This document discusses a new visitor attendance estimation model developed by Tourism Whistler for the destination of Whistler, British Columbia. The model integrates data from hotel surveys and visitor intercept surveys to estimate the total number and types of visitors to Whistler each year. It provides valuable information on trends in visitor volumes, timing, and market segments. The model has been validated through correlations with other tourism data and provides a cost-effective way for Tourism Whistler to monitor visitor flows and support strategic decision making.

1. Travel & Research
British Columbia. The Premium Tourism Magazine.
Special Edition Whistler
Destination Management – Visitor Volume Model p. 2-22

2. OVERVIEW
2
This current Edition of „Travel & Research“ focuses on one of North
America´s premier resort destinations „Whistler“ and its year-round
tourism system which is managed by a legally mandated organization –
„Tourism Whistler“.
Therefore we present the new developed „Visitor Attendance
Estimation Model“ which is currently highly discussed in the tourism
industry. Our findings are based on an exclusive interview with Mr.
Peter Williams from Tourism Whistler complemented with further
research done by the Travel & Research Team.

3. W HISTLER S TATS & FACTS
3
2 Mio. Visitors per year
815,000 (Winter)
1,3 Mio. (Summer
Winter remains key season
(Whistler Canada, 2011)

4. W HISTLER S TATS & FACTS
4
(Whistler Canada, 2011)

5. I NTRODUCTION W HISTLER
5
Whistler, British Columbia, is one of the North America´s premier resort destinations
and offers numerous lakes, wetlands and alpine areas which represent a diverse
range of recreation venues, accommodation units and hospitality facilities.
Whistler has emphasized the attractions of its destination outside of the ski season
and is approaching the ideal “four season” situation. By marketing its golf courses,
hiking trails and lakes it has become an outdoor recreation center. Furthermore, it
has built on its overall reputation to attract conferences and seminars in the
shoulder seasons.
Moreover, Tourism Whistler coordinates the marketing and promotion of the
destination´s commercial tourism operations and is responsible for its performance
to a board of directors. To provide them with more detailed information about
visitor numbers, Tourism Whistler has developed an innovative destination flow
estimation model.

6. R ELEVANCE V ISITOR V OLUME M ODEL
6
“Why are Visitor Numbers of such importance to DMOs, Mr. Williams?”
The economic impact of regional tourism depends on the number of visitors to the
region and the average behavior of those visitors.
Reliable tourist attendance information is a core metric for evaluating the
performance and competitiveness of tourism destinations.
Tourist attendance information is a key indicator in planning, development and
management decisions for Tourism operators, DMOs and state and community
governmental organizations.
Accurately measuring visitor attendance has been a challenging and problematic
exercise for tourism managers for decades.

7. TOURIST REGIONS
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“Why is it so difficult to come up with exact visitor numbers?”
Tourist regions have multiple gateways (e.g. highways, airports, railway stations)
unlike events or attraction parks, where ticket sales or gate counts may be used to
estimate total visitation.
Consequently, in most areas, accurate tourist counts for geographic regions are
typically unavailable.
Further typical challenges associated with accurate visitor estimation in a non-gated
tourist region include:
Identification of visitors – defining attendees
Establishing reliable and credible counting procedures
Identifying representative sampling sites
Providing timely, credible and relevant information

8. C OMMON A PPROACHES
8
“How did DMOs estimate tourist figures until now?”
Range of traditional measurement strategies which are commonly used:
Aerial photographs or Electronic cordon counts
Direct observation tallying schedules
Sampling surveys of visitor use patterns and expert estimates
A further common approach is the summation of the number of hotel occupants,
airport passengers and festival attendees. However, because residents participate in
many of these same activities and not all visitors will engage in any one activity,
these variables are not very significant.

9. C OMMON A PPROACHES
9
“simply counting the number of visitors to a destination is not as simple as
one might initially think.” (Smith, 1995).
As there is no single measurement technique providing a bulletproof
approach for measuring attendance, more robust methods need to be
developed.

10. V ISITOR E STIMATION A TTENDANCE MODEL
W HISTLER – B RITISH C OLUMBIA
10

11. V ISITOR E STIMATION M ODEL W H I S T LER
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Whistler´
„I guess Whistler´s visitor estimation model is such a new method. Mr.
Williams, please tell us more about it“
Developed for the Destination Management Organization „Tourism Whistler“
Effective tool for describing visitor attendance at the destination
Tracks trends in the number, timing and mix of visitors to the resort
Integrates data from multiple sources:
Audited visitor counts from hotels
Weighted survey data from footloose visitors
Provides valuable information and enhances long-term destination decision making.

12. TYPES OF V ISITORS
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It is important to distinguish between the different types of visitors such as day
visitors, overnight visitors or seasonal residents. Knowledge of these subcategories
enables market segmentation strategies.
The Visitor Volume model focuses on four target groups:
Commercial accommodation visitors anchored visitors
Day visitors
Visitors staying in second homes footloose visitors
Visitors staying with friends and family

13. D ATA C OLLECTION
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Model integrates two main sources of data for its attendance calculations:
Commercial
Accommodation Visitors
Day Visitors
Visitors of Family & Friends
Secon Home Visitors

14. D ATA – C OMMERCIAL V ISITORS
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Aggregate Hotel Data is used to calculate the number of commercial
accommodation visitors at the destination´s hotels
Sample = 75% of all rooms available in Whistler
(The sample totals are scaled up to develop an estimated total for the entire resort)
Collected through Commercial Accommodation Survey – CAS on a monthly basis
via e-mail
Data collected provides information on:
room nights sold
average length of stay
average number of people per room
Tourism Whistler is able to estimate total room nights sold, occupancy, average daily
rate, revenue per available room, area of origin and traveler type for the entire
resort.

