yield management

CEJOR (2008) 16:43-66DOI 10.l007/sI0100-O07-0O42-y

ORIGINAL PAPER

Pricing strategies for perishable products: the caseof Vienna and the hotel reservation system hrs.com

Jorg Schiitze

Published online: 8 January 2008© Springer-Verlag 2008

Abstract Consider a retailer who sells perishable products for which there isuncertain demand. Yield management with dynamic pricing is a standard practicethat firms use for revenue management. For perishable products, recent analysis hasfocused on the distribution of flight capacity, referred to as ticket sales. Other non-storable, non-trans portable, immaterial hospitality products include hotel capacity.The article discusses the extent to which hotel pricing strategies vary within the inter-net distribution system hrs.com. This study focuses on the distribution of hotel roomsavailable for booking on the internet for Vienna and gives an outlook to Eurolandcapitals. The main research interests are the underlying pricing models and the settingofthe end price. Data was taken from hrs.com, which is the most important specialistfor hotel room internet distribution in Germany according to recent studies by KMPGand others. The results include the identification of different pricing strategy clusterswith regard to hotel category and hotel availability over a 22-day period for Viennaand one city from all Euroland countries (the capitals were studied for all cases exceptfor the Netherlands, for which data was collected for Amsterdam). The study took thearrival days Mondays, Tuesdays, Wednesdays and Thursdays into account, and useddata for all these days from the 11th of July, 2005, to the 10th of October, 2005, forVienna, and the first and the last of these dates as a comparison base for the otherEuroland cities.

Keywords E-commerce • Perishable product • Yield management •Dynamic pricing • Internet distribution system • Hospitality • Room rate

J. Schutze (IS])•Department of Business Education and Development,University of Graz. Universitatsstr. 15 RESOWIGl,Graz 8010. Austriae-mail: [email protected]

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44 J, Schutze

1 Introduction

The internationalization of former local and regional markets via internet usage hasbeen revolutionizing the distribution of many products and services.

Due to the ease of data access and relevance, recent research into business modelsand pricing strategies has been focused on a few important hotel groups.

Hotels can use different channels to distribute hotel rooms (Fastbooking.com 2005,p.4):

- The hotel's/hotel group's distribution system- Global Distribution System (GDS, principal companies are Sabre, Galileo, World-

span, and Amadeus, which provide global connectivity and service to travel agents(Lewis 2005. p.45) and. partly, sell directly via the internet as well)

- Internet Distribution System

As a result ofthe increasing use of internet distribution systems, data access to hotelroom prices for small and medium-sized companies on the internet has improved.According to KPMG, internet distribution systems offer in most cases better pricesthan traditional travel agents, who work via a GDS, direct calls to the hotel or useof the hotel's own web site (Geo 2005, p.l). A number of companies offer internetdistribution systems. According to the 2(X)4 Travel Sector Spotlight Report, hrs.comis the hotel room specialist with the highest sector reach (Nielsen and Netrating 2004,p. I), in most cases offering better prices for the same hotels than hotel.de, expedia.deand ehotel.de (Arbeiterkammer 2005, p. 1). According to the German version of theFinancial Times, hrs.com is the hest known online service in Germany: the hrs.comsites are accessed around 300 million times per month (Kwapik 2005. p.2).

Therefore, this study uses hrs.com as a data collection base. Hrs.com. a Cologne-based company, offers more than 180,0(K) hotels worldwide. In 1999 about half of thebookings came from Germany (Marcussen 1999, p.l31), now hrs.com has offices inLondon. Paris and Shanghai and provides an intranet solution for enterprises, hrs.comclaims to have contracts with more than 12,(X)0 companies (hrs.com 2006). Half of itsbusiness in 2004 came from companies (Ohnsmann 2(X)4. p.2). Hotels participatingin hrs.com determine the offered hotel room capacity and prices up to one year inadvance but have access to the system to change their prices according to their needs.The research uses data based on room rates for certain dates, each of which weremonitored over a 3-week period for the Viennese hotel market with an outlook toall Euroland capitals. For the Netherlands. Amsterdam, as the economic center, wasstudied instead of Den Haag. Data for these additional cities were collected for thefirst and the last day ofthe study.

2 Significance of the study

On the one hand, the end price strategies utilized by a hotel can significantly impactthe profitability of a hotel's operation. Any additional room rate and/or higher roomrate earned contributes directly to the profitability of the hotel. There are no additionalexpenses involved, with the exception of any commission expense, in this case tohrs.com, and possibly higher variable costs (Collins and Parsa 2005, p.3).

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Pricing strategies for perishable products 45

On the other hand, knowledge of the usage of snch strategies could lead to a furtherincrease in time-sensitive booking, with clients trying to get the lowest price possible.With the high availability of pricing data accessible on the intemet, it is possible forconsumers to find out the pricing strategies used by firms and to develop strategicresponses (Zhou et al. 2005, p. 1).

