Use it or Lose It Transforming Big Data into Meaningful Information. November 16, 2015

Transcription

1 Use it or Lose It Transforming Big Data into Meaningful Information November 16, 2015

2 Mike Brown Former Director of Guest Services, Exploratorium Head of Client Services, ACME Technologies Chris Rizzo Director of Business Operations The Franklin Institute Kenny Wagener Vice President Management Resources Chris Wallace Director of Operations, Retail and Attractions Division, The Coca-Cola Company

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4 Management Resources With over 30 years experience, MR provides business, operational and marketing planning and advisory services to a diverse group of clients throughout the world. Planning We specialize in helping start-up clients plan their operations effectively, answering their most pressing questions. Is their vision economically viable? How will they drive the attendance and revenue necessary to be successful? Will they be able to manage their operations profitably and deliver the quality visitors demand? Advisory We assist established clients in enhancing existing operations and getting the most out of their asset. Can they increase revenue and cut costs while still delivering a great visitor experience? Are they getting the most out of their marketing? Would new programs help distinguish them in a crowded field?

5 Experience Cultural Attractions clients - Museums, Hall of Fames, Children s Museums, Zoos, Aquariums, and Science Centers Commercial Attractions - Worldwide - Theme parks, water parks, family entertainment centers, experience parks Corporate Attractions - Brand centers, special events, and corporate attractions Expos and Events - World expositions and Olympiads

6 Today s Objective Data Collection - Operations Data Analysis & Key Performance Indicators Implementation Experiences at Exploratorium, The Franklin Institute, the World of Coca-Cola and other institutions and attractions. Step 1 Step 2 Step 3 Develop Goals and Collect Data Points Analyze Data and Develop Strategy Implement and Start Saving Money

7 Common Themes Connecting data to development of future goals Connecting data to overall strategy Sharing of data across the organization

8 Data Collection What to Collect Attendance Mix Who is coming, tourist, residents? Members, school groups, GA? Online, on-site? Why to Collect Pricing sensitivity Staffing distribution, discounting Seasonality Daily and Hourly Arrival Transaction Time and Size Operational Flow When are guests coming? Seasonality How many guests came last year? What is the target today? How do guests arrive How many guests per transaction? How long is an average transaction? How long are guests staying in galleries, retail, café, etc.? How many guests enter the area? Staff scheduling Program planning Staff scheduling & distribution Budgeting and planning Standards and training Staff scheduling & distribution Space planning Staffing needs Providing better guest experiences

9 Glossary / Key Performance Indicators Term Attendance Distribution Seasonality Peak Day Design Day Arrival Pattern Departure Pattern Peak On-Site Definition/Calculation How many people from each visitor segment (Adult, Child, Senior, Student, Tourist, etc.) visit each year How many tickets sold by ticket type (General Admission, Membership, Programs, Adult, Child, etc.) Also known as monthly distribution is the monthly attendance as a percent of the total year The highest attended day during the year The number of people that your facilities(s) are designed to accommodate in an individual day The measurement of how many visitors arrive at various times during the day The measurement of how many visitors depart at various times of the day The highest number of guests within your facility during the day

10 Glossary / Key Performance Indicators Term Average Length of Stay Average Transaction Time Average Group Size Gross Square Footage Circulation Space Instantaneous Capacity Hourly Capacity Definition/Calculation The average time a visitor stays at/in the facility or specific area The average time from the start of one transaction to the start of the next transaction The average number of guest per transaction The total square footage of a given area (including exhibit footprints) The total square footage of a given area (excluding exhibit footprint and any obstructions) The total number of guests that can be accommodated within your facility at any particular time The number of guests your facility can/needs to support over a one hour period

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12 Exploratorium

13 Exploratorium

14 Exploratorium

15 Exploratorium

16 Exploratorium

17 Exploratorium

18 Exploratorium

19 Ticket Station Planning Staff Planning and Scheduling Queue Line Planning Using Your Data

20 Exploratorium Ticket Stations ANSWER: (1) Building for a design year and (2) projecting a greater percentage of members and online ticket sales therefore (3) fewer guests buying tickets on site.

