursday Room Number Occupied in 2007

Friday RNO in 2007

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50 60

Week

RN

O

Friday

Figure 4.9 Friday Room Number Occupied in 2007

50

Saturday RNO in 2007

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60

Week

RN

O

Saturday

Figure 4.10 Saturday Room Number Occupied in 2007

Sunday RNO in 2007

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60

Week

RN

O

Sunday

Figure 4.11 Sunday Room Number Occupied in 2007

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Demand vary by Day

2450

2500

2550

2600

2650

2700

0 1 2 3 4 5 6 7 8

Day

RN

O

Demand

Figure 4. 12 Daily Demand in 2007

Figures above show the room number occupied (RNO) in Grand Pasundan Hotel in 2007. This graph indicates the number of

daily room occupied. This historical data will be analyzed to be use in the proposed solution. The proposed solution is only for

Studio, Superior, and Deluxe type for the reason that the customer of the Suite and Presidential Suite type is consider being less

price-sensitive and also the total number of rooms is minimum.

4.3.1 Proposed Solution

Normally, hoteliers wanted to maximize their profits and charge the highest rate possible for their rooms. They still do.

However, the rate cannot be so high that it discourages guests from staying at the hotel. Similarly, the rate cannot be so low

that it prevents the hotel from being profitable. Therefore, the room rate charge should not result from a mere “guess” about its

appropriateness, but, ideally, would evolve from a rational examination of guest demand and a hotel’s costs of operation.

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Mathematically, such a rate should be easy to compute with specific and accurate assumptions.

This is an effective revenue management tactic to charge a higher price during the peak period and a lower price during off-

peak periods. The goal is not to shift demand but to increase demand during periods of low demand by attracting price-

sensitive customers, such as vacationing families, with a price discount.

Demand for hotel rooms is known to vary by day of the week. For Grand Pasundan Hotel, which targets business customers,

peak demand days occur in the middle of the week.

The first step is to analyze the historical data:

o RNO: Total number of room occupied

o RNA: Total number of room available

o Price: The published rate in the current strategy

Analyzing the RNO is to identify the type of data whether it is normal or Poisson. After that, we can get the mean of the data

distribution to be set as the expected high rate demand per day.

The price used in the current strategy (published rate) will be set as the high rate. The low rate in this strategy is the discount

rate, which is offered by the hotel for the customer who booked in advance.

The second step is to calculate the critical ratio, which is

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Cu = rh - rl = rh - rl

Co + Cu rl + (rh – rl) rh

In words, we want to find the Q such that there is a percent probability of high fare demand is Q or lower in the distribution

function table that we calculate with mean as the expected high rate demand. When the critical ratio falls between two values in

the distribution function table, choose the entry that leads to the higher decision variable.

After we identify the Q (optimal protection level), we could also find the expected lost sales (L(Q))with the distribution and

loss function table.

The third step is calculating the booking limit. The booking limit is the number of available capacity that can be reserved at low

fare.

Booking limit = RNA – protection level

The fourth step is to calculate the expected high rate booking. If we have the protection level, that means we protect the rooms

that will be sell at high rate. Then, we can compute the expected high rate booking as:

Expected high rate booking = Expected high rate demand – Expected lost sales

In other words, we expect to have the high rate bookings if we have protected rooms. We also calculate the expected number of

empty rooms which is:

Expected empty rooms = Q – Expected high rate booking

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The final step is to calculate the revenue as shown in Table:

o Revenue without protection level:

Low Rate x RNA

o Revenue with expected high rate booking:

(Low Rate x Booking limit) + (High Rate x Exp. high fare booking)

o Maximum expected revenue:

Exp. high fare demand x High rate + (RNA - Exp. high fare demand) x Low rate

o Expected revenue increase:

Rev. with exp. High fare booking – Rev. without any protection level

Rev. without any protection level

Table 4.9 Proposed Solution Remarks

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Dynamic Pricing with Protection Level and Booking Limit

High Rate Published Rate

Low Rate Discounted Rate

RNA Room Number Available

Exp. Hrate demand Mean of RNO (historical data)

Critical Ratio (High Rate - Low Rate)/High Rate

Protection level (Q) Q of Probability High Rate demand

Exp. Lost Sales L(Q) Loss function

Booking Limit RNA - Protection level

Exp. Hrate Booking Exp. Hrate demand - Exp. Lost sales

Exp. Empty room(s) Q - Exp. Hrate booking

Rev. w/o Protection level Low Rate x RNA

Rev. w/ exp. Hrate Booking (Low Rate x B.limit)+(High Rate x Exp. Hrate booking)