O'Conner surveyed the prices over different distribution channels for internationalhotel chains: The price at which a product is offered for sale has been identified as oneof the key motivators for encouraging customers to buy online (O'Conner 2002, p. 1).Due to possible bias, resulting from no clarification of the collection period and onlyfive sets of alternative dates, the results might not offer much insight (Tso and Law2005, p.4).

The use of the hrs.com service to offer a specified room arrangement for a definedarrival date can been seen as part of a complex decision within a hotel managementstrategy. The success of a hotel, i.e., yield, is not part of the analysis due to the limi-tations of the data being collected. These limitations are mainly the following:

- Only data from one service on one distribution channel is collected.- On the hrs.com platform no data is available for the deals which took place. Only

the joining or the withdrawal of an offer can be observed.- Non-availability of rooms previously available on hrs.com could be due to multiple

reasons.- A booked hrs.com room arrangement often includes the following option: The

room arrangement is reserved free of charge until 6 p.m. on the arrival date. Cus-tomers who do not show up on the booked arrival date are not charged.

Judging from research into major scientific research platforms (springerlink, businesspremier, ebsco, ingenta), this is the first analysis of hotel pricing strategies based onthe intemet distribution system hrs.com over the open booking period with a focus onthe underlying pricing model.

3 Fundamentals of pricing

Hotels offer the same rooms to different types of guests (i.e., business customers). Animportant decision to be made is whether to accept a booking request and generaterevenue now, or to reject it in anticipation of a more profitable booking request inthe future (Goldman et al. 2001. p.2). Offered capacity for a specific date on hrs.comobliges the hotel to accept bookings.

There are various room pricing approaches. These range from statistical models tocommon-sense best practices. No commonly accepted approach exists. Hotel roompricing refiects the complexity of human activities and environmental circumstances(Emmett and Gu 2005, p.3).

Dynamic pricing is a standard practice that firms use for revenue management. Forperishable products, recent analysis has focused on the distribution of flight capacity,referred to as ticket sales.

Perishahle product/service yield management assumes the following common prod-uct/service characteristics:

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46 J. Schiitze

- a day when the product/service becomes available after which it becomes unavail-able, ages or perishes,

- a fixed number of units/a limited capacity,- the possibility to segment customers according to their price sensitivity. The mech-

anism employed is the time of booking (Weatherford and Bodily 1992, p.831).

Hotel capacity is a non-storable, non-transportable, immaterial hospitality product(Henschel 2005, p.80f).

One of the two most used general business models in ticket sales, mostly for air-line ticket sales, uses dynamic pricing: Pricing strategies based on yield managementsystems, such as early discounting, and limited early sales for perishable productswhich are capacity constrained (Desiraju and Shugan 1999, p.lf). Based on yieldmanagement the remaining ticket capacity is priced dynamically (Gallego and vanRyzin 1994, p.lO17; Zhao and Zheng 2000, p.387f; Zhou et al. 2005, p.4; Perakis andSood 2004, p.4).

Dynamic pricing models represent demand as a controllable stochastic point pro-cess with price-dependent intensity (Perakis and Sood 2004, p.4). The other type ofmodel assumes that hotel customers can be categorized into different classes {e.g.,leisure and business travelers) and focuses on the allocation of capacity among theseclasses (Henschel 2005, p.393; Zhou et al. 2005, p.4).

Fencing and best-price strategies describe major industry concepts for reacting toIhe ongoing process of increased room rate harmonization over different distributionchannels. The Interconti Group has started to offer only six different rates for theirrooms, for example, while Mariott has introduced a single price policy (Geo 2005, p.2).

The general hotel room pricing methods are competition-driven pricing (market-competition driven approach) (Nagle and Holden 1995, p.9; Henschel 2005, p.392f),co.st-based pricing (financially driven approach) and customer-driven pricing (mar-ket-customer-driven approach).

Hospitality revenue management can be described as a method that aims to sellthe right inventory unit to the right customer, at the right time and for the right price(Kimes 1989, p.l5f). hrs.com lists only one room per hotel per date per location ona request for a single room. This makes it difficult to distinguish between customergroups, taking into account price level, service level, and location of a hotel roomoffer.

Therefore, the product distribution concept of hrs.com provides only restricted dataon each booking customer for the selling hotel, similar to low cost airlines (start anddestination point, day of travel, days left before the day of travel, accepted price)(Spann et al. 2(X)5. p.2O-

We will focus on the underlying pricing model and distinguish between three basicapproaches.