15. D ATA - V ISITOR S URVEY
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The second source is monthly survey data randomly obtained from footloose visitors
during their visit to Whistler.
Daily surveys conducted at high-traffic locations
Sample size = at least 15 surveys per day
Used to establish ratios of hotel attendance to footloose visitation

16. ESTIMATION PROCEDURE - EXAMPLE
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Hotel data:
Average visitors per day staying in hotels: 10,000
Survey Data:
For every 100 footloose respondents intercepted:
75 stayed in paid accommodation
10 stayed with friends and relatives
5 stayed in second homes
10 were day only visitors
The number of e.g. visitors that stayed with friends and relatives per day is
calculated by multiplying 10,000 by the ratio of 10 to 75.
Friends and relatives per day: 10,000 x 10/75 = 1,300
Second home visitors per day: 10,000 x 5/75 = 667
Day only visitors: 10,000 x 10/75 = 1,300
The annual number of visitors is then calculated by multiplying the average number
of visitors staying at the resort per day by 365 days and dividing by the average
length of stay (except day only visitors).

17. M ODEL OUTPUT
17
Number
Total number of visitors & visitors per day at the resort
Timing
Per year & per month
Mix
Commercial accommodation visitors
Visitors staying in second homes
Visitors staying with friends and relatives
Day only visitors
These metrics help to track trends in overall resort attendance
Valuable measure for assessing
Carrying capacity issues
Impacts of various events such as 9/11 on the destination´s overall visitor
performance.
The effectiveness of various marketing programs

18. M ODEL V ALIDATION
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To demonstrate the validity of the Visitor Volume Model, it was compared to four
existing data sources. A positive correlation between all variables was examined.
Correlation between hotel tax revenue and the average number of visitors staying
in paid accommodations 90%
Correlation between total skier visits and the average number of visitors per day at
Whistler 93%
Correlation between total golf rounds and the average number of visitors per day
93%
Correlation between the average annual daily traffic count and the average number
of visitors per day 87%

19. S TRENGTH OF M ODEL
19
Credible and cost effective method of monitoring visitor flows
Provides relevant output:
Information not only about absolute visitor numbers but also about the mix of
visitors and timing of visitor arrivals.
Valuable information about the destination´s overall use patterns and trends
in peak and shoulder seasons visitor flows.
Reliable estimation procedures tied to substantive information base
Representative sampling procedures confirmed through positive correlations of
existing data sources
Enhances strategic & long-term decision-making

20. L IMITATIONS OF M ODEL
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The model does not have any explanatory capabilities It can only suggest
capabilities:
correlations between management measures taken by Tourism Whistler rather than
identifying explicit cause-and-effect relationships.
Confidentiality issues The model is based on a foundation of audited commercial
issues:
accommodation visitor volumes. As most commercial accommodation providers
consider such information proprietary and hesitate to expose their visitation figures
other DMOs may not be able to obtain this critical input information.
Therefore, this model requires a high involvement of a destination´s
stakeholders (hotel operators)
Human & financial resources required due to very intensive data requirements
Other destinations may not have the budgetary resources or systems in place to
collect such information
More detailed information of visitor travel behavior to make the model more robust
is needed

21. R ECOMMENDATIONS OF F UTURE
21
RESEARCH
Other key data sources as a base for model estimations (other than commercial
accommodation data)
Methods to model visitor flows for more detailed market segments
More efficient survey and sampling methods
Effects of destination visitation on attendance figures at other sites
Cause-and-effect relationships between effects of planning and management
decisions on visitor flows

22. C ONCLUSION
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The model became an important tool in Tourism Whistler´s
performance-monitoring program.
According to the growing global competitiveness for destination travel markets,
information about the attendance volume is increasingly important for strategic
decision making activities. This information provides data on the success of
promotional campaigns as well as on changing customer patterns and emerging
market trends and is therefore invaluable for destination management
organizations.
The visitor volume model, therefore, provides a first framework for constructing a
credible and useful approach.

23. REFERENCES
23
Kelly, J., Williams, P.W., Schieven, A., Dunn, I. (2006a): „Toward a Destination Visitor Attendance Estimation
Schieven,
Model: Whistler, British Columbia, Canada“. Journal of Travel Research, Vol. 44: 449-456. Sage Publications.
Leiper,
Leiper, N., (1989): “Main Destination Ratios: Analysis of Tourist Flows”. Annals of Tourism Research, Vol. 16:
530-541. In: Kelly, J., Williams, P.W., Schieven, A., Dunn, I. (2006a): „Toward a Destination Visitor Attendance
Schieven,
Estimation Model: Whistler, British Columbia, Canada“. Journal of Travel Research, Vol. 44: 449-456. Sage
Publications.
Murphy, P.E. (20083): The Business of Resort Management. Elsevier Ltd. Burlington.
Smith, S., L. (1995): Tourism analysis: A handbook . Essex, England: Longman Group. In: Tyrrell, J.T, Johnston,
R.J. (2002): „Estimating Regional Visitor Numbers“. Tourism Analysis, Vol. 7: 33-41. Cognizant Comm. Corp.
Tyrrell, J.T, Johnston, R.J (2002): „Estimating Regional Visitor Numbers“. Tourism Analysis, Vol. 7: 33-41.
R.J.
Cognizant Comm. Corp.
Whistler Canada (2011): Whistler Statistics & Research. Online: http://events.whistler.com/about-
whistler/statistics-and-research/. Enquiry: 29/11/2011.
http://www.linkbc.ca/torc/downs1/KellyetalTowardaDestinationVisitorAttendance.pdfattendance%20estimati
on&f=false