Maximum Exp. Revenue (Exp. Hrate demand x Hrate)+((RNA - Exp. Hrate demand)x Lrate)

Exp. Revenue Increase (%)(Rev. with Exp. Hrate booking - Rev. without Protection level)/ Rev. without Protection level)

Studio type

o RNA: 15

o Price: Rp 380,000. (High rate)

o Discount Rate: 35%

o Expected High rate demand (mean):

• Monday: 8.8, Poisson

• Tuesday: 8.83, Poisson

• Wednesday: 9.56, Poisson

56

One-Sample Kolmogorov-Smirnov Test

52 52 52 52 52 52 52

32.1346 32.7885 35.2885 35.3077 32.6346 31.5192 33.0577

.151 .138 .167 .176 .186 .187 .135

.151 .116 .167 .173 .186 .187 .131

-.134 -.138 -.145 -.176 -.151 -.183 -.135

1.089 .998 1.207 1.271 1.343 1.345 .976

.187 .272 .108 .079 .054 .054 .297

N

MeanPoisson Parametera,b

Absolute

Positive

Negative

Most ExtremeDifferences

Kolmogorov-Smirnov Z

Asymp. Sig. (2-tailed)

Monday Tuesday Wednesday Thursday Friday Saturday Sunday

Test distribution is Poisson.a.

Calculated from data.b.


52 52 52 52 52 52 52

32.1346 32.7885 35.2885 35.3077 32.6346 31.5192 33.0577

.151 .138 .167 .176 .186 .187 .135

.151 .116 .167 .173 .186 .187 .131

-.134 -.138 -.145 -.176 -.151 -.183 -.135

1.089 .998 1.207 1.271 1.343 1.345 .976

.187 .272 .108 .079 .054 .054 .297

N


Absolute

Positive

Negative








52 52 52 52 52 52 52

32.1346 32.7885 35.2885 35.3077 32.6346 31.5192 33.0577

.151 .138 .167 .176 .186 .187 .135

.151 .116 .167 .173 .186 .187 .131

-.134 -.138 -.145 -.176 -.151 -.183 -.135

1.089 .998 1.207 1.271 1.343 1.345 .976

.187 .272 .108 .079 .054 .054 .297

N


Absolute

Positive

Negative







• Thursday: 8.71, Poisson

• Friday: 9.77, Poisson

• Saturday: 10.02, Poisson

• Sunday: 9.33, Poisson

Table 4.10 below shows the optimal protection level and booking limit for Studio type of room. This strategy is suitable to

maximize the revenue in Grand Pasundan Hotel. The number of protection level represents the number of room we protect to

be sell at high price. In this case, Studio type has 9 rooms average of protection level and 6 rooms to be sell at low price as the

booking limit. The expected revenue increase is 32% average, this is the increase between the revenue without protection level

and revenue with expected high rate booking.

Table 4.10 Studio type with 35% discount rate


High Rate 380,000 380,000 380,000 380,000 380,000 380,000 380,000

Low Rate 247,000 247,000 247,000 247,000 247,000 247,000 247,000

RNA 15 15 15 15 15 15 15

Exp. Hrate demand 8.8 8.83 9.56 8.71 9.77 10.02 9.33

Critical Ratio 0.35 0.35 0.35 0.35 0.35 0.35 0.35

Protection level (Q) 8 8 9 8 9 10 9

Exp. Lost Sales 0.48 0.47 0.51 0.5 0.48 0.58 0.54

Booking Limit 7 7 6 7 6 5 6

Exp. Hrate Booking 8.32 8.36 9.05 8.21 9.29 9.44 8.79

Exp. Empty room(s) -0.32 -0.36 -0.05 -0.21 -0.29 0.56 0.21

Rev. w/o Protection level 3,705,000 3,705,000 3,705,000 3,705,000 3,705,000 3,705,000 3705000.00

Rev. w/ exp. Hrate Booking 4,890,600 4,905,800 4,921,000 4,848,800 5,012,200 4,822,200 4822200.00

57

Maximum Exp. Revenue 4,875,400 4,879,390 4,976,480 4,863,430 5,004,410 5,037,660 4945890.00