3.1 Dynamic pricing

The first pricing strategy can be described as dynamic pricing, in which the remain-ing ticket capacity is priced dynamically. This pricing strategy is used by hotels with

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Pricing strategies for perishable product.s 47

a market-competition focus (Fig. 1). We can state the following hypothesis for theresearch:

Hi: Hotels on the internet distribution system hrs.com use dynamic pricing

3.2 Pre-fixed constant pricing

The other main pricing approach is based on a categorization of different customertypes (e.g.. business and leisure traveler classes) and focuses on the alkxation ofcapacity among these classes with the strategic use of service levels and add-on pack-ages. Therefore, the second pricing approach, fixed pricing, could be divided into aglobal single price policy and a policy with fixed pricing per stay, where the price is setaccording to historic or expected demand. The governing principle behind this pricingapproach is cost-based pricing and/or historic customer-driven pricing (Fig. 2).

H2: Hotels on the internet distribution system hrs.com use pre-fixed constant pricesper stay.

3.3 Pre-fixed mixed pricing

The governing principle behind this pricing approach is cost-based pricing and/or his-toric customer-driven pricing. This third pricing approach consists of a combinationof different pricing approaches: Constant and decreasing prices for arrival dates areused for uncertain or expected low demand with a last minute bonus for the remain-ing capacity. In analogy to airline ticket sales, it can be expected that hotels will useincreasing prices for expected high demand, incorporating an early booking bonus

Pricing Dynamic

ICASE

unclear if 1

uncl«jr 02

1

TIME

Fig. 1 Dynamic pricing patterns

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J. Schutze

Pricing Prefix constant

2 0 - —— » - - * — — — - — — — — - - - —

15

200

CASE

constjnt #1

constant #2

1

TIME

Fig. 2 Pre-fixed constant pricing; typical patterns

(Koenigsberg et al. 2004, p.lfO (Pig- 3). Therefore, we can state the followinghypothesis: : -

H3: Hotels on the intemet distribution system hrs.com use pre-fixed mixed pricesper stay.

The pre-fixed constant pricing and pre-fixed mixed pricing approaches are indepen-dent of the market and/or the remaining capacity monitoring, whereas dynamic pricingadapts to one or both of those two factors (Fig. 3, Table 1).

Pricing Prefix mixed

at

ro

^0

OD1

TME

Fig. 3 Pre-fixed mixed pricing: typical patterns

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Table 1 General pricing strategies

Pricing strategy Description

Dynamic Price selling according lo remaining capacity and/or market situation as the dif-

ference between expected demand and real demand (Engelmann 2004, p.56f)

Pre-fixed constant Global single price policy

Season-dependent constant pricing

Pre-fixed mixed Increasing pricing , • "

Decreasing pricing

Constant pricing

The three main pricing strategies are as followsHotel capacity is a non-storable, non-transportable, immaterial hospitality product

(Henschel 2005. p.SOfO- Due these characteristics, hotels—as with airlines in recentyears—are expected to increasingly use dynamic pricing when distributing this per-ishable product to maximize their yield. Due to its perishable character, the activity inprice changes for hotels using dynamic pricing can be expected to change increasinglyas the arrival date approaches. Therefore, we can state the following hypothesis:

Dependent on the existence of dynamic pricing on the internet distribution systemhrs.com:

H4: Dynamic pricing hotels increase the frequency of price changes with theapproach of the arrival date.

4 Research methodology

The subject of this paper is an empirical study over 3 months focusing on typical traveldays for business as well as for leisure. Mondays, Tuesdays, Wednesdays, and Thurs-days were chosen as such typical week days in order to include business customersand to exclude hotels only offering weekend stays. The analysis focused on one-nightstays in a single room in 3, 4 and 5 star hotels offered on hrs.com.

4.1 Collection time and pricing patterns

To find a reasonable point for data collection, important issues included a minimaldata change regarding offered hotel room capacity.

Business customers are expected to book during business office times, at leaston workdays. Therefore, it is not probable for them to book or cancel on Saturdayevenings. For the vast majority of private customers. Saturdays, especially Saturdayevenings, are work-free and followed by a work-free day (Sunday). These times areoften used for social life.

It is also important to consider how likely it is for the hotel capacity to be addedor canceled or the properties of the capacity to be changed on Saturdays. Typically,the hotel management is responsible for these above tasks and also for assigning work

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50 i Schutze

schedules for the hotel. Due to the above-mentioned importance of Saturday eveningsfor social life and the usual absence of important business matters at weekends, weassume that the hotel management is unlikely to be on duty on Saturday evenings andtherefore the hotel capacity data is not likely to be changed at that time.

In conclusion, the relevant parties for supply and demand of hotel capacity areless likely to act on Saturday evenings. For these reasons, data collection on Saturdayevenings can be regarded as a good choice.

To be able to gain insight into the pricing approaches used, data of hotels withdraw-ing their offer within the monitored 22-days prior to the arrival date were not includedin this study. Therefore, all data sets range over the full monitored 22-day period. As aresult, the study takes into account 4 prices collected on the last four Saturdays beforethe arrival date over a 22-day period.