21 Align Ticketing Staff with Projected Attendance in 4 Steps Weekly attendance projections by season Season AVG Att ADJ AVG # Weeks Total Peak 30,000 19, ,000 Mid-Peak 20,000 13, ,000 Shoulder 10,000 6, ,000 Adjusted attendance excludes guests who bypass the Buy Tickets line (e.g. members, online sales, group sales, tourism inititatives, etc.) Daily percent of week attendance by season Season Mon Tue Wed Thu Fri Sat Sun Peak 0% 15% 15% 15% 18% 20% 17% Mid-Peak 0% 15% 15% 11% 17% 23% 18% Shoulder 0% 9% 9% 9% 13% 34% 27%

22 Sample Output (peak period) Season Mon Tue Wed Thu Fri Sat Sun Derive average daily traffic to ticketing Peak 0 2,992 2,947 2,956 3,437 3,946 3,223 Mid-Peak 0 1,950 1,950 1,430 2,210 3,050 2,370 Shoulder ,189 1,743 Apply hourly arrival pattern by season Hour % Day 21% 20% 17% 16% 15% 9% 2% 10-11a 11a-12p 12-1p 1-2p 2-3p 3-4p 4-5p

23 Sample Output (peak period) Hourly Arrivals by Day of Week Hour Tue Wed Thu Fri Sat Sun a a-12p p p p p p

24 Use Hourly Arrivals to Stagger Staffing Hour 10-11a 11a-12p 12-1p AVG Wkdy AVG Wknd AVG Important Wkdy KPIs AVG Wknd Transaction time minutes (75 secs) transactions 3.6 per 4.2 min AVG Wkdy AVG Wknd p 2-3p 3-4p 4-5p Average 3.4 party size 4.0 = Guest processing capacity 1.9 per hour 2.2 per cashier 0.4 = Determine processing requirements by hour Calculate demand-based staffing requirement Adjust to experience and save money!

25 Sample Savings Shift Length Wkdy Staff Wknd Staff 8 hours hours hours 3 4 Total Hours Hours Saved 24 # Weeks in Season 9 Peak Total Hours Saved 216 Shift Length Wkdy Staff Wknd Staff 8 hours hours hours 0 4 Total Hours 8 20 Hours Saved 48 # Weeks in Season 11 Shoulder Total Hours Saved 528 Shift Length Wkdy Staff Wknd Staff 8 hours hours hours 2 4 Total Hours Hours Saved 48 # Weeks in Season 32 Mid-Peak Total Hours Saved 1,536 Total Hours , = 2,280 Cost per Hour $15.00 (wage) + $7.50 (benefits) = $22.50 Total Savings 2,280 * $22.50 = $51,300

26 Saving by Waiting What is the maximum acceptable length of time a guest should wait in line to buy a ticket? Is the payroll expense required to achieve this goal the best use of my budget?

27 Saving by Waiting feet Queue Capacity capacity Average party size Example = 3.0 guests Transaction time Example = 1.25 minutes

28 Saving by Waiting Is the incremental gain in queue speed the best use of your payroll dollars? Queue Length (ft) Number of Cashiers Guests in Queue Transactions

29 ACME Technologies ACME is a cloud-based, enterprise SaaS platform designed to run consumer and group ticketing, resource management, CRM, reseller/account management, and related back office operations for museums, zoos, aquariums, historic sites, passenger vessels, amusement parks, ski resorts, and the like. ACME provides white glove client service and customer support, including monthly product releases to respond to clients changing needs. We operate our own U.S.-based call center for customer support and inquiries on our clients behalf.

30 ACME Reporting

31 ACME Reporting

32 ACME Reporting

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34 History-We open our new facility in 2007 The Original World of Coca-Cola operated from

35 We had many learnings

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40 Master Planning

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44 The Franklin Institute

45 The Franklin Institute

46 The Franklin Institute

47 Using Data to Reallocate Resources The Franklin Institute actively manages Group Reservation list to encourage all groups to pay in advance

48 Using Group Data to Reallocate Staff Resources Day of Staff 4 Cashiers 4 Hours per Day for incoming group processing 80 Hours/Week Contact Center Reservation Staff 1 Contact Center Representative 4 Hours per Day for contact/fulfillment 20 Hours/Week Results Reduce Day of Staff by 50% or 40 Hours/week

49 Using Reservation Data to Manage Day of Operations 3 Touch Points Prior to Group s Visit 4 weeks Prior to Visit Re- 3 weeks Prior to Visit Call 2 weeks Prior Results Achieved Nearly a 0% No Show Rate Increased Paid In Full reservations by 40% Positive Feedback From our Group Visitors

50 Low Tech Solutions The Franklin Institute hosts many new exhibits each year and the most effective way to determine operational throughput is via a clipboard, hand counter, and index card.

51 Low Tech Data Easy Answers to the following questions: How many people fit in a queue line How long are your wait times What are the most popular areas What is the capture rate of a particular special events room What is the turn time in food outlets

52 Next Steps Step 1 Develop Goals and Collect Data Points Step 2 Analyze Data and Develop Strategy Step 3 Implement and Start Saving Money

53 Mike Brown Head of Client Services ACME Technologies Chris Rizzo Director of Business Operations The Franklin Institute Kenny Wagener Vice President Management Resources Chris Wallace Director of Operations, Retail and Attractions Division, The Coca-Cola Company