Exp. Revenue Increase (%) 32 32.41 32.82 30.87 35.28 30.15 30.15

Superior type

o RNA: 9


o Discount Rate: 35%









Table 4.11 below shows the optimal protection level and booking limit for Superior type of room. This strategy is suitable to


be sell at high price. In this case, Superior type has 6 rooms average of protection level and 3 rooms to be sell at low price as

the booking limit. The expected revenue increase is 33% average, this is the increase between the revenue without protection

58


52 52 52 52 52 52 52

32.1346 32.7885 35.2885 35.3077 32.6346 31.5192 33.0577

.151 .138 .167 .176 .186 .187 .135

.151 .116 .167 .173 .186 .187 .131

-.134 -.138 -.145 -.176 -.151 -.183 -.135

1.089 .998 1.207 1.271 1.343 1.345 .976

.187 .272 .108 .079 .054 .054 .297

N


Absolute

Positive

Negative








52 52 52 52 52 52 52

32.1346 32.7885 35.2885 35.3077 32.6346 31.5192 33.0577

.151 .138 .167 .176 .186 .187 .135

.151 .116 .167 .173 .186 .187 .131

-.134 -.138 -.145 -.176 -.151 -.183 -.135

1.089 .998 1.207 1.271 1.343 1.345 .976

.187 .272 .108 .079 .054 .054 .297

N


Absolute

Positive

Negative








52 52 52 52 52 52 52

32.1346 32.7885 35.2885 35.3077 32.6346 31.5192 33.0577

.151 .138 .167 .176 .186 .187 .135

.151 .116 .167 .173 .186 .187 .131

-.134 -.138 -.145 -.176 -.151 -.183 -.135

1.089 .998 1.207 1.271 1.343 1.345 .976

.187 .272 .108 .079 .054 .054 .297

N


Absolute

Positive

Negative







level and revenue with expected high rate booking.

Table 4.11 Superior type with 35% discount rate


High Rate 950,000 950,000 950,000 950,000 950,000 950,000 950,000

Low Rate 617,500 617,500 617,500 617,500 617,500 617,500 617,500

RNA 9 9 9 9 9 9 9

Exp. Hrate demand 6.42 6.15 6.5 5.9 5.96 6.27 6.01

Critical Ratio 0.35 0.35 0.35 0.35 0.35 0.35 0.35


Exp. Lost Sales 0.54 0.58 0.53 0.46 0.45 0.56 0.6

Booking Limit 3 3 3 4 4 3 3


Exp. Empty room(s) 0.12 0.43 0.03 -0.44 -0.51 0.29 0.59

Rev. w/o Protection level 5,557,500 5,557,500 5,557,500 5,557,500 5,557,500 5,557,500 5,557,500Rev. w/ exp. Hrate Booking 7,438,500 7,144,000 7,524,000 7,638,000 7,704,500 7,277,000 6,992,000

Maximum Exp. Revenue 7,692,150 7,602,375 7,718,750 7,519,250 7,539,200 7,642,275 7,555,825Exp. Revenue Increase (%) 33.85 28.55 35.38 37.44 38.63 30.94 25.81

Deluxe type

o RNA: 50



59








Table 4.12 below shows the optimal protection level and booking limit for Deluxe type of room. This strategy is suitable to


be sell at high price. In this case, Deluxe type has 33 rooms average of protection level and 17 rooms to be sell at low price as

the booking limit. The expected revenue increase is 35% average, this is the increase between the revenue without protection

level and revenue with expected high rate booking. We can see the revenue sensitivity to discount rate below in Table 4.11,

Table 4.12, Table 4.13, Table 4.14, Table 4.15, Table 4.16, and Table 4.17.