In order to gain insight into the pricing trends, we first analyze the price differ-ence between the collection dates (Table 2). Afterwards we can combine the founddifferences between the consecutive collection dates using the defined symbols.

The differences in pricing from one collection date to the next can be described asfollows:

As a mixture of the three given price changes, 27 possible pricing patterns over the22-day period can be distinguished.

Example on hrs.com: On the 17th September 2CX)5 a room in a certain hotel for onenight arriving on the 10th of October costs 100 Euros. Therefore, the price 23 daysprior to arrival is 1(X) Euros. On the 24th September, 16days prior to arrival, the pricehas dropped to 90 Euros. On the 1st of October, 9 days prior to arrival, the price isstill90Euros. On the 9th of October, 2 days prior to arrival, the price has risen to 100Euros.

Therefore, we find a decreased price between 23 and I6days prior to arrival, theprice being held constant when checked 9 days prior to arrival, and finally increasedto 100 Euros.

We can summarize this as follows:

1. Observation 2. Observation 3. Observationdecreasing price constant price increasing price

Resulting price pattern: T = ABy taking the general price trends into account, such pricing patterns are grouped

into increasing, decreasing, mixed and constant pricing trends. These groupings areas follows (Table 3):

In order to gain a comparison base for the analysis of Viennese data, data was col-lected for the following cities: Amsterdam, Athens, Berlin. Brussels, Dublin, Helsinki,

T^ble 2 Symbols for price p^^^ ^^ ^^^^^ collection dates Symbolchanges between collection

Unchanged

Decreased

Increased

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Table 3 Categorization ofpricing patterns into pricingtrends

Pricing trends

Increasing

= = A

= A =

= AA

A = =

A = A

AAA

AA =

Decreasing

= = T

= T =

=TT

T = =

T = T

TTT

T T=

Mixed

= TA

= AT

T = A

A = T

TA =

AT =

TTA

AAT

TAT

ATA

TAA

ATT

Constant

=

Lisbon, Luxemburg, Madrid, Paris and Rome. The two monitored arrival dates wereMonday, 11th of July 2(X)5, and Monday, 10th of October 2005. The price data forthese two dates was collected on the last four Saturdays before these dates.

For example, the collection dates for the arrival day Monday, 11th of July are the18th of June, 25th of June, 2nd of July and 9th of July, as shown in Fig. 4 part a.

countdown

—1

n

lltt

5 101

•

151

•

20

a

25

a a a

Jul 1 € Aug 05 Aug 25 Sep 14 Okt 04

arnvaldate

Fig. 4 a Collection dates for Monday, 1 Ith of July, b Data coUeclion points for Vjenna—it Arrival datesand remaining days before arrival date (countdown)

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52 J. Schtitze

The collection took place on all Saturdays from the 23th of April, 2005, to the 8thof October, 2(K)5. Therefore, the study includes the period starting on Monday, UthJuly, 2005, and ends with Monday, 10th October, 2(X)5. Data for Vienna includes allMondays, Tuesdays, Wednesdays and Thursdays in the given time frame.

For the following please refer to Fig. 4 part b.Data collection took place on Saturdays starting at 6 p.m. It usually took 4 h, depen-

dent on the server response time and internet-related speed limitations.The study includes all hotels on hrs.com which offered a room for 4 consecutive

data collection points in a 3-week time frame with the last available booking day 2(for Mondays) to 5 (for Thursdays) days prior to the arrival date.

A data set therefore includes four prices over a 3-week period.

5 Results and discussion

5.1 Collected data overview

The study consists of 5,454 data sets for Vienna (53days) and 1,765 data sets for theother Euroland cities.

These 1,765 monitored price development periods for the Euroland cities includethe data of 1,120 hotels. The 1,120 hotels consist of 433 three star hotels, 533 fourstar hotels, and 154 five star hotels, as shown in Table 4:

5.7./ Hotel category

The 5,454 monitored price development periods for Vienna include the data of 181hotels. The 181 hotels consist of 64 three star hotels, 100 four star hotels and 17 fivestar hotels, as shown in Table 5:

Tttble 4 Euroland cities without Vienna-collected data sets and hotel category

Hotel category Percentage of hotel count Hotel counl Data sets Percentage of data sets

3 stars

4 stars

5 stars

Total

35

559

100

64

100

17

181

1.715

3053

686

5.454

3156

13

100

Table 5 Vienna-collected data sets and hotel category

Hotel category Percentage of hotel counl Hotel counl Data sets Percentage of data sets

3 stars4 stars

5 stars

Total

3948

14

100

433

533

154

1.120

676

839

250

1.765

38

48

14

100

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Pricing strategies for perishable pRxlucts 53

5.1.2 Hotel availability

The number of available hotels on the last collection day 2,3,4 or 5 days before arrivaldate is shown in Fig. 5:

As can be seen in Fig. 5 the number of available hotels on hrs.com for the givenperiod was volatile. The minimum of offered hotels in the middle of September wasprobably due to a peak in the number of company trainings traditionally taking placein Austria in September and/or a peak in holiday makers in Vienna.

Due to the usage of different distribution channels, hotel policy, being sold out,etc., the hotels contracted by hrs.com were not available on hrs.com for all ofthe 53monitored arrival dates. The hotels were categorized according to their availability(Table 6):

- mostly available: for at least 37 (minimum) out of 53 monitored periods- often available: available between 26 (min.) and 36 (max.) out of 53 monitored

periods- poorly available: available for less than 26 periods out of 53.

Table 7 gives an overview ofthe availability for the 181 Viennese hotels.

5.2 Pricing patterns

5.2.] Price .setting: Results for Euroland cities without Vienna

For the Euroland cities without Vienna, Table 8 shows the price patterns found.

Fig. 5 Vienna—available hotels for monitored arrival dates

Table 6 Viennese hotels availability categorization according to the number of open booking periods

Category

Excellent availability

Average availability

Poor availability

Count

59

62

60

Number of open booking periods

Minimum

37

26

1

Maximum

49

36

25

Mean

42

31

20

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54 J. Schutze

Table 7 Viennese hotels: availability and hotel category

Availability category Hotel category and hotel count


Average availability

Poor availability

Total

Three star

14

20

30

64

Four stiir

33

38

29

100

Five star

12

4

1

17

Sum

59

62

60

181

Categorized pricing patterns for Euroland cities without Vienna

Pricing trend Constant Decreasingpricing pattern

1,118(100%) =^ T 148(37%)

- T - 103 (26%)

= TT 21 (5%)

T = = 92 (23%)

T = T 20 (5%)

T T = 12(3%)

TTT 2(1%)

Total 1,765 1.118 (100%) 398 (100%)

Increasing

= = A 58(41%)

= A = 35 (25%)

= AA 8(6%)

A ^ - 32 (23%)

A = A 3 (2%)

A A ^ 3(2%)

AAA 3(2%)

142 (100%)

Mixed

= TA

= AT

T = A

TTA

TA =

TAT

TAA

A ^ T

AT =

ATT

ATA

AAT

24 (22%)

15(14%)

15(14%)

5(5%)

18(17%)

4(4%)

2(2%)

10 (9%)

7(7%)

1(1%)

5(5%)

1 (1%)

107(100%)

As shown in the Tahle 8. all 27 pricing patterns were found for Euroland cities with-out Vienna. As only two pricing patterns were observed, we can neither distinguishthe general price setting strategy for each individual hotel, nor distinguish whether anobserved increased or decreased price is part of a dynamic pricing strategy. Accord-ing to the interpretation for the mixed pricing pattern, hotels nsing a pre-fixed mixedpricing pattern, however improbable, will be regarded as dynamic pricing hotels. Theexistence of dynamic pricing leads us to the Hi.

Hi: Hotels on the intemet distribution system hrs.com use dynamic pricing.

We could find dynamic pricing for 107 out of 1,765 observed cases. Therefore, wecan state without further investigation into the Viennese data:

ResultiA: Dynamic pricing takes place on the intemet distribution system hrs.com.

Since dynamic pricing takes place on the intemet distribution system hrs.com, we willfurther investigate the matter with;

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H4: Dynamic pricing hotels increase the frequency of price changes with theapproach of the arrival date.

6 Price setting: results for Vienna

For Vienna., Table 9 shows the price patterns found.As shown in the Table 9, all 27 pricing patterns where found. Since we monitored

up to 52 out of 53 price settings, we can analyze the individual hotels for their pricingstrategy.

The abbreviations for the four categories are given in Table 10.

6.1 Pre-fixed constant pricing per stay

The Viennese hotel availability and used pricing for the pricing strategy pre-fixedconstant pricing is shown in Table 11.

Regarding H2: Hotels on the internet distribution system hrs.com use pre-fixed con-stant prices per stay.

Table 9 Categorized pricing patterns for Vieona

Pricing trend Coostantpricing pattern

= 3.431(100%)

Total 5,454 3,431 (100%)

Table 10 Abbreviations forcategorized pricing behavior

Decrea.smg

== T 498(41%)

= T = 337 (28%)

= TT 59(5%)

T = = 219(18%)

T = T 50 (4%)

TT = 37 (3%)

TTT 5(0%)

1,205(100%)

Behavior

Increasing

Constant

Mixed

Decreasing

Increasing

= = A

= 4 ^

= AA

A ==

A = A

AA =

AAA

195 (35%)

207 (38%)

38 (7%)

82(15%)

19(3%)

7(1%)

4(1%)

552 (100%)

Mixed

= TA

= AT

T = A

TTA

TA =

TAT

TAA

A = T

AT =

ATT

ATA

AAT

91 (34%)