Table 4.12 Deluxe type with 35% discount rate


High Rate 600,000 600,000 600,000 600,000 600,000 600,000 600,000

Low Rate 390,000 390,000 390,000 390,000 390,000 390,000 390,000

RNA 50 50 50 50 50 50 50

Exp. Hrate demand 32.1 32.8 35.2 35.3 32.6 31.5 33.1

Critical Ratio 0.35 0.35 0.35 0.35 0.35 0.35 0.35


60


52 52 52 52 52 52 52

32.1346 32.7885 35.2885 35.3077 32.6346 31.5192 33.0577

.151 .138 .167 .176 .186 .187 .135

.151 .116 .167 .173 .186 .187 .131

-.134 -.138 -.145 -.176 -.151 -.183 -.135

1.089 .998 1.207 1.271 1.343 1.345 .976

.187 .272 .108 .079 .054 .054 .297

N


Absolute

Positive

Negative








52 52 52 52 52 52 52

32.1346 32.7885 35.2885 35.3077 32.6346 31.5192 33.0577

.151 .138 .167 .176 .186 .187 .135

.151 .116 .167 .173 .186 .187 .131

-.134 -.138 -.145 -.176 -.151 -.183 -.135

1.089 .998 1.207 1.271 1.343 1.345 .976

.187 .272 .108 .079 .054 .054 .297

N


Absolute

Positive

Negative








52 52 52 52 52 52 52

32.1346 32.7885 35.2885 35.3077 32.6346 31.5192 33.0577

.151 .138 .167 .176 .186 .187 .135

.151 .116 .167 .173 .186 .187 .131

-.134 -.138 -.145 -.176 -.151 -.183 -.135

1.089 .998 1.207 1.271 1.343 1.345 .976

.187 .272 .108 .079 .054 .054 .297

N


Absolute

Positive

Negative







Exp. Lost Sales 0.54 0.5 0.53 0.52 0.5 0.51 0.54

Booking Limit 18 18 15 15 18 19 17


Exp. Empty room(s) 0.44 -0.3 0.33 0.22 -0.1 0.01 0.44

Rev. w/o Protection level 19,500,000 19,500,000 19,500,000 19,500,000 19,500,000 19,500,000 19,500,000

Rev. w/ exp. Hrate Booking 25,956,000 26,400,000 26,652,000 26,718,000 26,280,000 26,004,000 26,166,000

Maximum Exp. Revenue 26,241,000 26,388,000 26,892,000 26,913,000 26,346,000 26,115,000 26,451,000

Exp. Revenue Increase (%) 33.11 35.38 36.68 37.02 34.77 33.35 34.18

Table 4.13 Deluxe Revenue Sensitivity to Discount rate on MondayMONDAY

Disc.ount Rate (%)

Rev. w/o Protection level

Rev. w/ exp. Hrate Booking

Max. Exp. Revenue

Exp. Revenue Increase

5 28,500,000 31,638,000 29,463,000 11.01

7.5 27,750,000 28,926,000 29,194,500 4.24

10 27,000,000 28,656,000 28,926,000 6.13

20 24,000,000 27,576,000 27,852,000 14.90

30 21,000,000 26,496,000 26,778,000 26.17

35 19,500,000 25,956,000 26,241,000 33.11

40 18,000,000 25,416,000 25,704,000 41.20

50 15,000,000 24,336,000 24,630,000 62.24

Table 4.14 Deluxe Revenue Sensitivity to Discount rate on Tuesday

TUESDAYDiscount Rate (%)



Max. Exp. Revenue


5 28,500,000 32,082,000 29,484,000 12.57

61

7.5 27,750,000 29,370,000 29,226,000 5.84

10 27,000,000 29,100,000 28,968,000 7.78

20 24,000,000 28,020,000 27,936,000 16.75

30 21,000,000 26,940,000 26,904,000 28.29

35 19,500,000 26,400,000 26,388,000 35.38

40 18,000,000 25,860,000 25,872,000 43.67

50 15,000,000 24,780,000 24,840,000 65.2

Table 4.15 Deluxe Revenue Sensitivity to Discount rate on Wednesday

WEDNESDAYDiscount Rate (%)



Max. Exp. Revenue


5 28,500,000 31,788,000 29,556,000 11.54

7.5 27,750,000 29,127,000 29,334,000 4.96

10 27,000,000 28,902,000 29,112,000 7.04

20 24,000,000 28,002,000 28,224,000 16.68

30 21,000,000 27,102,000 27,336,000 29.06

35 19,500,000 26,652,000 26,892,000 36.68

40 18,000,000 26,202,000 26,448,000 45.57

50 15,000,000 25,302,000 25,560,000 68.68

Table 4.16 Deluxe Revenue Sensitivity to Discount rate on Thursday

62

THURSDAYDiscount Rate (%)



Max. Exp. Revenue


5 28,500,000 31,848,000 29,559,000 11.75

7.5 27,750,000 29,193,000 29,338,500 5.2

10 27,000,000 28,968,000 29,118,000 7.29

20 24,000,000 28,068,000 28,236,000 16.95

30 21,000,000 27,168,000 27,354,000 29.37

35 19,500,000 26,718,000 26,913,000 37.02

40 18,000,000 26,268,000 26,472,000 45.93

50 15,000,000 25,368,000 25,590,000 69.12

Table 4.17 Deluxe Revenue Sensitivity to Discount rate on Friday

FRIDAYDiscount Rate (%)