40(15%)

44(17%)

5(2%)

28(11%)

11 (4%)

10 (4%)

19 (7%)

8(3%)

3(1%)

3(1%)

4(2%)

266(100%)

Abbreviation

i

cmd

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56

Table 11 Viennese hotel availability and used pricing for pre-fixed constant pricing

Hotel availabilityUsed pricings

Global single price policy

Season-dependent constant pricing

Total

Poor

17 (77%)

5 (23%)

22(100%)

Average

8 (73%)

3 (27%)

11(100%)

Excellent

11 (85%)

2(15%)

13(100%)

J. Schtitze

J

All

36(78%)

10(22%)

46(100%)

We can state:

Result H2: Viennese hotels use pre-fixed constant prices per stay: Global singleprice policy and season-dependent constant pricing.

6.2 Dynamic pricing

The pricing behavior ofthe Viennese hotels using dynamic pricing is shown in Table 12.The abbreviations are explained in Table 10.

The classification of dynamic pricing hotels only depends on the existence of mixedstrategies. These could be found with all above shown hotels. Regarding H\:

H]: Hotels on the internet distribution system hrs.com use dynamic pricing.

We could find dynamic pricing for 60 out of 181 hotels (Table 14). Therefore, we canstate:

Result HIB: Dynamic pricing is a widely used strategy for Viennese hotels onhrs.com.

The three main combinations of pricing found are: dc for decreasing and constant, idcfor increasing, decreasing and constant and ic increasing and constant, as found in thefollowing Table 13: . ,

Regarding H3:

Table 12 Viennese hotel availability and pricing behavior for dynamic pricing

Hotel availabilityused pricing

icna

idem

idm

im

dm

dcm , i

Total

Poor

0(0%)

7(44%)

1(6%)

0(0%)

0(0%)

8 (50%)

16(100%)

Average

1(4%)

13 (57%)

1(4%)

0 (0%)

1(4%)

7 (30%)

23 (100%)

Excellent

1(5%)

15 (68%)

0(0%)

1(5%)

0(0%)

5 (23%)

22(100%)

All

2(3%)

35 (57%)

2(3%)

1(2%)

1(2%)

20 (33%)

61 (100%)

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Table 13 '^ennese hotel availability and used pricings for pre-fixed mixed pricing strategy

Hotel availabilityused pricing

Poor Average Excellent All

dc

d

iicidcTotal

13(59%)2(9%)

0(0%)

2(9%)

5 (23%)

22(100%)

15 (54%)0(0%)

0(0%)

7 (25%)

6(21%)

28 (100%)

9 (38%)0(0%)

1(4%)

4(17%)

10(42%)

24(100%)

37 (50%)2(3%)

1(1%)13(18%)

21 (28%)

74(100%)

Table 14 Summary of Viennese hotel availability and used pricing strategie.s

Hotel availabilityused pricing strategies

Poor Average Excellent All

Dynamic pricing (Icm, idem, idm, im. dm, dcm)

Pre-fixed constant pricing (c)

Pre-fixed mixed pricing (Idc, dc, d, ic, i)

Total

16 (27%)22 (37%)

22 (37%)

60(100%)

23 (37%)11(18%)

28 (45%)

62(100%)

21 (36%)13 (22%)

25 (42%)

59(100%)

60 (33%)46(25%)

75 (41%)

181(100%)

H3: Hotels on the intemet distribution system hrs.com use pre-fixed mixed pricingper stay.

We can state:

Result H3: Pre-fixed mixed pricing is a widely used pricing strategy for Viennesehotels on hrs.com.

The summary of hotel availability and hotel pricing strategy are given as follows(Table 14):

Important result; All three pricing strategies are used by Viennese hotels on hrs.com.

6.3 General price change activity

If we have a look at the data collection points as days remaining prior to the arrivaldate, for Mondays these are the days 23,16,9,2 days prior to arrival, for Tuesdays theseare 24,17,10,3 days prior to arrival, for Wednesday 25,18,11,4 days prior to arrival andforThursdays 26,19,12,5 days prior to arrival.

We can calculate the mean price activity by categorizing the found pricing, whichunderwent price changes between the data collection points. If a change was made, aprice activity has been set and this is denoted with 1; if no price change was made itis denoted with 0.

In the following Fig. 6, we find the chart showing the hotel pricing strategy and theprice changing activity for Euroland cities and Vienna;

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58 J. SchUtze

<N _O

oc -o

0.15

o

o

ino -o

oO -o

«

8- \ ^ o

^ \

Legend— EurolanO

Vienna

1 1 r 1 1

• 0

1 r

100 200 500300 400

Countdown (h)

Fig. 6 Price changing activity and hotel pricing strategies 5-23 days prior to arrival

600

In general, Euroland cities show a similar behavior in terms of the fraction of pricechanges taking place in comparison to Vienna.'

As follows we further analyze the price changes for Vienna using the defined hotelpricing strategies dynamic and pre-fixed mixed:

Regarding H4: Dynamic pricing hotels increase the frequency of price changes withthe approach of the arrival date.

After a comparison ofthe first and the last countdown data collection point for all fourqueried weekdays, we can state (Fig. 7):

Result H4: Dynamic pricing hotels increase the frequency of price changes with theapproach ofthe arrival date.

The price change behavior of the different hotel availability clusters is given as followsin Fig. 8:

The visual findings indicate that all three availability clusters follow a similar ten-dency; About 3 weeks (23-26 days) prior to arrival, for all hotel availahility clusters,we find approx. 10% price activity set. A few days (2-5 days) before arrival, we findapprox. 20% price change activity, independent ofthe hotel availability cluster.

The following bar chart is used to analyze the hotel availability cluster and hotelpricing strategy for the differences in price activity set (Fig. 9):

We find a similar proportion of price activity level for the different hotel pricingstrategies: the pre-fixed mixed pricing strategy cluster shows a price activity of approx.10% while the dynamic pricing strategy cluster shows an activity level of approx. 30%.

Future in-depth statistical analysis will use an increased data base to account for the multiple dependencies(working day classes, week classes, hotels, etc.)

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LegendDynamic

— Prefix mixed

0 100 200 300 400 500 600

Cotintdovm (h)

Fig. 7 Fraction of price changing activity and hotel pricing strategies 5-23 days prior to arrival for Vienna

LegendPoor aval lability

Average availabilityExcellent availability

0 100 200 300 400 500 600Countdown (h)

Fig. 8 Price change activity and hotel availability 5-23 days prior to arrival

Tbe price cbange behavior of the different hotel category clusters (3-5 star hotels)is given as follows in Fig. 10:

The visual findings indicate that all three hotel category clusters follow a similartendency: About 3 weeks (23-26days) prior to arrival, for all hotel category clusters,we find approx. 10% price activity set, with the 5 star hotels being less price active.A few days (2-5 days) before arrival, we find approx. 20% price change activity withthe 4 star hotels being more price active.

A bar chart (Fig. 11) is used to analyze the hotel category cluster and hotel pricingstrategy for the differences in price activity set.

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60 J. SchOtze

0,5-

*- 0,4

Availability

D Poor

B Average

O ExceHert

Prefix constant Prefix mix«d Dynwnic

Hotel price strategy

Fig. 9 Hotel pricing strategy cluster: availability and price activity

Legend3-sEar holals•a-stsr noteis5-siar hotels—I—

100 300Countdown (h)

Fig. 10 Price change activity and hotel category 5-23 days prior to arrival

We find a similar proportion of price activity level for the different hotel pricingstrategies: The pre-fixed mixed pricing strategy cluster shows a price activity levelof approx. 10% while the dynamic pricing strategy cluster shows an activity level ofapprox. 30%.

6.4 Relative price change i

We analyze the relative price change of the pricing pattern for the pre-fixed mixed andthe dynamic pricing cluster. For that purpose we use the mean modulus of ail price

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Pnlix conitani Prefix mixed

Hot»l pric« strategy

Fig. 11 Hotel pricing strategy cluster hotel category and price activity

Dynamic

o _

oo -

to -

CN -

o -

Legend

\0

— Prefix mixed

0 100 200 300 400 500 600

Countdown (h)

Fig. 12 Mean modulus of relative price change and hotel pricing strategy 5-23 days prior to arrival

changes for the analyzed data sets. All pricing patterns are categorized into the pricingstrategy clusters pre-fixed mixed and dynamic. The pricing strategy cluster and themean modulus of relative price change is given in Fig. 12.

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62 J. Schutze

• • . . ^

Legend

— Poor availability— Average availability


0

,. o * 0

' * • 8 o ••.

100 500 600200 300 400

Countdown (h)

Fig. 13 Mean modulus of relative price change and hotel availability 5-23 days prior to arrival

We find the same general falling trend for pre-fixed mixed pricing and for dynamicpricing. While the pricing pattern for the pre-fixed mixed pricing strategy starts witb anaverage of about I/IO price change activity 23 days prior to arrival, the pricing patternofthe dynamic pricing strategy shows about 1/5 price changes at the beginning of themonitored period.

The mean modulus of relative price change of the different hotel availability clustersis given in Fig. 13.

The visual findings indicate that all three availability clusters follow a similar trend:about 3 weeks (23-26days) prior to arrival, for all hotel availability clusters, we findapprox. 2% mean modulus of relative price changes, with the poor availability clustershowing a lower mean modulus of relative price change. A few days (2-5 days) beforearrival, we find approx. 4% mean modulus of relative price change, with the pooravailability cluster showing a lower mean modulus of relative price change.