Max. Exp. Revenue


5 28,500,000 31,956,000 29,478,000 12.13

7.5 27,750,000 29,250,000 29,217,000 5.41

10 27,000,000 28,980,000 28,956,000 7.33

20 24,000,000 27,900,000 27,912,000 16.25

30 21,000,000 26,820,000 26,868,000 27.71

35 19,500,000 26,280,000 26,346,000 34.77

40 18,000,000 25,740,000 25,824,000 43

50 15,000,000 24,660,000 24,780,000 64.4

63

Table 4.18 Deluxe Revenue Sensitivity to Discount rate on Saturday

SATURDAYDiscount Rate (%)



Max. Exp. Revenue


5 28,500,000 31,866,000 29,445,000 11.81

7.5 27,750,000 29,139,000 29,167,500 5.01

10 27,000,000 28,854,000 28,890,000 6.87

20 24,000,000 27,714,000 27,780,000 15.48

30 21,000,000 26,574,000 26,670,000 26.54

35 19,500,000 26,004,000 26,115,000 33.35

40 18,000,000 25,434,000 25,560,000 41.3

50 15,000,000 24,294,000 24,450,000 61.96

Table 4.19 Deluxe Revenue Sensitivity to Discount rate on Sunday

SUNDAYDiscount Rate (%)



Max. Exp. Revenue


5 28,500,000 31,668,000 29,493,000 11.12

7.5 27,750,000 28,971,000 29,239,500 4.40

10 27,000,000 28,716,000 28,986,000 6.36

20 24,000,000 27,696,000 27,972,000 15.40

30 21,000,000 26,676,000 26,958,000 27.03

35 19,500,000 26,166,000 26,451,000 34.18

64

40 18,000,000 25,656,000 25,944,000 42.53

50 15,000,000 24,636,000 24,930,000 64.24

Table 4.20 Deluxe Weekly Revenue Sensitivity to Discount rate

Discount Rate (%)



Max. Exp. Revenue


5 28,500,000 31,835,143 29,496,857 12

7.5 27,750,000 29139428.57 29,245,286 5

10 27,000,000 28882285.71 28,993,714 7

20 24,000,000 27853714.29 27,987,429 16

30 21,000,000 26825142.86 26,981,143 28

35 19,500,000 26310857.14 26,478,000 35

40 18,000,000 25796571.43 25,974,857 43

50 15,000,000 24768000 24,968,571 65

Revenue without Protection level

0

5,000,000

10,000,000

15,000,000

20,000,000

25,000,000

30,000,000

0 10 20 30 40 50 60

Discount Rate

Rev

enu

e

Deluxe

Figure 4.13 Revenue without Protection level in Deluxe

65

Sensitivity to Revenue with expected High rate Booking

0

5,000,000

10,000,000

15,000,000

20,000,000

25,000,000

30,000,000

35,000,000

0 10 20 30 40 50 60

Discount Rate

Reven

ue

Deluxe

Figure 4.14 Sensitivity to Revenue with expected high rate Booking

Sensitivity to Maximum Expected Revenue

24,500,00025,000,00025,500,00026,000,00026,500,00027,000,00027,500,00028,000,00028,500,00029,000,00029,500,00030,000,000

0 10 20 30 40 50 60

Discount Rate

Reven

ue

Deluxe

Figure 4.15 Sensitivity to Maximum Expected Revenue

66

Sensitivity to Expected Revenue Increase

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60

Discount Rate

Reven

ue

Deluxe

Figure 4.16 Sensitivity to Expected Revenue Increase

Figures above show us the revenue sensitivity to discount rate. As we can see, the revenue will decrease if we increase the

discount rate, but the expected percentage of revenue will be increase except at discount rate 5% to 7.5% this is because the

differences in protection level.

This strategy offer the daily dynamic pricing with protection level, This method will attract more customers for the reason that

this strategy put a discount rate to attract price-sensitive customers such as vacationing family who usually tend to reserve

rooms well in advance of their stay, and set the high price in last minute reservation or closer to the time of their stay for the

reason that customer mostly are business traveler where they are less price-sensitive. This dynamic pricing strategy reaches the

opportunity to attract more customers with the different price, which leads to increase in revenue. This strategy is the solution

67

to maximize the revenue by using the dynamic pricing strategy with protection level and booking limit.

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