A bar chart is used to analyze the hotel availability cluster and hotel pricing strategyfor the differences in mean modulus of relative price changes (Fig. 14):

We find a similar proportion of mean modulus of relative price changes for thedifferent hotel pricing strategies: The pre-fixed mixed pricing strategy cluster shows aprice activity of approx. 2% while tbe dynamic pricing strategy cluster shows approx.5% mean modulus of relative price changes, with a lower figure for the poor avail-ability cluster and a higher for the excellent availability cluster. The mean modulus ofrelative price change ofthe different hotel category clusters is given in Fig. 15.

The visual findings indicate that to a certain extent all three hotel availability clus-ters follow a similar trend: about 3 weeks (23-26days) prior to arrival, for all hotelcategory clusters, we find approx. 2% mean modulus of relative price changes with

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lo-

iCn

1 4H

cra

Av»tabili!yD PeerQ Aveiage

Prefix contivt Prefnr mnred Dyntnic

Hotel price strategy

Fig. 14 Hotel pricing strategy cluster: hotel availability and mean modulus of relative price change

Legend3-star hotels' -star hotels

• 5-star hofels

100 500 600200 300 400

Counldown (fi)

Fig. 15 Mean modulus of relative price change and hotel category 5-23 days prior to arrival

the 5 star hotel cluster showing a lower mean modulus of relative price changes andthe 4 star hotel cluster showing a higher mean modulus of relative price changes. Afew days (2-5 days) before arrival, we find approx. 3-5% mean modulus of relative

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1 0 -

gha

nge

c_™ 4 -

0£.cRIVS 2-

I0 -

Hotel starsD 3

°= - . IT

1i• i

Prefix constart Piefix mixed DynamcHotel price strategy

Fig. 16 Hotel pricing strategy cluster: hotel category and mean modulus of relative piice change

price change with the poor availability cluster showing a lower mean modulus of rel-ative price change and the 4 star hotel cluster a higher mean modulus of relative pricechange.

The following bar chart is used to analyze the hotel category cluster and hotel pricingstrategy for the differences in mean modulus of relative price changes (Fig. 16);

We find a similar proportion of mean modulus of relative price changes for thedifferent hotel pricing strategies: The pre-fixed mixed pricing strategy cluster showsa mean modulus of relative price change of approx. 2% while the dynamic pricingstrategy cluster shows 5% mean modulus of relative price changes with a higher figurefor the 5 star hotel cluster.

7 Conclusion

The purpose of this paper is to analyze pricing strategies for perishable products:It focuses on Vienna and the internet distribution system Hotel Reservation Systemhrs.com.

The results include the identification of different pricing clusters with regard tohotel category and hotel availability over a 22-day period for Vienna and cities fromall Euroland countries, mostly the capitals.

A general comparison of used pricing patterns and price change activity in thecountdown to the arrival date showed the similarity of the Viennese hotel market onhrs.com with the aggregated hrs.com data from Euroland capitals.

As a result of all found pricing patterns, Viennese hotels could be categorizedaccording to their pricing strategies. The used pricing strategy clusters were identifiedas dynamic pricing, pre-fixed constant pricing and pre-fixed mixed pricing.

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For Vienna, the results include:

- Dynamic pricing cluster: Dynamic pricing is a widely used strategy for Viennesehotels on hrs.com.

- Pre-fixed constant pricing cluster: Viennese hotels use pre-fixed constant pricesper stay: Global single price policy and season-dependent constant pricing.

- Pre-fixed mixed pricing cluster. Pre-fixed mixed pricing is a widely used pricingstrategy for Viennese hotels on hrs.com.

- Dynamic pricing hotels increase the frequency of price changes with the approachof the arrival date.

Therefore we summarize: Three pricing strategies are used by Viennese hotels onhrs.com: dynamic pricing, pre-fixed constant pricing and pre-fixed mixed pricing .Allthree are widely used according to the results ofthe study.

The dynamic pricing and pre-fixed constant pricing were found to differ in relationto

- the number of price changes made,- the mean modulus of the relative price changes made.

Apart from giving insight into the application of general packaging options, mostimporiantly the study gives Viennese hotels the opportunity to decide

- when to commence, stop or continue to use internet distribution channels likebrs.com,

- whether to review their general pricing strategy,- whether to review their own fraction of price changing activity in comparison to

the peer hotels in terms of availability and hotel category,- whether to review their percentual price changes in comparison to their peer hotels

in terms of availability and hotel category.

This study provides the first framework of a series for the analysis of the collecteddata, which include room rate, breakfast price, location information (distance to thecenter, the airport, the next station, and the motorway). This study could provide thebase for future research with an extension ofthe observed period, a larger geographicalcoverage, the inclusion of more distributors and/or distribution channels and inclusionof estimations of previous demand based on an annual comparison.

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yield management

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